The Market Doesn't Know It's Planning Season

American football has a useful feature: the game stops. Often. In planned, consistent ways.

The team huddles. Coaches assess what happened. They choose the next play. Everyone gets an assignment. The teams line up.

Then the work starts again.

A surprising amount of enterprise planning assumes work behaves the same way.

We plan annually. Allocate budgets. Establish priorities. Assign resources. Then execute.

The problem is that the market never agreed to stop playing while we did that.

I work across companies with different fiscal years, planning cycles, budget processes and geographic rhythms. The instinct is often to treat this as a coordination problem:

How do we get everyone aligned to the same plan at the same time?

I'm increasingly interested in the opposite question.

What if they shouldn't be?

Imagine a global campaign launching in the U.S. in July.

Europe has lower capacity in August. Traditionally, we might see EMEA as behind. The rollout isn't synchronized.

Or the U.S. launches first. Customers respond. Sellers learn. Messaging gets tested. Assumptions break.

Then September arrives.

Instead of EMEA launching the same campaign two months late, it launches version two.

The stagger wasn't a bug.

It created a learning opportunity.

Partner ecosystems make the problem even more obvious.

Partner A's fiscal year starts in January. Partner B's starts in July.

Each company has its own annual planning process. So every six months, someone is entering a new year, setting new priorities, reallocating budgets and asking the partnership to plan again.

We experience each of those moments as a reset.

But why?

The partnership doesn't reset because one company's fiscal year does. The customers don't reset. The market doesn't reset. Everything we learned together during the previous six months still exists.

Maybe the useful unit of planning between these companies isn't a year at all.

Maybe it's six months.

Partner A's new planning cycle incorporates what the ecosystem learned during Partner B's previous six months. Six months later, Partner B does the same.

What looks like incompatible planning calendars becomes a shared learning cadence.

Organization design has wrestled with versions of this problem for decades. Jay Galbraith framed organizations partly as information-processing systems: as uncertainty increases, organizations need greater capacity to process information and coordinate work. Research on temporal structuring has similarly examined how organizations create rhythms—deadlines, planning cycles, reporting periods—to coordinate activity.

And of course AI necessarily changes some of the economics underneath those principles. Information can move faster…a lot faster. Analysis can happen faster. What one market—or one company—learns can potentially reach another before it acts.

That doesn't eliminate planning, but it changes what planning can be for.

Instead of asking how every part of an ecosystem can execute the same plan at the same moment, ask:

What actually needs to be synchronized?

Strategy might. Economic constraints might. Product truth and brand might.

Execution? Maybe not. The goal may not be synchronization. It may be coherence without simultaneity.

We keep trying to manage the market like a single football game: predictable opportunities to stop, huddle, call the next play, line everyone up and snap the ball.

We plan as though the market will wait for everyone to huddle, align and call the next play. It won’t. The opportunity is to build GTM systems that can learn and adapt while the game is already underway. Concept and direction by Megan Arnold; image created with ChatGPT.

But the market looks more like football on a Sunday.

Multiple games are happening at once. They're on different clocks, at different points in the game, with different players, different leaders and different narratives unfolding in real time.

There is no moment when everyone huddles together.

And there is no reason they should.

The challenge isn't getting every team onto the same play at the same time.

It's building a system that can learn across all those games while each team continues to play the one in front of it.

Standardize the principles. Share the learning. Adapt the execution.

The calendar isn't the strategy. It's a constraint the strategy should exploit. Why should the corporate fiscal year should be the natural unit of time for work that crosses corporate boundaries at all?

We Digitized the Beads. We Never Digitized the String.

I recently worked on a global event series spanning multiple cities and regions.

Every location had its own moving pieces: dates, speakers, booth staffing, lead capture, swag, creative, localization, and different event requirements.

None of this information was missing. That was the problem - it was everywhere.

Some lived in email. Some in Teams conversations and meeting recordings. Some in planning decks, shared folders, spreadsheets, and different versions of assets. Different people knew different pieces.

We had successfully digitized almost everything.

But there was no place that represented the actual state of the campaign.

If I wanted to know what was happening in a particular market, I had to reconstruct it.

Which speakers were confirmed? Had the creative been localized? Who was staffing the event? Were the right assets ready? What was still blocked?

The answers existed.

The campaign didn't.

We digitized the beads

Enterprise software is very good at storing things.

Emails. Files. Tasks. Contacts. Opportunities. Conversations. Presentations. Recordings.

Think of each as a bead.

Over the last several decades, we've gotten extraordinarily good at digitizing them.

But my brain was still holding the string.

A surprising amount of my work wasn't actually creating any of those artifacts. It was maintaining an understanding of how they related to one another.

And then repeatedly explaining that understanding to other people so they could place their bead in the right spot.

The campaign existed as a coherent object largely because humans were continuously reconstructing it and ensuring we were all visualizing the same necklace (i.e. “aligning”).

The artifacts are digital. The orchestration is human. A campaign exists because people continuously connect fragmented information into a coherent whole—and then synchronize that understanding with each other. Concept and direction by Megan Arnold; image created with ChatGPT.

Communication isn't the same as coordination

Research on shared mental models and transactive memory has long examined how teams coordinate by developing shared understandings of the work and knowledge of who knows what. Research on the “hidden profile” problem has similarly shown how information distributed across group members can remain difficult for the group to integrate.

More communication doesn't automatically solve that.

You can have more meetings, more messages, more documents and more dashboards while still lacking a shared representation of what is happening now.

That's the gap I find interesting for AI.

I built a Copilot-based tool that let me ask questions about the event series across the information already available to me.

Suddenly I could ask:

What's happening in this market? What's confirmed? What's missing? What changed?

It wasn't valuable because AI created another artifact.

It was valuable because it helped reconstruct the work itself from the artifacts surrounding it.

That feels like an important distinction.

For years, enterprise software has largely asked us to navigate to the places where information lives.

Open the CRM. Find the email. Search the chat. Locate the deck. Check the spreadsheet. Ask the person.

AI gives us an opportunity to invert that relationship.

Stop showing me the artifacts. Show me the work.

A campaign shouldn't exist only as a collection of files, messages, tasks and memories.

Neither should a product launch, a customer initiative, or a partnership.

Before we build agents to do more of the work, we may need systems that understand what the work is.

Because digitizing every bead isn't the same thing as knowing how they form the necklace.

Your People Are the Operating System

Recently, I needed to map two company's sellers to a list of leads. This is exactly the kind of tedious work AI should be good at.

We had seller directories. We had territories. We had company information. Instead of asking someone to spend hours matching the lists manually, AI could do the first pass.

And it did. Then we looked at the results.

A lead located in the UK had been mapped to a UK seller. Reasonable.

Except the company could be Boeing.

The employee may be sitting in London. The global account may be owned in the United States.

The AI hadn't made a stupid inference. It had made a perfectly reasonable inference from the information available to it.

The system didn't contain enough of the operating context required to make the right decision.

And suddenly I could see a much bigger problem.

AI can automate what the system makes legible

Enterprise work contains enormous amounts of context that formal systems don't fully represent.

• Who actually owns the account.

• Why this campaign was designed this way.

• Which executive cares about the relationship.

• Which process you're supposed to use.

• Which process actually works.

• Who can get something approved.

• Why the obvious option was rejected six months ago.

• Which partner will interpret a decision as an escalation.

• Which seller knows the customer.

• Which exception exists because of something that happened three fiscal years ago.

• Organizations have always depended on this kind of knowledge.

Research on organizational knowledge distinguishes between knowledge that can be articulated explicitly and knowledge that remains more tacit. Ikujiro Nonaka's foundational work argued that organizations create knowledge through interactions between tacit and explicit forms, with organizations playing a role in articulating and amplifying knowledge developed by individuals.

James Walsh and Gerardo Ungson approached a related problem through organizational memory: how organizations acquire, retain and retrieve information from their history.

Thirty years later, this problem is becoming an AI problem, because AI inherits the organization's information architecture. If important operating context exists only in people, AI can't reliably reason over it.

The present is an accumulation of decisions

When I left a previous role, I wrote roughly 60 pages for the person replacing me. Then I built an AI agent on top of it.

I cared about the handoff for an unusual reason: I was moving to another company, and my old company was becoming one of my partners. I had a direct interest in my replacement succeeding.

So I tried to document everything. Not just the formal process. The actual process. The history. The things we had tried. Why certain decisions had been made. The unofficial rules. The things everyone “knew” but nobody had written down. The heresies.

The context someone would otherwise spend a year slowly accumulating.

Because when you step into a role, the present is an accumulation of decisions that only understanding the past can explain.

And most enterprise systems are surprisingly bad at preserving that explanation.

They preserve artifacts:

• The deck.

• The campaign.

• The opportunity.

• The email.

• The approval.

• The spreadsheet.

They don't necessarily preserve the causal story connecting them.

Humans do.

Relationships aren't the problem

The argument is not that relationships are inefficient legacy infrastructure we should automate away.

Research on interorganizational governance provides a useful warning against that conclusion.

Laura Poppo and Todd Zenger studied formal contracts and relational governance in interorganizational exchanges and found evidence that they can function as complements, rather than one simply replacing the other. Formal mechanisms and relationships can reinforce each other.

That matches my experience.

Strong partnerships need humans. They need trust, judgment, negotiation, creativity, leadership, empathy.

The ability to understand something nobody has encountered before.

The willingness to try something that doesn't yet have a process.

The problem isn't that partnerships run on relationships. They should. The problem is what we're asking those relationships to do.

Humans are doing middleware work

I've watched a person schedule nearly 100 individual seller meetings between two companies because that was the mechanism available for connecting leads to the right people.

Spreadsheet. Email. Meeting invite. Reschedule. No-show. Follow-up. Repeat.

I've watched formal ticketing processes coexist with an unofficial alternative: know the person who handles the request. Message them. It gets done.

I've inherited campaigns where understanding the current state required reconstructing why dozens of decisions had been made months earlier.

None of this work is inherently irrational.

In fact, it is usually rational adaptation by intelligent people operating inside fragmented systems.

But look at what the human is doing.

• Carrying state.

• Routing information.

• Resolving identity.

• Translating between systems.

• Remembering history.

• Tracking dependencies.

• Reconciling exceptions.

• Maintaining context.

• Connecting people.

Following up when the workflow has no enforcement mechanism. That is infrastructure work.

And we have quietly embedded enormous amounts of it inside human relationships.

Now we're adding AI to every box

Imagine the enterprise as a collection of systems. CRM, marketing automation, co-sell platforms, content systems, email, collaboration tools, sales systems, analytics, each one gets AI.

The content system generates faster. The CRM analyzes faster. Marketing launches faster. Sales researches faster. Analytics reports faster. Every individual stove burns hotter.

And sitting in the middle is a human being carrying context between them.

We keep pouring AI into the nodes.

We haven't necessarily redesigned the architecture between the nodes.

In fact, making each node dramatically more productive may increase the amount of coordination the system requires.

More content.

More campaigns.

More insights.

More possible partner combinations.

More signals.

More decisions.

More things that can happen.

That's not an argument against AI.

It's an argument for following AI's impact one level higher.

“The Real AI Energy Crisis.” We’re pouring powerful new fuel into old enterprise infrastructure while humans remain the integration layer connecting it all. Concept and direction by Megan Arnold; image created with ChatGPT.

What should humans actually be doing?

If we could remove enough of the integration work, I don't think the goal should be to remove the humans.

I'd want them doing more of the work we're actually good at: leading people, building trust, managing change, communicating meaning, experimenting, learning hard lessons, being accountable.

Negotiating the genuinely difficult tradeoffs between organizations that want different things. And making judgments where the available information cannot produce an obvious answer.

The infrastructure should remember the history.

• It should carry state.

• It should know the workflow.

• It should surface dependencies.

• It should make ownership visible.

• It should route information.

• It should preserve context.

• It should make the work legible enough that AI can actually help.

Then the humans can do the human part.

Because your people should be the intelligence in the system.

They shouldn't have to be the system.

Research behind this essay

Nonaka, A Dynamic Theory of Organizational Knowledge Creation (1994); Walsh & Ungson, Organizational Memory (1991); Poppo & Zenger, Do Formal Contracts and Relational Governance Function as Substitutes or Complements? (2002).

AI Made Me Faster. The Work Still Took Two Weeks.

Recently, I had a marketing problem across about a dozen strategic accounts.

I had customer context scattered across calls, notes and other sources, and I wanted an agency to come back with campaign ideas grounded in what was actually happening inside those accounts.

Historically, getting them started would have been work in itself.

Gather the context. Synthesize it. Write the brief. Explain the accounts. Package everything so someone outside the day-to-day work could understand the problem.

Instead, I used Copilot.

Within minutes, I had synthesized the relevant context and turned it into a usable creative brief.

This is the kind of AI productivity gain we're all talking about.

Then I sent it to the agency.

The meeting was scheduled for two weeks later.

When we finally met, much of the conversation consisted of questions that could have been answered asynchronously. Notes were taken by hand. And when the hour was over, we still hadn't meaningfully advanced the work.

AI had dramatically increased the speed at which I could operate.

The work still took two weeks.

That's the problem I'm increasingly interested in.

We tend to think about AI productivity at the level of the individual task.

• How much faster can I write this brief?

• Analyze this data?

• Build this presentation?

• Research this customer?

But most go-to-market work doesn't happen inside one task, or even inside one person's job.

It moves from:

• Marketing to agency.

• Product to marketing.

• Global to regional.

• Vendor to partner.

• Marketing to sales.

• Sales back to product.

Every transition introduces some combination of waiting, context transfer, prioritization, interpretation, approval and rework.

Make one step 10x faster and the system doesn't necessarily become 10x faster.

It may barely move at all.

You can make one part of a system almost infinitely fast without making the system fast. Concept and direction by Megan Arnold; image created with ChatGPT.

That's GTM latency: the accumulated time and information loss between a market signal and the organization's coordinated response to it.

AI can compress enormous amounts of execution time without touching the latency between the people, teams and companies doing the executing.

In fact, there's a strange possibility here.

The faster AI makes each of us, the more visible the waiting between us becomes.

A brief that once took me three days might have made a two-week wait feel normal.

A brief that takes ten minutes makes the same two weeks look very different.

This isn't an argument for eliminating agencies, meetings, partners or collaboration. Those relationships often create enormous value.

It's an argument for looking at the whole system.

If AI makes every individual worker dramatically faster while the work still waits in queues, calendars, handoffs and organizational boundaries, we haven't transformed go-to-market.

We've made the spaces between the work easier to see.

Organizational Modernization: We Upgraded the Technology. What About the Organization?

There is an enormous conversation happening in technology right now about modernization.

Modernize the data estate. Modernize applications. Modernize infrastructure. Modernize security. Build the technical foundation required for AI.

It makes sense. You can't bolt a fundamentally new technology onto infrastructure built for another era and expect transformation.

But we seem remarkably comfortable doing exactly that with the organization.

We’re investing enormously in modernizing the infrastructure beneath AI. What happens when the organization running on top of it was designed for a different information environment? Concept and direction by Megan Arnold; image created with ChatGPT.

Many of the basic structures we still recognize inside large companies were developed to solve the information and coordination problems of an earlier industrial economy.

The functional organization grouped people by specialization: engineering with engineering, marketing with marketing, finance with finance. Specialization created expertise and efficiency, but coordinating work across those functions became a management problem.

As companies grew larger and more diversified, the structure evolved.

In 1962, Alfred Chandler's Strategy and Structure documented the rise of the multidivisional organization at companies including DuPont, General Motors, Standard Oil and Sears. Instead of trying to run an increasingly complex enterprise entirely through centralized functions, companies created more autonomous operating divisions, while a central office coordinated, planned and evaluated their work.

Then organizations became more complex along multiple dimensions at once.

Products mattered. Functions mattered. Geographies mattered. Projects mattered.

Enter the matrix.

In 1971, Jay Galbraith described matrix structures as an attempt to capture the advantages of both functional and project-based organizations. His case study is strikingly recognizable today: engineering, manufacturing and marketing needed to coordinate work while maintaining their specialized capabilities. Rules, planning processes, hierarchical escalation, direct contact and liaison roles were all mechanisms for integrating work across those boundaries.

Three years later, Galbraith articulated an idea that I think matters enormously in the age of AI.

Organization design is, in part, an information-processing problem.

As uncertainty increases, the amount of information an organization must process to coordinate its work increases too. Organizations can respond in different ways: create slack, make work more self-contained, invest in better information systems, or create more lateral relationships that allow information to move across the organization.

That was 1974.

The theory doesn't suddenly become wrong because it's fifty years old.

If anything, it gives us the question we should be asking now.

What happens when the information environment changes beyond recognition?

The constraint changed

The modern enterprise doesn't suffer from a shortage of information. It operates inside an almost continuous stream of it.

Customer calls. CRM records. Product telemetry. Emails. Teams messages. Documents. Competitive intelligence. Partner data. Market research. Support tickets. Seller feedback. Meeting transcripts. Usage data. Financial results.

And now AI can synthesize, generate, retrieve and transform much of that information at a speed that would have been difficult to imagine when many of our organizational mechanisms were developed.

A marketer can go from an idea to a campaign brief in minutes, a product manager can interrogate thousands of pieces of customer feedback, a seller can ask a question across a body of organizational knowledge instead of finding the person who knows the answer, a small team can produce work that once required a much larger chain of specialists.

But then that work enters the organization.

It hits the planning cycle.

The functional boundary.

The approval chain.

The meeting cadence.

The regional handoff.

The partner boundary.

The person who needs to be brought up to speed. The other person who has to be brought into the meeting because they own one piece of the decision.

And suddenly we have an extraordinary new information-processing technology moving through an organizational architecture built around very different information-processing economics.

AI is increasing our capacity to act while simultaneously increasing the speed at which the environment changes around us.

The challenge is no longer simply processing more information. It's sensing what changed, understanding what matters, coordinating a response and adapting again before that understanding becomes obsolete.

Our modernizations are falling short

The increase of uncertainty in our markets, our economies, our communities is why I'm increasingly interested in organizational modernization.

Not restructuring for the sake of restructuring. Not declaring hierarchy dead. Not replacing every function with small autonomous teams. And certainly not arguing that decades of organization-design research suddenly stopped applying.

The opposite.

If organizational structures evolved in response to the economics of specialization, coordination, uncertainty and information processing, then a dramatic change in those economics should make us revisit the structures.

• What work still benefits from specialization?

• What information still needs to travel up a hierarchy?

• What decisions actually require consensus?

• What should be standardized, and what should remain variable?

• What happens when execution can be local while learning becomes shared?

• What is the smallest unit of an organization capable of sensing, deciding, executing and learning?

• And what organizational infrastructure would allow hundreds of those units to learn from one another without requiring everyone to coordinate with everyone else?

Those aren't primarily questions about AI adoption.

They're questions about organization design.

We are spending enormous energy preparing our technical infrastructure for AI.

We should be asking what kind of organization we're preparing it to run.

Partnerships Aren't Symmetrical. Stop Designing Them Like They Are.

One of the most persistent assumptions in partnership design is also one of the most understandable. We expect the other company to look something like ours.

We have a product marketer. Where's your product marketer?

We have regional funding. Where's yours?

We have sellers covering these accounts. Who are their counterparts?

We have a process for this. What's your version?

Sometimes there is an equivalent. Often there isn't.

And then we treat the difference as a problem to solve.

The asymmetry is often the reason the partnership exists.

Different companies should bring different things

Consider the relationship between a hyperscaler and an independent software vendor.

The hyperscaler may want access to the ISV's installed base and customer relationships. The ISV can become a route through which customers consume more cloud.

The ISV may want access to the hyperscaler's marketplace, reach and enormous sales organization. The hyperscaler can become a route through which the ISV reaches more customers.

In an oversimplified version, each company would quite like the other to behave like its channel. Neither is.

So the partnership becomes a continuous negotiation between interests that overlap but do not coincide.

This is not an edge case in ecosystem theory. It is foundational.

Michael Jacobides, Carmelo Cennamo and Annabelle Gawer describe ecosystems as arrangements of distinct but interdependent organizations coordinated without full hierarchical control, with complementarities at the core of why the ecosystem exists.

Adner similarly treats ecosystem strategy as a problem of aligning different actors around a value proposition. Those actors can have different goals, positions and dependencies.

So asymmetry itself isn't the interesting discovery. The interesting question is why our operating models keep trying to erase it.

Organizational symmetry is comforting

Symmetry makes planning easier.

Company A contributes a marketer. Company B contributes a marketer.

Both contribute budget. Both activate sellers.

Both localize. Both report results.

The spreadsheet is beautiful. Reality is less cooperative.

One company has an enormous centralized marketing organization but almost no regional flexibility. Another has regional marketers with autonomy but little centralized support.

One has money. Another has people.

One has deep product expertise. Another owns the customer relationship.

One has an elegant partner program. Another has three extraordinarily committed humans who somehow make everything work.

Culture matters too. Some organizations escalate everything.

Some avoid escalation at almost any cost. Some are chronically under-resourced.

Some are so heavily resourced that coordinating their own participants becomes part of the partnership workload.

A partnership can be all chefs and no line cooks. Or six line cooks waiting for someone to decide what they're making.

A partnership is a continuously splitting strategic atom.

What begins as one executive relationship quickly divides into products, regions, sellers, marketers, funding sources, customer segments, industries and individual people—each with slightly different incentives and constraints.

What looks like a single partnership strategy at the center fractures as it moves toward execution. Each marketer sits inside a different field of global, regional, sales and functional priorities—so by the time two partner marketers meet at the corporate boundary, they may be representing very different systems of strategic gravity.

“The Strategic Atom.” A partnership may look singular at the corporate boundary, but strategy fractures as it moves through different organizational structures, priorities and lines of accountability. Concept and direction by Megan Arnold; image created with ChatGPT.

What if we dumped the resources onto the floor?

I've sometimes wondered what would happen if we stopped looking at the participating companies as organizations for a moment.

Imagine dumping every resource available to the partnership onto the floor.

People. Budget. Technical expertise. Customer relationships. Marketplace access. Sales reach. Industry knowledge. Marketing capabilities. Implementation capacity. Data. Executive sponsorship.

Then rebuild an organization around the work that actually needs to happen:

The partnership collectively has a complete organization.

It just doesn't exist as one org chart.

Its resources belong to different companies. They report through different hierarchies. They have different incentives. Different planning cycles. Different systems. Different definitions of success.

And nobody has hierarchical authority over the whole thing.

That's not a failure of partnership management. That's the defining organizational condition of an ecosystem.

Strategy has to survive contact with executability

This becomes particularly important when companies standardize partner programs. Standardization is attractive because bespoke execution is expensive.

Build the campaign. Package the messaging. Create the assets. Send the play to five partners. Scale.

Sometimes that works beautifully. Sometimes the differences between the partners are precisely what make the standardized motion unusable.

The dimensions multiply quickly:

• Geography.

• Account segment.

• Product.

• Industry.

• Persona.

• Partner type.

• Customer maturity.

• Seller coverage.

• Available budget.

• Existing relationships.

Suddenly the “program” is an infinite Rubik's cube of possible GTM configurations.

Classical strategy asks which opportunity is most attractive.

Ecosystem strategy has to ask another question:

Can this particular configuration of organizations actually execute it?

An attractive market opportunity that requires regional coverage no participating company has is not necessarily good strategy.

A campaign that assumes localization will happen in three weeks when the operating process requires nine months is not good strategy.

A motion that requires a seller population nobody has the ability to activate is not good strategy.

Executability is part of strategy.

AI may change what asymmetry costs

This is where AI becomes interesting, but not because it will make every partner look the same. It may do the opposite.

Historically, heterogeneity has been expensive. Different partners require different messaging, different account strategies, different enablement, different workflows and different levels of support. So companies standardize.

AI can lower the cost of configuration. That may allow us to design around difference rather than constantly trying to remove it. But the goal shouldn't be perfect customization either. Complexity has a cost, and every ecosystem still needs shared rules, interfaces and governance.

Which things need to be standardized so different organizations can work together—and which things should remain different because the differences create the value?

Ecosystem theory has already told us that interdependent organizations do not need to be identical.

Our operating models need to catch up.

The goal isn't to eliminate asymmetry.

It's to make asymmetry operable.

Research behind this essay

Jacobides, Cennamo & Gawer, Towards a Theory of Ecosystems (2018); Adner, Ecosystem as Structure (2017).

The Market Moves Faster Than the Meeting Invite

The slowest part of partner GTM is often surprisingly mundane. Hint: it’s the calendar.

You have an opportunity. The companies agree it's worth pursuing. Now you need product, sales, marketing, partner teams and perhaps another company involved.

So the coordination begins:

Can everyone do Thursday?

No.

Next Tuesday?

Someone is traveling.

The following week?

There is a sales kickoff.

Eventually, everyone gets on the call. And while we've been negotiating with Outlook, the market has continued operating.

Markets don't wait for organizational alignment

Technology ecosystems are constantly being reconfigured by events no joint GTM plan controls.

• An acquisition changes a partner's economics.

• A licensing change suddenly makes migration urgent.

• A material shortage affects product availability.

• A regulation changes what customers can buy.

• A political event changes the conversation around sovereignty.

• A cloud provider reorganizes its sales territories.

• Compensation changes.

• A product slips.

• A competitor launches.

• The kaleidoscope rotates.

And the GTM motion designed for the previous configuration may no longer make sense.

Management research has spent decades studying a version of this problem.

David Teece's work on dynamic capabilities describes the organizational capacity to sense opportunities and threats, seize opportunities, and reconfigure resources as circumstances change. Importantly, those capabilities aren't simply a matter of executives recognizing that something has happened. They depend on processes, structures, decision rules and organizational disciplines that make adaptation possible.

Research on organizational reconfiguration makes the same underlying point: adaptation requires firms to be able to redeploy and recombine resources rather than treating organizational structure as fixed.

Ecosystems make this harder.

Because the resources being reconfigured don't all belong to the same organization.

Agreement isn't alignment

This is one reason I've become suspicious of the phrase strategic alignment. I've participated in plenty of meetings where everyone was strategically aligned.

“We need to accelerate pipeline.” Absolutely.

“Pull more deals forward.” Sounds great.

“Drive consumption.” Also great.

Until you get one level lower. A company built around traditional infrastructure revenue may benefit economically from pulling a transaction into the current quarter. A cloud business may care much more about whether the customer deploys and consumes the service over time.

Both companies can enthusiastically agree that they want to “accelerate growth.” They have not necessarily agreed on the behavior the system should optimize.

Strategic alignment often isn't alignment. It's agreement at a level of abstraction where the contradictions haven't surfaced yet.

Adner's ecosystem research makes a related distinction. Ecosystem participation isn't enough; the actors required to produce the value proposition must align around their positions and activity flows even though they may have different goals. The thing is….this takes a lot of time.

The opportunity has a clock

I've seen joint solutions that everyone believed in. Leadership supported them. The value proposition made sense. The technology worked. What didn't exist was a credible GTM system.

• Who are the target accounts?

• Which sellers own them?

• Who leads?

• What's the message?

• Have the sellers been enabled?

• Who funds demand generation?

• Where do leads go?

• Who follows up?

Nobody owned the whole motion, and I wasn't willing to spend demand-generation money until there was a credible mechanism for converting demand into action. We never got there.

Not because the strategy was obviously wrong, but because the organization couldn't become executable before momentum disappeared.

That distinction has led me to a concept I've started calling GTM latency.

GTM latency is the time between recognizing a market opportunity and becoming operationally capable of pursuing it. The exact definition will need refinement. But the underlying relationship is simple:

If the opportunity window stays open longer than the system takes to reconfigure, the opportunity is executable.

If the system takes longer to reconfigure than the opportunity remains valuable, the opportunity decays while the companies coordinate.

“Look at That Speed!” AI can make individual parts of GTM move dramatically faster without increasing the speed of the system carrying them. Concept and direction by Megan Arnold; image created with ChatGPT.

Sometimes the fastest motions barely look like programs

Some of the fastest partner GTM I've seen has been almost guerrilla-style.

A technical conference is coming up. A handful of engineers and sellers are genuinely excited about a customer problem. They find several compatible partners. They assemble something in six or eight weeks.

The messaging isn't produced through an elaborate centralized process. The people closest to the technology help write it. The graphics get made. The sellers care. The engineers care. Everyone knows why they're there.

And it works.

That isn't an argument against professional marketing or operating discipline.

It's evidence that motivation, ownership and proximity to the work can dramatically reduce coordination latency.

The opposite happens too:

I've watched three companies try to approve one piece of joint collateral when no single person on Earth technically had authority over the integrated object.

Eventually, you get three separate approvals and someone takes responsibility for treating them as one.

The work has an owner-shaped hole.

Speed is an organizational capability

We tend to talk about speed as though it means people working faster. AI certainly makes that possible.

Content can be created faster. Analysis can happen faster. Account research can happen faster. Plans can be generated faster.

But none of that necessarily means the system can move faster.

The more interesting measure may be:

How quickly can this ecosystem translate strategic intent into coordinated action—and reconfigure when the world changes?

Because the market keeps rotating while the operating model schedules the meeting. Eventually we capture the opportunity. Sometimes.

Other times we're doing a floppy seal shuffle across a rocky beach and hoping the opportunity is still there when we arrive.

Research behind this essay

Teece, Explicating Dynamic Capabilities (2007); Karim & Capron, Reconfiguration: Adding, Redeploying, Recombining and Divesting Resources and Business Units (2016); Adner, Ecosystem as Structure (2017). SMS

Your Partner Network is an Asset. If You Can See It.

There is a particular kind of question that comes up constantly in large partner ecosystems.

Who do we know who can do this?

Sometimes the answer is immediate: “I know Cindy.”

Cindy knows the customer. Or the industry. Or the systems integrator. Or the person inside the systems integrator who actually understands this particular workload. Fantastic.

But I've started to think there is something strange about this: the company may have relationships with thousands of partners employing hundreds of thousands of people with an extraordinary range of technical, industry, geographic and customer expertise.

And our query interface is:

I know Cindy.

The network possesses the capability, but the organization has trouble seeing it.

Having a network and being able to use one are different things

Researchers have described network competence as an organizational capability: not simply possessing relationships, but being able to develop and manage relationships with customers, suppliers and other organizations while dealing effectively with the interactions among them.

Alliance research has similarly moved from studying individual relationships toward the challenge of managing entire alliance portfolios. Werner Hoffmann's work on multi-alliance management, for example, identified portfolio-level tasks including strategy, monitoring, coordination and the creation of alliance-management systems.

And ecosystem research pushes the problem another step further.

Ron Adner defines an ecosystem around the multilateral set of partners whose alignment is required for a particular value proposition to materialize. That matters because an ecosystem cannot always be understood as a large pile of independent bilateral relationships. The relationships interact.

That describes the environment many technology companies now operate in.

But there is another practical capability I think we need to examine:

Can the organization see the possible configurations contained inside the network?

The option set is hidden

Imagine, for example, a customer facing a sovereign-cloud requirement.

Somewhere in the ecosystem may be exactly the right combination of infrastructure, software, security expertise, regional capability, regulatory knowledge and services capacity to solve it.

Or a customer may be trying to migrate away from a legacy virtualization environment.

Again, the pieces may already exist; the problem isn't necessarily capability. It's legibility.

• Who has done this before?

• Which partners are technically compatible?

• Which have relationships in this account?

• Which sellers cover it?

• Who has delivery capacity in the relevant geography?

• Which commercial models work together?

• Which companies have successfully worked together before?

• Who is actually good at this?

Most large organizations can answer pieces of those questions. Few can query the network as easily as the question implies.

So humans traverse it. We ask around. We call Cindy.

Sometimes we literally architect the relationships ourselves

I've seen cross-company motions that required aligning sellers across companies to a common set of accounts.

Conceptually, this is straightforward:

1. Here are the accounts.

2. Here are the sellers at Company A.

3. Here are the sellers at Company B.

4. Find the relevant people and connect them.

In practice, it became an Excel exercise followed by meetings designed to introduce the humans to one another.

We were, quite literally, architecting seller friendships.

This isn't because anyone involved was incompetent. It's because the information necessary to assemble the motion lived in different organizational systems.

• Account ownership lived somewhere.

• Territory information lived somewhere else.

• Customer relationships were partly documented and partly tacit.

• Partner expertise lived somewhere else again.

And context was distributed across humans.

The network knew, the individual didn't.

But the network had no reliable mechanism for making what it knew accessible to the individual.

“Seller Matchmaking.” The network may already contain the right people and capabilities. The hard part is knowing where they are—and connecting them. Concept and direction by Megan Arnold; image created with ChatGPT.

This is going to matter enormously for AI

There is an understandable tendency to imagine AI eventually solving this problem: ask an agent to identify the ideal partner configuration for an opportunity.

Great.

But AI doesn't make an invisible network visible by magic. It can reason across information it can access and interpret. It can make previously expensive matching and synthesis dramatically cheaper. But the underlying capabilities still need to become sufficiently legible.

Identity matters, permissions matter, account ownership matters. Capabilities need descriptions, relationships need representation, customer context needs to be accessible.

Different systems need enough shared semantics for a useful answer to exist. The prerequisite to intelligent orchestration may therefore be something more basic:

The ecosystem has to become queryable.

A good ecosystem operating layer should make valuable relationships easier to discover, form and activate. Humans should still build trust, understand nuance, negotiate interests and recognize opportunities that weren't obvious from the data. But we should stop requiring years of accumulated personal experience just to know which capabilities exist inside a network the organization already has.

A network can be extraordinarily valuable, but the first requirement for exploiting an asset is being able to see what you own.

Research behind this essay

Ritter, Wilkinson & Johnston, Measuring Network Competence: Some International Evidence (2002); Hoffmann, How to Manage a Portfolio of Alliances (2005); Adner, Ecosystem as Structure (2017).

The Best Seat in the House is in the Partner Org

Partner organizations are often measured on parter-sourced opportunities and pipeline.

That makes perfect sense if you think of a partner primarily as a channel: did the relationship produce demand? Did that demand become pipeline? Did it become revenue?

It makes less sense when the partner is an independent software company sitting inside a much larger technology ecosystem.

Because after years working in partner organizations, I’ve become convinced that one of their most valuable outputs is something we barely measure at all.

Partners see things.

They see which product launches make sense to customers and which require months of explanation. They see when a technically compelling integration fails to produce a compelling customer story. They see which sales motions actually reach the field and which remain beautiful PowerPoint slides.

They see how customer priorities differ across regions, how competitors change conversations, how organizational structures accelerate or constrain execution, and where theoretically elegant GTM models break when they encounter actual buyers.

And, critically, they see many versions of these things.

A product team sees deeply into its product. A sales organization sees deeply into its accounts. A regional organization sees its market. A partner organization can sit at the intersection of all of them—and then repeat that experience across companies.

That should be an extraordinary learning system, but we don’t operate it like one.

Exposure isn't learning

Organizational researchers have been thinking about this distinction for decades.

In 1990, Wesley Cohen and Daniel Levinthal introduced the concept of absorptive capacity: a firm's ability to recognize the value of external information, assimilate it and apply it commercially. Importantly, they distinguished organizational absorptive capacity from the knowledge possessed by any one individual. An organization does not become capable of learning simply because someone inside it noticed something important.

Alliance research makes the point even more directly.

Prashant Kale, Jeffrey Dyer and Harbir Singh found that companies with a dedicated alliance function designed not only to coordinate alliances but to capture and disseminate alliance knowledge reported greater alliance success. Later work by Kale and Singh identified a more explicit alliance-learning process: articulating, codifying, sharing and internalizing what an organization learns through alliances.

That distinction matters.

Because partner organizations can be extraordinarily information rich while remaining learning poor.

I can observe the same problem across multiple partners. Someone else can see it across another set of multiple partners. A regional team may discover an approach that works remarkably well. A seller may understand why a joint solution suddenly resonates with customers. A campaign may dramatically outperform another.

But unless those observations are captured, compared, interrogated and eventually allowed to change something, the organization hasn't necessarily learned.

The people have.

We are very good at asking what happened

This becomes especially visible when something works.

Someone runs a great campaign. The natural question is: How did you do it?

“How Did You Do It?” Success often looks repeatable only after we strip away the conditions that made it possible. Concept and direction by Megan Arnold; image created with ChatGPT.

So we document the audience, messaging, tactics, channels, budget and results. We turn the campaign into a case study. Maybe we call it a best practice. But success is rarely that clean.

Maybe executive attention suddenly shifted and an initiative that had struggled for support became a priority.

Maybe product readiness aligned perfectly with the campaign.

Maybe the messaging, approvals and operational infrastructure were already in place.

Maybe the field had enough time to prepare.

Maybe a regional team found a faster way to adapt the motion to its market.

Maybe, for once, all the dependencies simply lined up.

The campaign worked.

But which parts of what happened constitute the repeatable capability?

The campaign worked.

But which parts of what happened constitute the repeatable capability?

That's a harder question. And at ecosystem scale, it becomes a fascinating one.

A large technology company may have hundreds or thousands of external relationships generating observations about customers, products, markets, competitors, sales motions and organizational behavior.

Imagine being able to interrogate that accumulated experience longitudinally.

• When did our expectations about this market change?

• Which assumptions repeatedly turned out to be wrong?

• Which early signals predicted successful joint motions?

• Which decisions looked right at the time but proved costly later?

• What conditions were consistently present when something worked?

• Which problems keep appearing across completely unrelated partners?

Those are not pipeline questions: they are learning questions.

The ecosystem as an observation network

This is where I think the role of partner organizations gets more interesting than the traditional channel model suggests. The ecosystem isn't only a route through which companies reach customers.

It is also an enormous observation network, and AI makes that possibility much more consequential.

Historically, the cost of capturing and synthesizing thousands of messy observations was prohibitive. The knowledge lived in people's heads, email, meeting notes, decks, CRM fields, partner plans and eventually whatever they remembered when someone asked.

AI dramatically changes the economics of making that information searchable and comparable, but AI can't learn from organizational experience the organization never captured. And it can't decide which observations should alter strategy, funding, product decisions or operating models unless there are mechanisms through which learning can become action.

That part is organizational design.

So perhaps we should expand what we mean when we talk about the performance of an ecosystem.

Of course I want to know what it sold, but I also want to know what it taught us, because the best seat in the house isn't valuable because it reveals a single winning formula.

It may be valuable because it gives us enough observations to stop pretending there is only one.

Research behind this essay

Cohen & Levinthal, Absorptive Capacity: A New Perspective on Learning and Innovation (1990); Kale, Dyer & Singh, Alliance Capability, Stock Market Response, and Long-Term Alliance Success (2002); Kale & Singh, Building Firm Capabilities Through Learning (2007).

The Next Layer of Cloud

A few weeks ago, I was on a call with a partner seller in a European territory reviewing the results of a campaign we'd run across two ISVs and multiple regions.

We'd sent him 30 leads. Three were actionable. Not three that converted. Three that could actually be worked.

Most of the others didn't have sales coverage at both ISVs. Some were SMB accounts, while the joint sales motion we'd built assumed enterprise coverage. There wasn't a clear path for what to do with them.

Could we figure one out? Probably.

We could create a new SMB sales motion. We could try to bring in another partner with the right coverage. Maybe there was another path through the broader ecosystem.

Except we didn't necessarily have permission to share the lead data with another company.

So first we'd have to figure out the PII issue. Then potentially a co-marketing agreement. Then lead ownership. Then routing. Then seller coverage. Then incentives. Then who actually calls the customer.

All solvable problems. It just takes time. Months, potentially.

Realistically, nothing is going to happen with most of those leads. The obvious lesson here is pretty boring: build a better target account list next time. And we will.

Thirty leads entered one multi-company GTM motion. Only three were immediately actionable after encountering constraints in sales coverage, customer fit, data permissions, and lead routing. Demand existed; the operating path did not. Illustration by Megan Arnold, created with AI assistance using ChatGPT.

But the campaign took somewhere between six and eight months to launch. During that time, two of the three companies crossed fiscal years.

If you've worked in a large technology company, you know what that can mean. Territories change. Account assignments change. Priorities change. Sales models change. People move.

A target account list isn't a fixed representation of the market. It's a snapshot of multiple organizations at a particular moment in time. By the time we launched the campaign, the ecosystem it had been designed for had already changed underneath it.

A campaign can remain fixed while the ecosystem around it changes. Over an eight-month campaign cycle, companies may cross fiscal years, change territories or sales coverage, release new product features, or delay product availability—altering the operating environment before launch. Illustration by Megan Arnold, created with AI assistance using ChatGPT.

I used to think these were partnership problems

I've spent a lot of my career working in cloud partnerships, first from the ISV side and now from inside a hyperscaler. For years, the prescription for making partnerships work has been some combination of the same things:

Alignment. Executive sponsorship. Relationships. Communication. Influence. Clear accountability. All good things. I'm just increasingly convinced they're insufficient. Because eventually you run into a problem that no amount of "alignment" actually solves.

• The customer gave one company their information. Can that company legally give it to another company?

• One company calls an account one thing. Another calls it something else. Are they actually the same account?

• Company A has a seller assigned to the customer. Company B doesn't. Can they still co-sell?

• Three companies agree on the customer outcome but have three different sales incentives for getting there. Who moves first?

You can put very collaborative people in a room and still have all of these problems. Because these aren't really relationship problems. They're infrastructure problems. And, importantly, the idea of orchestration isn't new.

In 2006, management scholars Charles Dhanaraj and Arvind Parkhe used the term to think about how hub firms coordinate networks of autonomous companies when they don't have hierarchical authority over everyone else. Their work was about innovation networks, not cloud GTM. But the fundamental organizational problem is strikingly familiar: how do you coordinate a network of companies when nobody can simply tell everyone else what to do?

More recent platform research treats ecosystem orchestration as a major part of how platforms create and capture value. A systematic review by Joost Rietveld and Melissa Schilling looked across 333 academic papers on platform competition published between 1985 and 2019 and identified ecosystem orchestration as one of the major themes in the literature.

So I'm not proposing a new theory of ecosystems here.

I'm interested in something much more specific:

What happens when all of this has to go to market?

Because I think we're still remarkably good at building ecosystems and remarkably bad at operating across them.

Cloud doesn't really feel like an ecosystem

"Ecosystem" makes me think of a forest. Lots of independent organisms distributed across a landscape, all interacting in complicated ways. That's not really how enterprise cloud feels to me. It feels more like a handful of enormous objects with gravitational fields.

In Q2 2026, three companies accounted for about 63% of worldwide cloud infrastructure services revenue. Synergy Research Group estimates that the market reached $143.4 billion that quarter, with AWS at 28%, Microsoft at 20% and Google at 15%. In public IaaS and PaaS specifically, the concentration was even higher.

Those aren't three companies sitting alongside thousands of other companies on a flat playing field. They're centers of gravity.

Around them are ISVs, GSIs, distributors, resellers, developers, data companies, services companies and customers. Many of those companies orbit multiple hyperscalers simultaneously.

And if you've ever worked inside an ISV, the gravity is not subtle.

• ISVs want access.

• They want attachment.

• They want favor.

• They want visibility within the hyperscaler and its broader ecosystem.

They want hyperscaler sellers to know who they are. They want to show up in account conversations. They want to become the obvious answer for a particular customer problem. They want marketplace transactions. They want executive attention. They want to be included.

They want just a little more of that enormous engine pointed in their direction.

A tiny change in how much of that engine flows toward an ISV can be enormously valuable to the ISV.

Which is why I think managing cloud partnerships with a direct GTM playbook misses a lot of the point.

The leverage isn't always where the revenue lands

The questions in a direct business are pretty obvious.

• How much pipeline did we generate?

• How much revenue did this campaign produce?

• How many opportunities did this event create?

We need those questions, but they can also make us stare directly at the transaction while missing the system around it.

Imagine an ISV gets hundreds more hyperscaler sellers to understand exactly where its product fits, or it becomes part of a repeatable customer architecture, or buying through the marketplace becomes dramatically easier, or it gets attached to another ISV whose product makes its own product substantially more useful, or one important field organization starts treating it as the default solution to a particular customer problem rather than something a seller has to go discover.

Where does the value of that intervention show up? Eventually, hopefully, in revenue.

But the leverage may have happened several steps—and several organizations—away from the place the revenue was finally recorded.

That's why I keep coming back to a different question:

Where are the points of leverage in the system that change how value flows through it?

And that's also what made me start thinking about the original promise of cloud differently.

Cloud already abstracted one enormous layer of complexity

The basic cloud proposition feels so normal now that it's easy to forget what changed. The canonical NIST definition of cloud computing describes on-demand access to shared computing resources that can be rapidly provisioned and released with minimal management effort or provider interaction.

Cloud did not eliminate infrastructure complexity (obviously): what it did was change how much of that complexity every customer had to manage directly. More of the underlying infrastructure became something you could consume. And then we built an astonishing amount of stuff on top of it: applications, APIs, data platforms, services, marketplaces, integrations, entire categories of software.

Which created more possible combinations of those things, and then more. And more.

The kaleidoscope keeps turning

This is where the cloud ecosystem stops looking like a solar system to me and starts looking like a kaleidoscope.

Same pieces.

Turn it.

• A hyperscaler launches a new capability.

• An ISV integrates with it.

• Another ISV solves the problem next to it.

• A marketplace incentive changes.

• A regulation changes.

• A customer standardizes on a different architecture.

• Turn it again.

Same companies. Different configuration. Different opportunity.

And now AI is in the middle of all of this.

I don't actually want to make the lazy argument that "AI means there will be more software companies." Maybe there will be. Maybe AI ultimately causes enormous consolidation. I don't think we know yet.

What seems much safer to say is that AI is accelerating change across an already enormous cloud market. Synergy estimates cloud infrastructure services grew 43% year over year in Q2 2026, with GenAI-specific cloud services growing substantially faster.

The kaleidoscope is turning quickly. Meanwhile, it can take me six to eight months to get a complicated GTM motion into market.

That mismatch bothers me.

Bottlenecks don't politely stay where they were

This is where the research changed how I was thinking about the problem:

Harvard strategy scholar Carliss Baldwin has spent decades studying the architecture of complex technical systems. Her work on bottlenecks looks at how firms can create value by solving important technical constraints—and capture value by controlling strategically important ones.

She is not writing about partner marketing (unfortunately for her, of course). But the idea made me wonder whether we're looking at a version of the same phenomenon one layer above the technology. Cloud solved—or at least dramatically reduced—an enormous set of infrastructure constraints. That made it possible to create far more on top of the infrastructure.

Now the value of all those capabilities increasingly depends on assembly.

And assembly isn't only technical - it’s commercial, it’s organizational, it’s contractual, it’s human.

We've become extraordinarily good at making technical components interoperable while routinely stitching the companies behind those components together with spreadsheets, meetings, contracts, Teams messages, email chains and a handful of humans who happen to know whom to call.

And then we act surprised when it takes eight months.

What if the constraint is moving?

This is where the research ends and my argument starts: I don't know that ecosystem orchestration is "the next bottleneck in cloud." I don't think anybody has established that. But I keep coming back to the possibility.

Cloud made computing infrastructure dramatically easier to consume. That helped create enormous ecosystems of independent technologies and companies capable of producing combinations of value no single company could create alone. But every additional dependency creates another question of assembly, another boundary, another interface. Another place where the technology can work perfectly and the companies behind it still can't figure out what happens next.

Which makes me wonder whether a meaningful constraint is moving upward.

From:

How do I get access to the computing capability I need?

to:

How do I make this ecosystem of capabilities actually work together for me?

Cloud abstracted much of the complexity of provisioning infrastructure. As cloud ecosystems expand, the next constraint may be moving upward—from infrastructure toward the orchestration of software, partners, and the ecosystem itself. Illustration by Megan Arnold, created with AI assistance using ChatGPT.

If that's right, ecosystem orchestration isn't just a more sophisticated way to manage partners. It starts to look like an abstraction problem.

Cloud abstracted infrastructure complexity so every customer didn't have to solve the same infrastructure problems independently.

• Could someone do something similar for ecosystem complexity?

• Could account identity become easier to reconcile across companies?

• Could seller coverage be mapped automatically?

• Could commercial permissions travel with the motion?

• Could different CRM stages be translated instead of standardized?

Could the seams between companies become actual interfaces instead of places where work disappears?

And if someone can make the ecosystem dramatically easier to assemble, buy, sell and operate, there's another question hiding behind that one:

Who gets to own that layer?

The hyperscalers are obvious candidates. They already sit at the intersection of infrastructure, marketplaces, billing, technical standards, sellers, customers and enormous partner networks. But they're not the only candidates. GSIs, distributors and other intermediaries occupy different positions in the network. Some have an advantage the hyperscalers inherently don't: they can operate across multiple gravitational systems.

The thing I can't get out of my head isn't really cloud architecture: it's those 27 leads. There was demand, there were products capable of solving customer problems, there were companies that wanted to work together. Nobody screwed up, no one refused to collaborate (well, some did, such is the nature of the beast), nobody did a bad job. Every individual problem was solvable.

It just takes time.

And by the time you've built the operating model required to capture the opportunity, the ecosystem may have moved again.

Cloud became enormously valuable by reducing the time and effort required to access computing infrastructure.

I wonder if we're watching the same problem emerge one layer higher.

The next challenge may be making the ecosystem cloud created as operable as the infrastructure underneath it.

Cloud created an abstraction layer for infrastructure complexity. Ecosystem complexity—relationships among companies, systems, data, incentives, permissions, and operating models—still lacks an equivalent abstraction layer. Illustration by Megan Arnold, created with AI assistance using ChatGPT.

Sources & further reading

Dhanaraj, C. & Parkhe, A. (2006).Orchestrating Innovation Networks. Academy of Management Review, 31(3), 659–669.
https://journals.aom.org/doi/10.5465/amr.2006.21318923

Rietveld, J. & Schilling, M. A. (2021).Platform Competition: A Systematic and Interdisciplinary Review of the Literature. Journal of Management, 47(6), 1528–1563.
https://journals.sagepub.com/doi/full/10.1177/0149206320969791?

Baldwin, C. Y.Design Rules, Volume 2: How Technology Shapes Organizations. MIT Press.
https://direct.mit.edu/books/oa-monograph/5887/Design-Rules-Volume-2How-Technology-Shapes?

National Institute of Standards and Technology. (2011).The NIST Definition of Cloud Computing (SP 800-145).
https://www.nist.gov/publications/nist-definition-cloud-computing?

Synergy Research Group. (2026).Q2 Cloud Market Passes $143 Billion; Highest Growth Rate in Eight Years.
https://www.srgresearch.com/articles/q2-cloud-market-passes-143-billion-highest-growth-rate-in-eight-years?

GTM Latency: When the Product Ships Faster Than the Organization Can Sell It

In enterprise technology, launching a product and getting a product to market are not the same thing.

A product can be generally available. The documentation can be live. The announcement can be published. The launch event can be over.

And the organization can still be months away from actually being able to sell it.

Marketing has to understand it. Positioning has to make its way into the field. Sellers have to learn it exists. Partners have to understand where it fits. Account teams have to recognize the customers who might need it. And eventually, somewhere in a customer conversation, someone has to connect a problem to this particular solution.

The time between product availability and effective market activation is what I think of as GTM latency.

And AI may be about to make it a much bigger problem.

Product velocity is accelerating. Is GTM velocity?

AI is already compressing parts of the product development lifecycle. Code can be written faster. Prototypes can be built faster. Features can be created, tested and iterated faster.

That should be an enormous advantage.

But only if the rest of the organization can keep up.

Imagine that AI allows a company to double the number of meaningful features it ships without materially changing the system responsible for bringing those features to customers.

Marketing now has more to position.

Sellers have more to learn.

Partners have more to understand.

Customers have more choices to navigate.

The technology moved faster. The organization didn't.

At some point, the constraint on growth moves downstream.

AI can accelerate product development while simultaneously making GTM latency worse.

We usually try to solve this with enablement

When a new product or feature isn't getting enough traction, one of the natural responses is to increase awareness.

Build the deck.

Create the sales play.

Run the webinar.

Send the email.

Train the sellers.

None of those things are inherently wrong. But they rely on an increasingly unrealistic assumption: that the way to make a growing portfolio of technology useful is to make more humans remember more of it.

Consider the actual moment that matters.

A seller is talking to a customer six months after a feature launched. The customer describes a problem that feature happens to solve.

For our traditional enablement model to work, the seller has to remember something they saw months earlier, recognize its relevance to a problem described in completely different language, find the right information, determine whether it's still current, and know what to do next.

At enterprise scale, that's a remarkable amount of GTM infrastructure resting on human memory.

AI gives us another option.

What if GTM optimized for retrieval instead of recall?

A seller shouldn't necessarily need to know that a product exists before they can find it.

They need to be able to describe the customer problem.

Imagine a seller asking an internal AI system:

"My customer is trying to accomplish X. They're already using Y, they have this constraint, and they're struggling with Z. What capabilities do we have that could help?"

A feature launched three weeks ago might be the best answer.

The seller never attended its enablement session.

They didn't read the launch announcement.

They didn't remember its product name.

They didn't need to.

The customer need became the query that activated the GTM system.

That starts to look surprisingly similar to an idea marketers have understood for a long time: inbound marketing.

Traditional inbound marketing recognized that customers were already searching for solutions to their problems. Instead of relying exclusively on pushing messages toward them, companies made themselves discoverable when customers went looking for answers.

There may now be an internal version of the same idea.

Sellers, solution architects, customer success teams and partners are already searching for answers to customer problems.

What if we treated them as an internal audience with intent?

Internal AEO/GEO

Companies are beginning to think seriously about how their products appear when customers ask AI systems questions.

But product teams inside enormous enterprises should probably be asking the same question about their own organizations:

How do I make my product legible to my company's intelligence layer?

That changes what internal product marketing could look like.

Instead of optimizing primarily for awareness, we also optimize for discoverability.

Product information would need to clearly explain not only what the product is, but the problems it solves, the scenarios in which it is relevant, the products it works with, the constraints it addresses, how it differs from alternatives, and what signals indicate that a customer might need it.

In other words, we make the product understandable in the context of the questions people will actually ask.

That's essentially an internal AEO/GEO strategy.

And it creates an interesting possibility for reducing GTM latency.

The traditional model requires knowledge about a product to travel through the organization before the product can reliably travel to the customer.

An AI-mediated model can potentially shorten that path.

Product ships → product becomes discoverable → customer problem surfaces it.

Not every seller needs to know everything.

The organization needs to know everything — and be able to retrieve the right thing at the right moment.

The bottleneck is moving

For years, one of the great constraints in technology was our ability to build.

AI is beginning to change that.

But removing one constraint doesn't remove the system's constraints. It exposes the next one.

If we can create technology dramatically faster than we can explain it, distribute knowledge about it, connect it to customer needs and activate the people responsible for selling it, then the competitive advantage of faster product development begins to erode.

The companies that win won't necessarily be the ones that ship the most.

They may be the ones that minimize the distance between something becoming possible and the right customer discovering that it's possible.

That's GTM latency.

And as product velocity increases, I suspect we're going to start measuring it.

The Missing Operating Layer Between Companies

Making the Space Between Companies Operable

The spacecraft we rebuild for every joint GTM campaign is made from predictable parts.

It needs a shared view of the accounts. It needs to know which sellers cover them and which company holds the customer relationship. It needs a way to capture demand, qualify it, route it, track it, and preserve context as the customer moves from one company to another. It needs permissions, ownership, governance, measurement, and a clear path from interest through adoption.

We know this before the campaign begins.

And yet, much of the infrastructure is still assembled through spreadsheets, meetings, email, personal relationships, and manual reporting. The companies may have sophisticated systems for managing their own customer journeys. The joint journey lives somewhere between them.

That space between companies is where multipartner GTM becomes difficult to see and manage. It is also where AI could make a meaningful difference.

The joint customer journey has no natural home

Within one company, a campaign moves through systems designed around that company’s view of the customer. Marketing automation captures engagement. A CRM holds accounts, contacts, sellers, and opportunities. Workflow tools assign tasks. Reporting connects activity to pipeline and revenue.

The experience can still fragment, but the company has an operating environment built to contain it.

Now distribute the same journey across three companies.

One company hosts the campaign and captures the initial response. Another has the technical expertise to qualify the need. The third has the strongest customer relationship. The opportunity appears in one CRM before it reaches the others. The transaction happens through a marketplace. Implementation belongs to a services partner. Adoption is measured through product data that only one participant can see.

Each company holds a valid piece of the story. None holds the whole story.

The missing information tends to be operationally consequential:

- What has the customer already communicated?

- Which partner currently owns the next action?

- Did the handoff occur?

- Is another company waiting for information?

- Has the opportunity progressed or stalled?

- Did the customer purchase, implement, and adopt the joint solution?

The collective customer journey exists in negative space. It is real enough for the customer to experience, but it has no natural system of record.

The joint motion needs a place of its own

Putting the entire motion inside one partner’s CRM creates a different problem. That company becomes the de facto owner of the customer view. The other participants receive whatever information the host company is willing and legally permitted to share. Visibility, control, and attribution begin to follow the architecture.

A multipartner motion needs a shared environment that sits between the companies.

That environment would not replace each company’s CRM, marketing platform, or internal reporting. It would maintain the operating state required for the companies to act together:

- The accounts included in the motion

- The customer problem and joint value proposition

- The sellers and partner teams attached to each account

- The known customer relationships

- The current stage of the joint journey

- The owner and deadline for the next action

- The status of each cross-company handoff

- The activity and outcome data the partners have agreed to share

- The decisions, approvals, and exceptions affecting the motion

Each company would continue to manage its own records. The shared layer would give the joint work somewhere to live.

It would function as a system of action between separate systems of record.

Reusable infrastructure does not require a universal GTM strategy

The partner ecosystem does not need every company to follow the same sales methodology, use the same qualification criteria, or define pipeline in the same way.

It needs a reusable set of operational capabilities that can be configured for different missions.

Every spacecraft needs navigation, communications, identity, controls, and a way to monitor its condition. The mission determines how those systems are configured. A multipartner GTM operating layer could work the same way.

One motion may involve a hyperscaler and an ISV targeting thousands of accounts through a marketplace offer. Another may combine a platform company, a security ISV, and a services partner around a small number of regulated customers. A third may involve several ISVs assembling an industry solution.

The account criteria, customer journey, seller roles, data permissions, and commercial path will vary. The operating components remain recognizable:

- Identity

- Shared state

- Workflow

- Permissions

- Governance

- Measurement

The current approach rebuilds those components around each campaign. A reusable operating layer would begin with them already available.

Temporary centralization may be enough

Three companies rarely need to integrate their entire enterprises. They need to centralize enough of the joint motion to pursue a defined customer outcome.

That operating environment may exist for one campaign, solution, group of accounts, or strategic initiative. Access can be limited to named participants. The partners can specify which data is visible, how it may be used, how long it is retained, and what happens when the motion ends.

Temporary does not have to mean disposable.

The individual environment can expire while the capability to create it remains. Each new motion begins with a proven architecture and adapts it to the partners, strategy, systems, and permissions involved.

That is very different from beginning with another blank spreadsheet.

It also gives legal and governance teams something concrete to evaluate. Instead of negotiating an undefined flow of customer data between companies, they can define access and use within a bounded environment built for a stated purpose.

AI does not remove those legal obligations. It can make the resulting rules easier to implement, monitor, and change.

AI changes the time required to assemble the system

Traditional software can provide shared portals, partner relationship management, data integrations, and joint reporting. The difficulty appears in the variation.

The same customer may have different names and hierarchies across three systems. An “engaged account” means something different to each company. Pipeline stages do not match. Qualification standards vary. Seller territories change. Every participant has its own security policies, privacy requirements, attribution rules, and internal workflows.

Traditional integration asks teams to define those mappings and rules in advance. That work can take longer than the campaign or market window allows.

AI can compress the configuration work.

It can reconcile account and contact identities, map sellers across changing territories, translate pipeline stages, interpret qualification criteria, and create workflows from the operating rules the partners agree upon. It can summarize permitted customer activity, detect missing handoffs, monitor deadlines, and produce different views of the same motion for each participating company.

When something changes, the system can adjust. A seller moves territories. A new partner joins the motion. A qualification rule changes. An account shows a new buying signal. AI can update the shared state and recalculate the work required without asking a team to rebuild the operating model manually.

The value is speed. The spacecraft can be configured for the mission while the launch window is still open.

From 5,000 accounts to the next action

This becomes especially important when a partner strategy operates across thousands of accounts.

At the portfolio level, three companies may agree on the market, the solution, and 5,000 target accounts. That agreement is a strategic achievement. It still leaves thousands of account-level questions unanswered.

At one account, the platform seller holds the executive relationship. At another, the services partner is already leading a transformation. The ISV may have an active opportunity in one account, an existing customer in another, and no presence in the third. Each account has its own product footprint, buying signals, relationships, timing, and best next action.

A shared operating layer could translate the portfolio strategy into account-level configurations.

For each account, it could identify the relevant sellers, show the relationships and activity the partners are permitted to share, surface the strongest joint opportunity, assign the next action, and monitor whether that action occurs.

The partners would still decide what the strategy means. The system would make it possible to execute that strategy at the resolution where revenue actually happens.

What the motion could look like

Imagine three companies preparing to launch a joint solution.

They agree on the customer outcome, target-account logic, seller roles, qualification criteria, data permissions, commercial path, and definition of success. Those decisions configure a shared environment for the motion.

The operating layer reconciles the three account lists and identifies the accounts where the joint hypothesis is strongest. It maps the relevant sellers and known customer relationships. Each account receives an initial owner and next action.

Campaign engagement enters from approved sources. Customer signals are matched to the shared account view. Responses follow the qualification and routing rules the partners established. Each company sees the information appropriate to its role.

When an opportunity progresses inside one company, the joint motion reflects that change without exposing restricted information. When a handoff stalls, the responsible team sees it. When the customer purchases, implementation and adoption remain connected to the original opportunity.

The shared environment keeps the customer journey coherent even though the participating companies remain separate.

This is the infrastructure we currently approximate through status meetings, spreadsheets, forwarded emails, and people who know whom to call.

AI can operate the agreement the humans create

The hardest decisions in a partnership are rarely data-matching problems.

The companies still have to agree on the customer outcome, partner roles, account priorities, information-sharing boundaries, seller incentives, commercial ownership, decision rights, and accountability. They have to decide what happens when their interests diverge.

AI cannot grant permission to share customer data. It cannot give one partner authority over another. It cannot decide which company should concede when the best collective outcome conflicts with an individual company’s economics.

Once those decisions are made, AI can turn them into an operating environment and help keep that environment running.

That distinction matters. A shared operating layer will make strong alignment easier to execute. It may also make weak alignment harder to hide. When the customer journey is visible and the next action is clear, a stalled motion can no longer be explained entirely by missing information.

The cost of coordination can become infrastructure

Today, every additional multipartner motion increases the demand for people who can manually coordinate it. Scale requires more meetings, more partner managers, more spreadsheets, and more institutional knowledge.

Reusable infrastructure changes that relationship.

Teams could launch joint motions faster, support more accounts, intervene earlier, and preserve customer context with less manual reconstruction. Partner leaders could spend less time determining what happened and more time making decisions about what should happen next.

The revenue benefit would come from several places: faster time to market, lower campaign operating costs, quicker customer follow-up, better conversion, more reliable implementation, stronger adoption, and the ability to run more missions without adding coordination headcount at the same rate.

The spacecraft does not have to be rebuilt for every launch. Much of its architecture is already known.

The next multipartner campaign is going to need account alignment, seller mapping, qualification, routing, visibility, ownership, governance, and measurement. Before another team opens a blank spreadsheet, there is a more useful question to ask:

How quickly could we create a secure place where the joint customer journey can actually exist?

We Rebuild the Spacecraft for Every Joint GTM Campaign

Three Products Work as One. We Rebuild the GTM Around Them Every Time.

We Rebuild the Spacecraft for Every Joint GTM Campaign

Every joint GTM campaign begins by building a spacecraft.

In astrophysics, Lagrange points are locations created by the gravitational relationship between two large bodies. A spacecraft operating near one of these points can hold a useful position with relatively little energy. Some Lagrange points are more stable than others. Near the less stable ones, the spacecraft needs regular station-keeping maneuvers to keep from drifting away.

Partner ecosystems create similar points of leverage. Two or more companies align around a customer need, a joint solution, a group of accounts, or a commercial opportunity. A campaign gives that alignment a mission. To carry it out, the partners need an operating system capable of moving customer context, decisions, work, and accountability across company boundaries.

That operating system is the spacecraft.

And in much of the ISV ecosystem, we build a new one for nearly every mission.

The five Lagrange points in the Sun–Earth system, with a joint campaign depicted station-keeping near unstable L1. Conceptual illustration, not to scale. Illustration by Megan Arnold, created with AI assistance using ChatGPT.

The payload is ready before the spacecraft

Three companies’ product teams engineer a joint solution. Three products now work together seamlessly. Then the customer tries to buy it.

The products function as one. The customer still has to navigate three companies.

Each company has its own account priorities, sales organization, marketing systems, qualification criteria, commercial model, and way of measuring success. Product integration does not automatically establish which company should introduce the solution, whose sellers should pursue it, where customer responses should go, or who owns the customer after the contract is signed.

Those decisions often begin after the technical work is complete. By then, the companies may have announced the partnership, established revenue expectations, and committed funding to a launch.

The payload is sitting on the ground. The teams still have to build the vehicle that will carry it to market.

We already know what the spacecraft needs

There is little about a multipartner GTM motion that is operationally unfamiliar. A company taking its own product to market selects an audience, identifies buyers, develops an offer, generates demand, captures engagement, qualifies responses, engages sellers, progresses opportunities, transacts, implements, and drives adoption.

The same functions exist when two, three, or four companies participate. Their ownership is distributed.

One company may host the campaign and capture the initial engagement. Another has the deepest product knowledge and is best equipped to qualify the need. A third has the strongest customer relationships and controls access to the buyers. The transaction may happen through a marketplace, implementation may belong to a services partner, and adoption may ultimately determine whether another partner recognizes revenue.

Every joint motion therefore needs some version of the same core machinery:

- Account alignment

- Seller mapping

- Customer identity and context

- Lead capture, qualification, and routing

- Opportunity ownership

- Shared visibility

- Commercial and post-sale handoffs

- Governance, attribution, and measurement

The specific partners, solution, audience, and route to market change. These requirements remain remarkably consistent.

Yet teams routinely assemble them from scratch. They create a new spreadsheet to reconcile the account lists and another to map the sellers. They establish a new meeting cadence, intake process, lead-routing agreement, reporting method, and set of definitions. Legal and privacy questions are worked through for that particular combination of companies. The people involved learn how to work together while the work is already underway.

Each campaign becomes a prototype.

A strategy for 5,000 accounts becomes 5,000 flight plans

At the strategy level, three companies may agree on 5,000 target accounts. They identify overlap using firmographic criteria, product usage, cloud consumption, purchase intent, or other signals. The list gives the partnership a market, a shared direction, and enough scale to justify investment.

But 5,000 accounts do not create one motion. They create 5,000 different versions of it.

In one account, the platform company may have the strongest executive relationship, the software company may already have an active opportunity, and the services partner may have no presence. In another, the services partner may be leading a transformation program while the other two companies are trying to gain access. One account may be ready to buy. Another may need education. A third may already use all three products but have no reason to think of them as one solution.

The portfolio strategy establishes where the mission will operate. At the account level, the system still has to resolve the customer need, existing relationships, product footprint, opportunity maturity, assigned sellers, commercial incentives, and best next action.

Matching company names across three lists is only the beginning. The partners need a shared reason for pursuing each account. They also need a way to identify the relevant people and determine who should act.

Doing that manually for ten accounts is possible. Doing it for 5,000 is an operating model.

This is where claims of scale tend to exceed the infrastructure supporting them. The strategy is built for thousands of accounts. Execution still depends on a small number of people resolving the motion one account at a time.

Campaigns create a temporary flight system

A campaign gives the partners a shared audience, budget, deadline, customer story, set of deliverables, and reason to meet. People are named. Approvals are pursued. Reporting is assembled.

For a limited period, the campaign creates an operating structure that the companies do not otherwise share.

That structure can place the joint motion near a useful point of equilibrium. Customer need, partner capabilities, seller interests, and commercial opportunity align well enough for the companies to act together.

The position rarely holds itself.

A partner marketer reconciles the account lists. An alliance manager tracks down the sellers covering each account. Someone carries customer context between systems, rebuilds the performance view, follows up on overdue actions, resolves an attribution disagreement, and reminds the teams who committed to the next step.

These are the campaign’s station-keeping maneuvers. They prevent the motion from drifting back into the separate priorities and systems of the participating companies.

The fuel is time, attention, budget, institutional knowledge, and political capital.

Some campaigns require modest corrections. The partners already have strong relationships, complementary incentives, clear ownership, and a familiar commercial path. Others need near-constant intervention. Remove one partner manager or stop the weekly meetings, and the shared motion begins to disappear.

That gives us a useful question for any joint campaign:

How much of the mission’s energy reaches the customer, and how much is consumed keeping the spacecraft in position?

A campaign can generate pipeline and still be structurally difficult to repeat. Its results may justify the investment while its station-keeping requirements prevent it from scaling.

The launch window keeps moving

Building the spacecraft takes time, and the mission does not wait.

The customer has a budget window, a current priority, and a problem they are trying to solve now. Another vendor may already be in the account.

The market is moving too. Products change. Competitors act. Regulations shift. New capabilities alter what customers expect. In a market moving as quickly as AI, a joint solution can lose its novelty while the participating companies are still deciding how to take it to market.

The partners have organizational clocks of their own. Each company operates through planning cycles, funding cycles, legal reviews, sales territories, and leadership priorities. The people who designed the original motion may change roles before the campaign reaches the field.

A lead-routing agreement finalized three weeks after launch cannot recover the customer interest lost during those three weeks. A target account list completed after territories change has to be mapped again. A commercial path agreed after the customer’s budget closes belongs to a mission whose launch window has passed.

Opportunity value decays while companies coordinate.

We tend to measure partner ambition through investment, pipeline targets, and the number of accounts in the plan. The time required to convert a joint decision into coordinated action may tell us more about the system’s ability to produce revenue.

The customer can feel the course corrections

Customers will never see most of the machinery holding the campaign together. They experience its instability.

They repeat information because context did not survive a handoff. They receive outreach from two partners while waiting to hear from the third. They hear slightly different versions of the value proposition. They struggle to understand who owns the next step. They discover late in the buying process that the path to implementation was never fully aligned.

The customer journey is where the internal physics of the ecosystem becomes visible, much as celestial bodies reveal the otherwise invisible forces acting between them.

When the journey fragments, the customer absorbs the coordination cost. That cost appears as delays, uncertainty, repeated work, and increased risk. For the partners, it appears as slower pipeline creation, lower conversion, longer sales cycles, delayed implementation, weak adoption, and missed expansion.

Three individually strong companies can create a collective experience that feels surprisingly fragile.

The next mission starts from scratch

At the end of a campaign, the temporary flight system often disappears.

The spreadsheets stop being updated. The meeting cadence ends. Sellers move to other priorities. The people who understood how the pieces fit together carry that knowledge into their next project or role. Results are reported back into separate company systems, each preserving a different version of what happened.

Then another joint solution or campaign appears, and another team begins assembling the same operational components.

The new mission may have a different payload, trajectory, and partner configuration. It will still need account alignment, seller mapping, customer context, qualification, routing, shared visibility, ownership, governance, and measurement.

We know what a spacecraft requires. We know where the station-keeping burden tends to fall. We know that the time spent building and correcting it consumes part of the opportunity it was created to pursue.

So why does every multipartner mission still begin in a new spreadsheet?

The physics of partner ecosystems

When you add a third partner, you change the whole system

There’s a concept in astrophysics called a Lagrange point.

Take two bodies in an orbital relationship, like the Sun and Earth. Together, their gravity and orbital motion create five mathematical locations called Lagrange points. Put a spacecraft near one of these points, and it can maintain a relatively consistent position in relation to both bodies with far less energy than it would require elsewhere.

The bodies are not stationary. They are moving constantly. But within that motion, there are points of equilibrium: places where the forces acting on an object align well enough to create stability and leverage.

Partnerships have Lagrange points, too.

The five Lagrange points in the Sun–Earth system. Conceptual illustration, not to scale. Illustration by Megan Arnold, created with AI assistance using ChatGPT.

In a partnership between two companies, there are usually a few places where customer need, company strategy, product capability, seller motivation, and commercial value align. A shared customer segment. A complementary product integration. A marketplace motion that benefits both companies. A field play that gives each seller a reason to bring the other company into an account.

These are the points where the partnership can produce disproportionate value. The companies keep moving—their strategies change, leaders rotate, products evolve, budgets shift—but the motion can remain relatively stable because the forces between them are understood.

Then we add a third partner.

We often talk about this as though we are simply expanding the opportunity. One company brings the platform, another brings the application, and a third brings the services required to implement it. Each participant adds capability, reach, credibility, or access to the customer. The combined value proposition becomes stronger.

At least, theoretically.

Because adding a third partner does not simply add another participant. It changes the physics of the system.

Three partners create more than one new relationship

With two companies, there is one bilateral relationship to manage. With three, there are three:

- Company A and Company B

- Company A and Company C

- Company B and Company C

And then there is the collective relationship among all three.

Each bilateral relationship may have its own history, economics, executive sponsorship, product dependencies, sales incentives, and definition of success. A decision that strengthens one relationship can destabilize another. A roadmap change at one company can alter the value proposition for all three. A seller incentive introduced by one partner can redirect attention away from the collective motion. A disagreement about customer ownership can stop the entire system at the moment it is supposed to create value.

The third partner has not just made the partnership bigger. It has introduced new forces and feedback loops.

This distinction matters because the way we plan multipartner growth often assumes that value is additive. If Partner A brings a certain set of customers and capabilities, and Partner B brings another, adding Partner C should increase the opportunity again.

But the size of the opportunity is only one part of the commercial equation:

Expected revenue = market opportunity × probability of execution × value captured

A third partner can increase the potential market opportunity while simultaneously reducing the probability of execution. The total value available may be larger, but there are now more dependencies, handoffs, approvals, incentives, systems, and decisions that must align before any of that value becomes revenue.

That is the multipartner paradox: the same partner that expands the opportunity can also make the opportunity harder to realize.

The strategy usually looks better than the system beneath it

Imagine a joint go-to-market motion among a cloud platform, an independent software company, and a services partner.

The customer story is compelling. The platform provides the infrastructure. The software company provides a specialized solution. The services partner helps the customer implement it and achieve the promised business outcome. Together, the companies can solve a larger and more consequential problem than any one of them could solve alone.

The executive slide practically writes itself.

Then the work begins.

Which accounts are the companies targeting? Do all three define the ideal customer the same way? Which sales representatives cover each account? Who already has the strongest relationship? Who makes the introduction? Who leads the customer conversation?

If the companies launch a campaign, who pays for it? Who builds it? Whose brand leads? Where do the responses go? Whose business development representatives qualify them—and according to whose criteria? Can the resulting customer data legally and technically move between all three organizations?

If a lead becomes an opportunity, which company records it? How do the other two know it is progressing? Which sellers receive credit? Who owns the next action? Who transacts? Who implements? Who is accountable for adoption after the contract is signed?

These questions can be dismissed as execution details. They are not peripheral to the strategy. They are the mechanisms through which the strategy either becomes revenue or does not.

A campaign can generate hundreds of responses and still fail commercially because no one designed the handoff between companies. A customer opportunity can exist in three CRM systems under three different names and still have no clearly accountable owner. Three sellers can all support the same strategic motion while each waits for someone else to initiate it.

The campaign did not necessarily fail. The system around the campaign failed.

Not every point of alignment is stable

The astrophysics metaphor is useful for another reason: not all Lagrange points are equally stable.

In partner ecosystems, some forms of alignment naturally reinforce themselves. If the solution solves a real customer problem, the commercial path is clear, sellers receive meaningful credit, and each company benefits from the customer’s success, the motion may continue with relatively little intervention.

Other motions only appear stable because partner and marketing teams are constantly correcting them. Someone manually reconciles the account lists. Someone tracks down the sales representatives. Someone carries customer context from one system to another. Someone schedules the meeting, follows up on the action items, resolves the attribution dispute, and reminds every participant why the work matters.

Remove that person, and the motion drifts.

This does not mean the opportunity is unworthy. But it does mean we should distinguish between a motion that is structurally aligned and one that is being held together through continuous human effort. They have different costs, risks, and potential to scale.

One of the most important questions in partnership strategy, then, is not simply, “Where can we create value together?” It is:

What would have to remain true across all participating companies for that value to be created repeatedly?

That question shifts the work from planning an activity to designing a system.

The customer is the center of gravity

There is also a danger in becoming so absorbed in the complexity between partners that we lose sight of why the ecosystem exists.

The customer does not care that three companies have different fiscal calendars, sales territories, privacy policies, CRM platforms, reporting models, or internal definitions of an influenced opportunity. The customer experiences only the collective result.

Did the companies understand the problem? Did they present one coherent solution? Did they know what the customer had already shared? Was there a clear next step? Could the customer buy, implement, and adopt the solution without becoming the project manager for the partnership?

The customer journey is where the internal physics of the ecosystem becomes visible, much as celestial bodies reveal the otherwise invisible forces acting between them.

If three companies promise an integrated outcome but deliver a fragmented buying experience, the solution is not truly integrated from the customer’s perspective. The customer ends up absorbing the coordination costs the partners failed to resolve.

That friction has direct commercial consequences: longer sales cycles, lower conversion, stalled procurement, weaker adoption, and fewer expansion opportunities. Conversely, when the companies make their boundaries nearly invisible to the customer, orchestration becomes a source of competitive advantage.

Partner leaders are designing systems of work

Multipartner growth cannot be achieved by stacking several bilateral go-to-market plans together. The collective system has to be designed intentionally.

That means designing more than the market message. It means designing how decisions are made, how information moves, how accounts are prioritized, how sellers engage, how customer context survives handoffs, how incentives reinforce the desired behavior, and how success is measured across organizational boundaries.

The highest-leverage work may happen in places that never appear in the launch announcement: the account-mapping process, the lead-routing agreement, the shared opportunity stages, the seller-credit model, the governance mechanism, or the decision about who owns the customer’s next step.

Those operational seams are where theoretical ecosystem value becomes—or fails to become—customer value and revenue.

The future of partner leadership is not simply managing a larger portfolio of relationships. It is learning to engineer dynamic systems among companies that continue moving independently but must create a coherent outcome together.

Because when you add a third partner, you do not merely expand the partnership.

You change its physics.

The five invisible inherent tensions in a strategic alliance

by Megan Arnold

Underneath every conversation, every negotiation, every activity in a business alliance, between the two companies there are fundamental opposing forces that constitute and define the playing field of the partnership – the boundaries that define how and to what extent to which you can achieve your company’s objectives. Most alliances are based on the expectation that two companies’ combined strengths and weaknesses can complement each other to the benefit of both companies. This strategic impetus is necessary to form the alliance, but the ability to execute and exploit those strategic advantages over several years and multiple financial cycles is where value is realized.

Unlike in a joint venture – where a new legal, financial, and operational entity is formed (an entirely new company) – a strategic alliance relies on the continuous combination and cooperation of every stitch of the partnership between two companies. As a result, one of the biggest challenges in the implementation and active management of a strategic alliance is that the negotiation between the two companies is perpetual: the conversation about how your partner invests in the partnership and each company’s accountability to the joint business never ends, and given the expansive and holistic nature of strategic alliances, every piece of the business is subject to those ongoing negotiations. Nothing is ever settled; very little about your partnership becomes the normal course of business.

Day to day in an alliance, conversations can center around product roadmap priorities, the number of sales headcount funded on each side, CRM integrations, marketing investment, and so on. The tangible, well-defined components of a partnership are critical to execution. However, all these investments occur in a partnership environment defined by subtle, almost invisible partnership dynamics. These dynamics define the playing field – the boundaries, the goals, the rules of the game. When partnership managers and executive leaders focus on expanding and influencing the playing field, it expands the potential for a successful partnership.

Flexibility vs. commitment

Many strategic alliances start with contracts. The negotiation of these initial contracts is often more important than the final contract itself, as that’s when the primary stakeholders at both companies work towards alignment on strategy and mutual commitment to the partnership’s success. Specific investments from both companies can be defined, certain legal issues committed to (like ownership of intellectual property), and often issues like pricing, margins, and so on are agreed upon.

These contracts can quickly go out of date. They can be unenforceable to the extent they are impractical. Further to that, there are other vehicles in a strategic partnership that can be far more binding than a contract, like the integration of systems that share financial and sales data or the transfer of funding from one company to another to support partnership activities, for example.

Hard, contractual commitments can ensure cooperation and investment from your partner for a defined period of time, but they can also lock you into certain investment expectations as well. In a business environment where markets are changing faster than ever, long-term commitments can also prevent a company from pursuing new, more lucrative opportunities both within the partnership and outside of it. 

Flexible commitments, in the form of annual business plans, executive handshakes, one-off projects, allow both partners to be agile and to continually revisit their expectations for the partnership based on their changing priorities. This can also present challenges around committing to long-term, multi-year goals and maintaining focus in executing on the business.

Every hard commitment a partner makes is a brick in the foundation of the alliance that becomes a step to increased collaboration and cooperation long term, until it becomes an anchor holding you back from new opportunity. Every soft commitment a partner makes allows for the agility required to pursue different opportunities together, but the impacts are rarely sustained. 

When partner managers wield hard and soft commitments in right places, the business not only builds endurance through those hard commitments, but uses flexibility in ways that don’t undermine long-term business execution.

Imbalance of power

There is almost always a more powerful partner in a strategic alliance. This imbalance is usually rooted in which company wields more power in the marketplace due its size and market cap, the competitiveness of its product offering, the scale of its customer base, or its brand value. It can also be based on how much or how little the company depends on the partnership. As abstract as the imbalance of power sounds, it shows up in everyday interactions between people at the two companies, like whose availability takes priority for scheduling meetings, whose office hosts business reviews, who is expected to contribute the majority of funding and headcount to joint initiatives. 

The more powerful partner can often be inaccessible, more difficult to influence, and more expectant that the less powerful partner adapt to their ways of doing business, like which metrics are used to measure and report on performance.

One of the ways to expand or change the environment of your partnership is to understand the levers of power, in particular what your partner wants to achieve from the alliance that your company is specifically positioned to provide. In some cases that can feel like capitulating to the more powerful partner, but when managed accordingly, it turns into a strong foundation to exert increased influence on that partner. It can be frustrating to feel pressure from your partner to focus on parts of the business that you don’t value as highly, but if you were to over deliver, it becomes a source of power for you in conversations with your partner. 

In sprawling enterprise partnerships in which multiple business units at each company collaborate with each other - and sometimes not in a centrally coordinated way - the imbalance of power can be localized to specific components of the partnership, like the balance of power between two product innovation groups or between geographies. 

Centralization vs. distributed management

Long-term strategic alliances grow as more products are added to the partnership, more geographies, more use cases, more teams involved to support the joint business. In large enterprises, sometimes hundreds, if not thousands, of people at a company are dedicated to a specific partner across multiple organizations and business units. This proves challenging to alliance managers and partner leaders to protect and grow the partnership itself when so many people are engaged with the partner every day. 

Distributed management can scale the joint business with more productivity, and it can also damage focus and strategic alignment, both internally and with the partner. Centralized management can bottleneck the running of the business, but it can also support a very focused approach to partnership development and ensuring that your company maintains its position of power and leverage. 

One of the biggest challenges facing strategic partnerships is how to scale the partnership itself. Partnership development is often overlooked in pursuit of business development, but without a partnership development strategy, the playing field for the partnership remains either static, controlled disproportionately by your partner, or subject to external, unpredictable influence. 

Partnership development and management is best scaled in the same way most enterprise businesses are managed: through a clear partnership development roadmap with milestones identified and agreed upon by internal stakeholders as the most likely to improve pipeline potential. Employees involved in the partnership should be briefed and trained on strategic objectives for the partnership as well as a consistent message to deliver and adhere to when engaging with the partner. 

This also prevents the pursuit of partner development activities that are low impact or divergent from the top priorities, or activities with your partner that put the partnership at risk. 

Visibility / information access

Many large organizations have systems in place to allow partners controlled access to proprietary data - like org charts and employee contact information - that allow the business to run smoothly. However, that access doesn’t typically include strategic information, like sales comp plans, marketing campaign priorities, budget information, product roadmaps, pipeline data, etc. There will always be blind spots in your ability to see into your partner, which is often why the most effective strategic alliance managers are former employees of the partner. 

For companies in a strategic alliance, managing a line of business together requires the sharing of business critical information, like pipeline, revenue, product roadmaps, or customer information. Access to this information is dependent on the level of human cooperation between organizations, and the operational infrastructure put in place to manage the business jointly. 

Working towards joint business management is incredibly challenging - scheduling executive alignment meetings, quarterly business reviews, monthly pipeline reviews and forecasting all require agreement on what metrics to report, whose data should be used, and what format the report takes. It requires calendar coordination, determining the right parties to have at the table, and how to facilitate productive strategic conversations between two companies whose corporate objectives and ability to execute are only occasionally aligned. 

With the right relationships and executive sponsorship, many partners can achieve joint business management on some level. Strategic partners are able to reach partnership milestones that further entrench themselves in the partner’s rhythm of the business. These milestones are hard commitments more effective than contracts - like CRM integration and shared dashboards for reporting. That level of technical integration is hard to unwind, whereas quarterly business reviews can always be canceled. 

When a company can identify the most critical business information and establish ways to entrench their access to it from a partner, they establish barriers to competition that haven’t achieved that level of joint business management. 

Intent vs. ability to execute

Often, strategic alliances are forged between parts of companies whose intentions require other teams to assist in their fulfillment. This gap between those accountable for a strategic alliance and the resources required to achieve the joint objectives present numerous roadblocks in partners’ ability to execute on their joint business plan. For example, product teams could find it advantageous to build a new offering together, but marketing teams could see that product as a low priority to support. Orchestrating resource alignment within a company and across two companies is a continuous effort as organizations restructure and corporate priorities for shared resource teams evolve over time. 

Establishing hard commitments are an effective way to insure against resources being allocated away from partnerships. One example would be establishing a marketing development funds program in which two companies agree contractually on joint marketing initiatives and then funds are disbursed from one company to another to execute on those plans, but these agreements often have to be renewed on a yearly basis. 

When working within the framework of a strategic alliance - like with most things - a good strategy has no value if it can’t be executed. If you have resources lined up in the United Kingdom to drive joint business, but it’s not a high priority country, that has more value every day than a high priority country without any resources. Your total available market is only as valuable as your ability to capture it. It can be hard to recognize that reality when confronted with it, and often endless cycles are spent lining up resources in order to execute on business, and then the business opportunity is missed. 

Symmetrical vs. complementary alignment

It’s human nature to expect symmetrical alignment in a partnership - that your partner has the same number of headcount dedicated to the joint business that you do, that there’s a marketing or sales leader at both companies that work together in the same capacity, that both companies are pursuing the joint opportunity in the same way against the same product, the same geographies, the same customers, the same use cases, and that what makes them partners is that business is being done together. Symmetrical alignment would certainly make it easier to do business together.

Not only is this unrealistic - as both companies have different investment models, different approaches to doing business, and are starting from different organizational structures at the start of a partnership - but it can also leave money on the table. If two companies were exactly the same and decided to partner, that would be a 1+1=2 equation - the same strengths, same weaknesses, just double. 

The best strategic alliances exploit complementary strengths and compensate for different weaknesses, which means that direct alignment in managing the partnership is counterproductive. If Company A excels in financial services, Company B does not need to invest in financial services. Ideally, Company B is strong in another area, say, manufacturing, in a way that Company A is weak. The partnership management task is jointly managing complementary businesses in order for 1+1 to equal 3. 

It’s a trap to expect 1:1 functional alignment across the partnership - for sales at Company A to have a mirror image sales team at Company B and across all functions from marketing to product to services. In some cases it’s appropriate, but when partner managers can clearly identify the complementary strengths of each company, there is also much less tension around the ways your partner might not be meeting expectations. 

These invisible conditions are running your business

Managing pipeline and business results with a partner is often the primary focus of a partnership, with partnership development seen as second priority. But often, most of the limitations on achieving the full potential of a strategic alliance come down to the two companies’ ability to execute on the partnership through clear alignment, mutual investment, and operations. 

The conditions of strategic alliances - like flexibility vs. commitment, imbalance of power, visibility vs. information access, centralized vs. distributed management, and symmetrical vs. complementary alignment, are invisible but powerful forces that define the potential of your joint business. With a clear strategic approach to managing these conditions, partners can expand their playing field intentionally to increase their ability to execute against their business goals. 

Knowing where you want a hard commitment from your partner and where you want flexible commitment helps you navigate changing market dynamics while resting on a solid partnership foundation. Understanding the positions of power you wield in the partnership helps you capitulate to your partner in ways that increase your value to them. Increasing your programmatic and systematic access to business critical information from your partner gives you a competitive edge. Finding ways to scale the management of your partnership across large organizations ensures focus and reduces risk to the partnership. Exploiting complementary strengths makes mutual investment efficient and best captures the value of the alliance in the first place. 

Organizations too overlook partnership management, often conflating partnership development as business development or revenue growth. Partnership growth expands the boundaries of what’s possible for how two companies work together efficiently and effectively, and raises the ceiling on true revenue potential. 

A three-tier AI content production model to meet the demands of your business

by Megan Arnold

Generative AI requires a tiered content production approach to maximize its potential. This is dramatically different from today’s methods of translating corporate strategy into messaging, copy, design, and review phases regardless of the type of content. To account for the impact of generative AI, as marketers we need to understand specifically the roles and responsibilities of AI in the production process and organize our teams and work streams accordingly. In an AI-driven content organization, three tiers of content exist that require different levels of management: original works, derivative works, and long-tail works. 

With AI-enabled content creation, organizations should build a different production process for three different types of content.

Tier 1: Original Works Creation

In the first tier, human-led efforts play a crucial role in developing foundational assets such as brand guidelines, messaging frameworks, value propositions, and personas. These original works serve as the source material for generative AI to create derivative content. Human involvement ensures strategic vision, creativity, and the incorporation of changes in corporate strategy, product updates, market feedback, and institutional knowledge.

Tier 2: Derivative Works Generation

In this tier, generative AI tools utilize the original works to produce derivative content that can be consumed by the target audience. The AI-led process allows for an infinite number of permutations, tailoring the content to specific segments, industries, and use cases. Human input is essential to curate and guide the AI's output, ensuring accuracy, relevance, and brand integrity.

Tier 3: Long-tail Works

In the final tier, generative AI takes center stage in producing long-tail assets, including call scripts, social posts, sales outreach emails, blog posts, and more. These assets often have limited use and are temporal, requiring personalization and customization for establishing a human-to-human connection with prospects. Generative AI empowers individual sales representatives to create authentic and relevant content in a decentralized fashion.

The three-tier content production model harnesses the capabilities of generative AI while incorporating human creativity and strategic direction. By capitalizing on generative AI's strengths at each tier, B2B marketing organizations can achieve unprecedented levels of content personalization and customization. This collaborative approach empowers marketers to connect with customers on a deeper level, driving engagement, and building long-lasting relationships.

What AI can’t or shouldn’t replace humans for in the business of marketing

by Megan Arnold

In the application and integration of artificial intelligence into daily business processes, there’s understandable and legitimate angst over how AI will replace humans. With what we understand today about AI, I believe that these new capabilities will let us be more human, not less. I believe that AI will remove the kinds of tasks from our to-do lists that give us more time to be the creative, inspired, intellectual, metacognitive, and talented humans we all are. 

When AI can automate a number of our daily tasks as marketers- like writing swipe copy, first drafts, scheduling meetings, using templates, etc.- we are left in the position of focus on critical management functions. These management functions aren’t relegated to managers or executives, every marketer makes strategic decisions at every level. As we work towards integrating AI into our production processes, understanding which functions remain owned by humans will help us all establish the roles and responsibilities of our newest team member - artificial intelligence. 

Here are a few management functions I see as primarily human:

Corporate strategy

AI will inevitably have a large role to play in the development of corporate strategy given its ability to analyze massive amounts of data quickly. AI algorithms rely heavily on historical data to predict future outcomes, and only on the historical data it has access to. In the near term, AI will remain blind to competitor data or data proprietary to entities outside the firm.

Prioritization

One fundamental responsibility of managers in executing corporate strategy is deciding what not to do. Managers are often faced with many options, many potential paths forward, many good campaign ideas to invest in, but the purpose of strategy is to maximize the results produced while minimizing the resources required. This involves saying no more than it involves saying yes. In an AI-driven world where so much more marketing work can be produced than ever before, the temptation to say yes more often will be strong. It is essential that managers remain committed to using AI production processes to drive efficiency in achieving strategic objectives, not in losing focus.

Decision making

While AI tools can provide recommendations on what decisions to make (like project prioritization) based on the resources required and the results expected, there are a number of factors involved in decision-making that can’t be reflected in data. These factors could include information humans would know that AI doesn’t, like a new product in development, anticipated changes in the market, unprecedented economic environments (like a global pandemic), or personality and power dynamics in a business.

Humans should continue to make major decisions in the marketing production process, the most important of which may be what gets published, or which campaigns go live. Marketing artifacts represents a company’s external image and reputation at scale, and artificial intelligence should not automatically publish those artifacts without human approval and oversight.

Quality assurance

AI is not perfect and cannot produce perfect results. Perfect is subjective, as is the definition of quality. In marketing terms, quality can mean anything from an asset’s adherence to brand style guidelines to technical accuracy or to just being fun to read. Generative AI tools can take into account many of the technical aspects of writing and content, even brand tone of voice, but it may not catch bias baked into its work. Humans must remain gatekeepers to AI-produced work to prevent unconscious bias, inaccuracies, or that the finished product matches the brief and follows through on its strategic intent. 

Innovation and original works

Marketing is inherently creative. It’s the practice of building meaningful connections between an individual and a brand at scale. When we operate at scale, we can identify trends and derive insights applicable to most of the people engaged with our campaigns, but each of those people remain unique, complex individuals looking for solutions to their specific problems. On the other side, each corporation sees itself occupying a unique position in the market relative to its competitors. It invests in producing new solutions through innovation, in bringing products to market that customers haven’t seen before. 

Today, generative AI relies on data and information that already exist and can be processed by machines. The complexity of the human individual can’t be entirely captured by AI (fears, regrets, hopes, insecurities, to name a few), and the ability to imagine what has never existed before remains distinctly human.  

As long as we see the production of new ideas as informed by AI but owned by humans, our markets will remain a step ahead of AI.  For marketers, this means that human talent should focus on the innovative work of producing the knowledge base the AI tool relies on (messaging frameworks, technical documentation, brand guidelines, etc.) and experimentation with new tactics to continue informing AI on best practices.

A culture of AI literacy, healthy skepticism, and a commitment to ethics

In an AI-driven production environment, marketers should be well-educated on what’s behind the algorithms - the knowledge base the tool uses, the rules it follows, and potential biases inherent in its outputs. When reviewing work produced by AI, marketers need to continually question if the asset reflects strategic intent, is free of unconscious bias, and represents the company appropriately. 

Ethical considerations have a necessary role to play in the development and adoption of AI tools. In marketing, some of those considerations include data privacy, systemic discrimination and unconscious bias, accuracy and veracity of information, corporate espionage (competitive intelligence), impersonation (deep fakes), and corporate social responsibility. 

As the application of AI evolves and becomes more understood, marketers can see AI as a powerful engine of information and analysis that enables us to be better managers. We maintain authority over AI as we leave critical management tasks to humans, from decision-making, to ethics, to innovation and new ideas. 

A B2B marketer’s AI wishlist

by Megan Arnold

Like many of us, I spent the better part of 2023 absorbing the impact of generative AI, exploring its use cases, and considering its potential to transform the ways we work. As a marketer in the tech industry, generative AI (and in particular ChatGPT) became widely available at a time when there were widespread doubts about the near-term resilience of our economy, increased interest rates forced a reckoning in the world of venture capital, and layoffs in the industry became ubiquitous. For much of the year, these forces created a working environment of intense pressure to perform with more uncertainty, change, and the fewest resources available to do the work in years. 

I became somewhat obsessed with the potential for AI to automate and simplify many rote marketing tasks - copywriting, first drafts of blogs, or perhaps even the localization or customization of content, for example. While I do currently use generative AI in my day-to-day work, without the organization around it (the features, processes, systems, and workstreams), its ability to transform the ways marketing teams work is extremely limited. 

In the spirit of the holiday season, here’s my wishlist of ways I’d like to see marketing organizations adopting AI tools to drive efficiency, improve ROI, and reduce burnout for their team members. Emphasis on wish list, by the way. 

Self-service content development and production

One of the biggest challenges every organization faces - especially enterprises - is content management. Core content teams can’t possibly achieve the “right message, right person, right time” holy grail of digital marketing with current production costs. Messaging takes too long to develop, content too long to produce, and products and services change too rapidly for true personalization of content. Another challenge is that in a world where everyone has Powerpoint and a LinkedIn profile, every single frontline go-to-market professional (from sales to partnerships to marketing) is creating content, no matter how good or how big an enterprise’s content team is. Today, this proliferation of content comes with inconsistent branding, inaccurate product information, and a long tail of unused and out of date assets on content management systems. 

How many times have you heard an account executive ask for a solution brief or sales presentation deck on one of your products, but specifically speaking to C-level decision makers in the automotive industry for a niche use case (and written in German)? With today’s production timelines, no organization can centrally generate or control the thousands of versions of a single asset needed by go-to-market teams to address each customer’s unique pain points. 
A self-service content generation tool built on the back of a foundational knowledge base (brand guidelines, messaging frameworks, and technical documentation), a marketing team could empower their entire organizations to achieve true personalization and deliver the right message at the right time to the right person. 

AI tools built for marketers today already include the ability to generate copy (and in some cases design) by relying on libraries of existing brand documentation and assets that ensure the AI tool generates content that is both technically accurate and adheres to brand guidelines.

Imagine if your highly intelligent, experienced, and capable content teams could focus on building the core foundational knowledge base of your AI tool. You could then allocate more resources to a brand review team to rapidly review content for quality assurance. Even two or three week’s turnaround time on content review is light-years ahead of where we are today in the time it takes to produce content. 

A fully-integrated and automated AI-fueled program management system

Today, marketers open up millions of Asana tickets (or Monday or Wrike or Trello or Basecamp tickets, etc.)  around the world. And those same marketers spend millions of hours managing their program management platform, not producing the work. And, if you’re like me, you experience a deluge of email inbox notifications on tasks. We also spend millions of hours in planning meetings, trying to agree on not just the work to be done, but how to do the work and softly (or not) negotiating who is going to do the work and by when. Projects stall because team meetings have to wait 2-3 weeks until there’s a free timeslot in the group’s calendar. People in geographically distributed teams get left out when they’re in a distant time zone. 

I’d like to have a program management system that is a marketing platform multi-tool: it combines a content management system, has access to performance data and analytics, integrates with Outlook, knows the company’s org chart (and roles and responsibilities), and understands the time it takes for tasks to be done. 

Imagine telling this AI-driven platform the business problem you’re trying to solve, or the business impact you want to have (generate 3,000 marketing qualified leads from this set of accounts in the next 8 weeks, help my sales team use LinkedIn more effectively, etc.) and that platform gives you three ways to solve that problem based on historical performance of your company’s own marketing campaigns that generated similar results. 

Then, that platform identifies all the tasks involved in producing that campaign: the bill of materials, vendors required, the people involved, and the cadence of the workflow required to meet the business objective. This platform then schedules a kickoff meeting, records the discussion, synthesizes the feedback, develops the brief, and tees up tasks in order for the right people with all dependencies taken into consideration. 

The teams review the progress asynchronously by providing written feedback that the AI tool consolidates and shares, eliminating the need for excessive meetings and improving global collaboration. It reduces to almost zero the amount of time your teams spend managing the task versus doing the critical thinking work only humans can do. 

In this case, that critical thinking work - the kind only humans can do - includes setting business goals and KPIs, negotiating costs with vendors, negotiating project prioritization with internal resources, resolving conflict between team members, ensuring that the output of the project meets quality requirements, and communicating the delivery of the project and its impact to stakeholders. Last - and perhaps the most important work only humans should do - is the final decision to go live with the campaign. 

A system like this not only saves time and reduces the hidden costs of inefficiency, but it is also highly fluid. With every reorganization or restructuring, every role change, there’s often months where your teams are left figuring out how to do the same work they’ve always done but with different people and processes that haven’t been rebuilt. An AI-fueled program management tool could be managed on the backend to take these changes into consideration and fluidly adapt to where each of the humans on your team sit in the organization as it changes. 

Further to that - in enterprise marketing organizations - best practices often don’t make their way around the world as important learnings. A centralized AI-fueled program management tool like this would automate the knowledge sharing without cycles being spent by teams teaching each other what they did that worked so well, it would be immediately applied. 

Performance optimization & resource management

Today’s martech stack remains fragmented and unable to produce consolidated digital insights across the full customer journey. From social media performance to web analytics to lead progression, we can only derive isolated insights and performance metrics. Currently, significant investments in IT are required to build data lakes and corresponding interfaces to tell a single story of the customer journey. Part of a centralized AI-fueled program management tool would inevitably have to be integrated with all these reporting platforms to understand campaign performance against business goals to identify best practices. 

To some extent we have these capabilities today, but they are based on “hard” diagnostic metrics like peak time of day for web traffic, referral source, time spent on page, number of clicks, etc. AI promises insights based on softer metrics like identifying that a certain tone of voice produces more engagement, or videos with three speakers instead of two are watched more often, or web pages with images of your product lead to more sales. 

As ROI requires an understanding of not just results, but the investment against them, this program management tool would observe the work being done, the time it takes and the dollars spent. This should be anonymized in order to not be misused by leadership as a surveillance tool, but used to better understand how many hours producing a major event costs, or how many hours marketers in sales-facing roles spend talking to reps, or how many hours it takes to build and launch a webinar. 

Competitive intelligence and market insights

Using publicly available information only (earnings calls, product launches, financial statements, analyst reports, customer reviews, product listings, press releases, conference session recordings, executive social media, etc.) I’d love to see an AI tool that could analyze historical information on a company’s competitive environment and provide insights into its position in the market, its strengths and weakness, and its opportunities relative to its competitors. Perhaps this tool could even identify patterns in competitor behavior to provide probabilities on certain moves - likely acquisitions, future product launches, risks, etc. 

Today that work is done by consultants, corporate strategy teams, and analysts, but all are prone to bias and none can process the vast amount of information and synthesize it fast enough to remain relevant and useful. By no means am I suggesting AI is not prone to bias, but because it can process massive amounts of data, it is less susceptible to biases like confirmation, affinity, or anchoring bias. 

Corporate strategy is an enterprise’s function of understanding itself, it’s the firm’s center for self-awareness. An AI tool would allow corporate strategy to see the world more objectively, not only through the lens of itself, and ultimately lead to more informed decision-making and better preparedness to meet its customers’ needs. 

A commitment to being good

Artificial intelligence, especially when baked into the very systems that determine how we work and what work we do, is a tremendously powerful tool. Like many innovations that came before it, it has the ability to exacerbate our worst flaws, or to amplify our virtues. My last wish list item is that every organization, public, private, or personal, makes a commitment to being good, not just doing good. AI will reflect back to us who we are, and as a result the risk to AI is not in itself, it’s in us. 

I’d like to see companies commit to being good through the intention to do right by their customers and employees by leading with compassion, kindness, and wholeheartedness. I’d like to see companies take action on their intent with policies that support decision making based on goodwill and global citizenship, not just shareholder wealth creation. I’d like to see those companies follow through on their actions by basing formal incentive structures on more than business metrics. I’d like to see those organizations maintain healthy and continuous skepticism of themselves and a desire to always strive for goodness. And I’d like to see organizations hold themselves accountable and responsible when they make mistakes, despite their best intentions. We can only allow mistakes when we see a collective willingness from corporations to hold themselves accountable, and we will make many mistakes with AI. 

I’d like to see organizations truly celebrate goodness from the inside out, not just in holiday ad campaigns or employee giving programs, but that see their core mission as being good, manifested in the business of solving customer challenges and investing in new ideas.