For the better part of the last decade, enterprise technology had a pretty clear hierarchy.

Software was where the growth was.

Hardware was the plumbing.

Recurring revenue, high gross margins and asset-light business models attracted the premium valuations. Servers, networking, storage and distribution were necessary, but they weren’t usually where people went looking for the most interesting growth stories.

AI is changing that equation.

And if you’re in partnerships, you should be paying attention.

Because one of the biggest shifts happening in technology right now isn’t just the rise of AI software.

It’s the return of the physical technology ecosystem required to make AI actually work.

The numbers are getting hard to ignore.

Gartner expects worldwide IT spending to reach $6.3 trillion in 2026, up 13.5% from last year.

Software spending is forecast to grow 15.1%.

IT services: 9%.

Devices: 8.2%.

But data center systems?

55.8%.

Gartner expects spending on data center systems to jump from roughly $506 billion in 2025 to almost $788 billion this year.

IDC is seeing the same thing. Worldwide server spending increased 30.7% year over year in Q1 2026, even though unit shipments grew just 3.3%.

And AI infrastructure spending reached roughly $318 billion in 2025, more than double the prior year.

The stock market has noticed.

Dell has been one of the standout performers as AI server demand exploded. HPE has rallied sharply alongside the infrastructure cycle. Cisco is reporting billions of dollars in AI infrastructure orders. Avnet, sitting much further down the technology supply chain, has been hitting record highs as demand for electronic components accelerates.

At first glance, this looks like a hardware story.

I think it’s actually a partnership story.

AI is rebuilding the technology value chain

Think about what it takes to deploy enterprise AI.

You need chips.

Servers.

Networking.

Memory.

Storage.

Power.

Cooling.

Cloud infrastructure.

Data platforms.

Security.

Models.

Applications.

Implementation.

Managed services.

No single company owns that stack.

Nvidia needs OEMs.

OEMs need component suppliers.

Infrastructure vendors need distributors.

Distributors need VARs and integrators.

Software companies need infrastructure.

Hyperscalers need ISVs.

Enterprises need systems integrators to put much of it together.

The more AI infrastructure grows, the more interconnected the technology industry becomes.

That’s very different from the SaaS model that dominated the last decade.

A SaaS company could build a product, hire a sales team and sell directly to customers. Partnerships were valuable, but in many organizations they remained an additional route to market.

AI infrastructure doesn’t work that way.

The ecosystem isn’t sitting next to the product. The ecosystem is part of the product.

Look at Dell

Dell might be the clearest example of the shift.

Its AI-optimized server revenue reached $16.1 billion in its fiscal first quarter, up 757% year over year.

AI server orders reached $24.4 billion.

Backlog ended the quarter at $51.3 billion.

Dell subsequently raised its fiscal 2027 AI server revenue expectation to $60 billion.

Those numbers are impressive on their own.

But think about the economic activity sitting around those servers.

Those systems need Nvidia or AMD accelerators.

They need networking.

They need storage.

They need power and cooling.

They often need data infrastructure and AI software.

They need to be installed.

They need to be integrated.

They need to be managed.

And they need to reach customers.

A dollar flowing through Dell can create opportunities across an entire network of partners.

That’s the difference between looking at AI as a product market and looking at it as an ecosystem.

Cisco tells a similar story

Cisco just reported $4 billion of AI infrastructure orders in a single quarter and $9.3 billion for the fiscal year.

Again, the obvious story is networking demand.

But networking doesn’t exist independently inside an AI deployment.

It’s attached to compute.

Compute is attached to storage.

Infrastructure is attached to cloud and data platforms.

And all of it eventually connects to applications and business outcomes.

That creates a fundamentally different partnership environment.

The question isn’t simply:

Who can resell our product?

It’s increasingly:

Which combination of companies needs to win together for the customer to deploy this architecture?

That’s a much more strategic role for partnerships.

Then look one layer deeper: distribution

This may be the most overlooked part of the story.

Companies like TD SYNNEX, Arrow and Avnet sit in the middle of enormous technology ecosystems.

Historically, we called them distributors.

That description increasingly feels incomplete.

Avnet recently reported fiscal fourth-quarter revenue growth of 48%, while its electronic components business grew 49%. Management pointed to AI, data centers and industrial demand as contributors to the rebound.

Why does that matter for partnerships?

Because complexity increases the value of the companies that connect the ecosystem.

AI infrastructure involves thousands of products and thousands of partners.

Someone needs to manage inventory.

Someone needs to finance purchases.

Someone needs to aggregate vendors.

Someone needs to help partners configure solutions.

Someone needs to enable the VARs and integrators selling into the customer.

Someone needs to understand where demand is appearing across the channel.

Distribution starts looking less like logistics and more like ecosystem orchestration.

And that makes distribution data increasingly interesting too.

A distributor can potentially see something an individual vendor cannot:

Demand forming across multiple vendors at the same time.

That’s a powerful market signal.

The opportunity isn’t just partner-sourced revenue

This is where I think partnership leaders need to expand the conversation.

For years we’ve tried to prove the value of partnerships using variations of the same metrics:

Partner-sourced pipeline.

Partner-influenced revenue.

Referrals.

Marketplace transactions.

Channel revenue.

Those metrics still matter.

But the AI infrastructure cycle creates something bigger.

Partnerships can become a source of market intelligence.

Imagine you’re trying to sell into a large enterprise.

Your CRM tells you what your company knows about that account.

But your ecosystem can tell you much more.

Which infrastructure vendors are already there?

Which distributor is seeing purchases?

Which SI has an active project?

Which cloud provider is involved?

Which data platform is expanding?

Which partners already have executive relationships?

Which technologies are being deployed together?

Which partners are seeing AI demand before it appears in your pipeline?

Suddenly partner data isn’t just attribution data.

It’s GTM intelligence.

This is why the hardware numbers matter

The growth in hardware spending tells us something about where the market is going.

But it also tells us something about how technology is going to be sold.

The larger and more complicated the infrastructure stack becomes, the harder it is for vendors to operate independently.

That increases the value of relationships between:

OEMs and semiconductor companies.

OEMs and distributors.

Distributors and VARs.

Cloud providers and ISVs.

Data companies and AI platforms.

Systems integrators and enterprise customers.

And increasingly, partnerships across all of them.

The opportunity for partnership teams is to understand those relationships better than anyone else inside the company.

Not simply who our partners are.

But how the market around the customer is connected.

Partnerships are becoming a data problem

This is the part I find most interesting.

Most partnership organizations still manage their ecosystems as lists.

Here are our partners.

Here are their tiers.

Here are the deals they sourced.

Here are our joint campaigns.

But the market isn’t a list.

It’s a network.

Dell connects to Nvidia.

Nvidia connects to cloud providers.

Cloud providers connect to data platforms.

Data platforms connect to ISVs.

Distributors connect hundreds of vendors to thousands of resellers.

Systems integrators connect all of them to enterprise transformation projects.

And enterprise customers sit in the middle of multiple overlapping ecosystems.

If you can map those relationships, you can start identifying things a traditional CRM can’t see.

Where demand is forming.

Where partners overlap.

Where an account is investing.

Where a new ecosystem is emerging.

And where the next opportunity might come from.

That’s the evolution from partner management to ecosystem intelligence.

Hardware is back. But that’s not really the story.

The technology industry spent the last fifteen years becoming increasingly software-centric.

AI is reminding us that software still needs infrastructure.

A lot of it.

Servers.

Networking.

Storage.

Memory.

Power.

Cooling.

Distribution.

Integration.

And an enormous network of companies required to put all of it together.

That’s why the hardware numbers matter.

Not because partnerships should suddenly become hardware experts.

Because the fastest-growing part of the technology market is becoming one of the most ecosystem-dependent parts of the technology market.

And that changes the partnership equation.

The companies that understand not only what customers are buying, but which ecosystems are forming around those purchases, are going to have an advantage.

Hardware is back.

And partnerships may be one of the best ways to understand where it’s going next.

Ask a partner leader what makes a great hire, and you’ll usually get some version of the same answer.

Someone who builds relationships.

Someone who can run a joint pitch without a script.

Someone the field trusts.

Those things still matter.

But they are no longer a very good description of what the market is actually hiring for.

We pulled every skill listed across the live partner and alliance job postings we track at BlueThread, covering roughly 90 companies with active partner hiring signal, and ranked them by how frequently they appeared in the requirements.

Not what partnership leaders say they want.

What companies are actually putting into job descriptions.

The results suggest the partner skill set is undergoing a fairly significant rewrite.

Look at what companies are actually asking for

Among the most frequently appearing skills and domains in the roles we track:

Partnerships: 376

Partner management: 172

Channel sales: 161

Security domain: 156

Partner enablement: 128

RAG / agents: 111

Co-sell: 98

LLM / GenAI: 91

Solutions architecture: 81

AWS: 75

API integration: 72

Distribution: 70

Business development: 65

Azure: 63

Some of those rankings are intuitive.

Others are not.

Security domain knowledge appears in 156 of the postings we track.

Co-sell appears in 98.

RAG and agent experience appears in 111.

Business development, arguably the credential that has anchored partner hiring for the last two decades, appears in 65.

That is a meaningful difference.

It suggests something bigger than companies simply adding a few AI keywords to existing partner jobs.

The underlying skill profile is changing.

Partnerships are becoming a technical discipline

For most of the modern SaaS era, partner organizations were fundamentally commercial organizations.

Their job was to build relationships, recruit partners, create joint value propositions, enable sellers, generate pipeline and help deals move through the field.

Technical expertise existed inside the ecosystem, of course.

But it often sat somewhere else.

Solutions engineering handled architecture.

Security handled security.

Product handled integrations.

Partner managers coordinated the relationships between all of them.

That model is getting harder to maintain.

Two forces are pushing technical fluency directly into the partner role.

Force #1: Security moved into the revenue process

Enterprise deals increasingly route through security before they reach a purchase decision.

That changes the job of anyone responsible for helping those deals move.

A partner professional who can create pipeline but cannot navigate a conversation about SOC 2, data residency, identity, permissions or a vendor security review becomes dependent on someone else to advance the deal.

Companies appear to have noticed.

Instead of treating security knowledge as something a partner manager can borrow from another team, they are increasingly putting that fluency directly into the hiring requirements.

That is why the security number matters.

It is not simply evidence that cybersecurity companies hire partner people.

It reflects a broader change in where technical credibility sits inside an enterprise buying process.

The closer partnerships move to revenue execution, the harder it becomes for the function to remain technically abstracted from the product being sold.

Force #2: AI created an entirely new partner surface area

The second force is happening even faster.

Every hyperscaler, infrastructure company and software vendor with an AI strategy is building some form of ecosystem around it.

That ecosystem includes model providers, cloud platforms, vector databases, observability companies, agent frameworks, data platforms, implementation partners and application vendors.

Those relationships require a different type of conversation.

A partner leader does not need to become a machine learning engineer.

But increasingly, they do need to understand enough to answer questions like:

Where does the model sit?

Where does the customer’s data sit?

What does retrieval actually do?

What is the difference between a workflow and an agent?

Where does an orchestration framework fit?

How does the integration work?

Who owns the customer relationship?

What happens to the data moving between systems?

Those aren’t edge-case technical questions anymore.

They determine whether two companies actually have something meaningful to partner around.

And you cannot fake that fluency very long in front of a technical buyer.

That helps explain why RAG / agents appears in 111 postings in our tracked cohort while business development appears in 65.

The partner function is not becoming less commercial.

It is becoming commercially AND technically accountable.

The compensation data points in the same direction

Skill requirements tell one side of the story.

Compensation tells another.

If technical ecosystem roles were simply an experiment by hiring managers, you might expect companies to add new titles without meaningfully changing what they were willing to pay.

That isn’t what we see.

Across roles in BlueThread Mesh where employers disclosed compensation, the median for AI Alliances is approximately $236,000.

For context:

Partner Leadership: $242,000 median

AI Alliances: $236,000 median

Partner Manager: $186,000 median

There is an important caveat here.

The AI Alliances number comes from only nine disclosed postings in our current coverage, compared with forty disclosed ranges for partner leadership.

It is a small sample.

I would not treat $236,000 as some definitive market benchmark for AI alliance professionals.

But I do think the relative positioning is interesting.

A role family that barely existed as a distinct hiring category a few years ago is being priced surprisingly close to roles responsible for running entire partner organizations.

That is a market signal worth watching.

The floor didn’t disappear. The ceiling moved.

This is where I think the interpretation matters.

Classic partnership skills are not becoming obsolete.

You still need to build trust.

You still need to understand incentives.

You still need to get two sales organizations to cooperate.

You still need to create joint value propositions, enable sellers and generate revenue.

Those skills remain the foundation of the profession.

But foundations rarely create differentiation.

The emerging premium appears to be going to people who can layer another capability on top of those traditional partnership skills.

Partner + security.

Partner + AI infrastructure.

Partner + solutions architecture.

Partner + cloud.

Partner + APIs.

That changes both how companies should hire and how partnership professionals should think about their careers.

If you’re hiring, stop writing generic partner job descriptions

Look at almost any partner job description and you’ll still find some variation of:

“Excellent relationship-building skills.”

“Strong communicator.”

“Ability to influence cross-functional stakeholders.”

“Experience building strategic partnerships.”

None of those requirements are wrong.

They are simply insufficient.

If the role requires someone who can navigate an enterprise security process, say that.

If they need to understand AI infrastructure, say that.

If they need to work alongside solutions architects, specify what level of technical fluency that actually requires.

“Technical aptitude” is not a useful requirement.

Security review experience and AI infrastructure literacy are not interchangeable skills.

Neither is equivalent to API integration experience.

The more specific the requirement, the more likely you are to hire the person you actually need.

And companies should be equally honest about which role family they are hiring into.

A Partner Manager role and an AI Alliances role should not be identical job descriptions with different titles.

The skill data suggests they are different jobs.

The compensation data suggests the market knows it too.

If you’re already in partnerships, build a second fluency

The career implication is equally important.

If your resume still leads with pipeline influenced, deal registrations, partner recruitment and co-sell wins, keep those things.

They prove you understand the core job.

But they increasingly describe the baseline.

The next question is:

What else can you speak fluently about?

Security might be that second language.

AI infrastructure might be another.

Cloud architecture, APIs, data infrastructure or solutions engineering could be others depending on the ecosystem you work in.

You do not need to become the technical expert in the room.

You need enough fluency that the technical experts don’t have to translate the entire conversation for you.

There are practical ways to start.

If you have never sat through a security review, join the next one as an observer.

If you work around AI but cannot explain RAG without using marketing language, spend an afternoon building a basic retrieval workflow.

Sit in on architecture calls.

Talk to your solutions engineers.

Ask technical buyers why they chose one integration architecture over another.

Read the documentation for the products your partners actually sell.

Technical credibility compounds surprisingly quickly when you are already good at the commercial side of partnerships.

This may also change who becomes a partner leader

There is a second-order effect here that I think is even more interesting.

Historically, partnership leadership has often grown out of business development, sales or existing alliance organizations.

That made sense when the core competency of the function was primarily commercial orchestration.

But if technical fluency becomes a bigger part of the job, the talent pool changes.

Some of the best future ecosystem leaders may come from solutions engineering.

Some may come from product partnerships.

Some may come from cloud architecture.

Some may come from security.

And some will be traditional partner professionals who deliberately built enough technical depth to operate across those worlds.

That could reshape the profession more than any individual AI tool does.

Because once the hiring profile changes, the leadership profile eventually changes with it.

A caveat about the data

This analysis reflects the companies currently tracked in BlueThread Mesh, not the entire partner job market.

Our current cohort includes roughly 90 companies with live partner hiring signal collected from public job postings.

That creates selection effects.

The companies hiring aggressively today are not necessarily representative of every company with a partner organization.

Our AI Alliances compensation sample is particularly small.

And job descriptions themselves are imperfect indicators of what people actually do once they get hired.

So I would treat the precise counts as a view into the market we are watching, not a census of the profession.

But the directional signal is difficult to ignore.

Security and AI fluency are appearing alongside, and in several cases ahead of, skills that have traditionally defined partnership careers.

That is enough to make the shift worth paying attention to.

The job description is catching up to the job

For years, the archetype of a great partner professional was someone who could walk into a room, build trust quickly, understand the incentives on both sides and get two organizations moving in the same direction.

I don’t think that archetype disappears.

I think another requirement gets added to it.

The next generation of great partner professionals will still know how to build relationships.

They will still know how to sell.

They will still know how to navigate organizations.

But increasingly, they will also understand the technology well enough to participate in the conversations that determine whether the partnership actually works.

The partner skill set already started rewriting itself.

The job descriptions are beginning to show it.

The resumes will be next.

I have been in partnerships long enough to watch the language change several times.

First, we called it channels.

Then alliances.

Then ecosystems.

Now co-sell is trending.

Now we talk about orchestration, partner intelligence, and agentic GTM.

The terminology keeps evolving because the market keeps evolving. New technologies create new business models. New platforms create new routes to market. New tools give us better ways to identify opportunities, coordinate teams, and measure outcomes.

All of that matters.

But beneath the language, the work has stayed remarkably consistent.

Understand the customer.

Choose the right partners.

Find the few economic levers that matter.

Focus on them maniacally.

Execute.

Learn.

Repeat.

That was true when I was building cloud and mobility businesses. It was true when I built the partner organization at Gong. And it is still true today as companies try to understand what AI means for their go-to-market strategy.

The tools have changed.

The fundamentals have not.

We often use new language to avoid old problems

Every few years, the partnership industry finds a new vocabulary.

Sometimes that language reflects a real shift.

“Ecosystem” is broader than “channel.” It recognizes that value can come from technology partners, services firms, marketplaces, hyperscalers, communities, and other participants that do not fit neatly inside a resale model.

“Co-sell” is more specific than “alliance.” It describes teams actively working accounts and opportunities together.

“Orchestration” reflects the reality that modern partnership motions cross sales, marketing, customer success, product, operations, and finance.

These are meaningful distinctions.

The problem starts when the new terminology becomes a substitute for doing the work.

A company announces an ecosystem strategy but has not decided which partners matter.

A team launches a co-sell program without identifying the accounts it wants to pursue.

A leader buys an orchestration platform before defining what exactly needs to be orchestrated.

The language sounds modern.

The operating model underneath it is often missing.

Most growth comes from a few things done exceptionally well

At one point in my career, we grew a business by roughly 12 times.

The philosophy behind that growth was remarkably simple.

Every year, I focused the organization on three things.

Those three things changed as the business changed, but the operating principle did not.

We identified the few levers that mattered most, and then we focused on them maniacally.

When we were building the Microsoft cloud business, the opportunity was not simply to sell more Office 365.

The real question was:

What could we attach to Office 365 that would create more top-line revenue and improve profitability?

So we went looking for the right vendors.

We focused on the products that naturally belonged around the Microsoft offering.

We built the motion around attach.

Not twenty priorities.

Not a broad innovation agenda.

Attach.

When we launched the Google business, the focus was different.

Google was strong in education.

That was the market opportunity.

So we concentrated on EDU.

We did not try to make the Google business everything to everyone. We focused on the segment where the company already had strength and where we had a credible right to win.

Then there was mobility.

We had meaningful businesses with Verizon, T-Mobile, and AT&T.

But the metric that mattered most was activations.

Activations drove profitability.

So that became the focus.

Cloud attach.

Google in education.

Mobility activations.

Three different businesses. Three different economic levers. One operating philosophy.

Find what matters.

Focus relentlessly.

Build the organization around it.

The profitability those motions created allowed us to add resources, strengthen the team, and invest further in growth.

That created a cycle.

Focus produced results.

Results produced resources.

Resources expanded our ability to execute.

The business grew because the priorities were clear enough for the entire organization to act on them.

Strategy still becomes real through execution

Partnership teams spend a lot of time creating strategies.

They map the market.

They recruit partners.

They build tiers.

They create presentations.

They announce programs.

But strategy only becomes valuable when it changes what someone does on Monday morning.

Which partner are we prioritizing?

Which customer problem are we solving together?

Which accounts should we pursue?

What is the economic lever?

Who owns the next action?

What evidence will tell us whether the motion is working?

These are not glamorous questions.

They are operational questions.

They are also where most partnership strategies succeed or fail.

The strongest programs I have seen did not win because they had the most complicated framework. They won because they turned strategy into a repeatable operating rhythm.

A small number of priorities.

Named owners.

Clear next steps.

Real customer opportunities.

Consistent follow-through.

The strategy mattered, but execution created the result.

Focus is not about doing less for the sake of doing less

People sometimes hear “focus on three things” and assume it means ignoring everything else.

That is not the point.

A business still has customers to support, teams to manage, budgets to build, and dozens of operational responsibilities.

Focus means recognizing that not every activity deserves the same level of attention.

Some work keeps the business running.

A few things change the trajectory of the business.

The job of the operator is to know the difference.

In the Microsoft business, attach changed the economics.

In the Google business, education gave us the strongest market position.

In mobility, activations drove profitability.

The priorities were not chosen because they sounded strategic.

They were chosen because they were connected to how the business actually made money.

That distinction matters.

A good priority is not simply important.

It has economic consequence.

Customers still want simplicity

One of the most expensive lessons of my career came while I was helping build an early cloud marketplace.

We built what we believed the market needed.

It was ambitious.

It was technically interesting.

It also was not working.

After burning through a significant amount of money, I called several of our best Microsoft partners and asked a much simpler question:

What do you actually need?

Their answer was not more features.

It was simplicity.

Can I buy this in a few clicks?

Can I manage my customers easily?

Can you remove the friction?

That feedback changed the product and changed my thinking.

Every new technology cycle creates more complexity. The best companies do not pass that complexity along to the customer. They absorb it.

That was true in the early days of cloud.

It is true now with AI.

Customers do not care how sophisticated the system is behind the scenes. They care whether it helps them get something done faster, better, or with less risk.

The technology can change dramatically.

The customer’s tolerance for unnecessary complexity does not.

Relationships still matter, but they are not the outcome

Partnerships have always been built on trust.

That has not changed either.

A strong relationship creates access. It creates candor. It allows two companies to work through difficult issues without immediately giving up.

But a relationship is not an operating model.

It does not automatically create pipeline.

It does not close deals.

It does not improve customer retention.

It does not assign ownership.

The relationship is the permission layer.

It allows the work to begin.

The mistake is believing that the existence of a relationship proves the partnership is productive.

Modern partner leaders need both.

They need the trust of a relationship manager and the discipline of an operator.

They need to understand the people and the economics.

They need to build goodwill and convert that goodwill into joint action.

The relationship still matters.

It simply cannot be where the work ends.

AI improves the loop. It does not remove the need to choose.

AI is changing partnership work quickly.

It can gather information faster.

It can analyze markets.

It can identify patterns across thousands of companies.

It can summarize meetings, recommend accounts, draft joint value propositions, and automate workflows.

That gives partnership teams leverage they have never had before.

But AI also creates a new problem.

It makes it possible to see too many opportunities.

When information was scarce, the advantage came from finding it.

Now information is abundant.

The advantage comes from deciding what deserves attention.

The operating loop is still simple:

Gather information.

Analyze what it means.

Choose the few things that matter.

Focus on them maniacally.

Execute with discipline.

Learn from the outcome.

Repeat.

AI can dramatically improve the gathering and analysis.

But it cannot remove the need for judgment.

Someone still has to decide what not to pursue.

Someone has to identify the economic lever.

Someone has to choose which partners matter.

Someone has to protect the team from distraction.

Someone has to turn insight into action.

That responsibility still belongs to the operator.

The future will look different. The work will feel familiar.

Partnerships will continue to evolve.

AI agents will manage parts of the workflow.

Marketplaces will become more important.

Data will become more connected.

Partner programs will become more embedded inside the broader revenue engine.

The vocabulary will change again.

That is healthy.

But when the next term arrives, we should ask a basic question:

Does this help us do the work better, or does it simply give us a new way to describe it?

Because the fundamentals are not complicated.

Understand the customer.

Find the economic lever.

Choose the right partners.

Simplify the motion.

Create clear ownership.

Focus on the few things that matter.

Execute consistently.

Measure what happened.

Adjust.

Repeat.

The vocabulary changed.

The work didn’t.

I built an advisory agency, and have been very public about the build.

This article is part observation, part operating note. I’m sharing what is working, what I’m learning, and how content is becoming a real inbound motion for Bluethread.

My hope is that other entrepreneurs and partnership professionals can use some of these principles to build trust, grow their networks, and create stronger opportunities over time.

Most people measure content by what happens in the first 24 hours.

How many likes?

How many comments?

How many impressions?

Those numbers are visible, immediate, and mostly incomplete.

Lately, I have been paying attention to something more interesting.

Prospects are showing up to our first conversation already familiar with how we think.

They have read an article. Then another. They have looked through the website, explored our Field Notes, joined the community, or followed the work for a while.

By the time we meet, they are often not asking:

“What does Bluethread do?”

They are asking:

“How would you approach our situation?”

That is a very different sales conversation.

I thought I was writing for LinkedIn

For a long time, I thought the value of publishing was mostly distribution.

Write a LinkedIn post. Get some engagement. Stay visible. Repeat.

LinkedIn still matters. It is often where the first interaction happens.

But I increasingly see LinkedIn as the front door, not the whole house.

The more durable value sits on the website.

A LinkedIn post may get attention for a day or two. A useful article on your own site can keep working for months or years. It can show up in search, get shared privately, support a sales conversation, or help someone understand how you think long before they are ready to buy.

That is where the compounding starts.

The signals are becoming easier to see

I have started building a lightweight system around this.

I use HubSpot to track website activity and identify engagement from people and companies already in our orbit. I get regular notifications when people are visiting the site, returning to certain pages, or moving through the content.

I use Cloudflare to understand traffic patterns and keep the site fast, secure, and available.

I use Hado to see crawler activity across search, social, and AI platforms. I can see traffic from sources such as Google, Bing, OpenAI, Anthropic, and Perplexity interacting with the site.

None of these tools tells the whole story by itself.

A website visit is not a lead.

A crawler is not a customer.

An article view is not revenue.

But the signals begin to matter when they stack up.

Someone follows a post on LinkedIn.

Then they visit the website.

Then they read two or three articles.

Then someone from the same company returns.

Then they subscribe to the community.

Then they fill out a form or send a message.

Then they tell us on the first call:

“We have been reading your content.”

That is not attribution in the clean, spreadsheet-friendly sense.

It is still a motion.

And it is becoming a strong inbound motion for us.

Content is not a campaign

The mistake is treating content like a campaign with a start date, an end date, and a short reporting window.

Good content behaves more like infrastructure.

Each piece adds another path into your business.

Each article answers another question.

Each framework gives someone another way to understand your point of view.

Each example gives a buyer more confidence that you have seen their problem before.

One article does very little.

Ten articles begin to create a body of work.

Fifty articles begin to create a category of expertise.

Add a few hundred community subscribers, consistent LinkedIn publishing, a useful website, and years of practical experience behind the ideas, and the effect becomes difficult to measure through any single channel.

That is the point.

The value is cumulative.

The first sale is trust

Most buyers are not ready to talk to you the first time they encounter your company.

They are gathering evidence.

Do these people understand the problem?

Do they have a real point of view?

Have they done this before?

Are they teaching something useful, or are they simply promoting themselves?

Content helps answer those questions without requiring a meeting.

It gives the buyer space to evaluate you privately.

That matters because people rarely tell you they are researching your company while they are doing it.

You see the evidence later.

They reference a phrase you wrote six months ago.

They mention an article you had forgotten about.

They already understand your framework.

They have forwarded your work to someone else on the team.

The sales process did not begin when they booked the meeting.

It began when they started reading.

AI makes the body of work more valuable

AI is adding another layer to this.

People are no longer only typing keywords into Google. They are asking ChatGPT, Claude, Perplexity, Gemini, and other systems to help them research problems, compare approaches, and identify experts.

That does not mean every article will suddenly become an AI-generated lead source.

It does mean useful, original, well-structured thinking has more ways to be discovered.

The old content playbook often focused on producing pages for search engines.

The better play now is producing the clearest, most useful answer you can to a real question your market is asking.

Not keyword filler.

Not generic thought leadership.

Not a rewritten summary of what everyone else already said.

Real experience. Clear thinking. Useful teaching.

That is the material people remember, share, and eventually buy from.

A practical way to think about it

You do not need an elaborate content machine.

Start with the questions you hear repeatedly from clients and prospects.

Write down your real answer.

Publish it somewhere you control.

Share it through the channels where your audience already spends time.

Connect the basic systems so you can observe what happens next.

LinkedIn can create discovery.

Your website can build depth.

HubSpot can help track engagement.

Cloudflare can show traffic patterns and protect the experience.

Haddo can help you see how search and AI systems are interacting with the site.

Your community can turn occasional readers into a recurring audience.

The goal is not to obsess over every signal.

The goal is to see the pattern.

The payoff is not always more leads

Sometimes the payoff is fewer bad conversations.

People read your work and decide they disagree with your approach.

That is useful.

Others read it and understand exactly what you believe before they speak with you.

That is even more useful.

Strong content qualifies both sides.

It helps the right buyers recognize themselves in your thinking. It also helps the wrong buyers move on without wasting anyone’s time.

The best result is not simply more inbound.

It is better inbound.

Better prepared buyers.

More relevant conversations.

Less time explaining the basics.

More time solving the actual problem.

The compound interest is trust

I used to think I was writing for this week’s audience.

I was actually building next year’s sales conversations.

The posts, articles, Field Notes, community discussions, search traffic, website visits, and AI crawlers all look like separate activities.

They are not.

They are small deposits into the same account.

Most of them produce very little on their own.

Together, over time, they create familiarity.

Familiarity creates credibility.

Credibility creates trust.

And trust is what eventually turns attention into a conversation.

The compound interest of content is not measured in likes.

It is measured in how much less selling you have to do when the right person finally reaches out.

One of the most common mistakes I see in partnerships is not choosing the wrong partner.

It is expecting one partner to matter too much.

The big cloud provider.

The category leader.

The consulting firm with the perfect customer base.

The technology company everyone wants on the logo slide.

A team spends six months getting the agreement signed, another three months building the integration, and then waits for the partnership to produce revenue.

Sometimes it works.

Usually, it does not work fast enough.

That does not mean the partnership was a bad idea. It means the company placed too much weight on a single bet.

Partnerships do not behave like a traditional sales pipeline.

They behave more like a portfolio.

The Hero-Partner Problem

Most partnership teams have a hero-partner story.

“If we can just get Microsoft engaged.”

“If this GSI starts taking us into accounts.”

“If this integration gets listed in their marketplace.”

“If their sales team brings us into three deals.”

The logic feels reasonable. The partner has distribution, customers, brand, or technical reach that your company does not have.

But scale on the partner side does not automatically create attention for you.

Large partners have hundreds or thousands of competing priorities. Their sellers are compensated for specific outcomes. Their technical teams have roadmaps. Their marketing teams have limited campaign capacity.

Being important to your company does not make you important to the partner.

That gap is where a lot of partnership plans stall.

The team builds a strategy around what the partner could do, not what the operating model is actually producing.

Partnerships Are Bets

Every partnership begins with a hypothesis.

We believe this partner serves the right customers.

We believe the integration will increase product adoption.

We believe their sales team will see value in bringing us into deals.

We believe a joint offer will create access we could not create alone.

Those are reasonable beliefs.

They are not yet evidence.

The partnership only becomes real when customers, sellers, and operators begin behaving differently.

Are account teams bringing each other into opportunities?

Are customers using the combined solution?

Are both sides investing time without being chased?

Are introductions becoming opportunities?

Are opportunities converting?

Until those behaviors appear, the partnership is still a bet.

The mistake is pretending every signed agreement is an asset of equal value.

It is not.

Think Like an Investor

A good investor does not expect every company in the portfolio to become the winner.

A good partnership leader should not expect every partnership to scale.

You might launch ten partnership experiments.

Three develop real activity.

Two begin producing repeatable opportunities.

One becomes strategically meaningful.

That is not failure.

That is portfolio math.

The problem is that many partnership teams do the opposite.

They select a small number of large partners, spend heavily on each one, and give them too much time to prove themselves.

By the time the team accepts that the motion is not working, a year has passed.

A portfolio model changes the operating cadence.

You run more focused experiments.

You define the behavior you expect to see.

You invest gradually.

You increase resources when evidence appears.

You reduce investment when it does not.

This is not about treating partners as disposable.

It is about earning conviction.

Not Every Bet Needs to Be Big

One reason companies concentrate on a few large partners is that they assume every partnership requires a major launch.

Executive alignment.

Legal agreements.

Technical integration.

Sales enablement.

Marketplace listings.

Joint marketing.

That is too much investment before the hypothesis has been tested.

Many partnership ideas can begin much smaller.

A shared account mapping session.

A referral experiment in one segment.

A joint customer workshop.

A simple services package.

A campaign aimed at ten mutual accounts.

A technical proof of concept before a full integration.

The goal of the first step is not to prove the partnership can become enormous.

The goal is to learn whether it deserves the next level of investment.

Concentration Risk Exists in Partnerships Too

Companies understand concentration risk when one customer represents too much revenue.

They are often less disciplined when one partner represents too much of the partnership strategy.

That dependence can show up in several ways.

One partner controls most of the sourced pipeline.

One marketplace drives the entire cloud motion.

One services firm holds the key customer relationships.

One technology integration accounts for most partner activity.

One executive relationship keeps the partnership alive.

When the strategy depends on a single relationship, program, or company, the team is more exposed than it realizes.

Leadership changes.

Compensation plans change.

Priorities shift.

Programs get reorganized.

The person who championed you leaves.

The partnership may still exist on paper, but the momentum can disappear overnight.

A portfolio protects against that.

The Portfolio Still Needs a Thesis

Running a portfolio does not mean collecting random partners.

More is not automatically better.

A portfolio without a thesis is just a directory.

The team still needs to know what it is trying to accomplish.

Are you trying to reach a new customer segment?

Increase product adoption?

Improve retention?

Add implementation capacity?

Enter a new geography?

Create credibility in a vertical?

Strengthen a cloud motion?

The thesis determines which bets belong in the portfolio.

It also determines how they should be measured.

A referral partner should not be evaluated like a technology integration.

A GSI should not be evaluated like an app marketplace.

A strategic alliance should not be evaluated like a local services partner.

Different partner types create different forms of leverage.

The portfolio should be diverse, but not confused.

Allocate Resources Based on Evidence

Most partnership teams divide time based on the perceived importance of the partner.

The biggest logo gets the most attention.

A better model is to allocate resources based on evidence and momentum.

An early-stage bet may only deserve a small experiment.

A partner showing repeatable customer activity deserves more enablement.

A partner generating qualified opportunities deserves operational support.

A partner creating consistent revenue may deserve executive sponsorship, dedicated resources, and deeper product investment.

This creates a simple progression:

Hypothesis. Experiment. Evidence. Investment. Scale.

Most teams jump from hypothesis straight to scale.

Then they wonder why the partnership feels expensive.

Your Best Partner May Not Look Strategic Yet

Portfolio thinking also helps teams avoid a second mistake: overlooking smaller partners.

The most productive partnership may not start with the biggest company.

It may begin with a focused services firm that understands the customer.

A specialized ISV with a highly complementary product.

A regional partner with strong relationships.

A consultancy that repeatedly encounters the problem you solve.

A platform team with a specific gap in its offering.

These partners may not look impressive on an ecosystem slide.

But they may have something more valuable than scale.

They have motivation.

And motivation is often the scarce resource in partnerships.

Stop Asking Which Partner Will Win

Partnership leaders are often asked:

“Which partner is going to be the big one?”

It is usually too early to know.

The better questions are:

Which partnership hypotheses are we testing?

What behavior would validate each one?

Where are we seeing real momentum?

Which bets deserve more investment?

Which ones are consuming time without producing evidence?

Where are we overly dependent on one partner?

That is a more honest way to operate.

It is also a more scalable one.

The goal is not to predict the winner perfectly.

The goal is to build a system that recognizes the winner early enough to invest.

Build the Portfolio

The best partnership strategies are not built around one logo.

They are built around a collection of deliberate bets.

Some create access.

Some create product value.

Some create credibility.

Some create delivery capacity.

Some create revenue.

A few may eventually create all five.

But you rarely know which ones at the beginning.

So stop asking one partnership to carry the entire strategy.

Build the portfolio.

Run the experiments.

Watch the behavior.

Follow the evidence.

Then place the bigger bet.

Every few years the industry declares the system of record obsolete. Social was going to kill it. Then data lakes. Now agents.

Here is what I keep seeing instead.

The hype cycle runs ahead of the reality cycle

That’s the whole pattern, and it has never once broken.

I’ve been through a few of these. On-prem to cloud at Microsoft. Two-tier distribution learning to sell subscriptions at SYNNEX. Perpetual license to recurring revenue across every category I’ve worked in. Each one got announced as a one-year shift and delivered as a five-year grind.

The technology arrives fast. The operating model arrives slow. The gap between those two is where most budgets get burned.

Agentic workflows are real. They are also early. The companies that get the most out of them in 2029 are the ones cleaning their record in 2026.

What people actually ask me for

I run an advisory business. I see what lands on the table before anything else does.

The single most requested piece of work right now is CRM. Not agents. CRM.

Field hygiene. Partner attribution that survives a CFO question. Deal registration that doesn’t live in a spreadsheet parked next to the pipeline. Someone who owns the object model.

That surprised me at first. It stopped surprising me around the fifth engagement.

Payroll is a monthly event

This is the part that sounds cynical and isn’t.

Companies are in business to make money this quarter. Payroll clears on the fifteenth whether or not the agent roadmap shipped. That constraint isn’t a failure of imagination. It’s the job.

An operator who spends Q3 on an agent pilot and still can’t tell the board which deals had partner involvement optimized for the wrong horizon. An operator who fixes attribution in Q3 has an asset that pays this year and compounds into whatever comes next.

Both companies believe in AI. Only one of them gets paid for it.

This works the same at 40 people and at 4,000. The small company can’t afford a bet that pays in three years. The big company can’t move the reporting line fast enough to matter in three years. Different reasons, same answer.

Time saved is not an outcome

AI is disruptive. I’m not arguing that. I’m arguing about when the disruption shows up in the numbers.

Right now most of the reported value is time saved. Hours back. Faster first drafts. Fewer clicks. That’s real and it’s worth having, and it is also the easiest thing in the world to claim and the hardest thing in the world to bank.

Time saved shows up on a slide. Outcomes show up on the P&L. Pipeline created. Cycle time compressed. Win rate moved. Partner-sourced revenue up as a percentage of total.

Nobody has ever been able to spend an hour saved. AI becomes disruptive the quarter it starts driving one of those four numbers, and not before.

Which brings me to why it mostly hasn’t yet.

What “evolved” actually means

CRM used to be where reps typed things. That era is over, and good riddance.

CRM is now the substrate. It’s the thing every other system reads from and writes back to. Enrichment writes to it. Product usage writes to it. Ecosystem data writes to it. Agents read from it and then act on what they find.

That last part is the whole argument.

An agent is only as good as the data it pulls from. It doesn’t fix your record. It inherits it. Point a capable model at a field where sourced and influenced mean whatever the rep felt that day, and you get confident, fast, beautifully formatted wrong answers. At volume.

Bad data used to cost you a slow quarter. Now it scales.

Here’s the part nobody wants to say out loud. We are entering the third decade of data cleansing. Same work. New reason.

We cleansed to make reporting trustworthy. We cleansed to make automation fire correctly. Now we cleanse so the agent doesn’t confidently hallucinate a partner attribution model that never existed.

The work didn’t get more glamorous. It got more consequential.

The partner data problem specifically

Partner data is the worst-maintained data in most CRMs. I say that having audited a lot of them.

Common state: a partner field that’s free text. Sourced and influenced with no written definition. Deal reg sitting in a portal that never syncs back. No partner object on the account, so nobody can ask a portfolio-level question. Attribution settled in a quarterly argument instead of by a rule.

If your partner leader can’t answer “which closed-won deals had a partner touch, and what was the touch” in under five minutes, you don’t have a partner program problem. You have a record problem wearing a program costume.

Fix the record and the program becomes measurable. Measurable programs get budget. That’s the whole chain.

What to do this quarter

Not a transformation. Five things.

1. Write the definitions. Sourced, influenced, co-sold. One page, signed by sales and finance. If two leaders define influenced differently, nothing downstream is real.

2. Put partner on the object model. A real association with a role, not a text field. Account to partner, opportunity to partner.

3. Make deal reg write back. If registration lives only in the portal, it doesn’t exist to the business.

4. Name one owner. One person accountable for the partner data model. Not a committee, not a rotating RevOps ticket queue.

5. Then pilot the agent. On clean data, against a defined question.

Do the first four and the fifth gets much shorter.

The certain thing

I don’t know what the tooling looks like in three years. Neither does anyone selling you a roadmap.

I do know the business still needs one place where what happened is written down. And I know every layer built on top of it will be worth exactly as much as that layer is trustworthy.

CRM isn’t dead. It stopped being the app and became the foundation.

Build accordingly.

“The will to win means nothing without the will to prepare.”

For decades, that was obvious.

Preparation meant late nights. Reading annual reports. Researching stakeholders. Mapping partner ecosystems. Watching product demos. Rehearsing the meeting. Doing work no one ever saw.

Then AI arrived.

Today you can generate an executive briefing, summarize a 10-K, analyze a company’s ecosystem, build an account plan, and draft a partner strategy before your coffee gets cold.

It feels like preparation.

Sometimes it isn’t.

The biggest misconception about AI is that it replaced preparation.

It didn’t.

It repriced it.

The work that used to take six hours now takes thirty minutes.

That’s incredible.

It’s also dangerous.

Because when everyone has access to the same models, the same prompts, and the same public information, preparation itself stops creating an advantage.

It becomes the minimum requirement.

That’s the new preparation gap.

It isn’t between people who use AI and people who don’t.

It’s between people who stop at the output and people who use that output to develop an original point of view.

The New Split

I think every knowledge worker now has two jobs.

Machine work

• Research

• Summarization

• First drafts

• Competitive intelligence

• Formatting

• Briefing documents

• Account plans

Let AI own as much of this as possible.

Doing all of it manually isn’t diligence anymore.

It’s usually just expensive nostalgia.

Then comes the work that actually creates leverage.

Operator work

• Forming the opinion

• Choosing the strategy

• Predicting objections

• Knowing which partner to involve

• Understanding the politics inside both organizations

• Deciding what not to do

• Making the call when the answer isn’t obvious

AI can inform these decisions.

It can’t be accountable for them.

And that’s where careers will increasingly separate.

Partnerships Make This Even More Obvious

I’ve spent my career building partnership programs at Microsoft, SYNNEX, Gong, and now Bluethread.

One thing has never changed.

Revenue comes from people trusting other people enough to work together.

You can generate the perfect account map.

You still have to convince an account executive to bring in the partner.

You can summarize a customer’s business.

You still have to earn enough credibility that the customer believes your recommendation.

You can build the cleanest QBR deck anyone has ever seen.

If the partner doesn’t answer your text when the deal gets stuck, none of it matters.

AI accelerates preparation.

Relationships accelerate outcomes.

Those aren’t the same thing.

Where the Best Operators Are Winning

The highest performers I know aren’t using AI to avoid preparation.

They’re using AI to eliminate the parts of preparation that don’t require them.

Instead of spending six hours collecting information, they spend thirty minutes.

Then they invest the recovered time where no model has context.

They call the partner before the meeting.

They pressure test the strategy with someone they trust.

They identify the political risk inside the account.

They prepare three different paths depending on how the conversation unfolds.

They walk into the room with something AI still can’t generate.

Conviction.

What This Means for the Next Generation

If you’re early in your partnerships career, here’s the trap.

You can now produce work that looks remarkably polished.

Beautiful briefs.

Perfect formatting.

Thoughtful summaries.

The danger is believing polished work is valuable work.

Your manager isn’t hiring you because you can create a document.

AI can already do that.

They’re hiring you because someone has to decide what matters.

Someone has to connect the right partner.

Someone has to make the judgment call when the data is incomplete.

Someone has to earn trust.

Those skills just became dramatically more valuable.

The New Definition of Preparation

The old definition of preparation was collecting information.

The new definition is creating perspective.

Everyone will walk into the meeting with an AI-generated briefing.

Not everyone will walk in with an original point of view.

That’s the new preparation gap.

It isn’t about who has access to better models.

It’s about who can combine AI with experience, relationships, and the courage to take a position.

The will to prepare was never about spending more hours than everyone else.

It was about refusing to show up with someone else’s thinking.

Today, everyone can generate someone else’s thinking in ninety seconds.

The operators who build the next generation of partnerships will use AI for everything they can.

Then they’ll invest themselves where no model can.

Because in the age of AI, preparation isn’t the competitive advantage.

Perspective is.

For the last twenty years, we’ve built systems of record.

CRM became the system of record for customers.

ERP became the system of record for finance.

HRIS became the system of record for employees.

Marketing automation became the system of record for campaigns.

These platforms answered one fundamental question:

What happened?

That was enough when business moved slower.

It isn’t enough anymore.

Today, competitive advantage isn’t determined by who has the most data.

It’s determined by who understands the most context.

Every Team Has a Different Window

An Account Executive opens Gong before a customer call.

A RevOps leader opens Salesforce to understand pipeline.

A marketing leader opens HubSpot to measure campaigns.

A product manager opens Jira to review the roadmap.

A partnership leader opens Crossbeam to identify shared accounts.

An executive asks ChatGPT what it all means.

Every team has a different window into the business.

Almost no one has the whole picture.

The information exists.

It’s just fragmented.

The Most Important Signals Live Between Systems

Think about what actually changes your strategy.

Not another opportunity entering Stage 2.

Not another customer record.

Instead...

Your largest cloud partner launches a new marketplace program.

A competitor hires twenty AI partnership specialists.

Microsoft changes partner incentives.

Three implementation partners begin winning deals together.

Five enterprise customers all adopt the same technology stack.

A startup you’ve never heard of suddenly appears in every strategic account.

None of those events live neatly inside your CRM.

But every one of them changes how you should operate.

That’s context.

Context is the layer between systems.

It’s where relationships, timing, market shifts, and business decisions intersect.

AI Changes the Rules

This is why AI feels both incredible and, at times, underwhelming.

AI can summarize.

Analyze.

Code.

Write.

Reason.

But its conclusions are only as good as the context it receives.

An AI connected only to your CRM answers questions about customers.

Connected only to Gong, it understands conversations.

Connected only to your product analytics, it explains adoption.

Useful.

But incomplete.

The real breakthrough happens when AI understands the relationships between all of those systems and the world outside your company.

Customers.

Partners.

Products.

Communities.

Hiring.

Marketplaces.

Industry trends.

Competitors.

That’s when context becomes intelligence.

Why I’m Watching GTM Engineers

One trend I’m paying close attention to is the rise of the GTM Engineer.

The title is appearing everywhere.

The mission is usually the same.

Connect the tools.

Automate the workflows.

Bring AI into the revenue engine.

I think that’s an important step.

But I also think we’re defining the role too narrowly.

Most GTM Engineer roles today are built around the direct sales motion.

CRM.

Marketing automation.

Sales engagement.

Conversation intelligence.

Product usage.

Those are critical systems.

They’re just not the entire go-to-market motion anymore.

Modern growth happens across an ecosystem.

Partners influence buying decisions.

Cloud marketplaces accelerate procurement.

Implementation firms shape adoption.

Communities generate trust.

Customers become advocates.

Developers create momentum.

AI connects it all.

The next generation of GTM Engineers won’t simply orchestrate internal systems.

They’ll orchestrate entire ecosystems.

The Next Competitive Advantage

For years, we’ve invested in systems of record.

The next decade belongs to systems of context.

Not software that stores information.

Software that explains why it matters.

Software that identifies what changed.

Software that connects customers, partners, products, conversations, market signals, hiring trends, and relationships into one operating picture.

The companies that win won’t necessarily have more data.

They’ll have better context.

And better context leads to better judgment.

In a world where AI makes execution increasingly accessible, judgment becomes one of the few sustainable competitive advantages.

That’s why I believe context is becoming the next system of record.

Not because it replaces the systems we’ve built.

Because it connects them into something far more valuable: understanding.

Over the past year, I’ve noticed something happening across nearly every client I work with.

The best operators are accelerating.

Not by 10%.

By multiples.

At first, I assumed they simply had better teams or bigger budgets. But after dozens of conversations, I realized that wasn’t it.

The difference was their tools.

The gap between operators is no longer defined by experience alone. It’s defined by how effectively they combine experience with AI.

I call it the Tool Gap.

The Operator Has Changed

For years, great partnership leaders were measured by their ability to coordinate.

They built relationships.

They navigated internal politics.

They managed partner programs.

They organized meetings, enablement, QBRs, business plans, and executive reviews.

Those skills still matter.

But they’re no longer enough.

Today’s best operators aren’t replacing those fundamentals. They’re amplifying them with an entirely new way of working.

They wake up to overnight market signals.

They use AI to research companies before breakfast.

They automate repetitive work.

They connect data that used to live in five different systems.

They spend less time collecting information and more time making decisions.

The work hasn’t changed.

The leverage has.

The Tool Gap

Every week I hear some version of the same sentence.

“I know AI is important. I just don’t know where to start.”

That’s the gap.

It’s not a lack of intelligence.

It’s not resistance.

It’s not even fear.

It’s simply that the people pulling ahead have developed workflows that compound their judgment.

Think about two partnership operators with ten years of experience.

Both understand ecosystems.

Both know how to build partner motions.

Both can lead executive conversations.

One still spends hours gathering information manually.

The other has AI researching accounts, summarizing partner activity, enriching CRM data, monitoring ecosystem changes, drafting content, and preparing recommendations before the workday begins.

They’re equally experienced.

They’re no longer equally productive.

This Isn’t About Becoming an Engineer

Here’s the interesting part.

I don’t consider myself a software engineer.

In fact, I still tell people I don’t know how to code.

Yet over the past year I’ve built research workflows, AI-powered dashboards, market intelligence systems, and products that simply wouldn’t have been possible for me a few years ago.

Not because I learned to write software.

Because I learned to work alongside AI.

That distinction matters.

Most business leaders don’t need to become developers.

They need enough technical literacy to understand what’s possible.

Just like we all learned CRM twenty years ago, today’s operators need to become comfortable with concepts like connected data, APIs, AI agents, automation, and intelligent workflows.

Not to build everything themselves.

To ask better questions.

To design better systems.

The Four Stages of the Modern Operator

I think we’re moving through four distinct stages.

Stage 1: Manual Operator

Everything runs through spreadsheets, email, meetings, and manual research.

Stage 2: AI Assistant

ChatGPT, Claude, or Gemini help with writing, summarizing, brainstorming, and research.

This is where many organizations are today.

Stage 3: AI Operator

AI becomes part of the workflow.

Research is automated.

Market signals arrive daily.

Partner intelligence is continuously updated.

The operator spends less time finding information and more time acting on it.

Stage 4: AI Operating System

Routine work happens automatically.

AI gathers evidence, surfaces opportunities, drafts recommendations, and monitors change.

Humans focus on judgment, relationships, and decisions.

We’re only beginning to see what this looks like.

The Competitive Advantage Isn’t AI

The biggest misconception I see is that AI itself is the advantage.

It isn’t.

The advantage is how experienced operators use AI to multiply decades of judgment.

Experience without modern tools is becoming less valuable.

Tools without experience create noise.

The winners will combine both.

That’s why I believe the next generation of leaders won’t simply have better technology.

They’ll have better systems.

Better workflows.

Better leverage.

Closing the Tool Gap

The good news is that this gap is still early.

Unlike experience, which takes years to accumulate, modern workflows can be learned surprisingly quickly.

You don’t have to become a programmer.

You don’t need to replace your team.

You don’t need to rebuild your business.

You simply need to start thinking like an operator who designs systems instead of completing every task manually.

That’s the shift I’m seeing.

Not AI replacing operators.

Operators becoming dramatically more capable because they’ve learned how to combine experience with better tools.

The Tool Gap is real.

The question isn’t whether it will affect your business.

The question is whether you’ll be on the widening side of it.

When I launched the State of Partnerships Survey, I made a commitment to share what I learn along the way, not just publish a report months from now.

We’re still early, so these aren’t conclusions.

They’re signals.

Here are three that surprised me.

1. AI adoption isn’t the story anymore.

Nearly everyone responding is already using AI as part of their daily workflow.

The conversation has shifted from “Should we use AI?” to “Where does AI create the most leverage?”

That feels like a major change from even six months ago.

2. AI is uncovering partner value that companies used to miss.

One respondent described using AI with conversation intelligence to identify partners mentioned during sales calls that otherwise would have gone unnoticed.

Another is using AI to analyze customer segments and identify where partner motions should focus.

This isn’t about replacing partner managers.

It’s about exposing signals that humans simply didn’t have time to find.

3. Human judgment still matters.

This was probably the strongest theme.

Respondents are happy to let AI help with research, QBRs, enablement, communications, and analysis.

But when it comes to relationships, negotiations, executive conversations, and strategic decisions, they still want a person in the loop.

That balance feels important.

This is only the beginning.

The responses are already challenging some assumptions I had, and I suspect they’ll challenge a few of yours too.

If you work in partnerships, alliances, channels, ecosystems, or Partner RevOps, I’d love your perspective.

The more voices we have, the more valuable this research becomes for everyone.

Take the State of Partnerships Survey here:
https://bluethread.io/state-of-partnerships

I’ll continue sharing the patterns as they emerge.

For the last decade, Revenue Operations has transformed how companies grow.

It connected sales, marketing, and customer success around shared data, common processes, and better decision making. It gave leadership a clearer picture of what was happening inside the business.

That work isn’t finished.

But the scope of RevOps is about to expand.

Growth no longer comes from internal teams alone.

Partners influence buying decisions before opportunities ever enter the CRM. Cloud marketplaces generate demand. Technology alliances create new routes to market. Consultants and service providers shape enterprise buying decisions. Communities, creators, and AI agents are becoming part of the revenue engine too.

Yet most RevOps teams have very little visibility into these external motions.

That’s the next challenge.

I believe the future of RevOps is Ecosystem Intelligence.

Not a new department.

Not another piece of software.

An evolution of Revenue Operations that brings partner, marketplace, alliance, and ecosystem data into the same operating model as sales and marketing.

Imagine a RevOps team that can answer questions like:

• Which partners consistently improve win rates?

• Which ecosystem motions create the most pipeline?

• Which marketplace investments actually generate revenue?

• Which partner plays shorten sales cycles?

• Where should we invest our next dollar for the highest return?

Today, many of those answers are based on anecdotes.

Tomorrow, they’ll be based on data.

AI is accelerating the shift.

The administrative work that once consumed partnership teams is becoming automated. That creates an opportunity for RevOps to expand its role from managing internal efficiency to orchestrating revenue across the entire ecosystem.

The best RevOps leaders won’t just optimize what happens inside the company.

They’ll understand everything that contributes to revenue, whether it happens inside their walls or outside them.

Revenue Operations isn’t going away.

It’s getting bigger.

The next generation of RevOps will connect CRM data with ecosystem data, partner signals, marketplace activity, customer intelligence, and AI.

That’s not a new function.

It’s the natural evolution of the one we already have.

I think we’ll look back in a few years and realize the companies that pulled ecosystem data into RevOps early built a lasting advantage.

That’s what I mean by Ecosystem Intelligence.

For decades, the world’s best companies have shared a common capability.

They are exceptional capital allocators.

Whether it’s Berkshire Hathaway, private equity firms, or companies studied by McKinsey, the pattern is remarkably consistent. The highest performers don’t simply manage what they have. They continually reallocate resources toward their highest-return opportunities and away from yesterday’s assumptions.

I’ve been thinking about what this means for partnership organizations.

The more I reflect on it, the more I believe we’ve been describing the role of a partnership leader incorrectly.

We often say partnerships are a relationship business.

That’s true.

But it’s incomplete.

Relationships are the foundation. They create trust, unlock opportunities, and make collaboration possible.

Managing relationships is table stakes.

Managing a portfolio of investments is what separates good partner leaders from great ones.

Every partnership is an investment

Most people think of capital as money.

Partnership leaders know better.

Capital takes many forms:

• Executive attention

• Partner manager capacity

• Engineering resources

• Product roadmap prioritization

• Sales overlay

• Marketing investments

• Enablement

• Marketplace resources

• Customer introductions

• Credibility

Every one of these resources is finite.

Every decision to invest in one partner means investing less in another.

That makes every partnership decision an allocation decision.

The question isn’t simply:

“Should we work with this partner?”

It’s:

“Is this the highest-return use of our limited resources?”

That’s a very different way to think about the job.

The portfolio mindset

Great investors don’t try to maximize the performance of every investment.

They optimize the portfolio.

The same should be true in partnerships.

Not every partner deserves the same level of investment.

Not every integration deserves engineering time.

Not every strategic alliance deserves executive sponsorship.

Not every marketplace listing deserves marketing dollars.

Some partnerships deserve to be scaled aggressively.

Others deserve to be maintained efficiently.

Some deserve patience.

Others should receive no additional investment at all.

That’s not relationship management.

That’s portfolio management.

The hidden cost of every decision

One of the hardest lessons in investing is understanding opportunity cost.

Every dollar invested somewhere cannot be invested somewhere else.

The same is true for partnership teams.

Every hour your best partner manager spends on an underperforming relationship is an hour they aren’t helping your highest-potential ecosystem partner.

Every quarter engineering spends building a low-value integration is a quarter they aren’t accelerating a strategic platform.

Every executive business review has a cost.

Every MDF investment has a cost.

Every roadmap commitment has a cost.

The question isn’t whether a partnership is “good.”

The question is whether it’s the best place to invest next.

Why relationship skills still matter

None of this diminishes the importance of relationships.

In fact, it elevates them.

Relationships are how you earn trust.

Trust creates opportunities.

But leadership begins after the relationship exists.

That’s when difficult allocation decisions have to be made.

Where do we double down?

Where do we stay the course?

Where do we stop investing despite the history we’ve built?

Those are investment decisions.

The future partnership leader

As AI automates more operational work, I believe the role of the partnership leader will become even more strategic.

Less time coordinating.

More time allocating.

The best leaders won’t simply know their partners.

They’ll know where every hour, every engineer, every marketing dollar, and every executive meeting creates the highest long-term return.

That’s a portfolio management problem.

The partnership leaders who embrace that mindset will build stronger ecosystems, make better investment decisions, and create more durable growth.

Because in the end, the job isn’t just building relationships.

It’s allocating scarce resources where they create the greatest value.

And that’s what great capital allocators have been doing all along.

ERP created Accenture and Deloitte.

Infrastructure created CDW and SHI.

Cloud created a generation of AWS, Azure, and Google Cloud consultancies.

AI will create a new category:

AI Deployment Firms.

Not resellers.

Not traditional systems integrators.

Organizations focused on turning frontier models into business outcomes.

Today, much of the AI conversation centers on the model providers themselves.

OpenAI.

Anthropic.

Google Gemini.

Meta.

The assumption is that the winners will be determined by model performance.

Reasoning benchmarks.

Context windows.

Agent capabilities.

Model quality matters.

But history suggests something else.

The biggest economic opportunity may emerge in the ecosystem surrounding the models rather than the models themselves.

The Real Challenge Isn’t Access to AI

Most enterprises can already access frontier models.

They can purchase ChatGPT Enterprise.

They can deploy Claude through Anthropic.

They can access Gemini through Google Cloud.

The challenge isn’t obtaining access.

The challenge is transforming how work gets done.

That’s where a new partner category emerges.

What AI Deployment Firms Actually Sell

The first misconception is that these firms will sell AI.

They won’t.

Customers don’t buy AI.

Customers buy outcomes.

They buy:

• Faster sales cycles

• Lower support costs

• Improved productivity

• Better forecasting

• Faster onboarding

• More effective partner programs

The model is simply the engine behind the outcome.

Workflow Transformation

Most organizations don’t need another chatbot.

They need someone who can answer:

• Which workflows should be automated?

• Which decisions should remain human?

• Where do agents fit?

• What changes operationally?

This looks more like business transformation than software implementation.

Context Engineering

The industry spent the last two years talking about prompt engineering.

The next phase will be context engineering.

A model becomes valuable only when connected to:

• CRM data

• Knowledge bases

• Product information

• Operational systems

• Ecosystem intelligence

The deployment partner becomes responsible for connecting intelligence to context.

Governance and Trust

As AI becomes embedded into core business processes, organizations need governance.

Questions quickly emerge:

• Which models can access customer data?

• How are decisions monitored?

• Who approves autonomous actions?

• How is performance measured?

These are operational challenges, not technology challenges.

Why Frontier Model Companies Need Them

This isn’t unique to OpenAI.

Anthropic is increasingly building enterprise relationships around trusted adoption and responsible deployment.

Google is embedding Gemini across Workspace, Cloud, and enterprise workflows.

Each company faces the same challenge:

Building a world-class model does not automatically create organizational change.

Someone still has to redesign workflows, train teams, integrate systems, and measure outcomes.

Historically, vendors struggle to scale this function themselves.

That’s why partner ecosystems emerge.

The New Power Brokers

Partnership leaders often ask:

“Which AI vendor should we partner with?”

A more strategic question may be:

“Which firms are becoming trusted advisors for AI deployment?”

Those firms will increasingly influence:

• Model selection

• Technology architecture

• Workflow design

• Platform adoption

• Long-term AI strategy

Just as cloud consultancies became influential during the AWS migration era, AI deployment firms may become the decision-makers enterprises trust most.

What This Means for SaaS Companies

Many SaaS companies are racing to add AI features.

That’s necessary.

But it may not be sufficient.

The winners may not be the platforms with the most AI features.

They may be the platforms most frequently recommended by AI deployment firms.

This creates a new ecosystem strategy.

Instead of focusing solely on technology partnerships, SaaS companies may need to build relationships with organizations responsible for AI transformation.

A Prediction

The most influential AI partners of the next decade probably aren’t household names today.

Some will emerge from consulting.

Some from cloud services.

Some from niche industry specialists.

But the biggest winners will likely be the organizations that learn how to combine:

• Frontier models

• Business context

• Workflow redesign

• Organizational change

into repeatable outcomes.

The AI race won’t be won solely by the companies building the smartest models.

It will also be shaped by the firms that make those models useful inside real organizations.

And that may become the most important partnership category of the next decade.

A few weeks ago, I was sitting in an executive roundtable when a leader shared a story that stuck with me.

Their company had effectively replaced about half of its SDR function with AI agents.

Not completely. There were still humans in the loop. People were overseeing the process, guiding the work, and stepping in where judgment mattered.

But the math was hard to ignore.

Productivity increased.

Output improved.

The experiment worked.

Nobody in the room seemed shocked.

Honestly, neither was I.

If you’ve been paying attention, these stories are becoming increasingly common. Every conference I attend, every executive conversation I join, every boardroom discussion eventually finds its way back to AI.

The excitement is real.

The opportunity is real.

And if we’re being honest, so is the tension.

Because later that evening, I wasn’t thinking about SDR productivity.

I was thinking about my kids.

They’re early in their careers, trying to figure out how to build meaningful futures at the exact moment the rules seem to be changing.

That’s what makes this conversation different for me.

It’s easy to discuss AI in terms of efficiency gains, cost savings, and productivity metrics.

It’s harder when you start asking what happens to the people whose careers are just beginning.

For decades, many careers followed a fairly predictable path.

You joined a company.

You learned the craft.

You gained experience.

You moved up.

The entry-level jobs weren’t always glamorous, but they served an important purpose. They taught people how business worked.

SDR roles are a good example.

They weren’t just about generating pipeline.

They taught people how customers think.

How buyers make decisions.

How to communicate.

How to handle rejection.

How to create value.

If AI can now perform a significant portion of that work, the question isn’t simply whether jobs disappear.

The bigger question is:

How will the next generation gain experience when the traditional entry points are changing?

I think that’s part of what we’re seeing in younger workers today.

Many leaders complain that younger employees seem less loyal to corporations than previous generations.

Maybe.

Or maybe they’re responding rationally to uncertainty.

The old social contract feels less certain than it once did.

If technology can reshape an entire job category in a matter of months, it’s understandable that people might place less faith in long-term corporate promises.

I don’t see that as a failure of commitment.

I see it as adaptation.

And yet, despite all of this, I still find myself landing on the side of hope.

Not because I think AI won’t disrupt jobs.

It will.

Not because I think some careers won’t disappear.

They will.

But because history suggests something important.

Humans are remarkably adaptable.

Every generation faces a technological shift that reshapes work.

My parents’ generation experienced globalization.

Mine experienced the internet.

My kids’ generation is experiencing AI.

The tools change.

The nature of work evolves.

But human ambition has a way of finding new outlets.

In fact, many of the skills that matter most seem to become more valuable as technology advances.

Curiosity.

Judgment.

Creativity.

Trust.

Resilience.

Relationship-building.

The ability to navigate ambiguity.

The ability to help people solve meaningful problems.

Those aren’t becoming less important.

They’re becoming more important.

The people who thrive won’t be the ones competing against AI.

They’ll be the ones who learn how to work alongside it.

That’s the advice I give my kids.

Learn the tools.

Experiment relentlessly.

Stay curious.

Don’t ignore what’s happening.

But don’t let fear paralyze you either.

Every generation gets its defining technology.

This one happens to be AI.

So if you’re asking whether I’m on the side of hope or fear, my answer is both.

I understand the fear.

I feel it myself sometimes.

Especially when I think about young people trying to find their footing.

But in the end, I still choose hope.

Not because the future is guaranteed to be easy.

Because I’ve seen enough of human adaptability to believe that we’ll figure out how to create value in whatever comes next.

The technology industry has been cycling through two emotional states for the better part of three years.

The first is existential dread. AI is coming. Jobs are evaporating. The future is uncertain. Every week brings another analysis of what will disappear, what can’t be replicated, what humans will be left doing when the machines get good enough. The tone is apocalyptic. The subtext is helplessness.

The second is its mirror image: obsessive optimization. Move faster. Ship more. Stack your routines. Maximize your output windows. Sleep less. Systemize everything. If you’re not compounding, you’re falling behind. The tone is relentless. The subtext is that rest is a form of failure.

Both of these are responses to the same anxiety.

Neither one is a framework for a life.

I had lived in California all my life, a large part of it, in the Bay Area.

People come here for a lot of reasons. The opportunity. The energy. The proximity to things that matter in tech. The gold rush, in whatever form it’s taking this decade.

What I’ve noticed is that the people who came to chase something are often the most anxious. The locals, the people who were already here, who built their lives here before the industry arrived, tend to have figured something out that the chasers haven’t.

They’re not slower. They’re clearer.

The ocean doesn’t care about your valuation. The beach at 6am doesn’t care about your pipeline. There’s something that happens when you actually live somewhere instead of just occupying it while you chase the next thing.

I’ve been building Bluethread for a year. Partnership advisory, RevOps, community. A small team. Clients I respect. Work that has evolved in ways I didn’t fully anticipate when I started.

What I’ve arrived at, after 25+ years in this industry and two years building something of my own, is a set of filters I apply to almost every decision.

Time. Will this protect or consume it? I’m fiercely protective of time now in a way I wasn’t at 35. Not because I have less of it, but because I finally understand what it’s worth. The work that belongs in the business is work that earns its place on the calendar. Everything else is noise.

Enjoyment. Will I actually want to do this? Not in a soft, preference-based way. In a practical, sustainability way. Work you resent is work you eventually do poorly. The clients I’ve kept longest are ones I genuinely want to solve problems with. That’s not coincidence.

Outcomes. Can we create something that actually matters? Not impact in the vague sense. Specific, measurable movement for the people I work with. If the answer is unclear at the start, we figure it out before the engagement begins.

These aren’t values statements. They’re filters. And they’ve shaped every decision I’ve made since I started Bluethread.

The tech industry doesn’t talk much about this kind of deliberateness.

It talks about disruption. Velocity. Market positioning. Competitive moats. Growth trajectories.

The doomer conversation and the hustle conversation share more DNA than either side would admit. Both are obsessed with external forces. Both measure success in terms of what’s happening to you, or what you’re producing, rather than what kind of life the work is actually supporting.

The question nobody asks in a pitch deck is: does this founder sleep?

The question nobody asks in an AI displacement conversation is: what would you actually choose to do with your time if you had more of it?

I don’t wake up with Sunday dread anymore. That sounds small. It isn’t.

For most of my career, the Sunday feeling was background radiation. The cost of playing the game. You absorbed it the way you absorbed airport delays and all-hands meetings and quarterly reviews that moved the goalposts.

I don’t miss it.

The beach is six miles away. My succulent garden is thriving. My spiritual life is trending in the right direction. I’m still on the adventure with my wife of 30+ years. These aren’t footnotes to the professional story. They are the point.

The business works because the life works. Not the other way around.

The people who come to Northern California to chase something often burn out or move on. The people who stay, who actually plant themselves here and build a life instead of a career, tend to understand something that the rest of the industry is still working out.

That the goal was never the gold.

It was always what you were going to do once you stopped running.

If you’re in the tech industry and you’ve been oscillating between the doom scrolling and the optimization content, I’d offer a third option.

Get clearer about what you’re actually building toward.

Not the product. Not the company. The life.

The work should support that. If it doesn’t, it’s not a strategy problem. It’s a design problem.

The window for building something deliberate doesn’t close at 40 or 50. In many ways it opens. You finally know enough about yourself to stop making choices based on what looks impressive and start making choices based on what actually fits.

That’s not a soft take.

That’s what the data looks like after enough time in the industry.

Rob Moyer is the founder of Bluethread LLC, a B2B partnerships advisory firm. He writes about partner revenue infrastructure, ecosystem strategy, and the operational mechanics of building something that lasts.

I’m fortunate to sit in on calls with partnership leaders across startups, scale-ups, and enterprise organizations.

Lately, I’ve noticed a consistent trend.

Partnership teams are becoming dramatically more efficient.

AI is helping automate partner recruitment, account mapping, content creation, communications, reporting, pipeline management, and dozens of other operational tasks that used to consume valuable time.

This is a good thing.

Most partnership organizations are resource constrained. Anything that helps teams eliminate manual work creates capacity to focus on higher-value activities.

But as I listen to these conversations, I think many partnership leaders are asking the wrong question.

The conversation is often centered around:

“How much of our partnership motion can we automate?”

A better question might be:

“Where does the human need to stay in the loop?”

Because if we’re honest about the purpose of partnerships, the answer matters.

Let’s Not Confuse Activity With Outcomes

Nobody builds partnerships because they’re running a nonprofit.

Partnerships exist to drive profitable revenue for both sides.

That’s the job.

The goal isn’t meetings.

The goal isn’t partner satisfaction scores.

The goal isn’t having hundreds of logos on a partner page.

The goal is creating economic value that benefits both organizations.

The best partnerships accomplish this by creating something incredibly valuable:

An annuity.

Not a financial product.

A business annuity.

A stream of recurring value that continues producing results long after the initial introduction, campaign, integration, or deal.

You see it when referrals continue to flow.

When co-sell motions become repeatable.

When marketplace transactions happen consistently.

When executive relationships create new opportunities year after year.

When customers continue buying because multiple companies are aligned around their success.

The most successful partnerships aren’t constantly starting over.

They’re compounding.

AI Can Scale Activities. It Can’t Create Trust.

This is where I think many organizations misunderstand the role of automation.

AI can help scale activity.

It can help identify prospects.

It can draft communications.

It can summarize meetings.

It can analyze partner performance.

It can automate workflows.

What it cannot do is create trust.

Trust is still built between people.

Trust is built when a partner answers the phone during a difficult customer situation.

Trust is built when an account executive gives a partner an opportunity in a competitive deal.

Trust is built when executives make introductions before there’s an obvious transaction attached.

Trust is built when two organizations invest in each other without knowing exactly when the return will show up.

Those moments matter because partnerships are fundamentally asymmetric.

Someone almost always has to go first.

Someone has to take a risk.

Someone has to make an investment before the outcome is guaranteed.

That’s where trust becomes the operating system.

And trust remains stubbornly human.

The New Competitive Advantage

For years, partnership leaders were rewarded for managing complexity.

The best operators could coordinate programs, communications, reporting, enablement, and execution across multiple organizations.

Many of those activities are becoming easier.

The advantage is shifting.

The future belongs to partnership leaders who understand exactly which activities should be automated and which moments require human involvement.

The winners won’t be the teams that automate everything.

They’ll be the teams that automate the repeatable work so they can spend more time on the irreplaceable work.

Building executive alignment.

Creating partner confidence.

Navigating conflict.

Making introductions.

Developing trust.

Finding opportunities where others don’t see them.

Those activities create leverage that compounds over time.

Relationships Are Not The Goal

One of the biggest misconceptions in partnerships is that relationships are the end objective.

They’re not.

Relationships are the mechanism.

Profitable recurring revenue is the outcome.

The relationship matters because it creates the trust required to produce that outcome repeatedly.

That’s why the best partnership leaders are never focused solely on transactions.

They’re focused on building the conditions that make future transactions inevitable.

The more AI handles the operational work, the more obvious this becomes.

Technology can accelerate relationships.

Technology can support relationships.

Technology can help scale relationships.

But technology cannot replace the human trust that sits underneath them.

And that’s ultimately where partnership annuities come from.

AI scales partnership activities.

Humans create partnership annuities.

I have a theory about why so many operators feel busier than ever and shipped less than ever.

AI lowered the cost of starting to zero.

It did not touch the cost of finishing.

The thing that used to be hard

For most of the last decade, your judgment showed up in what you chose to build. Execution was expensive. You had to want something badly enough to spend the time, the budget, the political capital. The cost of doing was a filter. It killed your bad ideas before they ate your calendar.

That filter is gone.

Now anyone can spin up a deck in an afternoon. A microsite before lunch. A scraper, a fifteen-question assessment, a full content calendar, a custom GPT trained on the whole back catalog. The friction that used to protect you from your own enthusiasm has been removed.

This is sold to you as a feature. For some of you it is. For a lot of you it is the most dangerous thing that has happened to your focus in years.

The shiny ball tax

Every AI project you start has a hidden cost. It is the attention you pulled off the thing that was already working.

The problem was never a lack of ideas. It was finishing. AI did not fix that. It gave the idea-rich, finish-poor crowd a faster way to start more things they will not finish.

You are not behind because you are using AI too little. Some of you are behind because you are using it to manufacture motion. Fourteen experiments open. None of them in market. All of them feeling like progress.

Motion is not the same as progress. AI is very good at producing motion.

No is the off switch

Here is the skill the next era rewards. The ability to say no to a use case you are fully capable of building.

That sentence is harder than it sounds. When something was hard to build, no was easy. The work said no for you. Now that everything is one prompt away, the no has to come from you. From judgment. From a point of view about what should exist, not just what can.

No is a positioning statement. When you say you do not do something, you are telling the market what you actually stand for. A partner program that does everything with AI stands for nothing. The operators who win are the ones who can say we do not do that with a straight face.

Judgment does not scale, and that is the point

AI scales production. It does not scale taste.

This is the part people get backwards. They want AI to replace the judgment. The judgment is the only thing that was ever scarce. A framework has value precisely because it is a set of opinionated nos. An Ideal Partner Profile without a buyer persona is a no. Treating your ecosystem as a relationship program instead of a P&L unit is a no. The method is the discipline of refusing the lazy version, over and over, in public.

Strip the nos out of your work and you are left with output. The market is about to be flooded with output. It will be worth nothing.

The assistive era runs on what you point it at

We are in the assistive era of these tools. Not autonomous. Assistive. That word matters.

Assistive tools amplify whatever you aim them at. Point them at clarity and they compound it. Point them at confusion and they manufacture more of it, faster and at higher resolution. The tool is neutral. The aim is not.

So the question is not how much AI you are using. The question is what you have decided not to do with it this quarter.

The job

AI made starting free. That is real and it is permanent.

Finishing is still the job. So is choosing. So is the no.

The operators who matter in five years will not be the ones who started the most. They will be the ones who finished the few things that mattered and had the judgment to leave the rest on the table.

The off switch is a skill. Start practicing it.

A partnerships leader at a frontier AI company told us how they get cloud partner data into their own pipeline. Not a hypothetical. Their actual process. “We don’t log into their partner portal. It’s just shared through like a Slack or a spreadsheet.”

A partnerships lead at a mid-market compliance SaaS company described how they target accounts with their advisory partners. “We’ll fill out an Excel sheet, download it to our desktop, send it via email for them to fill out on their end.”

A partner ops leader at a Fortune 100 industrial OEM walked us through their propensity-to-buy workflow. Send an account list to the regional leads. Wait for them to run manual lookups. Get the data back. Type it into a specific Salesforce field by hand. Two human handoffs per batch.

These are sophisticated companies. One of them builds the AI everyone else is buying. And their partner motion runs on Slack threads, downloaded spreadsheets, and a person re-keying values into a CRM field. I worked through this research with Sam Gong at WorkSpan for a report we published in May, drawing on primary calls with partnership leaders at nine companies. The pattern was so consistent we gave it a name. Manual as default.

The thing that’s actually broken

Read those three workflows again and notice what’s not the problem.

It’s not awareness. Everyone in those stories knows the data should be in Salesforce. Nobody thinks the spreadsheet on their desktop is the ideal end state. The OEM ops leader isn’t confused about where the P2B data belongs.

It’s not access. The portal exists. The compliance SaaS team could log into it. The data they need is sitting right there.

It’s not capability. These are companies with budget, headcount, and in at least one case the most advanced AI models on the planet.

The portal exists. The process exists. The data exists. And almost none of it is in a state where AI can act on it, because the data is in a Slack message, in an email thread, in a spreadsheet someone downloaded to their desktop. The work happens. It just happens in places nothing can reach.

That’s the pattern. Not an absence of process. An absence of process in a system of action. Manual is the default state, and every workaround quietly reinforces it.

Why your own AI hits a ceiling

This is the part that should bother anyone who’s been told AI is transforming the partner function.

There’s a whole category of partner managers bringing their own AI to work right now. They open ChatGPT, they paste in a deal, they get a referral draft, they feel faster. And they are faster, at that one task. This is real and I’m not knocking it.

But watch what happens when that motion meets manual-as-default. The model writes a perfect referral. Then the human copies it, opens the partner portal, and re-keys twelve fields to submit it, because the portal is not connected to anything the model can touch. The model produces P2B scores. Then the human types them into the Salesforce field by hand, because that’s the only way they get there. The model drafts the account list. Then it gets downloaded to a desktop and emailed, because that’s how account lists move between these two companies.

The AI got better. The plumbing did not. So the gains stop exactly where the model’s reach stops, which is the edge of the chat tab. Every downstream step is still a person carrying data between systems that were never wired together.

This is why bring-your-own-AI has a ceiling, and why the ceiling is low. It can only help with the parts that happen inside the tab. Everything that happens in Slack, in the portal, in the spreadsheet, in the CRM field stays exactly as manual as it was. You can put the smartest model in the world on top of a manual-as-default motion and you will get a manual-as-default motion with better drafts.

The bottleneck was never the model

We spent the last couple of years arguing about model capability. Which one reasons better. Which one writes better. For most partner work, that argument was beside the point.

The bottleneck was never whether the model could write the referral. It always could. The bottleneck is that the referral has nowhere to go without a human walking it there. The data the model needs to be useful lives in places the model can’t see, and the actions the model produces land in places the model can’t reach. Capability was never the constraint. The constraint is that the data was never in a system of action in the first place.

That reframes the whole problem. The work isn’t to find a better model. It’s to get the partner motion out of Slack and spreadsheets and into a system where an agent can actually act: read the live deal state, submit the referral, write the result back, without a person re-keying anything. The model was ready. The motion wasn’t.

What it takes to change the default

Changing manual-as-default is not a prompt and it’s not a smarter model. It’s structural. The referral has to file into the partner’s portal through an integration, not through a human and a clipboard. The P2B data has to write to the CRM record directly, not arrive by email and get typed in. The account list has to live in a shared place both companies can act on, not get downloaded to somebody’s desktop.

That’s a different kind of work than picking an AI tool. It’s the work of making the partner motion legible to software in the first place, so that when you do point an agent at it, there’s something for the agent to act on. Until that’s done, every AI you add sits on top of the manual layer and inherits its ceiling.

The companies that fix the default first are the ones who’ll actually get the transformation everyone else is only talking about. Not because they found a better model. Because they finally gave the model a motion it could operate.

And here’s the part most people miss. As partner tools start closing the gap, they don’t remove the need for a human. They sharpen what the operator is for. Someone has to wire the deal state to the portal, decide what the agent is allowed to submit on its own, watch where the automation breaks, and own the handoff between two companies’ systems. That’s not relationship management and it’s not admin. It’s engineering the last mile of the partner motion.

This is the operator’s newest discipline. Partnership operators already run systems instead of portfolios: inputs, levers, loops. Partner GTM engineering is what that looks like once the agents arrive. Same operator, one level up the stack, now accountable for whether the machinery actually executes across the trust boundary. The tools are finally good enough to act. The constraint shifts to whoever can make the motion legible enough for them to act on, and trustworthy enough to let them. As the gap closes, the operator who can engineer that last mile has never been more needed.

Veterans Day is for the living. The thank-yous, the airport handshakes, the discounts, those belong to the people still walking around. Memorial Day is for the ones who aren’t. It is a day for the dead. I think we only do it the right way when we keep that line bright and refuse to blur it, even when blurring it would feel warmer and easier.

So before anything else: today I am thinking about the people who did not get the life I got.

I grew up poor. Not the soft kind of poor that makes for a tidy origin story, the real kind, where the math at the end of the month never works and you learn early that most doors are closed. The military was the door that wasn’t. It didn’t ask what my parents earned or what zip code I came from. It asked whether I would show up and do the work, and that was a deal I could take. I went through Army ROTC and came out an officer. I didn’t understand it at the time, but that commission opened every door back then, doors a kid like me had no business expecting to walk through.

Think about what that actually is. A country reaches into a household with no money and no connections, finds a kid, and decides to trust him with command. Not a job. Command. Responsibility for other people’s sons and daughters. Nobody owed me that. It was offered, and offering it to someone like me is the kind of thing a country does only when it has decided that where you start is not where you have to finish.

There is another thread in this, and I have come to see it as the whole point.

My mother immigrated from Japan. She came at a time when that word still carried weight in this country, when the distance between the two nations was measured in something heavier than an ocean. And here is what America did with that. A generation later, the son of that immigrant put on the uniform of the country that had once been on the other side, and rose to lead in it. Nobody stopped me at the door to ask where my mother was born. The country that had every historical reason to hold a grudge instead made room, and then made an officer.

I don’t think you can tell that story in most places on earth. The willingness to take a former adversary’s child, call him one of your own, and then trust him with command, that is not a small thing. It is, I would argue, close to the best thing about this country. We are a nation that keeps deciding, slowly and never perfectly, to widen the circle of who belongs.

But none of it was free, and that is what brings me back to today.

The door I walked through, the commission, the second chances, the room my mother was given, all of it rests on a bill that other people paid in full. The ones who went and did not come back. They didn’t get the career, the family, or the slow ordinary Monday this holiday has somehow become for the rest of us. They bought all of it for people they would never meet, including a poor kid with a Japanese mother who would grow up to wear the same uniform.

So I won’t spend today thanking myself, or the others who made it home. We can do that another day. Today I’ll sit with the harder, more honest version of gratitude, the kind that knows exactly what it cost and who paid.

That, I think, is the right way. Not flags as decoration and sales as ritual, but a clear-eyed thank you aimed at the only people this day was ever for. Quiet, specific, and a little uncomfortable, the way real debt always is.

And if you’re reading this from somewhere that doesn’t mark this particular Monday, the holiday is ours, but the debt isn’t unique to us. Every country still standing is standing on a bill someone else paid. The names and the dates change. The arithmetic doesn’t.

To the ones who didn’t come home: I got the life you paid for. I know it. And I’m grateful in the only way that counts, which is to try to be worth it.

If you’re a partnership leader, you’ve probably had a conversation that felt a bit like a cultural mismatch. You’re talking about “ecosystem synergy,” “co-sell momentum,” and “influence loops.” Meanwhile, the person across the table is staring at a spreadsheet, asking about customer acquisition cost (CAC) and payback periods.

Welcome to the world of FP&A (Financial Planning & Analysis).

If you want to protect your budget, scale your team, and unlock serious resources for your partner program, FP&A shouldn’t be a department you fear. They should be your closest strategic ally.

Here is your Substack guide on how to speak their language, align on the metrics that actually move the needle, and prove why partnerships are finance’s best friend.

1. First Things First: What is FP&A (and Why Should You Care)?

Think of FP&A as the navigation system of the company. While the accounting team looks backward to record what has happened, the FP&A team looks forward to project what will happen. They manage the company’s budget, forecast future revenue, and decide where the business should invest its next dollar.

💡 Why they matter to you: FP&A holds the keys to the castle. If they don’t understand how partnerships drive the bottom line, your program will always be viewed as a “discretionary cost” rather than a “growth engine.” When macroeconomic shifts happen and hard choices must be made, discretionary costs are the first to get cut.

To win them over, you have to move past vanity metrics (like “number of registered partners”) and talk about the mechanics of revenue efficiency.

2. Why Partner Attribution is Non-Negotiable

FP&A loves predictability. If they put $1 into marketing, they want to know exactly how many dollars come out the other side. Because partnerships can be structurally complex, strict partner attribution is your ticket to internal credibility.

You need to cleanly categorize your revenue into three clear buckets:

• Partner-Sourced: Net-new pipeline brought entirely by a partner. Finance views this as highly efficient, ultra-low CAC revenue.

• Co-Sell: Deals where a partner and your direct sales team worked hand-in-hand to navigate the account and close the business.

• Partner-Influenced: Deals where a partner didn’t originate the lead, but accelerated the deal velocity, shaped the scope, or unblocked a stuck executive stakeholder.

Why FP&A cares: If you can’t accurately attribute revenue, FP&A automatically assumes it was a pure direct sales win. By cleanly segmenting sourced, co-sell, and influence, you give FP&A the exact data points they need to build accurate revenue forecasting models and calculate the true ROI of your channel.

3. The Metrics FP&A Actually Cares About: Partner vs. Direct

To really grab finance’s attention, you need to show how partner-assisted deals compare to pure direct-sales deals. When you pit them head-to-head, partnerships almost always win on unit economics.

Here are the specific comparisons you should bring to your next FP&A alignment meeting:

📊 Win Rates (Partner vs. Direct)

• The Metric: The percentage of pipeline opportunities that successfully close.

• Why FP&A cares: A higher win rate means your sales team wastes less time on dead ends. If partner-led or co-sell deals have a 15% higher win rate than direct deals, FP&A realizes they can model higher future revenue without needing to hire additional high-cost sales reps.

⏱️ Deal Velocity (Sales Cycle Time)

• The Metric: How many days it takes a deal to move from initial creation to closed-won.

• Why FP&A cares: Time is money. If partner influence shrinks the sales cycle by 20%, it means working capital frees up faster, cash flow improves, and revenue is recognized on the balance sheet much sooner.

💰 Average Contract Value (ACV) & LTV

• The Metric: The size of the initial deal, and the lifetime value of the customer.

• Why FP&A cares: Co-sell and partner-influenced deals often result in larger initial contract sizes and better long-term retention (lower churn). When FP&A sees that partner-backed customers stay longer and buy more, your channel instantly becomes a capital-allocation priority.

Quick-Reference Comparison Table

• Win Rate * Direct: Baseline

  • Partner Co-Sell / Influenced: Higher

  • Why FP&A cares: More predictable revenue, less wasted sales effort.

• Sales Cycle * Direct: Baseline

  • Partner Co-Sell / Influenced: Faster

  • Why FP&A cares: Accelerates cash flow and revenue recognition.

• ACV / Deal Size * Direct: Baseline

  • Partner Co-Sell / Influenced: Larger

  • Why FP&A cares: Better utilization of account executives.

The Bottom Line

FP&A isn’t trying to slow you down; they are trying to optimize the business.

When you show up to a finance meeting armed with clear attribution data and a side-by-side comparison of partner efficiency versus direct sales, you change the conversation entirely. Stop asking for budget based on “ecosystem potential.” Start proving your impact using win rates, deal velocity, and ROI.

Once FP&A sees that partnerships are the most efficient growth lever in the company, they won’t just fund your program—they’ll help you scale it.

What about you? How does your organization handle partner vs. direct attribution in your financial models? Drop a comment below or reply directly to this email newsletter to share your strategies!