When I sat down to do it, it took about ten minutes to go from “time to follow up” to a proposal and email in the prospect’s inbox.

Dust pulled everything it needed: the call from Granola and Gong, the email thread from Gmail, and the deal history from HubSpot. Then it went into my Obsidian vault for the frameworks and precedent I use in engagements like this, and wrote a directional brief: what they need, what to scope, what to price, what to leave out. I handed that to Claude. Claude wrote the proposal, then helped me draft the email. I read both, made my edits, and hit send.

That same follow-up used to take me a few hours.

And since I put this system in place, my close rate has been above 50%.

This isn’t a post about how great my agency is. BlueThread is nine months old. It’s a post about the operating model underneath it, because I think any small firm, or any partner team inside a bigger company, can run the same thing. So steal it.

The problem: context is the job

A small firm doing enterprise-grade work lives or dies on context.

What did we scope for a similar client last time? How did we price it? What did this prospect say on the call about their partner program, their board, their timeline? What’s our position on co-sell versus resale for a company at their stage?

In most firms, that context is scattered across six tools and one founder’s head. Every proposal starts from a blank page and a memory search. That’s where the hours go, and it’s why quality drifts depending on how tired you are.

I didn’t want to hire my way out of that. I wanted to build a brain first.

The model, layer by layer

1. Sources: where the work already lives

Gmail, Gong, Drive, Slack, HubSpot, Granola, Obsidian.

The first rule: don’t move the work. Connect to it. Every system you ask people to copy things into is a system that goes stale in three weeks. The calls are already recorded. The emails are already in Gmail. The deals are already in HubSpot. The brain reads from where the work happens. The one exception is our Obsidian vault, which we maintain on purpose. It lives in GitHub so the whole team shares it, and we update it every Friday.

In practice, that means when I sit down to follow up, Dust already has the call, the emails and the deal record. I don’t go hunting for anything.

2. Dust knows: context, precedent, answers

Dust is the knowledge layer. There’s one front door, @brain in Slack, and it routes every question to the right specialist:

• Deal Desk: pricing, scope and SOW precedent. “What did we charge for a partner program assessment at a company this size?”

• Brand: voice, look and house style, so nothing we send sounds like a template.

• Playbook: our methodology and co-sell frameworks. This is the IP.

• Client Threads: one agent per client, so the full history of an engagement is one question away.

The point of splitting it up is trust. When Deal Desk answers a pricing question, I know it’s drawing on real deals, not a guess.

3. Claude drafts: skills that produce the deliverable

Dust knows things. Claude makes things.

The difference matters. I don’t want a chat reply about what a proposal could say. I want the proposal. So Claude runs as a set of skills, each built to produce one specific deliverable in our format:

• Proposals: from intake to a deal in the CRM

• SOWs: scope and pricing

• Research: account intel before a call

• Brand and voice: decks, docs and posts

Dust hands over the context. Claude hands back a draft that’s close to final.

4. Operators decide: nothing ships without a human

This is the non-negotiable. Every proposal, every SOW, every email gets my sign-off before it leaves.

AI gets me to 90% in minutes. The last 10% is judgment: reading between the lines of what the prospect said, knowing when to push on scope and when to leave room. That part is the job, and it’s the part clients are paying for.

The part most people miss: close the loop

Here’s the idea that makes the whole thing compound.

When a proposal or SOW is final, meaning signed off, sent and ideally signed, it goes back into the brain as a gold document. Tomorrow’s answers run on today’s best work.

That’s why the proposal I sent last week was better than one I would have sent in month two. The system isn’t just faster. It gets sharper with every engagement, because it learns from what actually shipped, not from drafts.

Most people using AI skip this step. They use it as a vending machine: ask, receive, move on. The value is in the loop.

What broke along the way

The biggest problem was the brain itself. It lived in an Obsidian vault on my Mac.

That vault holds everything: our methodologies, all our client work, a breakdown of our HubSpot, and our website. It’s the best record of how BlueThread thinks. But it sat on one laptop, so the only person who fully benefited from it was me. Everyone else was working from partial context, and we spent extra cycles chasing consistency. Which version of the framework is current? Did that pricing change make it into the SOW? Is this the latest client summary?

The fix was simple. We pushed the vault to a GitHub repository. Now the whole team works from the same source, Dust reads from it, and it gets updated every Friday. One brain, one version, shared by everyone.

The lesson: a brain only one person can reach is just a very good notebook. The value shows up when the whole team, and the AI, draws from the same place.

How a partner team could run this inside a company

This isn’t only a solo-founder play. Swap the deliverables and it’s a partner operating model:

• Sources: the partner CRM or PRM, co-sell call recordings, partner Slack channels, marketplace data.

• Brain: agents for partner program rules, co-sell precedent, and one thread per strategic partner.

• Drafts: joint account plans, co-sell briefs, QBR decks, partner business reviews.

• Operators decide: the partner manager signs off. Always.

• Close the loop: every approved account plan and winning co-sell brief goes back in.

Most partner teams spend their week rebuilding context that already exists somewhere. This gives that time back to the work that actually moves pipeline.

If you’re starting Monday

Don’t build all of it. Build the smallest version that closes the loop:

1. One source. Your call recordings.

2. One brain. A single knowledge space with your five best past deliverables in it. Ours is an Obsidian vault in a shared GitHub repo, covering our methodologies, client work, HubSpot and website, updated every Friday. Start smaller than that. Five great documents beats five hundred average ones.

3. One skill. Whatever you write most often. For most of us, that’s the follow-up.

4. One rule. Nothing ships without your read.

5. One habit. Every final goes back in.

Do that for a month and you’ll feel the compounding.

Dust knows. Claude drafts. Operators decide.

What does your operating model look like? Reply and tell me what’s in your stack.

Every major shift in enterprise technology creates a multi-billion-dollar partner ecosystem. Not because tech vendors plan for it, but because new architecture creates new friction.

When technology was hard to source, we got Resellers.

When it was hard to configure, VARs created value.

As systems grew complex, Systems Integrators (SIs) built the bridges.

When enterprise IT got too painful to run internally, MSPs took the keys.

Cloud shifted the model to consumption and marketplaces. APIs created the integration partner. Automation gave birth to workflow orchestrators.

Now, the shift toward autonomous agents, MCP (Model Context Protocol), and AI orchestration is forming the next wave. But this wave carries a fundamentally different kind of friction.

From Connections to Delegation

APIs gave software a voice. Automation platforms gave software a schedule.

AI agents introduce something entirely different: agency. Software is no longer just passing data through a pipeline; it is interpreting context, picking tools, and executing actions on its own.

This is why MCP is becoming the most critical protocol in modern software. If APIs were the standard interface for app-to-app communication, MCP is becoming the interface layer for agents to discover and execute tools.

That triggers a brand-new set of ecosystem questions:

• Who prepares an enterprise’s data and workflows so agents can safely consume them?

• Who orchestrates multi-agent systems operating across disparate platforms?

• Most importantly: Who is accountable when an agent gets it wrong?

The Delegation Paradox: Where Liability Lives

Every previous partner wave involved a human operator acting on behalf of the customer. A VAR configured the server. An SI wrote the custom code. An MSP clicked “deploy.”

Agents invert that sequence. An agent doesn’t wait for a human to confirm every step; it interprets context and acts.

This isn’t just a faster version of traditional integration. It is delegation. And delegation introduces liability.

If an agent misinterprets an MCP tool context and executes an invalid transaction, issues a bad credit, or leaks sensitive data, the problem isn’t technical deployment; it’s governance, risk, and trust.

None of the legacy partner categories were built to hold that risk.

We don’t have a settled name for this new category yet (Agent Orchestrators, AI Governance MSPs, or Ecosystem Operators). But the pattern is undeniable, and the platform giants are already laying the tracks:

• Anthropic opened MCP as an industry standard.

• AWS launched its AI Agent Marketplace and wired its Partner Central into an MCP server.

• Google Cloud added native MCP support across its agent suite.

• OpenAI built its Apps SDK directly on top of MCP.

• Microsoft integrated MCP into enterprise engines like Dynamics 365.

This isn’t a mature partner motion yet. It’s the early smoke before the fire, much like the first hyperscaler marketplace listings in 2016.

Will AI Eliminate Partners Entirely?

It’s worth confronting the elephant in the room: Will agents just do the partner’s job?

Yes, this wave carries higher disintermediation risk than any before it. Agents that can self-integrate and self-configure will wipe out low-value SI headcount and basic software resale margins.

But history shows us a clear rule: New capability creates new friction faster than it automates old work. The work doesn’t vanish; the friction just moves upstream.

Shift, Don’t Snap: The Hardware Lesson

It is also worth remembering that these transformations rarely happen overnight.

Whenever a breakthrough architecture arrives, the market tends to over-index on instant disintermediation. Consider the hardware industry. A decade ago, as workloads migrated to the cloud, conventional wisdom declared local hardware dead and traditional IT resellers on borrowed time.

Yet hardware didn’t vanish; it adapted.

As cloud bills ballooned, hybrid architectures stabilized, and specialized compute like AI clusters became table stakes, hardware didn’t just survive. It re-emerged at the center of the enterprise conversation. The partners who thrived weren’t the ones selling commodity boxes; they were the ones who shifted from selling hardware to orchestrating high-density compute, sovereign data centers, and specialized infrastructure.

The same transition dynamic will apply to agents.

Traditional SIs, MSPs, and VARs won’t be replaced by an agent deployment overnight. Instead, we’ll see a multi-year bridge where existing partners absorb agent capabilities into their current motions before entirely new, specialized category leaders fully emerge.

Transition takes time. But directionally, the vector is clear.

Follow the Value

To predict where the revenue goes, look at where the friction lives, and where new value is generated:

• Hard to buy? Reseller (Margin / Markup)

• Hard to deploy? VAR (Services Markup)

• Hard to customize? Systems Integrator / SI (Project Fees / SOW)

• Hard to run? MSP (Recurring Retainers)

• Hard to connect? Integration & Automation Partner (Usage / Licensing Fees)

• Hard to trust, govern & coordinate agents? The Next Partner (Outcome-Based / Value Share)

Nobody has solved the business model for this next layer yet. Will they charge subscriptions like SaaS? Project fees like SIs? Or take a share of the actual business value an agent creates?

Whoever answers the monetization question first won’t just build a huge business, they will define the category.

Technologies change. Interfaces change. Labels change.

But the core rule remains: Partners don’t just follow friction, they follow where value is being created.

The Takeaway: Don’t ask what work AI will take away from your partners. Ask where your customers will experience the newest, highest-stakes friction when agents run wild, because that’s exactly where the next generation of value creation (and partner revenue) is hiding.

The best partner leaders in the next few years will not be the best relationship managers.

They will be system thinkers.

That does not mean relationships matter less.

They are still the core.

Trust matters.
Credibility matters.
Knowing who to call matters.
Being able to get people aligned around a problem still matters.

But relationships are not a system.

That distinction is becoming more important.

One of the biggest mistakes I see in partnership organizations trying to transform is taking what worked 10 years ago and simply adding more technology around it.

More tools.
More data.
More automation.
More dashboards.

But underneath it all, the operating model is still built around individual relationships.

That does not scale.

The modern partner leader has to understand how the pieces of GTM actually work together.

Sales.
Marketing.
RevOps.
Product.
Customer success.
Partners.
Data.
AI.

Historically, partnerships could operate as a separate function.

Build relationships.
Recruit partners.
Run enablement.
Create some joint pipeline.
Report activity.

That model worked better when companies were smaller, channels were simpler, and much of the motion could live in the heads of a few experienced people.

That is not the environment we operate in now.

The customer does not experience your company in departments.

They experience one system.

And increasingly, the partner sits somewhere inside that system.

The best partner operators are starting with the customer and working backward.

Where does trust come from?

Who already has the relationship?

What signals tell us there is an opportunity?

How does that signal get into the CRM?

Who owns the next action?

Where does the partner enter the sales process?

How do we move from introduction to validation to conversion?

And ultimately, did the partnership help us close the deal?

That is system thinking.

It is also why the lines between Partnerships, RevOps, Sales and GTM Ops are beginning to blur.

The partner leader of the future needs to understand CRM architecture, account mapping, attribution, automation, hyperscaler co-sell, AI workflows, sales process and partner economics.

Not because they need to personally operate every system.

But because they need to understand how the system produces revenue.

This is the shift I think we are underestimating.

AI will automate more of the activity around partnerships.

Research.
Account mapping.
Partner discovery.
Briefing.
Enablement.
Follow-up.
Reporting.

The differentiator will not be who can produce more activity.

It will be who can design a better system.

And this is where the distinction matters most.

Relationships create trust.

Systems create repeatability.

You need both.

If you have systems without relationships, the motion becomes mechanical.

If you have relationships without systems, the motion becomes dependent on individuals.

That is the trap.

A lot of partnership teams are trying to modernize without actually changing how they operate.

They are digitizing the old model instead of building a new one.

The best operators will do something different.

They will look across GTM, find the friction, connect the right pieces and create leverage.

They will use technology to make relationships more valuable, not replace them.

The next generation of partnership leaders will need both.

Human trust.

And system thinking.

I’m all in on AI.

I use it every day. I believe it will make us faster, smarter and dramatically more productive.

But I’m starting to wonder if we’re optimizing the wrong thing.

Everywhere you look, the conversation is about more.

More output.
More automation.
More agents.
More workflows.
More loops.
More dashboards showing how all the loops connect.

More. More. More.

And I get it.

I spend a lot of my time helping companies build better operating systems. Better partner motions. Better workflows. Better data. Better ways to turn signals into action.

But there’s another side of performance we don’t talk about enough.

Imperfection.

Not sloppiness.

Not lowering the bar.

The willingness to leave enough space in the system for something unexpected to happen.

That matters in life.

And it matters in partnerships.

We spend a lot of time trying to define the perfect partner.

The perfect Ideal Partner Profile.

Right segment.
Right customer overlap.
Right capabilities.
Right geography.
Right economic model.
Right level of commitment.

All of that is useful.

You should absolutely know who you are looking for.

But every once in a while, a partner shows up that doesn’t fit the model particularly well and creates a total unlock.

A new route to market.

A new buyer.

A new use case.

A completely different way of thinking about the business.

Some of the best partnership opportunities I’ve seen were obvious only after they started working.

That happened to me at Gong.

There were plenty of traditional partnership lanes we could have pursued.

Sales methodology companies made sense.

Large consulting firms and GSIs made sense.

Technology integrations made sense.

The ecosystem map looked pretty familiar.

But I started spending more time around a different group.

Boutique RevOps firms.

They weren’t necessarily the partners you would have put at the top of a traditional partner segmentation exercise.

But they were close to the buyer.

They understood the workflow.

They had trust.

They were often inside the customer helping shape how revenue teams actually operated.

And suddenly there was a lane.

Not because someone had perfectly modeled it six months earlier.

Because we noticed something.

We paused.

We followed the signal.

And we let the strategy evolve.

That is a kind of imperfection I think companies need to protect.

AI is going to make it very tempting to optimize everything.

We can score every partner.

Rank every opportunity.

Automate every follow-up.

Model every possible route to revenue.

Eventually we’ll probably be able to create an incredibly precise picture of where to spend our time.

That’s valuable.

But precision can also create a false sense of certainty.

You still need judgment.

You still need curiosity.

You still need someone willing to say:

“This doesn’t quite fit our model, but there’s something here.”

The same thing is true personally.

High performance is often presented as a perfectly optimized calendar.

Every minute accounted for.

Every meeting productive.

Every workflow automated.

Every task prioritized.

Every hour somehow producing an output.

I don’t think that’s how high performance actually works.

The best work usually comes from a clear head.

Knowing what matters.

Having the ability to focus on it.

Having enough room to notice something you weren’t looking for.

Sometimes that means taking the afternoon off.

Going for a walk.

Having a conversation with no agenda.

Building something before you have every answer.

Changing your mind.

Letting something be good enough.

The irony is that AI should make more of this possible.

If AI can take away administrative work, repetitive tasks and unnecessary friction, the payoff shouldn’t simply be that we cram another twenty percent of work into the day.

Maybe the payoff is space.

Space to think.

Space to notice.

Space to explore the partner that doesn’t fit the IPP.

Space to follow the strange idea.

Space to spend time with people.

Space to do your best work when your best work is actually required.

We’ve spent years designing systems around optimization.

AI is going to make those systems incredibly powerful.

But I don’t want a perfectly optimized life.

And I don’t want a perfectly optimized company.

I want enough structure to create focus.

And enough imperfection to leave room for discovery.

The goal isn’t perfection.

It’s growth.

There’s a difference.

The question every VP of Alliances asks eventually is not, “Which PRM should we buy?”

It is, “Who actually owns this relationship when the tooling changes?”

That question used to have an easy answer. Whoever held the CRM record owned the relationship. Whoever ran deal registration owned the motion. Ownership was wherever the system of record lived.

AI is breaking that assumption.

We are moving toward a partner tech stack where an agent can identify account overlap, research the partner, recommend an introduction, draft the outreach, update the CRM, and initiate the co-sell handoff without either side opening a portal.

That creates a tempting conclusion: the PRM matters less because AI can sit above the systems and orchestrate the work.

I believe the opposite is true.

As AI handles more of the partner motion, the need for clean data, durable context, clear governance, and human accountability becomes more important. Done right, the PRM becomes the foundation that allows AI to scale a partnership motion without hollowing it out.

Done wrong, AI simply helps companies accumulate relationship debt faster.

The hidden debt inside the partner tech stack

Relationship debt is the gap between the partnerships represented in your systems and the relationships capable of producing a commercial outcome.

It accumulates quietly.

Another account-mapping exercise. Another AI-generated introduction. Another registered opportunity nobody actively co-sells. Another partner added to the program because the data showed overlap, even though neither side has a reason to invest.

The system gets busier. The ecosystem map gets larger. The dashboards look better.

But when an important deal stalls, nobody knows who can make the call.

That is when the debt comes due.

AI does not create this problem. It exposes it, and potentially accelerates it.

An agent can find 500 account overlaps in seconds. That does not mean you have 500 partner opportunities.

It can write a personalized introduction. That does not mean the recipient trusts the person sending it.

It can recommend the next best action. That does not mean anyone is accountable for taking it.

It can update every system perfectly. That does not mean the underlying relationship is real.

The risk is not that AI will take ownership of the partner relationship. The risk is that AI will reveal nobody owned it in the first place.

Why the PRM matters more in an AI stack

To be clear, I am a strong proponent of PRMs.

At scale, spreadsheets, tribal knowledge, and relationships living inside one partner manager’s head are not an operating model. They are a risk.

A PRM should not be viewed as another portal that partners are forced to visit. In an AI-enabled stack, its greater value is as the structured relationship layer between the CRM, partner data, workflows, and people.

The CRM tells you what is happening with the customer.

The PRM should tell you what is happening with the partner.

The AI layer should use both to recommend and execute the next best action.

But the operating model still has to determine who owns the outcome.

A well-designed PRM gives AI the context it needs to be useful:

• Which partners actually matter

• What each partner is trying to accomplish

• Which relationships exist on both sides

• What has been committed

• What has already been tried

• Which opportunities are active

• Who owns the next action

• How the partnership creates economic value

Without that context, AI does not create intelligence. It creates more activity.

This is why I do not believe AI replaces the PRM. I believe it changes the job of the PRM.

The old PRM was often designed as a destination: log in, complete training, register a deal, download content.

The next PRM needs to operate more like infrastructure. It should preserve partner context, govern workflows, feed the right data to agents, and capture the outcomes of work happening across email, meetings, CRM, collaboration tools, and marketplaces.

The partner may never open the portal. The PRM can still be essential.

AI can scale trust, or scale noise

I have watched partner motions survive major technology changes.

At SYNNEX, scaling Microsoft’s cloud business from roughly $100 million to more than $1 billion required technology. We could not have operated at that scale without platforms, data, and repeatable processes.

But the relationships survived changes in distribution platforms, programs, incentives, and systems because the trust between the people predated the tooling. When something changed, the reps still knew whom to call. They understood the shared economics and knew how to get work done together.

The technology scaled the motion. It did not create the reason for the motion.

I saw the same thing at Gong.

The partners who mattered were not necessarily the ones who logged into the portal most often. They were the ones whose reps picked up the phone when our reps called. They had context. They understood the customer. They had earned the right to make an introduction or ask for help.

Portal activity was a signal. It was never the relationship itself.

That distinction matters even more with AI. If the underlying motion is built on trust, shared economics, clear ownership, and consistent execution, AI and the PRM can scale it.

If those things are missing, the same technology scales noise.

Three tests for your AI-enabled partner stack

Before adding another agent or automation, ask three questions.

1. If the tool disappeared tomorrow, would the relationship survive?

If the answer depends on a login, an account map, or an automated workflow, you do not have a partnership. You have a technology dependency.

2. Can the AI see context, or only activity?

Deal registrations, logins, certifications, and account overlaps are useful signals. But they do not explain why the relationship matters, what each side has committed to, or who can move an opportunity forward.

AI acting on incomplete context will automate the wrong work with impressive speed.

3. Who is accountable when the agent completes the handoff?

An automated introduction is not co-selling. A recommended action is not execution. A registered deal is not pipeline.

The agent can coordinate the motion. A person still has to own the outcome.

The more autonomous the tooling becomes, the more explicit human accountability needs to become.

Build the stack in the right order

The AI-enabled partner stack should have distinct jobs.

The CRM owns the customer and opportunity record.

The PRM owns the partner context, program rules, commitments, and relationship history.

The data layer supplies ecosystem and account intelligence.

The AI layer identifies signals, recommends actions, and automates execution across the stack.

The human owner applies judgment, builds trust, and remains accountable for the commercial outcome.

This is where Partner RevOps becomes critical. Partner RevOps is not simply connecting the PRM to the CRM and adding an agent on top. It is designing an operating model in which the data, workflows, incentives, systems, and people reinforce the same commercial motion.

Before deploying more AI, pull up your ten most important partners and ask:

Who on our team can call whom?

What have we actually built together?

What context does the system preserve?

What actions could AI safely automate?

Who owns the next commercial outcome?

Would the partner still take the call?

That is your real ecosystem. The rest is infrastructure.

AI is going to transform the partner tech stack. It will eliminate administrative work, surface opportunities humans miss, and make smaller partner teams far more productive.

But it will not manufacture trust, judgment, or accountability.

The winners will not be the companies with the most agents or the largest ecosystem maps. They will be the companies that use a PRM to preserve the right context, AI to accelerate the right actions, and people to own the relationships and outcomes that matter.

Build the relationship. Design the motion. Structure the data. Then let AI scale it.

Done in that order, the partner tech stack becomes an unlock.

Done in reverse, it becomes a very efficient way to accumulate relationship debt.

I am too young to retire. at least in my mind.

I am also too old for the grind.

I have no interest in spending the next several years sitting on a beach with nothing to build. But I have equally little interest in returning to the endless meetings, corporate politics, packed calendars, and Sunday-night anxiety that can come with running a large business.

For a long time, those appeared to be the two choices.

Keep grinding or stop working.

I am discovering a third option.

You can build a thriving small business around what you are good at, what you care about, and how you actually want to live.

This is part passion and part lifestyle

When I started BlueThread, I knew I wanted to remain in the partnership world.

I love the work.

I like sitting with a company, understanding how it grows, finding the right partners, and turning a collection of ideas into an operating model that produces revenue.

I still get energy from being in the weeds with customers. Probably more than I ever got from sitting in executive meetings talking about the work.

That is the passion part.

The lifestyle part is just as intentional.

I wanted control over my calendar. I wanted to travel with my wife. I wanted to continue knocking things off the bucket list while I was healthy enough to enjoy them. I wanted the freedom to work from different places without asking anyone’s permission.

I also wanted to create opportunities for a small team of people I enjoy working with.

This was never about working as little as possible.

It was about building a life where work fit into the whole picture instead of consuming it.

A thriving business does not have to become a large company

Corporate life conditions many of us to measure success by size.

How many people report to you? How large is the budget? How much revenue do you control? How quickly can you scale?

When you build your own business, it is easy to carry those same assumptions with you.

More employees. More clients. More products. More complexity.

But I have started asking a different question:

What if the goal is not to build the largest company possible?

What if the goal is to build a highly capable, profitable business that does meaningful work and gives everyone involved a good life?

That is not a lack of ambition.

It is a more precise definition of ambition.

I want Bluethread to thrive. I want our revenue to grow. I want our work to matter. But I do not want to rebuild all the complexity I spent a career learning to navigate.

Small can be a design decision, not a temporary stage before becoming big.

Experience becomes an unfair advantage

Starting a business later in your career has real advantages.

You know what you are good at.

You have lived through enough technology cycles to recognize hype. You have seen strategies fail in the field. You can usually tell the difference between a real customer problem and a fashionable idea looking for a market.

Most importantly, you have accumulated judgment.

I could not have built this business when I was 30.

I might have had more energy, but I would not have had the pattern recognition, relationships, or confidence to ignore what everyone else was doing.

At this stage, I do not need to pretend to know everything. I know where I can create value, and I know when to bring in someone better than me.

That makes the work more focused and, honestly, more enjoyable.

The market still gets the final vote

Experience does not mean you get everything right.

I originally expected Bluethread to be primarily a partnership strategy firm.

Customers pushed us somewhere more interesting.

They did not just need advice about partnerships. They needed help making partnerships work operationally. Their CRM, PRM, automation, attribution, and co-sell workflows were often disconnected.

They needed the machinery fixed underneath the strategy.

That pulled us into Partner RevOps, AI workflows, automation, and hands-on implementation.

The business became better when I stopped protecting my original vision and followed what customers were willing to pay us to solve.

This is one of the things I enjoy most about running a small business.

You can listen, adjust, and move.

There is no eighteen-month transformation plan. There are customers, problems, decisions, and results.

Technology changed what is possible

AI and automation have fundamentally changed the economics of a small company.

A small team can now research markets, analyze accounts, create content, build internal tools, automate workflows, and serve clients with capabilities that once required a much larger organization.

That does not mean everything should be automated.

It means experienced people can spend less time on mechanical work and more time applying judgment.

For someone at my stage of life, that is an incredible combination.

I can bring decades of experience to the work without recreating the infrastructure, headcount, and overhead of the companies where I gained that experience.

We can stay small and operate much larger than we are.

“It doesn’t feel like work” does not mean it is easy

There are still difficult days.

Clients have expectations. Revenue is never guaranteed. Something always needs fixing. When your name is on the business, there is nowhere to hide when you get something wrong.

But difficulty and grind are not the same thing.

The grind is spending your time on work that drains you because the system demands it.

This business feels different because I chose the work, the customers, the people, and the operating model.

Most days, I am doing something I would probably be talking about even if nobody paid me.

Now they do.

That is a pretty good business model.

The third option

We talk a lot about preparing financially for retirement.

We talk much less about what happens when you still have energy, experience, curiosity, and something left to contribute, but no longer want the life that comes with a traditional executive role.

The answer does not have to be another full-time corporate job.

It also does not have to be retirement.

You can build something smaller, more personal, and more aligned with the life you want now.

Something that makes money without making money the only point.

Something built around your experience without being trapped by your past.

Something that gives you a reason to stay engaged while giving you the freedom to enjoy the years you worked so hard to reach.

I am too young to retire and too old for the grind.

It turns out that is a very good age to build.

I was wrong about Microsoft Copilot.

Not because I thought it was bad. I just thought about it too narrowly.

I started this week doing what looked like a fairly normal training session. There was some AI involved, some prompt work, some discussion around how a sales team could use Copilot more effectively.

Then the conversation changed.

Instead of asking, “What are the best prompts?” we started asking a much better question:

What would an actual operating system for this team look like if we built it entirely on the Microsoft stack?

That changed everything.

The front door became Teams.

SharePoint became the knowledge layer.

Microsoft Lists became a way to structure content instead of burying it in folders.

Copilot became the interface for finding, summarizing, and applying that knowledge.

Agents started to make sense as a way to guide people through repeatable jobs.

Power Automate became the plumbing underneath the system.

And suddenly this was no longer an AI training exercise.

It was a go-to-market workflow problem.

That distinction matters.

There is so much attention right now on models, benchmarks, agents, and whatever the newest release happens to be. I understand why. The technology is moving incredibly fast.

But most companies do not have a model problem.

They have a workflow problem.

They have a knowledge problem.

They have an execution problem.

They have ten different places where people store the same information, no clear operating rhythm, and too much institutional knowledge sitting in someone’s head.

AI does not magically fix that.

In fact, if the underlying system is messy, AI often just gives you a faster way to interact with the mess.

What became interesting in this project was that we stopped thinking about AI as the destination.

We started thinking about the job.

A seller has a customer meeting.

What do they need?

They need to understand the account.

They need the right value proposition.

They need the relevant product knowledge.

They need discovery questions.

They need the right sales play.

They need competitive context.

They need a call brief.

They need a good follow-up.

They need to know what to do next.

That is the operating system.

AI becomes valuable when it helps move the seller through that workflow faster and with better context.

That is a very different conversation than “Here are 50 great prompts.”

The prompt library still matters.

But a prompt sitting in a library is just another document.

A useful prompt is connected to a workflow.

Account research.

Call preparation.

Opportunity planning.

Executive communication.

Cross-sell.

Competitive strategy.

Next-step recommendations.

The same thing is true for agents.

An agent is much more useful when it is grounded in the way the organization actually works.

Not a generic chatbot.

An agent that understands:

Here is how we prepare for a call.

Here is our value proposition.

Here are our product plays.

Here are the questions we ask.

Here is what good looks like.

Here are the examples from the field.

That is where the technology starts becoming operational.

One of my biggest takeaways from the week was also a reminder of something I have believed for a long time:

Being close to real customers is still one of the best ways to understand where technology actually creates value.

You can read the announcements.

You can watch the demos.

You can follow every AI release.

But when you sit with a team trying to hit a real number, the conversation gets simpler very quickly.

Does this help someone prepare better?

Does this improve a customer conversation?

Does this make the right information easier to find?

Does this create a repeatable process?

Does this help a seller move an opportunity forward?

Those are much better tests than whether something sounds impressive in a keynote.

And that is where I changed my mind on Copilot.

I had been evaluating it too much as an AI product.

I now think the more interesting opportunity is to view it as part of a broader operating layer.

Teams.

SharePoint.

Lists.

Copilot.

Agents.

Automation.

All connected around the actual work.

That combination may not be as exciting as arguing about which model is best this week.

But for go-to-market teams, it may be a lot more useful.

The lesson for me is pretty simple:

Do not start with the AI. Start with the workflow.

Then decide where AI actually makes the workflow better.

That is when the hype starts turning into something useful.

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.