In my previous piece on the WEF’s Growth in the New Economy blueprint, I flagged five forces that should reshape how B2B technology vendors think about their partner ecosystems. One of them, the AI dilemma, is likely to land hardest and deserves its own treatment.
To borrow from the language of the report, AI is simultaneously a ‘no-regret’ move for productivity and a source of ‘dilemma’ for how its benefits are diffused across an economy. Translate that into channel or ecosystem terms, and the same tension appears with sharp focus: AI is both the largest single contributor to the IT services growth vector and yet the largest threat to the partner economic models that built today’s channel.
What AI is doing to partner P&Ls
The public version of the AI partner conversation, currently running across most vendor channel teams, is about enablement. Certification tracks. Co-selling AI products. Sharing customer use cases. Marketing development funds for AI campaigns.
In private, it’s about labour cost compression happening inside partner businesses right now.
The economics of the typical MSP, VAR, or SI have been built over the past two decades on a fundamentally similar structure. Licence or SaaS resale at modest margin. Implementation services are billed on a day-rate or project basis. Managed services billed on a recurring per-seat or per-asset basis. Underneath all three, a labour stack, engineers, consultants, support staff, whose cost is the partner’s largest line item and whose billable hours are the partner’s primary revenue lever.
AI is compressing the billable side of that ledger faster than the cost side. Implementation projects that took 80 days can now take 20. Service desk ticket volumes that supported headcount are being absorbed by autonomous remediation. Configuration work that justified consulting hours is being collapsed into agent-driven workflows. The partner can lay off the engineer, but not as quickly as smarter customers are renegotiating the project fee or the per-seat managed services rate.
For partners that built their business on that model, this is not an enablement problem. It is a recalibration of their business model. The vendors who pretend otherwise, by sending the same partners more product training and calling it AI strategy, are accelerating the compression rather than mitigating it.
The rose-tinted glasses of enablment
I’m not arguing that enablement doesn’t matter. Of course it does, but it sits at the wrong altitude for the problem.
Enablement assumes the partner business model is sound and that what the partner needs is more and more capability inside an unchanged structure. However, the AI dilemma is structural in changing what partners are paid for, what they are trusted with, and how they make money. No amount of certification will fix a partner whose entire margin came from labour cost arbitrage that no longer exists.
The vendors making the most progress are the ones who have stopped treating AI as a product to be sold through partners and started treating it as a force restructuring partner economics. That changes the channel team’s job from enablement to leading the partner base through a transition, making explicit choices about which parts of the base to lead, and at what speed.
Go solo or team up , but pick one
The WEF frames the broader choice as competition versus coordination. In the channel, it shows up as race versus coordinate.
A race posture concentrates investment, partner economics and field engagement on a relatively narrow set of partners, typically the top 10%, and pushes them to the frontier of AI-native advisory, managed services, and outcome-based delivery. The vendor’s role is to fund, equip and clear the path. The trade-off is that the rest of the partner base gets less attention and may churn faster than expected. The upside is speed: by 2027, the vendor has a credible AI-capable channel layer.
A coordinate posture takes the opposite stance. The vendor leads a structured transition across the whole base, sequenced by partner readiness. Common standards, shared playbooks, joint AI delivery frameworks, and economic transition support are central. The trade-off is speed, as the army marches at the pace of its slowest man. The upside is breadth: more of the base survives the transition, and the vendor preserves regional and segment coverage.
Most vendors are currently doing neither. They are running a race in PowerPoint while coordinating in practice, communicating ambition and pace through marketing materials, while still funding the same partner programmes, the same incentives, and the same enablement formats that produced the last decade’s results. The dissonance shows up as partner cynicism, missed targets, and a slow-motion erosion of programme credibility.
The choice itself isn’t binary, but the posture needs to be explicit. Drift is the worst option.
What compounds, what compresses
Inside the partner base, the differentiation is becoming visible if you look for it.
Partners that compound in this environment share a number of characteristics. They are increasingly priced on outcomes rather than effort. They have built or acquired AI advisory capability that operates at the strategy layer, not just the implementation layer. They specialise, by industry vertical, by use case, by complexity of regulatory environment, in ways that make them harder to substitute. They take liability for the result, which means they can charge for the trust premium that AI uncertainty creates. And, often, they are smaller and more focused than the partners that occupied the same spot in the vendor’s top 20 three years ago.
Partners that compress also share characteristics. They are still priced on day rates or per-seat fees in service lines that AI is automating. They have horizontal capability across many vendors but limited depth in any. Their certification badges are extensive, but their delivered outcomes are commoditised. Their competitive moat is incumbency and a long-standing customer relationship, both of which are eroding due to customer buying behaviour, AI agents and platform marketplaces.
The honest exercise for any vendor is to map the top 50 partners against those two profiles however uncomfortable the results may prove.
Five ‘no regrets’ pursuits for 2026
If the WEF analysis holds true for the B2B tech channel, the implications are significant. Here are five potential ‘no regrets’ moves to mitigate.
1: Redesign at least one element of partner economics
Margin structure, MDF allocation, rebate triggers, or co-investment terms, pick one, and rewire it to reward AI-attached and outcome-based delivery rather than transactional licence attach.
2: Audit your top 50 partners on AI readiness
Use a framework that goes beyond certifications. The questions are: where does revenue come from today, what proportion of it is exposed to AI compression, what is the partner’s plan for the next two years, and is the leadership credible on that plan. The answers will reshape your investment allocation.
3: Co-invest materially with five to ten partners
Not enablement materials. Capital, expertise, joint go-to-market, and shared P&L on specific verticals or use cases. This is the ‘race’ posture, applied with discipline.
4: Build an explicit transition pathway for partners who won’t make the leap
This is the hardest move because it touches the politics of the channel team’s relationship but pretending that every existing partner has a future in an AI-native channel is more damaging in the long run than acknowledging that some don’t.
5: Recruit the partner archetypes that don’t exist in your channel today
AI consultancies, vertical-AI specialists, system integrators with AI-native delivery models. Most channel programmes are still optimised to recruit and onboard partners who look like the partners who joined five years ago. The next generation looks different.
The Blue Barn take
The AI dilemma is not, fundamentally, a technology question. It is a partner economics question, and beyond that, a vendor strategy question. AI is doing what cloud did between 2010 and 2018, restructuring the unit economics of the partner business, except that AI is doing it faster, deeper, and with a more direct line to the customer through hyperscaler marketplaces and emerging buying agents.
The vendors who navigated the cloud transition well, Microsoft being the canonical example, did so because they made explicit, contested, expensive choices about partner programme structure ahead of the curve. They redesigned margin economics. They invested directly in partner capability. They retired programme elements that no longer fit. They were prepared to lose some partners to gain the structure they needed.
The vendors who navigated the cloud transition with difficulty did so because they treated it as an enablement issue.
So what song will you choose to sing?
