Channelnomics chief analyst Larry Walsh argues that a new global class of roughly 35,000 to 50,000 AI-native consultancies is already influencing enterprise technology decisions outside traditional reseller and integrator partner programs. That matters because these firms are not waiting for vendor authorization, margin schedules, or tier badges before shaping customer architecture. The next channel fight is not simply about whether MSPs sell enough Copilot seats. It is about whether vendors can see the people who are already steering AI demand before the purchase order exists.
The traditional partner ecosystem has always been good at counting what it can invoice. Resale revenue, registrations, certifications, co-sell motions, marketplace transactions, deployment projects, and renewal attach rates all fit neatly into the machinery vendors have built over decades. If a partner moves product, the vendor sees the partner.
AI is exposing the limits of that worldview. A consultancy that helps a manufacturer choose a model strategy, build an agentic workflow, or decide between hosted APIs and an open-source stack may never appear as a partner of record. It may not resell anything. It may never ask for MDF, sales training, or a higher program tier.
Yet that consultancy may be the most important influence in the room. It can determine whether the customer uses Azure OpenAI Service, Amazon Bedrock, Google Vertex AI, Anthropic, OpenAI directly, a local model, or some stitched-together combination of all of the above. By the time a vendor sees consumption rise, the architectural decision may already be over.
Walsh’s Channelnomics piece puts a number around a phenomenon many IT buyers and practitioners have already felt: the AI advisory layer is expanding faster than the formal channel can classify it. The estimate of 35,000 to 50,000 AI consultancies globally is necessarily imperfect, built from company formation, service listings, and engagement patterns. But even if the number is off at the edges, the point is not. Influence has escaped the CRM field where vendors expected to find it.
That is a low bar. It is also not useless. For many SMBs, licensing Microsoft 365 Copilot, Google Gemini for Workspace, Salesforce Einstein, ServiceNow AI agents, or security products with AI-assisted triage may be the first practical exposure to enterprise AI. The channel has always made money translating vendor packaging into customer adoption.
But Walsh draws a harder line between partners selling AI-adjacent products and partners building or supporting AI systems. By that stricter standard, he estimates the active slice of traditional partners is closer to 10%. That feels plausible because building AI into business operations is not merely a SKU expansion. It is data engineering, governance design, workflow analysis, software integration, change management, and risk containment bundled into one messy service motion.
This distinction is especially relevant for WindowsForum readers because Microsoft’s ecosystem is the clearest example of both sides of the story. Copilot has given Microsoft partners an obvious AI resale and adoption path. But the deeper customer work — grounding models in enterprise data, preventing data leakage, redesigning processes, and deciding when a local model beats a cloud service — often falls outside the neat shape of a license transaction.
Their caution is not irrational. A partner that built heavily around custom LLM wrappers in 2023 may now be maintaining brittle code, abandoned frameworks, or customer expectations that no longer match the economics of modern AI platforms. The market has moved from prompt demos to retrieval-augmented generation, from RAG to agentic orchestration, from single-model enthusiasm to multi-model pragmatism, and from experimentation to governance in a brutally compressed cycle.
Smaller AI-native consultancies did not carry the same baggage. Many formed around the actual work customers were asking for: make this support process faster, turn this document pile into a searchable assistant, automate this internal approval chain, connect this ERP data to a conversational interface, or evaluate whether an agent can safely perform a bounded task. They started from the workflow rather than the vendor program.
That origin matters. A classic reseller asks what the vendor can package. An AI-native consultancy asks what the customer is trying to change. The first instinct leads to licensing and deployment. The second leads to architecture, data movement, model choice, and organizational redesign.
AI-native consultancies are often aligned to outcomes instead. They may use OpenAI for one use case, Claude for another, a small local model for regulated data, a vector database from one provider, orchestration from another, and a cloud platform chosen because the customer’s data already lives there. Their credibility comes from not sounding like an extension of a single vendor’s sales organization.
That vendor neutrality is powerful in the current AI market because customers are skeptical. They have been told that every enterprise application now contains AI, that every cloud has the best model strategy, and that every software platform is about to become an autonomous agent platform. Buyers know some of that is true, some of it is roadmap theater, and some of it is pricing strategy dressed as innovation.
A consultancy that can say “don’t use a frontier model here,” or “this is cheaper with a narrow model,” or “the hard part is your permissions model, not the chatbot” earns trust quickly. That advice may still lead to major consumption on a hyperscaler’s platform. But the influence belongs to the advisor, not the vendor.
Those numbers should not be read as proof that AI is useless. They should be read as proof that enterprise AI is not a normal software rollout. A company cannot simply buy a model, point it at a workflow, and expect durable value to emerge. The system has to be connected to real data, bounded by policy, accepted by users, measured against business outcomes, and maintained as models and costs change.
That is exactly the kind of gap consultancies fill. They sit between vendor capability and customer reality. They translate abstract model power into specific process change, and they often do it before the customer is ready to issue a formal RFP.
The high failure rate also explains why these firms are growing outside partner programs. Customers in the messy middle of AI adoption often do not begin by asking for a resale quote. They begin by asking someone to help them understand what is feasible. The vendor that waits for the resale motion is arriving late.
But Microsoft’s partner machine is still shaped by a familiar logic: competencies, designations, co-sell incentives, marketplaces, and solution areas. That system is not obsolete. It is enormously effective at organizing large-scale commercial execution. But it can struggle to recognize a five-person consultancy that never resells a license yet causes a customer to standardize on Azure AI Foundry, Microsoft Fabric, or Copilot Studio.
The same issue applies across the cloud and SaaS market. AWS, Google Cloud, Salesforce, ServiceNow, Oracle, IBM, Databricks, Snowflake, and OpenAI all have partner motions, but AI influence is often happening in workshops, prototypes, Slack communities, GitHub repos, and advisory engagements that do not look like old-channel activity. The consultant writing the architecture memo may matter more than the partner registering the deal.
For Microsoft-focused administrators, this matters because the AI strategy inside many organizations will not be dictated solely by Microsoft licensing. It will be shaped by consultants evaluating permissions, data residency, Purview governance, SharePoint sprawl, endpoint security, application integration, and whether Copilot can be trusted for a given workflow. The winning recommendation may still be Microsoft. But the person making it may not be in Microsoft’s partner directory.
A three-person consultancy may build an impressive agentic workflow, but that does not mean it can clear a Fortune 500 procurement process. Large customers want security documentation, liability insurance, financial stability, data processing agreements, privacy reviews, SOC 2 reports, indemnity language, support commitments, and someone accountable when the project becomes operationally critical. Procurement is not a formality. It is the moat around enterprise revenue.
Legacy integrators and MSPs know how to cross that moat. They have vendor management teams, legal processes, security questionnaires, compliance staff, and existing master service agreements. They may not be the fastest at prototyping a multi-agent workflow, but they can get a statement of work signed, survive a risk review, and support the customer after the demo becomes production.
This is where the future looks less like replacement and more like recombination. AI-native consultancies bring speed, technical experimentation, and workflow fluency. Established partners bring trust, scale, governance, and contractual cover. The channel’s next model may be less about a single partner owning the whole motion and more about partnerships between partners.
This is not new in principle. The channel has always used subcontractors for niche security, compliance, migration, data, and development work. What is different is that AI expertise is not a narrow add-on. It can determine the architecture, the customer’s platform commitment, and the long-term consumption pattern.
That makes subcontracting strategically sensitive. If the AI specialist quietly chooses the model stack, data platform, and orchestration layer, then the prime partner may own the paperwork but not the technical influence. Vendors that only reward the prime partner will fail to see the actual source of platform selection.
The smarter vendor play is to create recognition and enablement for both sides. The established partner needs incentives, governance templates, and commercial structures for bringing AI specialists into enterprise accounts. The independent consultancy needs access to technical resources, sandbox environments, reference architectures, training, and perhaps non-transactional recognition that does not force it into a resale identity it does not want.
AI advisory influence breaks that logic. A consultancy may never hit a revenue threshold for a vendor but may still be responsible for millions of dollars in downstream consumption. It may not hold vendor certifications because its market value comes from being independent. It may not want MDF because it does not run campaigns around a single vendor’s solution.
That does not mean vendors should abandon rigor. Quite the opposite. AI raises risk, and vendors have legitimate reasons to care who is advising customers on governance, data access, security, and automation. But the measurement system has to expand beyond resale.
Vendors should be asking different questions. Which consultancies are repeatedly appearing in customer architecture decisions? Which open-source projects, templates, and implementation patterns are driving consumption? Which advisory firms are publishing playbooks customers actually use? Which small firms are being subcontracted by major integrators? Which communities are shaping buyer confidence before a sales team is invited?
Those questions are harder to answer than “who sold the licenses?” They are also closer to where AI demand is being created.
Independent AI consultancies vary widely in maturity. Some are excellent engineering shops with a deep understanding of identity, permissions, auditability, and compliance. Others are fast-moving prompt-and-agent builders learning enterprise security in real time. The absence of a vendor relationship does not automatically mean poor quality, but it does remove one traditional layer of vetting.
That creates a role for established partners that is more important than resale. They can become the control plane for responsible AI services. They can validate security practices, integrate work into customer governance, manage support, and ensure that prototypes do not become unmonitored production dependencies.
The danger is that vendors and large partners treat these consultancies either as competitors to be ignored or subcontractors to be hidden. Both approaches waste the moment. The better model is to surface the specialist, wrap it in enterprise-grade governance, and make the combined offering safer than either side could deliver alone.
That confidence rarely comes from a benchmark chart. It comes from someone who can say, “We have done this workflow before,” or “Your data is not ready,” or “This should not be an agent,” or “Use a deterministic workflow here and reserve the model for the ambiguous step.” This is why consultancies have influence even when they lack scale.
The best AI advisors are not merely model enthusiasts. They are translators between business process and technical constraint. They know when the customer’s problem is permissions, not prompts. They know when latency will kill adoption, when inference cost will break the business case, and when a human-in-the-loop step is not a weakness but a requirement.
Vendors that understand this will stop treating enablement as a product training exercise. The advisory class does not need another slide deck explaining why a platform is strategic. It needs practical reference architectures, cost models, security guidance, evaluation harnesses, migration paths, and escalation access when real customer deployments hit edge cases.
That structure may look more like a network than a ladder. It may include verified advisory roles, technical communities, implementation labs, consumption attribution models, subcontracting marketplaces, and lightweight governance programs for firms that do not want to become classic resellers. It may reward published patterns, validated deployments, customer references, and responsible implementation practices rather than pure booked revenue.
This will be uncomfortable for vendors because it dilutes the clean hierarchy they prefer. It will also be uncomfortable for some partners because it acknowledges that technical influence can live outside the companies that own the account relationship. But the alternative is worse: a vendor ecosystem that sees the transaction after losing the recommendation.
For customers, the change could be positive if handled well. A more open advisory ecosystem can give buyers better technical options and reduce lock-in disguised as guidance. But without governance, it can also produce fragmented architectures, unsupported prototypes, and security gaps. The channel’s job is to turn that chaos into repeatable value without crushing the independence that made the new consultancies useful.
Vendors and established channel players should take several concrete lessons from this shift:
The next five years of AI adoption will not be won only in model labs, cloud regions, or productivity-suite licensing dashboards. They will be won in the advisory conversations where customers decide what is safe, affordable, useful, and real. Vendors that keep treating the channel as a resale hierarchy will see only part of that market; vendors that learn to engage the new AI-native advisory class without smothering it may discover that the most important partner of the AI era is the one that never asked for a badge.
The Channel Is Looking at the Wrong Dashboard
The traditional partner ecosystem has always been good at counting what it can invoice. Resale revenue, registrations, certifications, co-sell motions, marketplace transactions, deployment projects, and renewal attach rates all fit neatly into the machinery vendors have built over decades. If a partner moves product, the vendor sees the partner.AI is exposing the limits of that worldview. A consultancy that helps a manufacturer choose a model strategy, build an agentic workflow, or decide between hosted APIs and an open-source stack may never appear as a partner of record. It may not resell anything. It may never ask for MDF, sales training, or a higher program tier.
Yet that consultancy may be the most important influence in the room. It can determine whether the customer uses Azure OpenAI Service, Amazon Bedrock, Google Vertex AI, Anthropic, OpenAI directly, a local model, or some stitched-together combination of all of the above. By the time a vendor sees consumption rise, the architectural decision may already be over.
Walsh’s Channelnomics piece puts a number around a phenomenon many IT buyers and practitioners have already felt: the AI advisory layer is expanding faster than the formal channel can classify it. The estimate of 35,000 to 50,000 AI consultancies globally is necessarily imperfect, built from company formation, service listings, and engagement patterns. But even if the number is off at the edges, the point is not. Influence has escaped the CRM field where vendors expected to find it.
Copilot Licenses Are Not the Same Thing as AI Transformation
The broadest reading of the channel’s AI participation can make the ecosystem look healthier than it is. If selling AI means moving Microsoft Copilot licenses, bundling applications with embedded AI features, or advising customers on SaaS tools that have added generative functions, then a large share of solution providers can plausibly claim to be in the AI business.That is a low bar. It is also not useless. For many SMBs, licensing Microsoft 365 Copilot, Google Gemini for Workspace, Salesforce Einstein, ServiceNow AI agents, or security products with AI-assisted triage may be the first practical exposure to enterprise AI. The channel has always made money translating vendor packaging into customer adoption.
But Walsh draws a harder line between partners selling AI-adjacent products and partners building or supporting AI systems. By that stricter standard, he estimates the active slice of traditional partners is closer to 10%. That feels plausible because building AI into business operations is not merely a SKU expansion. It is data engineering, governance design, workflow analysis, software integration, change management, and risk containment bundled into one messy service motion.
This distinction is especially relevant for WindowsForum readers because Microsoft’s ecosystem is the clearest example of both sides of the story. Copilot has given Microsoft partners an obvious AI resale and adoption path. But the deeper customer work — grounding models in enterprise data, preventing data leakage, redesigning processes, and deciding when a local model beats a cloud service — often falls outside the neat shape of a license transaction.
The New Consultancies Were Born Where the Old Channel Hesitated
Traditional MSPs and integrators are often accused of moving too slowly on AI. That criticism is only partly fair. Many of them have spent years building recurring revenue, security practices, cloud migration capabilities, compliance services, and automation offerings. They know from experience that every vendor “revolution” comes with hidden margin pressure and operational debt.Their caution is not irrational. A partner that built heavily around custom LLM wrappers in 2023 may now be maintaining brittle code, abandoned frameworks, or customer expectations that no longer match the economics of modern AI platforms. The market has moved from prompt demos to retrieval-augmented generation, from RAG to agentic orchestration, from single-model enthusiasm to multi-model pragmatism, and from experimentation to governance in a brutally compressed cycle.
Smaller AI-native consultancies did not carry the same baggage. Many formed around the actual work customers were asking for: make this support process faster, turn this document pile into a searchable assistant, automate this internal approval chain, connect this ERP data to a conversational interface, or evaluate whether an agent can safely perform a bounded task. They started from the workflow rather than the vendor program.
That origin matters. A classic reseller asks what the vendor can package. An AI-native consultancy asks what the customer is trying to change. The first instinct leads to licensing and deployment. The second leads to architecture, data movement, model choice, and organizational redesign.
Vendor Neutrality Is Becoming a Sales Weapon
The partner programs built by the largest technology vendors reward loyalty. That has never been a secret. Certifications, access to specialists, co-selling priority, rebates, market development funds, and executive attention all push partners toward stack alignment.AI-native consultancies are often aligned to outcomes instead. They may use OpenAI for one use case, Claude for another, a small local model for regulated data, a vector database from one provider, orchestration from another, and a cloud platform chosen because the customer’s data already lives there. Their credibility comes from not sounding like an extension of a single vendor’s sales organization.
That vendor neutrality is powerful in the current AI market because customers are skeptical. They have been told that every enterprise application now contains AI, that every cloud has the best model strategy, and that every software platform is about to become an autonomous agent platform. Buyers know some of that is true, some of it is roadmap theater, and some of it is pricing strategy dressed as innovation.
A consultancy that can say “don’t use a frontier model here,” or “this is cheaper with a narrow model,” or “the hard part is your permissions model, not the chatbot” earns trust quickly. That advice may still lead to major consumption on a hyperscaler’s platform. But the influence belongs to the advisor, not the vendor.
The AI Project Failure Problem Creates the Market
The rise of these consultancies is not happening because every customer has a clean AI roadmap. It is happening because many do not. Gartner warned in 2024 that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs, and unclear business value. MIT’s NANDA initiative later drew wide attention for research indicating that most enterprise generative AI pilots were failing to produce measurable profit-and-loss impact.Those numbers should not be read as proof that AI is useless. They should be read as proof that enterprise AI is not a normal software rollout. A company cannot simply buy a model, point it at a workflow, and expect durable value to emerge. The system has to be connected to real data, bounded by policy, accepted by users, measured against business outcomes, and maintained as models and costs change.
That is exactly the kind of gap consultancies fill. They sit between vendor capability and customer reality. They translate abstract model power into specific process change, and they often do it before the customer is ready to issue a formal RFP.
The high failure rate also explains why these firms are growing outside partner programs. Customers in the messy middle of AI adoption often do not begin by asking for a resale quote. They begin by asking someone to help them understand what is feasible. The vendor that waits for the resale motion is arriving late.
Microsoft’s Ecosystem Shows the Opportunity and the Blind Spot
Microsoft is unusually well positioned for this shift and unusually exposed to it. The company owns the productivity surface through Microsoft 365, the identity and management layer through Entra and Intune, the developer surface through GitHub and Visual Studio, the cloud foundation through Azure, and the enterprise AI entry point through Copilot and Azure OpenAI Service. If any vendor can turn AI influence into platform gravity, it is Microsoft.But Microsoft’s partner machine is still shaped by a familiar logic: competencies, designations, co-sell incentives, marketplaces, and solution areas. That system is not obsolete. It is enormously effective at organizing large-scale commercial execution. But it can struggle to recognize a five-person consultancy that never resells a license yet causes a customer to standardize on Azure AI Foundry, Microsoft Fabric, or Copilot Studio.
The same issue applies across the cloud and SaaS market. AWS, Google Cloud, Salesforce, ServiceNow, Oracle, IBM, Databricks, Snowflake, and OpenAI all have partner motions, but AI influence is often happening in workshops, prototypes, Slack communities, GitHub repos, and advisory engagements that do not look like old-channel activity. The consultant writing the architecture memo may matter more than the partner registering the deal.
For Microsoft-focused administrators, this matters because the AI strategy inside many organizations will not be dictated solely by Microsoft licensing. It will be shaped by consultants evaluating permissions, data residency, Purview governance, SharePoint sprawl, endpoint security, application integration, and whether Copilot can be trusted for a given workflow. The winning recommendation may still be Microsoft. But the person making it may not be in Microsoft’s partner directory.
The Enterprise Procurement Moat Still Protects the Incumbents
The obvious counterargument is that small AI consultancies cannot replace established channel partners because they lack enterprise reach. That is true, and it is the part of Walsh’s argument that should make legacy partners pay attention rather than panic.A three-person consultancy may build an impressive agentic workflow, but that does not mean it can clear a Fortune 500 procurement process. Large customers want security documentation, liability insurance, financial stability, data processing agreements, privacy reviews, SOC 2 reports, indemnity language, support commitments, and someone accountable when the project becomes operationally critical. Procurement is not a formality. It is the moat around enterprise revenue.
Legacy integrators and MSPs know how to cross that moat. They have vendor management teams, legal processes, security questionnaires, compliance staff, and existing master service agreements. They may not be the fastest at prototyping a multi-agent workflow, but they can get a statement of work signed, survive a risk review, and support the customer after the demo becomes production.
This is where the future looks less like replacement and more like recombination. AI-native consultancies bring speed, technical experimentation, and workflow fluency. Established partners bring trust, scale, governance, and contractual cover. The channel’s next model may be less about a single partner owning the whole motion and more about partnerships between partners.
Subcontracting Becomes the Quiet AI Channel
The most likely near-term structure is subcontracting. An enterprise partner owns the customer relationship, while a specialist AI consultancy performs discovery, prototyping, model evaluation, workflow design, or implementation under the larger partner’s umbrella. The customer gets innovation without taking on the full vendor risk of a tiny firm. The consultancy gets access to enterprise work it could not win alone.This is not new in principle. The channel has always used subcontractors for niche security, compliance, migration, data, and development work. What is different is that AI expertise is not a narrow add-on. It can determine the architecture, the customer’s platform commitment, and the long-term consumption pattern.
That makes subcontracting strategically sensitive. If the AI specialist quietly chooses the model stack, data platform, and orchestration layer, then the prime partner may own the paperwork but not the technical influence. Vendors that only reward the prime partner will fail to see the actual source of platform selection.
The smarter vendor play is to create recognition and enablement for both sides. The established partner needs incentives, governance templates, and commercial structures for bringing AI specialists into enterprise accounts. The independent consultancy needs access to technical resources, sandbox environments, reference architectures, training, and perhaps non-transactional recognition that does not force it into a resale identity it does not want.
The Old Tier Badge Cannot Measure Advisory Power
Partner tiers were designed for a world where value eventually became transaction volume. Sell enough product, earn a higher tier. Certify enough people, gain access. Register enough deals, receive support. It is a rational system for a resale-centric channel.AI advisory influence breaks that logic. A consultancy may never hit a revenue threshold for a vendor but may still be responsible for millions of dollars in downstream consumption. It may not hold vendor certifications because its market value comes from being independent. It may not want MDF because it does not run campaigns around a single vendor’s solution.
That does not mean vendors should abandon rigor. Quite the opposite. AI raises risk, and vendors have legitimate reasons to care who is advising customers on governance, data access, security, and automation. But the measurement system has to expand beyond resale.
Vendors should be asking different questions. Which consultancies are repeatedly appearing in customer architecture decisions? Which open-source projects, templates, and implementation patterns are driving consumption? Which advisory firms are publishing playbooks customers actually use? Which small firms are being subcontracted by major integrators? Which communities are shaping buyer confidence before a sales team is invited?
Those questions are harder to answer than “who sold the licenses?” They are also closer to where AI demand is being created.
The Security Stakes Are Bigger Than Channel Politics
For Windows admins and security teams, this shift is not merely an industry-channel story. It affects the quality and risk profile of AI deployments landing inside real environments. A poorly governed AI workflow can expose sensitive documents, automate bad decisions, create shadow data pipelines, or grant model-driven tools access that no human user would have been allowed to exercise casually.Independent AI consultancies vary widely in maturity. Some are excellent engineering shops with a deep understanding of identity, permissions, auditability, and compliance. Others are fast-moving prompt-and-agent builders learning enterprise security in real time. The absence of a vendor relationship does not automatically mean poor quality, but it does remove one traditional layer of vetting.
That creates a role for established partners that is more important than resale. They can become the control plane for responsible AI services. They can validate security practices, integrate work into customer governance, manage support, and ensure that prototypes do not become unmonitored production dependencies.
The danger is that vendors and large partners treat these consultancies either as competitors to be ignored or subcontractors to be hidden. Both approaches waste the moment. The better model is to surface the specialist, wrap it in enterprise-grade governance, and make the combined offering safer than either side could deliver alone.
Customers Are Buying Confidence, Not Just Capability
AI vendors often sell capability: larger context windows, cheaper tokens, faster inference, better reasoning, richer agent frameworks, tighter integrations, or safer model controls. Customers are buying something more psychological. They are buying confidence that a project will not become another expensive pilot that dies after the demo.That confidence rarely comes from a benchmark chart. It comes from someone who can say, “We have done this workflow before,” or “Your data is not ready,” or “This should not be an agent,” or “Use a deterministic workflow here and reserve the model for the ambiguous step.” This is why consultancies have influence even when they lack scale.
The best AI advisors are not merely model enthusiasts. They are translators between business process and technical constraint. They know when the customer’s problem is permissions, not prompts. They know when latency will kill adoption, when inference cost will break the business case, and when a human-in-the-loop step is not a weakness but a requirement.
Vendors that understand this will stop treating enablement as a product training exercise. The advisory class does not need another slide deck explaining why a platform is strategic. It needs practical reference architectures, cost models, security guidance, evaluation harnesses, migration paths, and escalation access when real customer deployments hit edge cases.
The Next Partner Program Will Look Less Like a Ladder
The tiered partner program is not going away. Large vendors still need predictable structures for global systems integrators, regional MSPs, ISVs, distributors, marketplaces, and resale-heavy partners. But AI will force a second structure to grow alongside it — one designed around influence rather than transaction.That structure may look more like a network than a ladder. It may include verified advisory roles, technical communities, implementation labs, consumption attribution models, subcontracting marketplaces, and lightweight governance programs for firms that do not want to become classic resellers. It may reward published patterns, validated deployments, customer references, and responsible implementation practices rather than pure booked revenue.
This will be uncomfortable for vendors because it dilutes the clean hierarchy they prefer. It will also be uncomfortable for some partners because it acknowledges that technical influence can live outside the companies that own the account relationship. But the alternative is worse: a vendor ecosystem that sees the transaction after losing the recommendation.
For customers, the change could be positive if handled well. A more open advisory ecosystem can give buyers better technical options and reduce lock-in disguised as guidance. But without governance, it can also produce fragmented architectures, unsupported prototypes, and security gaps. The channel’s job is to turn that chaos into repeatable value without crushing the independence that made the new consultancies useful.
The Signal Vendors Cannot Afford to Miss
The practical lesson from Walsh’s Channelnomics argument is not that traditional partners are doomed. It is that the definition of partner value is widening faster than partner programs are. The firms shaping AI adoption may not look like partners, transact like partners, or want to be called partners.Vendors and established channel players should take several concrete lessons from this shift:
- AI resale activity is not the same as AI implementation capability, and vendors that blur the distinction will overestimate channel readiness.
- Independent AI consultancies are already shaping model, platform, and architecture decisions before many vendors can attach a partner of record.
- Legacy MSPs and integrators retain a major advantage in procurement, compliance, contracting, and enterprise risk management.
- The strongest near-term model is likely to pair AI-native specialists with established partners rather than force one group to replace the other.
- Partner programs need mechanisms to recognize advisory influence, technical patterns, and downstream consumption even when no resale transaction occurs.
- Customers will reward advisors who can reduce AI project risk, not merely those who can demonstrate the newest model feature.
The next five years of AI adoption will not be won only in model labs, cloud regions, or productivity-suite licensing dashboards. They will be won in the advisory conversations where customers decide what is safe, affordable, useful, and real. Vendors that keep treating the channel as a resale hierarchy will see only part of that market; vendors that learn to engage the new AI-native advisory class without smothering it may discover that the most important partner of the AI era is the one that never asked for a badge.
References
- Primary source: Channelnomics
Published: 2026-07-06T15:50:08.331924
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