This channel field guide explores many of the elements solution providers need to take into account as they bring AI to their customers in meaningful ways.
AI is infiltrating both the IT channel and the customers it serves: It’s inside the tools MSPs run their businesses on, it’s inside the customer workflows that solution providers support, and it’s inside the boardroom conversations that are driving IT investments.
This shift means that solution providers are no longer being asked to introduce AI but rather help customers operationalize it, secure it, govern it and—critically—prove its value.
Across CRN’s coverage over the past year, a consistent pattern has emerged: Customers want AI, but they don’t want chaos. They want faster resolution times, better productivity and competitive advantage but not at the expense of security, data integrity, compliance or trust. And they increasingly see solution providers as the means to those ends.
This channel field guide explores many of the elements solution providers need to take into account as they bring AI to their customers in meaningful ways. From autonomous cybersecurity attacks to control planes to governance, neoclouds, tokens and zero-trust thinking, each entry, from A to Z, reflects a real pressure point that solution providers are navigating right now and provides guidance on how many of them are turning those challenges into opportunities.
A — Autonomous Cybersecurity Attacks
It’s impossible to talk about the state of the cybersecurity market today without considering the impact of AI, and that impact, in many cases, is unsettling. Bad actors are tapping into AI tools to make their attacks not only more dangerous but also easier to build and deploy.
In the case of autonomous cyberattacks, which represent a new but growing threat model, AI agents are doing the heavy lifting, with little human intervention required.
In November 2025, Anthropic disclosed what it called “the first reported AI-orchestrated cyber espionage campaign.”
“In that attack, the model worked as an autonomous agent: It executed commands, exploited vulnerabilities, stole credentials and made tactical decisions, only requiring human input at a few key moments,” San Francisco-based Anthropic said in a recent analysis of AI-enabled cyber threats.
The advent of autonomous attacks being conducted at machine speed means that defensive decisions can no longer be left to human speed, security experts have told CRN.
It is also critical that solution providers help customers secure the identities of their users—both humans and AI agents—so that they do not become compromised and open the door to attacks.
“Agents in and of themselves are identities. And what they can do—or what they should be able to do—needs to be tracked, reviewed, attested to,” said Rob Gregory, CISO at Denver-based solution provider Optiv, in a recent interview.
As a result, many security-focused solution providers are embedding agentic tools into Security Operations Center operations, driving more automated detection and response, deeper identity visibility, and faster, intelligence-driven incident response as part of their core security services— all while educating customers on the rapidly changing cybersecurity landscape.
Channel takeaway: Autonomous cybersecurity attacks require a machine-speed defense.
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B—Buyer Expectations
Customers’ interest in AI has become more sophisticated. They aren’t asking solution providers whether to use AI anymore; they’re asking how their trusted adviser can help make its use safe, measurable and operational. Customers’ AI expectations now are focused on driving outcomes, including productivity gains, cost reduction, time-to-resolution and risk reduction.
For solution providers, this is a sign to reset the sales motion. “AI readiness” can be an opening salvo that enables them to demonstrate immediate value, and then educating customers about AI governance can establish their in-depth expertise.
Customers might start by asking for Microsoft Copilot or AI agents, but successful solution providers will quickly turn that discussion toward data hygiene, permissions and risk management—especially once the customer realizes how quickly and widely AI can share or overshare a company’s crown jewels when proper precautions are not in place.
“Everybody says they have to have AI, but if you don’t know your data, if you don’t know your risk, if you don’t know your business problem, then you’re not really talking about strategy yet,” Michael Goldstein, market president, Southeast Florida, for Fort Myers, Fla.-based Entech, recently told CRN. “You’re just talking about tools.”
It’s safe to assume potential customers are already experimenting with AI and may even have already gone rogue. That means expectation management needs to be part of the sales conversation: what AI can do, what it can’t and how to measure that.
Channel takeaway: Sales discussions should lead with AI readiness and business outcomes because buyers are done paying for AI curiosity.
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C—Control Planes
As copilots and agents become more ubiquitous, businesses are discovering that deploying AI is relatively easy … it’s the governing that’s hard. That’s the reason for the emergence of a new layer in enterprise architecture: a control plane for intelligence where AI guardrails such as policies and permissions are created and enforced.
CRN’s recent coverage of the launch of Cisco Cloud Control illustrates how important the control plane is becoming to the AI solutions, converging into a single operational layer that incorporates networking, security, observability and AI operations.
“As customers look at their environments now, they’ve got higher expectations of what they need to get from that environment, and that’s where Cisco and others really need to use agentic AI agents to go,” said Lane Irvine, alliance leader at Long View Systems, a Calgary, Alberta-based solution provider. “The question is how are we going to manage this from an operational perspective? How are we going to get insight into what’s going on? We want to be able to get predictive insight. … Agentic AI really opens the door for that.”
Microsoft’s March rollout of its E7 suite and Agent 365 is another reflection of the broader move toward unifying governance, security and observability for AI agents into a centralized layer. Other vendors such as IBM, Palo Alto Networks and Snowflake have all talked to CRN this year about the important role of a control plane in AI deployments.
For solution providers, the need to incorporate an AI control plane creates significant opportunities. Control planes are no longer just policy layers; they are environments that must be architected and integrated. Solution providers will play a central role in designing and managing these platforms—spanning identity mapping, policy enforcement, telemetry integration and agent life-cycle control.
Channel takeaway: A control plane that incorporates governance, observability and policy enforcement is a core requirement for the AI solutions the channel is building.
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D—Data Gravity
Data gravity—a term first coined in 2010—considers a large dataset as if it were a planet. Just like a planet with more mass will have a stronger gravitational pull, so too does a dataset attract more services, applications and other smaller sets of data the larger it gets.
It’s a concept that shapes architecture conversations: Where the data lives dictates where AI runs, how fast it can respond and how secure it can be because, according to conventional wisdom, it’s better to bring the compute to the data rather than undertaking the potentially risky and expensive route of moving a massive dataset to the compute.
Meanwhile, some vendors such as Hammerspace argue that data gravity and the need to consider it as a driving force can be eliminated.
For solution providers, data gravity—and how to deal with it—should be a consideration in every discovery meeting: latency, cost, sovereignty, retention and access controls.
As customers juggle structured and unstructured data across clouds and on-premises environments, AI is turning that sprawl into a performance and governance problem. At the same time, AI workloads are reviving the data center conversation, driven by compute and power demands.
Data gravity is the reason why hybrid architecture is now so en vogue.
Channel takeaway: Map out where data lives and where AI should run, tapping into hybrid architecture that distributes resources rather than centralizes them.
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E—Expertise Gap
In many customer organizations, the gulf between the advanced AI capabilities they want to deploy and the in-house knowledge that currently exists within their workforce is wide, leaving a huge opportunity for solution providers to step in and help.
To capitalize on that opportunity, solution providers are looking inward at how AI can reshape their own workforce in a world where technical know-how alone is no longer enough and AI literacy has become the new currency.
“You can’t look at AI as a niche skill anymore,” Anthony Marino, chief administrative officer at Florham Park, N.J.-based Conduent, recently told CRN. “It really has to be a necessary skill for all employees.”
This shift has real implications for hiring, training and career development inside solution provider organizations. Deep expertise in AI infrastructure, data science and software engineering is in high demand, solution providers say, but so is something more essential from the channel perspective: the ability to translate technology into business value.
Solution providers that can bridge that gap are better positioned to win trust and long-term engagements, particularly as customers grapple with how to deploy AI responsibly and effectively.
Where AI literacy cannot be hired, it has to be developed, and the underlying indicators that an employee, or potential employee, can become an AI powerhouse lie largely in that person’s level of agility and curiosity.
They have to be able to learn— and they have to want to learn—new things quickly.
Channel takeaway: AI is sharpening the need for human expertise that can connect technology to outcomes, a channel strength.
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F—FinOps for AI
AI is creating a new financial reality for customers: costs that are variable, usage-driven and difficult to predict. Unlike traditional software pricing or even cloud infrastructure, AI spend is tied to how often models are used, how much data they process and how agents behave in production. The need to help customers manage, attribute and optimize the costs of running AI workloads is driving the rise of a new discipline for solution providers: FinOps for AI.
AI is not just changing the technology landscape; it’s changing how customers pay for it. Microsoft, for example, has described a shift away from traditional per-seat licensing toward usage-based and outcome-based models, where cost is tied directly to the value generated by AI agents.
At the same time, CRN reporting shows that AI consumption is scaling rapidly. At Google Cloud Next, executives said hundreds of customers are now processing trillions of tokens, highlighting how quickly usage-based pricing can expand as AI moves from experimentation into production environments.
This creates a new customer challenge for solution providers to solve: cost visibility. AI spending doesn’t grow linearly—it spikes based on usage, model selection and agent activity.
For solution providers, there is a fresh opportunity to help customers understand what technology vendors are doing to optimize AI economics, including efforts to reduce cost per token and improve predictability in multi-user environments.
Most customers do not yet have frameworks for managing AI costs at scale. Solution providers can step in with usage visibility and reporting, cost attribution by team or use case, optimization strategies such as model selection and workload placement, and governance policies to prevent runaway spend. FinOps for AI becomes a natural extension of managed services—helping customers control costs while scaling adoption and proving ROI.
Channel takeaway: Solution providers can build AI FinOps into their services stack to help customers get a handle on usage, cost control and optimization as AI pricing shifts to consumption-based models.
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G—Governance
As business users build AI agents and incorporate them into their workflows at a rapid pace, organizations need observability and governance layers that extend beyond people and devices to include agent behavior.
While customers might be impatient to get moving so they don’t feel left behind, it’s the job of the solution provider as a trusted adviser to introduce elements of risk management, compliance, auditing and ethics for the agentic era.
For MSPs, governance can be a product strategy that is incorporated into their service portfolios as assessments, implementation services and/or ongoing managed services focused on areas such as policy, access control, logging, monitoring and incident response when AI does something it shouldn’t.
“Agentic AI isn’t a ‘set-and-forget’ technology,” Bryan Rogers, CEO of Brisbane, Australia-based Redd, recently told CRN Australia. “It relies heavily on good data, secure identity and information permissions, and ongoing governance.”
Many solution providers recognize that this is no easy feat. Governance and compliance were cited by 51 percent of solution providers as the primary barrier preventing customer AI adoption, according to a recent survey commissioned by AvePoint.
The opportunity for solution providers to help customers overcome that barrier is sizable. Compliance-focused MSPs are more likely to project revenue growth of over 50 percent in 2026, according to ScalePad’s 2026 MSP Trends Report.
Channel takeaway: Don’t let customers push governance aside. It’s not flashy, but it’s critical to AI success.
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H—Hallucinations
Nearly everyone who has spent much time researching AI has come across horror stories about AI hallucinations that have come to light in embarrassing ways. In business settings, these hallucinations—the term used for when AI models confidently deliver incorrect or even fictitious information and citations—can have a big impact if they are delivered at scale inside workflows that users trust.
“Overall, 51 percent of respondents from organizations using AI say their organizations have seen at least one instance of a negative consequence, with nearly one-third of all respondents reporting consequences stemming from AI inaccuracy,” according to a McKinsey report on the state of AI in 2025.
AI hallucinations can be damaging to an employee’s or a business’ reputation. Fear of being stung by an AI hallucination can also have a chilling effect on internal usage if employees stop trusting AI.
For the channel, hallucination mitigation represents a service opportunity. Solution providers can deliver frameworks for evaluation, monitoring and continuous improvement. Recommending a Retrieval-Augmented Generation architecture, deploying observability tools, directing agentic workflows to escalate unverified or ambiguous responses for further review and rolling out continuous AI governance and auditing services are all ways MSPs can help ensure customer trust in AI solutions.
Channel takeaway: Educate customers on the risks and deliver services to mitigate them.
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I—Infrastructure Pressure
AI is creating an infrastructure squeeze that’s as much about physics as budgets: compute, storage, networking, cooling and power all matter more when AI workloads move from pilots to production.
The unprecedented demand for data center infrastructure to fuel the AI boom is at the heart of the current component shortage that is causing supply disruption and price increases across the market.
It’s not just hyperscalers that are thirsting for AI-capable infrastructure. AI is renewing interest in private data centers and driving hybrid infrastructure sales, driven by cost, performance and security considerations. Vendors such as Cisco, Dell Technologies, HPE and Lenovo are all reporting blowout financial results on the strength of AI-optimized infrastructure sales.
“The buildouts that are happening for data centers right now are starting from hyperscalers, but they’re very soon going to go into enterprises, and they’re going to go into the edge of the enterprise,” said Cisco President and Chief Product Officer Jeetu Patel in an interview earlier this year. “I think there’s a lot of opportunity for services and value-add and architectural services and making sure that you have use cases for AI.”
For solution providers, the opportunity is to find ways to help customers navigate infrastructure constraints by prioritizing workloads, mapping out realistic deployments and helping them make informed trade-offs across cost, availability and performance.
Channel takeaway: The ability to navigate constraints—across supply, performance and cost—is emerging as a key point of differentiation in infrastructure deals.
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J—Justifying ROI
Amid economic uncertainty, inflation, supply chain constraints and continued layoffs, it’s no wonder that AI spending is getting scrutinized, even though it’s seen as a critical driver of success both now and for the future.
That means solution providers have to build ROI narratives that survive the CFO, not just the CIO. To do that, they are focusing on how they can help customers deploy AI solutions to drive key business outcomes.
For those business outcome conversations, Michael Cervino, co-founder and CEO of Radnor, Pa.-based Circle Square Consulting, said his team runs discovery sessions with clients, analyzes the conversations with AI and generates potential solutions and ROI models. Then they build a proof of concept.
“We take the highest-priority problem, the thing with the most return that’s easiest to build, and we let AI help us create a working proof of concept,” he said. “Once they see it, that’s when the conversation really changes.”
In many cases, customers are already experimenting with AI, so solution providers can add value by helping businesses turn those early experiments into something structured and sustainable, said Sandy McGrath, co-founder and president of Sherwood Park, Alberta-based managed intelligence provider MIPGlobal.
“What we’re able to bring is stability and clarity to the customer’s AI journey,” McGrath told CRN recently. “They can get access to experts, knowledge and thought leadership that helps clarify their vision around what they’re trying to do. A lot of them have already used tools like ChatGPT or [Microsoft] Copilot. But now they’re trying to build out corporate structure and compliance around it. How do they make use of it? How do they stay safe and secure? How do they meet regulatory requirements?
“We’re not necessarily the ones introducing AI,” he added. “We’re the ones helping them implement it properly, add structure and actually get value and ROI out of it.”
Channel takeaway: The solution provider role is increasingly defined by the ability to bring structure, clarity and measurable outcomes to AI adoption efforts.
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K—Knowledge Management
Knowledge management is emerging as one of the most practical AI profit pools for the channel: Take what a business already knows—documents, tickets, policies—and make it searchable, reliable and usable inside workflows.
Melvin Williams, CEO of Blue Bell, Pa.-based M&N Communications, told CRN in May that his company is developing three core AI agents: an HR agent, a virtual receptionist agent and an on-boarding agent for employees that will leverage the in-house knowledge base customers have already built up. The early reception has been positive, he said.
“We followed up with one of our best clients and said, ‘Hey, listen, this is what we’re trying to do in reference to building out this bot from an HR standpoint. Would you like to be part of our pilot program?’”
The client is now preparing to commercialize those systems. “We want to go live and start to receive income from this,” Williams told CRN.
Solution providers, particularly those with service desks, are also tapping into their own documentation and best practices as the underpinnings of AI-powered automation to improve customer experience.
Instead of sending incoming help desk calls to an automated phone tree, for example, some MSPs say they are utilizing AI voice receptionists that interact with customers using natural language.
“It can have a back-and-forth conversation just as if a human was answering the phone and then use natural language to route the person accordingly,” Jack Skinner, co-founder and CTO of Lewisville, Texas-based Oversee My IT, recently told CRN. “It can even act as your service desk dispatcher, take the call, identify the problem, work through the ticket and handle some of those initial qualification steps.”
That is all built on a foundation of strong knowledge management.
The reason it’s accelerating is simple: AI makes unstructured data finally monetizable—if it’s governed and contextualized. Vendors are responding with knowledge discovery and context layers, but adoption hinges on data foundations and access hygiene.
For solution providers, a focus on knowledge management manifests as a three-part engagement: classify and clean the data, lock down access and governance, then deploy retrieval and search experiences that users actually adopt.
Channel takeaway: What companies already know is becoming a monetizable asset, with partners using AI to turn internal knowledge into customer-facing automation for themselves and their customers.
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L—Liability Questions
As AI agents become autonomous entities that make independent decisions and take actions based on those decisions, the landscape of corporate identity and risk management is undergoing significant, complex changes.
AI agents acting as digital workers have to be governed by rules and guardrails, just as their human counterparts do. Agents performing autonomously could open a liability gap when they act outside their intended behaviors, particularly if their actions create operational or security risks.
To help mitigate that liability, solution providers are ensuring that their customers are incorporating strong data governance and integrity principles into their operational frameworks.
The liability question also speaks to why so many security experts are now emphasizing the importance of managing identity, particularly amid predictions that there will be 50 or more autonomous agents for every human identity in the near future.
“If you have a compromised human identity that’s now running 50 autonomous agents, you have kind of permissive accesses and capabilities across an organization,” said Nicole Carignan, senior vice president of security and AI strategy at Cambridge, U.K.-based vendor Darktrace, in a recent interview. “That’s quite terrifying.”
Liability is also tangled up in shadow AI. When employees use unsanctioned tools, they can accidentally leak intellectual property and regulated data.
For solution providers, the liability conversation creates an opportunity to define scope, implement logging and controls and document what they are or are not responsible for.
Channel takeaway: Strong identity controls and governance over autonomous agents have to be key pieces of any agentic AI discussion.
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M—Managed AI Services
One of the new frontiers AI is opening up for the channel is the opportunity to launch managed AI services.
With AI agents poised to handle service desk tasks, generate content, automate workflows and make decisions in real time, solution providers are building portfolios to manage not just customers’ IT systems but their digital workers.
In CRN’s May cover story, Microsoft Commercial CEO Judson Althoff pointed to managed services as the channel’s ‘AI superpower,’ emphasizing that partners—not just platforms—will be responsible for helping customers deploy, secure and operate AI at scale.
“The opportunity for partners around delivering a managed service to help customers get value out of their agents, tune their agents, secure their agents and govern and manage them is massive,” Althoff said. “Most of the commercial customer base out there is not going to be equipped to be able to handle managing these agentic processes, so [we’re looking at this] both from a build opportunity and a manage opportunity. It’s why I’m convinced that the opportunity for our partners has never been greater.”
In practice, managed AI means solution providers should be treating AI not as a feature to deploy but as an operational system to run. That includes managing access and permissions for agents, monitoring outputs, accuracy and behavior, tuning workflows and updating policies, optimizing cost and performance over time, and responding when AI-driven processes fail or create risk.
Managed AI services opportunities for the channel include managed adoption (on-boarding users and use cases), managed governance (policies, controls, oversight), managed operations (monitoring, tuning, life-cycle management) and managed economics (usage tracking, optimization, cost control).
This is also where differentiation emerges. As vendors continue to package AI capabilities, the MSP’s role becomes less about providing access and more about ensuring outcomes.
Channel takeaway: The MSP that can operate a customer’s AI environment—reliably, securely and efficiently—becomes indispensable.
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N—Neoclouds
Neoclouds are becoming one of the most important infrastructure stories in the AI economy—and one of the more underappreciated partner opportunities. These AI-focused cloud providers are emerging as an alternative vehicle for enterprise adoption, offering GPU-rich environments for training and inference at a moment when capacity, power and procurement constraints continue to complicate AI projects.
A report by Synergy Research Group last October said that neocloud companies were on track to exceed $23 billion in cumulative revenue last year and forecast that number to grow to nearly $180 billion in 2030.
Earlier this year, Nvidia Americas Channel Chief Craig Weinstein called neoclouds the “next phase of opportunity” for partners, pointing to players such as CoreWeave and Nebius as an increasingly important market segment. In April, TD Synnex reserved more than 1,000 Nvidia GPUs through Nebius specifically to give partners access to scarce cloud AI capacity. That is not a niche side bet; it is the channel responding to real scarcity with new routes to market.
For solution providers, neoclouds are not just another place to spin up workloads. They are becoming a practical answer to AI infrastructure bottlenecks, a way to accelerate proofs of concept and a bridge between hyperscale dependence and customer urgency.
For solution provider giant World Wide Technology, neocloud partners like Nebius are generating profitability around GPU as a service and “giant” deal sizes, said Chris Campbell, senior director of AI solutions and GPU as a Service, in a recent interview.
“Imagine a one-year deal for 1,000 [Nvidia] B300s chips at $5 an hour. That would be $45 million for one year. So if you just take 5 percent of that, it’s a couple million dollars for WWT,” Campbell said. “So we see this as a massive opportunity for us. We continue to see that these deals will be big in scale.”
Savvy solution providers will guide customers on when a neocloud is the right fit and where it complements hyperscalers, turning GPU access into architecture, migration and managed services revenue.
Channel takeaway: Tap into neocloud providers as an architecture and procurement option, especially when GPU scarcity, performance needs or time-to-value make hyperscaler-first plans less practical.
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O—Operational Efficiency
Operational efficiency is the least flashy AI story—and, when it comes to the inner workings for MSPs—the most impactful one. In the channel, AI is already being applied in areas such as service desk triage, ticket routing, automation, documentation and other work that keeps MSP margins under pressure.
For example, MSPs like MRE Consulting are using AI to streamline ticket triage, automatically categorizing incoming requests and helping with routing and early stage resolution right inside the service desk workflow.
“It’s really put a lot more time back into our day because what used to be a manual process is now automated,” said Shayon Mazumder, managed IT services practice leader at the Houston-based company.
In some cases, he said, AI-assisted triage can even help end users resolve tickets themselves. “That’s the kind of power we’re looking for … first-line resolution without escalation.”
At the same time, MSP platform makers are adding AI-driven triage and zero-touch on-boarding capabilities that compress time-to-resolution and reduce labor intensity.
For solution providers, the ROI becomes easy to see: resolve more Tier 1 requests without adding head count, improve ratios and reduce back-office friction.
Channel takeaway: Start where the money is: service desk plus on-boarding/off-boarding plus billing automation.
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P—Partner Program Evolution
Partner programs are being redesigned around AI because vendors recognize that the channel is essential for making AI real. Channel chiefs across the industry are reshaping training, incentives and resources to support AI solution selling as partner readiness remains a key focus.
And it’s also not just the usual suspects that are vying for channel expertise. The AI platforms themselves, most notably, ChatGPT-maker OpenAI and Claude-maker Anthropic, are in the midst of launching strong channel charges to woo solution providers. OpenAI in June launched its OpenAI Partner Network, backed by a $150 million investment, while Anthropic expanded its push into the channel with a services track in its Claude Partner Network.
But program evolution isn’t just about new badges. It’s about providing the AI enablement pieces solution providers need for success, including enablement on use cases, packaging, governance, and how to build practices that deliver outcomes.
AI enablement for partners is something technology vendors see as critical. In CRN’s 2026 Channel Chiefs project, for example, 30 percent of surveyed channel executives said lack of skills among the partner base is the biggest challenge they face in making GenAI offerings available through the channel.
In 2026, partners are judging channel programs by one key element: Do they help build a repeatable, profitable AI business?
Channel takeaway: Choose vendors whose programs help build a practice, not just resell a SKU.
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Q—Quality Of Data
Every AI conversation a solution provider has with a customer will eventually become a data conversation. At issue is whether that data is accurate, properly permissioned, contextualized and accessible in ways that won’t create new risk once it’s readily available to users via AI.
Solution providers are responding with structured AI readiness offerings focused on data governance, data foundation assessments and operational deployment.
Michael Patterson, vice president of Databricks solutions at St. Louis-based Perficient, told CRN in a recent interview that Perficient has done a lot of work defining clients’ Databricks strategies and hardening the platform with improved governance. Many CIOs want to move beyond dashboards and traditional business intelligence to natural language queries and other possible actions in the AI era, he said.
“It is changing fundamentally the operations model of how businesses work because of the data availability,” he said.
Having the right data at the foundation is key to being able to build AI solutions that solve big customer pain points, said Zach Paulson, vice president of technology at Fargo, N.D.-based ABM Technology Group.
“It always comes down to data,” he said. “One finance client was spending thousands on basic reporting software. We replaced that with an agent for a one-time cost. They’ll never have to pay for that again.”
With the risk of AI hallucinations already a challenge, it’s critical that the underpinning data AI solutions are built on be clean and accurate to reduce the risk of wrong answers being served up with confidence. Low-quality data is not only less useful but also potentially more dangerous, which is why data cleanup and access hygiene are necessary for AI to scale.
Channel takeaway: Data quality is emerging as a core channel opportunity because clean, governed data determines whether AI deployments deliver value or risk.
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R—Regulation Readiness
Even in a fragmented U.S. regulatory environment where Congress is drafting a federal AI mandate and individual states are establishing their own laws around transparency and data privacy, regulation readiness is becoming a buyer expectation. It’s not because every customer is regulated but because AI risk now, at minimum, touches privacy, security and governance in ways senior leaders need to be able to explain and defend to their company boards.
It also makes sense to prepare now for a future where stronger state and federal AI regulations exist, even though it’s unclear how those laws might impact U.S. businesses.
Customers are asking, “How do we control this?” before they ask, “How do we expand it?” That’s especially true as shadow AI—the unsanctioned use of AI tools—accelerates and organizations realize they don’t even know what tools employees are experimenting with or how their data is being used and exposed.
Data exposure through shadow AI usage by employees is the biggest challenge many customers are facing, said Jennifer Roy, CEO of Vancouver, B.C.-based Nucleus Networks, during a panel session at the XChange March 2026 event hosted by CRN parent The Channel Company earlier this year.
“It’s an unknown [and] it’s scary,” she said. “You could lose your IP because of someone who’s put it out in OpenAI.”
Regulation readiness doesn’t mean every solution provider needs to become a law firm. It means they should be implementing the controls that auditors and risk teams care about, including visibility, access management, data loss prevention, monitoring and documentation.
Channel takeaway: AI controls, documentation and monitoring can form the basis of a practical compliance offering that puts clients in a good position ahead of anticipated regulations.
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S—Security Amplification
AI is amplifying both sides of the security equation: Defenders get automated detection and faster response, and attackers get speed, scale and automation to the point where autonomous cybersecurity attacks now exist. It is serving to accelerate an already rapidly accelerating arms race.
The new baseline for customers is deep visibility into AI usage, strong identity controls, AI-aware data loss prevention and continuous testing because AI expands the attack surface and introduces new potentially vulnerable entry points, such as agent integrations and MCP servers.
Vendors see the opportunity to push agentic capabilities into security operations, and they are equipping channel partners to capitalize on those opportunities.
Following the mainstream emergence of GenAI, the industry quickly recognized, for example, that security operations teams could massively benefit from the analytics and productivity-boosting capabilities of the technology.
New agentic SOC tools are starting to prove that an easing of many of the biggest challenges in cyber defense, such as alert fatigue and tool sprawl, is entirely possible, said Kurt Wagner, director of sales at Austin, Texas-based BlackLake Security. Without a doubt, “having the ability to augment your SOC [with the new tools]—it becomes a force multiplier,” he said.
At the same time, security amplification is happening inside customer organizations through shadow AI. Unsanctioned usage is creating exposure of sensitive data, and MSPs and other solution providers are being asked to plug the holes.
Channel takeaway: AI is amplifying both cyber risk and defense, creating a growing opportunity for solution providers to help customers secure an expanding, AI-driven attack surface.
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T—Tokens
Tokens are becoming one of the clearest new units of account in the AI economy. They are not just a technical measure of model input and output—they are increasingly a budgeting, pricing and optimization problem for solution providers and customers trying to move AI from the experimentation phase to real-world, repeatable operations.
At Google Cloud Next, the vendor said 330 customers had each processed more than 1 trillion tokens over the prior 12 months, while about 40 customers reached the 10-trillion-token milestone. Nutanix, meanwhile, is explicitly marketing lower and more predictable token costs as part of its agentic AI stack. And Microsoft is signaling that AI-era pricing is moving beyond seat count toward combinations of user-based, usage-based and even outcome-based billing.
For the channel, that means tokens matter for more than model performance. They affect margin, customer planning, workload placement and the eventual shape of managed AI services. The partners that understand token consumption—and can help customers forecast, optimize and govern it—will have an advantage as AI pricing moves from abstract promise to very concrete bills.
Channel takeaway: Build token governance into AI advisory and managed services: Usage forecasting, cost controls, workload placement and pricing guidance are becoming core customer needs.
Learn more:
Google Cloud Next 2026: The Biggest News In Gemini, Agentic AI, TPUs
Nutanix’s New Nvidia Agentic AI Platform For GPUs And AI Factories Unveiled
Microsoft Q3 Earnings: Nadella Says AI Agents Change How Customers Pay
U—Use-Case Specialization
The same general principle that governs the IT channel—that successful solution providers act as trusted advisers who know their customers’ businesses inside and out—also holds true when it comes to implementing AI.
Customers need tailored solutions tied to their industries that deliver measurable outcomes, and solution providers are in a prime position to build and implement those types of AI offerings.
At their core, industry-specific AI offerings need context and understanding of vertical industry lexicon, unique data and data models, regulations and workflows—all of which specialized channel partners can provide.
During a panel session at XChange March 2026, hosted by CRN parent The Channel Company, Nucleus Networks CEO Jennifer Roy described one solution for a 600-user nonprofit customer that only had budget for two HR personnel.
To help the nonprofit overcome its budgetary restrictions, Nucleus automated its workflows for new-user on-boarding and off-boarding, taking some weight off a staff that was overstretched.
“We’ve automated the whole process, including building their user in their HRIS [Human Resources Information System], so that their HR team doesn’t have to do that additional work as well, and that makes us sticky with the client because why would they want to move when we’ve reduced probably one [full-time employee] from their HR team?” Roy said. “So instead of them working all these extra hours and overtime and not getting to these initiatives, they’re able to be more efficient.”
Channel takeaway: AI adoption is reinforcing the channel’s traditional strengths, with specialized expertise and vertical knowledge driving the most meaningful deployments.
Learn more:
MSPs Turn To Specialization, AI To Stand Out
Why AI’s Next Wave Runs Through The Channel: ‘MSPs Already Live There’
GenAI’s Year Three: Solution Providers Zero In On ROI, Industry Use Cases
V—Vendor Consolidation
The advent of the AI age is driving a strategic shift toward vendor consolidation as customers seek to break down data silos, optimize cost, limit security risk and improve scalability, all things that are easier to accomplish if resources aren’t quite so spread out.
In short, AI is accelerating tool sprawl, and customers are pushing back. The result is a new wave of consolidation conversations where buyers want fewer platforms, tighter integration and less operational overhead.
For solution providers, consolidation carries both risk and opportunity. Risk, because platform vendors will try to own more of the stack, meaning there’s more pressure to put all of one’s eggs into the same proverbial basket. But there is also opportunity because customers need help rationalizing their tech stacks, migrating cleanly and preserving continuity.
True end-to-end visibility across cloud, on-premises, hybrid and the internet itself unlocks self-healing IT and real business impact, said LogicMonitor CEO Christina Kosmowski in CRN’s CEO Outlook project earlier this year.
“Partners who lean in will lead this shift as trusted advisers,” she said. “They’ll help customers cut through tool sprawl, trust their data, and put AI to work preventing problems instead of babysitting dashboards. This is a transformation every customer knows they need but cannot do alone. That’s our opportunity to lead.”
Solution providers that can lead that simplification process will earn long-term trust.
Channel takeaway: AI is accelerating a shift toward vendor consolidation, as customers push for fewer, more integrated platforms to reduce complexity and risk.
Learn more:
Omnissa CMO: AI-Enhanced Platform Driving New Customer Conversations For Partners
Navigating The AI Revolution: Insights From Steve Burke For Channel Partners
W—Workflow Re-Engineering
Workflow re-engineering is how solution providers can morph AI from an assistant to an operator. By leveraging their intimate knowledge of customer processes, they can tap into automation platforms to enable natural-language workflow creation and secure agent interactions through structured interfaces.
Some of the biggest AI wins for solution providers will come from this kind of re-engineering, creating new customer workflows that are optimized for speed, automation and knowledge reuse.
IT services giant Cognizant see an opportunity to help bridge the gap between AI capability and the “production value” of most AI implementations, said CEO Ravi Kumar during a panel discussion at PegaWorld earlier this month.
“Why is there a gap between the capability and the production value? That is because we don’t use this technology to reinvent businesses, reimagine businesses. You can’t apply this on old processes and assume there is going to be magic coming out of it,” Kumar said. “If you’re taking this technology and putting it into old processes, you’re really not going to get the productivity you’re looking for. You’re really not going to get the throughput you’re looking for.”
Customers want AI solutions that improve their day by freeing up time and releasing them from having to focus on mundane tasks. Workflow re-engineering is where solution providers can prove their value.
Channel takeaway: Don’t sell prompts—sell workflow redesign with automation and governance baked in.
Learn More:
Monday.com Expands Partner Capabilities With Eye On AI For Workflow Automation
10 Hot MSP Tools To Expand Automation, AI, Agentic AI Capabilities
X—X-Factor Differentiation
As AI becomes more and more ubiquitous, it will become harder for solution providers to differentiate themselves. It will also be more important to stand out. Developing an “X factor”—a unique, standout blend of domain knowledge, delivery expertise and the ability to provide measurable results—will be critical for solution providers of all types.
For many solution providers, developing that X factor will start with becoming their own customer zero by using AI internally to build real-world insight and operational muscle that translates into better customer engagements.
“We are customer zero for our own AI services that we offer,” said Sanjay Singh, CEO of AI cloud solution provider Onix, in a recent interview. “Everybody has to learn and certify themselves on the AI stack. So it’s a mindset and it’s a hard-core discipline that we design that helps us win more [AI] customers.”
In a market full of AI claims, one true differentiator will be credibility. Solution providers that can show what they’ve built internally, prove what they’ve delivered externally and quantify outcomes will stand out.
Channel takeaway: Differentiation in the AI era is increasingly defined by a combination of domain expertise, delivery capability and demonstrated success.
Learn more:
Cloud Solution Providers Find AI Customer Success By ‘Drinking Our Own Champagne’
CWX CEO On Agentic AI ROI, Google Gemini Enterprise Push, Hiring And New Funding
Y—Your Data, Your AI
AI solutions are most valuable when they are tailored to customers’ specific needs and built on a foundation of their own data. The more a model is grounded in a customer’s own data, the more strategic advantage it can provide.
That grounding requires hard work that is tailor-made for the channel: connecting enterprise data sources securely, providing context and controlling access. Model Context Protocol (MCP) is emerging as a key enabler, connecting assistants and agents to real-time enterprise systems through governed interfaces.
“Everybody says they have to have AI, but when you go in and do an analysis, they don’t know what to do with it,” said Michael Goldstein, market president, Southeast Florida, for Fort Myers, Fla.-based Entech. “They don’t know where their data is. … Most midsize corporations don’t know where their data exists, or it exists in another entity. That’s the problem.”
For solution providers, telling a “your data, your AI” story can be the entry point to a high-margin engagement built on data assessments, governance, secure connectors, retrieval architectures and ongoing optimization once AI is in production.
Channel takeaway: AI value is increasingly tied to how well it is grounded in a customer’s own data, elevating the role of solution providers in connecting and contextualizing that information.
Learn more:
CData Looks To Bridge The Data Infrastructure Gap With Latest Offering
Commvault Tackles AI Data Requirements With New Data Room, MCP Server
New Salesforce Partner Network, MCP Tools Target AI Agent Success
Z—Zero-Trust Thinking For AI
Zero trust is already a common concept in cybersecurity that’s heralded by vendors such as ThreatLocker and Zscaler (trust should never be the default position, always verify), but it is being rewritten for the agentic AI era. Traditional architectures were built for humans and devices. AI agents introduce a new challenge: Controlling access is no longer enough. It also requires controlling the actions they are allowed to take.
As agent usage grows, solution providers are rolling out task-based permissions, monitoring and policy enforcement to prevent agents from becoming a new, unmanaged workforce with broad access.
“For us as MSPs, the more we can consolidate tools into one or two platforms, the better. And the more that we can make these tools zero-trust-oriented, the better,” said Reagan Roney, CEO of Sterling, Va.-based Solvere One, in a recent interview with CRN.
Some steps solution providers can implement to espouse the zero-trust ethos include sandboxing agents so they stay isolated, developing a security architecture that examines intent of an agent’s actions and ensuring that agents do not hold direct API keys to enterprise systems, according to security vendor Zentera.
Solution providers can also explore the Agentic Trust Framework developed by the Cloud Security Alliance, which aims to bring zero-trust principles to bear in managing AI agents.
Channel takeaway: Zero trust is taking on new urgency in AI deployments, as agent activity introduces fresh layers of risk beyond traditional access models.
Learn more:
Zscaler CEO On Why Zero Trust Is The Real ‘Foundation’ For Deploying AI Agents
Cisco Unveils Zero Trust For AI Agents: 5 Things To Know
Microsoft Security’s Vasu Jakkal: E7, Agent 365 Tackle Shadow AI, ‘Double Agents’
The Final Channel Takeaway
If there’s one unifying takeaway from this examination of the channel AI opportunity from A to Z, it’s that AI is becoming a complex discipline that requires deep expertise, hands-on experience and a strong focus on the fundamentals.
The most successful solution providers are slowing the customer conversation down long enough to do the hard work—cleaning data, locking down access, re-engineering workflows, defining governance and making sure everything can be measured.
That may sound less exciting than the current hype cycle would imply, but it’s where durable revenue lives for the channel: Governance turns into recurring services, security becomes an AI adoption enabler instead of a blocker, and workflow re-engineering creates tangible efficiencies.
In short, 2026 is the year AI has stopped serving as a conversation starter for the channel and instead has become a credibility test. The solution providers that pass won’t just be selling AI—they’ll be the ones who turn it from hype into impact, backed by experience, execution and trust.
Kyle Alspach, CJ Fairfield, Mark Haranas, Joseph F. Kovar, Dylan Martin, Wade Tyler Millward, Gina Narcisi and Rosalyn Page contributed to this report
