The technology distribution channel is at a crossroads. Artificial intelligence promises to transform businesses, but the reality is that many projects remain in the testing phase. Are we facing a bubble or a poorly managed opportunity? We analyze industry voices to understand how AI is truly being monetized and what partners must change to avoid being left behind.

The Gap Between Pilot and Production

Fernando Maldonado, principal analyst at Foundry Spain, points to a recurring problem: many AI projects start as technical pilots, not as operational solutions. "A common explanation is that many of these initiatives were born to test technical capability, not to sustain an operational process," he explains. During the pilot phase, data is manually corrected, dedicated teams are in place, and organizational friction is absorbed through human effort. But when those conditions disappear, inconsistencies, data governance issues, and unforeseen maintenance costs emerge.

For the channel, this alters traditional commercial logic: before selling technology, you must sell preparation. "A contract that avoids prior auditing may close faster, but it is more likely to end up in a project that no one renews," warns Maldonado. Not all cases require deep transformation; limited automations or well-defined processes have lower thresholds. The problem arises when the system stops being a test and becomes operational infrastructure.

Three Speeds of AI Adoption

Moisés Camarero, CEO of Compusof, describes a heterogeneous landscape. "We are in a much more exploratory phase than the headlines suggest," he says. He observes three speeds: a first layer of licensing (Copilot for M365) with good acceptance but tight margins; a second layer of professional services (assessments, data governance, training) where margins are healthy because the client needs guidance; and a third layer of custom projects with generative models or agents, where there is media noise but modest billing. "Our clients tell us there are many proofs of concept that never make it to production, and that is something the channel still does not dare to say out loud," he confesses.

Alejandro Soto, General Manager of Sales at Arrow Enterprise Computing Solutions Spain, agrees that monetization is heterogeneous. "There are very profitable and well-executed projects, but also a significant portion of enterprise AI initiatives that stall before reaching their true potential." The problem is not technological but strategic: "Some companies have been carried away by the wave and invested in pilots without a solid plan, generating skepticism where there should be confidence."

Who Really Monetizes AI?

Ismael Benito, Sales Engineer at Applivery, argues that real monetization is not about selling AI, but using it to sell better. The partners that truly monetize are those who use AI internally to generate technical proposals in minutes, create custom automation scripts, or produce content tailored to each vertical. "Thanks to AI, a partner can generate responses to complex RFPs in minutes, when before it took hours. This allows bidding on 3X more projects with the same team. That is real monetization: more billing with the same resources."

Benito identifies three main brakes: lack of knowledge on how to apply AI to specific problems (companies see impressive demos but don't know how to apply them to their reality); fear of the learning curve (they think it requires intensive training, when any IT manager can start by asking AI about an error message); and the perception that it is only for large companies (they don't understand that an SME can use AI to generate security reports in minutes). The solution: evangelize with micro-concrete use cases, not macro-strategic visions.

Most Demanded AI Services

IDC forecasts that AI will generate a cumulative global economic value of $22.5 trillion by 2031. Santiago Méndez, General Director of Advanced Solutions at TD SYNNEX Iberia, points out that the greatest demand comes from AI solutions applied to business: copilots, generative assistants, predictive analytics, and use cases in regulated sectors. There is also growing interest in services that enable industrializing AI: specialized pre-sales, architecture validation, deployment support, cloud integration, and support. "The market is asking for partners capable of combining technology, consulting, and operations, not just reselling hardware or software."

Oliver Crespo, Head of Vendor and Channel Strategy for Iberia and Latin America at Zaltor, highlights solutions for IT support and operations automation (AIOps), intelligent monitoring, predictive maintenance, AI applied to cybersecurity, intelligent assistants, and document automation. "We also see a lot of interest in integrating AI within existing platforms, rather than deploying entirely new projects. Companies seek to simplify operations without increasing structure or complexity." In the MSP environment, AI becomes an operational multiplier that allows managing more clients and offering more proactive services.

The Role of the Channel Transforms

Alberto Pascual, Executive Director of Ingram Micro Spain, states that AI drives the evolution of the channel toward a more consultative and service-oriented model. "Value is no longer in distributing technology, but in accompanying the client to use it safely and efficiently." Partners assume a strategic role, while distributors expand their offering with training, technical support, and accompaniment services. "We all evolve toward more flexible, personalized, and scalable models."

Moisés Camarero is direct: "The traditional distributor, who lived off logistics and credit, has a structural problem if they don't adapt." AI is consumed as a service; there are no boxes to move. Distributors who understand this become technical enablers with competence centers, training, and specialized pre-sales. For the integrator, the change is even deeper: "We have gone from selling and implementing products to having to understand the client's business with a level of detail that was not a priority before." A Copilot project does not fail because of technology, but because no one redesigns processes. "That brings us closer to consultancies and moves us away from the purely technical profile." AI demands profiles such as change management, functional analysts, and data professionals.

Obstacles Slowing Real Adoption

Santiago Méndez points to the main brake: the lack of specialized talent to design and operate AI projects with guarantees. Added to this are the complexity of integration with existing environments, the difficulty of demonstrating return, and the need to align security, compliance, and data governance from the start. He also observes uneven maturity in the channel: many partners need support to move from product sales to building high-value services.

Oliver Crespo adds the difficulty of converting interest into projects with real impact and clear return. "Many companies have not defined where to apply AI, how to integrate it, or how to measure its long-term value." This is compounded by the lack of talent, technological complexity, doubts about privacy and regulation, and excessive expectations. The market is now entering a more mature stage, where practical solutions and tangible results are prioritized.

Beyond AI: Other Drivers of Change

Alejandro Soto reminds that AI is not the only driver of change. "Transformation has been underway for some time, driven by how technology is licensed and consumed: the rise of XaaS models, the proliferation of hyperscaler marketplaces, and new commercialization dynamics." AI acts as an accelerator, not a starting point. Value-added distributors are now orchestrators of a complex ecosystem: business developers, cloud business managers, financial solutions managers, and specialized technical service providers. "These are roles that did not exist a few years ago and are now central to our offering."

The value of partners no longer lies in deploying infrastructure, but in understanding the client's business problem, designing the adoption strategy, and accompanying implementation. "Integration, not technology itself, is where value is generated and where customer loyalty is built."

Capabilities the Channel Must Develop

Alberto Pascual lists key capabilities for the coming years. First, "stop thinking of yourself as a technology seller and start thinking of yourself as a process transformer with technology." This implies real capability in data governance, with information architects, not just infrastructure architects. Second, applied AI engineering: professionals who know how to design agents, orchestrate models, evaluate results, and monitor drift. "It is not enough to know how to call an API; you must know how to industrialize the solution and keep it in production reliably and economically." Third, vertical specialization: generic AI commoditizes quickly; value will lie in solving specific problems in sectors such as public administration, healthcare, energy, or banking. Fourth, consulting and change management: "This is what costs us the most in the traditional technical channel, and it is where the client demands the most value. Those who do not incorporate that muscle will be relegated to low-margin implementation tasks." Finally, an internal culture of real AI use: "Leading by example matters. An integrator that does not use AI in its own development, support, or pre-sales is not credible selling it."

Pascual concludes that the next two years will separate the wheat from the chaff in the channel, not because of the technology itself, but because of each one's ability to transform their own business model while helping the client transform theirs.

Iván Rodrigo, Solutions Architect at Westcon-Comstor, summarizes: "AI is not going to diminish the importance of the channel. Quite the opposite. But it will demand a higher level. The channel that knows how to unite business, data, cloud, security, integration, and adoption will have a long way to go. The one that stays only selling licenses or saying 'this has AI' will have a much harder time."

To delve deeper into how AI is transforming other technological areas, we recommend reading our analysis on Snowflake and AI agent governance, as well as the debate on the lack of agreement among AI models. Additionally, managing critical infrastructures such as Kubernetes and network security are essential complements to any enterprise AI strategy.

Original source: ComputerWorld. Analysis and adaptation by ForgeNEX.