2026 - AI’s year of Consolidation and Clarity

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MSN: Paul Michael Curtis: 2026 - AI’s year of consolidation and clarity

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A strange thing happened when artificial intelligence went mainstream. The technology became less mysterious, but the path to value got messier.

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In 2024, the conversation was about access, as tools like ChatGPT put generative AI into the hands of anyone with a browser. In 2025, companies moved from fascination to experimentation, launching pilots and proofs of concept at speed.

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2026 will feel different. “This is the year of discipline. This is the year where AI kind of grows up," says Paul Michael Curtis, a battle-tested technology leader with more than 25 years of driving innovation and strategic transformation across early stage companies and global enterprises. For many organizations, that shift will be less about flashy demos and more about how to build, align, and lead cross-functional teams that can convert AI capability into measurable business outcomes.

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If 2025’s urgency was driven by fear of missing out, Curtis thinks 2026’s urgency will be shaped by a more specific anxiety: profitability.

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“The time frame for delivering something meaningful, and of value, is so compressed that you have to come out of the gates running – and with a well-defined strategy that ensures early successes,” he says. “In many cases, you have to get it right the first time.”

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From tool sprawl to platform discipline

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The two years have been necessary, but chaotic, steps. “2024 was really the year of access and availability,” he says, pointing to the moment when AI shifted from an abstract promise to a daily utility. Then 2025 became “the year of exploration and exploitation,” with organizations trying to translate novelty into practical use.

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That experimentation also created a predictable hangover, with an overwhelming amount of disconnected tools and poor integration effort contributing to a growing sense of fatigue. “There’s all of these disparate tools that sit out there with some level of AI baked into them,” Curtis says. “You’ve got AI in productivity suites, you’ve got AI in design and creative software, you’ve got AI in a multitude of backend operations tools.” In large organizations, this sprawl turns into procurement complexity, duplicated spend, and inconsistent support.

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The natural response, Curtis predicts, is consolidation into fewer, stronger and value-driven platforms. “Organizations are going through another cycle of tool fatigue,” Curtis says, which triggers “the rise of the platforms.”

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In practical terms, that means fewer vendors, a clearer support model better suited for enterprise standards, and systems that align with how enterprises actually buy and run software. The leadership challenge will then be in orchestrating the decision-making across business, technology, procurement, legal, and security teams.
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Build versus buy becomes a leadership test

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“Now, we have to make crucial decisions about build versus buy,” he says. For some organizations, buying a mature platform will accelerate adoption. For others, building internal capability may be essential, especially when proprietary workflows or regulated data environments demand tighter control.

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Either way, the decision forces alignment. Teams need a shared definition of value, clear ownership, and a realistic view of what it takes to operate AI at scale. Many organizations still treat AI as point solutions for individual departments. They ask, “How do we solve this for marketing? How do we solve this for shipping and logistics?” but stop short of designing for a unified, enterprise-wide experience.

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That gap often explains why pilots stall. “It’s more like 95% of AI pilots had no sponsorship, or a clear definition of meaningful outcomes,” says Curtis, reframing a widely cited failure rate. "There was no plan for adoption. There was no plan for organizational transformation,” In other words, teams built something technically interesting, then discovered the organization was not structured to use it.

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Cross-functional leadership becomes the difference maker. The strongest teams do not treat adoption as an afterthought. They weave change management, training, governance, and workflow redesign into the initiative from day one.

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ROI pressure will reshape teams and incentives

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If 2025’s urgency was driven by fear of missing out, Curtis thinks 2026’s urgency will be shaped by a more specific anxiety: profitability. “I think it’s just going to take a bit of a different form.” The new question is not whether AI is relevant. It is whether a competitor has already figured out how to turn it into margin.

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“There’s greater accountability from the CFO’s office,” Curtis says, and leaders who cannot connect AI budgets to outcomes will lose credibility fast. He expects companies to seek specialized, boutique partners that can deliver. “There’s no more room for fluff. Nobody can tolerate that anymore.”

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Inside organizations, incentives will shift toward delivery. Curtis sees 2026 favoring teams that can run small, measurable bets, rather than sprawling, long-running programs. “The organizations that are winning are the ones who have a ‘fail fast’ mentality,” he says, describing “small projects that have a direct correlation to productive and profitable outcomes.” That approach requires cross-functional teams that can prototype quickly, validate with the business, and scale only when results are proven.

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The move from AI talks to AI doing

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Curtis believes 2026 is when AI graduates from chat-based novelty to operational execution. “If last year was the year of AI talks,” he says, “2026 is the year of AI doing things.”

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For cross-functional leaders, this is a cultural challenge as much as a technical one. Automation shifts roles, decision rights, and accountability. Curtis argues that AI should first absorb “the mundane task, the boring stuff – areas where human accuracy is affected by attention fatigue” and then move into augmentation, reducing cognitive load so humans can reach “flow state” faster.

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“If we don’t get that part right,” he says, “we’re going to have a lot of automated things that are ultimately going to be abandoned by the very organizations they were built to serve.” The organizations that succeed will treat AI adoption as a workforce strategy, with clear communication, training, and guardrails that keep humans in the loop where it matters.

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Consolidation and clarity matter

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Consolidation is a forcing function for organizational maturity. Fewer tools mean clearer standards, more consistent governance, and better odds of scaling what works. Clarity, meanwhile, shows up in sharper choices: the right platform, the right metrics, the right operating model, and the right team structure to deliver outcomes.

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That's why Curtis puts regulation on the short list of leadership priorities. While regulation can slow movement, he sees it as an enabler, arguing that guardrails can help clarify expectations so teams can build with confidence. “If you’re building something, you really need to know the rules by which you need to build the thing. It’s like building a house without a clear understanding of the building codes.” he says.

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In 2026, winners will look less like organizations chasing hype and more like organizations building discipline: cross-functional teams that can make hard decisions, ship in tight cycles, and connect AI initiatives to meaningful results.

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Follow Paul Michael Curtis on LinkedIn for more insights.