Stop Treating AI Like a Revolution: Why 2026 Needs a Different Mindset

Across the enterprise landscape, AI is being described as the next great technology revolution. Boards are asking for strategies, data and analytics leaders are scrambling to productionalize AI, chief AI officers are springing up in the market, and suppliers are signaling a future defined by acceleration and transformation. No doubt, the investment, momentum, experimentation, and popular intrigue (and consumption) is different this time when compared to “AI revolutions” of the past. But with so much urgency in the air, it is easy to assume that slowing down invites risk. For over fifteen years, IIA has been advising large, complex organizations in making the difficult and necessary transition to advanced analytics and AI, and our work with these organizations points in another direction. The real risk emerges when tech hype convinces leaders that they need to act immediately, rather than pausing to decide what makes sense for their organization.

History gives us a useful lens. Many technology waves were introduced as revolutions—data warehousing, big data, Hadoop, blockchain—and each promised to change the nature of business. Vendors defined the narrative and direction, and enterprises inherited the systems they then had to manage and fund. Too often, investors are the short-term beneficiaries in these waves of revolution, while companies and the practitioners who run these systems inherit technical debt. The history of hype cycles should shape how leaders interpret today’s AI moment, as it underpins several governing assumptions that form the basis of IIA’s point of view.

1. Every “revolution” begins on the supply side and should be met with skepticism.

Each major technology revolution, from data warehousing to big data to blockchain, was driven by suppliers seeking to sell the next wave of products. These waves historically ended with short-term gains for investors and long-term costs for enterprises, leaving practitioners burdened with expensive legacy systems to wrestle with. IIA believes every new “revolution” should first be examined through the question, “What do the suppliers want, and why now?”

2. The line between marketing and reality has eroded.

Historically, practitioners could separate vendor hype from operational truth. That distinction has blurred. Modern marketing practices amplified by venture capital, analyst firms, and the media have created a feedback loop of rosy optimism that now influences enterprise decision-making more directly than experience or evidence.

3. Technology cannot solve fundamentally human problems.

A recurring industry delusion is that technical capability can automate away human complexity. Yet the hardest business challenges—what game theorists call “wicked decisions”—involve uncertainty, interdependencies, and consequences that defy modeling. These problems demand human judgment, context, and accountability. Technology can assist, but it cannot replace these functions.

4. The “easy button” mentality is expanding.

Demand-side stakeholders increasingly expect analytics and AI to deliver simple, fast, plug-and-play solutions. This expectation distorts investment priorities and devalues the slow, disciplined work of building data quality, literacy, and governance. Enterprises are chasing ease instead of effectiveness.

These assumptions inform how we think about and advise clients on AI initiatives. For us, they push the conversation away from hype cycles and back toward the practical question that ultimately decides whether a technology becomes part of the way a company works: What does it take to run this well over time? The answer never begins with the technical solution. It begins with clarity on the business problem, the technology fit for that problem, the processes that must change to adopt the technology, and the people who will ultimately carry the work forward.

What complicates today’s environment is the speed at which claims outpace evidence. Capabilities evolve quickly, but expectations evolve even faster. Leaders are under pressure to produce visible wins before their organizations have the data quality, governance, or operating structures to support them. An expanding gap has opened between what AI tools seem to offer and what enterprises can realistically absorb. Closing that gap depends on steady judgment and a clear view of organizational readiness.

It also requires accepting that AI does its best work when paired with human expertise, not positioned as a substitute for it. The decisions that shape strategy, risk, customer trust, and operational continuity depend on context and accountability. These elements cannot be automated. They can, however, be augmented through better retrieval, more consistent reasoning patterns, and workflows that help people scale their judgment across the organization.

At the same time, the demand for ease continues to rise. Leaders ask for turnkey copilots, instant value, minimal configuration, and predictable outcomes. Those expectations are understandable, but they rarely align with how enterprise systems behave in practice. Organizations that orient their AI investments around convenience often find themselves repairing the consequences for years. The ones setting themselves up for success invest early in literacy, guardrails, observability, and the operating rhythms that make new capabilities safe to run at scale.

This is the context for IIA’s new AI resource hub. Across IIA’s advisory and assessment services, including hundreds of one-on-one consultations each year between clients and Experts and a portfolio of diagnostic tools that score over 100 competencies and qualitative dimensions in analytics and AI maturity, we see the same concerns surface again and again. How do we connect AI ambition to business outcomes? How do we deploy safely when foundations are still maturing? How should teams be structured to deliver AI consistently? Which capabilities should be built internally, and which should be sourced? How do we govern systems that change over time? These questions reflect shared pain points across the enterprise landscape and go well beyond isolated struggles.

The hub brings together the expert frameworks, advice, and peer-to-peer insights that help data and analytics leaders navigate these challenges with confidence. Nan Li’s eBook helps you bridge traditional analytics and AI. Expert Exchanges help you rethink data governance, introduce concrete examples of applying AI to supply chain problems, and demystify agentic AI. Roundtable Peer Insights give you an exclusive view into peer-to-peer discussions about change management and AI—and building and leading AI teams. These resources showcase the immense power of IIA’s Expert Network and client community working hard to make AI progress sustainable inside real organizations with real constraints and real consequences for failure. We will add resources and update IIA’s community through this newsletter as the field develops.

As enterprises plan their AI direction for 2026, a more grounded mindset can make all the difference. Treat supplier-led revolutions with caution. Resist easy-button narratives. Keep human judgment at the center. And evaluate new capabilities by how well they can be governed, integrated, and maintained. And, most importantly, scrutinize how well your analytics and AI techniques solve business problems that map to company objectives. We’re sometimes told that IIA should rebrand as “IIAAI,” as if analytics is fading into the background. We disagree. AI is an extension of analytics, and the foundational pillars necessary to mature in analytics maturity hold true for AI maturity.

Data debt. Operating models. Strategic integration. User adoption. Wicked decisions. These challenges reflect human dynamics, and only human judgment and coordination can move them forward. Thus, the strength of the partnership between the business and the analytics organization still determines whether teams choose the right problems and apply the right methods. From that starting point, meaningful and lasting momentum becomes possible.