#ai-implementation

#ai-implementation

As 2026 begins, many organizations are launching AI transformation initiatives. The new year brings with it fresh budgets, renewed strategic focus, and mounting pressure to capture value from artificial intelligence. Yet studies consistently show that most AI projects fail to generate meaningful returns. Companies pour resources into promising experiments that never scale, accumulate tools that are never integrated, and watch initial enthusiasm curdle into skepticism.

The question for the coming year, then, is no longer whether AI will transform your organization, it's whether your leadership team will guide that transformation thoughtfully or let it happen haphazardly, tool by tool and team by team. I have spent much of the past year working with my research team and industry partners to think through the most pressing challenges organizations face as they implement AI at scale. Drawing on this work, I have identified seven key priorities for leaders preparing for 2026.

The new program - which forms part of the company's Destination AI enablement framework - offers a three-phase pathway oriented around discovery, scoring, and activation. Partners work with customers to identify pain points across their organizations, with use cases then scored and prioritized based on factors such as feasibility, impact, and strategic alignment. The workshop then concludes with a shortlist of two to three high-value AI initiatives and a tailored 90-day roadmap for implementation.

A MIT study last year showed that 95 percent of generative AI implementations fail. They generate little to no business value. There can be various reasons for this. Think of poor integration, little focus on solving real problems, and unrealistic expectations. As far as Appian CEO Matt Calkins is concerned, this is an absurd percentage for something that is seen as the breakthrough technology of this generation.

AI agents did not take off in 2025. Obstacles include legacy systems and architecture. Companies that succeeded were thoughtful about implementation.

When it comes to agentic artificial intelligence, the fear of missing out factor is clear. Organizations are plopping down agents, in part, because that's what everyone else seems to be doing. But FOMO is not a business strategy. To make agentic AI work, business leaders need to ignore the hype and concentrate on establishing exactly what agents can do for them, how, and at what cost.

Our commitment to clarity and security drives our operations, taking inspiration from Johannes Kepler to illuminate the path for our Fortune 500 clients.

AI doesn't fail because of the tech. It fails because there's no business case, no clean data, or no one truly accountable for results.