Year in review: how AI evolved in 2025

As 2026 opens, the question isn’t whether to use AI. It’s whether you’re leveraging it effectively to create value and competitive advantage. While model breakthroughs made headlines last year, the most meaningful shifts happened inside organizations working to bring clarity, control, and confidence to their AI efforts. 

Throughout 2025, the Staying competitive podcast series explored how AI evolved in real time. Across those episodes, hosts Ray Beste and John Eisenhauer connected with leaders, reflected on lessons learned, and offered guidance on how businesses can move forward. Their conversations revealed how AI adoption matured, what worked, and what organizations must focus on next. 

Clarity: AI strategy starts with defining the problem 

The first major theme was clarity. Many leaders still opened conversations with the same question, “What exactly is AI, and where should we begin?” Ray captured the challenge well when he said, “AI isn’t the strategy. AI supports the strategy. And without a problem statement, there’s really nothing to measure.” 

Too many teams are feeling pressure to “do AI” without understanding the actual business needs. This often creates stalled initiatives rooted in technology, rather than business impact. 

Technical jargon also became a barrier for many executives. Concepts like “context windows” or “tokens” surfaced in everyday conversations. When leaders had even a basic understanding of these ideas, they were better positioned to make decisions, evaluate vendors, and avoid costly missteps. 

The lesson was simple. Clarity builds confidence, and confidence accelerates progress. 

Control: governance moves from optional to operational 

Throughout 2025, the broader market began to understand AI requires more than experimentation, it requires governance. Organizations wrestled with questions of ownership, responsibility, transparency, repeatability, and data protection. As Agentic AI (autonomous, ambient, intelligent tools operating in the background) accelerates, literally hundreds of new regulations have emerged throughout the United States, Europe, and South America. The message couldn’t be clearer: governance is not an option. 

The most forward‑thinking companies recognize the need for and value of designing AI solutions with governance in mind. They are treating governance as an enabler instead of a hurdle. They embed controls into workflows and architect systems with privacy and reliability in mind. They treat governance as a first-class passenger in their AI journey. It’s part of the design, not just an afterthought. 

Competitiveness: moving from exploration to execution 

By mid-year, AI was no longer viewed as a separate project. It was infused into daily work, especially through AI assistants embedded directly into core business applications. 

Tools like Microsoft Copilot exemplified this shift, meeting people where they already worked. Users discovered gaps in their data, uncovered new efficiencies, and built AI fluency simply by using familiar tools. Other platforms followed suit, creating a growing expectation that business apps should include AI support. 

As pricing dropped and multimodal capabilities—the ability for AI models to interact with text, images, audio, and video—matured, AI became an everyday utility rather than a specialty tool. Workers who effectively and consistently utilize AI as part of their daily work realized significant gains in productivity. 

The workforce implications became crystalized 

With more automation on the horizon, organizations are rethinking how they develop talent. While some are considering reducing roles and headcount; more innovative minds are taking a different tack. AI can enhance productivity, but it cannot replace the foundational experience that comes from hands-on work. AI elevates workers and enables them to “operate at the top of their license.” A modern workforce enabled with AI tools can align itself with higher-order work efforts and deliver great value to their markets. This is creating a new competitive opportunity: one where organizations are starting to leapfrog their competitors and into bigger, broader markets. 

This opportunity comes with a strategic imperative: preserve the pathways that create expertise. As AI automates many early-career tasks, organizations must redesign how they develop talent. The best are creating intentional development programs that ensure employees still build foundational knowledge and judgment, just in new ways. Long-term competitiveness depends on cultivating talent with real experience and context, augmented by AI rather than replaced by it. 

Lessons leaders should carry into 2026 

Several key takeaways emerged that will define the next year of adoption: 

• Start with the business problem, not the tool. “Doing something with AI” isn’t a strategy. 
• Human judgment remains essential. Oversight, ethics, and expertise can’t be automated. 
• Words matter. Whether describing outcomes to an AI model or discussing AI internally, clear language drives better results. 
• Automation isn’t new, but its urgency is. AI is accelerating traditional automation initiatives that should have happened years ago. 
• Copilots and embedded AI become expectations, not experiments. If a business app doesn’t include AI support, users will notice. 
• Expertise still wins. AI amplifies human insight; it doesn’t replace it. Organizations must continue developing talent, not shortcutting the process. 

Looking ahead: 2026 will reward deliberate operators, not reactive adopters 

If 2025 was about understanding the landscape, 2026 is about building systems that work. The gap won’t be between companies that use AI and those that don’t. It will be between organizations that use AI deliberately and those that constantly react to it. 

Staying competitive will continue exploring how AI evolves over time with more industry voices, more practical examples, and more conversations from leaders in the trenches navigating AI every day. 

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