5 Action Items for AI Decision Makers in 2026
By Faiszal Anwar
Growth Manager & Digital Analyst
If you’ve been feeling like AI has been all talk and not enough action lately, you’re onto something. According to Thomas Davenport and Randy Bean in their latest analysis for MIT Sloan Management Review, we’re hitting a level-set year for AI. The hype cycle is slowing, and that’s actually good news for anyone who’s been trying to cut through the noise.
Here’s what you need to focus on in 2026.
1. Start Experimenting with AI Agents (But Keep Humans in the Loop)
Agentic AI systems that can perceive, reason, and complete tasks independently? Still not ready for prime time. Ongoing accuracy issues and security concerns around prompt injection have slowed adoption. But here’s the thing: Davenport and Bean predict AI agents will handle most transactions in large-scale business processes within five years.
The move now is to start building internal capabilities. Pick use cases that can be reused across your organization. Begin fostering the skills to create and test agents. Don’t wait until everyone’s doing it.
2. Prepare for an AI Bubble Correction
AI has dominated boardroom discussions and inflated stock valuations. This year, expect a reckoning. The emphasis on user growth over profits is reminiscent of the dot-com bubble. The key insight: “Often technologies are overestimated in the short term, but their transformational impact is very much underestimated in the long term.”
What should you do? Maximize the value from AI tools you already have. At the same time, explore how AI investments can shape your future business strategy. The companies that survive the correction will be the ones using AI wisely, not the ones spending the most.
3. Move from Individual Productivity to Enterprise Value
Most organizations have taken the individual approach: employees using generative AI to boost their own productivity. That’s useful, but it’s not where the real value lives. Companies need to apply generative AI to enterprise workflows and processes. Until then, it’s difficult to aggregate results and quantify actual business value.
Think bigger. How can generative AI facilitate new product development? How can it enrich the customer experience? These are the questions that separate AI dabblers from AI drivers.
4. Figure Out Your AI Reporting Structure
This one might surprise you: most companies still don’t have a clear reporting structure for AI leadership. In the 2026 AI & Data Leadership Executive Benchmark Survey, 38% of companies said they’ve appointed a chief AI officer or equivalent. But there’s little consensus on who that role reports to. Business? Technology? Transformation?
The result is chaos. Davenport and Bean note that this scattered structure is likely contributing to the widespread problem of AI not delivering sufficient business value. Consider appointing someone to unify data, analytics, and AI, reporting directly to business leadership.
5. Build Your AI Factory
An AI factory isn’t just a data center full of GPUs. It’s a capability within your organization: combinations of platforms, methods, data, and algorithms that make it fast and easy to build AI systems. Instead of requiring data scientists to replicate work or figure out what data is available, AI factories establish a foundation that lets your firm efficiently build AI at scale.
Forward-thinking firms should use this year to ramp up AI factories and expand the number of use cases internally.
The Bottom Line
2026 is the year of execution, not experimentation. The companies that win won’t be the ones with the most AI tools. They’ll be the ones who figured out how to deploy AI at enterprise scale, with clear ownership and measurable business value.
The hype is fading. The real work begins now.
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