What are 3 big AI Shifts in the midst 2026?

AI is leaving the “wow” phase and entering the “prove it” phase. Many companies are about to realize their strategy was built for demos, not reality. In other words, AI is growing up. It’s time to move on from the hype toward the hard tradeoffs. What are 3 big AI shifts?

• Magic to Money 
• Demos to Deployment 
• Capabilities to Consequences

For the past few years, AI has lived in the realm of spectacle—impressive demos, viral moments, and “did you see what it can do?” reactions. That phase is ending. What’s replacing it is more grounded, more valuable, and a lot less comfortable. 

AI is shifting from magic to money, demos to deployment, and capabilities to consequences. Let’s examine these more fully.

Magic to Money

The novelty is wearing off. Organizations are no longer impressed that AI can generate content, write code, or analyze data. People are asking whether it actually drives revenue, reduces cost, or creates a defensible advantage. This is where many AI initiatives encounter friction: what initially appears magical, struggles when tied to real business metrics, messy data, and existing workflows. A current challenge is proving consistent, measurable ROI, not just isolated wins and talk.

Demos to Deployment

We’ve all seen the polished demos. But deploying AI into production is a different game entirely. Integrations, governance, reliability, edge cases, and user adoption quickly surface. The gap between “it works in a demo” and “it works every day in real life” is where most efforts stall. The winners are no longer the ones with the flashiest models, but rather those who can operationalize them at scale. A current challenge of getting to “real life” is bridging the last mile from prototype to dependable, repeatable execution.

Capabilities to Consequences

As AI capabilities grow, so do concerns about accuracy, bias, job displacement, environmental cost, security, and trust. Leaders are increasingly forced to weigh not just what AI can do, but what it should do and what risks they’re willing to accept. The conversation is shifting from innovation to responsibility, often faster than organizations are prepared for. A current challenge is how to manage risk and accountability without slowing innovation to a crawl.

What shifts are you seeing, and what challenges are you facing in getting to the full usage of AI?

What is a CM Tool?

Does this title sound silly? I mean, doesn’t everyone know what a Configuration Management (CM) tool is? Isn’t a Configuration Management CM tool something that provides version control functionality? Well, the short answer is yes (in part) but only in its most simplistic form. CM as a discipline goes well beyond simple version control. It is important to look beyond what vendors define as classic CM tools and consider CM in terms of the full practice and processes they offer. CM at its very essence covers identification, control, audit, and report. Many would expand control to include version control, change control, change management, build management, and release engineering. Now the answer to “what is a CM tool” becomes complex.

Agreeing on exactly all of the CM capabilities is not the point. The point is to ensure you are clear on what possible CM (or CM-related) capabilities there are and then deciding which ones are important to you. By first identifying and understanding the capabilities, provides a good starting point to identifying your needs and then further drilling down to capture requirements for a CM tool evaluation. More details on this topic can be found at: http://www.cmcrossroads.com/article/what-cm-tool.

Thoughts? Feel free to share with others.