The mistake I see today is focusing on what agentic AI can do without defining where it can operate reliably and when human review remains necessary.

Model selection conversations tend to focus on capability, and that is a reasonable starting point but a terrible stopping point.

Skepticism should not automatically be confused with wisdom, just as enthusiasm should not automatically be confused with progress.​

In industrial IoT, human-centric AI must be built around human involvement in analysis and decision-making and be based on several fundamental principles.

Companies that skip the data cleanse may see short-term movement, but a long-term AI launch requires data architecture evaluation as step one. ​

AI is a means to an outcome—not the outcome itself.

Data sovereignty is becoming a major barrier to enterprise AI. Here’s how leaders can overcome regulatory, architectural and compliance challenges.

But they reveal a practical problem for platform leaders: customers can want the product and still be unable to buy it.

The opportunity is enormous, but realizing it means building the operational and cybersecurity foundations that allow organizations to use AI with confidence.

Plenty of people are doing the work, but no one person clearly owns the outcome all the way through.