Solwey

AI is finally showing ROI. Just not the way most companies expected.

A recent analysis covered by Business Insider, based on McKinsey data, shows that leading companies are now seeing real financial returns from AI, with some reporting around $3 back for every $1 invested. That is a meaningful shift from the earlier narrative where AI was mostly framed as experimentation and long-term potential. What stands out even more is how these companies are actually achieving those results.

The organizations seeing real returns are not spreading AI across every function at once. Instead, they are focusing on a small number of high-impact use cases and executing them deeply. Rather than chasing broad adoption, they are prioritizing specific problems where AI can directly improve revenue, reduce costs, or increase efficiency.

This is where many companies are still getting it wrong. There is a tendency to measure progress through activity, such as how many pilots are launched or how many teams are using AI tools. But activity does not translate into value, and adoption alone does not guarantee outcomes.

The companies highlighted in the analysis are taking a different approach. They tie AI initiatives directly to business metrics and track performance in terms of real financial impact. In many cases, they begin to see measurable results within one to two years, which reinforces the importance of staying focused and committed rather than constantly shifting direction.
There is also a deeper shift happening beneath the surface. Many organizations are still layering AI on top of existing workflows, which leads to incremental improvements but limits the overall impact. The companies seeing stronger returns are redesigning how work happens, using AI as a core part of the process rather than an add-on.

This distinction matters more than the technology itself. AI is no longer the constraint, and access to powerful models is becoming widespread. What differentiates companies now is how clearly they define problems, how disciplined they are in ex*****on, and how willing they are to rethink their operations.

Came across this and it’s worth your time: https://www.businessinsider.com/mckinsey-ai-adoption-return-on-investment-analysis-enterprise-2026-5

When not to use AI agents

AI agents are powerful - but they’re not always the right answer.

You shouldn’t use AI agents when the problem isn’t well defined. If success criteria are vague or constantly shifting, an agent will automate confusion instead of reducing it.
You also shouldn’t use agents when the data is unreliable or fragmented. Agents act on what they see. If the inputs are wrong, they’ll move fast in the wrong direction - and make it harder to spot the issue.

Another red flag is when human judgment is still the primary value. Decisions involving nuance, ethics, or high-stakes tradeoffs need people in the loop. Agents can prepare information, but they shouldn’t own the outcome.

Finally, if there’s no owner for the workflow, don’t add an agent. Without accountability, agents become background automation that no one trusts or uses.

The best use of AI agents isn’t to replace people.
It’s to support clear decisions, clean data, and well-owned workflows.

If those aren’t in place yet, AI agents won’t help - they’ll just make the mess more efficient.