AI Should Solve Problems, Not Create Complexity

Interest in AI is high, and many organizations feel pressure to adopt it quickly. That pressure can lead to projects that begin with the technology and then search for a problem to solve. The result is often a proof of concept that impresses in a demo but never becomes part of daily work.

Useful AI starts the other way around: with a clearly defined problem, a measurable outcome, and a realistic view of how people will use the result.

Define the problem in operational terms

"Use AI in customer service" is not a problem statement. "Reduce the time agents spend searching for policy answers" is. A precise statement tells you what data is needed, who the users are, and how success will be judged.

Check whether AI is the right tool

Many problems are better solved with clearer processes, better reporting, or simple rules-based automation. AI is most valuable where inputs are varied or unstructured — documents, free text, images — and where a good-enough answer that a person can review is still a meaningful improvement.

Start from the data you actually have

Model quality depends on data quality. Before committing to a build, confirm that relevant data exists, that you are permitted to use it, and that it reflects the situations the system will face. Gaps discovered late are a common reason AI projects stall.

Design for people, not around them

Keep humans in the loop where it matters

For decisions with financial, legal, or safety consequences, AI output should support a person rather than replace their judgment. Make it easy to see why a suggestion was made and to correct it.

Fit into existing tools

Adoption rises when AI appears inside the systems people already use, rather than in a separate application they have to remember to open.

Plan for operation, not just launch

AI systems need monitoring, periodic evaluation, and clear ownership. Inputs change over time, and performance can drift. Budget for this ongoing work from the start.