Technology & Insights

Interest in AI has moved well past experimentation. Executives want to know where it can reduce cost, shorten cycle times, or improve service, and operations leaders are often the first to be asked. The challenge is that "use AI" is not a plan. Getting real value depends on choosing the right problems and setting up the conditions for success.

Start with the work, not the model

The best early candidates are processes your team already understands well and can measure. Look for work that is high in volume, repetitive, and dependent on reading, classifying, or summarizing information. Common examples include processing invoices and forms, triaging service requests, answering recurring internal questions, and preparing routine reports.

For each candidate, write down the current baseline: how many items are handled each month, how long each one takes, and what the error or rework rate looks like. Without that baseline, it is very hard to show whether an AI solution made a difference.

Check your data readiness early

AI solutions depend on the data and content they can access. Before committing to a use case, confirm a few basics:

• Is the information the model needs available in digital form?
• Is it reasonably consistent and current?
• Who owns it, and who is allowed to see it?
• Can it be accessed securely from the environment where the solution will run?

A knowledge assistant built on outdated policy documents will confidently give outdated answers. A document extraction model trained on one invoice layout will struggle with the next. Data issues are rarely a reason to stop, but they should shape the scope of a pilot.

Design for people in the loop

In most enterprise processes, the right goal is not full automation on day one. It is handling routine cases automatically and routing exceptions to people with the context they need to decide quickly. Confidence thresholds, review queues, and clear audit trails let teams adopt AI without giving up accountability.

This approach also builds trust. When staff can see what the system suggested and why, and can correct it, they become active participants in improving it rather than skeptics waiting for it to fail.

Pilot small, then scale deliberately

A focused pilot should run for weeks, not quarters. Define success criteria up front, test with real data, and involve the people who do the work today. If the results hold up, plan the production rollout with the same care as any other system: security reviews, integration with existing tools, monitoring, and support.

Put governance in place before it is urgent

As AI use spreads, organizations need simple, practical rules: which tools are approved, what data can be used with them, how outputs are reviewed, and who is responsible for each solution. Establishing this early prevents a patchwork of unmanaged tools and makes it easier to scale what works.

AI can make a meaningful difference in day-to-day operations. The organizations that benefit most treat it like any other operational improvement: clearly scoped, carefully measured, and owned by the business.