THE UTILITY AI JOURNEY
A practical path from possibility to progress.
You don’t need every answer to begin. You need a useful next step.
Wherever your utility is today, these five stages help connect learning with action. Start where you are, involve the people doing the work, and build confidence through evidence.
Find your starting point
Five stages. A continuous learning process.
The stages overlap. You can revisit a decision, narrow the scope, or improve the foundation as you learn.
Understand
See what AI can make possible.
Start with the people doing the work. Look for repeated searching, reconciliation, drafting, and handoffs. Learn enough about AI to recognize opportunities—and the evidence needed to judge them.
Where does useful work get held up today?
Put it into practice
- Follow a real task from its trigger to an accepted result.
- Ask practitioners where information, effort, or time is lost.
- Explore examples across departments, including the work they share.
Your checkpointA short list of real problems, with people who understand them.
Choose
Find a starting point worth pursuing.
Compare value, feasibility, and the consequences of an error. Choose a task with a clear output, an accountable owner, and a practical way to tell whether the result is better.
Which workflow can your team own, evaluate, and improve?
Put it into practice
- Describe the task, its users, essential inputs, and desired outcome.
- Record today’s effort, delays, quality, and rework as a baseline.
- Define the limits of the pilot and what would justify continuing.
Your checkpointOne bounded workflow, a named owner, and a pilot brief.
Prepare
Build the foundation around the work.
Connect the essential records, make their meaning clear, and give people a way to review proposed actions. Improve the data needed for this task as you build. An imperfect starting point can still support useful progress.
What information and controls does this workflow need?
Put it into practice
- Identify source owners, access permissions, update frequency, and gaps.
- Keep original evidence and distinguish verified facts from inferred values.
- Set review responsibilities, allowed actions, and a working fallback.
Your checkpointUsable source information and an agreed boundary for the workflow.
Prove
Test it against the work that matters.
Try representative cases with actual users. Include missing evidence, conflicting records, and exceptions. Measure the effort needed to reach an accepted result, including review and corrections.
Does it improve the complete process at an acceptable quality?
Put it into practice
- Keep evaluation examples separate from the material used to tune the system.
- Compare quality, total effort, adoption, exceptions, and operating cost.
- Confirm that people can reject, escalate, pause, and recover the work.
Your checkpointEvidence for a clear decision: expand, revise, or stop.
Grow
Turn a useful result into lasting capability.
Assign ongoing ownership, keep sources current, and evaluate changes. Expand the patterns that work. Reuse the data connections, evidence, review processes, and learning that your first project created.
What will keep this useful—and help the next team?
Put it into practice
- Name the owners of service quality, source updates, incidents, and budget.
- Review performance and revisit assumptions as use grows.
- Choose the next workflow based on evidence and shared capabilities.
Your checkpointA maintained workflow and an informed plan for the next step.