How to Implement AI in Your Business: Start With Core Processes

Map your core processes to find what to eliminate, simplify, automate, or improve with AI.

By Tony Chiappetta · 6 min read

You want AI to give your team time back. Then a promising project hits a familiar obstacle: nobody can quite explain how the work gets done.

The main steps sound straightforward. But one person knows which customer needs an exception. Another knows why a spreadsheet must be updated twice. A third knows when to skip the usual handoff. Each answer reveals another unwritten rule.

Soon, the team is spending more time untangling the work than improving it. The project stalls, and it becomes difficult to see where to start again.

You can spend your AI budget discovering how your business works—or make that work visible before choosing what to improve. At Modern IT, we start with that clarity. It helps you find the opportunities worth pursuing and the work worth removing altogether.

Start with the big picture

The Entrepreneurial Operating System (EOS®) offers a useful foundation. Its Three-Step Process Documenter™ guides teams to identify their core processes, document and simplify them, and package them so people can easily find and use them. Its 20/80 approach keeps documentation focused on the essential detail. Consistent adoption, called Followed By All, is an ongoing discipline beyond those three steps. See EOS Worldwide’s explanation of the methodology.

That gives an AI conversation a practical starting point. Agree on the major flows that keep your organization working, then look at how work passes between them.

Where does a sale become a delivery commitment? When does completed work become an invoice? Who notices when something is missing?

You can begin with a shared view of those handoffs. The detailed instructions can follow where they are needed.

Make one flow visible

Bring together the people who do the work, including someone who receives its output. Choose one flow that regularly causes delays, confusion, or rework.

For example, a service business might sketch:

Inquiry → Scope → Proposal → Agreement → Delivery → Invoice → Follow-up

For each stage, capture who owns it, what information it needs, and what must be handed to the next person. Mark where work waits, information gets entered again, or someone says, “You have to ask me about that.”

Keep a separate list of exceptions. Explore an exception immediately if it changes the main flow; save the smaller procedural details for the area you choose to improve.

The aim is a shared picture of how work happens today, including the gaps. An idealized chart will hide the very opportunities you are trying to find.

Decide what deserves to happen next

Once the flow is visible, remove unnecessary work, simplify what remains, and choose where technology can help. Then measure whether the change is working. This is Modern IT’s application of process thinking to AI improvement.

Keep human judgment where relationships, accountability, or consequential exceptions require it. Measure the improvement whether it comes from AI, automation, or a simpler process.

A better sales-to-delivery handoff

Consider an illustrative sales-to-delivery handoff. The team wants AI to write a weekly status report. Mapping the work reveals that nobody uses the report to make a decision, while delivery repeatedly chases sales for missing commitments.

Removing the unused report and agreeing on required handoff information addresses the underlying friction. AI might then help draft a handoff summary from approved notes, with the account owner checking it before delivery relies on it.

The first improvement creates value even if the AI idea goes no further.

Go deep where it matters

A high-level map helps you select an opportunity. Implementing it still requires enough detail to handle real work.

For the chosen step, document its inputs, decision rules, exceptions, owner, and definition of a good result. If AI is involved, establish what information it may use, who checks its output, and what happens when the output is wrong. NIST’s AI Risk Management Framework similarly calls for defining business context, application scope, and human oversight. Read the NIST framework’s Map guidance.

Record how the process performs today before testing a small change. Agree on what counts as a successful result and where timing begins and ends.

Make the agreed process easy to find, practice it with the team, and update it as you learn.

Make progress visible with a Scorecard

The EOS Scorecard™ gives this work a weekly rhythm: a small set of measurables, each with an owner and a goal. See EOS Worldwide’s Scorecard guidance. Use an operational dashboard for the detail, then bring the most useful signals into the team’s weekly Scorecard.

For an AI-assisted process, we recommend tracking:

• AI workload. Show how many items AI handled, alongside total eligible work.
• Success and failure. Separate results accepted on the first pass, results needing correction, failed attempts, and work still awaiting review. Show counts and rates with clear denominators.
• Cycle time. Track elapsed time from the agreed starting point to an accepted result, including waiting and rework.
• Process time. Track time actively spent doing the work, separating human effort from AI or system processing time.
• Human time saved. Compare the human effort previously needed for comparable work with the effort now required, including review, corrections, and exception handling.

For the sales-to-delivery example, that means tracking handoffs processed, summaries accepted without correction, handoffs with missing commitments, elapsed handoff time, and human minutes spent per handoff. Give each selected measure an owner and a weekly goal.

Review trends against the baseline. A faster AI step may still leave people waiting downstream. More completed tasks may come with more correction work. When a measure misses its goal, add the issue to the team’s Issues List, identify the cause, assign the next improvement, and review the result the following week.

Keep setup and ongoing maintenance effort visible when judging the overall return. Share verified wins with the team: fewer returned handoffs, shorter waits, or hours freed for customer work. Seeing that progress helps build confidence and momentum for the next improvement.

Give AI a clearer starting point

Process work is less glamorous than an AI demonstration. It can also reveal why a demonstration has been so difficult to turn into dependable everyday work.

You do not need to document your entire business before making progress. Start with the big picture, select one point of friction, and learn enough about it to make a better decision.

PUT IT INTO PRACTICE

Start with one difficult handoff

1. Gather the people who do the work and receive its output.
2. Map the major steps and mark where work gets stuck.
3. Choose one thing to remove or simplify. Assign an owner and measure the change.

Give your team an improvement to work toward—and your AI budget a clearer purpose.