How to Find High-ROI AI Opportunities

AI creates an almost endless list of possibilities.

A new model launches. Someone on your team finds a tool that can generate presentations, analyze documents, or automate emails. Before long, the company has dozens of ideas and no clear answer for which one deserves attention.

One of our clients has a phrase that we come back to often:

What is interesting, and what is valuable?

There are plenty of interesting ways to use AI. The real challenge is identifying the opportunities that can make more money, save money, reduce risk, or help people use their time more effectively.

The success of an AI initiative is determined long before you choose a model, platform, or vendor. It begins with choosing the right business problem to solve.

Start With Value

Business leaders often begin by asking what AI can do.

That question is understandable, but it can quickly lead to a long list of tools, demonstrations, and hypothetical use cases. A more productive starting point is to look at the business itself.

• Where is time being wasted?

• Where are errors creating unnecessary cost?

• Where are customers waiting?

• Where is growth constrained by a process that no longer scales?

• Where are employees doing work that technology could reasonably support?

These questions keep the conversation grounded in business value.

Before evaluating whether AI can solve a problem, we typically ask whether solving the problem would matter.

A useful opportunity should create value in at least one of the following ways:

• Increase revenue

• Reduce cost

• Save meaningful time

• Improve throughput or capacity

• Reduce operational risk

• Improve the customer or employee experience

An idea may be technically impressive and still have little impact on the business.

That distinction matters because every AI project requires time, attention, and money. Even a relatively small initiative pulls people away from other priorities.

Your team’s time is one of the most valuable resources you have. The goal is to invest it where the return can be measured.

AI Is Rarely Step One

One lesson we’ve learned across projects is that the highest ROI AI initiatives rarely begin with AI. They begin by organizing data, connecting systems, simplifying workflows, and building technology that makes information accessible and actionable.

Only then does AI become truly valuable.

Without that foundation, AI has limited context, fragmented data, and very little ability to take meaningful action.

In reality, AI is often one of the final layers built on top of well-designed systems, high-quality data, and effective automation.

Interesting vs. Valuable

There are plenty of interesting ways to use AI. But interesting doesn’t always mean valuable, and some of the highest-ROI opportunities are surprisingly ordinary.

The strongest AI opportunities are frequently buried inside ordinary business processes:

• Reviewing invoices and documents

• Searching for internal information

• Preparing recurring reports

• Routing emails and requests

• Scheduling people, machines, or work

• Entering data across multiple systems

• Troubleshooting equipment

• Following up on incomplete tasks

• Identifying exceptions that require attention

These processes create significant returns because they happen repeatedly and consume valuable time.

The Value Filter

Before discussing AI, evaluate the business opportunity.

1. How much time does the process consume?

A task that takes five minutes may seem insignificant. If it happens 500 times every week, it becomes a meaningful opportunity.

2. What does the process cost today?

Labor is only part of the equation. Think about errors, delays, missed revenue, customer frustration, downtime, and the opportunity cost of employees spending time on low-value work.

3. What would improve if the problem were solved?

• Would the company process more orders?

• Would employees respond faster?

• Would equipment spend more time running?

• Would leadership make better decisions?

• Would the business scale without adding the same amount of overhead?

4. Where are there bottlenecks in your process?

A great way to identify bottlenecks is to ask your team "What part of your job feels unnecessarily difficult?" The answers are usually very specific:

The answers are usually very specific:“I enter the same information into three systems.” or “We have the data, but it takes too long to understand what it means.”

These frustrations are valuable. They point toward places where time, information, and decision making aren’t flowing efficiently.

5. Can success be measured?

Strong first projects have a clear before and after.

• Processing time

• Labor hours

• Downtime

• Error rates

• Throughput

• Revenue

If success can’t be measured, it’s difficult to know whether the investment worked.

The AI Filter

Once an opportunity passes the value test, determine whether AI or automation is the right solution.

The strongest candidates usually share a few characteristics.

• The work is repetitive

• The information is digital

• The volume is meaningful

• The process has consistency

• The data already exists

• People will actually use the result

The more of these boxes a process checks, the stronger the opportunity tends to be.

What This Looks Like in Practice

One manufacturing client had years of machine data flowing from their production floor.

While they were collecting tons of data, it was hard to understand what it meant.

Leaders wanted to know which machines, products, and production lines were creating inefficiencies, but answering those questions required manually pulling reports and interpreting thousands of data points.

We built a digital operations platform that continuously collects machine data, analyzes performance, and surfaces actionable insights instead of raw information. Rather than asking employees to determine what the data meant, the platform highlights where attention should be focused, allowing leaders to make faster and better operational decisions.

Another client had a very different challenge.

Their employees spent the day logging into hundreds of third-party websites to retrieve documents that supported downstream business processes.

Every login required the correct URL, username, password, and retrieval process. Individually, each task only took a minute or two. Across hundreds of logins every day, it became a significant operational burden.

We built a credential management platform designed specifically around their workflow. It became much more than a database. The platform manages credential health, integrates with downstream systems, automatically retrieves documents where possible, supports automated credential updates, and gives employees a single place to manage the entire process.

Both projects started by solving expensive operational problems.

In both cases, the technology created the foundation that makes future AI capabilities significantly more valuable. Once the right systems, data, and workflows exist, AI can generate insights, automate decisions, and orchestrate work in ways that simply aren’t possible without that foundation.

Start With One Meaningful Problem

Your first AI project doesn’t need to transform the company. It should solve one meaningful problem exceptionally well. Choose a workflow with measurable value, accessible data, a clear owner, and a group of users willing to adopt it.

Keep the initial scope narrow.

1. Learn

2. Measure

3. Improve

4. Expand

Momentum built on measurable business value is far more powerful than momentum built on excitement.

Final Thoughts

AI gives businesses more opportunities than ever before. It also creates more distractions.

Every business has interesting AI ideas. New technology will keep appearing. The companies that generate meaningful returns won't chase every one of them. They'll spend their time identifying the problems that create the greatest business value, solving them thoughtfully, and building the right foundation for AI to create even more value over time.

Interesting ideas generate excitement. Valuable ideas generate results.

Find one meaningful problem, solve it well, then build from there.