You Bought the AI. Now What?

Your company has purchased AI tools. Employees have access, the licenses are active, and leadership expects results. So what’s happening with that investment?

Are employees using AI regularly? Is it helping them work faster or produce better work? And can you put a number on the value you’re getting?

These are questions worth asking, especially when AI spending continues to grow.

According to Boston Consulting Group’s 2026 AI at Work study, companies still have a gap between providing AI tools and preparing employees to use them effectively.

Source: Boston Consulting Group, AI at Work 2026 (full citation at the end of this article). Survey of close to 12,000 frontline employees, managers, and leaders in more than a dozen global markets.

That’s a concerning number for companies spending money on AI subscriptions and expecting productivity improvements. Buying the technology is one decision. Making sure your people know how to use it deserves just as much attention.

Why aren’t employees using the tools you’ve purchased?

We’ve all seen this story play out before with enterprise software.

Leadership selects a platform, IT handles the rollout, and employees attend an introductory session. Everyone receives access, and the expectation is that people will start using it.

A few enthusiastic employees begin experimenting. They discover useful applications and gradually incorporate AI into their routines. Others use it occasionally, often for simple questions or drafting emails. Some barely touch it.

This doesn’t necessarily mean employees are resistant to change. Most already have full workloads, and learning something new takes time, especially when they’re unsure how it fits into their responsibilities.

A generic product demonstration may show what Claude can do, but someone working in finance, sales, operations, or marketing still needs to figure out what it means for their job. When deadlines are approaching, familiar ways of working usually win.

Before purchasing additional tools or upgrading subscriptions, spend some time understanding what’s happening with the ones you already have. Talk to your teams about what they’re using AI for, where they’re struggling, and what would make it more useful.

You may discover that the biggest opportunity is helping people get more out of the technology you’ve already purchased.

Are you measuring usage or business value?

Most enterprise AI platforms provide usage information. Administrators can see how many licenses have been assigned, how many employees have logged in, and how frequently people use the tools.

Those numbers are useful, but they don’t tell you whether the investment is paying off.

Someone could open Claude every day without saving much time or improving their work. Another employee might use it a few times a week and eliminate hours of repetitive tasks.

Leadership should look beyond usage and focus on four measures.

Adoption

How many employees are using AI consistently? Look at which departments are using it, how frequently, and whether participation is growing over time.

Time saved

Which activities are taking less time? If preparing a weekly report previously took three hours and now takes one, that’s worth measuring. Remember to include time spent reviewing and correcting AI-generated work.

Quality of work

Is AI helping people produce better results? Are reports more complete? Are teams responding to customers faster? Are employees able to explore more options before making decisions?

Business impact

What is the organization doing with the time it saves? Are salespeople spending more time with customers? Are analysts reviewing more information? Are managers making decisions faster?

BCG’s 2026 research found that 42% of regular frontline AI users reported saving eight hours per week. Yet 66% received limited or no guidance on what to do with the time they saved.

That raises an important question for leadership. If your employees are saving time with AI, how are you making use of it?

What is your AI investment worth?

Before expanding your AI rollout, it’s worth developing a financial picture of what you’re spending and what you expect to gain.

Start with your costs. Include software subscriptions, training, implementation, ongoing support, and the time employees spend learning the tools.

Then look at the potential benefits.

Imagine a company with 100 employees using AI. If each person saves an average of two hours per week on recurring tasks, including time spent reviewing the results, the numbers begin to add up.

At an estimated fully loaded labor cost of $60 per hour, the potential annual productivity value would look like this.

That’s a lot of time, but there’s an important distinction to make.

The $576,000 represents the estimated labor value of time made available. It isn’t automatically a cash saving.

If employees use that time to serve more customers, complete additional projects, or reduce overtime, the organization may capture financial value. If the time isn’t redirected toward useful work, the financial benefit may be much smaller.

You also need to account for the cost of the AI program and how much of the time saved translates into measurable business results.

We’ve built an AI ROI calculator to help organizations explore these numbers. You can adjust the assumptions for your team and estimate the potential value of time saved through AI.

Give AI training its own budget and owner

Companies generally have a clear process for buying technology. Someone evaluates vendors, negotiates contracts, gets budget approval, and manages implementation.

Training often receives less attention. It may be assigned to HR, handled through a vendor webinar, or left to individual departments to figure out.

If your company expects AI to improve productivity, training should have its own budget, clear objectives, and someone accountable for the results.

That person should work closely with leadership, IT, and department heads to understand where AI could help and what employees need to learn.

A sales team may need help preparing for meetings and developing proposals. Finance may focus on reporting and analysis. Operations might see opportunities to improve documentation or repetitive administrative work.

Training should reflect those differences. It should also cover security, privacy, and how to review AI-generated information.

A short introductory session can get people started, but continued practice and support help them become comfortable using AI in their work.

What should your first 90 days look like?

You don’t need a year-long program to find out whether AI can make a difference. A focused 90-day effort can give leadership a clearer view of what’s working, what isn’t, and where to invest next.

The first 30 days should be about understanding where you stand.

Start with the tools you’ve already purchased. How many licenses are assigned? Who’s using them? Which departments have embraced AI, and which haven’t?

Then spend time talking to employees. Ask them about the activities that consume the most time, particularly repetitive work or tasks that involve gathering information from several places.

You might discover that salespeople spend hours preparing for client meetings, finance teams manually assemble recurring reports, or managers spend too much time sorting through information before making decisions.

Choose a handful of those activities and document how the work gets done today. Record how long it takes, what a good result looks like, and where people experience frustration.

By the end of the month, you should know which opportunities are worth exploring.

During days 31 through 60, put AI to work on those tasks.

This is where practical training comes in. Help employees use AI with the reports, research, documents, and other responsibilities they’ve identified.

Give them time to experiment, ask questions, and refine their approach. Some tasks will work well with AI. Others may need a different approach or may not be worth changing.

Measure what happens. Did the work take less time? Was the quality comparable or better? How much review was required? Would the employee use the approach again?

Keep track of the results, including what didn’t work. You’ll learn just as much from the unsuccessful experiments as the successful ones.

The final 30 days are about building on what you’ve learned.

Review the results with department leaders and identify the activities where AI consistently helped.

Look for opportunities to share successful approaches across teams. You may find that several departments prepare similar reports, conduct comparable research, or handle the same kinds of administrative tasks.

Provide additional training where it’s needed and make sure employees have a way to ask questions as they continue using the tools.

Most importantly, decide how the organization will use the time that’s being saved. If a team has gained several hours each week, what higher-value work could they take on?

After 90 days, you should have enough evidence to make more informed decisions about training, additional licenses, and opportunities to expand AI use across the business.

Make AI adoption a leadership responsibility

AI adoption shouldn’t sit entirely with IT or whoever manages the software licenses.

Department leaders understand where work slows down, what their teams are responsible for, and what good performance looks like. Their involvement can help connect AI initiatives to business priorities.

Leadership should be learning to use AI as well.

Executives who understand the tools can ask better questions, recognize promising opportunities, and make more informed decisions about where to invest.

They can also help employees make time to learn and encourage them to share what’s working.

When leaders are engaged, AI becomes part of the conversation about improving the business.

You’ve made the investment. Now make it count.

If your company has already purchased AI tools, you’ve made a start. The next step is understanding how people are using them, where they’re seeing value, and what would help them do more.

That means measuring improvements in everyday work, giving employees opportunities to learn, and making someone accountable for the results.

At Trackmind, this is the thinking behind our AI Foundations program.

We work with leadership to identify where Claude can make a difference, then help employees develop practical skills using tasks from their own work. Our approach includes hands-on training, clear guardrails, and continued support.

If you’re evaluating what AI could be worth to your organization, start by putting some numbers around the opportunity.

Calculate your ROI

And if you’re looking for help getting your teams started, explore Trackmind’s AI Foundations training.