How a mini app built with AI improved call center metrics at a Fortune 500 company

← AI Profit Garden

Companies large and small struggle with the same quiet problem. Internal systems that do not keep pace with the work and company growth, and staff who are required to hit ambitious goals without adequate tools to get there.

A large part of my work as a fractional CFO means stepping inside companies, large and small, to evaluate their internal infrastructure, determine what is truly effective, and strengthen the areas that are lagging behind to help improve the company's bottom line. That kind of evaluation is rarely accurate from the outside, and in this case, it took putting myself into the role, hands on, to see it clearly.

The company

I had the opportunity to evaluate the effectiveness of the customer service channels of a Fortune 500 tech company, and the metrics they used to track key performance indicators on every call, including how well their customers were being served.

I sat on the team that fielded incoming calls from some of their most valued customers.

The challenge

Leadership tracked multiple metrics on every customer service agent, and used them to determine how well customers were being served. Some of the metrics scrutinized most closely by the management team were average call handling time, the productivity of each call, transfer rates, and how quickly issues got resolved.

In working this role, I quickly realized there were ambitious goals that needed to be met, but no effective way of accomplishing them. At the same time, meeting those goals was simply part of the job for every agent.

I found that the systems the company had in place lacked the key features that would have made the work easier, and that were driving up after-call handling time. Proper case documentation, along with the other documentation different departments required, was taking longer than it should have.

Even with a lifetime spent serving customers, I still needed extra time after every call to document it properly, so the company had the historical record it needed.

After-call handling time was one of the metrics the company tracked closely, and they wanted it as low as possible. I quickly realized there was a real gap between the goals the company had set and the means available to its customer service agents to achieve them, so I set out to find the best solution.

What I built

I designed and built a lightweight tool, a small application made for one purpose. It gave every field a home. Dictation instead of typing, wherever dictation was faster. One click to copy a section instead of retyping it into the various fields it needed to live. Multiple fields that were pre-filled from what had already been entered once. Formatting handled automatically, every time, the same way, as well as customized notes to guide the agent on what data to collect.

It was not complicated. It did not need to be. It needed to exist, and it did not, so I built it.

I am not a software developer or a coder. What I created was a software tool built with the help of AI, using an approved large language model to turn what I needed into something real.

The results

I used it on my own calls first. Before the tool, after-call documentation was taking me roughly 35 minutes across an eight hour day. With the tool, it dropped to 5. That is thirty minutes returned every day, a reduction of 86%, and it happened within the first few days. Not eventually. Immediately.

Put a dollar figure on it. Thirty minutes a day, across a working year of 260 days, computes to roughly $3,900 in one agent's time.

I would not sell that as the win, though, and it is worth saying why. Nobody goes home early. The agent works the same shift and draws the same pay, so the company does not pocket that money. What changes is what fits inside the shift.

One more call. Every day. Five more a week, 260 more calls a year from the same person in the same eight hours. That is real capacity, and capacity is what keeps a company from hiring another body to answer the same volume.

After-call time was the number I set out to fix. But it was not the only one that moved. The minutes that came off documentation went straight back into the calls themselves, and other metrics the company tracked improved right alongside it.

Why the goals matter more than the money

Here is the thing worth understanding about this company. The metrics are not a report card that gets filed away. They are the whole system. Leadership sets a daily goal for every agent and measures whether it is hit, because that is how the company knows its customers are being served well.

It is also how the company decides who is ready for more. An agent who wants to be considered for advancement has to be succeeding in their current role, and succeeding means hitting those numbers.

Which is where the old workflow was quietly doing damage. A person could be excellent with customers, genuinely good at the job, and still fall short of the goal because documentation was eating the time the goal required. The company was asking for something it had not equipped anyone to deliver.

The tool closed that gap. Agents using it hit their targets, which is exactly what the business wants to see from its people, and exactly what an agent needs on the record before putting a hand up for the next role.

The company got its numbers. The people got a fair shot at their own careers. That is the part I am proudest of.

Company-wide today

The tool was adopted across the rest of the customer support division and is still in use today, company-wide, continuing to give customer service agents improved metrics, as it did for me.

The work continues

That experience shaped the work I do now. My mission is to use AI solutions as an effective way to solve the problems that hold a company back, for businesses of every size.

AI does not replace the judgment a business still needs. What it replaces is the manual work standing between a good idea and a working solution. A tool like the one I built once required a developer, a project timeline, and a budget most companies could not justify for an internal fix. Today, the same result can be built in days, not months, at a fraction of the cost.

That shift changes what is possible inside a company. Work that once took a person hours, retyping the same information into three different systems, can be handled in seconds. Time that was lost to repetitive documentation gets returned to the people doing the actual work of serving customers, closing deals, or solving problems that require a human mind. And because the tool itself costs so little to build, the return on that investment shows up almost immediately, rather than something a company has to wait years to see.

This is the endeavor I have built my practice around. Not AI for its own sake, but AI aimed squarely at the internal friction that quietly drains a company's time, labor, and money, replaced with something built specifically for the problem at hand.

What it proves

One small application, built without a development team and without a long timeline, cut documentation time dramatically and gave an entire department a faster, more accurate way to do their jobs. That is the same opportunity sitting inside most companies still relying on manual processes to do what a well built tool could do in a fraction of the time. Fewer hours lost to repetitive data entry. Fewer errors from retyping the same information more than once. More time for the work that actually requires a person.

AI is not here to replace people. It clears the ground so people can do what they were gifted to do.

Questions people ask about this

Do you have to be a developer to build an internal tool like this?

No. I am not a software developer or a coder. I built this tool with the help of an approved large language model, which turned what I needed into working software. The skill that mattered was knowing the work well enough to specify exactly what the tool had to do.

How long does a tool like this take to build?

Days rather than months. A tool like this one used to require a developer, a project timeline, and a budget most companies could not justify for an internal fix. That is no longer the constraint.

What kind of problem is this approach best suited to?

Repetitive documentation and retyping. Any place where a person is entering the same information into more than one system, formatting the same thing by hand every time, or spending time after the real work is finished just to record that it happened. This was one tool, built for one company, solving one specific problem. Your business is carrying a different one. The question worth asking is not whether a solution exists, but what it would look like built for you.

Does this only work at large companies?

No. The example here happens to be a Fortune 500 company, but the problem is not a big company problem. Small businesses run on the same manual processes, usually with fewer people to absorb them, which makes the hours returned worth even more.

What was actually measured, and by how much did it change?

After-call handling time, which is the documentation work a customer service agent completes once a call has ended. It went from roughly 35 minutes across an eight hour day to 5 minutes, a reduction of 86%. It was one of several metrics leadership tracked on every agent, alongside average call handling time, transfer rates, and how quickly issues were resolved.

What was the return to the business?

The thirty minutes saved each day computes to roughly $3,900 a year in one agent's time, but the agent works the same shift either way, so the company does not bank that as cash. The real return is capacity. The recovered time allows one more call a day, 260 more a year from the same person, and agents hitting their daily goals instead of missing them.