Why Your Best AI Use Cases Are the Most Boring

On average, office workers spend more than three hours a day on manual, repetitive tasks that are not part of their core responsibilities. This was the finding of a OnePoll survey conducted for Automation Anywhere among 10,500 employees in 11 countries.

Three hours a day. That’s more than 40% of a workday spent on re-entering data, reconciling records, filing, and sending reminders. That’s where the value of AI in business lies, and it’s precisely the area no one looks at, because there’s nothing spectacular about it.

The argument of this article is simple—and a bit disappointing: your best AI use cases aren’t the ones that impress people in meetings. They’re the most mundane ones. Here’s why, and how to identify them in your own organization.

Money goes to what stands out; the payoff lies in what bores

There is a natural bias in the way companies choose their AI projects: they fund what’s visible.

An assistant who drafts a response during a demo meeting gets noticed. A model that reconciles three thousand accounting entries without a single error overnight doesn’t get noticed. As a result, budgets go to the use cases that look good, while the real value remains where no one thought to look.

However, data on value creation points in a very different direction. In its seminal June 2023 report on the economic potential of generative AI, McKinsey estimated the potential annual value at between $2.6 trillion to $4.4 trillion, of which approximately 75% focused on four functions : customer operations, marketing and sales, software engineering, R&D. The key point is not the amount—which is always staggering in this kind of exercise—but the nature of these functions: they involve a high volume of structured and repetitive tasks. The same study estimated that AI technologies could automate activities involving 60 to 70% of working hours employees.

In other words, value lies in volume and repetition, not in technical prowess. It lies in the arduous, not in the brilliant.

The real opportunity: the hours wasted on repetitive tasks

To understand where the value lies, you have to look at where the time goes.

The Automation Anywhere survey mentioned above, published in January 2020, estimated that more than three hours a day the time spent on repetitive, manual tasks outside of core business activities. A 2017 Smartsheet study confirmed the scale of the problem: more than 40% of workers spent at least a quarter of their workweek on such tasks. These two surveys predate generative AI; they do not measure its impact, but they quantify the potential. And that potential is enormous.

That’s where the hidden cost lies. Re-entering data takes a few minutes, and no one complains about it out loud. Multiplied by the number of employees, days, and errors it generates, it becomes a significant expense—one that goes unnoticed because it isn’t counted. Tedious work is precisely the kind that isn’t measured, and therefore the kind whose cost is underestimated.

Three "tedious" projects that pay off

To illustrate this point, here are three types of mundane tasks—and why they pay off.

Invoice processing. This is the classic example of a tedious yet profitable task. Market benchmarks agree: a manually processed invoice costs between $12 and $30, compared to $1 to $5 for automated processing, with an error rate dropping from a few percent to a fraction of a percent. [TO BE VERIFIED: figures from software vendors and integrators, to be presented as market ranges and not as an independent study.] No one is going to give an exciting demonstration of invoice processing. Yet it is one of the processes to be automated for maximum efficiency.

Reconciliations and controls. Reconciling entries, checking for consistency, and identifying discrepancies in a data flow: repetitive work with low visibility and high costs associated with errors. This is exactly the type of task where value is measured in errors avoided rather than time saved, and where a business management When properly implemented, it turns a chore into an opportunity.

Follow-ups and scheduling. Following up on an overdue payment at the right time, rescheduling a route, adjusting a team schedule: these are repetitive micro-decisions that, when made collectively and in a timely manner, can really boost profitability. Nothing spectacular, but highly effective.

What these three tasks have in common is that they’re tedious enough to have remained manual for a long time, and repetitive enough that a custom application absorbs them silently.

What we see on the ground

The most common reflex at the start of a project is to be presented first with the use case that would make for a great demonstration. It’s almost always the wrong one. The right one is right there, in the task that no one mentions because it’s too mundane to be mentioned: the morning data entry, the Friday check, the follow-up that gets forgotten one out of every five times.

When you put a number on this mundane task, the hidden cost is surprising, and the decision becomes obvious. The most tedious work in a company is often the most worthwhile to automate.

Where to start

To identify your best use cases, forget about what would make for a great demo and look for what annoys you.

List the tasks that no one has volunteered to take on. Re-entry, reconciliation, verification, follow-up, filing. The more tedious it is, the more promising it is.
Look for loud, repetitive sounds. A tedious task done a thousand times is better than a brilliant task done once. Volume is the key to productivity.
Calculate the hidden cost. Total time, errors generated, delays caused. Tedious work is underestimated because it isn't measured.
Aim for the unseen, not the spectacular. If the use case sounds impressive in a meeting, be wary. If it would save a department three hours a week without anyone noticing, go for it.

The best enterprise AI goes unnoticed, because it does the thankless work. If you want to identify the tedious tasks that pay off in your organization, Let's talk about it.

FAQ: Choosing Your AI Use Cases

What are the most cost-effective AI use cases in business? The most tedious tasks: processing invoices, reconciling accounts, conducting audits, sending reminders, filing documents, and scheduling. These are high-volume tasks with high hidden costs, whereas McKinsey identifies the bulk of value potential in operations, sales, engineering, and R&D.

Why do spectacular use cases often fall short of expectations? Because they are chosen for their impact in a demonstration, not for their efficiency. A brilliant task performed rarely creates less value than a tedious task performed thousands of times. Volume and repetition are the true drivers of return.

How can I identify a good use case for my business? Look for repetitive, high-volume tasks with low visibility and high error costs that your teams have been doing manually for years. Quantify their hidden costs (cumulative time, errors, delays): that’s often where you’ll find the best return on investment.

How much time do teams waste on repetitive tasks? A OnePoll survey conducted for Automation Anywhere (2020, 10,500 employees, 11 countries) found that employees spent more than three hours a day—or over 40% of their workday—on repetitive, non-core manual tasks. These pre-AI surveys measure the potential for automation, not the impact of AI.

How much does it cost to process an invoice manually? Market benchmarks estimate the cost of a manually processed invoice at around $12 to $30, compared to $1 to $5 for automated processing, with a significantly lower error rate. These ranges come from software vendors and integrators: they should be taken as a rough estimate, not as an independent study.

Sources

McKinsey, “The Economic Potential of Generative AI: The Next Productivity Frontier” (June 2023) — $2.6 trillion to $4.4 trillion, 75% across four functions, 60–70% of the time
Automation Anywhere / OnePoll, “Global Research Reveals the World’s Most Hated Office Tasks” (January 21, 2020, 10,500 employees, 11 countries)
Smartsheet, “Automation in the Workplace” (2017)
Invoice Processing Market Benchmarks (Resolve, NetSuite, 2025–2026) — Cost Ranges per Invoice