It’s a question I’ve been asking myself a lot lately. In the rush to innovate and keep up with trends, how often do we pause and scrutinize our day-to-day work? Before diving into any shiny new tool or technology, we need to take a hard look in the mirror and ask: What are the key things we’re wasting time on? Often, this means confronting inefficient workflows, redundant processes, and “busywork” that creeps into every corner of the organization. The truth is, we can’t fix or automate something we haven’t first acknowledged as a problem.

The Temptation of Flashy AI vs. the Hard Questions

I’ve noticed many companies eagerly experimenting with flashy AI tools, from chatbots to analytics platforms, hoping they’ll magically boost productivity. But there’s a catch: it’s much easier to play with new tech than to have tough internal conversations about unproductive workflows and low-value tasks.

Streamlining work can be uncomfortable; it might reveal that a report we’ve produced for years is rarely read, or that two departments have been unknowingly duplicating efforts.

If we skip this step and try to layer AI on top of chaos, we risk automating the inefficiencies, essentially making the mess run faster. In fact, experts warn that automating a flawed process only amplifies the existing issues.

So before asking “Where can we apply AI?,” leaders should first ask “Where are we wasting time?” Identifying these friction points is not glamorous, but it’s crucial.

Hunting for information

One of the biggest productivity killers is employees scrambling to find data, documents, or answers. Studies have found that knowledge workers spend roughly 30% of their workday searching for information, equivalent to one day a week lost. That’s time spent digging through emails, shared drives, or intranets instead of delivering value. In a 2023 survey, researchers even estimated this search time costs a 1,000-person company around $2.5 million per year in lost productivity.

Manual, repetitive tasks

Routine administrative work sucks up hours that could be automated or eliminated. Data entry, copying and pasting between systems, filing forms, and compiling reports are common culprits. In one global study, office workers admitted they waste over 40% of their time on manual digital processes like these. Little wonder that tasks like data entry, managing emails, and report prep rank among the most detested parts of the day.

Fragmented workflows & duplicative processes

How often do we unintentionally redo work because information isn’t shared, or use five different tools that don’t talk to each other? This “work about work” is a silent time thief. Employees are building ad-hoc spreadsheets or forwarding the same data around because systems don’t integrate. Such “gray work” not only frustrates teams, it also adds zero value.

Low-value meetings and admin busywork

Think of standing meetings with no clear outcome, or filling out lengthy reports that no one acts on. Many organizations are rife with legacy activities that continue “because we’ve always done it this way.” Every hour spent on administrative busywork or sitting in an aimless meeting is an hour not spent on strategic, creative, or revenue-generating activities. We should be ruthless in questioning these time sinks.

Identifying these issues isn’t about pointing fingers at employees, it’s about fixing the environment they work in. Before introducing AI, the priority should be to streamline, simplify, and eliminate any work that isn’t moving the business forward. Only then can technology make a meaningful impact.

AI in Action: Streamlining the Time-Wasters

Emerging evidence shows that when applied thoughtfully, AI can give back time to employees by taking over mundane activities. The Adecco Group’s late-2024 survey of 35,000 workers found that AI tools are saving users an average of about one hour per day , which they can reinvest into more creative or strategic work. These efficiency gains appear across industries; in energy and tech sectors, for example, workers reported around 60–75 minutes saved daily thanks to AI assistance.

And tellingly, almost three-quarters of employees using AI say it’s made them more productive.

In other words, when we target real internal problems, AI is already helping to streamline operations in a very tangible way. Recent examples from various departments illustrate how focusing on the right use cases can pay off.

Finance/Internal Audit

A Canadian investment company adopted AI to automate parts of its internal reporting and analysis. By removing manual data-crunching tasks, they saved over 2,300 person-hours of work and cut the time spent writing internal audit reports by 30%. That’s a huge boost to efficiency, freeing the finance team to focus on interpreting results and advising the business, rather than just compiling numbers.

Client Management & Reporting

At a global consulting company, employees now use generative AI to auto-generate quarterly business review decks. The AI pulls data, drafts slides, and even suggests insights. Each report now takes about 90 minutes less to prepare than before.

Those saved hours add up, allowing account managers to spend more time with clients instead of pushing PowerPoint. Consistency has improved too, since the AI ensures every report covers the right points without starting from scratch.

IT & HR Support

One telecommunications giant built an internal AI assistant to field common IT and HR questions from employees. The bot gives staff fast answers to basic HR queries (like benefits, policies, troubleshooting tech issues) without waiting on a helpdesk email. This not only increased efficiency and cut support costs, but also improved employees’ experience – people can get what they need in seconds and get back to work.

General Administration

Everyday administrative tasks across departments are being trimmed down by AI. For instance, employees at a dairy cooperative leveraged AI tools to automate routine tasks (like drafting emails or updating forms) and reported saving up to 20 hours per month of work time each, essentially reclaiming half a week’s worth of productivity every month.

In another case, an energy company deployed an AI tool to summarize meetings and generate report drafts automatically, significantly reducing the admin workload on staff . AI turned what used to be hours of note-taking and formatting into minutes, letting teams concentrate on more meaningful projects.

These examples have a common thread: in each case, the organization identified a specific time-consuming problem and then applied AI as a targeted solution. It wasn’t AI for AI’s sake or some grand, splashy innovation experiment. It was about using technology to relieve a genuine pain point, whether it’s answering repetitive questions, sifting through data, or preparing documents. And the results speak for themselves in saved hours, happier employees, and faster turnaround times.

Importantly, none of these improvements required sci-fi-level AI. They came from tools we have right now, from chatbots and generative writing assistants to intelligent search and workflow automation. The key is prioritizing what truly needs fixing inside your organization. As one operations research put it, companies that thoughtfully reinvent their processes with AI are seeing productivity jump and tangible ROI, whereas those that don’t often get stuck in pilot purgatory. The technology works when it’s solving the right problem.

Looking Inward Before Leaping Ahead

At the end of the day, the biggest lesson I’ve learned is to start with purpose, not with technology. Before chasing the next AI use case, take a step back and audit where your teams are actually spending their time. Which activities soak up hours but add little value? What do people complain about doing, or avoid doing until the last minute? Those are your prime candidates for elimination or improvement. In many cases, you may find that the fastest way to boost productivity is simply to stop doing something unnecessary.

Once you’ve cleared the decks of truly wasteful work, you can approach AI from a position of clarity. You’ll know exactly what problems you’re trying to solve. And when you do decide to implement an AI solution, it will be in service of a real need – not just because it’s trendy.

Technology should follow strategy, not the other way around. AI can be a powerful ally in reclaiming lost time (automating reports, fetching knowledge in seconds, speeding up routine paperwork) but its impact will be limited if you haven’t fixed your foundational processes first. So I encourage every leader: pause and reflect. Ask yourself and your team where time is leaking away in your organization, and whether those activities truly move the business forward. By tackling that question head-on, you’ll be in a much better position to decide if and where AI can meaningfully help. After all, the goal isn’t to use AI, it’s to free up our people to do the work that matters most.

Before leaping into the future, make sure you’ve cleaned house in the present. Audit your team’s time, eliminate the busywork, and then bring on the AI tools to accelerate what truly counts. That approach might not be as exciting as a splashy tech demo, but it’s far more likely to create lasting, meaningful improvement.