What’s Hiding in Your Time Logs? 6 Agency Challenges AI Can Reverse Engineer for You

We couldn’t agree more. Running a profitable business is about mindset and the willingness to test, tweak, and challenge your assumptions, then stay open to what your experiment might reveal.

And what better lab data to test with than the hours your team is already logging?

Time is money (you’ve heard it before), but in 2025, it’s also insight and forecasting power. According to the Toggl Productivity Index, 80% of C-suite leaders believe that rethinking productivity this year could increase company revenue by up to 20%. That’s not small potatoes.

The problem: most of us have messy productivity data. Maybe your tags are inconsistent, your task names are vague, and your category names made sense two project managers ago. And that’s fine, because imperfect tracking is better than no tracking at all. But when it comes to making sense of the numbers, the human brain isn’t up to the task.

The limitations of human analysis

Us human folk like to think we’re great at spotting patterns, but often we’re fooled by them. Psychologists refer to the “clustering illusion,” which is the brain’s tendency to see patterns in random data and assume there’s meaning behind it. The same cognitive glitch makes us think one process feels like a bottleneck when the data says otherwise, or assume a task type always takes too long, when it only happened twice.

To make things worse, our brains hit a brick wall when juggling too many variables at once. A recent study published in the National Library of Medicine reveals humans experience significant cognitive overload when trying to evaluate complex datasets. Translation: if you’re trying to analyze 12 months of time logs across eight clients, six team members, and 14 different task categories … you’re probably not seeing what you think you’re seeing.

Enter AI, your productivity superpower

Instead of getting into the weeds yourself, AI does the legwork for you. Built on machine learning models, it can detect patterns across thousands (or millions) of data points — patterns that would be impossible for a human to spot without a whiteboard, forty coffees, and a couple of weeks’ free time.

Even better, AI excels at anomaly detection. It spots the outliers that might signal over-servicing or bottlenecks; and correlation analysis, which reveals relationships between variables that don’t look connected at first glance. Like the link between last-minute time entries and delayed project delivery, or how internal admin work spikes right before team burnout.

Reverse engineering your time logs with AI in this way, grows your knowledge from what happened to why it happened to what will happen next, all in just a few clicks.

Your mountain of Toggl Track entries might appear neglected and unloved. No matter what state it’s in, this data is the foundation of your reverse engineering project. And the more data you have, the more context AI has to work with, which is everything when you're trying to spot trends. A few weeks of time entries might give you a short overview of what happened. But a year or two of logs tells a story — how client demands shift over time, how certain task types balloon quietly in the background, and where team inefficiencies have been blowing your margins.

Just like a detective needs more clues to crack the case, AI needs a fuller picture to deliver accurate, useful insights. Even if the numbers are a little fudged, more data gives you a better chance of identifying what needs your attention.

And remember: it doesn’t have to be perfectly labeled or categorized. You’re not feeding it into a tax return; you’re training it to think critically about how your agency runs. “Don't get me started on clean data,” says Talkshop Media operations manager, Hannah McClenechan, proving that imperfect data is the reality for most agencies.

Turning raw, imperfect time logs into strategy gold takes a bridge, and AI is the scaffolding that connects the dots between what you’ve tracked and what you need to know.

Here are six real challenges your agency can solve when you start reverse engineering your time data, starting with one that impacts your bottom line the most.

Challenge 1: Spotting your most (and least) profitable clients from ambiguous entries

You’ve got your team logging their time, adding entries like Client A: General Work, or Client B: Meeting. And yes, the time is technically tracked, but you haven’t got the foggiest what’s really going on? Are you pouring 80 hours a month into a client who only brings in $5k? Are your most demanding clients your least profitable?

With AI, you can spot underperforming clients and reveal true client profitability numbers by combining the hours you’ve logged in Toggl Track with simple revenue data. From there, you’ll let AI do the heavy lifting for you. Your AI output will flag the clients quietly draining your team’s time and margin, and spotlight those pulling their weight. Eli Rubel of Profit Labs, explains the benefits of knowing the granular details:

“It's funny how many agency owners have a hard time with time tracking. An agency is in the business of selling someone else's time ... you can't sell time efficiently if you don't even know what that time is in the first place.”

Here’s how to cross-reference your total volume of time logged per client, so you can identify those peeps consistently stealing too many hours from your firm.

Prepare to be amazed as AI reveals the difference between those hidden “money pit” clients and the efficient “goldmine” clients you’ll want to hang onto over the long haul.

Challenge 2: Uncovering hidden workflow bottlenecks and delays

Work is technically getting “done” but it feels like trudging through mud. If your tasks are piling up leading to projects stalling, your profitability will take a nosedive, and you risk losing your clients.

AI becomes your backstage pass to what’s really happening. After auditing the timestamps and duration of consecutive tasks in Toggl Track, AI can spot your delays and bottlenecks by answering “What’s taking longer than it should?” It looks for unusual transitions, prolonged durations, or patterns of idle time between handoffs.

Tony Bradberry, Managing Director at Grey Matter, has lived this problem firsthand. When he joined the agency, it had more client demand than it could handle, but lacked the systems to deliver efficiently and profitably. The solution was to use time studies to pinpoint their workflow bottlenecks. Now, Tony’s team uses structured time expectations for every process, and constantly reviews the outcome.

“We’ve probably built out a program and tore it down about three or four times in the last four years. We’re constantly looking at our processes and how we can build those out in a better way to be more efficient, more profitable.”

Even if your agency isn’t doing full-blown time studies, you can still get similar insights by feeding your Toggl Track data into AI, and letting it show you where things are clogged. Here’s how:

Expect to learn the exact stages, tasks, or handovers that are silent project killers, all of which could point to a team capacity or utilization problem.

Challenge 3: Deconstructing actual team utilization and capacity vs. perceived

Your dashboard says everyone’s at 80% utilization, but your team is struggling to breathe. Someone’s always overwhelmed, your top performer is showing signs of burnout, and a simple Monday brief turns into a Tuesday meltdown. That’s likely because your agency utilization looks neat on paper, but it’s not telling the full story. Kyle Hunt, owner of Agency Operators, says:

“Utilization is a very poor metric. It only tells you the number of hours worked. It doesn’t tell you anything about the value delivered.”

Instead, Kyle suggests focusing on capacity planning, which is essentially how much high-quality work someone can take on and still perform with excellence. This insight comes from deeper visibility into how long things actually take and how teams are working.

AI can flip the lights on, giving you detailed patterns that are easy to overlook: unlogged overtime (like weekend activity or late-night logins), short and fragmented tasks that suggest constant context-switching, and excessive time spent on admin work. It compares these across team members and roles to show who's at risk of workload burnout, and why.

“Wouldn’t it be nice,” asks Eli Rubel, “if as a manager, I could say: Kim’s been logging over 40 hours a week for three weeks straight. Let’s take some work off her plate before she burns out?” That’s the power of reverse engineering your time tracking with AI.

Your output is a truer picture of team bandwidth, from their availability to how well they’re coping. With better intel, you can rebalance workloads fairly, and make smarter resourcing decisions before things hit crisis mode.

Challenge 4: Spotting unaccounted scope creep through time deviations

The dreaded scope creep is a problem faced by all agency owners. It’s one of the oldest threats to agency profitability, and still one of the hardest to catch in real time, especially when a “quick revision” here and there doesn’t seem like an unreasonable request.

But a 2025 Agency Pricing & Cashflow Report reveals that 57% of agencies lose between $1,000 and $5,000 per month to unbilled scope creep. And nearly 80% admit they rarely or only sometimes charge for that extra work.

AI gives you a way to plug this recurring leak. If you’ve set estimated hours in Toggl Track, or can pull in rough project baselines, AI compares what you expected to happen with what actually got logged. From there, it can go beyond looking at totals to pinpoint which phases, deliverables, or task types run over most often, and by how much.

Even without airtight data, you’ll start to see where things balloon, and which projects would have been profitable if they’d stayed in scope.

Once you can see where scope creep causes damage, you’ve got options. You might renegotiate your retainers or tighten up your estimates, for example. And if you need to engage in difficult conversations with your clients, at least you’ll have the confidence of data behind you.

Challenge 5: Identifying unprofitable service offerings and deliverables

84% of agencies identify as specializing in one thing — they stay in their vertical and excel at what they do. But over time, it may be tempting to branch out horizontally and offer a broader pick of service offerings and deliverables. Kyle Hunt believes this is a mistake:

"What usually happens is you're good at one service and then a client asks for another service and because you're revenue hungry, you say yes. I think there might be certain business models where that can work. I don't think that agencies are one of them."

Instead, his advice is to “Find something you do really well and focus on that one thing and try to scale it.”

Nevertheless, if you’ve already got your fingers in a few pies, learning which are truly profitable could be the key to growing your business, so you can really hone your niche. In fact, a recent Promethean Research report found those agencies that shifted their service offering mix in 2024, not by expanding or reducing but by changing focus, grew faster than the average.

AI runs the numbers you’ve been too busy (or too close) to calculate. It compares the hours your team spends on different deliverables against the revenue each service brings in. Once you’ve got that visibility, you’re in a position to make smart calls: double down, refine the scope, adjust pricing, or quietly retire those client favorites that just aren’t working. And if you’re still saying yes to everything? At least now you’ll know what it’s costing you.

From here, you’ll have data-backed insights at your fingertips, which you can use to refine your service catalog and pricing strategy.

Challenge 6: Revealing behavioral patterns for better project management

We talk a lot about how long people work. But very few agency owners know how their teams work, or whether they’re set up to succeed.

The numbers aren’t pretty. According to Microsoft 2025 Work Trend Index Annual report, the average employee now receives 117 emails and 153 Teams messages per day. And that’s before you factor in surprise meetings, which account for 57% of all calendar events.

No wonder time logs can feel like a blur of short entries, disjointed sprints, and “just catching up” admin time after hours. But when you use AI to reverse engineer your team’s time logs, you start to see the shape of their workday, including both the quantity of hours and how they’re structured. Are your designers getting the 2-3 hour blocks they need to think creatively? Are your developers doing deep work at 10am or 10pm?

Even better: by analyzing when work happens, AI can align your project schedules with your team’s natural rhythms. Half of all meetings already take place between 9-11am or 1-3pm, which is right when most people’s circadian rhythms are at their natural productivity peak. Do you really want them stuck in meetings, or getting into flow? It can also highlight dependency blockers that are hard to spot manually. If time is repeatedly logged on tasks like “waiting for feedback” or “internal review,” AI can flag these issues.

You’ll be rewarded with detailed insights to encourage deep work and guide better project scheduling. You’ll be able to redesign your delivery rhythms and protect your team’s best hours.

Time tracking is where it all begins — the raw, honest view of how your team actually spends their time. But if you stop there, you’re only seeing part of the picture. To unlock the real magic, you’ll want to connect that time data with the rest of your operational ecosystem. Here’s what it should include overall:

• Toggl Track: This is your source of truth for who’s doing what, when, and for how long. Your time data doesn’t have to be perfect, but it becomes the fuel for everything that follows.
• Data integration layer: This is where things start talking to each other. Whether you’re using a Zapier connection, a custom API setup, or even just a few good exports, the goal is to link your time logs with financial data (like revenue or cost from your CRM or accounting tools) and project data (from Asana, monday.com, Jira, etc.). That’s when you can start answering real questions like, “Are we undercharging for content design?” or “Which projects are always behind schedule, and why?”
• AI analytics platforms: Once your data is in one place, choose how you want to analyze it. Use:

• ChatGPT with Advanced Data Analysis for flexible, no-code pattern detection and visualizations.
• Business intelligence tools (like Looker, Power BI, Tableau) with AI features if you’re already invested in dashboards.
• Custom Python or R scripts if you’ve got a technical team or want to automate recurring analysis across clients or teams.
• Specialized operational AI tools if you’re managing at scale and want something purpose-built.

Top tip: Don’t wait for “perfect” data — it’ll never come. Start small by asking one question or pulling one report. As the insights roll in, you’ll naturally get better at tagging, naming, and capturing the kind of detail AI thrives on. Every iteration sharpens the next.

The tech stack isn’t the hard part; the hardest part is getting started. But once you connect the dots between time, cost, and output, you’ll start to see your agency ops in an entirely new way.