We’ve never had more tools to make work easier. So why are so many of us still working harder than we need to?
I’ve spent much of my career examining processes, evaluating risks, and finding opportunities to improve how organizations operate. And if there’s one thing I’ve learned, it’s that inefficiency rarely announces itself.
It hides in everyday routines.
It’s the report someone manually updates every Friday because that’s how it’s always been done. The spreadsheet that gets copied, pasted, reformatted, and emailed to five different people. The meeting that exists because nobody has questioned whether it’s still necessary.
None of these activities seems particularly problematic on its own. But multiply them across a team, a department, or an entire organization, and the impact becomes significant.
The interesting part? Most of us know these inefficiencies exist.
So why do we keep doing things the hard way?
The Comfort of “We’ve Always Done It This Way”
There’s something reassuring about a familiar process.
We know the steps. We know what the finished product should look like. We know who needs to approve it.
And perhaps most importantly, we know it works. Or at least, we know it hasn’t failed badly enough to force us to change it.
But familiarity isn’t the same as effectiveness.
Over time, processes accumulate additional steps. Someone requests another review. A new report gets added. An exception becomes a permanent workaround.
Eventually, nobody remembers why half the steps exist.
I’ve seen how easily organizations can confuse following a process with achieving its intended objective.
It’s an important distinction.
A process can be performed perfectly and still be fundamentally inefficient.
We’re Measuring the Wrong Things
In many workplaces, productivity is still associated with activity.
How many tasks did you complete? How many hours did you spend? How many reports did you produce?
But activity doesn’t necessarily translate into value.
Consider two employees.
One spends eight hours manually compiling a report. The other develops a repeatable, automated process that produces the same report in 20 minutes.
Who’s more productive?
The obvious answer seems to be the second employee. But organizational cultures don’t always reward efficiency that way.
Sometimes the person who appears constantly busy receives more recognition than the person who quietly eliminates unnecessary work.
That’s a problem.
If we want meaningful improvement, we need to shift our attention from how much work gets done to how much value the work actually creates.
The Real Cost Isn’t Just Time
When we discuss inefficient processes, we tend to focus on wasted hours.
But the consequences go much further.
Every unnecessary manual step creates another opportunity for error. Every redundant approval introduces delay. Every disconnected spreadsheet increases the possibility of inconsistent information.
And then there’s the cost we rarely quantify: attention.
People have a limited amount of mental energy available each day.
When we consume that energy performing repetitive administrative tasks, we have less capacity for critical thinking, creative problem-solving, and strategic decision-making.
In my work, I’ve become increasingly interested in this relationship between efficiency and risk.
Because eliminating unnecessary work isn’t just about saving time.
It can also improve consistency, strengthen controls, and allow people to focus on the decisions that actually require human judgment.
Of course, not every manual step is wasteful. Some exist for very good reasons, particularly when oversight, independence, or accountability is important.
The goal isn’t to eliminate controls in the name of speed.
It’s to distinguish between the steps that protect the organization and the steps that simply keep everyone busy.
Enter AI: A Tool, Not a Strategy
Artificial intelligence has made this conversation even more interesting.
Suddenly, tasks that previously required hours of manual effort can potentially be completed in minutes.
We can summarize large volumes of information, identify patterns, draft documentation, analyze data, and automate portions of workflows that once demanded substantial human involvement.
The possibilities are exciting.
But I think we’re at risk of repeating an old mistake.
We’re introducing new technology without always questioning the processes we’re applying it to.
If a workflow is unnecessarily complicated, adding AI doesn’t automatically make it better.
It might simply help us execute a bad process faster.
And sometimes, that’s exactly what happens.
Before asking, “How can we use AI to do this?” I think we should be asking a more fundamental question:
“Should we still be doing this at all?”
That question can be uncomfortable because it challenges assumptions that may have existed for years.
But it’s also where some of the most valuable improvements begin.
What I’ve Learned From Building Better Processes
Over the years, I’ve worked on initiatives involving automation, reporting, data analysis, and process improvement.
More recently, I’ve been exploring how generative AI and emerging automation capabilities can support the work professionals do every day.
What continues to surprise me isn’t necessarily the sophistication of the technology.
It’s how much opportunity exists in relatively simple improvements.
Sometimes the biggest win isn’t a complex AI solution. It’s removing a redundant step, connecting two systems, or eliminating the need to manually reconcile information that already exists somewhere else.
I’ve also learned that technology is often the easier part.
The harder part is getting people comfortable with changing how they work.
A process might be inefficient, but it’s familiar. And changing it introduces uncertainty.
Will the new approach be reliable? Will it introduce new risks? Will people trust the results?
Those are legitimate concerns, and they deserve thoughtful answers.
Successful improvement requires more than technical capability. It requires understanding the people, processes, and risks involved.
That’s one reason I find the intersection of technology, risk management, and organizational improvement so interesting.
It’s not enough to build something that works.
You have to build something that makes sense.
Five Questions Worth Asking About Your Work
You don’t need to be a developer, data scientist, or AI expert to identify opportunities for improvement.
Start by looking at the work you do regularly and asking a few questions:
- Why does this process exist? Is it still solving the problem it was originally designed to address?
- Which steps require actual human judgment? Could the remaining steps be simplified, standardized, or automated?
- Where are we entering or manipulating the same information more than once? Repeated handling of data is often a sign of an improvement opportunity.
- What happens if we stop doing this? Sometimes the most revealing answer is that nothing meaningful would happen.
- Are we solving the right problem? Before investing in a new tool, make sure you’re addressing the underlying issue rather than its symptoms.
These aren’t particularly complicated questions.
But asking them consistently can fundamentally change how you approach your work.
The Future of Work Shouldn’t Be More Work
There’s a tendency to assume that technology should help us accomplish more.
More reports. More analysis. More meetings. More output.
But I’m not convinced that more should always be the objective.
What if the goal were better?
Better decisions. Better information. Better processes. Better use of people’s time.
What if we measured the success of a new technology not just by how much faster it completes a task, but by whether that task creates meaningful value?
As AI becomes more integrated into our professional lives, I believe the ability to question existing processes will become increasingly valuable.
Knowing how to use a tool is important.
Knowing when, why, and whether to use it is even more important.
And perhaps the most useful skill of all is being willing to challenge the familiar.
Because sometimes the smartest way to work isn’t finding a faster way to do something.
It’s realizing you don’t need to do it in the first place.
I’m Julie Cella, an IT audit and risk professional with a passion for technology, automation, and finding smarter ways to work. I explore how emerging technologies, thoughtful process improvement, and a healthy dose of curiosity can help us create more value without creating more work.
