The Illusion of Progress

We’ve built dashboards that no one reads.
Launched bots that solve questions nobody had.
Automated tasks that were never the problem to begin with.

It’s easy to feel like things are moving. Tools get deployed. Workflows get faster. But faster isn’t better if you’re accelerating in the wrong direction.

This is the new trap of digital progress:
We reward motion, applaud efficiency, and forget to ask the one question that really matters: is this helping anyone?

🔍 The Unseen Problem Behind Most AI Projects

In one of the companies I worked with, a leadership team proudly announced that their new internal AI assistant saved 4,000 hours per year by automating knowledge queries.

Six months later, they quietly admitted something else:
Nobody was using those 4,000 hours.
Decisions weren’t faster. Meetings weren’t shorter. Strategy wasn’t clearer.

They had optimized a pain point, not a priority.

And they’re not alone. Across industries, we’re measuring success in outputs, not outcomes.
We track “hours saved” but never ask: what did we use them for?

Progress becomes an illusion when it doesn’t show up in the experience of the people it was meant to help.

📉 Where AI Initiatives Quietly Fail

A report from MIT CISR found that only 7% of organizations fully integrated AI into their decision-making workflows, and a study from S&P Global states that over 42% abandon pilots due to resistance and unclear objectives.

What does this tells us? The problem isn’t adoption. It’s relevance.

• Tools are rolled out without rethinking the surrounding process.

• Data is surfaced but not made actionable.

• Speed is added, but alignment is missing.

When you automate without context, you create what I call organizational noise: tools that buzz around without changing how people think, work, or collaborate.

🧭 A Better Starting Point: Useful > Impressive

Before launching your next AI feature, hold a one-hour friction session.
Ask your team:

• What slows us down in practice, not in theory?

• Where do we make decisions we regret later?

• What are people constantly working around?

You’ll find the real opportunities don’t sound like innovation.
They sound like fixing what’s broken quietly. That’s where AI has the most leverage: not in impressing stakeholders, but in removing daily friction.

🧱 The Practical Antidote: A Framework for Meaningful Automation

If progress isn’t measured in outputs, what should it be?

Most organizations track deployment. Few track behavioral change. They measure how many automations were launched, not how they shifted the way people work, decide, or feel.

To change that, here's a structure I call Utility Over Optics: a three-part lens to evaluate whether automation is truly valuable or just another shiny thing.

1. Friction Relevance
Does this automation remove friction that people actually experience, or just what leadership assumes exists?
Tip: Interview three people in the workflow. Ask what slows them down daily. If your AI doesn't touch that, it’s probably solving the wrong problem.

2. Decision Proximity
Does this automation make critical decisions faster, smarter, or more transparent?
Tip: Map the five most frequent decisions in your process. Look for points where people hesitate or over-rely on meetings. That’s where automation can help most.

3. Purpose Visibility
Can teams clearly explain how this tool supports their goals, or is it a black box labeled “innovation”?
Tip: If your automation needs a 20-minute explainer to justify its value, it may be a solution in search of a problem.

These three checks (friction, decisions, purpose) act like a filter. They don’t just prevent waste. They direct investment toward tools people actually want to use.

📊 Progress That Looks Boring on Paper but Feels Transformational

The most impactful automation efforts I’ve seen don’t make headlines.
They aren’t about replacing entire departments. They’re about elevating the parts of work that matter most.

One client trimmed 18 percent of internal review time by embedding decision-tracking into meeting summaries. There was no flashy interface. Just context where it was needed, when it was needed. The result? Fewer repeated discussions. More space for deep work.

Another added a generative assistant inside their documentation. Not to replace people, but to answer repeat questions instantly. Within weeks, team frustration dropped and self-sufficiency improved. No big announcement. Just less noise, more clarity.

These aren’t dramatic breakthroughs. But they work. Because they’re built for how people actually behave, not just how tools are designed to perform.

🪞 The Deeper Reflection: Are We Still Solving Human Problems?

The more advanced the technology, the more we must ask what it’s for:

• Are we reducing unnecessary tension, or just adding new layers of complexity?

• Are we improving decisions, or just speeding them up?

• Are we making work more meaningful, or just more efficient?

Most AI waste isn’t caused by bad tech. It’s caused by solving the wrong problem.

And that problem often isn’t technical. It’s human.
Lack of clarity. Misaligned incentives. Pressure to perform innovation without understanding what needs to change.

💡 Let Progress Be Measured by Relief, Not Hype

There’s a kind of progress that doesn’t need a pitch deck.
You feel it in how teams move. In how fast good decisions get made. In the way people stop apologizing for not “keeping up,” because the systems finally help them catch up.

If automation doesn’t feel like relief, it probably isn’t helping.

Behind the scenes, there are teams helping others rethink what innovation really looks like. Not just through more projects, but through better judgment. Sharper focus. Quieter impact.

That’s the kind of work that actually moves things forward.