AI and Agile: I’ve Seen This Cycle Before
- Mindset180
- 6 days ago
- 3 min read
Over the last year or so, I’ve noticed something interesting.
A lot of people I used to know from the agile community are now deeply involved in AI work. Myself included.
At first glance, that might look like trend-hopping. But to me, it makes perfect sense.
If you take the how out of the equation, the goal hasn’t really changed. We’re still trying to answer the same fundamental question:
How do we shorten the time between sensing a real customer need and delivering something valuable in response?
That was the promise of agile. And in many ways, it’s also the promise of AI.
AI as an Extension of the Agile Promise
When you strip away consumer apps and flashy demos, most enterprise AI capabilities are a natural extension of what agile was trying to do all along.
Early agile was deeply human and manual:
Sticky notes
Whiteboards
Face-to-face conversations
Teams co-located in the same room
The goal wasn’t the artifacts. The goal was speed, feedback, learning, and adaptability.
Over time, the world changed.
Technology replaced the need to keep 3M in business with sticky notes. Digital tools enabled collaboration across geographies at scale and at a reasonable cost.
AI is the next step in that evolution.
It helps us sense patterns faster, reduce workflow friction, and respond more quickly. But here’s where the comparison starts to break down.
AI Isn’t “Agile 2.0”
Is AI just agile 2.0? Or 3.0? Or 4.0?
Not really.
Agile primarily changed how teams delivered products. AI touches every aspect of work life, or it will over time.
Decision-making. Analysis. Risk management. Communication. Knowledge work itself.
That’s what makes this moment both powerful and dangerous.
What Happened to Agile (and Why It Matters Now)
Agile has largely lost its marketing appeal.
The buzzwords faded. The certifications multiplied. The hype cycle moved on.
But the core idea didn’t disappear.
Creating and keeping happy customers by solving their problems quickly became part of the fabric of product delivery. Even if the popular “brands” of agility lost their luster, the underlying principles stuck.
And that raises an uncomfortable question:
How do we keep AI from becoming the next corporate albatross?
The never-ending cycle of:
Hiring consultants
Running pilots
Declaring success
Quietly shelving initiatives that never quite delivered
I saw this play out during the agile boom.
Some organizations did it well. Many… did not.
What Actually Worked in Agile Transformations
Looking back, the patterns were clear.
Organizations that succeeded didn’t do anything magical. They did a few things consistently well.
1. Clear-eyed executive expectations
Leaders understood the real cost, effort, and time required. They didn’t expect miracles in a quarter. They made informed decisions about whether the benefits were worth the investment.
2. Incremental rollout with real governance
The work was decentralized, but not chaotic. Teams learned and improved within a clear decision-making framework. Progress wasn’t perfect, but it was measurable and compounding.
3. Well-defined workflows and meaningful measurement
Successful organizations changed how they thought about productivity, quality, and efficiency. They didn’t just slap agile language onto legacy metrics and hope for the best.
4. An engaged, agile-literate workforce
They invested in people, not just frameworks. The result was a workforce capable of thinking beyond prescribed processes and adapting to changing conditions.
The Implications for AI in the Workplace
The parallels to AI adoption are hard to ignore.
If you want your organization to survive, and benefit from, the AI boom, the lessons are familiar.
1. Executives: be clear-eyed
This will take time. It will cost money. You have to honestly decide whether the long-term benefits outweigh the investment.
2. Build governance before you scale
Create decision-making structures that address ethics, security, and risk without suffocating progress. Governance should guide the work, not paralyze it.
3. Don’t automate bad processes
AI will happily accelerate dysfunction. Take the time to understand how value actually flows before deciding what to automate.
4. Invest in your people
AI augments human capability; it doesn’t replace judgment, responsibility, or thinking. The organizations that win will be those that help their workforces understand that difference.
Why This Is the Work We’re Doing at Mindset180
This perspective didn’t come from theory. It came from decades of watching large-scale change succeed and fail.
That’s why our work at Mindset180 is grounded in three pillars:
Strategy, governance, and risk management
Workflow optimization and automation
AI literacy and enablement
Not hype. Not fear. Not chasing the next shiny tool.
If any of this feels familiar, if you’re seeing gaps between AI ambition and real results, we’d welcome a conversation.
Because the goal isn’t to relive the agile boom.
It’s to learn from it, and do better this time.
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