Everyone’s talking about AI productivity. No one’s asking what it’s replacing
In strategy and research functions, AI has become the go-to accelerator. Faster reports, quicker turnarounds, broader coverage- it all looks like progress. But underneath the surface, a quieter erosion is taking place. The very processes that build judgment, improve thinking, and grow talent are being skipped in the name of speed.
The problem no one’s naming
AI has undeniably improved the speed and scale of research delivery. Teams are producing more reports, responding faster to leadership, and covering more ground with fewer people.
But as deadlines shrink and throughput rises, something crucial is quietly breaking: the internal feedback loops that once sharpened insight, corrected course, and taught teams how to think better over time.
This isn’t about bad AI
It’s about the disappearance of friction.
Your AI workflow is starving your team of resistance
In healthy research environments, every project used to include space for debate, revision, peer review, and post-mortems. Those moments of friction weren’t just quality control—they were where judgment got built.
Now?
AI lets you skip to the final draft. The feedback loop gets bypassed. Everyone moves on. Nothing gets challenged. And with that, your team loses something deeper: the ability to refine its thinking through exposure, conflict, and correction.
The cost isn’t just lower quality. It’s slower learning
The most dangerous impact of broken feedback loops isn’t that your current output gets worse, it’s that your future output stops improving.
Junior researchers never get pushback.
Mid-level analysts never get asked “why.”
Teams repeat flawed patterns because there’s no pause for review.
You’re not just producing less robust insight.
You’re training your team to stop getting better at producing it.
This is how research teams quietly become stale
You won’t notice it at first. Reports will still go out. Insights will still sound reasonable.
But over time:
- Your team will stop spotting weak assumptions early.
- Your conclusions will flatten into consensus summaries.
- Your stakeholders will start sensing that something’s “off.”
Eventually, someone else who kept their learning muscle alive will walk into the same boardroom with sharper insight and more confidence.
If you don’t fix this, AI will make you efficient and irrelevant
The real threat isn’t AI-generated garbage. It’s AI-assisted mediocrity that no one bothers to question. And if your team can’t tell the difference anymore, you’re done, because insight isn’t just about output. It’s about judgment, and judgment only gets built through feedback
What high-performing teams are doing instead
The best research teams aren’t moving slower.
They’re inserting friction on purpose.
They’re:
- Building feedback checkpoints before final delivery
- Pairing junior analysts with seniors for live critique
- Running “second draft” reviews, even for AI-polished outputs
- Holding post-mortems not just on what was delivered—but how the thinking evolved
In other words: they’re rebuilding the loop.
The question isn’t how fast your team moves.
It’s whether they’re still learning as they go.
If AI has made your research more productive but less reflective, you’re not building an edge, you’re just building a backlog.
What research teams can do now
If you're serious about preserving insight quality while using AI, here’s where to start:
- Bake in second-draft reviews: No AI-generated output goes forward without a human pass + pushback.
- Institutionalize peer review: Set up short, weekly critique sessions across pods, 10 minutes goes a long way.
- Create a “why” culture: Every mid-level output should get at least one “why do you think that?” before delivery.
- Document how thinking evolved: Keep a quick log of assumptions made, discarded, or refined: build a culture of visible iteration.
- Assign reflection time: After major deliverables, run 15-minute retros: What did we learn? What would we challenge?
These aren’t blockers, they’re multipliers. Judgment compounds. You just must make space for it.
