Let Your Experts Lead: Use AI to Unlock and Scale What They Know
Every team has a few unsung heroes.
The ones who know the infrastructure inside and out.
Who remember why that firewall rule exists.
Who understand the weird behavior that pops up once a quarter—because they’ve been there before.
They’re the calm during chaos.
The Slack DM you send when no one else knows what to do.
The glue holding legacy and modern systems together.
But here’s the problem:
That knowledge lives in their head.
And that’s exactly where risk, burnout, and slowdowns begin.
The Hidden Cost of Tribal Knowledge
Tribal knowledge works great—until it doesn’t.
Until someone’s on PTO.
Until they leave.
Until someone tries to follow the docs but doesn’t see the latest change.
This is when:
Onboarding slows to a crawl
Incidents repeat because no one learned from the last one
Engineers get hesitant to make changes
And progress halts because “the one who knows” is in back-to-back meetings
Everyone feels it, but few talk about it directly.
This isn’t about headcount or capability.
It’s about access, continuity, and scale.
When Experts Aren’t Involved, Things Break
Let’s be real: most outages aren’t caused by bad actors.
They’re caused by good engineers working without the full picture.
The experts often aren’t involved because they’re too busy—or the team assumed everything was documented.
They probably did document it.
But someone didn’t read it.
Or didn’t know there was an update.
Or skipped the nuance only the SME would’ve flagged.
Here’s the thing: experts don’t just know the steps.
They know the why, the what if, and the what breaks if you don’t.
They double-check everything.
They think in systems.
They’re the safety net.
But safety nets only work when they’re available—and they often aren’t.
This Is Where AI Comes In
AI isn’t a threat to your experts.
It’s the reinforcement they’ve needed for years.
Used right, AI becomes a force multiplier:
It surfaces relevant internal documentation at the right time
It answers the 10 repeat questions that distract engineers every week
It explains infrastructure or IAM changes before they hit prod
It onboards new engineers with actual context, not just confluence links
More importantly:
It lets your experts focus on what they do best—leading, guiding, and solving real problems.
What Changes (and What Doesn’t)
This isn’t a job elimination story.
It’s a job evolution story.
Just like the shift from racking servers to building cloud-native apps.
Here’s what stays:
Deep knowledge
Strategic input
Problem-solving under pressure
Here’s what goes:
Repeating yourself in Slack
Digging up the same configs for the 5th time
Babysitting systems no one else wants to touch
Experts aren’t automated away.
They’re freed up to lead.
Real-World Examples Already in Motion
This is happening today in high-performing orgs:
Internal LLMs trained on runbooks, tickets, and PRs
AI copilots embedded in Slack and IDEs to surface recommendations
Platform bots that auto-suggest tagging, IAM roles, and cost anomalies
Incident summarizers that capture what happened and what to fix—without meetings
These aren’t hypothetical.
They’re working—and they’re saving hours, dollars, and drama.
The Cultural Shift: Letting Experts Let Go
This change also requires a mindset shift—from both leadership and SMEs.
It’s not about hoarding knowledge.
It’s about creating leverage.
You’re not making yourself replaceable.
You’re making your knowledge resilient.
The best experts already want to mentor, guide, and build systems that outlast them.
AI just makes that possible—at scale, in real time, and without interruption.
Final Thought
Your experts know the systems better than anyone.
They care deeply.
They think ahead.
And they usually see the problem before it becomes one.
But if their knowledge stays locked in their head or scattered across docs, the team stays fragile.
AI doesn’t replace them.
It helps everyone think like them—even when they’re not in the room.
Let your experts lead.
Give them tools to scale.
And build a culture where knowledge isn’t hidden—it’s amplified.