As the Founder of ReThynk AI, I’ve seen DevOps make the same mistake with AI:
They choose use cases that sound impressive… instead of use cases that create measurable outcomes.
So I use a simple way to choose AI use cases without wasting months.
How DevOps Should Choose AI Use Cases
AI adoption fails when DevOps start with excitement:
- “Let’s add AI everywhere.”
- “Let’s automate everything.”
- “Let’s build tools fast.”
That creates experiments, not results.
DevOps doesn’t need more experiments. DevOps needs first wins that build trust and momentum.
The 3 rules I use to pick the right AI use case
1) Pick the pain that repeats daily
If it happens once a month, AI won’t become a habit.
I start with work that repeats:
- customer questions
- sales follow-ups
- marketing content creation
- proposal drafting
- internal reporting
- SOP and training needs
Repetition creates adoption.
2) Pick the workflow that has a clear “before vs after”
If you can’t measure improvement, your team & clients won’t believe in it.
Good use cases have visible metrics like:
- response time
- turnaround time
- hours saved per week
- conversion rate
- error rate
- customer satisfaction
One KPI beats ten vague ideas.
3) Pick low-risk, high-leverage first
Many DevOps teams start with high-risk automation and lose trust fast.
So I start with areas where mistakes are recoverable:
- drafts, not final decisions
- assistance, not approvals
- suggestions, not replacements
Trust first. Automation later.
The quick scoring method (5 filters)
When I evaluate a use case, I ask:
- Frequency: Does this happen daily/weekly?
- Friction: Does it drain time or cause errors?
- Impact: Will improvement be meaningful?
- Measurable: Can I track a KPI easily?
- Risk: If AI is wrong, is the damage controllable?
If a use case scores high on 1–4 and low on 5, it’s a perfect starting point.
The leadership insight
DevOps wins with AI when they stop thinking like tool buyers and start thinking like system designers.
The goal is not “AI adoption.”
The goal is:
one workflow, one outcome, one repeatable win.
That’s how democratisation of AI becomes real for small teams.
Top comments (1)
DevOps needs first wins that build trust and momentum.