Short answer: Pick one business problem. Set a number to improve. Run a small test. Keep what works. Kill what doesn’t. Repeat.
That’s the core play. But to make AI adoption stick and to avoid hype, burnout, or wasted budget, you need structure.
The Reality Check
Before we talk about “how,” it’s worth looking at the current state of play. AI may be everywhere in headlines, but the numbers show a harder truth.
Over 70% of companies say they’ve tried AI.
Only about a quarter actually see strong ROI.
Fewer than one in five pilots scale.
In smaller markets like Greece and Cyprus, adoption rates are climbing fast, but budgets are tight and talent is limited. That makes smart choices even more important.
CEOs can’t afford to ignore AI, but they also can’t afford to get lost in experiments that never deliver.
1. Start with “Why now?” and one clear outcome
Once you see the reality, the first move is simple: don’t chase tools but do chase results. The question every CEO should ask is, “Why now, and what exactly do we want to improve?”
Pick one outcome tied to revenue, cost, or risk.
Revenue: Lift conversion rate by 10%.
Cost: Cut first-response time in support by 50%.
Risk: Reduce invoice fraud incidents to near zero.
Write it in one line. Add a deadline and an owner. If you can’t do that, you’re not ready to buy anything.
2. Choose use cases that move the needle
Focus on work that pays back. This can be one or few of the following:
Marketing & Sales
Lead scoring in the CRM.
Personalized campaigns.
Meeting notes and follow-ups auto-pushed to the CRM.
Operations
Demand forecasting.
Routing and scheduling.
Quality checks with computer vision.
Document automation.
Service & Success
AI triage for chat/email.
Draft replies for agents.
Ticket summaries and tags.
Finance & HR
Invoice and expense anomaly detection.
Expense audit flags.
CV screening assistance.
Pick two. Ignore the rest for now.
3. Build, Buy, or Partner?
Ask these questions in order. Stop when you hit “Yes.”
Is this already a solved problem in my industry? → Buy.
Do I have unique data or workflows that create advantage? → Partner or add a custom layer.
Does speed matter more than perfection? → Buy now, refine later.
Do I have a team to run this 24/7? If no → Buy/Partner.
Is this core to our competitive edge? If yes, and talent exists → Build.
There is a default for SMEs: Buy > Partner > Build that might be useful to anyone.
4. Check readiness
You don’t need to be perfect from start. You need “good enough” and start building on that.
Data: Is it accessible and clean enough to start?
Process: Who does the work now? Can they describe it?
People: Who owns the outcome? Who uses the tool daily?
Compliance: Any rules or privacy laws to respect?
Budget: Setup + monthly + people time.
If any answer is a hard “no,” fix that first. It’s cheaper than rescuing a failed rollout.
5. Run a 30–60 day pilot
Keep it small. Keep it measurable. And always check your numbers.
Scope: One use case, one team.
Baseline: Capture the “before” number.
Target: One clear metric. Example: “Cut response time from 6h → 2h while CSAT stays above 4.5.”
Guardrails: Keep a human in the loop.
Exit:
Go → Hit target.
Tweak → Close, adjust, extend.
Stop → Miss badly. Document why, move on.
No endless pilots. Decide.
6. Scale what works
A pilot that hits its target is not the finish line. It just gives you the green light. Now turn the win into standard practice. Scale with discipline, not a big bang.
When a pilot wins:
Integrate it into systems. No swivel-chair work.
Write the SOP. If you can’t, you don’t understand the work.
Train the team.
Monitor the same metric at scale.
Expand step by step—team by team, region by region.
7. Governance without drama
AI doesn’t just need results. You need to give it guardrails. Governance doesn’t have to be heavy or bureaucratic, but it does have to exist.
Keep it simple:
Ownership: Business, tech, compliance.
Data: Know what you’re using, where it lives, who accesses it.
Review: Quarterly checks for bias, drift, privacy.
Transparency: Tell people when they’re dealing with AI.
Appeals: Keep a human path for exceptions.
That’s enough to start.
8. ROI: hold yourself to a number
AI is only worth it if the numbers add up. Treat it like any other investment. Measure, track, and demand a clear return. Do the math.
Costs: Setup + monthly + internal time + integrations.
Benefits: Time saved, cost avoided, revenue lifted.
Payback: Cost ÷ Monthly benefit. Aim for <12 months.
No ROI, no scale.
9. Avoid common traps
Most failed AI projects fail because leaders fall into the same traps. Steer clear of these.
Tool first, problem later.
“AI everywhere.” You’ll get nowhere.
No data plan. Garbage in, garbage out.
No change plan. People won’t use it.
Shadow AI. Teams buying tools without oversight.
Vendor theatre. Demos impress, contracts don’t. Test on your data.
Over-automation. Humans still matter.
10. A simple 90-day plan
Want the bonus section? We’ve shared it exclusively with subscribers.
👉 Unlock it here 👈
Don’t worry, it’s free.
Real-world proof
AI can sound abstract until you see it in practice. These cases show what happens when companies tie AI directly to business outcomes.
Walmart: AI in logistics saved $75M in a year and cut emissions.
JPMorgan: AI contract review replaced 360,000 hours of legal work.
BMW: AI vision cut defects by 60% and sped up inspections.
CarMax: AI summarized 100,000+ customer reviews into 5,000 highlights, boosting SEO and freeing staff time.
Each win tied to a clear business outcome, not hype.
If you’re in a smaller market
Budgets are tighter. Talent is scarce.
Favour buying tools over building.
Pick narrow, high-impact use cases.
Stick with cloud tools that meet EU compliance.
Partner locally only when needed.
Focus on one visible win. Momentum matters more than perfection.
Find the results
You don’t need to go to the moon. You just need the momentum.
Pick one problem worth solving. Set a number. Prove it in weeks, not years. If it works, make it normal. If it doesn’t, move on. Change things.
That’s how CEOs decide their AI path by making small, clear bets that pay for themselves.