The AI market is flooded with buzzwords and exaggerated claims. Every vendor promises transformation. Every tool claims to be revolutionary. Without a framework for evaluation, it's easy to chase shiny objects instead of real value.
Before you invest time and resources into an AI initiative, run it through this reality check.
1. Feasibility Assessment
Start with the practical questions:
- Do you have quality data to support it? AI is only as good as the data it learns from. Garbage in, garbage out.
- Is it compatible with existing systems? Integration complexity kills more AI projects than technical limitations.
- Do you have the talent to maintain it? Someone needs to own this after launch. Who is that person?
If you can't answer these confidently, you're not ready—regardless of how promising the opportunity looks.
2. Business Impact Clarity
Can you measure the impact in concrete terms?
- Efficiency gains: Hours saved, throughput increased, cycle times reduced
- Cost savings: Labor costs, error reduction, resource optimization
- Better decision-making: Faster insights, improved accuracy, reduced bias
If you can't articulate the value in terms the CFO would understand, it's probably hype. "AI-powered" isn't a business case. Measurable outcomes are.
3. Pilot Before You Commit
Vendor claims are just that—claims. The demo always works perfectly. The case studies always show impressive numbers. But your environment isn't their demo environment.
Run a controlled pilot to verify results before going all-in. Define success criteria upfront. Set a timeline. And be willing to walk away if the results don't materialize.
4. Complexity Check
This is the question most teams skip: Could a simpler solution work?
Sometimes basic automation handles the job. Sometimes a process improvement eliminates the problem entirely. Sometimes a well-designed spreadsheet outperforms a machine learning model.
AI should be the answer when simpler approaches fall short—not the default starting point.
5. Long-Term Viability
Will this solution still make sense in two years?
- Is the vendor stable and growing?
- Does the technology require constant retraining or manual intervention?
- Are you building on open standards or locking into a proprietary ecosystem?
The worst AI investments are the ones that work great initially but become maintenance nightmares over time.
The Ultimate Test
Before greenlighting any AI project, ask yourself this:
"Would this problem exist if we just improved our current processes?"
If the answer is no, you don't need AI—you need optimization. Fix the process first. Then, if a gap remains, AI might be the right tool to fill it.
Knowing when to say no is just as important as knowing when to say yes. The companies getting real value from AI aren't the ones implementing the most tools. They're the ones implementing the right ones.