Cedario Analytics

Every week, another vendor promises that their AI platform will transform your business. Every week, another article warns that companies not adopting AI will be left behind. The pressure to act is real — but so is the risk of acting for the wrong reasons.

The truth is that AI is genuinely powerful for certain problems and genuinely wrong for others. The companies that get the most value from AI aren’t the ones that move fastest — they’re the ones that ask the right questions first.

The 4 Questions to Ask Before Any AI Project

1. Do you have the data?

Machine learning models learn from historical data. If you don’t have enough of it, or if what you have is messy, incomplete, or siloed across different systems, an AI project will struggle from day one. This doesn’t mean you can never pursue AI — but it does mean data readiness needs to be part of your plan.

2. Is the problem well-defined?

“We want to use AI to improve our operations” is not a problem statement. “We want to reduce equipment downtime by predicting failures 48 hours in advance using sensor data from our production line” is. The more specific the problem, the more likely AI can be built to solve it effectively. Vague objectives produce vague — and expensive — outcomes.

3. What does success look like?

Before building anything, you need to define what “working” means. What metric improves? By how much? Over what timeframe? Without a clear definition of success, there’s no way to evaluate whether the investment paid off — and no way to know when to stop iterating.

4. Do you have the internal capacity to maintain it?

A deployed AI model isn’t a set-it-and-forget-it solution. Models need to be monitored, retrained as data drifts, and maintained as your business evolves. If your team doesn’t have the skills or bandwidth for this, the model’s performance will degrade over time. Planning for ownership from day one is not optional.

Signs AI Will Create Real Value for Your Business

• High-volume, repetitive decisions. If your team makes the same type of judgment call hundreds or thousands of times — approving transactions, classifying documents, flagging anomalies — a well-trained model can do this faster, cheaper, and with fewer errors.
• Large datasets you’re not using. Most organizations are sitting on years of operational data that has never been systematically analyzed. If that data captures patterns relevant to a business outcome you care about, AI can extract value from it.
• Clear bottlenecks in existing workflows. If a specific step in your process is consistently slow, error-prone, or resource-intensive, that’s a candidate for automation or optimization. AI works best when the target is specific.

Signs You’re Not Ready Yet (and What to Do About It)

• Your data is messy or siloed. Start with a data audit and consolidation effort before pursuing AI. The investment in data infrastructure will pay off regardless of whether AI follows.
• No clear problem statement. Work with your team to define specific, measurable outcomes you want to improve. Talk to the people closest to the problem — they usually know where the pain is.
• No executive buy-in. AI projects that lack leadership support rarely survive contact with the organization. If you can’t get a sponsor at the leadership level, the project will stall at integration and adoption — even if the model works perfectly.

The Bottom Line

The best AI projects start with an honest assessment, not a technology choice. Before you evaluate vendors, build a business case, or hire a team, spend time with these four questions. The answers will tell you whether you’re ready to move forward — and if so, where to start.

If you’d like a structured way to work through this, we offer a free 30-minute strategy call where we walk through your specific situation and give you an honest read on readiness and fit.