It’s easy to feel the pressure around artificial intelligence. Board members are asking about it. Competitors are touting “AI-powered” capabilities. And headlines promise a future that’s already here. For CIOs and business executives, the urgency to “do something” with AI is real — but so is the risk of doing the wrong thing too quickly.
The truth? Most AI initiatives underdeliver not because the technology fails, but because the business didn’t ask the right questions from the start. Before greenlighting your next AI project, consider these five questions that can make or break your ability to drive meaningful value.
- What business problem are we actually solving — and is AI the best way to solve it?
AI is a powerful tool, but it’s not a silver bullet. Too often, companies lead with the technology — “Let’s build something with generative AI!” — without a clear link to business value. The result is often a proof of concept that never gets off the whiteboard.
Instead, start by identifying pain points, inefficiencies, or missed opportunities in your business. Then ask: would AI make this better, faster, or smarter in a way that other approaches wouldn’t? For example, a global logistics company recently realized that 80% of customer delivery inquiries could be answered by analyzing tracking and weather data. Instead of hiring more agents, they built an AI-powered assistant that reduced call volume by 40% and improved customer satisfaction — because the business problem was clearly defined from the start.
According to Gartner, 80% of AI projects never make it past the pilot stage. Clear problem definition is one of the top reasons why.
- Do we have the data — and the data practices — to support this initiative?
AI lives and dies on data. But having lots of data isn’t the same as having the right data — in the right format, with the right level of quality and governance.
Before pursuing any AI initiative, ask: Where will the data come from? Is it clean, complete, and accessible? Who owns it, and how is it managed? Are there privacy, compliance, or security concerns?
A recent MIT Sloan study found that only 13% of organizations feel confident in their data strategy to support AI at scale. If your AI idea depends on pulling data from five siloed systems — some of which are still running on spreadsheets — it’s worth addressing those foundational issues first.
- How will we measure success, and what’s our threshold for ROI?
Not all AI wins are flashy. In fact, many of the best applications are operational improvements that fly under the radar — reducing manual effort, speeding up decisions, or improving accuracy.
But if you don’t define what success looks like, it’s impossible to know whether your initiative is working. Be specific about the KPIs you’ll track, whether that’s time saved, cost avoided, revenue unlocked, or customer experience improved. A consumer goods company recently used AI to forecast demand more accurately at the SKU level. They didn’t automate the entire supply chain — just reduced overproduction by 9% in one category. That alone delivered a 7-figure impact.
Pro tip: Include both business metrics and adoption metrics. If people don’t use the AI solution, it doesn’t matter how smart it is.
- Do we have the right cross-functional team to support this effort?
AI is not just an IT initiative — it’s a business capability that cuts across functions. That means your team should include more than just data scientists and engineers. You’ll need input from Business owners (who define the problem), Operations leaders (who own the workflows), Legal and compliance (for governance and risk), and End users (to validate usability and adoption).
Some of the most successful AI teams we’ve seen are small, agile, and business-led — with just enough technical expertise to get things built and enough business context to ensure they get used. If your AI initiative is run entirely by the tech team with no executive sponsorship or functional involvement, pause and recalibrate.
- How will we manage risk, compliance, and ethical considerations?
Executives are increasingly aware of the reputational and regulatory risks associated with AI — from biased algorithms to data privacy issues to hallucinating chatbots that give the wrong answers.
Even in the absence of formal AI regulations, you need guardrails:
What data can and can’t be used?
Who reviews and approves models before deployment?
What happens if the AI makes a mistake?
How will we maintain transparency with customers and employees?
The most mature companies are building lightweight but effective AI governance frameworks — not to slow things down, but to ensure trust and sustainability. As McKinsey notes, “risk and responsibility are quickly becoming differentiators for AI success.”
Start Smart, Not Fast
AI has the potential to transform how your organization operates — but only if it’s grounded in real business problems, backed by good data, and supported by the right team and governance.
So before you say “yes” to that exciting AI proposal, ask these five questions. The answers may not always be easy — but they’ll save you time, money, and frustration down the road.