“How do we make sure our AI projects actually deliver business value?”
It’s a good question that executives are asking, and an urgent one too. AI is moving fast, and most organizations are still figuring out how to go from early experiments to measurable results.
Here’s the hard truth: most AI projects don’t deliver. Studies show 85% of AI initiatives fall short of expectations, and nearly three-quarters never even make it to full production. That’s almost twice the failure rate of traditional IT projects. The reasons aren’t technical, they’re strategic.
AI doesn’t fail because the tech isn’t capable. It fails because no one defined what success looked like from the start. Follow these six steps to ensure that AI delivers real business outcomes.
1. Start with a Business Goal, not a Tech Demo
If the first sentence in your AI plan is about “testing a new model” or “exploring what ChatGPT can do”, stop. That’s a recipe for shelfware.
The organizations that win with AI start with a real, high-value business objective. Reduce claims processing time by 40%. Improve on-time deliveries. Flag compliance issues before they become fines.
Make that goal the anchor. Tie it to a KPI the business already tracks. Then, and only then, consider the technology.
2. Pick Use Cases Where You Have Both Impact and Data
The best idea in the world will fail without the right data foundation. Before you greenlight a project, check:
- Do we have the data?
- Is it accurate, accessible, and complete enough to train and run an AI model?
- Can we measure results against our baseline?
Research highlights the top reasons for AI failure: misunderstanding the real problem, insufficient training data, and focusing on technology over user needs. Mismatches between business goals, data readiness, and execution plans account for the bulk of AI initiative failures.
Start with accessible, high-value areas. Early wins build credibility and help you earn the buy-in to tackle harder problems later.
3. Put Governance in Place Early
If you want AI to scale, you have to be able to trust it. This is where governance comes in. That means:
· Clear ownership, one business sponsor accountable for outcomes, one technical owner accountable for performance and risk.
· Defined success metrics that are tracked.
· Guardrails for data usage, bias mitigation, and regulatory compliance.
· Repeatable processes for monitoring, updating, and retiring models.
This isn’t just “AI risk management”. It’s the difference between isolated experiments and a sustainable, organization-wide capability.
4. Foster Cross-Functional Leadership
AI can’t live in IT or data science alone. The magic happens when domain experts and technologists work side by side.
The share of organizations with AI executives reporting directly to CEOs increased from 17% in 2023 to 31% in 2024, signaling the shift toward AI as a strategic imperative.
“AI success doesn’t mean hiring full time CAIO, it’s about building the smartest bridge between technology and business value.”
5. Measure, Review, Improve, Repeat
Without clear, agreed-upon metrics tied to business value, AI loses momentum fast. In fact, only 11% of firms report successful outcomes from most of their AI initiatives, with 44% citing a gap between expectations and results.
Track things like:
- Hours saved
- Errors avoided
- Costs reduced
- Revenue or customer engagement gained
Review progress regularly and refine as you go. The best AI programs are living, breathing systems, not one-and-done launches.
6. Make AI Part of the Culture
AI isn’t a launch it and leave it initiative. Ongoing training, change management, and user experience focus are essential for adoption.
It’s a shift in how your organization thinks, works, and measures success. That means training people on how to use AI in their work, designing change management around adoption, and creating feedback loops so the tech keeps getting better. When teams are educated, empowered, and engaged, AI doesn’t just work, it sticks.
Shaping the Future with Purpose-Driven AI
AI is not a magic wand. But when it’s tied to a clear business purpose, backed by governance, and driven by measurable results, it becomes one of the most powerful levers you have for growth and efficiency.
The companies that take a business-first approach will shape their markets. The ones that don’t will end up as another failed AI statistic.
If you want to talk about how to align AI to your organization’s goals, contact Hartman Executive Advisors today for a free consultation.
