Too many companies are still approaching AI like it’s a parallel track. Something that lives in an innovation lab or gets its own press release.
But the real value of AI isn’t found in demos or isolated experiments. It’s found in alignment.
- Alignment with core business goals.
- Alignment with measurable outcomes.
- Alignment with the people who are accountable for results.
In my experience working with global enterprises, the AI initiatives that deliver real value don’t start with “What can we automate?”
They start with “What’s holding us back?”
Then it’s a matter of applying the right tools, whether that’s GenAI, machine learning, or automation, to the roadblocks that are slowing the business down.
And it works.
Back in the early days of cloud, I co-founded a company at the forefront of that shift. I’d ask CIOs what was driving their move to the cloud.
The most common answer? Cost savings.
And I’d have to explain, often awkwardly, that lift and shift doesn’t always save money. Sometimes it does the opposite. The real value came when companies used the cloud to directly support their core business objectives. Fortunately, showing real value helped us grow and get acquired.
It’s the same with AI.
When AI is tied to specific and measurable goals, like improving customer retention, reducing fraud, or accelerating decision cycles, it becomes a lever for transformation, not just another productivity tool.
This isn’t just my experience. It’s backed by research.
MIT Sloan found that companies aligning AI to strategic business objectives are significantly more likely to see real performance gains.
Harvard, McKinsey, and Forbes all emphasize setting SMART goals, involving stakeholders early, and embedding AI into the culture of the business, not keeping it off to the side.
Still, one of the most common mistakes I see?
AI projects that are disconnected from business outcomes.
- No KPIs.
- No clear definition of success.
- Just “we’re experimenting.”
If your AI effort can’t draw a straight line to a business result, it’s not going to scale. Or survive.
So what’s the better approach?
- Start with the friction points that are already costing you time, money, or opportunity.
- Set specific goals.
- Align the right people who know the business.
Build. Test. Measure. Improve.
Treat AI as a core capability, not a side project.
Because the future of AI doesn’t belong to the companies with the biggest models.
It belongs to the ones who know how to apply them to the right problems.
