Cutting through the AI noise: When to invest and when to wait

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Most business leaders today aren't asking, "Should we use AI?" but rather, "Where should we apply it first?" It's the right question.

The AI hype cycle feels all too familiar: big promises, vendor pitches, and pressure to adopt. But if you approach it strategically, there's real potential beneath the marketing froth.

Here's how to cut through the noise.

Start with Problems, Not Solutions

The biggest mistake executives make is starting with AI rather than with their business challenges.

Instead of asking, "How can we use AI?" ask, "What are our most significant inefficiencies or opportunities?" Then, evaluate whether AI is genuinely the right answer.

Three areas where AI consistently delivers real value today:

• Data processing automation: Tasks involving high-volume document processing, classification, or extraction

• Predictive analytics: Forecasting based on historical data patterns that humans might miss

• Customer interaction optimization: Personalization and next-best-action recommendations

Execution Matters More Than Technology

I've never seen technology alone transform a business. What transforms businesses is implementing technology and executing the business strategy with that technology.

Before approving any AI project, ensure you have:

1. Clear success metrics defined upfront

2. Adequate data infrastructure

3. A culture ready to adapt to AI-augmented processes

4. Implementation experts who understand both the technology and your business domain

One manufacturing client shelved their ambitious AI initiative for six months to strengthen their data governance. It was the right call, and ultimately led to a successful implementation.

When to Wait

Despite what vendors tell you, sometimes the best move is strategic patience. Wait when:

• Your core data is fragmented or of poor quality

• The AI solution addresses a peripheral rather than core business need

• The ROI calculation requires heroic assumptions to pencil out

• Your team lacks the expertise to evaluate AI claims critically

Remember this little nugget I’ve learned over my career: being second or third to implement often lets you learn from others' expensive mistakes.

The most successful AI implementations aren't always the most technologically impressive. They're the ones where leaders correctly match specific business problems with appropriate technology solutions and execute them with discipline.

In this age of AI hype, a measured, problem-first approach isn't just prudent…it's your competitive advantage.

If you found this post helpful, consider sharing it with another executive grappling with AI, technology, and data. If you want to explore AI and other Technology strategies, grab some time on my calendar, and let's chat.

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