Implementing AI without a problem is a fast road to failure

No matter where you turn, someone is talking about AI. It’s in C-suite conversations, team standups, strategy decks and town halls. Entire companies are rebranding themselves as AI-first, with some setting bold internal mandates to get ahead.

There’s real momentum behind AI — and just as much FOMO. Too often, teams start with the wrong question. Instead of asking, “What are we trying to solve?” they jump straight to “How can we use AI?”

That leads to trouble. It’s how you end up with high-priced pilots that don’t scale, tools that don’t fit the team or initiatives that fizzle out quietly. These missteps often leave everyone a little more skeptical about using AI the next time around.

AI should never lead. It must come after a clear business need and a defined problem worth solving.

Chasing AI without a cause

The pressure to do something with AI is real. When competitors are bragging about AI-generated content, automated workflows or AI cost savings, it’s easy to feel like you’re behind if you don’t have an AI use case ready to showcase.

But adopting AI for the sake of appearances or because it’s trending is a trap. Here’s what can happen:

• A team spins up a pilot using whatever AI tool is most accessible.
• It runs, but the data isn’t clean or the use case isn’t meaningful enough to matter.
• Internal teams disengage and stakeholders don’t see ROI.
• And the next time AI comes up, people are reluctant to get on board.

The opportunity cost? You’ve wasted time, budget and attention. Meanwhile, the real problems draining resources or slowing growth remain unsolved.

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AI is a tactic, not a strategy

AI is not a strategy. AI is a tool. It is a powerful tool, one that only works in service of clearly defined goals. Before you think about implementation, ask these questions to get to the heart of the matter. 

• What problem are we trying to solve? Be specific. Is it lead conversion? Churn reduction? Content production speed?
• Why does this problem matter to the business? Tie it to tangible outcomes like revenue, customer satisfaction or efficiency.
• What’s your current approach? What’s broken or slow? Understand the existing process to help clarify the opportunity.
• Do you have the correct data to support a solution? Where is that data? Is it accurate, accessible and structured? What else do we need?
• Who will use the solution and how? You need buy-in from those impacted. Tools that don’t fit into workflows won’t get used.
• How will we measure success? Define KPIs early. Otherwise, how will you know it’s working?
• Is AI even the answer? Sometimes the best solution isn’t AI. Would you be better served by training or better processes?

These questions will force you to clarify your intent, which is your most valuable asset when exploring AI.

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Common problems where AI can help

Once you’ve grounded your thinking in business needs, you might discover areas where AI could unlock real value. Here are some common marketing and operations challenges that lend themselves well to AI-powered solutions:

• Conversion gaps: Leads are coming in but not closing. Why? AI can help score leads, personalize touchpoints or identify drop-off points in the funnel.
• Content bottlenecks: Your team is drowning in content requests. AI can assist with first drafts, translations, repurposing and even tagging.
• Customer churn: You’re losing customers, but don’t know why. Predictive models can flag at-risk users earlier, giving teams a chance to act.
• Lack of personalization: You know customers want relevance. AI can help create micro-segments or even real-time personalization.

A business-first framework for AI

If you want to operationalize this thinking, here’s a simple framework I like to use.

• Define the business need: What are you trying to improve? Growth? Retention? Speed? Cost savings?
• Diagnose the root cause: Is the problem about data? Process? Technology? Resources? People?
• Evaluate possible solutions (AI or not): Would AI help? Or is there a more straightforward fix?
• Pilot with purpose: Pick a narrow use case with a clear KPI. Start small, learn fast.
• Measure, refine and scale: Prove it works. Then build on the momentum.

This approach is deliberate and agile. Rather than locking into a 12-month AI roadmap, you’re testing assumptions and learning in the context of real business needs.

Dig deeper: How marketers can go beyond random acts of AI and why they should

Beware of FOMO

One of the most significant risks of AI adoption is the fear of missing out. It looks like this:

• Trying a tool for the novelty.
• Launching a flashy pilot with no owner.
• Playing with outputs that never get used.
• Moving on when the next shiny thing appears.

This doesn’t just waste time. It sets your team back, creates confusion and breeds cynicism. Instead, treat AI like any other strategic investment. Ask what business outcome you’re looking to achieve. Starting with something small and valuable builds trust. 

AI won’t fix a broken process, clarify an unclear strategy or drive results if it’s adopted just to say you’ve adopted it. The next time someone asks, “What’s our AI strategy?” Try reframing it: “What are the biggest problems we need to solve?” Then you can determine if AI is the right tool to help.

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