The Ripple Effects of AI

Why slowing down now can save leaders from costly mistakes later.

I was speaking with a CTO recently who made a profound point during our discussion on AI adoption. Essentially he said:

Quick AI decisions being made today are going to lead to people sitting in depositions three years from now—having to explain how and why they made them.

That statement captures what I’m increasingly seeing in the marketplace: AI FOMO. Many organizations feel pressure to jump into AI, often without a cohesive strategy, simply so they can say, “Yes, we’re using it.”

But here’s the reality: studies show that 80–95% of AI investments don’t deliver expected ROI or are deemed unsuccessful. Rushing in can create ripple effects that last far longer than the initial hype.

How to Avoid the Ripple Effects

1. Establish an AI Strategy

Develop a strategy and vision for AI at your organization, anchored in outcomes.

• Align initiatives with business goals and user experience
• Create a formal process for evaluating, approving, and introducing AI solutions
• Ensure each solution fits into the enterprise vision and avoid one-off “quick wins”
• Focus on integration and scalability, avoiding “agent sprawl”
• Incorporate AI literacy and education
• Identify areas where reskilling or upskilling will be needed and plan accordingly
• Continue to iterate and reassess as new capabilities are available

Without a clear strategy, organizations risk deploying fragmented AI tools that become unmanageable, impact the user experience and fail to deliver sustainable value.

2. Rethink Vendor Vetting

The market is exploding with new AI vendors and solutions that offer impressive features quickly. However, while it can be tempting to go for the quick AI win, leaders must properly vet the solution to ensure the proper elements are in place.

• Get in writing how data secured, stored, and leveraged
• Understand whether models are trained on your data
• If sensitive data is involved (e.g., PHI, student records), ensure the proper controls are in place to maintain compliance and confidentiality
• Clarify ownership of data and IP
• Gain transparency into which models are being used and how they might change over time.

Skipping this diligence risks regulatory exposure, compliance issues, and loss of control over critical business assets.

3. Invest in AI Literacy & Training

Policies and strategies only work if teams understand and adhere to them. That’s why AI literacy is essential for every organization:

• Train staff on proper use (e.g., avoiding sensitive data uploads)
• Build awareness of risks, retention policies, and vendor practices
• Ensure employees know how their AI usage is aligned, or potentially not aligned, with organizational policies

Without awareness and a clear understanding, even the best-written policies will fail.

The Bottom-line

AI isn’t going anywhere. Every technology wave follows the same cycle: rapid expansion, then consolidation. Right now, we’re deep in the expansion phase.

Don’t be afraid to slow down, step back and be intentional.

• Develop a cohesive strategy that prioritizes user experience and ROI
• Vet vendors and their solutions carefully
• Invest in literacy and governance

By doing this, IT leaders can avoid the negative ripple effects of rushed AI adoption and position their organizations for long-term, sustainable success.r organizations for long-term, sustainable success.