The leadership blind spot with AI

A manufacturing CEO showed me his new AI system earlier this year. He spent $250,000 on predictive maintenance technology that promised to cut downtime by 50%.

He looked confused when I asked how he assessed their readiness for the tool. "The vendor showed us case studies where it worked well."

Six months later, the system sat unused. The maintenance team didn't trust it, and data quality issues remained unfixed. The dashboard looked great in meetings, but they used the same old processes on the floor.

This happens across industries. Executives rush to buy AI without evaluating whether their organizations can effectively utilize these tools. The gap between AI's promise and real value isn't about technology but leadership blindness.

The Seduction of AI Vendor Promises

I've sat through hundreds of AI vendor presentations. They follow a pattern: impressive case studies, big ROI claims, and slick dashboards that make complex problems look easily solved. Executives often leave these meetings convinced they've found their solution.

The problem? Vendors aren't selling what you need.

They're selling the final chapter of a book when you haven't written the first chapter. They show results from organizations that have already developed the capabilities, data infrastructure, and processes necessary to utilize AI effectively.

What the sales decks don't show:

• Months of data cleaning and integration work.

• Process changes needed to act on AI insights.

• The training required to build trust in the system.

• Governance frameworks for responsible use.

When leaders choose AI tools based on vendor promises and demos, they rely on intuition that often fails in this domain. Even experienced executives fall into this trap because AI readiness involves technical and organizational factors not visible in normal management metrics.

Why Executive Intuition Fails with AI

Your leadership intuition comes from years of business experience. It's valuable, but has specific blind spots with AI:

1. The Visibility Problem: You can't directly see your organization's data quality, integration capabilities, or technical debt until they derail your AI project.

2. The Case Study Fallacy: Vendor examples typically come from organizations that have already solved foundational problems. They rarely show the preparation work that made success possible.

3. The Demo Illusion: Polished demos show AI working with clean data in controlled settings, conditions that rarely exist in real operations.

4. The Timeframe Disconnect: AI implementations often take 2-3 times longer than projected, especially when readiness issues emerge midway.

5. The Learning Cost: First-time AI implementations require substantial organizational learning not captured in vendor ROI calculations.

You don't need to become a technical expert to make good AI decisions. You need frameworks that make the invisible visible and overcome the blind spots in your business intuition.

A Practical Readiness Framework for AI Selection

After working with dozens of organizations on AI implementations, I've developed a simple framework that helps leadership teams evaluate their readiness before selecting AI tools.

Step 1: Assess Data Foundations

Evaluate your organization's data foundations:

• Data Quality: How clean, complete, and accurate is the data for your AI use case? Be honest. Most organizations overestimate their data quality.

• Data Integration: How connected are your data sources? Can information flow between systems without manual work?

• Data Governance: Do you have clear ownership, security, and privacy protocols for your data?

Score each from 1-5, where 5 is excellent. If your average is below 3, fix these foundations before selecting any AI tool.

Step 2: Evaluate Process Readiness

Examine the business processes that will need to change:

• Decision Clarity: Is there a specific decision or action the AI will inform? Who owns this decision?

• Process Flexibility: How adaptable are your current processes? Can they incorporate AI-driven insights?

• Feedback Loops: Can you track outcomes and feed that information to improve the AI system?

Again, score each dimension. If your average is below 3, focus on process redesign before AI implementation.

Step 3: Assess People Readiness

Evaluate your organization's human capability:

• Technical Skills: Does your team have the skills to implement, maintain, and troubleshoot AI systems?

• Change Capacity: How much change can your organization handle right now? Are you managing other significant changes?

• Trust Factors: Is there a culture of trust around data-driven decisions? Or resistance to algorithmic recommendations?

If your average score is below 3, you need to spend some time investing in your people’s skills before proceeding.

After assessing these three areas, you should begin evaluating specific AI tools and vendors. This ensures your selection matches what your organization can use, not just what looks good in a sales pitch.

A Real-World Example

A healthcare provider wanted an AI system for patient flow optimization. Their first approach was to evaluate vendors based on features and claimed ROI.

Instead, we ran them through the readiness framework:

Data Foundations: They scored 2/5. Patient data was spread across three unconnected systems. Historical data had significant gaps.

Process Readiness: They scored 3/5. Clear decision owners existed, but processes were rigid.

People Readiness: They scored 4/5. The team was technically skilled and open to new approaches.

Based on this assessment, they shifted their approach. Before implementing AI, they:

1. Started a data integration project to connect their systems

2. Launched a data quality initiative focused on inputs needed for the AI model

3. Ran a process redesign workshop to ensure their workflows could use AI recommendations

Six months later, they successfully implemented the AI system, achieving results that matched vendor promises because they had first built the necessary foundations.

Breaking the Pattern

To avoid AI failure, break the pattern of selection based mainly on vendor promises:

1. Start with use cases, not tools: Define the business problem before looking at specific AI solutions.

2. Assess your readiness honestly: Use the framework to evaluate your organization's ability to implement AI successfully.

3. Invest in foundations first: Be willing to delay AI implementation to build necessary capabilities.

4. Pilot and iterate: Start small, with limited implementations that allow for learning.

5. Measure what matters: Define success metrics focused on business outcomes, not technical milestones.

This approach might seem slower at first, but it dramatically increases your odds of getting real business value from AI. In almost every case I've seen, organizations that invested in readiness first achieved faster time-to-value than those who rushed into implementations they weren't prepared for.

The Executive Advantage

As AI transforms industries, the differentiating factor won't be which executives bought the most advanced tools. It will be those leaders who accurately assessed their organization's readiness and built the necessary capabilities before major investments.

The leadership blind spot in AI selection isn't technical—it's strategic. By recognizing the limitations of executive intuition and applying frameworks that make the invisible visible, you can avoid the expensive lessons many organizations are learning the hard way.

I help leadership teams evaluate and build their AI readiness. If you're considering significant AI investments this year and want to discuss how this framework applies to your situation, reply to this email. I'd be happy to share more detailed assessment tools.

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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