04.28.2026

Why Most AI Initiatives Fail: The Executive Prioritization Problem

By Dave Sandhoefner

The Eroding Moat

The Executive Prioritization Problem

Most executives I talk to believe they have an AI problem.

 

They don't.

 

They have the same prioritization problem they've always had, and AI just made it harder to ignore.

 

In my last post, I promised to cover why most AI investments in go-to-market are failing and the three places it actually drives revenue. That's what this is.

 

But stepping back, this is bigger than GTM.

AI is exposing a broader executive challenge that has always existed: there will always be more opportunities, more ideas, and more things a business could improve than there is time, focus, or organizational capacity to pursue.

 

AI is expanding what's possible for leaders.

 

But it's also amplifying the hardest part of the job: the number of things a company could improve keeps growing, and the pressure to pursue all of them keeps mounting.

 

Deloitte recently surveyed more than 3,000 senior leaders and found that while two-thirds reported efficiency gains from AI, only one in five were seeing meaningful revenue growth.

The gap isn't technology. It's prioritization and execution.

The cost compounds quickly. Misallocated resources and disconnected pilots create the illusion of progress while real constraints go unsolved. By the time leadership recognizes the pattern, competitors have pulled ahead and internal belief in AI as a capability has already started to erode.

 

Leadership has never been about doing the most things.

 

The best executives identify the few things that matter most and avoid the rest.

 

AI makes that discipline more important, not less.

 

The companies pulling ahead aren't starting with tools or possibilities.

 

They are working through three questions, with one critical step in between.

1. Why Do Most Companies Struggle to Prioritize AI Initiatives?

Most companies don't struggle because they lack good ideas.

 

They struggle because they lack clarity.

 

Before prioritizing anything, leaders need an honest picture of where the business is constrained:

 

     - Where is growth slowing?

     - Where is execution inconsistent?

     - Where is customer value eroding?

     - What are we good at and what should we build on?

     - What will have the biggest impact on our goals if improved?

 

Visibility and prioritization are inseparable.

 

One without the other is either analysis without action or action without direction.

 

AI changes this.

 

Getting that level of visibility used to require significant time, resources, and specialized analysis, and often produced conclusions that were already stale by the time they were delivered.

 

Now leaders have access to pattern recognition across the business, outside perspectives, benchmarking, and a tool that can actively challenge assumptions instead of simply reinforcing them.

 

The prioritization decision gets faster, sharper, and harder to rationalize away.

2. How Should Executives Identify the Right AI Strategy?

Many organizations start their AI journey in the wrong place.

 

They begin with AI tools looking for problems instead of starting with the most important business problems and applying AI intentionally against them.

 

Once a business objective is defined, the fundamentals still matter: design the right solution, identify the right tools, align the team, and execute with discipline.

 

AI changes this too.

 

In go-to-market, leaders can now apply AI directly against the revenue constraints that matter most: identifying where pipeline quality breaks down, where deals stall, where execution becomes inconsistent, where onboarding slows, and where customer value erodes.

 

And they can increasingly do it in ways tailored to their specific business, competitive position, product, market, customers, and operating realities instead of relying solely on broad generic playbooks or tools.

 

The goal is not broad AI adoption.

 

The goal is measurable business impact.

3. How Do Companies Measure AI ROI and Business Impact?

 

Execution without a closed loop isn't execution. It's activity.

 

The companies seeing sustained impact from AI are not treating implementation as the finish line.

 

They are building accountability, measurement, iteration, and learning directly into the operating motion from the beginning.

 

The best organizations systematically identify which behaviors, workflows, and operating motions are driving measurable impact, then accelerate and replicate them across the business.

 

AI changes this too.

 

Closing the loop used to require dedicated resources, manual analysis, and separate reporting motions that often got deprioritized as soon as the next initiative emerged.

 

Now feedback cycles can happen continuously, providing up-to-date analysis that is visible to all stakeholders.

 

That's not a small efficiency gain. It fundamentally changes how quickly organizations can learn, adapt, and improve.

Most companies are generating AI activity.   The ones pulling ahead are generating AI impact.

None of this is a new way to run a business.

 

     - Identify what matters most.

     - Design the right solution.

     - Execute and iterate.

 

I've run this motion hundreds of times.

 

What's new is that AI makes every step faster, sharper, more adaptive, and more accessible than ever before, without the overhead that used to make this level of rigor available only to the largest companies.

 

The companies winning with AI won't be the ones doing the most.

 

They'll be the ones who got the right things right and built the discipline to keep improving faster than everyone else.

Next Artcle: “AI Is Giving SaaS Companies a Memory. The Winners Will Use It to Create More Customer Value.”