Why AI Investments May Not Deliver the Growth Leaders Expect

Technology changes how work gets done. People determine whether the change succeeds.

Organizations are investing heavily in AI.

The promised outcomes are compelling:

• Increased productivity

• Greater efficiency

• Lower costs

• Faster execution

• Improved decision-making

For leaders under pressure to grow, those outcomes are difficult to ignore.

Yet many organizations are discovering something familiar.

The technology works.

The results are taking longer than expected.

In some cases, implementation has created new friction, confusion, and operational challenges that leaders never anticipated.

The problem may not be AI.

The problem may be implementation.

We've Seen This Before

This isn't the first time organizations have expected technology to accelerate growth.

Over the past several decades, businesses have invested heavily in ERP systems, CRM platforms, supply chain software, marketing automation, and digital transformation initiatives.

The technology often delivered what it promised.

Implementation was a different story.

Hershey (1999)

Hershey implemented a major ERP, CRM, and supply chain software initiative intended to improve operations.

Instead, implementation challenges disrupted order fulfillment during the company's most important selling season.

The result was approximately $100 million in unfulfilled orders during Halloween season.

The technology wasn't the problem.

The implementation was harder than expected.

Nike (2000)

Nike experienced significant supply chain software implementation issues that contributed to inventory imbalances and demand-planning problems.

The company reported an estimated $100 million impact on sales, missed earnings expectations, and saw its stock price decline sharply following the announcement.

Again, the lesson wasn't that technology failed.

The lesson was that implementation proved more complex than anticipated.

Zimmer Biomet (2024)

More recently, medical technology company Zimmer Biomet reported operational disruptions tied to an ERP implementation.

Shipping delays, production challenges, reduced guidance, and investor concerns followed, contributing to a significant decline in share price.

The pattern remains remarkably consistent.

Technology changes.

Implementation challenges remain.

AI Is Following the Same Pattern

Many organizations are approaching AI as a technology project.

But AI implementation is actually an organizational change initiative.

Technology enters an environment already shaped by:

• Existing workflows

• Existing habits

• Existing communication patterns

• Existing customer expectations

• Existing culture

AI does not arrive fully integrated into those systems.

People must adapt.

Processes must evolve.

Communication must change.

New expectations must be established.

The organizations that struggle are often focused almost entirely on AI readiness.

The organizations that succeed focus equally on leadership readiness.

What Successful Implementation Looks Like

I've also seen the other side of the story.

Years before today's AI boom, I participated in a partnership between Oklahoma State University–Oklahoma City and Amazon Web Services to implement one of the institution's early conversational chatbot initiatives.

At the time, this wasn't as simple as today's AI tools. The work required significant development, testing, refinement, and collaboration across departments.

What I remember most wasn't the technology.

It was the people.

Participation across departments was voluntary. Some teams were excited. Others were skeptical. Adoption varied based on comfort levels, perceived value, and willingness to invest the time required to learn something new.

A small group of us became champions for the initiative.

The work was often tedious.

We tested, refined, adjusted, and learned as we went.

The results were significant.

Financial Aid phone inquiries decreased by approximately 60%, improving the student experience while reducing pressure on staff.

The lesson I carried forward had very little to do with chatbots.

The technology helped.

The success came from people who were willing to learn, adapt, communicate, and help others navigate change.

Technology changed how work was done.

People determined whether the change succeeded.

Leadership Readiness vs. AI Readiness

Most organizations are evaluating AI readiness.

Do we have the tools?

Do we have the systems?

Do we have the budget?

Those questions matter.

But they overlook a more important one.

Are leaders prepared to guide the transition?

Technology adoption depends on people.

People require:

• Communication

• Expectations

• Training

• Feedback

• Confidence

• Support

Without those elements, even powerful technology can create friction rather than momentum.

Competence and Trust

Successful implementation requires more than training.

It requires competence and trust.

Competence asks:

Can our people successfully use these new tools?

Trust asks:

Do they feel supported while learning?

Not everyone experiences change the same way.

Some employees are energized by learning new systems.

Others become cautious.

Others become overwhelmed.

For some people, being asked to learn something new triggers curiosity.

For others, it triggers uncertainty.

They worry about making mistakes.

They worry about appearing incompetent.

They worry about job security.

Leaders often interpret these responses as resistance.

Many are actually signs of uncertainty.

Without trust, competence develops more slowly.

Without competence, confidence suffers.

Organizations need both.

Growth Breaks in the Connections

One of the biggest risks during AI implementation is that important connections break.

Communication pathways change.

Processes shift.

Responsibilities evolve.

Customer experiences are affected.

Information moves differently.

What appears to be a technology issue is often a connection issue.

Growth breaks in the connections between:

• People

• Processes

• Communication

• Customer experience

• Execution

AI simply exposes those gaps faster.

The Organizations That Will Win

The organizations that gain the most value from AI may not be the ones with the most advanced technology.

They may be the ones that prepare their people most effectively.

They invest in:

• Communication

• Training

• Leadership support

• Change management

• Organizational readiness

They understand that technology changes how work gets done.

People determine whether the change succeeds.

Conclusion

AI may be one of the most powerful business tools organizations have ever adopted.

But technology alone does not create growth.

People do.

Organizations that focus only on AI readiness may struggle to achieve the outcomes they expect.

Organizations that invest in leadership readiness, competence, trust, and implementation are far more likely to realize the value of their investment.

The question is no longer whether organizations should adopt AI.

The question is whether they are preparing their people to succeed alongside it.