The Biggest AI Mistake Organizations Make Happens Before Deployment

Learn the biggest AI mistake organizations make before deployment and the four leadership questions every executive should answer before adopting AI.

Artificial intelligence has quickly become a strategic priority for organizations across nearly every industry. Executive teams are evaluating use cases, employees are experimenting with new tools, and vendors continue promising greater productivity, efficiency, and automation.

Yet many organizations are asking the wrong question.

Instead of asking which AI platform they should deploy, leaders should first ask whether they understand what they actually want AI to accomplish.

That distinction may determine whether AI strengthens an organization or quietly weakens it over time.

During a recent Insight Jam Expert Keynote, Dr. Laura Spencer argued that the biggest AI mistake organizations make doesn’t occur after deployment. It happens before deployment, when AI adoption is driven by competitive pressure, executive expectations, or fear of falling behind instead of a clearly defined organizational purpose.

AI Strategy Often Begins With the Wrong Motivation

Every major technology arrives with bold promises.

The internet promised publishing. Social media promised connection. Video conferencing promised flexibility. Each delivered on those promises to some degree, but they also fundamentally reshaped how people work, communicate, and behave in ways few organizations anticipated.

Dr. Spencer argues AI represents another example. While many organizations view AI primarily as a productivity tool, its lasting impact is likely to be far broader. Rather than simply changing how work gets done, AI has the potential to reshape how people think, solve problems, and make decisions throughout the organization.

The problem is that many AI initiatives begin with external pressure rather than internal clarity.

Competitors are adopting AI.

The board wants an AI strategy.

Employees expect new tools.

Leadership fears being left behind.

Those motivations may explain why an organization is exploring AI, but they do not define what success actually looks like.

Without that foundation, organizations risk implementing technology without ever establishing the outcomes they hope to achieve.

Technology Should Follow Organizational Intent

One of the keynote’s strongest ideas is the distinction between accidental design and intentional design.

Many organizations adopt technology first and discover its organizational consequences later. New tools arrive, workflows evolve, and only after months or years do leaders begin recognizing the capabilities that have quietly improved—or deteriorated.

Intentional organizations reverse that sequence.

They first define what they want to be true about their organization.

Then they design the processes needed to achieve those goals.

Only then do they choose technology that supports those decisions.

That shift reframes AI from the center of strategy to an enabler of strategy.

Rather than asking, “How can AI transform our business?” leaders begin asking, “What kind of organization are we trying to build, and where can AI help us get there?”

Four Questions Every AI Leader Should Ask

To help organizations evaluate AI more intentionally, Dr. Spencer proposes four leadership questions:

• What is this for?
• What does it strengthen?
• What does it replace?
• What does it make too easy to do poorly?

Notice that none of these questions begin with technology.

Instead, they focus on purpose, capability, tradeoffs, and organizational quality.

For example, asking what AI strengthens moves the conversation beyond efficiency metrics and toward long-term capability development. Leaders should ask whether AI is improving employee judgment, strengthening decision-making, and helping teams solve increasingly difficult problems—not simply whether it allows work to be completed faster.

Likewise, understanding what AI replaces requires leaders to think beyond tasks and consider the human capabilities attached to those tasks. When technology consistently performs work that once developed judgment, analysis, or communication skills, organizations may unknowingly erode the very expertise they hope to scale.

AI Doesn’t Just Automate Work. It Changes Capability.

Perhaps the keynote’s most compelling insight is that AI adoption is never simply about automation.

Every tool changes people.

Dr. Spencer illustrates this through familiar examples like GPS navigation and smartphone calculators. While these technologies made daily tasks easier, they also gradually reduced the need to develop or maintain certain human skills. The convenience was real, but so was the tradeoff.

AI presents organizations with a similar challenge.

When AI drafts reports, conducts research, summarizes information, or prepares communications, leaders must ask which capabilities remain essential for employees to practice themselves.

Some repetitive work can and should be automated.

Other activities are “load-bearing” for professional development because they cultivate judgment, critical thinking, communication, and expertise over time. Eliminating those opportunities entirely may improve short-term efficiency while weakening long-term organizational capability.

That is why Dr. Spencer’s fourth question may be the most important.

What does AI make too easy to do poorly?

Technology often removes friction, but friction is not always a problem to eliminate. Preparing for a difficult meeting, writing a first draft, researching opposing viewpoints, or delivering constructive feedback all require effort for a reason. Those moments often develop stronger leaders and better decision-makers. AI can streamline those processes, but it can also make it easier to skip the thinking and human interaction that gave them value in the first place.

The Organizations That Win Will Read the Terrain First

Organizations often view AI adoption as a technology decision.

Dr. Spencer argues it is fundamentally a leadership decision.

AI will almost certainly improve productivity across countless business functions. But productivity alone does not guarantee stronger organizations. AI can accelerate good strategy just as easily as it accelerates poor strategy. It can strengthen excellent leadership or amplify existing organizational weaknesses.

Ultimately, technology is not the deciding variable.

Leadership is.

The organizations that realize the greatest long-term value from AI will not necessarily be the ones that deploy it first or deploy it most aggressively. They will be the ones that first define their purpose, understand the capabilities they want to strengthen, and carefully evaluate the tradeoffs AI introduces before implementation begins.

In other words, the biggest AI mistake organizations make doesn’t happen after deployment.

It happens long before the first AI tool is ever turned on.