Artificial intelligence is showing up in nearly every leadership conversation right now.

Most of those conversations start with technology.

What tools should we buy? What can we automate? Which AI platform should we standardize on? Where can we reduce manual work?

Those are reasonable questions, but they are not necessarily the most important ones.

Because AI does more than change the technology a company uses. It changes how work gets done, what employees are responsible for, where human judgment matters, how decisions are made, and even how people define the value they bring to their roles.

In a recent episode of Pipeline to Profit, Revenue Ops Founder and CEO Heather Davis Lam sat down with Drew Fortin, Founder and CEO of Lever Talent, to talk about what AI transformation really requires from leaders.

The conversation centered on an important idea:

AI transformation is not primarily a technology implementation. It is an organizational transformation.

The Technology Is Only Part of the System

Companies have spent decades implementing software.

CRMs, ERPs, marketing automation platforms, workflow tools, analytics platforms, and countless other systems have changed how businesses operate.

But most traditional software still relies on humans to define exactly what should happen.

A workflow says, “When this occurs, do that.”

A CRM gives people a structured place to enter and retrieve information.

An automation moves data from one system to another based on predetermined rules.

Artificial intelligence introduces something different.

AI can interpret information, work with unstructured data, generate outputs, make recommendations, and increasingly perform portions of work that previously required human interpretation.

That means leaders cannot simply add AI to the technology stack and expect transformation to happen.

Drew describes AI less as another tool and more as a resource that businesses must determine how to deploy.

That distinction matters.

If AI is treated as another software purchase, organizations may focus almost entirely on implementation.

If it is treated as a resource, leaders have to ask much broader questions.

Where should we use it?

Where should we not use it?

What work should remain human?

What decisions require judgment?

Who remains accountable for the outcome?

And how should jobs change as AI begins handling more of the transactional work?

Start With the Problem, Not the AI

One of the easiest mistakes companies can make is finding an interesting AI capability and then searching for somewhere to use it.

A new application can generate emails.

Another can summarize meetings.

Another can create presentations.

Another can automate customer interactions.

The technology may be impressive, but that does not automatically make it valuable.

The better question is:

What problem are we actually trying to solve?

Drew describes the importance of slowing down long enough to understand the system before introducing a solution.

Where is the bottleneck?

What process is breaking down?

What information is missing?

What outcome are we trying to improve?

Once those questions are answered, organizations can determine whether AI, automation, process redesign, training, organizational changes, or some combination of those things is the right answer.

Sometimes technology will be the answer.

Sometimes it will only be one part of it.

Automation Changes What We Need People to Do

There is understandable concern about what happens to jobs as AI becomes more capable.

But another way to look at the change is to ask:

If technology handles more transactional work, what becomes more valuable for humans?

Drew points to capabilities such as:

• Judgment
• Creativity
• Curiosity
• Systems thinking
• Relationship building
• Contextual decision-making
• Adaptability

As repetitive work decreases, those skills become increasingly important.

The goal should not simply be to automate someone’s existing responsibilities and leave the rest of the job untouched.

Leaders need to rethink the role itself.

If AI removes five hours of administrative work each week, what should happen with those five hours?

Should the employee spend more time with customers?

Analyze more complex problems?

Improve processes?

Develop new capabilities?

Coach other team members?

Experiment with new ways of working?

The productivity gain only becomes meaningful when the organization intentionally decides where that capacity should go.

Efficiency Cannot Be the Only Goal

There is a strong temptation to judge every AI initiative by how much time or money it saves.

Efficiency matters.

But efficiency is only one possible outcome.

An automation can reduce costs while simultaneously damaging customer experience.

It can save employees time while removing the part of their job where they felt they created the most value.

It can produce more output while reducing trust.

It can accelerate decisions while lowering decision quality.

During the conversation, Drew shared an example of an employee whose company began using AI to draft client emails.

From a productivity perspective, the use case made sense.

But the employee loved writing those messages. They viewed that communication as part of their personal contribution to the client relationship.

The organization had automated a task without fully understanding what the task meant to the person doing it.

That does not necessarily mean the company should stop using AI to help write emails.

It means the role needs to be redesigned thoughtfully.

Perhaps AI creates the initial draft while the employee adds the context, personality, and judgment that turns it into a meaningful client interaction.

The conversation shifts from:

“How do we automate this?”

to:

“How do we use technology to help this person create more value?”

That is a very different transformation strategy.

AI Literacy Is Not the Same as AI Fluency

Many companies have started educating employees about AI.

They explain what large language models are.

They offer prompting training.

They introduce tools such as ChatGPT or Claude.

They establish basic policies.

That is important, but Drew makes an important distinction between AI literacy and AI fluency.

AI literacy is understanding the technology.

Employees become comfortable with AI, understand common tools, know how prompts work, and begin identifying tasks where AI might help.

AI fluency goes further.

It is the ability to rethink how work gets done when humans and AI operate together.

Drew compares the shift to moving from single-player mode to multiplayer mode.

Instead of individual employees experimenting with AI independently, the organization begins building a shared way of working with it.

That requires more than technical training.

It requires a framework.

The Four Cs of AI Fluency

Drew recommends leaders think about AI initiatives through four areas:

1. Context

What information does the AI need?

What documentation exists?

Where is the source of truth?

What knowledge should the system use when making recommendations or generating outputs?

Many organizations discover during AI projects that the real problem is not the AI at all.

It is that their processes were never clearly documented or their information is scattered across multiple systems.

AI forces organizations to confront those gaps.

2. Constraints

What is the AI allowed to do?

What is outside its scope?

Which parts of the workflow should remain human?

What data limitations exist?

Where does the organization need additional safeguards?

Just because AI can participate in a process does not mean it should own every part of that process.

Defining boundaries is just as important as identifying opportunities.

3. Connections

Where does the information come from?

Which systems will AI interact with?

Where will its outputs go?

AI rarely exists in isolation.

It may need data from CRM, ERP, support, marketing, HR, collaboration, or other business platforms.

Understanding those connections is essential if AI is going to become part of an actual operating process instead of a standalone experiment.

4. Control

Who owns the outcome?

Who is responsible when the AI is wrong?

What governance is required?

When should AI usage be disclosed?

What review or approval should happen before an output becomes a business action?

AI may perform the work, but accountability cannot disappear.

Organizations still need people who are responsible for decisions and outcomes.

Healthy Experimentation Still Needs Direction

Companies should experiment with AI.

Employees need opportunities to learn what works, what does not, and where AI can genuinely help them.

But experimentation and fragmentation are not the same thing.

Without coordination, a company can quickly end up with dozens of disconnected AI tools, personal workflows, unofficial processes, and inconsistent approaches to sensitive information.

Drew recommends beginning with education and conversation.

Some organizations are forming cross-functional AI groups.

Others are creating internal town halls or collaborative sessions where employees can share what they are testing.

Some dedicate specific time for teams to experiment with AI.

Those efforts help employees move past the initial uncertainty and begin seeing possibilities.

Then leadership can start identifying the strongest ideas, applying a consistent framework, and turning experimentation into coordinated organizational capability.

AI Transformation Requires Leadership

One of the most important takeaways from the conversation is that AI transformation cannot simply be delegated to IT.

It cannot be delegated entirely to HR either.

Technology leaders absolutely need to be involved.

HR leaders will play an important role in skills, roles, training, organizational design, and change management.

But AI increasingly touches nearly every part of the business.

That makes it a leadership issue.

Senior leaders need to determine where AI supports the company’s strategy, what capabilities the organization needs, how roles will change, how success will be measured, and where human judgment remains essential.

They also have to create the space for the organization to learn.

Transformation rarely happens because someone purchases the right technology.

It happens because the organization changes how it operates.

Go Back to the System

Toward the end of the conversation, Drew offered a simple piece of advice:

Go back to the system.

Every organization is made up of people, processes, technology, information, incentives, responsibilities, and relationships.

Changing one part affects the others.

AI is accelerating that interconnectedness.

Automating a task may change a role.

Changing a role may change a customer interaction.

Changing that interaction may change what success looks like.

Changing success metrics may change how employees behave.

That is why leaders need to create enough space to look at the whole system instead of focusing only on the newest technology.

The companies that get the most value from AI will not necessarily be the ones that automate the most.

They will be the ones that make deliberate decisions about where technology creates value, where human judgment matters, and how the two should work together.

AI can remove low-value transactions.

It can increase capacity.

It can help people analyze information, generate ideas, and make better decisions.

But realizing that potential requires much more than implementation.

It requires leadership.

Listen to Pipeline to Profit

Want to hear the full conversation?

Listen to this episode of Pipeline to Profit with Drew Fortin, Founder and CEO of Lever Talent, for a deeper discussion about AI transformation, organizational design, talent, leadership, AI fluency, and the future of work.

And if your organization is considering how AI, automation, CRM, and revenue operations should work together, Revenue Ops can help you turn the technology into a practical operating strategy that supports the way your teams actually work.