What it takes to become an AI company: Four lessons from the top

Every company is under pressure to become “an AI company.” Over the past several months, we’ve been undertaking that transformation at Precisely, changing how we build, work, and measure progress.

That experience has reinforced something for me: the hardest parts of becoming an AI company have relatively little to do with the technology.

AI can help an organization move faster, automate work, increase capacity, and fundamentally change what people can accomplish. But none of those things guarantees better business performance.

“The hardest parts of becoming an AI company have relatively little to do with the technology.”

Four leadership lessons have emerged from that experience:

Start with the business objective

Simplify before accelerating

Redesign how people work

Measure the results that matter

Start with the business objective

First, clarify what the business is trying to achieve. That needs to be a business outcome: growth, faster innovation, stronger customer relationships, better products, improved margins, or something else that matters to the strategy.

AI dramatically expands what is possible. That makes focus critically important. Leaders must establish clear priorities: what needs to improve, what the organization needs to start doing, and just as importantly, what it needs to stop doing.

That clarity filters where to invest, which processes to change, and where AI can create the most value.

Without it, AI can proliferate complexity rather than reduce it. Organizations deploy more tools, launch more agents, and generate more activity without a clear connection to the business outcome they are trying to achieve.

That’s why I think of AI as an operating model, not a strategy. Strategy establishes where the business is going and why. AI can change how quickly and effectively you get there.

Simplify before you accelerate

Once the objective is clear, leaders need to examine how work gets done.

AI can make almost anything move faster. That’s both the opportunity and the problem.

Apply AI to a process that no longer adds value, and you’ve simply made a bad process run faster. Automate one step while leaving the same unnecessary approvals, handoffs, meetings, and decision points around it, and the bottleneck moves from one point to another.

“Apply AI to a process that no longer adds value, and you’ve simply made a bad process run faster.”

I’ve seen this firsthand. As AI accelerates areas like software development, constraints often shift to the processes around the work. If an engineer can generate code 4x faster, but that code still waits in the same queues and moves through the same handoffs, the organization hasn’t created 4x the value.

The entire system has to evolve.

The principle is straightforward:

Eliminate what doesn’t add value à Simplify what does à Then use AI to accelerate it

Redesign how people work

Technology alone doesn’t transform an organization. People have to change how they work.

I saw this in an unexpected place at Precisely: our summer intern program.

Our interns arrived with different levels of AI experience and different ideas about what it might mean for their careers. Over ten weeks, I watched those perceptions change. Once they could apply AI to real work, they moved faster, explored challenges more deeply, and became more confident in what they could accomplish.

What struck me most was how quickly their sense of what they were capable of changed, and how contagious that confidence became.

That experience reinforced something I believe leaders need to understand about AI: the baseline for what people can accomplish is changing. And this extends well beyond people entering the workforce.

As AI takes on more execution and routine work, people at every level have greater capacity to set direction, evaluate outputs, solve difficult problems, build relationships, and apply judgment and expertise.

“Giving employees access to AI and measuring usage will only take an organization so far. Leaders need to examine how the work itself should change.”

That has significant implications for how leaders think about jobs, skills, and development. The assumptions we have built around what a role looks like, how long it takes someone to become proficient, and what people at different career stages can contribute will have to evolve.

Workflows need to evolve, too. Giving employees access to AI and measuring usage will only take an organization so far. Leaders need to examine how the work itself should change: where AI belongs in the process, what it can take on, where human judgment remains essential, and what people can now accomplish with the capacity AI creates.

Our interns gave me an early glimpse of what that can look like. The leadership challenge is creating the conditions for that same shift to happen across the organization.

Measure what the business gets back

Finally, leaders need to be disciplined about measuring progress.

At some point, AI adoption became a business metric. Organizations track how many employees are using AI, how many agents they’ve deployed, or how many tokens they process.

Those numbers can tell you whether people are using AI. They can’t tell you whether AI is creating value.

We never measured the ROI of email. We didn’t track spreadsheet adoption or ask for weekly reports on how many documents were created in Word. We treated them as infrastructure and measured what the business did with them.

Adoption is an input, not an outcome.

Productivity should create capacity. That capacity should let teams move faster, bring ideas to market sooner, spend more time on work that requires judgment and expertise, and create better customer experiences. Those gains then need to translate into growth and stronger business performance.

“Adoption is an input, not an outcome. The greater opportunity is expanding what an organization can accomplish with the people and expertise it already has.”

That’s why I believe leaders should see AI as an opportunity to do more with the same, not as an opportunity to do more with less.

If your AI strategy focuses on headcount reduction, you’re treating AI as an efficiency tool. That can reduce costs in the short term, but it doesn’t provide long-term value. The greater opportunity is expanding what an organization can accomplish with the people and expertise it already has.

The leadership challenge ahead

I’m optimistic about what AI will mean for work.

Technology has always changed what people do and raised the ceiling on what they can accomplish. AI is accelerating that pattern.

But simply having the technology won’t create competitive advantage.

The differentiator will be how organizations use it.

That means staying focused on the business objective, eliminating complexity before accelerating it, redesigning work around what people and AI can accomplish together, and holding the transformation accountable to business results.

The true measure of AI is what your business can now do that it couldn’t do before.

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