Philips Trains 70,000 Employees to Use AI, Starting at the Top

Philips is scaling AI literacy across its entire 70,000-person workforce, moving the technology out of specialised machine learning teams and into the hands of every employee. The 134-year-old healthcare technology company, which operates across personal health, diagnostics, image-guided therapy, and patient monitoring, says the shift is less about adopting new tools than about changing how the whole organisation thinks.

The executive running that effort is Patrick Mans, Head of Data Science & AI Engineering at Philips. His framing of the journey is deliberately simple: "You start playing with it, then you start working with it—and from there, you start innovating with it."

Not a new technology, a new scale

AI is not new at Philips. The company has embedded specialised AI and machine learning systems in its products for years. What has changed is the ambition: Philips wants AI to become a capability that every employee can confidently use, not just experts in dedicated teams.

According to Mans, broad transformation required something different from the specialist work Philips already did well. It required AI literacy across everyone.

One factor made the rollout easier than it might have been: familiarity. Philips chose to work with OpenAI because employees already knew the tools. "People were already using OpenAI tools privately—so the curiosity was there. We just needed to channel it into real work," Mans says.

The Toy → Tool → Transformation curve

Philips is intentionally moving employees along a curve the company describes as Toy → Tool → Transformation. The rollout combined pressure from the top with pull from the bottom.

First, executives were trained hands-on, so leadership could lead by example rather than by mandate. Then a company-wide challenge invited employees to propose their own use cases. Finally, access to ChatGPT Enterprise increased demand and momentum across the organisation.

The result, Philips says, was momentum from both directions: leadership endorsement combined with grassroots pull.

Culture before technology

As a healthcare technology company, Philips operates under strict safety, privacy, and regulatory expectations. That constraint shaped the entire programme. Trust and responsible use of AI are foundational, and Mans argues that implementation alone is not enough.

"You can't just implement AI as technology. You have to shift the culture—how people think, and how they trust," Mans says.

To build that confidence, Philips sequenced the rollout carefully. The company began with low-risk internal workflows. Teams were encouraged to experiment in controlled environments. Responsible AI principles—transparency, fairness, and human oversight—were formalised and adopted organisation-wide. Only after confidence and skill grew did Philips begin moving toward workflows with patient impact.

That sequencing matters beyond Philips. Healthcare is one of the most heavily regulated sectors for AI deployment, and the industry is watching how large medical technology companies build internal trust before touching clinical processes. Philips' approach—internal experimentation first, regulated workflows second—offers a template for any organisation where AI errors carry real-world consequences.

Fifteen minutes to save a life, fifteen minutes to document it

The strategic priority now is reducing administrative burden, especially in clinical environments where time is critical. Mans grounds the goal in a story from a hospital visit:

"I was in a hospital where a clinician spent 15 minutes saving a life—and then had to spend 15 minutes documenting it. He could have saved two lives in that same time."

That arithmetic drives the company's focus: give clinicians time back to care for patients. Administrative reduction, Philips argues, is the fastest path to meaningful impact, because it attacks the documentation load that sits between clinicians and the patients in front of them.

What the programme has delivered so far

Philips summarises the results of the rollout in five points:

• AI literacy and hands-on use are expanding across the organisation.
• Executive leadership has been trained directly, modelling the change.
• Bottom-up idea challenges are accelerating experimentation.
• A trust-building approach is enabling movement into regulated workflows.
• Strategic focus has settled on reducing administrative burden in clinical environments to give time back to healthcare professionals.

Notably, the company reports no single headline benchmark or cost-saving figure. The results are structural: literacy, leadership engagement, experimentation volume, and readiness for regulated use. That reflects the stage of the programme—Philips is building capability now and targeting workflow-level impact next.

The lessons Philips is drawing

Philips distils its experience into five lessons for other large organisations undertaking similar transformations:

1. Lead from the top. Train leadership hands-on so they model usage, not just mandate it.
2. Fuel bottom-up momentum. Give people ways to propose, test, and own their use cases.
3. Align early—AI moves faster than most organisations. Prepare stakeholders upfront so momentum becomes an advantage, not a blocker.
4. Make responsible AI principles real. Transparency and human oversight are essential, especially in healthcare.
5. Focus where time matters most. Administrative burden is the fastest path to meaningful impact.

The third lesson carries particular weight for companies of Philips' age and size. A 134-year-old firm with strict regulatory obligations cannot move as fast as a startup, and Philips' answer is to align stakeholders before momentum builds, not after.

What comes next

Philips is now moving from individual productivity gains to workflow-level automation and agent-supported processes, with a clear AI policy and responsible AI principles already in place. The company frames the goal in human terms: giving clinicians back time so they can spend it on their patients.

Mans puts the end state plainly: "We want to deliver better care for more people. AI is one of the most powerful tools we have to do that."

The next phase—agents operating inside clinical and clinical-adjacent workflows under formal responsible AI governance—will test whether the literacy and trust built over the past rollout can carry Philips from employee experimentation into the regulated heart of healthcare delivery.