How our CIO and CPO partner to turn AI adoption into a people-first transformation

AI transformation usually starts with tools: a new assistant, a workflow automation, or a team experimenting with prompts to save time. But as AI becomes part of our everyday work, the challenge quickly becomes bigger than technology. Scaling AI requires people, processes, and technology to evolve together.

This realization shaped how our Chief Information Officer (CIO), Michal Lewy Harush, and Chief People Officer (CPO), Nirit Peled-Muntz, partnered to guide AI adoption across HiBob. This story is about that partnership—and the role Bob played in helping us turn a people-first AI philosophy into a company-wide transformation.

As Michal explained, “AI is a revolution. It’s not just another technology we use. It’s a mindset.” 

This perspective changed the conversation from choosing AI tools to asking a broader question: How do we help people work differently in ways that feel clear, safe, and useful?

For us, technology was never the hardest part. The real challenge was helping people build the confidence, habits, and mindset to work differently. That’s why we approached AI transformation as a people transformation. 

When the Technology and People & Culture teams aligned around a shared strategy, the answers began to take shape. Because Bob already served as our foundation for people data, feedback, performance, and workforce insights, we had a platform we could build on as AI adoption accelerated.

The shift started when experimentation became operational

At first, AI experimentation appeared everywhere. Teams were testing prompts, automating repetitive work, and discovering faster ways to handle manual tasks. The energy was exciting, but it also introduced a new challenge. Experimentation scales much faster than operating models do.

Nirit and Michal quickly realized they were solving two sides of the same problem:

Technology focused on building the right foundations with secure infrastructure, governance, and scalable capabilities. People & Culture focused on helping people embrace change by building trust, confidence, new skills, and the habits needed to adopt new ways of working. 

A shared understanding shaped our broader approach to AI transformation. Instead of relying only on top-down adoption, we combined leadership direction with bottom-up experimentation, creating the conditions that made managers and people want to explore AI in ways that felt relevant to their work.

As Nirit often says, adoption works best when it starts with curiosity rather than compliance.

Rather than asking how to get people to use AI, we asked how to remove the barriers that prevent naturally curious people from experimenting. We believed that when people feel supported, trusted, and equipped, adoption follows naturally. 

In practice, this meant:

• Creating safe spaces to experiment
• Giving teams ownership to explore AI in ways that made sense for their work
• Equipping managers with the tools and confidence to lead change
• Making room for experimentation—and learning from failure
• Redesigning workflows instead of layering AI onto existing processes
• Building new skills in AI, process design, and new ways of working

The challenge wasn’t introducing more AI tools. It was redesigning how work happened across the organization.

From AI tools to better workflows

One of our earliest decisions was to refuse to treat AI as a side initiative. Instead, we approached it as a workflow redesign.

As Michal explained, the conversation shifted from asking whether AI could perform a task to asking how the process itself should change once AI became part of it.

This shift changed how experimentation happened across the company. The people closest to operational friction became responsible for identifying opportunities where AI could genuinely improve work, not simply automate activity for the sake of it.

Removing repetitive work in HR operations

One example came from HR operations. Every month, HR business partners spent time manually following up on employee time reports, a repetitive task that added little strategic value and caused frustration. One team member built an AI-powered Slack workflow connected to Bob that automated reminders and follow-ups.

Nirit explained, “It’s repetitive work that needs to happen every month. It’s something we could automate and run in the background while we continue our other work.”

Ultimately, every repetitive task AI removes creates more space for the work only people can do: building relationships, solving complex problems, making better decisions, and helping others grow. 

Bringing AI guidance into performance reviews

Another example emerged during HiGrowth performance reviews in Bob. Instead of requiring people to leave the workflow and use separate AI tools, we embedded AI support directly into the review experience, enabling them to structure feedback, summarize accomplishments, and improve consistency without leaving the process.

Nirit highlighted that people appreciated having AI guidance embedded directly into the process, helping them complete reviews more efficiently while still reflecting thoughtfully on their work.

Across multiple use cases, we saw the same pattern emerge: AI created the most value when it supported human decision-making instead of trying to replace it.

Creating the conditions for adoption

As adoption accelerated, we had to create sufficient structure to keep experimentation safe while preserving the curiosity that had people experimenting in the first place.

Michal’s team created a safe sandbox for experimentation, giving employees room to explore AI while maintaining appropriate governance, infrastructure, and security. At the same time, People & Culture focused on psychological safety, making sure employees understood that experimentation, learning, and even getting things wrong were part of the process.

One of our biggest learnings was that lasting transformation doesn’t come from Technology or People & Culture alone. It comes from leaders who create environments where people feel safe to experiment, learn, and gradually build new ways of working.

While Technology and People & Culture could provide tools, training, and guardrails, managers helped teams figure out what AI adoption looked like in practice. They often saw first where confidence was growing, where people needed support, and where new ways of working were taking hold.

That role became especially important because adoption didn’t happen uniformly across the organization. Some people immediately embraced new tools and workflows. Others needed more guidance, examples, and encouragement before they felt comfortable changing how they worked.

As managers helped teams navigate that transition, Bob became a central place to connect performance conversations, employee feedback, learning initiatives, and workforce insights. Rather than treating AI adoption as a separate initiative, managers could incorporate it into the development conversations they were already having with their teams.

Measuring progress without losing trust

As AI became part of everyday work, we needed ways to understand how adoption was progressing without turning measurement into a source of pressure. That meant looking beyond usage alone and asking whether people felt confident, supported, and safe enough to experiment.

We built that reflection directly into the employee experience.

One way we did this was by adding a new AI section to the HiGrowth performance review in Bob. The goal wasn’t to score people on AI usage. It was to encourage reflection and help people think about their own progress, confidence, and mastery.

As Nirit explained, we wanted people to pause and ask themselves whether they were moving in the right direction, where they might need more support, and how they could continue building the skills they need to be successful.

We also used Bob to listen at the organizational level.

Whenever we do something meaningful at HiBob, we want to understand how people feel, what they’re missing, and what we can do better. So we added a six-question AI deep dive to an employee engagement survey in Bob.

We believed the strategy was working, but the favorability ratings confirmed that people were experiencing AI adoption as we intended: as something safe, useful, and connected to their work.

One response stood out more than any other:

“I’m allowed to experiment and fail.”

That response mattered because it showed that the culture around AI was taking hold.

As Michal explained, the AI team worked to encourage democratization, creativity, and experimentation by making it clear that there was no wrong idea when people were trying to improve their work, their department, or a process they faced.

For us, that became one of the strongest signals that AI adoption was moving beyond tools and into culture. The real progress showed up when people felt safe enough to try, learn, and build new ways of working.

AI transformation became human transformation

One of the biggest surprises in our AI journey was how quickly the conversation shifted from technology to people.

The tools mattered, but the bigger transformation happened when we started redesigning workflows, creating space for experimentation, and helping people build confidence in new ways of working. What made this possible was the partnership between Technology and People & Culture, combining governance and infrastructure with mindset, adoption, and employee experience.

Bob helped connect those efforts. By embedding AI into performance conversations, development workflows, and employee feedback, we were able to bring AI adoption into the systems people were already using every day.

Looking back, our biggest lesson wasn’t about AI itself. It was about people. Technology will continue to evolve, but lasting transformation happens when people feel confident enough to learn, adapt, and rethink the way they work. That’s what turns AI adoption into lasting business change. 

Key takeaways

• AI transformation is an operating-model shift, not a technology rollout. Its purpose goes beyond efficiency—it creates more space for people to do what they do best.
• Bring AI into the flow of work. Embedding AI into existing workflows drives greater adoption than introducing another tool.
• Bob helped make AI part of everyday work. By embedding AI into existing people workflows, Bob made adoption feel like a natural extension of the way people already worked.
• Trusted people data is the foundation of effective AI. A reliable source of truth helps teams make better decisions and builds greater trust in AI.
• Technology and People & Culture must work hand in hand. One builds the capabilities. The other builds the confidence, skills, and habits that make change stick.
• Psychological safety fuels adoption. People are more willing to experiment, learn, and share when they feel safe to do so.
• Measure progress in ways that build trust. Reflection and feedback reveal more than monitoring usage alone.
• Managers turn experimentation into lasting change. They create the environment where new tools become new habits.