With 87% of UK businesses planning global expansion, managing a dispersed workforce is a top priority for business leaders. At Jamf, with over 3000 locations, I knew the answer lay in automation — and GenAI was the tool to help us achieve that.
But with a crowded market and countless options, selecting the right GenAI solution wasn’t easy. The GenAI market is set to surge from $40bn in 2022 to $1.3trn in the next decade and many developers are trying to add their software into the mix.
As the CIO, I had to carefully evaluate what would bring the most value to Jamf, and this meant cutting through the noise and focusing on scalability, security and user acceptance, while avoiding the pitfalls of tech sprawl.
Here’s how we approached GenAI integration and the lessons I learned along the way.
Defining GenAI Goals
At Jamf, our journey with GenAI began nearly two years ago, and it’s been a continuous process of adapting to rapid change.
We began by identifying specific pain points and figuring out what we wanted GenAI to accomplish. For us, it was automating repetitive processes and freeing up employee time for higher-value work.
We wanted to streamline tasks across IT, HR and other operational areas to support our hybrid workforce. The goal wasn’t just to implement GenAI for the sake of following a trend, but to drive efficiency where it mattered most.
As well as our goals, we also began by considering any potential risks and challenges. Security and data privacy quickly became top issues that we needed to account for.
I worried that employees might start using unauthorized AI tools without oversight from IT and security teams, for example. We needed safeguards that protected our systems without stifling innovation.
For CIOs starting their GenAI journey, it’s so important to have a north star. Without an end goal in mind, there’s no direction during implementation and you’ll soon hit roadblocks.
Having a clear purpose allowed us to focus on the right tools that aligned with our organizational objectives. With those fundamentals in place, it allowed me to measure the value and impact of GenAI tools on our workforce.
Selecting the Right GenAI Tools
Knowing our goals was only half the battle, however. Navigating the GenAI marketplace was one of the most challenging aspects of the process.
With so many SaaS platforms offering similar solutions, it’s tough to know if you’ve made the right decision.
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I had to constantly ask, "What value does this bring to Jamf?", and I soon ended up coming to the conclusion that less is often more when it comes to effective GenAI integration.
The temptation to adopt multiple tools was real, but I quickly realized that more tools didn’t mean better results.
Instead, I prioritized finding a solution that integrated well with our existing systems and organizational structure.
It would have been ridiculous for me to buy five different tools. It would have confused and frustrated users and added unnecessary complexity and security risks.
Streamlining our approach helped us avoid the tech sprawl trap while ensuring scalability for the future.
Remember GenAI is supposed to make things easier for everyone not tougher.
Preparing for Implementation and Addressing Pushback
It was critical that when we didn’t rush the implementation of GenAI. I’d seen colleagues and other companies make the mistake of rushing cloud deployment and IoT, and they’re still paying the price for it.
It had never been more important for me to foster cross-team collaboration and communication. As the CIO, I was the glue between explaining GenAI to the board and directing technical teams.
In the end, we set up a cross-functional governance council, which included representatives from IT, HR, security and data privacy teams, to ensure the GenAI tools we implemented aligned with our organizational standards.
However, even with all best laid plans and preparation, implementation was not easy. Like any technology that gets rolled out, we experienced resistance, however, it was not where we expected.
When we started rolling out GenAI tools and functions, it was really well received by users. However, soon our engineers expressed discomfort with how AI was being used, particularly around code recommendations. They were skeptical of AI’s ability to suggest meaningful changes to their work.
It’s at this point, I realized the importance of communication, especially when you make changes after initial pushback. To address this, we took a measured approach.
We rolled out GenAI to pilot groups first, allowing teams to test the tools in real-world scenarios. This helped us gather feedback and refine our implementation before full-scale deployment.
Ultimately, success was down to open communication – ensuring that everyone felt heard and that their concerns were being addressed.
Embracing Feedback and Refining the Process
Like any successful tech implementation or process change, bringing in AI needs a continuous plan, not for it to be treated as a one-time project. Ongoing feedback was a critical part of our GenAI journey.
The dialogue also allowed us to catch potential problems early and make necessary adjustments. This level of collaboration and openness was essential to ensuring the tools aligned with our broader strategic goals.
Also, open communication meant that employees started coming to us with new GenAI tools they were interested in. It was great to see – we had managed to convince users to explore and engage with GenAI tools, but in a safe manner that we could control.
Keeping the End Goal in Mind
Ultimately, deploying GenAI is about more than just automation – it’s about ensuring long-term success.
Throughout the process, I kept asking myself, “What’s the endgame? What do we hope to achieve in the long run?”
At Jamf, the goal was to build a scalable, secure, and efficient GenAI system that could grow with our needs. By staying focused on that objective, we avoided the common mistake of adopting AI just to follow trends. Instead, we implemented a solution that genuinely added value to our operations.
For any CIO navigating the complex world of GenAI, my advice is to stay focused on your core objectives, embrace feedback, and always measure your results.
GenAI can be transformative, but only if it’s implemented thoughtfully and strategically.