How to Realize Value from a GenAI-Enabled Workforce

Thanks to OpenAI’s ChatGPT, pretty much everyone knows about GenAI today. Its ability to satisfy people’s thirst for knowledge with just a simple prompt sent it viral. This tool's usage is truly impressive. It gained a million users in just five days and attracted more than 100 million visitors in its first few months. Individuals and organizations are integrating it into their daily lives and activities with great enthusiasm.

And yet – while GenAI is globally famous, few have moved far beyond experimentation. Organizations are excited by its potential but often struggle to adopt it at a scale that can ultimately create measurable value.

In my role, I’ve been fortunate enough to be able to witness how AI is evolving the way organizations operate and the value it can deliver to customers. Yet, businesses need a guide to turn potential into performance. With these challenges in mind, my team implemented a roll out experimentation with Microsoft’s 350 Copilot, to develop valuable insights and practical strategies for companies aiming to achieve successful adoption and meaningful ROI.

Our path to GenAI value

As we looked into adopting Copilot, our approach helped us identify where its capabilities could add value.

Our experience could be helpful for any organization looking to introduce GenAI into its workflows.

Here are some of the actions that helped us along the way:

• Start with a structured adoption framework. For introducing GenAI capabilities, we began by identifying personas in our organization who might benefit from them, and then specific and highly targeted use cases for the technology. Finally, we have created personalized training plans for each role or persona that guide users carefully, so they know exactly how to make the most of the capability.
• Use experimentation to validate the technology. For Copilot, we ran an exercise with three groups of users. Group A had no Copilot licenses, while for Group B, we simply gave these users access to the tool, with no training or guidance: it was up to them to work out what to do. Group C got our full adoption framework. The results? We saw a 31% boost in adoption in Group C compared to Group B. Moreover, Group C registered time savings of 2.5 hours per week versus 1.8 hours per week for Group B. The exercise also gave us baseline data, for example on how much time teams could save on specific tasks such as creating presentations. This was another strong example and argument confirming that our adoption framework was working.
• Involve employees closely in the process. Exercises like our Copilot experiment help ensure that people engage more readily with new technology. We got people closely involved in selecting the use cases for Copilot, which makes it more relatable, driving adoption and ultimately improving ROI. This process creates evangelists, too. Because our Group C cohort could clearly see the technology’s value for them, they championed it across the company and especially with their teams, encouraging further adoption.
• Create hyper-personalized and continuous training plans. We worked with project managers and process owners to make sure that the Copilot use cases were relevant to their everyday tasks, such as producing presentations at very short notice. Armed with this understanding, we created highly tailored training that showed how technology could help them reach their goals. Also, we found that continuous training around creating prompts was very valuable in helping people get the best value out of GenAI. It is also fun and helps keep the community united. For example, we have created a group in which we are sharing useful prompts, and we also have regular short sharing sessions.
• Leveraging partners. We tapped our partner to help us by coming in with specific use cases and training offers that helped build our skillsets. In a domain that changes as fast as GenAI, partnership and collaboration are essential to getting good outcomes.
• Communicate proactively about employees’ concerns. Questions about ethical AI and whether it will steal people’s jobs are common. It’s therefore important to ensure that the adoption framework clearly defines ethical AI and the ethical use of AI. To ensure responsible and secure use of AI, we leveraged our Responsible AI framework. This framework provides clear guidelines for our employees, aligning with our company values and helping them use AI responsibly. And to allay concerns about GenAI’s impact on jobs, we focused on its ability to take over unpopular mundane and time-pressured tasks such as minute-taking, drafting communications, or sifting through a crowded email inbox. As their proficiency grew, we introduced more sophisticated techniques, including enhancing their ability to create advanced prompts that yield more precise and tailored outputs.

Time, innovation and training

Our experience with Copilot and other GenAI projects is that a structured pilot phase is key, and that people need time to learn the innovative technology. It is also necessary to have a framework for AI adoption and change management that is tailored to your team’s specific needs. Coupled with training and active engagement of users, this will motivate and clear up concerns about GenAI.

Once the technology embeds itself in the organization and spreads out, it becomes part of the culture and accelerates your path to realizing real value from GenAI.

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