During the second year of Kyndryl’s 24-month global IT transformation, we made a strategic decision to invest in generative artificial intelligence technology.
Copilot for Microsoft 365 was the logical choice for us and, after collaborating with Microsoft on a rollout strategy, our Copilot team launched a multiphase implementation in July 2023. Over the following 12 months, we worked with multidisciplinary teams across Kyndryl to execute and refine our strategy before vetting use cases and staging rollouts of Copilot licences.
By July 2024, we had assigned nearly 20,000 Copilot licences and amassed more than 600 approved use cases for the technology.
We learned a lot throughout the process and have distilled our lessons learned into five best practices to help guide other organisations on their Copilot journey.
1. Prepare your data to optimise performance
Data is the fuel for large language models that power generative AI tools like Copilot. Therefore, your data structure and information architecture must be flawless and adhere to the latest data privacy and cybersecurity standards to perform optimally.
Over the course of four months, Kyndryl’s Copilot team reviewed existing Microsoft 365 data controls, content lifecycles and data classifications to prepare the information architecture for Copilot.
During this process we tightened access controls in Microsoft SharePoint and reduced the number of internal public sites to approximately 10,000. We also automated the deletion of roughly 20,000 inactive SharePoint sites, developed a data classification module based on sensitivity, tagged content with relevant keywords and organised data.
2. Establish a robust governance model
Without proper guardrails for generative AI in place, users are at risk of misusing the technology or exposing sensitive data that can cause significant operational, financial or reputational damage.
During implementation, the Copilot team collaborated with legal, risk and human resources teams to review each use case request individually to ensure alignment with compliance standards and risk considerations.
Each month, administrators reviewed dashboard reports to measure the frequency of use and reclaimed licences from employees who hadn’t used the technology within a specified period. Reclaimed licences were then reallocated to individuals with approved use cases, ensuring employees who would benefit most from the technology had access to it.
3. Grant access incrementally
With tools like Copilot, it’s better to allocate licences over time than to immediately grant full access to the technology. Staggering implementation lets administrators review use cases and assess any associated risks that arise before deploying the technology to your entire workforce.
We had a two-phase rollout of Copilot following the preflight assessment period. This approach allowed a limited number of Kyndryl employees to experiment with the technology prior to purchasing thousands of licences, giving our implementation team time to determine which use cases provided the most business value.
4. Provide extensive training
Education is crucial for maximising the potential of Copilot, so it’s critical to start training users with the technology early in the implementation process.
Before we granted employees access to Copilot, they were required to attend training conducted in partnership with Microsoft. These educational sessions covered proper Copilot usage, including how to create effective prompts and ways to properly handle data.
We also worked with Microsoft to conduct role-specific training sessions to address the unique needs of different business units within Kyndryl. For example, Copilot specialists led interactive video training with our finance team to highlight use cases and demonstrate the technology’s capabilities in Excel, showcasing how Copilot can help with data summarisation and analysis in finance scenarios.
5. Create robust feedback cycles
It’s essential to collect feedback once employees start using Copilot. Internal administrators and Microsoft product teams can use these insights to fine-tune deployment, increase engagement and enhance the Copilot user experience.
Our Copilot team established feedback protocols for users to document their experiences in four categories: use case application, overall quality and performance, unauthorised access and technical questions.
We also empowered Copilot users to improve the quality of our organisational data. For example, if a user discovered outdated or inaccurate information, they were asked to report it to the Copilot team so administrators could correct the data.
The bottom line
Generative AI is rapidly evolving and new use cases are being discovered constantly, so early adopters will need to continually refine their systems and processes and offer ongoing education and training to take full advantage of tools like Copilot.
Even as Kyndryl explores the full power of generative AI, Copilot has already yielded significant time savings and contributed to a more efficient work environment for our teams. Our approach and lessons learned can serve as a blueprint for other organisations starting to use generative AI.
Peter Hudacko is lead infrastructure specialist at Kyndryl
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