FEATURE: SKILLS GAP ambitious approach by launching more than 60 pilot projects at once. These pilots spanned a wide range of functions – including services, the call centre, free-form language queries on engineering data, analytics and more. However, most of these projects did not progress to full production, largely because they faced challenges in scaling effectively at the enterprise level.
Early in the process they realised that focusing on processes around customer experience( CX) is a good idea. According to Gartner, 76 % of organisations already use GenAI to enhance their internal efficiency and achieve cost savings and growth in processes related to customer experience. The team kicked off with a pilot project, building a GenAI-powered virtual assistant capable of understanding and resolving common support inquiries such as billing questions or technical troubleshooting. This was one of the few projects that made it to production.
By starting with this focused, high-volume use case, they could test, learn and refine their solution with realworld data and feedback. They relied on robust data governance, enabling the virtual assistant to access up-to-date information from key business systems while enforcing strict privacy and compliance controls.
Lessons learned on the path to GenAI success
In summary, realising the full potential of GenAI in the enterprise requires more than exciting technology – it demands a strategic approach, strong data foundations and a commitment to privacy, security and scalability. By learning from successful organisations, focusing on practical business outcomes and ensuring AI initiatives are grounded in trusted, real-time data, companies can turn GenAI from pilot projects into sustainable sources of value. The journey may be complex, but with the right mindset and solutions, true GenAI impact is well within reach. p
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