Current gen AI use cases focus on internal efficiency gains
The existing use cases of generative AI are mainly focused on internal processes and tasks. AI and generative AI — they flow into each other — have already found their way into many areas.
The technology is used, for example, to generate content for media inquiries, categorize customer service requests, and pick out invoice details to automate account assignment. The focus here is on efficiency gains.
In content management, many companies are currently testing generative AI for its ability to create text, including product descriptions and translations. Some organizations are also using AI to automate simple image processing tasks. Here, too, the focus is on saving time.
While gen AI is yielding internal efficiency gains, very few have ventured far into customer-facing use cases — at least not without the support of "humans-in-the-loop."
Start and experiment with the lean business case
Managers are often faced with the challenge of creating a business case for generative AI. The lean business case or the use of innovation budgets for proof-of-concepts that you continually fine-tune can help here without having to show clear financial results right from the start. Lean thinking reminds us that the experimentation itself is crucial. The outcome isn’t only about commercial results, but also about gaining valuable insights and building AI skills in the team.
Infrastructure, processes, and people: laying the foundation
The quality of the data is crucial to the success of generative AI. As a result, companies that want to see success here should make investments in cornerstones like data strategy, cloud strategy, and governance. At the same time, the data must be aligned with the technology, people, and processes within the organization. Without this foundation, both experimentation and implementation become a real challenge.
Upskilling up to management level
Companies that want to see generative AI applied successfully need to offer employee training and education paired with access to resources. Laying the foundations we mentioned above comes first (and may feel like a given for people with AI experience). Companies can’t stop there, though. They need to support AI adoption with training and internal discussions, especially at the management level.
The future: focus on the customer interface
Moving forward, the experts gathered at our breakfast roundtable expect to see more gen AI uses cases centering on call center analytics and knowledge mining. They predict that the future of generative AI lies in the customer interface. Many participants hope gen AI can help to deliver an optimized customer experience, including the scaling of individual and fully personalized communication. For consulting-intensive services, efficiency gains from AI should maximize the time team members can spend directly interacting with the customer.
Generative AI will continue to transform companies and unleash new potential. As a result, companies benefit from starting now to define relevant use cases and carry out early tests and initiatives. That starts with laying the needed foundations, whether that’s in the area of data strategy, technology, employee training, or the process landscape.
