Not only will these teams of collaborative generative AI agents work together, but they’ll also delve into the data stores, software systems, networks and processes that are already distributed throughout the business. Sitting in front of all this background orchestration is the conversational user interface that makes meaning out of all this activity.
Here’s how this might work. Let’s say the product design team needs to modify a home thermometer to include a speech interface to accommodate people with a visual impairment. You ask the conversational gen AI agent to analyze the existing product and suggest some alternatives. As the product modifications take shape, the gen AI agents align it with regulatory requirements, generate blueprints and prototypes, and keep other team members in the loop—all the way through marketing and promotion to launch day.
Meanwhile, the sales team is also building a generative AI-driven platform to develop customer insight reports. Because the two efforts are linked, the sales team is always in the loop with sales materials, and the product design team is continuously updated with real-time feedback, like the desire for larger buttons for easier operation.
The value is multiplied. Siloed systems are integrated, cumbersome user interfaces become intuitive, “swivel-chair” processes are obliterated. When all of this is achieved, we won’t even be talking about the term “ease of use” when it comes to generative AI-driven systems because it will be as natural to work as a search bar or voice prompt.
Three gen AI principles that maximize value
When generative AI is adopted in this way, all the previous technology investments form the foundation on which the generative AI-driven system will be built. This is because generative AI encompasses three principals that should be incorporated into any implementation strategy:
- Discoverability: Turning hidden data into treasure.
Legacy systems often become repositories of vast amounts of hidden data that are underutilized and forgotten. Generative AI can transform this scenario by crawling, indexing, and understanding the data that’s there. As a result, the gamut of enterprise systems, data and processes can become discoverable and usable by the generative AI agents, elevating their value.
- Interoperability: building a bridge to everywhere.
Generative AI agents can bridge the many existing systems and technologies that support the tasks done by disparate business functions like marketing, legal, procurement, operations, R&D, sales, marketing and customer service. When these connections are made, it eliminates hand-offs, streamlines workflows and delivers new insights to previously disconnected areas of the business.
- Repeatability: Scaling success without reinventing the wheel.
Sunk costs are most troubling when businesses need to repeatedly reinvent solutions for every new use case that arises. But with generative AI, businesses don’t need to continuously start from scratch. Businesses can create AI model repositories and retrain these models with new data sets when similar problems arise. The same model, using different data, can be used for a variety of uses, such as optimized pricing structures, new product design, logistics delivery network optimization and problem prediction in manufacturing.
Future-proofing your investments
Forward-thinking businesses will see generative AI as a way to amplify the value of their existing data and systems, uncovering insights, efficiencies and entirely new ways to work. When gen AI systems work together to orchestrate work, whatever businesses spend on modernizing their existing systems yields results beyond that area of expenditure.
Technology leaders should view generative AI as a powerful ally that can bring out the best in their existing systems, propelling their business toward a future marked by growth and innovation.
To learn more, visit the Generative AI section of our website or contact us.