When it comes to real generative AI use cases, the number of enterprises that are in the experimentation and expansion stages jumped from 62% to 71% between July and September 2023, said Forrester recently.
That percentage growth represents one of the fastest mass adoption rates of a new technology in the enterprise, the firm noted.
Success in real generative AI use cases requires full enterprise support and turning the technology’s friction points—BYOAI and coherent nonsense—into opportunities, Forrester advised.Â
The company recommends that organisations should do the following to harness the full potential of their real generative AI use cases.
Make trust an intrinsic part of their enterprise’s genAI foundation. A deliberate and cohesive approach to trust is essential for long-term success.
Companies that help their employees and customers understand the nuances of privacy and security will empower their users to be more confident and innovative in their use of this technology.
Stay focused on practical, measurable use cases. GenAI use cases most often involve the augmentation or transformation of an existing product, service, or business process.
In initial prototypes and use cases, select projects that lean toward employee-facing or offline generation, as opposed to real-time generation with a chatbot.
Explore and invest in skills that are both known and emerging. To harness genAI successfully, both employees and leaders need to upskill continually.
These include technical as well as soft skills to communicate the impact of genAI transparently and openly with employees.
Differentiate by relying on their own business data. Foundational models are powerful tools but accessible to everyone.
To create differentiation, organisations should rely on their own business data to build genAI models and applications.
