I recently came back from Gartner's Data and Analytics Summit in Orlando, Floria. As expected, GenAI was a big area of focus and interest. One of the sessions that I attended was "10 best practices for scaling Generative AI."
The session highlighted the rapid adoption of generative AI, with 45% of organizations piloting and 10% already in production as of September 2023. While the benefits like workforce productivity, multi-domain applications, and competitive differentiation are evident, there are also significant risks around data loss, hallucinations, black box nature, copyright issues, and potential misuse.
Through 2025, Gartner predicts at least 30% of generative AI projects will be abandoned after proof-of-concept due to issues like poor data quality, inadequate risk controls, escalating costs, or unclear business value.
To successfully scale generative AI, the session outlined 10 best practices:
- Continuously prioritize use cases aligned to the organization's AI ambition and measure business value.
- Create a decision framework for build vs. buy, evaluating model training, security, integration, and pricing.
- Pilot use cases with an eye towards future scalability needs around data, privacy, security etc.
- Design a composable platform architecture to improve flexibility and avoid vendor lock-in.
- Put responsible AI principles at the forefront across fairness, ethics, privacy, compliance etc. Evaluate risk mitigation tools.
- Invest in data and AI literacy programs across functions and leadership.
- Instill robust data engineering practices like knowledge graphs and vector embeddings.
- Enable seamless human-AI collaboration with human-in-the-loop and communities of practice.
- Apply FinOps practices to monitor, audit and optimize generative AI costs.
- Adopt an agile, product-centric approach with continuous updates based on user feedback.
The session stressed balancing individual and organizational needs while making responsible AI the cornerstone for scaling generative AI capabilities.
Hope you found these useful. What are you thoughts on best practices for scaling GenAI?
