Embrace the generative AI era: Six adoption essentials 3 Get your proprietary data ready Customizing foundation models will require access to domain-specific organizational data, semantics, knowledge, and methodologies. In the pre-generative AI era, companies could still get value from AI without having modernized their data architecture and estate by taking a use-case centric approach to AI. That’s no longer the case. Foundation models need vast amounts of curated data to learn and that makes solving the data challenge an urgent priority for every business. Companies need a strategic and disciplined approach to acquiring, growing, refining, safeguarding and deploying data. Specifically, they need a modern enterprise data platform built on cloud with a trusted, reusable set of data products. Because these platforms are cross-functional, with enterprise-grade analytics and data housed in cloud- based warehouses or data lakes, data is able to break free from organizational silos and democratized for use across an organization. All business data can then be analyzed together in one place or through a distributed computing strategy, such as a data mesh. Read more on the practices data-mature companies are using to maximize enterprise data value: A new dawn for dormant data: Unleash the intrinsic value of enterprise data with a strong digital core on cloud. 4 Invest in a sustainable tech foundation Companies need to consider whether they have the right technical infrastructure, architecture, operating model and governance structure to meet the high compute demands of LLMs and generative AI, while keeping a close eye on cost and sustainable energy consumption. They’ll need ways to assess the cost and benefit of using these technologies versus other AI or analytical approaches that might be better suited to particular use cases, while also being several times less expensive. As the use of AI increases, so will the carbon emissions produced by the underlying infrastructure. Companies need a robust green software development framework that considers energy efficiency and material emissions at all stages of the software development lifecycle. AI can also play a broader role in making business more sustainable and achieving ESG goals. Of the companies we surveyed that successfully reduced emissions in production and operations, 70% used AI to do it. 12 17 A new era of generative AI for everyone |