Companies will have thousands of ways to apply generative AI and foundation models to maximize efficiency and drive competitive advantage. But they’ll need to reinvent work to find a path to business value from this technology. Business leaders must lead the change, starting now, in job redesign, task redesign and reskilling people.
To get started, consider the following adoption essentials:
Dive in, with a business-driven mindset. Organizations must take a dual approach to experimentation. One, focused on "low-hanging fruit" opportunities using consumable models and applications to realize quick returns. The other, focused on reinvention of business using models that are customized with the organization's data. A business-driven mindset is key to define, and successfully deliver on, the business case.
Take a people-first approach. Focus on people as much as on technology, ramping up talent investments to address both creating AI and using AI. This means developing technical competencies like AI engineering and enterprise architecture and training people across the organization to work effectively with AI-infused processes.
Get your proprietary data ready. Foundation models need vast amounts of curated data to learn and that makes solving the data challenge an urgent priority for every business. Take a strategic and disciplined approach to acquiring, refining, safeguarding and deploying data. Ensure the organization has a modern enterprise data platform built on cloud with a trusted, reusable set of data products.
Invest in a sustainable tech foundation. Consider requirements for infrastructure, architecture, operating model and governance structure in order to leverage generative AI and foundation models—keeping a close eye on cost and sustainable energy consumption.
Accelerate ecosystem innovation. Access resources and expertise needed to build and scale AI applications. Take advantage of industry best practices and insights offered by ecosystem partners—big tech players, start-ups, professional services firms and academic institutions.
Level up responsible AI. Urgently assess whether the company's responsible AI governance regime is sufficiently robust before scaling up generative AI applications. Build in controls for assessing risks at the design stage and embed responsible AI principles and approaches throughout the business.