Because of the way they are built, structured, enterprise-aligned AI applications offer multiple benefits. Integration with multiple systems means a wider array of data, which improves recommendations and outputs. It’s easier to achieve scale, because the same tool can support thousands of users.
Outputs are more consistent, as they can be standardized. Use is more closely controlled and auditable, reducing governance risk. Because they are evaluated closely for impact on cost, speed, quality, or innovation, we also believe they will be more likely to yield greater ROI, since with measurement comes the ability to rapidly address flaws and calibrate costs.
Some companies have already made the necessary pivot to building these kinds of systems. Earlier this year, for example, Johnson & Johnson concluded that most individual experimentation was not yielding measurable business value and decided not to devote additional resources to it.
Instead, the firm prioritized just a few enterprise-level gen AI projects focused on applying the technology to strategic priorities for the firm: drug development, HR policy access, assisting sales reps in communicating with physicians, and identifying and mitigating supply chain risks.
Coca-Cola has also decided to focus on large-scale, enterprise-orchestrated AI projects, such as creating new marketing content. The company is using gen AI to customize 10,000 different versions of 20 proprietary marketing assets for the 180 countries and 130 languages in which it does business around the world.
The same qualities that make enterprise systems more beneficial, however, make them difficult to build. Integrating high-quality data across complex systems, orchestrating data flows, and aligning outputs with business are huge challenges.
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