Companies Shift Focus to Delivering Real Value from AI

In their latest quarterly survey on AI readiness, Bain & Company found that companies are moving quickly to explore ways for generative AI to enhance their business, with most already developing or deploying initiatives. The survey shows that across industries, companies have high expectations for the value that generative AI can add to their businesses, and they are investing appropriately in talent and other resources.

According to Gene Rapoport, Sanjin Bicanic, Jue Wang, Richard Lichtenstein, and Arjun Dutt, partners at Bain & Company, some use cases are already showing more promise than others, and executive teams will need to adjust their expectations and funding as they learn more. The survey highlights the importance of data quality and the ability to operationalize use cases to meet users’ needs. As third-party solutions mature, companies may shift from building their own solutions to buying off-the-shelf applications.

The article discusses the adoption of generative AI across various industries, highlighting the successes and challenges faced by companies. The data is based on a quarterly survey conducted by IncQuery, which provides quantitative surveys for primary research.

Here are the key takeaways:

1. Success stories: Five use cases show signs of success: sales and sales operations, software code development, marketing, customer service, and customer onboarding. These areas have seen promising results from the implementation of generative AI.
2. Challenges: On the other hand, use cases in legal, operations, and HR appear less successful. This could be due to various reasons, including data quality issues or difficulties in operationalizing these use cases.
3. Tech companies leading the way: Technology companies are more likely to say that their data, resources, and policies are ready to support generative AI use cases than non-tech companies. However, even tech companies feel less ahead than previously, indicating that implementation is proving more difficult than expected.
4. Buy or build?: Both approaches are being tested across use cases. Companies are buying third-party solutions when available but investing in tailoring them for their needs. As the technology matures, we may see more decisions to buy rather than build.
5. Data quality and operationalization: The survey highlights that poor performance and low-quality output were the main reasons why generative AI has not met expectations. This emphasizes the importance of high-quality data and the ability to operationalize use cases to meet users’ needs.

Overall, the article suggests that companies have high expectations for the value that generative AI can add to their businesses, but they are also facing challenges in implementation. As the technology continues to evolve, we can expect to see more successes and failures, with data quality and operationalization playing critical roles in determining outcomes.