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7 Keys to Getting the Most From Generative A.I.

In writing my latest book, Brain Rush, I have been surprised at how much large consulting firms have invested to serve what they anticipate to be strong demand from companies for their help in developing high payoff applications of generative A.I. For example, Accenture is investing $3 billion to scale up to meet such demand and others are spending more than $1 billion to add the technology resources they will need to serve clients.

Of all the large firms helping companies to use generative A.I. applications -- such as ChatGPT and others -- the one with the most interesting ideas in my view is McKinsey. A McKinsey Digital article, "What every CEO should know about generative AI," shared seven useful insights.

1. Organize for Generative A.I.

McKinsey advised CEOs to assemble cross-functional teams whose composition would vary depending on the generative A.I. application. For example, the firm recommended that clients assemble teams from functions such as data science, engineering, legal, cybersecurity, marketing, and design to build generative A.I. for applications to personalize marketing messages.

2. Brainstorm fresh ways to perform essential processes.

McKinsey urged CEOs to use generative A.I. to make dramatic improvements in how their company performs essential business functions to create new ways of working both inside the company and with stakeholders such as partners and customers.

3. Enable a fully loaded technology stack.

CEOs should charge their chief technology officers to build or collaborate with outside firms to deploy the technical capabilities -- such as computing resources, data systems, data governance and security processes, tools, and access to models -- required to deploy generative A.I. applications that will enhance the company's performance, McKinsey noted.

4. Build a "lighthouse."

To generate enthusiasm and inspire creativity, McKinsey advised CEOs to pilot generative A.I. applications that add value quickly. An example was a "virtual expert" that allowed workers to provide the most relevant content to customers. Such "early wins" would help pave the way for more important applications of the technology.

5. Balance risk and value creation.

CEOs should initially roll out lower-risk generative A.I. applications that may deliver less value, McKinsey advised. This approach aimed to help leaders develop and master procedures -- such as requiring humans to review generative A.I. content -- to prevent hallucinations and biases in large language model training data from reaching customers.

6. Collaborate to obtain needed capabilities.

Business leaders should acquire or collaborate with providers to gain access to capabilities when the firm lacks sufficient expertise, McKinsey noted. For instance, the firm advised CEOs to "team up with model providers to customize models for a specific sector, or partner with scalable cloud computing" service providers.

7. Develop talent and add skills.

Finally, to make better use of generative A.I., CEOs should train their people in prompt engineering and develop clear guidelines restricting the use of proprietary company data. Leaders should also consider hiring new technical people in fields such as engineering, data, design, risk, and product, McKinsey advised.

Follow these tips and you will get more from your investment in generative A.I.



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