Gen AI Present and Future: A Conversation with Jim Swanson, CIO at Johnson & Johnson | Greylock

Greylock · Greymatter · Publicado el 14 mar 2025

Jim Swanson describes how Johnson & Johnson organizes AI deployment, governs its use, and brings AI tools into employees’ workflows. He discusses secure enclaves, vetted data, usage policies, and expert review of use cases. Lee 4 puntos de vista con sus evidencias y enlaces a las fuentes.

Asheem ChandnaJim Swanson

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Three-layer AI deployment framework
Johnson & Johnson organizes its AI deployment into three layers: (1) improving productivity around enterprise capabilities, (2) enabling end-to-end processes—such as drug discovery, clinical operations, market access, and customer engagement—and (3) embedding AI into the core of products and services.
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Dual-council AI governance model
J&J established an AI Council and a Data Management Council to mature its technology stack, ensure ethical AI use, and prioritize scalable, high-impact AI use cases.
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Workflow-native AI integration
J&J embeds AI tools directly into employee workflows—for example, integrating AI into CRM platforms so sales representatives can access provider-specific insights, and into drug safety workflows to improve case identification and resolution.
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Secure enclaves and vetted-data AI infrastructure
J&J mitigates AI security risks by deploying large and small language models within secure internal enclaves, using only vetted and curated data, restricting public generative AI use via policy, and requiring expert review—from security and architecture teams—for every proposed generative AI use case.
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AI governance

Dual-council AI governance model

Extracto original

First, we have created two councils, an AI Council and a Data Management Council, which allow us to properly mature the technology stack and apply these tools to use cases that matter. These bodies help ensure we adhere to ethical standards and have a clear understanding of how to use these technologies in ways that are scalable.

Jim Swanson·14 mar 2025

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AI security

Secure enclaves and vetted-data AI infrastructure

Extracto original

To defend against threats, we’ve created secure enclaves within the company for our large and small language models, and use vetted, curated data. Additionally, we created policies that educate our employees on the risks and restrictions around use of public Gen AI and direct them towards use of our internal generative AI application. We also review every proposed Gen AI use case through a process that includes security and architecture experts.

Jim Swanson·14 mar 2025

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AI adoption

Workflow-native AI integration

Extracto original

Third, we’re placing a heavy emphasis on embedding AI tools directly into employees’ workflows. For our sales representative, these capabilities are integrated directly into their CRM platform. When a representative prepares for a visit with a provider, they can get information on the kinds of patients the provider usually sees, the interactions we’ve had with them previously, the types of advances or insights they might want to know about, the channel the provider prefers to be engaged on, etc. Similarly, with our drug safety teams, we have embedded these tools into their workflows so they can better identify and resolve cases.

Jim Swanson·14 mar 2025

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AI deployment strategy

Three-layer AI deployment framework

Extracto original

We have three main layers of AI deployment. The first layer is about improving productivity around our enterprise capabilities. The second layer is centered around enabling our end-to-end processes, whether it be drug discovery, clinical operations, getting a product to market, or engaging with customers. The third layer is about working to embed AI into the core of our products and services.

Jim Swanson·14 mar 2025

Fuente y metodología

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