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

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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. Read 4 viewpoints with supporting evidence and source links.

Asheem ChandnaJim Swanson
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4 key points

Synthesis

  1. 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.

    Supporting evidence 1

    Original excerpt

    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 · Paragraph 5

    Gen AI Present and Future: A Conversation with Jim Swanson, CIO at Johnson & Johnson | Greylock · Mar 14, 2025

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    AI deployment strategy →

  2. 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.

    Supporting evidence 1

    Original excerpt

    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 · Paragraph 18

    Gen AI Present and Future: A Conversation with Jim Swanson, CIO at Johnson & Johnson | Greylock · Mar 14, 2025

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  3. 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.

    Supporting evidence 1

    Original excerpt

    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 · Paragraph 21

    Gen AI Present and Future: A Conversation with Jim Swanson, CIO at Johnson & Johnson | Greylock · Mar 14, 2025

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    AI adoption →

  4. 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.

    Supporting evidence 1

    Original excerpt

    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 · Paragraph 34

    Gen AI Present and Future: A Conversation with Jim Swanson, CIO at Johnson & Johnson | Greylock · Mar 14, 2025

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    AI security →

Key passages4

Attributed passages with the context to verify them. Open the original text to check the source.

Paragraph 18 (opens in a new tab)
AI governance

Dual-council AI governance model

Original excerpt

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·
Paragraph 34 (opens in a new tab)
AI security

Secure enclaves and vetted-data AI infrastructure

Original excerpt

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·
Paragraph 21 (opens in a new tab)
AI adoption

Workflow-native AI integration

Original excerpt

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·
Paragraph 5 (opens in a new tab)
AI deployment strategy

Three-layer AI deployment framework

Original excerpt

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·

Source & methodology

These viewpoints are linked to their original sources. Paraphrases are labeled and are not verbatim quotes.

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