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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. Lisez 4 points de vue avec leurs éléments à l’appui et les liens vers les sources.

Asheem ChandnaJim Swanson
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En un coup d’œil

  • 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.

    Lire le moment probant · Paragraphe 5

  • 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.

    Lire le moment probant · Paragraphe 18

  • 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.

    Lire le moment probant · Paragraphe 21

  • 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.

    Lire le moment probant · Paragraphe 34

Passages clés4

Passages attribués et accompagnés du contexte nécessaire à leur vérification. Ouvrez le texte original pour vérifier la source.

Paragraphe 18 (s’ouvre dans un nouvel onglet)
AI governance

Dual-council AI governance model

Extrait 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·
Paragraphe 34 (s’ouvre dans un nouvel onglet)
AI security

Secure enclaves and vetted-data AI infrastructure

Extrait 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·
Paragraphe 21 (s’ouvre dans un nouvel onglet)
AI adoption

Workflow-native AI integration

Extrait 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·
Paragraphe 5 (s’ouvre dans un nouvel onglet)
AI deployment strategy

Three-layer AI deployment framework

Extrait 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·

Source et méthodologie

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