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Biotech Researchers

Randy Bean

How is AI transforming the world’s original biotech company?

Genentech is an American biotechnology corporation headquartered in South San Francisco, California and celebrating its 50th anniversary this year. Founded in 1976, Genentech pioneered recombinant DNA and essentially founded the biotech industry. Today, Genentech is looking to lead the next revolution in human health by pioneering the integration of AI. For Genentech’s leadership, this new technological frontier isn't just a digital upgrade; it is the natural evolution of a 50-year legacy built to solve the most urgent health challenges for patients and society. Genentech's South San Francisco campus serves as the headquarters for Roche pharmaceutical operations in the United States.

Some of the most important and beneficial applications of AI will be likely made in the life sciences and human health. I recently spoke with members of Genentech’s leadership team about how they view the potential of AI and how it can transform the biopharma industry. They note that one of the greatest challenges in the modern healthcare landscape is that while the volume of scientific data is growing exponentially, the time that a physician has available to consume that data is shrinking. We discussed how AI can help overcome this challenge and can be employed to enhance human health.

Data and AI Leadership and Organization Structure at Genentech

Xingchu Liu serves as senior vice president and Chief Data and Analytics Officer, U.S. Pharma Commercial Operations for Genentech. He previously was Chief Commercial Analytics and AI Officer with Pfizer. Liu characterizes the CDAO role at Genentech as being the “orchestrator of the common digital thread" at Genentech.

Liu explains, “The CDAO’s mandate at Genentech is to ensure that data, digital, analytics, and AI work as an orchestrated ecosystem across the commercial value chain—not as isolated tools or pilots.” He continues, “The role is not about owning technology, but about designing how the enterprise learns, decides, and acts faster.” Results include:

• Scalable Impact: Capabilities are built once and reused many times, allowing wins in one therapeutic area to be rapidly extended across brands, indications, and markets.
• Enterprise Alignment: The CDAO partners with commercial, medical, and market access leaders early, shaping roadmaps and operating models so solutions scale beyond pilots.
• Execution at Speed: By embedding AI and analytics directly into workflows, the Data, Digital and Analytics (DDA) organization accelerates decisions and improves execution where it matters most.

“The bottom line is that the CDAO’s role is to make data and AI a force multiplier—driving faster decisions, better execution, and measurable impact for patients,” comments Liu.

Using AI to Transition from a Service Model to a Product Model at Genentech

As the Genentech data and AI leadership team explains, Genentech leadership is focused on turning organizational alignment into “muscle memory”. Erik Lundgren is senior vice president of Genentech’s Commercial Portfolio Organization. He explains that the leadership commitment at Genentech goes beyond budget approval; it is about active immersion.

Lundgren explains, “Our U.S. Leadership Team (USLT) participates in intensive ‘Bootcamps’—not to learn how to code, but to create a shared language around AI ethics, the Product Model, and the digital roadmap.” He elaborates, “Immersion turned AI into leadership muscle memory—so we steer with clarity as the landscape shifts.”

This leadership "muscle memory" is what paved the way for System 1.0—Genentech’s AI and digital-first operating model. By rebuilding its systems to respond at the speed of the physician’s and patient’s needs, the company is ensuring that its technical rigor matches the scale of its innovation. With dozens of new investigational medicines in clinical development, Genentech is using this model to ensure that a technical breakthrough in one area becomes a repeatable success across the entire portfolio. In high-stakes areas like early-stage breast cancer and Multiple Sclerosis, this speed is more than a metric; it is the difference in getting a life-changing therapy to a patient sooner.

“At Genentech, we believe the primary application of AI is to act as a bridge between scientific complexity and clinical action. Every delay in that bridge is a delay for patients, which is why speed must be designed into how we operate, and not added on later, notes Lundgren. To achieve this objective, Genentech has undertaken a structural redesign called the "Two-in-the-Box" model, “where we pair a Business Leader with a Technology Leader as equal ‘co-pilots’ for every digital initiative,” explains Lundgren. This transformation is driven by the sheer scale of the current Genentech portfolio.

With dozens of new investigational medicines in clinical development, the company has moved toward a unified enterprise strategy. As leadership explains, building AI throughout the end-to-end commercialization cycle is the only way to effectively scale this volume of science to waiting patients. By industrializing how they work, Genentech ensures that a technical breakthrough in one therapeutic area becomes a repeatable success for their entire portfolio of over 40 medicines. The benefits of this shift to an industrialized product model include:

• Speed, Sourcing, and Scale “In this new model, we don't just "build to order. We evaluate how our Data, Digital, and Analytics ‘DDA engine’ and existing tech tools can be leveraged to quickly source, iterate, and test solutions in real-time,” explains Lundgren.
• Industrialized Building: “Because we think in terms of "products" (modular, reusable capabilities), a solution built for one therapeutic area can be immediately adapted for another,” says Lundgren.
• Responsive Care: Lundgren continues, “This shift ensures we are responding to the market at the speed of the physician’s need. When a provider seeks information about a complex medicine, our systems aren't "under construction"—they are ready and responsive, ensuring that we support the patients who need us most with precision and empathy.”

By integrating its DDA Engine directly with its Commercial Strategy, Genentech has built an AI "Back-office Co-pilot." Lundgren comments, “We aren't just theorizing about removing administrative friction; we are proving it. In just six weeks, we launched a voice-based copilot assistant with 12 Therapeutic Area Managers.” Lundgren concludes, “We no longer ask, 'What can we build for you?' but rather 'How can our Data, Digital, and Analytics “engine” solve this for every patient and get our patients closer to the care they need.”

DDA and AI Innovation Initiatives at Genentech

Historically, marketing in biopharma operated as a centralized "content factory." Xingchu Liu explains, “We ‘pushed’ materials out to our field teams—representatives who directly support physicians and healthcare providers (HCPs)—hoping the assets were relevant. This was a slow, manual ‘Service Model’ where teams spent weeks getting a single piece of content approved and distributed.” Today, Genentech has industrialized this process.

Liu explains, “We’ve moved from a ‘Push’ to a ‘Pull’ system, co-developed with the field. Instead of waiting on a manual request, our integrated content and analytics engine use real-world signals to 'pull' the exact asset that a representative needs for a specific physician's hurdle.” According to Liu, resulting benefits include:

• Innovation in Velocity: By utilizing a "modular content" approach—where AI helps design, deliver, and measure messages—Genentech has transformed throughput. Liu explains, “We have moved from a 12-week manual cycle to a process where we can iterate in days. Early returns show a 50% increase in content volume with a 50% reduction in time spent by the team.”
• Efficiency & Cost: Early returns demonstrate a 45% reduction in cost per asset.
• Velocity: The overall approval time for Print, Social, Banner, and Email has been reduced by 45%, effectively moving from 12-week manual cycles to a process that iterates in days.
• Industrialized Reuse: “This isn't just about speed; it's about scale,” comments Liu. He explains that because these capabilities are built as "products" rather than one-off projects, a messaging framework built for one complex therapy can be instantly adapted and deployed across the entire Genentech portfolio of 40+ medicines.

A second pillar of innovation within Genentech is Voice AI, which the company has scaled to power an automated “Insight Engine.”

• The "Insight" Engine: Previously, capturing what a physician needed required a representative to manually log notes across multiple disparate systems after a meeting—a clear bottleneck to real-world intelligence.
• The Strategic Shift: Rather than recording live interactions, Voice AI allows field teams to capture spoken insights and post-engagement summaries hands-free immediately after a meeting concludes. This eliminates administrative burden for the representative and provides immediate, actionable themes to better prepare for their next physician interaction.

Liu explains that this shift turns the field force into the company's most valuable data asset: “Our field teams are no longer just the end-point of a strategy; they are the strategic sensors for the entire organization. By capturing high-fidelity insights in real-time, our field team provides ground-truth intelligence that allows us to replace intuition with precision.” He adds, “We co-create these digital tools alongside the field to ensure that the most relevant clinical resources reach the right physician at the exact moment they are needed.”

“We industrialize innovation. When you can take a complex idea like a voice-based field assistant and put it in the hands of field leadership in just six weeks, you’ve moved past the 'pilot phase' and into a new era of digital leadership,” concludes Liu.

Building a Data & AI–Driven Business Culture at Genentech

Genentech believes that an AI-ready culture is built by marrying a 50-year heritage in biological science with world-class technical execution. Jacki Dioguardi, vice president of Roche Digital Technology Global Pharma Commercial Chief Information Officer explains, “To own our future, we made a strategic choice to internalize our technical capability rather than relying on a traditional outsourced model.” This shift ensures that technical teams are core strategic partners in Genentech’s business mission. As Dioguardi explains, benefits include:

• Industry-First Talent: “We have aggressively recruited approximately 40% of our technical leadership from Tier 1 tech firms,” comments Dioguardi. She adds, “By blending high-tech ‘builders’ who know how to scale systems with deep-pharma experts who know how to solve disease, we’ve created a unique competitive advantage.”
• Competitive Advantage of Speed: Pairing deep biology with an "engineering mindset" allows Genentech to build modular, reusable platforms. A technical win in one therapeutic area can now travel to others in weeks, not quarters. This speed is the result of a deliberate, rigorous architectural choice that allows Genentech to scale AI capabilities as a high-velocity commercial engine.

Dioguardi concludes, “We are intentionally putting the ‘tech’ in Genentech. Technology alone doesn't transform a company; leadership does. We initiated a systemic reset of our roles, skills, and even our terminology to ensure we weren't just 'using' AI but were becoming an AI-first organization.”

Responsible Use of Data & AI at Genentech

Genentech leadership believes that compliance must serve as a compass, not as a roadblock. Dioguardi comments, “In an industry where clinical accuracy and empathy are non-negotiable, we ensure that AI remains human-accountable. We don't build a product and then seek approval; we embed Legal, Risk, and Compliance at the very beginning of our product roadmaps.” Illustrations include:

• Guardrails by Design: Genentech co-builds safety frameworks alongside technology. This includes rigorous "Human-in-the-Loop" checkpoints. For example, when exploring AI-generated imagery for patient materials, Genentech label assets clearly and vet them with patient groups and physicians before deployment. This ensures technology augments, rather than undermines, the trust built over five decades.
• The Global Blueprint: This commitment to responsible, industrialized AI has become the gold standard for our broader global organization. Genentech’s Product Model has inspired global programs which utilize a pilot/co-pilot model to scale AI-driven content generation responsibly across seven functional workstreams.

“We’ve moved away from seeing compliance as a final hurdle and instead treat it as a compass that guides our design. By embedding these guardrails—we’ve created a model that is now being adopted as the global blueprint for the entire Roche enterprise. We aren't just protecting our company; we are ensuring that as we scale AI, we keep clinical judgment and human empathy firmly at the center,” adds Dioguardi.

This commitment to a global digital backbone is further underscored by the recent announcement of the Roche AI Factory. Powered by a strategic collaboration with NVIDIA, this enterprise-wide initiative provides the high-performance computing infrastructure necessary to scale AI across the entire organization. For Genentech, this represents the 'industrial' scale of their vision—pairing their refined Product Model with world-class technical power to ensure that AI-driven insights can be generated and deployed at the speed of the global market.

Looking ahead, Xingchu Liu comments, “Our North Star is to serve more patients, faster.” He continues, “We have moved away from standard financial ROI to focus on Impact for Patients. We measure success by the friction we remove from the patient’s path to therapy.” This means tracking cycle-time compression—the speed at which a scientific insight becomes a helpful reality for a physician.

Liu concludes, “If an initiative doesn't move the needle on patient reach or the speed of clinical support, we reassess. By focusing our data engine on the speed of clinical delivery, we ensure that our technology is always in service of the patient’s journey. In the Genentech ledger, the ultimate value of AI is measured by how much time it returns to the humans who save lives!”