Why Pharma Should Study Palantir

Forget AI for a moment. The company pharma needs to study should be the one that didn’t build the model. Not because models are bad, but because the obsession with AI made pharma close its eyes on the gold mine that lays in their existing data, hidden under layers of bureaucracy and protected by incentives to survive rather than innovate.

When I first came into biotech six years ago, I started pulling my hair out and asking a lot of “why” questions. I saw an industry whose “not invented here” thinking outlived the dot-com boom and fall. Some of its information technology and red tape could only compete with those of US federal government and people were equally content with the state of affairs - its employees were often difficult to differentiate from zombies. Evidently, self-driving cars on the streets of San Francisco and the tech revolution didn’t impress enough executives to switch from Windows to web-based ELNs. Benchling “innovated” the industry and finally offered a web-based SAAS product, as Salesforce Tower smiled while celebrating the 20th anniversary of turning on premise software into fossils.

Bureaucracy survived but raised an AI flag to signal that its brand stands for something fresh. The tech industry felt its gravitational pull. They instantly built models. Then benchmarks. Then better benchmarks. More capable reasoning. Generative chemistry. Protein prediction. Autonomous laboratories.

And now, every few months we see another demonstration suggesting that some previously stubborn intellectual task can be performed faster by a machine. This is definitely important and quiet useful. It may also encourage pharma companies to focus on the part of the AI stack where they have the least durable advantage. Frontier models are becoming extraordinarily capable, but they are also becoming widely available and commoditization might be around the corner.

This is great, nonetheless. But the harder problem inside a big pharma is considerably less glamorous: getting the right data, from the right systems, under the right permissions, into the right workflow, at precisely the moment somebody has to make a consequential decision.

That is why pharma executives should spend more time studying Palantir. AI vendors like Anthropic and OpenAI became famous by making intelligence dramatically more accessible. Palantir built its reputation around making complicated organizations computationally legible. Pharma needs plenty of the former. Its chronic disease, however, is often the latter.

Consider what actually happens between a scientific insight and a medicine reaching a patient. Discovery data live in one environment, preclinical results in another, clinical information in several more. Manufacturing has its own systems. Quality has its own systems. Regulatory teams assemble evidence according to exacting requirements. Commercial organizations build forecasts from still another collection of datasets. Supply-chain teams worry about capacity, inventory and geography. Patient information is governed by strict access controls. External data arrive from CROs, laboratories, hospitals and other partners. Each function may be individually sophisticated while the organization connecting them remains surprisingly dependent on spreadsheets, reconciliations, meetings and heroic employees who know which database contains the number everyone else is arguing about. This complexity is sometimes orchestrated by hundreds of people dancing between back-to-back meetings, where confused faces try to assemble puzzle pieces together, hoping that gatekeepers will actually unleash some of these pieces before they retire. Data integration project timelines look suspiciously similar to those of clinical trials, and project managers do voodoo dances similar to those of lab scientists - cute and usually not easily reproducible. Meanwhile, thousands of boxes of paper records from previous decades of clinical trials rot somewhere in the warehouse, waiting for the new generation of zombies to scan them and put behind the red tape.

A more intelligent AI chatbot does not automatically solve this problem. Neither does a better molecular model. What matters is creating a usable representation of the enterprise: what a batch is, what a clinical site is, what a patient cohort is, what inventory exists, which trial depends on which supply, who is authorized to see which information, what actions are permitted and what happens when circumstances change. Palantir calls the organizing layer at the heart of its approach an ontology. The terminology matters less than the idea. Data become substantially more valuable when they are mapped to the objects, relationships, rules and actions through which an organization actually operates.

There are certainly glimpses of this approach and some success in biotech and pharma. You can now get faster access to validated clinical data and shorten the path from trial planning toward submission. Palantir has also described healthcare and biotech customers using its product to harmonize disparate datasets and make clinical and real-world evidence more accessible. Unfortunately, none of this proves that Palantir has discovered the universal operating system for drug development. Vendor case studies deserve the same healthy skepticism as exceptionally attractive before-and-after photographs. But they illustrate a useful principle: the economic value of AI often appears only after information can travel through an organization quickly enough to change what somebody does.

This matters because pharma’s instinct is often to turn AI into a portfolio of use cases. One team builds a medical-writing assistant. Another experiments with protocol generation. Scientists acquire a research copilot. Commercial teams automate content. Manufacturing gets predictive maintenance. Procurement gets another dashboard. Each project can produce a respectable return while leaving the company fundamentally unchanged. The result is a technologically impressive archipelago: dozens of clever islands separated by oceans of organizational friction.

The Palantir lesson is to begin with the decision rather than the model. Which decisions materially affect development speed, probability of success, manufacturing reliability or patient access? What information is required to make them? Where does that information currently live? How fresh is it? Who may access it? What dependencies connect one decision to another? Which actions can safely be automated, and which require accountable human approval? Only after answering those questions does the model become interesting. In this architecture, an LLM is a powerful component rather than the architecture itself.

That distinction becomes especially important as foundation models improve. If model capability continues to diffuse among competing providers, pharmaceutical companies are unlikely to derive a lasting advantage merely from having access to excellent models. Their competitors will have them too. OpenAI’s expansion into life sciences makes the point rather neatly. In April 2026 Novo Nordisk announced a strategic partnership with OpenAI intended to apply advanced AI from drug discovery through commercial operations, including manufacturing, supply chain and distribution. Thermo Fisher has similarly described embedding OpenAI APIs across areas including product development, service delivery and operational efficiency. OpenAI, in other words, is itself moving beyond the chatbot.

The competitive question therefore shifts. If everyone can rent intelligence, what remains scarce? Proprietary scientific data certainly do. So do experimental capabilities, clinical-development expertise and biological judgment. But another scarce asset is the institutional machinery that allows intelligence to encounter proprietary data safely and then affect reality. A model that can predict a supply disruption is useful. A system that knows which products are affected, which patients depend on them, which manufacturing constraints apply, which alternative facilities have capacity, which regulatory restrictions matter and which executive has authority to intervene is something else entirely. Prediction creates information. Operational integration creates leverage. Speed of information availability and automatic representation of interlinked data puts you at a different level against competition.

Pharma has an unusually strong reason to care about this distinction because its decisions occur inside a dense web of regulation, validation, privacy and accountability. The industry’s caution about autonomous AI is therefore not merely conservatism. A system that recommends an action must often provide enough provenance, permissions and auditability for somebody to understand why that action was possible in the first place. Enterprise AI in pharmaceuticals consequently has to answer questions that consumer AI can often ignore: Which data was the system allowed to use? Which version? Who approved the workflow? What happened afterward? Can the decision be reconstructed six months later? Intelligence without governance is a demonstration. Intelligence with governance can become infrastructure.

This also suggests that the industry’s obsession with AI talent may be slightly misallocated. Pharma certainly needs machine-learning scientists, computational biologists and excellent software engineers. But some of the most valuable people in an AI transformation may be translators who understand both the company’s data architecture and the physical or regulatory process represented by that data. Palantir’s much-discussed model of embedding engineers close to customers reflects a recognition that enterprise software rarely succeeds by being dropped elegantly from the cloud. Somebody has to understand why the manufacturing planner distrusts the inventory field, why two clinical systems disagree about site status, and why the apparently irrational approval step exists because a regulator once asked an awkward question in 2017.

There is a also a somewhat awkward implication for senior management. If this argument is right, many supposed AI problems are actually organizational problems exposed by AI. A company discovers that its data are fragmented, its definitions are inconsistent, its permissions are Byzantine, its workflows are poorly documented, and each piece of data has a red tape attached to it. The tempting response is to wait for a sufficiently clever model to reason through the mess. This feels like responding to a chaotic warehouse protected by blind security guards by hiring a more intelligent forklift driver. Intelligence helps. Knowing where anything is helps more. Educating gatekeepers is gold.

None of this diminishes the significance of frontier models. OpenAI and its peers may contribute enormously to drug discovery, scientific reasoning, clinical development and knowledge work. Better models could unlock capabilities that are difficult to anticipate today. The mistake would be to assume that model intelligence automatically translates into enterprise productivity. Between an answer and an outcome lies an organization.

The companies that capture the most value from AI may therefore be those that become unusually good at connecting three layers: models that can reason, proprietary context that makes the reasoning relevant, and operational systems that allow the reasoning to change a decision. The first layer will attract most of the headlines because intelligence makes for spectacular demonstrations. The second and third layers involve data plumbing, permissions, ontology design, validation and workflow engineering, which is why nobody has yet made a blockbuster film about master-data management.

Pharma should certainly watch the AI space. It should experiment with the best models available and exploit every legitimate improvement in scientific reasoning. But it should study Palantir for a different reason. Palantir’s central insight is that enterprise intelligence becomes valuable when it is connected to the machinery of the enterprise itself.

The next pharmaceutical AI breakthrough may indeed be a model that understands biology better than any scientist. But the next billion dollars of AI value may come from something much more boring: a company finally knowing what it knows, knowing who is allowed to use it, and getting that knowledge to the person who has to decide what happens next.