Artificial intelligence & machine learning

When a drug fails, it’s not always because it doesn’t work. In many cases, the problem is linked to a limitation in trial design. This is especially true in mental health research, where studies of conditions like depression, and post-traumatic stress disorder, rely on subjective symptom ratings, not objective measures such as tumor size or blood markers — a challenge that can introduce bias.

Symptom ratings are sometimes inflated by investigators who may be eager to help a patient meet study criteria, according to research published in Journal for Clinical Studies. Tweaking these numbers may boost enrollment, but could lessen the chances a drug will succeed. When baseline scores are artificially high, it becomes harder to see the actual difference between the drug and the control group, said Gary Zammit, founder of Clinilabs, a central nervous system-focused clinical research organization.  

This long-held hurdle to improving outcomes is one of the many issues in clinical trials AI is being tapped to fix. 

“AI models not only seem to be more accurate in their ratings, but they help to eliminate the biases that investigators might bring to assessment,” Zammit said. 

AI is making its mark in drug development in other ways as well.

Promoting safety and expanding diversity

AI holds substantial potential in safety monitoring as well and is being used to mine trial data to detect safety signals before patients experience an adverse event, Zammit said.

Companies are also exploring whether AI can shrink trial size using so-called digital twin studies, which use computational models to predict a patient’s trajectory if they were taking a placebo. 

“In essence, you get an AI-generated or artificial twin that can be used by pharmaceutical companies to derive estimates on sample sizes and on statistical approaches that might be taken in the study,” Zammit said.

This digital twin technology can help fill diversity gaps in drug trials by helping to predict outcomes in different populations such as pregnant women, children or groups that are difficult to enroll due to ethical or regulatory challenges, according to an article in Nature.

AI is also delivering measurable gains in efficiency. A McKinsey & Company analysis found that companies adding AI and machine learning can increase trial enrollment by 10% to 20%, which can lead to faster trials.

One study at Mass General Brigham Health System in Boston found that an AI-assisted patient screening tool significantly reduced the time it took to determine trial eligibility and enrollment compared with manual screening, according to a research letter published in JAMA.  The rise of agentic AI, which can carry out some tasks independently, could further speed processes. 

Together, small gains could add up to substantial time savings.

“If you can shave off 5% of the time required for a clinical trial by improving subject recruitment and another 5% by improving subject screening, and reduce the sample size because now you’ve got a better edge on rating accuracy,” Zammit said. “Taken in the aggregate, that can have a huge impact on what we’re able to deliver to patients.”

Oversight is critical

Despite growing enthusiasm for AI, its full impact is still being realized, Zammit said. 

“People see the promise, but adoption is going to take some time, and it’s only after that adoption that we're going to be able to see the real value,” he said.

As the technology evolves, it’s critical to ensure AI won’t introduce errors into the system and that people know how the technology is arriving at its conclusions. 

“For us as scientists and people who have to produce datasets with integrity, we need to have a good understanding of that underlying process,” Zammit said. 

One of the largest concerns about AI is that its models could be systematically biased, depending on the logic used to develop it. AI that’s asked to assess the efficacy of a particular therapeutic, for instance,  might generate its response based on historical data. 

“But the data it used could be based on narrowly defined populations. Maybe there wasn't enough diversity in that clinical trial data set. So, the caution is in understanding what's behind AI’s answer,” he said.

Human oversight will remain critical as the technology expands its footprint. 

“AI is not going to replace the ethical oversight of studies,” Zammit said. “We still need people.”

For example, as companies integrate AI systems to monitor for potential safety signals, the role of a local qualified person for pharmacovigilance will evolve, not disappear, according to IQVIA. The position will simply gain a new focus on ensuring that safety monitoring is accurate, compliant with regulations and ethical.

Super Bowl ads have long been a cultural bellwether, and at this year’s big game, AI was a top trend with about 23% of commercials related to the technology.

Like any impactful innovation, AI has led to lots of handwringing, from concerns about the heavy environmental toll of AI data centers to the potential negative effects on children’s cognitive growth. For workers across industries, the threat of job losses also remains heavy. 

As large corporations pursue greater efficiency and bottom-line growth, AI is replacing human workers at an astonishing clip. Amazon announced 16,000 layoffs after it axed 14,000 jobs earlier in the year.

“As we roll out more generative AI and agents, it should change the way our work is done. We will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs,” Amazon CEO Andy Jassy wrote in June 2025.

Despite this worrying shift, biopharma will likely be spared from such losses for now.

“I wouldn't say that AI is necessarily replacing jobs one-for-one,” said Jae Yoo, executive director of EPM Scientific, a recruitment firm specializing in pharma, biotech and R&D hiring. “It's rehousing and reshaping the types of jobs that are now coming in.”

A recent poll of industry executives echoed that sentiment, with pharma’s C-suite leaders reporting that they don’t think AI will lead to major job losses.

“Rather than replacing jobs, AI creates new roles and elevates existing ones, making curiosity, creativity and critical thinking essential skills for the future,” according to Pfizer’s December announcement of its second annual AI Festival.

Reshaping the job landscape

Although biotech job losses have surged, last year’s spike hasn’t necessarily been linked to AI. 

In fact, AI has added jobs at some Big Pharma companies. Eli Lilly announced it’s working with NVIDIA to build an “AI factory for drug discovery” and co-innovation lab in San Francisco that will create new scientific and technical roles.

AI hiring is also top of mind for the life sciences industry, with more than half of surveyed biotech execs saying that AI experts are among the top three roles they need to fill in the coming years. The search for AI talent is especially critical as life sciences companies invest in and build more in-house AI teams.

“I'm seeing more in-house analytics buildouts versus in previous years where they would outsource a lot of these to vendors,” Yoo said. “Instead of using one person to oversee a vendor, they'll create that team internally.”

Pharma job titles are also being reshaped by AI, creating hybrid roles that merge several functions and capabilities, such as commercial analytics and market access.

“AI is allowing a lot of companies to transform some roles and make them more productive, versus hiring routine and repetitive rules-based positions,” Yoo said. “Our clients are asking us more for cross-functional skill sets than maybe a technical expert in just one area.”

Yoo pointed to one company that combined departments after adding AI capabilities, requiring employees to work more cross-functionally.

“You can call it restructuring, but there was not a single employee that was displaced,” he said.

Where AI skills are in demand

Drug discovery is the biggest area where life sciences companies will not only add AI functions but jobs as well, Yoo said.

“AI is allowing companies to discover therapies and drugs at a fraction of the cost that it took before, and a fraction of the resources. So there's a booming demand for AI and [machine learning] engineering talent, specifically in discovery,” he said.

Commercial analytics roles are also in high demand, with a need for workers who can analyze commercial data and real-world evidence.

“Any positions and departments that directly affect time-to-market and any type of regulatory success is going to remain in high demand,” he said. “That cross-functional piece, I think, is going to be really important.”

Whether AI will eventually lead to major job losses in pharma remains to be seen, but for now Yoo believes the industry’s broader focus beyond revenue will help it retain jobs that are being lost in other industries.

“At companies like Amazon or any tech company, there's more of a bottom line that you have to prioritize,” he said. “Whereas in pharma, you're prioritizing therapies, drugs and patients.”

Sponsored content

By Quest Diagnostics

Artificial intelligence (AI) has become synonymous with drug discovery, allowing pharma companies to identify drug targets, screen compounds, and optimize clinical trial designs. The market is expected to exceed $35 billion in 2030 as pharma works toward improved preclinical and clinical success and shorter development timelines.

Commercial teams have started to recognize that the same analytics can unlock value later in the product life cycle. Applying machine learning (ML) to large-scale diagnostic datasets helps pharma move beyond retrospective analysis to predictive targeting. 

“AI is showing its value in accelerating drug discovery,” says Parag More, executive director of life sciences data and analytics solutions at Quest Diagnostics. “Now we’re seeing that same power applied to the commercial side to help companies understand where patients and providers are hiding in the healthcare system.” 

From discovery to commercialization

Commercial teams have relied on historical data and broad physician targeting to guide their commercial strategies. The approach overlooked large populations of patients who met the clinical criteria for a disease but remained undiagnosed. 

AI models can detect signals within lab data that point to a high likelihood of a disease. By analyzing diagnostic-testing data alongside real-world data sources, AI models can identify patterns that suggest a patient may have (or be at a high risk of developing) a disease before a formal diagnosis is made, revealing patient populations that traditional datasets miss. 

“If you only look at confirmed diagnoses, you may identify a million patients,” More says. “But when you apply predictive analytics to diagnostic datasets, you may find three million people who meet the clinical criteria but haven’t yet been diagnosed.”

This expanded view doesn’t just change how pharma companies estimate the size of the market; it reshapes how they approach commercialization. Besides allowing more precise targeting of physician-education programs and marketing, predictive analytics streamlines traditional sales and marketing strategies, reducing inefficiencies in sales force deployment and marketing spend.

“Machine learning shifts the model from reactive outreach to proactive engagement,” More says. “It helps identify which doctors are most likely to see undiagnosed patients and uses that data to prioritize outreach.” 

Turning insight into action 

Using AI-enabled insights in commercialization pathway planning can inform initiatives designed to close diagnostic gaps. It could help pharma companies deploy sponsored testing programs in healthcare settings where predictive models suggest a high prevalence of undiagnosed patients or launch targeted physician education to improve awareness of symptoms and testing protocols.

AI also provides feedback loops that help refine strategy over time. As diagnostic testing increases and more patients enter the treatment pathway, machine learning models can incorporate new data to improve prediction accuracy and refine targeting to enhance how pharma companies provide support throughout the patient journey.

Delivering these insights at scale requires more than advanced algorithms. As the pharmaceutical industry becomes increasingly data-driven, the role of technology and analytics partners is growing—but applying AI in commercialization is not without its challenges.

Data fragmentation across healthcare systems, evolving privacy considerations, and the need to integrate new insights into existing workflows can all create barriers to adoption. Predictive models should be transparent, validated, and trusted to realize their full value. Moreover, AI and ML platforms require access to large, high-quality datasets and specialized modeling expertise that creates a need to partner with data-solutions providers with predictive-analytics tools designed specifically for pharmaceutical commercialization.

These partnerships can support multiple stages of the product life cycle. During early research and development, real-world diagnostic data may help with validating hypotheses and refining patient-population estimates as well as refining market-access strategies, physician targeting, and patient-identification programs.

“Pharma companies are realizing that commercial success increasingly depends on data partnerships,” More says. “The organizations that can integrate diagnostic data, real-world evidence, and predictive analytics will have a significant advantage in identifying patients and accelerating adoption.”

AI and machine learning are no longer experimental, moving from pilot programs to scaled deployment and becoming foundational to commercial success. As AI adoption continues to grow across the pharmaceutical industry, its effect will extend far beyond the lab to reshape how therapies reach the market and the patients they are designed to help.

Contact Quest Diagnostics to learn more about AI solutions for pharma commercial strategy.

As artificial intelligence increasingly takes on new patient-facing roles that bring privacy and safety risks, the laws governing its use have made far less progress. Comprehensive federal rules are largely absent, so individual states have stepped in to fill the void. 

The lack of legal clarity triggers challenges and confusion across healthcare, including pharma where more companies are launching platforms that directly interact with patients. 

“For companies looking to think through a homogenous AI workflow or business plan, it's very difficult, because you're looking at each of the states that have implemented AI legislation, and trying to figure out a single, cohesive path,” said Aaron Maguregui, a healthcare AI attorney at Foley & Lardner.

The Trump administration has sought to accelerate the technology through initiatives like the Stargate Program aimed at building infrastructure, earning early criticism for its deregulatory bent and for not developing national standards that would give the industry guardrails as it grows. 

But in December, the administration moved in that direction when the HHS issued a Request for Information aimed at learning how to encourage AI use in clinical care while avoiding patient harm and privacy risks. 

“Data ownership in the healthcare space is heavily regulated, and so understanding where HIPAA and AI mix is part of this RFI process,” Maguregui said.

The goal is to ease the path to innovation while allowing companies to maintain their intellectual property rights and products. In the meantime, pharma companies using AI in consumer-facing ways are being left in limbo. 

“I hope that a federal framework comes fast, but I don't see it happening. And even if it does come, it's going to take time to mature,” he said.  

The DTC factor

Inconsistent regulation is becoming increasingly problematic as in the rapidly-expanding AI field. Major pharma companies, for example, have been rolling out direct-to-consumer platforms featuring AI-led patient education. Some states passed laws that regulate aspects of these direct interactions, focusing initially on the most pressing threats. Others still rely on existing, non-specific privacy and consumer rules. 

“The first one that I think is fairly common and obvious is just disclosure laws,” Maguregui said. “Are you using AI? Is AI being utilized during this consumer interaction?” 

Some state provisions require a human to remain in the AI loop, ensuring that a person, not a machine, is the ultimate decision maker. Others are trying to make it clear to patients that AI is not a medical professional by barring chatbots from using medical titles, such as doctor or nurse, Maguregui said.

California has been at the forefront in AI regulation, taking steps to ensure AI developers are transparent about their data sources while requiring them to ensure their products operate in an ethical, non-biased way, he said. Colorado, another leader in the space, introduced the concept of high-risk AI to operationalize governance around algorithm discrimination and documentation, Maguregui said.

Innovation without harm

The hope is to ultimately find a balance between innovation and regulation with unifying guidance at the federal level. 

“On one hand, we want to emphasize innovation and pushing the needle in terms of technology and how helpful AI could be,” Maguregui said. But it’s critical to also understand how AI is built and deployed to ensure safety and quality, he added. 

Keeping up with technology as it advances will be a major challenge as new iterations are released. While federal rules are hashed out, compliance is a piecemeal proposition. 

“It's really understanding that as of right now and probably for the foreseeable future, understanding where the state law trends and regulation are going are paramount,” Maguregui said. 

Companies should also be aware that it’s not only their internal processes that need to comply with these state regulations. If they have a substantial supply chain with companies also using AI, vendor diligence is crucial, Maguregui said, pointing out that it’s critical to ask hard questions about how they are developing and incorporating the technology. Organizations should also keep a close eye on the shifting sands at the state and federal levels to ensure they are in compliance as rules evolve so that AI tools can deliver promised value, while limiting harm.

The era of AI in drug discovery and development is just beginning. And with it, industry insiders foresee a massive shift not only in speed and scale, but also in what it takes to become a successful drugmaker.

With advances in machine learning overlapping with new biological discoveries, the early work of AI integration will likely yield concrete benefits for patients within even the next five years, according to Dr. Mikael Dolsten, former chief scientific officer at Pfizer.

“The next couple of years are going to be the time when precision medicine can live up to its promise because of these powerful mathematical algorithms and the size of the datasets,” Dolsten said. “We would hope between now and 2030 to see a dramatic shift in how we treat and define disease.”

Now an adviser to the AI-based drug discovery and development company Immunai, Dolsten envisions a lab of the future “where science is integrated with medical practice almost in real time.”

Between industry firsts like Insilico Medicine’s AI-designed pulmonary fibrosis candidate rentosertib and Google DeepMind’s Nobel Prize-winning AlphaFold platform for three-dimensional protein mapping, the industry already can point to a few AI success stories.

Where does it go from here? Much of AI’s trajectory in the life sciences still remains theoretical. But experts in pharma and tech are optimistic that they will soon be solving some of the industry’s toughest challenges.

Data, data, data

Like Pac-Man chomping his way through a maze, AI thrives on power pellets of data. And over the course of just a few years, AI models have had their fill. 

AbbVie’s R&D search engine “ARCH,” for example, holds some 450 terabytes of data. 

But experts say big data is just the first step. Going forward, researchers hope to mitigate the types of hallucinations and errors that could steer development programs in a false direction.

That’s why the next phase of AI will require a higher level of introspection, said Stef van Grieken, CEO of the AI drug discovery software company Cradle. In other words, platforms will have to discern to a greater degree which outputs make sense and which ones don’t.

“We need to be very careful that we don’t send scientists down the wrong path,” van Grieken said. “Instead of hallucinating incorrect answers from a lack of information, AI models that are better at introspection would be able to say, ‘I don’t know,’ or ask for further clarification.”

With Cradle’s platform, a researcher starts with a target or a binder — or a wider library of binders — and builds a “plate” of potential outputs. What once took many tries to discover whether a drug compound would succeed or fail can be compressed into one process by cross-referencing many of the contradicting requirements for a viable candidate, van Grieken said.

Understanding AI’s limitations is just as important as amplifying its successes, he added.

“There are no protein models today that know about disease, in the same way [that] you don’t expect a self-driving car to start teaching college math,” van Grieken said. “They’re not designed to do that, and they’re not magic boxes that suddenly understand every disease profile and come up with a molecule that works. That’s just fantasy.”

Danish pharma giant Novo Nordisk has worked with Cradle to search for new cardiometabolic drug candidates that could improve upon GLP-1 blockbusters like Ozempic and Wegovy. The speed and relative low cost of sifting through AI libraries is a clear competitive advantage, said Mishal Patel, senior vice president of AI and digital innovation at Novo.

“It’s one thing having a good model, but it’s another thing getting it to work in a real-life drug discovery environment and operate on a super-fast timescale,” Patel said. “Like weaving your way through the woods on a bicycle, you need to be agile and quite fast at making decisions.”

Patel likened finding novel targets to searching for new stars in the galaxy.

“An AI model is like a super powerful telescope that shines a focus on an area of biology that we maybe don’t understand, and it gives us an opportunity to deconvolute that to get enough data and confidence to explore it further,” Patel said.

Failing quickly

Failure is just as important for machine learning as any success, which is why much of the headway being made in drug discovery and development tends to happen behind the scenes.

For Immunai CEO Noam Solomon, whose company is mapping the immune system using its AI platform Amica, partnerships with pharma partners like Pfizer as well as with dozens of smaller drugmakers, are critical.

“There are already success stories, but other times we‘re recommending a clinical program be terminated, for instance,” Solomon said. “Those are less sexy, and no one wants to acknowledge them, but they can be some of the most important things we do.”

Amica is built around single-cell multiomic sequencing of human immune systems. Solomon said the platform includes 50 million cells currently, a total he estimates will reach 1 billion cells in two years and 10 billion cells in five years. By then, this would represent samples from 1 million patients altogether in the database, he said.

The company is embarking on a new academic collaboration effort to provide immunology researchers with free access to the sequencing technology on an application basis — a program that will hopefully fill a gap left by cuts to the NIH — and serve to bulk up Amica’s datasets.

As former CSO of Pfizer, Dolsten is no stranger to this type of public-private partnership, but mostly from a Big Pharma perspective, he said.

“We’re looking for innovations to come out of this, not just through single-cell sequencing, but in the broader context of big scientific questions to pinpoint the convergence of technology, AI and clinical science and make a more significant leap forward in knowledge,” Dolsten said.

The lab of the future

Through the hype and fear of an AI-saturated future, the technology’s impact on the industry in the long run isn’t yet clear. But pharma leaders are already seeing how it’s changing their approach, from early research to clinical development.

“There was a period when you could put people into boxes and say, this is a computational person or this is a traditional biologist or chemist,” Novo’s Patel said. “But that has changed in the last five years, and the gap is shrinking day by day.”

To that end, AI developers are working closely with scientists to understand how the convergence can happen as seamlessly as possible.

“We spend a lot of time making sure scientists understand why models make certain decisions while bringing their knowledge of the disease target into the process,” van Grieken said. “There’s a whole set of tacit knowledge these experts have, but they need to see it work more than one or two times — we need to provide value not just in one cool demo, but at scale.”

For veteran leaders like Dolsten, getting the two pieces to fit together is critical.

“Early in my career, 5% of the staff needed to be dry lab while 95% were wet lab, and that was forward looking at the time,” Dolsten said. “Now, we’re moving toward a complete change in the ability to model outcomes, and that lowers the amount of resources we need in the wet lab. But we will always need human intelligence in the final product.”

Pharma is betting big on AI, pouring billions into the technology. But how it will ultimately influence the trajectory of the drug industry remains a mystery. Can pharma leaders’ priorities provide a glimpse into that future?

A recent Bloomberg Intelligence survey of pharma’s C-suite execs shows that while leaders are still leery of the technology, they see its potential benefits, predicting it will cut drug development costs by an average of 16% over time, with limited job losses.

“What was a little bit surprising for me, at least, was that they did say there would be a high impact on near-term sales potential, but they also said they see low headcount reduction potential from AI integration,” said Andrew Galler, a biopharmaceuticals analyst at Bloomberg Intelligence.

Pharma execs seem to be scrambling to position themselves for the inevitable changes AI will bring. Almost nine out of 10 pharma industry leaders surveyed anticipate a high or very high degree of disruption in the industry from the technology. 

“The 50 biopharma C-suite executives in our survey don't regard AI as fully validated in drug development and have concerns about the patentability of AI developed drugs,” the survey report found. But Galler said these patent fears may be overblown. While the current framework requires a human inventor, the patent would likely be granted to the scientist directing the AI development process. 

There’s also uncertainty regarding other aspects of the regulatory process. 

“The current administration is supportive of AI,” Galler said, noting, however, that there’s limited formal regulatory guidance indicating how the FDA will view AI-generated data. 

And although the media has recently trumpeted fears of an AI bubble, survey respondents believed there might not be enough AI infrastructure to support their plans, and were less concerned that AI companies were overbuilding.

Putting AI to use

While companies are taking an individualized approach to AI, many pharma leaders plan to task AI with preclinical work. Respondents anticipated 10% to 30% of those functions will go to the machines. 

“This coincides well with the FDA's intentions to move more preclinical work away from animal models, with the agency specifically calling out AI models as a potential alternative,” the surveyors reported. “As many as 94% of respondents indicated they planned to decrease their reliance on animal models for preclinical work, and we expect that a significant portion of that should flow through to AI models.”

The technology could also shorten drug development timelines by six to 18 months, Galler said. 

“That's important, because when you get to market quicker, it increases the [net present value] of your drug [with] a longer time on the market for patent exclusivity,” he said.

Compressing the development timeline could help close, but likely not eliminate, the widening speed gap between U.S. and Chinese companies. 

“That’s still a little bit longer than what we typically see in China,” Galler said.

Shorter timeframes also mean cost reductions, which could theoretically translate into job cuts. But Galler said it’s likely that large pharma companies and biotechs, which always need to replenish their pipelines with new programs, will instead choose to use those savings to expand their development programs. 

“You might see leaner teams, but more individual product teams,” he said. 

Certain drugs will likely benefit more than others from AI, particularly small molecules, because AI could help reduce off-target effects they’re prone to causing. Biologics and gene therapies could also benefit, but to a lesser degree because they’re newer and have generated fewer datasets to train AI programs, Galler said.

A new paradigm

As AI takes on new tasks, it will likely disrupt more operations on pharma’s back end, such as automating office functions, Galler said. GSK, for example, created a custom AI tool to assist employees with HR-related questions, he said. 

“We saw a pretty broad mix of what companies are planning to do between internal and external use of AI,” Galler said. “Obviously, they're going to try to build their own models for R&D.” 

This could put to work troves of internal data from decades of running preclinical experiments that may not have been published. 

Many companies are employing AI for generative molecule design with varying success. But uncertainty is causing some to move cautiously. 

“I think people are waiting for a little bit more validation, where they start investing super heavily, because pharma is a very conservative industry, typically,” Galler said.

Some companies are more aggressive in their AI adoption than others, though. 

“You have companies like Insilico [Medicine] that are kind of pure-play, AI-enabled drug developers, and they do everything from target discovery all the way to candidate nomination in an AI model,” Galler said. 

Others are taking a measured approach, but more could come on board as the technology gains validation. 

“Once you start getting some actual breakthroughs from companies developing first-in-class or best-in-class molecules with [AI], [we’re] going to start more rapidly seeing acceptance,” Galler said.

With TV ads and direct-to-consumer marketing under fire in pharma, the industry needs a new way to reach patients. Cue the influencers. 

“Social media influencers are a big play for pharma in the future,” said Faruk Capan, chief innovation officer at Eversana and CEO of Eversana InTouch.

A parade of celebrities has graced pharma ads in recent years. Pop star Lady Gaga became the face of Pfizer’s campaign to sell Nurtec ODT, while tennis star Serena Williams teamed up with AbbVie to promote a different migraine drug, Ubrelvy.

But the new ideal influencer isn’t just always “famous,” Capan said. Instead, they should be “more altruistic.”

“[Influencers] should have more health-related expertise,” Capan suggested. “[It may be] people who only have a few thousand followers, but are more critical [to the industry].”

Now, influencers with those specific criteria can be generated from AI.

“We built one of the first AI social media influencers in pharma,” Capan said.

Eversana unveiled a new marketing platform in 2025 designed to deliver end-to-end commercialization services through AI agents trained in omnichannel development, market strategy, branding and content creation. Called Agency AI, the platform was created in partnership with technology titan Google Cloud and can whittle a year’s worth of agency work into minutes, the company claims.

It's also part of an emerging push to leverage agentic AI — an ideal fix for burdensome and repetitive tasks across the industry. In the clinical trials space, for example, Medable recently rolled out a platform powered by AI agents that monitor data submission, flag potential errors and communicate directly with sites about needed information.  

And because agentic AI is relatively new for pharma, a bevy of use-cases remains largely untapped. 

After analyzing 270 workflows from 1,200 tasks in the life sciences, McKinsey recently estimated that 75% to 85% of the work could be augmented by AI agents.

If a company implements Agency AI, the workflow shifts will amount to 80% of tasks being generated by AI, with humans in the loop for the remaining 20%.

“We didn’t build this to replace or cut people, but the reality in the market is that clients want efficiency,” Capan said. “AI won’t replace you, but people using AI will.”

But alongside the efficiencies pharma can gain from AI, the technology is also giving rise to potential hurdles.

AI’s thorny new era

AI’s steady creep into marketing has triggered a host of ethical concerns for drugmakers. A social media influencer that’s outed as being fake, for example, could erode public trust in the industry — already a long-standing challenge.

Questions also remain over the legal accountability for AI-generated content if it’s later accused of or found to be being misleading. 

The Federal Trade Commission took aim at the issue with rules banning AI-generated consumer reviews and testimonials that “misrepresent the experience of the person giving it.”

The FTC isn’t the only agency becoming more hostile toward marketing practices. Drug regulators are also on the case.

The FDA in September launched a broad “crackdown on deceptive drug advertising” that includes plans to target companies that “conceal critical safety risks.” The agency called out social media for being part of the problem by leveraging “undisclosed paid influencer promotion” and blurring “the lines among editorial content, user-generated media and pharmaceutical advertising.”

While the FDA didn’t mention AI specifically — except to say the agency will be using AI to review drug ads — it promised to “aggressively deploy” all of its “enforcement tools” to ensure drugmakers aren’t misleading the public.

How can pharma stay out of regulators’ crosshairs? Marketing pros generally suggest that companies clearly disclose when content is sponsored and when it’s generated by AI.

The Agency AI platform will also be updated monthly or quarterly to help keep companies in compliance, Capan said.

“The technology is not a problem — it’s about compliance and making sure [companies] know which changes they are going to face,” Capan said.

Data safety, especially for intellectual property, is also a chief concern for pharma when using new platforms. But Capan pointed to Salesforce as an example of a massive platform that’s proven it can keep sensitive information under a digital lock and key, and said the company developed AI Agency with security top of mind.

“You have your own playground on the platform and [data] doesn’t go outside of it and become a bigger issue,” Capan said. “We have a lot of safeguards.”

Biogen is on a mission to transform — from building a new headquarters in Kendall Square near Boston to further expanding the aperture beyond its multiple sclerosis beginnings — and emerging technologies are set to be key players in that effort.

Spearheading the IT side of that overhaul is Guy Hadari, senior vice president and global chief information officer, who’s guiding the company in a way he hopes will set the stage for a new era of therapies. His approach isn’t to opt in to “dozens of Blue Sky pilots,” but to diligently and carefully assess which technology needs the company should pursue that are directly linked to business outcomes, he said.

Biogen has already established newer directions past the outsized success of its MS blockbuster Tecfidera, with the rare disease drug Spinraza and the Alzheimer’s groundbreaker Leqembi, as well as a series of partnerships with fellow biopharmas Genentech and Sage Therapeutics. And although 2025 was a relatively mediocre year when it comes to revenue, the company is building a nascent pipeline with blockbuster hopefuls of the next decade.

To get there, Hadari said Biogen will be integrating all aspects of the business with technology like revamped patient service platforms and cost-saving AI models. For Hadari, that means his role — like many CIOs in biopharma — is changing at a rapid clip.

Here, Hadari explains how his role has shifted, where the company is heading in the AI arena and how these new directions will serve Biogen’s trajectory in solving complex biological problems.

This interview has been edited for brevity and style.

PHARMAVOICE: How has the role of CIO changed over the last decade or so?

GUY HADARI: The CIO is moving from the basement — where everything is being done but nobody sees or understands what IT is doing — to the front line. And it’s not just because of AI. That’s just one wave. The CIO is becoming more visible because what they do is more critical to the evolution of a company. This is an evolution not just of the role but of the business understanding of the role. It used to be perceived as providing hardware and making sure PCs are working to now being run as a business. AI will push it even further, and then the potential for things like quantum computing will make the role of technology even more important in 20 or 30 years.

What is the “new Biogen,” and what is your role in shaping it?

Biogen is going through a massive change as a company. We are moving away from being a single-franchise, single-therapeutic-area company to taking on a diverse pipeline and multiple products across immunology, rare disease, Alzheimer’s and more. From a one-stem company to a tree. The real challenge there is that the company evolved over the last 25 to 30 years with designs, processors, technology and ways of working for a single franchise. Now, we are becoming a different company. And all those underlying processes need to support it.

I’ll give you an example: Our patient services center where we support patients starting from reimbursement and through the whole process is designed to support multiple sclerosis. Now that we need to support a diverse portfolio, we need to redesign those business processes. We are paving the new highways of the company, and if we didn’t do that, the changes that [CEO] Chris [Viehbacher] wants to drive could not take place. We started this process two years ago, and we have two to three more years to go. This is a big step, and we work hand in hand with the business to adopt and define these new processes.

What have you accomplished in those two years?

The first area we addressed was R&D because we are heavily focused on development. Biogen’s development systems, from a technology perspective, were a result of 30 years of tailored investment, and we’ve replaced that with one integrated system that is industry standard. We’ve done the same for quality systems in manufacturing and the patient services platform, which we’re still working on. We still have things to do in development and manufacturing, and in commercial, and I would say 2026 to 2027 it will be done. That fits very nicely with our pipeline evolution and readouts.

As you integrate these new systems, you’re introducing more AI. Broadly speaking, as pharma pans for gold in the AI space, what do you think are the nuggets that will stand strong?

AI is the new kid in town, but it’s not the first kid. We’ve had digital and dot-com. This kid is different, though. I’m a skeptical, careful guy, and I think there is important promise for pharma, but we have to set up our expectations. A study from MIT found that 95% of AI projects fail. I’m a little skeptical of that number, but I’m sure it’s high, and there are plenty of reasons, like unrealistic expectations and chasing technology.

I don’t chase technologies. I chase outcomes. We know very well what technology is out there, don’t get me wrong. We keep our eyes and ears open. But if I can’t see a well-defined outcome, preferably linked to revenue or cost, I’m very skeptical. So I tell people, don’t tell me about technologies. Tell me about the problem and the expected outcome. I don’t want to get stuck in proof of concept land. And as for AI in drug discovery, the jury is still out. I think it will take years longer than what was expected. Quantum computing will be another jump, but that’s also years down the road.

At Biogen, we break AI into a pyramid. At the foundation is the table stakes, like [Microsoft’s] Copilot and agentic AI that we’ve already implemented because you need to start changing people’s way of thinking. AI is not just technology and products — it’s also culture. So that’s baseline. In the middle of the pyramid are tactical projects, an example being the painful, lengthy, expensive process of due diligence in the case of an acquisition. For our acquisition of HI-Bio [in July 2024], instead of reviewing thousands of contracts at high cost for three to four months, we fed the model and in 10 hours we’re on our way. The third layer is strategic bets, and this gets into the R&D world. It takes two years just to develop the regulatory documents required during the life cycle of drug development. If we can cut that time in half, we gain another year of the product on the market, and for us and the patients, that’s a game changer. We’ve done a pilot, and we want to develop our muscles here more.

Biogen is at the forefront of some very complex and difficult science. What’s the importance of your role in getting to that next level of targets and treatments in areas like Alzheimer’s, for instance?

A lot of value sits with the different types of data we have and how we use it. In Alzheimer’s, there’s a ton of data, especially for biology, and getting a sense of this data in a way that can help us in development and help patients is a monumental task. It’s a task that neither the business nor IT can do alone, and it touches everything. We have data from R&D, from manufacturing, from finance, from commercial — and IT’s main job is to integrate the stakeholders and the discussion around an agenda like Alzheimer’s and take it to action. That is the gold, and it’s easier said than done. Our team is at the center because we’re the only ones who can bring everyone to the table. We see everybody. We’re in a good place for that kind of journey.

Our hands for the next two and a half years are completely full, with the AI agenda, the data agenda and the growth of the company. I want to allow Biogen to fulfill its strategy, and if [the IT team] doesn’t do that, we will be inhibiting its success. So our agenda is very clear.