Shawn Opatka 

AI’s Next Step in Pharma Quality: From Detection to Closed-Loop Prevention

According to Shawn Opatka, VP and GM Honeywell Life Sciences, nearly all organizations—about 99%—are engaging with artificial intelligence in some capacity, though the degree and focus of implementation vary widely. Many companies are piloting AI tools within quality, regulatory, and traceability operations to address persistent challenges such as labor shortages, skills gaps, and the overwhelming volume of data inherent to modern pharmaceutical operations.

Opatka emphasized that while AI experimentation is widespread, the industry remains in the early stages of full-scale deployment. Companies are still testing how best to integrate AI into regulated environments, where oversight and validation requirements remain stringent. This experimentation phase, he suggested, reflects a broader willingness within pharma to embrace innovation while maintaining compliance with evolving standards for data integrity and product oversight.

Ultimately, the sector’s cautious but active engagement with AI represents a balancing act: driving efficiency and insight through automation and predictive tools while ensuring transparency and control remain uncompromised. As regulators sharpen expectations around digital accountability, AI’s role in improving data traceability, quality assurance, and operational resilience will only grow more central to the pharma enterprise of the future.

He also dives into the biggest risks companies face if they attempt to scale AI solutions without a strong quality and compliance framework in place; the role of AI evolving within pharmaceutical quality systems; the definition of successful AI adoption in pharma; and much more.

How do you see the role of AI evolving within pharmaceutical quality systems, from reactive issue management toward more proactive assurance and risk prevention?

 From a reactive and proactive standpoint, we've actually been doing a bit of that for a while now, and have some adoption with our customers. What we would really like to see and focus on, and starting to see some traction is, closing the loop in the process.

You could take a simple, proactive or reactive example where you present a user with some data and allow them to make a choice based on some data that AI has. It's hey, here's what we think happened, and let them make that decision and finally decide, okay, this is the reason why that happened, but using AI to actually assist the operator, but then close the loop on the process to go back and then provide that data in a way that provides more insight and knowledge in a closed loop. That's really where we're focused on, and I think the use cases that are really exciting.

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In the third part of his Pharma Commerce video interview, Shawn Opatka, VP and GM, Honeywell Life Sciences, explains that as pharma quality systems evolve, AI is shifting from reactive problem-solving to proactive, closed-loop risk prevention, enhancing operator insight, decision-making, and continuous process improvement.

Pharma Accelerates AI Adoption as Transparency and Traceability Take Center Stage

What are the biggest risks companies face if they attempt to scale AI solutions without a strong quality and compliance framework in place?

 It depends on where in these companies you work with. As I said, we're focused mainly in manufacturing and quality, which is obviously a very regulated part of these companies’ business. I think there is some risk to jumping in potentially too far there, specifically in the regulated part, without taking note of the FDA guidances and what they've put in place, whether that's pharma manufacturing or medical device manufacturing.

I think the FDA has done a good job of putting out some guidances around what to do, but I think the biggest risk is probably how it's going to scale and how it's going to be used, and not having that comprehensive strategy in place before jumping in. I do see, and I would say, more adoption outside of the regulated part. I think we all use AI in our general lives and work environments, and some of those use cases aren't the regulated part, and I think that's where we're seeing the most adoption today, but certainly very exciting stuff coming in the manufacturing and quality spaces.

In the second part of his Pharma Commerce video interview, Shawn Opatka, VP and GM, Honeywell Life Sciences, notes that nearly all pharma organizations are experimenting with AI to address labor shortages, data challenges, and regulatory demands, but full-scale adoption remains on the horizon as companies balance innovation with compliance.

Pharma’s AI Evolution: Balancing Innovation, Transparency, and Regulation

Artificial intelligence (AI) is rapidly transforming the pharmaceutical industry, but companies are still navigating how best to align innovation with growing regulatory expectations around transparency, traceability, and control. According to Honeywell’s insights, approximately 99% of organizations are already experimenting with some form of AI. However, the scale and depth of adoption vary significantly across the sector.

Shawn Opatka, representing Honeywell Life Sciences, noted that pharma companies are leveraging AI primarily to address persistent workforce gaps, skill shortages, and inefficiencies in knowledge management. Many organizations are using the technology to bridge operational gaps and enhance regulatory or quality systems, though the industry remains far from full-scale integration. AI’s appeal lies in its ability to automate complex tasks, improve compliance, and strengthen product traceability, key requirements as global oversight becomes more stringent.

Adding to the momentum is the White House’s recent AI Action Plan, designed to establish the United States as a global leader in artificial intelligence. Linda Malek highlighted that the plan emphasizes not just acceleration but also smarter regulation. It proposes measures such as regulatory sandboxes, controlled environments where companies can safely test AI technologies, as well as streamlined approval processes to reduce bureaucratic hurdles for AI-driven medical devices and software.

The plan also calls for investment in critical infrastructure, including large-scale data centers and greater access to high-quality datasets, enabling more robust model training and algorithm development.

Together, these shifts signal a pivotal moment for pharma and healthcare organizations: as AI becomes integral to quality, safety, and efficiency, balancing innovation with regulatory confidence will define the next era of digital transformation in life sciences.

How are evolving regulatory expectations around transparency, traceability, and control shaping the way pharma companies approach AI adoption?

 I think companies are, from what I'm seeing, really jumping into AI, and we see different levels of engagement, but our data actually shows that about 99% of organizations are doing something with AI. What are they doing? I think it varies widely. If I think about it, particular to the businesses we serve as Honeywell, how are they doing it in quality and regulatory areas, as you mentioned, and traceability, that really varies, but we do see a lot of adoption.

I think what we see in it is the willingness to try new things with AI and fill gaps that companies are struggling to fill today. I think we all know the reasons why AI has been adopted, whether it's skill gaps, worker shortage, just the wealth of knowledge. I think that's where a lot of companies are experimenting, but I think we're a long way from full-scale adoption.

Meanwhile, how might the White House AI Action Plan influence the pace and scope of AI adoption across hospitals, health systems, and health-tech companies?

The White House AI Action Plan that came out recently was interesting in that it really focuses on trying to make the United States a leader as it relates to AI. There's a lot of discussion throughout the document around trying to accelerate development of AI in the United States, trying to keep pace with and get ahead of our competitor countries, and ways to do that.

I think in terms of how that plan will influence the scope of AI adoption across hospitals and health systems and health tech companies, is that there are a number of different mechanisms whereby the Action Plan tries to ease the regulatory burden, like through regulatory sandboxes—which we can talk about in more detail later—and trying to streamline approval processes for AI adoption, in terms of cutting what they term as red tape to ease approval pathways for AI medical devices and software.

Invest in infrastructure, government infrastructure and calling upon agencies like NIST and like the FDA and others to try to build out data centers, to try to increase access to large datasets and so on, which are essential for really training AI models and algorithms.

As AI adoption accelerates across life sciences, Shawn Opatka, VP and GM of Honeywell Life Sciences, and Linda Malek, partner at Crowell & Moring, discuss how regulatory expectations and federal initiatives—like the White House AI Action Plan—are shaping the technology’s future role in pharma quality, traceability, and healthcare systems.

Shawn Opatka

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