AI in Life Sciences: How Sales and Marketing Leaders Are Embracing – and Shaping – Our New Reality

An Executive Roundtable Discussion for Healthcare Sales & Marketing - Moderated by Colleen Burns, Managing Partner and Co-Founder | G2M Health

Artificial intelligence (AI) is poised to change the way life sciences leaders do business. Today, the question is no longer “will AI transform my business?” It is “how quickly can I adapt?” 

By harnessing AI's capabilities to solve complex problems, uncover hidden insights, and create competitive advantages, forward-looking leaders are becoming architects of change, actively shaping the future of industry using emergent technology.

But amid the growing hype and frenzied investment, what's working? Where are the genuine breakthroughs happening? And how are real-world commercial leaders navigating the unique challenges of implementing AI in one of the world's most heavily regulated industries?

To answer these questions and provide a clear-eyed view of AI's true potential, we recently assembled a panel of senior life sciences executives who are on the frontlines of this revolution. Their insights reveal not just how AI is being deployed today, but how it's helping them scale up faster, work more efficiently, reach more patients, and ultimately, improve more lives.

Meet our esteemed panel of experts:

Paul Murasko, Azurity Pharmaceuticals, Head of Digital Innovation & Marketing Operations

Paul is Head of Digital Innovation & Marketing Operations at Azurity Pharmaceuticals, where he leads efforts to transform commercial operations through technology. With decades of experience in life sciences, Paul champions the strategic integration of AI into marketing and sales functions while maintaining regulatory compliance, focusing on practical applications that deliver measurable business value.

Amanda Todorovich, FACHE, Cleveland Clinic, Enterprise Executive Director, Digital Marketing

Amanda is Enterprise Executive Director of Digital Marketing at Cleveland Clinic, where she leads digital strategy for one of the world's premier healthcare organizations. Under her leadership, Cleveland Clinic has pioneered innovative approaches to healthcare marketing, including strategic implementation of AI tools to enhance content creation, social media management, and audience engagement.

Anindita Sinha, Shionogi Inc., Vice President, Commercial Operations

Anindita is the Vice President of Commercial Operations at Shionogi Inc., where she leads analytics, field operations, data management, training, and other strategic initiatives. With her background in data-driven decision making, Anindita is pioneering the integration of AI into commercial operations to enhance predictive analytics, improve HCP targeting, and develop more personalized marketing approaches.

Rohit Sood, Sorcero Inc., Chief Revenue Officer

Rohit is the Chief Revenue Officer of Sorcero, a growing health tech company offering a medically tuned AI platform that is the intelligence platform for life science organizations.  With 3 decades of experience and multiple leadership roles in life sciences digital transformation, he is responsible for a positive customer experience throughout the customer journey.

Jieun Choe, Viz.ai, Chief Product and Marketing Officer

Jieun is Chief Product and Marketing Officer at Viz.ai, a company that specializes in healthcare AI technologies. With her extensive background in healthcare technology marketing, Jieun oversees product strategy and marketing initiatives, helping healthcare organizations implement advanced analytics solutions that improve operational efficiency.

Colleen Burns, G2M Health, Managing Partner, Panel Moderator

Colleen is Managing Partner and Co-Founder of G2M Health, a specialized consultancy whose mission is to empower health and med tech innovators to elevate their go-to-market strategies and successfully commercialize high-value solutions.  Colleen has driven growth and market penetration for health and med tech organizations of all sizes for the past 25 years and is well versed in utilizing AI-powered tools to support critical business goals like improving efficiency, reducing costs, and driving growth.

Finding the Right Starting Point

Colleen Burns, G2M Health: Let's start with the big picture. How is AI currently influencing your commercial strategy and what approach are you taking to adoption?

Paul Murasko, Azurity Pharmaceuticals: We're approaching AI strategically at Azurity, focusing on specific use cases rather than trying to implement it everywhere at once. No one doubts the potential value, but it isn't a one-size-fits-all solution. We're making significant headway on the data side with forecasting and predictive analytics—moving from an ML model for next best action and next best customer initiatives to an AI-driven approach.

Amanda Todorovich, Cleveland Clinic: I couldn't agree more with Paul's point about strategic implementation. In our experience at Cleveland Clinic, 2024 was all about dipping our toes into the AI waters, focusing on marketing areas where we could see quick wins—tangible improvements in efficiency and real time savings. Now in 2025, we're ready to implement it on a large scale. We're prioritizing AI education for everyone on the team. We want our people to be AI-fluent, so we're identifying even more opportunities to streamline processes and boost efficiency across the entire division.

Anindita Sinha, Shionogi, Inc.:   At Shionogi we've embraced the need to leverage artificial intelligence in various ways. From a company standpoint, we've started promoting the use of various off-the shelf tools amongst various teams. AI can really help us from a productivity standpoint—for example, don't make all your slides yourself, don't summarize all your emails yourself – an AI summarization tool can help. My analytics teams are also leveraging different methodologies involving machine learning and artificial intelligence to help us get to insights that inform our marketing and sales strategies better.

What Business Problems Are You Solving with AI?

Colleen Burns, G2M Health: What primary objectives are you targeting with your AI implementations? Are you focusing on efficiency, personalization, or something else?

Anindita Sinha, Shionogi, Inc.: We're leveraging AI to develop prediction models around market events and brand performance. We're also exploring innovation through data operations and management automation by considering a "little AI brain" sitting on top to do quality checks—not just checking rows and numbers but identifying trends and issues. Beyond that, we're partnering with companies that have built AI-driven models to help us understand consumer sentiment outside of traditional market research. If we know what consumers are organically thinking, our insights are far more intuitive and therefore our campaigns and targeting become much more efficient and effective.

Jieun Choe, Viz.ai: Picking up on what Anindita mentioned, efficiencies are at the top of our list too. We're expanding into more therapeutic areas and innovating, which requires more throughput from our go-to-market teams. We're also focusing on improvements in our content and user experience — how do we personalize content to different personas? Last year, we launched a new AI-powered chat on our website so users could get to the right answers faster.

Amanda Todorovich, Cleveland Clinic: Efficiency has been our initial focus at Cleveland Clinic, and the results have been dramatic. Our social media team slashed their platform management and post creation time by a whopping 57%—going from 154 hours a week down to just 66. Our photography team experienced an 82.5% time savings on a recent photo editing project. And our podcasting team has cut their script development time in half thanks to AI. These aren't just incremental improvements — they're game-changing shifts in how we operate.

Paul Murasko, Azurity Pharmaceuticals: To Amanda's point about efficiency, the foundation for personalization is always data. Overlaying AI allows us to analyze that data quicker and more effectively. Eventually, we'll use AI for true GTM campaign personalization — not just for segmentation, but for the actual content of what we'll say to specific HCPs. We're not there yet, but that's where we're headed.

Rohit Sood, Sorcero Inc.:  Business objectives vary but tend to center on accuracy, speed, time saved, and ROI. In Medical Affairs, customers want to generate insights faster while increasing breadth and depth. In Scientific Communications, teams often want to save time on key documentation. The common thread across all use cases is delivering measurable business value and impact. For instance, we’ve been able to deliver 18x faster insight generation at threefold depth for Medical Affairs teams; reduce plain language text summary generation time by over 40% for Sci-Com teams; and ensure 96% accuracy in detection of individual case safety reports (ICSRs) for Safety teams.

Which AI Tools Are Delivering Results?

Colleen Burns, G2M Health: Let's get specific about tools and applications. What AI implementations are delivering value for your teams today?

Amanda Todorovich, Cleveland Clinic: We've seen some major wins with our AI toolkit. Lately has been a game-changer for social media. Descript is streamlining our podcast and video editing. And Evoto is making photo editing a breeze. In our trial of Lately alone, it auto generated over 100,000 versions of social copy and saved our team over 21,000 hours of work.

Beyond specialized tools, our team is also embracing enterprise-approved AI like ChatGPT and CoPilot for everything from research and brainstorming to crafting interview outlines and email copy. We're also experimenting with generative AI to repurpose existing content—like turning a published article into an interactive quiz.

Paul Murasko, Azurity Pharmaceuticals: We've invested heavily in an internal engine we call ACE (Azurity Commercial Excellence), where data fuels next best actions and next best customers for the field and soon to be sales. When I talk about overlaying AI on data, it's not just for dashboards and predictive analytics for leadership, but for day-to-day decision-making in the field. Through our CRM efforts, we've seen a correlation in step-change performance when the model is followed. The big difference with AI is it's quicker and more robust because of the scalability and amount of data you can process.

Rohit Sood, Sorcero Inc:  Our most successful implementations have been in Medical Affairs, Scientific Communications, Safety, Commercial, and Market Access. Medical insight management and field medical excellence tools are providing significant value and allow medical teams to measure their impact on their stakeholders. By unifying disparate data sets, our customers can generate meaningful insights across multiple medical themes. They're particularly excited about measuring scientific engagement impact both at aggregate and individual HCP levels. Safety applications are also gaining traction, helping teams proactively message stakeholders to improve patient outcomes. In each case, the AI platform is specifically tuned to life sciences workflows and terminology.

Anindita Sinha, Shionogi, Inc.:  I'm particularly excited about dynamic targeting. Traditional targeting relies on criteria for driving brand awareness and adoption, but these criteria change as you impact awareness and behavior. Typically, we revisit these attributes every three to six months. But imagine having an AI engine sitting on top of our data, crunching numbers in real-time, telling us: "In the past two weeks, 60% of your original targeting attributes still hold true, but 40% have changed, and you should now focus on these 10 doctors instead of the previous 10." That's an extremely exciting use case that's being established well in pharma already.

Overcoming Adoption Hurdles

Colleen Burns, G2M Health: What challenges have you faced in adopting AI and how are you addressing them?

Anindita Sinha, Shionogi, Inc.:   The challenges fall into two primary buckets. First is the mindset shift. Some people are fast adopters, using ChatGPT or Copilot for everything. Others are afraid and don't realize that the time gained allows for more thoughtful engagement versus low-value, repetitive work.

The second challenge is infrastructure. Many teams lack the appropriate data infrastructure to support AI tools. Your data must be clean, structured, and ready for an AI model to learn from it. Most analytics teams spend too much time cleansing and structuring data before they can analyze it. If we're going to bring in powerful models, we must invest in strong data infrastructure—without that foundation, none of this is possible.

Amanda Todorovich, Cleveland Clinic: Navigating the AI landscape in healthcare comes with its own set of challenges. Security is one of the biggest hurdles, and our internal review process is understandably rigorous. We can't just jump in and start testing every new tool. There are layers of scrutiny to ensure everything is secure and compliant.

We've also learned that AI adoption isn't just about technology—it's about people. There's natural skepticism, even bias, around AI. New technologies can be intimidating or even scary. So, we've prioritized education and demonstrating tangible benefits with our teams. We're focusing on small wins, showcasing how AI can make everyone's lives easier and more productive. AI fluency is becoming a core business skill, and we want to ensure everyone embraces these opportunities rather than fearing them.

Jieun Choe, Viz.ai: While we're discussing AI applications in healthcare, our primary focus is developing tools that enhance clinical workflows through data analysis. In our marketing operations, we face challenges like what Anindita mentioned—AI tools for images and videos are improving but not quite there yet where we'd feel comfortable making a significant investment. However, this will most likely change sooner than later as the technology matures. The rapidly evolving nature of the technology makes it challenging to know when to commit to specific solutions.

Paul Murasko, Azurity Pharmaceuticals: Organizations often get caught up in the concept of AI instead of breaking it down into specific use cases. If you have an issue with your process or data, overlaying AI doesn't fix it—it's not a magic wand. You still need governance for your approach. If the underlying model is broken, AI might tell you more quickly it's broken, but it doesn't solve the fundamental issues.

Rohit Sood, Sorcero Inc:  We frequently encounter user skepticism about accuracy, hallucination, and potential impact on existing roles. IT integration with data lakes like Snowflake and Databricks is another area requiring deeper technology discussions. We invest considerable time engaging with IT teams to demonstrate seamless integration and with users to show the validation process. What we've found is that this initial skepticism typically wanes with hands-on experience and training.

Navigating Compliance Realities

Colleen Burns, G2M Health: How does operating in a heavily regulated industry like healthcare impact your AI approach?

Paul Murasko, Azurity Pharmaceuticals: Transparency and constant communication with key partners and stakeholders is crucial. When introducing new technology, I proactively involve regulatory, legal, IT, and compliance stakeholders as early as possible. These teams aren't proactively investigating emerging technologies and updating policies accordingly—they're focused on day-to-day operations. The responsibility falls on innovation leaders to bring new technologies to these stakeholders early, explain the value and differences from previous approaches, and help them properly calibrate their response.  Collaboration is key!

Rohit Sood, Sorcero Inc:  Compliance must be central to any AI provider’s approach. At Sorcero, we have invested and continue to keep up with new regulations to keep our platform compliant. Critically, we also invest to solve complex challenges that affect user compliance. One such challenge in life sciences AI use is minimizing “hallucinated,” responses that are inaccurate. Using our specialized AI agents, we’ve achieved a near-zero hallucination rate – an early win for the agentic model in general, which could be its own topic of conversation. Ultimately, when teams see that AI can be accurate, compliant, and credible in all medical contexts, adoption barriers fall more quickly.

Anindita Sinha, Shionogi, Inc.: The way we ask questions and interact with AI systems creates unique challenges in our highly regulated healthcare environment. With precision and accuracy paramount, the nuanced interaction with AI tools becomes especially important. When using AI in regulated contexts, we need to ensure users are properly trained not just on the tool itself, but on how to effectively query it to get compliant, accurate information. This is particularly important when the output might influence clinical decisions or marketing claims. Unlike in other industries, where an imprecise AI response might be merely inconvenient, in healthcare it could have significant compliance implications.

Future and Emerging Applications

Colleen Burns, G2M Health: What emerging AI trends do you foresee having the most significant impact on healthcare marketing and sales?

Amanda Todorovich, Cleveland Clinic: There are two areas that I can see impacting us in some big ways. First, as AI agents become more advanced, I could see us having many different personas of distinct kinds of patients that we use for asking questions and testing ideas or campaigns against to learn what will do well and be impactful for the human audience. Second, as the quality of AI-generated video, images, and graphics improves, there are many opportunities that could be made more efficient with those capabilities.

Jieun Choe, Viz.ai: Building on what Amanda said about AI agents and patient personas, enhanced video capabilities would be another game-changer. Generating quality video content currently demands significant resources and time. As these tools improve, they'll transform how we create and distribute video content, making it possible to produce more personalized educational materials at scale.

Paul Murasko, Azurity Pharamceuticals: I see several areas where AI will be transformative. First, I believe it can help streamline the MLR process.  Using AI to help with submissions and ultimately approvals can allow the process to effectively and efficiently work at scale and speed. Second, AI can dramatically improve next best action applications by making them more dynamic and responsive to real-time conditions. Third, as more interactions become virtual, AI can help with training and improving communication methods — for example, transcribing video calls with HCPs to provide valuable coaching opportunities.

Anindita Sinha, Shionogi, Inc.: I see three critical areas where AI will have significant impact. First, day-to-day productivity gains through tools like Copilot and ChatGPT, which provide quick efficiency wins. Second, advanced predictive analytics where AI models make forecasting more quantitatively driven and statistically sound, giving us a powerful way to understand market dynamics and adapt resources accordingly. Third, dynamic targeting for physician engagement, where AI continuously analyzes targeting criteria in real-time, automatically adjusting physician prioritization and engagement strategies.

Rohit Sood, Sorcero Inc:  We're tracking two significant trends. First, even as LLMs grow more powerful, we're seeing an increase in hallucination: something covered in a recent New York Times article looking at research done by OpenAI. Second, AI agents are already transforming operations by handling mundane tasks, allowing commercial teams to focus on relationship-building and strategic market development. The result is a dramatic increase in meaningful customer touchpoints while simultaneously reducing operational costs, accelerating sales cycles. I believe this approach will quickly become the standard in life sciences portfolio management.

Key Lessons for Commercial Leaders

The executives on our panel shared powerful insights that reveal a clear roadmap for AI implementation in life sciences marketing and sales. From their collective experience, several crucial lessons emerge:

1. Focus on Data Infrastructure First

Before implementing advanced AI tools, ensure your organization has clean, well-structured data. Without this foundation, even the most sophisticated AI models will struggle to deliver meaningful results. The time spent cleansing, structuring, and organizing data upfront pays dividends when AI tools can efficiently process information and generate valuable insights. At Shionogi, Anindita Sinha emphasizes this approach: "Your data infrastructure has to be clean and structured. Without that foundation, none of this is really possible."

2. Start with Specific, High-Value Use Cases

Rather than attempting to implement AI across all operations simultaneously, identify specific challenges where AI can deliver immediate value. Breaking down the broad concept of "AI" into targeted applications makes implementation more manageable and increases the likelihood of early success. Sorcero’s Rohit Sood advises his customers to “clearly define the business problem” before they venture into any AI solution.

3. Engage Regulatory Stakeholders Early

In a highly regulated industry like life sciences, involving compliance, legal, and IT stakeholders from the beginning is essential. These teams may not be proactively investigating emerging technologies, so innovation leaders must take responsibility for educating and involving them early in the process. Paul’s team at Azurity proactively engages these stakeholders: "I bring it to them as early as I can, explain the value add, and how it's different from what we've done in the past."

4. Invest in AI Education and Culture Change

The human element of AI adoption is often overlooked but critical to success. Address skepticism and resistance by teaching teams about AI's benefits and demonstrating how it can make their jobs easier and more productive. Focus on building trust in both the data and the AI-driven insights it produces. For Viz.AI’s Jieun Choe, this means keeping the conversation focused on what AI excels at delivering today: “enhancing clinical workflows through data analysis.”

5. Measure and Scale from Quick Wins

Start with small, focused pilots that can demonstrate measurable value before scaling to broader initiatives. By proving AI's impact through concrete metrics, you build organizational momentum and confidence for more ambitious implementations. Cleveland Clinic's Amanda Todorovich has witnessed remarkable efficiency gains: "Our social media team reduced platform management time by 57%, and our implementation of AI tools saved more than 21,000 hours of work."   

The Path Forward

At the beginning of our discussion, we posed a fundamental question: "How are real-world commercial leaders navigating the unique challenges of implementing AI in one of the world's most heavily regulated industries?"

The answer is now clear. Forward-thinking life sciences leaders are becoming architects of change, systematically transforming their operations by starting small, focusing on fundamentals, and building momentum through measurable wins.

In an industry dedicated to improving human health, AI represents more than a technological shift; it offers a pathway to fulfill our collective mission more effectively. The future belongs to those who can harness these capabilities not as a distant aspiration but as today's achievable reality—scaling faster, working more efficiently, reaching more patients, and ultimately, improving more lives.