Growth in life sciences has traditionally meant adding head count, increasing budgets, and acquisitions. However, these strategies often introduce inefficiencies rather than agility, with larger org charts, disconnected workflows, and increased costs. In an industry where regulatory requirements, data volume, and operational costs are already high, the traditional approach to scaling is becoming increasingly unsustainable and challenging to innovate. 

Leaders are looking for ways to scale intelligently. The answer lies in radical simplification. By rethinking workflows, modernizing data foundations, and integrating agentic AI, life sciences enterprises can expand capabilities, accelerate decision-making, deliver high-value HCP experiences, and improve patient outcomes—without unnecessary operational drag. 

^Forrester

AI’s impact depends on the quality of the data it can access—making modernization essential. But data alone isn’t enough. To drive real transformation, AI must move beyond isolated insights and become an active decision-making agent embedded in enterprise workflows. This is where agentic AI comes in.

Agentic AI: The next frontier of intelligent decision-making

NVIDIA CEO Jensen Huang predicts 2025 will be the “year of AI agents,“ with AI handling more enterprise-level decisions. OpenAI CEO Sam Altman believes this will be the year we see the first “virtual employees” entering the workforce.  Fortune reports that a quarter of employers will try out agentic AI this year, with adoption expected to grow to 50% by 2027.  
  
Agentic AI isn’t just another automation tool. It represents a fundamental shift in how decisions are made, how work gets done, and how enterprises operate. Rather than replacing human expertise, agentic AI enhances decision-making, operational efficiency, and collaboration. These systems are designed to solve complex, domain-specific problems and are part of a broader decision-making framework that may also include simpler automation solutions.

Rather than replacing human expertise, agentic AI enhances decision-making, operational efficiency, and collaboration. Slalom helps organizations identify which tasks within a role can be augmented or automated—and which should remain human—through accelerators like EnhanceIQ.

As enterprises move away from rigid hierarchies and embrace AI-powered networks, workflows must evolve as well. Traditional processes designed for manual decision-making often slow down innovation. AI-driven workflows eliminate these inefficiencies, enabling faster, more intelligent operations. 

Breaking free from legacy workflows

For life sciences enterprises to truly capitalize on the benefits of AI, they must rethink how work flows through the organization. AI is not just an automation tool—it is a catalyst for simplifying complex processes, eliminating inefficiencies, and enabling real-time decision-making at scale.

AI-driven workflows streamline operational bottlenecks by reducing dependency on manual interventions, ensuring that data and insights move effortlessly across teams. By embedding AI into business processes, supply chains, and patient care pathways, organizations can enhance responsiveness, improve efficiency, and accelerate innovation.

For example, AI-augmented workflows can dynamically allocate resources, prioritize tasks, and optimize decision velocity—enabling enterprises to function with greater agility. This shift ensures that work is no longer constrained by rigid structures or outdated hierarchies but is instead governed by real-time intelligence that adapts to business needs.

By embracing AI-driven operational fluidity, enterprises can reduce friction in day-to-day processes and create an environment where technology and human expertise work in harmony—enabling teams to focus on high-impact activities that drive competitive advantage.

Agentic AI for improved patient experiences

The healthcare system is notoriously fragmented, creating friction for patients and providers alike.

• For patients: Navigating healthcare can be overwhelming, slow, and impersonal. Long call center wait times, disconnected records, and a lack of personalization contribute to frustration and poor experiences.
• For providers: The burden of data overload, regulatory demands, and outdated systems takes time away from what matters most—delivering excellent patient care.

How agentic AI can address these challenges

• Enabling hyper-personalized patient care—AI-driven insights connect patient history, clinical data, and real-time information to create individualized, context-aware care pathways. This ensures more tailored treatments, faster interventions, and improved patient engagement.
• Enhancing predictive capabilities—AI-powered models can identify risks earlier than human teams alone, leading to better outcomes, proactive treatment plans, and more streamlined interactions between patients and healthcare providers.
• Reducing administrative burdens—AI-powered assistants can handle paperwork, billing, and documentation, freeing up valuable time for providers to focus on patient care rather than administrative tasks. AI can also support field teams by serving as a more effective curator of the rapidly expanding body of high-value scientific data and evidence that’s doubling every 72 days. 
• Improving compliance efficiency—Agentic AI can automate regulatory reporting, ensure adherence to treatment protocols, and reduce documentation errors, making compliance more streamlined and accurate.

Using an integrated, agentic AI platform, healthcare organizations can eliminate inefficiencies, improve outcomes, and create a more seamless and personalized experience for patients.

Future-proofing AI adoption: From experimentation to enterprise transformation

AI’s potential in enterprise decision-making is clear, but many organizations struggle to move beyond the pilot stage. Too often, AI initiatives remain isolated experiments or one-off projects rather than long-term transformations. To fully embed AI into business operations and move to production, organizations must focus on upskilling, cross-functional collaboration, and overcoming resistance to change.

At the same time, regulatory compliance and governance should be embedded from the start—ensuring AI systems align with ethical, legal, and industry standards while maintaining trust and accountability.

Consider these strategies:

• Establish a dedicated AI office—Centralizing AI as a department will help move the technology from a novelty to a core operational driver, ensuring long-term integration and impact.
• Address adoption barriers proactively—Overcome fear, compliance concerns, and lack of transparency by establishing clear governance frameworks. A successful pattern has been to prioritize legal and compliance teams as early stakeholders with their use cases to realize the benefit directly. 
• Foster a culture of AI fluency—Ensure leaders and teams understand how to work effectively alongside AI agents, evolving skill sets and ways of working to make AI a natural part of decision-making.
• Ensure leadership adoption and advocacy—While early experimentation may start with individual teams, lasting transformation requires leadership to adopt AI tools themselves and set the tone from the top. When leaders model usage and sponsor AI initiatives, it accelerates cultural change and signals strategic commitment.

AI agents will be responsible for 15% of day-to-day work decisions within the next two years, empowering businesses to reimagine workflows, problem-solving, and collaboration. Organizations that embrace this innovative technology now will lead the way in the future. Novo Nordisk, for example, is developing a production-ready multiagent framework for clinical data analytics.  

AI adoption is no longer a question of “if“ but “how fast.“ Life sciences enterprises that proactively integrate agentic AI will gain a competitive advantage—enhancing decision-making, optimizing workflows, and delivering better outcomes.