From weeks to minutes: How AI transforms pharmaceutical market entry strategy

Market entry decisions in pharmaceuticals carry extraordinary weight. Each choice influences billions in investment, years of development effort, and ultimately patient access to innovative therapies. Yet, most organisations still navigate these high-stakes decisions using fragmented data systems and decades-old sequential analysis processes.

Critical information sits scattered across disconnected databases. Analysis that should take hours stretches into weeks as multiple teams work in isolation, often reaching conflicting conclusions. A recent survey found that 41% of companies struggle with this type of fragmentation.

A new generation of artificial intelligence is transforming this landscape. Agentic AI systems can access and analyse information across all data sources simultaneously, understand questions posed in natural language, and deliver unified strategic insights in minutes, rather than weeks. But before scaling these capabilities, organisations must first implement robust data organisation and governance frameworks that set the stage for “unified intelligence”.

The price of scattered intelligence

Market entry teams typically have access to extensive data resources, but these remain scattered across the organisation. Sales performance metrics live in one system while promotional effectiveness tracking sits in another. Market forecasts and competitive intelligence occupy separate platforms entirely. Critical insights often arrive as static reports that quickly become outdated.

These disconnected systems compound delays at every stage. A standard market assessment might require multiple analysts working in sequence. One group extracts sales figures. Another pulls promotional metrics. A third gathers competitive data. Finally, a separate team attempts to synthesise these findings into strategic recommendations. Each analysis cycle consumes several days and severely limits the number of strategic questions teams can explore each month.

Beyond time lost, fragmentation often results in troubling inconsistencies. When analysts work in isolation with their specific data sets, they develop distinct perspectives and methodologies. Teams examining identical market opportunities through different data lenses frequently reach conflicting conclusions. This creates confusion, rather than clarity for decision-makers. This lack of unification undermines confidence in strategic recommendations and delays critical market entry decisions.

Unifying intelligence through natural interaction

Agentic AI directly addresses these fragmentation challenges by allowing access to and analysis of information across all data sources in a single interface. Users can pose questions using everyday language and eliminate the need for specialised technical skills or database expertise.

Consider a brand leader who is investigating performance issues. Instead of commissioning multiple analyses across different teams, they can directly ask: “Why are we losing market share in France while our promotional investment has increased 30%?” Within minutes, the system can reveal connections that might have taken weeks to uncover, such as deployment of a competitor’s new sales force, shifting physician preferences, or channel saturation issues.

The ability to explore strategic questions in real time, rather than waiting days or weeks for analytical support, can transform how leadership teams operate. Executives can test hypotheses, examine scenarios, and refine strategies in real time and turn static presentations into dynamic strategic discussions.

When systems learn to think strategically

We’ve witnessed a remarkable evolution from basic data retrieval to genuine reasoning that marks a transformative shift in pharmaceutical analytics. Initial AI implementations functioned primarily as sophisticated search engines, locating and summarising existing information. Current agentic systems demonstrate analytical reasoning, identifying patterns, evaluating correlations, and generating strategic guidance based on a comprehensive understanding of market dynamics.

These systems perform multifaceted analyses when tasked with developing recommendations for market entry of a new therapy. They evaluate competitive positioning, identify successful launch patterns from analogue products, assess regional variations in prescribing behaviour, and synthesise these findings into tailored strategic frameworks. Recommendations are adapted based on specific geographic contexts, therapeutic categories, and competitive environments.

Transparency in this reasoning process proves essential for building trust. Agentic AI allows users to examine the logic behind any recommendation. Teams can request detailed explanations, explore alternative scenarios, and learn exactly how conclusions were reached. This transparency can build confidence and enable more informed strategic decisions.

Agentic AI provides another benefit that resonates with anyone who’s led strategic analysis sessions: the elimination of analyst fatigue. While human experts understandably lose patience after repeated rounds of granular questions, automated systems maintain consistent analytical rigour regardless of query volume or complexity. Users can pursue deep lines of inquiry without concerns about resource availability or interpersonal dynamics. This unlimited analytical depth often reveals insights that would remain hidden by traditional analysis constrained by human limitations.

Real impact across the product lifecycle

The real-world impact of agentic AI becomes clear through specific use cases spanning the pharmaceutical product lifecycle. During early development, teams leverage AI-powered analysis to evaluate commercial viability and identify optimal positioning strategies, which can compress timelines from days to hours. Prelaunch applications include competitive assessment and analogue identification that previously required weeks of manual research.

Picture a launch team that discovers their promotional strategy isn’t resonating. Previously, diagnosing the root cause meant weeks of analysis. Now, they can explore whether the issue lies in message content, channel selection, or competitive response, all during a single strategy session. These insights enable course corrections within days, rather than months.

Lifecycle management also benefits from AI-augmented analysis. Marketing teams can optimise resource allocation by understanding channel-specific returns on investment and identifying underutilised opportunities for engagement.

Geographic granularity adds another dimension of agentic AI’s practical value. Advanced implementations will one day enable analysis from national strategies down to individual territories or customer segments. This capability will allow organisations to develop nuanced market entry plans that account for local prescribing patterns, regional payer policies, and geographic variances.

Market access planning particularly demonstrates the power of AI-driven simulation. Teams can model how different pricing strategies might perform across European markets with varying health technology assessment (HTA) requirements and reimbursement frameworks.

The partnership between human and machine intelligence

Rather than displace human expertise, agentic AI amplifies strategic value by changing how analysts spend their time. By automating data gathering and initial analysis, these advances can free human experts to focus on strategic interpretation, stakeholder engagement, and implementation planning.

This evolution dramatically expands analytical capacity. Teams that previously managed 10 strategic analyses each month can now address dozens of business questions to enable a more comprehensive understanding of the market and deliver a faster response to competitive threats. This reduction in time allows organisations to maintain closer alignment with rapidly evolving market conditions.

However, human oversight remains necessary for several reasons. While AI models have delivered increasingly accurate results, output still requires human validation through the dual lens of business judgment and market expertise. Agentic frameworks excel at identifying patterns and generating directional insights, but transforming these findings into executable strategies demands human strategy and creativity.

Unified intelligence: A competitive edge

The message for pharmaceutical leaders is clear. Agentic AI isn’t just another analytical instrument. It represents a reimagining of how strategic intelligence is generated and applied. Organisations that successfully integrate these capabilities into their planning processes can gain the ability to compete with unprecedented speed, precision, and strategic sophistication. As advances continue and adoption accelerates, early adopters will find themselves with substantial advantages in delivering innovative therapies to patients around the world.

About the authors

Kapil Chaddha brings over 22 years of experience in leadership and operational roles across pharmaceutical, healthcare, financial services, and automotive industries. As director of new product development at IQVIA, he leads product development within Global Market Insights, driving development and commercialisation of GenAI solutions. His expertise spans end-to-end client engagement management, business development, and solution design for complex business challenges across Fortune 500 customers in the US, UK, Europe, and APAC regions.

Abhishek Jaiswal is a product and strategy leader with 17 years of experience in market research and agentic AI. He leads development and commercial strategy of agentic AI solutions layered across IQVIA’s gold standard syndicated data assets. With over 11 years at IQVIA, he drives incremental value to customers through business strategy, product development, and AI-powered solutions and data-driven insights. His responsibilities include leading product development roadmaps and implementing pricing and go-to-market strategies for next-generation analytics solutions.