July 15, 2025
Pharmaceutical companies are increasingly leveraging artificial intelligence tools across market research functions – from tracking competitors and analyzing real-world patient data to forecasting sales and automating report writing. Below, we profile a wide range of AI-powered tools (both established vendors and startups) organized by key subdomains.
Competitive Intelligence
AI-driven competitive intelligence platforms help pharma teams stay ahead of rivals by monitoring pipelines, clinical trial developments, regulatory news, and market signals in real time. These tools aggregate vast data sources and use AI models to surface actionable insights.
Maven Bio
Maven Bio is an AI-driven biopharma intelligence platform designed to automate, accelerate, and deepen pharmaceutical market research and competitive intelligence (Maven Bio). Leveraging advanced AI-agentic workflows, Maven Bio systematically analyzes and synthesizes insights from vast amounts of data from clinical trials, scientific literature, press releases, earnings calls, and regulatory filings (Maven Bio). The platform rapidly generates detailed competitive landscape reports, real-time market alerts, and delivers promising opportunities for a given therapeutic area. By transforming fragmented market data into actionable insights, Maven Bio enables pharma stakeholders to proactively identify risks and opportunities, streamline due diligence processes, and accelerate strategic decision-making—ultimately delivering a measurable competitive edge (Y Combinator).
AlphaSense
AlphaSense is an AI-powered market-intelligence platform that aggregates thousands of premium, public, and proprietary sources—including SEC filings, patents, broker research, expert-call transcripts, and news—into a single, concept-aware search experience for biotech and pharma teams (AlphaSense). Its Smart Synonyms™ semantic engine interprets researcher intent, while Smart Summaries—a generative-AI feature—condenses lengthy earnings calls and filings into crisp bullet briefs (AlphaSense). The Enterprise Intelligence module lets customers index internal SharePoint, Box, or S3 repositories so the same AI search covers private and external content (AlphaSense). For pharma competitive-intelligence teams, this toolkit can generate non-obvious market insights (AlphaSense).
Signum.AI
Founded in 2019, Signum.AI provides a cross-industry competitive-intelligence platform and offers a life-sciences-focused lens (F6S). Its engine continuously monitors open-web indicators—website changes, hiring patterns, ad campaigns, social communication—and pairs them with pharma-specific sources such as ClinicalTrials.gov records, FDA filings, and patent publications to deliver real-time alerts (Signum.ai). Beyond aggregation, Signum applies trend-detection algorithms that flag early signals—like sudden oncology hiring spikes or a newly granted fast-track designation—giving strategy teams lead time to recalibrate (Signum.ai). User reviews highlight how the platform replaces manual analysis with an automated workflow that keeps CI, marketing, and product teams ahead of competitor moves (g2.com).
Gosset
Gosset is a Palo Alto–based, AI-focused biotech-intelligence platform founded in 2023 that equips investors, corporate-development analysts, and pharma R&D/CI teams with fully structured clinical-trial data (LinkedIn) and analysis. Its AI agents automatically extract efficacy, safety, eligibility criteria, placebo, and comparator results from papers, conference posters, investor decks, press releases, and regulatory filings (GitHub). By reducing manual literature mining—“no more hunting down numbers in dozens of papers”—the platform accelerates diligence (Gosset).
Real-World Evidence & Patient Journey Analytics
In this domain, AI is used to harness real-world data (RWD) – such as insurance claims, electronic health records (EHRs), and patient-reported outcomes – to derive insights on treatment patterns, outcomes, and the patient journey. These platforms often combine big data with machine learning to identify patient cohorts, predict outcomes, or reveal unmet needs, empowering teams in R&D, HEOR, medical affairs, and commercial strategy.
Komodo Health
Komodo is a U.S.-based RWD analytics company built on the Healthcare Map, which stitches together more than 330 million de-identified patient journeys spanning claims, labs, genomics and EHR data (Komodo Health). Inside its MapLab™ workspace, generative-AI services such as MapAI transform those longitudinal records into on-demand dashboards that size disease incidence, trace time-to-diagnosis and pinpoint therapy drop-off nodes for commercial and HEOR teams (Komodo Health). Analysts report the no-/low-code interface collapses the data-wrangling cycle—what Komodo calls a “velocity advantage”—to mere hours (Komodo Health). A May 2025 deal with Nasdaq now syndicates the dataset to hedge-fund and equity analysts as Medical Claims Insights, letting them forecast drug uptake and revenue curves from real-time claims trends (Business Wire).
Flatiron Health
Flatiron, a wholly owned subsidiary of Roche since 2018 (Roche), curates the industry’s largest oncology EHR RWD asset with 5 million+ patient journeys across 22 tumor types and multiple geographies (flatiron.com). Its ML/NLP pipeline abstracts progression, response and safety endpoints directly from unstructured notes, delivering regulatory-grade real-world evidence to life-science customers in weeks (MedicalEconomics). In July 2025, Flatiron researchers showed that large-language models could match human abstractors when extracting progression events across 14 cancers—paving the way for automated comparator-arm construction and competitive-landscape analytics. Market-research users tap the cleaned tables via cohort builders, APIs and curated disease reports to benchmark line-of-therapy share, sequencing patterns and real-world comparator outcomes in both U.S. and Japanese settings (flatiron.com).
ConcertAI
ConcertAI, founded in 2018 and based in Cambridge, MA, couples one of oncology’s deepest clinico-genomic warehouses with CARA™ and the newly launched Precision Suite of generative- and agentic-AI SaaS tools to arm commercial and R&D strategists with real-time market intelligence (Business Wire). Deployed at 2,000+ provider sites and 50 bio-pharma companies, its Explorer and Trials modules simulate eligibility tweaks, forecast enrollment velocity and surface high-density patient clusters—accelerating study start-up and go-to-market planning (concertai). An ASCO-2024 study showed the multi-model screening stack significantly cut manual chart-review time while maintaining accuracy—translating into faster market-access projections for commercial teams (concertai).
Sales Forecasting & Commercial Analytics
AI is also transforming commercial analytics in pharma – from forecasting drug sales under various scenarios to optimizing marketing campaigns. The tools in this space leverage rich datasets (prescription trends, physician behavior, payer data, promotional response) and machine learning to provide more accurate forecasts and guide tactical decisions.
IQVIA
IQVIA operates the cloud-native Human Data Science Cloud, integrating large-scale longitudinal prescription, claims and sales records that capture 93% of all U.S. outpatient retail-Rx activity. (IQVIA). Its “health-care-grade AI” underpins a portfolio of market-research tools: the Next Best Action engine delivers real-time, channel-agnostic recommendations to field teams, while the Forecast Horizon platform blends competitive events into forward-looking demand models (IQVIA). In June 2025 IQVIA unveiled custom NVIDIA-accelerated AI agents that automate workflows such as market assessment, literature review, and HCP engagement (IQVIA). Together, these capabilities let commercial strategists quantify the impact of rival launches, size new-indication opportunities and optimize resource allocation with real-world evidence.
Evaluate
Evaluate Pharma couples long-standing analyst-consensus datasets with Intelligent Forecasting, an AI layer that dynamically recalibrates market-size and epidemiology models as fresh clinical, demographic and pricing inputs arrive (Evaluate). Its latest World Preview 2024 extends revenue projections through 2030, estimating global prescription-drug sales will top $1.7 trillion. Users can tap into Omnium, which applies machine learning to millions of pipeline datapoints—R&D cost curves, success probabilities, time-to-market—to stress-test asset valuations and competitive scenarios in minutes (Evaluate). Since the 2022 combination that placed Evaluate alongside MMIT, Panalgo and Citeline inside Norstella, its forecasts can also be cross-referenced with payer, pricing and RWE signals, giving market-research teams a panoramic, AI-augmented view of commercial potential.
Axtria
Axtria supplies cloud-native analytics platforms—SalesIQ™, MarketingIQ™, DataMAx™ and the new InsightsMAx.ai—that embed machine-learning and “agentic AI” throughout sales-planning and omnichannel-engagement workflows (Axtria). Launched in April 2025, InsightsMAx.ai ships with a library of 30+ pre-built AI agents that forecast granular demand, optimize call plans and surface next-best actions directly inside Veeva or Salesforce, cutting rep-planning cycles (axtria.com). Marketing and market-research users draw on propensity-to-engage models, GenAI-driven budget-shift simulations and territory uptake views from MarketingIQ and CustomerIQ, all without building in-house ML pipelines. (Axtria).
Conclusion
Across the pharmaceutical industry’s market research and analytics spectrum, AI tools are driving significant change. RWE and commercial analytics groups can analyze billions of data points – patient journeys, payer policies, social media posts – with machine learning finding the patterns and outliers that matter. Even the traditionally human-intensive tasks like report writing and content creation are being accelerated by generative AI solutions, allowing experts to focus on interpretation and strategy rather than rote production.
It’s important to note that while these AI tools greatly augment capabilities, they are most effective when paired with pharma experts who provide context, ask the right questions, and validate AI-driven outputs. For a U.S. pharma market researcher or strategy leader, the landscape of tools is richer than ever: from established vendors (IQVIA, Clarivate, Evaluate, MMIT) that have infused AI into their offerings, to nimble startups (Komodo, Intelligencia, Aktana, Yseop, and many more) that bring novel AI-first approaches to longstanding challenges.
The bottom line is that AI is no longer experimental in pharma market research; it’s here, delivering value today. Companies that embrace these technologies – while maintaining scientific rigor and ethical use – are finding they can navigate markets more adeptly, anticipate changes better, and ultimately make decisions that lead to improved patient and business outcomes. The tools profiled above provide a reference starting point for anyone aiming to bolster their market research arsenal with the power of AI-driven intelligence.
Frequently Asked Questions
What is “AI market research” in the pharmaceutical industry, and how does it differ from traditional desk research?
AI tools automate two big steps: (1) data aggregation and normalization, (2) research and analysis—turning what once took hours into minutes and freeing experts to focus on strategy rather than data wrangling. For example, Maven Bio’s agentic-AI platform automates the manual data analysis and repetitive research steps for consultants, investors, and pharma companies. This allows them to generate complex competitive landscapes, identify the most promising drugs, or screen the most relevant opportunities, much faster than traditional desk research (Mavenbio.com).
How does generative AI accelerate pharmaceutical competitive-intelligence (CI) workflows in 2025?
Generative-AI has compressed the entire pharmaceutical competitive-intelligence (CI) cycle. Large language models aid in analyzing biomedical literature, clinical-trial registries, regulatory filings, and financial disclosures and act as always-on agents that can continuously harvest and classify new information, draw insights from complex data, and draft briefs that quantify potential market impact. Maven Bio exemplifies this shift: its AI-agentic stack can benchmark each new signal in the market against thousands of historical data points so CI teams can develop strategy, anticipating threats or partnership openings. The result is a move from episodic, manual data analysis to a conversational decision engine that keeps strategists ahead of the curve (mavenbio.com).
How can small biotechs bootstrap AI market-research capabilities without hiring a full data-science team?
Small biotechs can stand up sophisticated AI-driven market-research programs by utilizing external SaaS platforms instead of building a full data-science department. The fastest path is to license vertical tools that already bundle data ingestion, ML models, and visualization layers—platforms like Maven Bio, for instance, deliver out-of-the-box agents that analyze large corpus of data including SEC filings, drug and trial data, press releases, scientific literature, and earnings calls. (mavenbio.com) Contract research organizations (CROs) and data vendors often provide “analytics-as-a-service” packages, so you can outsource heavy modeling projects while keeping ownership of insights. Taken together, this mosaic of ready-made AI tools and light external expertise lets a resource-constrained biotech get 80–90 % of the insight speed enjoyed by Big Pharma—at a fraction of the head-count cost.
How will agentic AI reshape the future roles of human analysts in pharmaceutical market research by 2030?
By 2030, agentic AI—self-directed software agents that can gather data, reason over it, and trigger downstream actions—will push human analysts in pharmaceutical market research up the value chain. Instead of spending most of their time hunting for trial updates, cleaning spreadsheets, or assembling slide decks, analysts will oversee fleets of AI agents that continuously harvest real-world evidence, sentiment signals, pricing shifts, and competitor disclosures, then draft forecasts and “what-if” scenarios autonomously. The human role will pivot to three higher-order tasks: (1) hypothesis framing—defining the nuanced business questions that steer AI agents; (2) sense-making and validation—stress-testing model assumptions, resolving edge-case ambiguities, and ensuring outputs align with clinical and regulatory realities; and (3) narrative influence—translating machine-generated insights into persuasive strategy recommendations for executive, medical, and commercial stakeholders.
Platforms such as Maven Bio already preview this future. Its agentic LLM stack synthesizes disparate information, freeing companies to deal with higher-level strategy questions (mavenbio.com).
