Mar 24
Global life sciences M&A hit USD 240 billion in 2025, up 81% year over year according to EY's Firepower report. Deals are getting bigger, faster, and more complex. And somewhere in that acceleration, AI has moved from a buzzword on conference slides to an actual tool sitting inside deal teams at AstraZeneca, Takeda, and a growing number of mid-sized biopharma companies.
AstraZeneca acquired Modella AI in January 2026 - embedding multimodal foundation models and agentic AI directly into its oncology R&D organization, supporting AstraZeneca's stated goal of reaching USD 80 billion in annual revenue by 2030. Takeda closed a USD 1.7 billion deal with Iambic Therapeutics in February 2026, acquiring an AI-designed small molecule pipeline targeting oncology and gastrointestinal disease. Xtalpi and Dovetree completed a USD 5.99 billion combination - one of the largest AI-driven biopharma transactions to date. And the Recursion/Exscientia USD 700 million merger created one of the most closely watched AI drug discovery entities in the sector.
These are not fringe transactions. They represent a structural shift in how biopharma companies think about what they are actually buying in an M&A deal.
But here is the honest question most CEOs are not asking loudly enough - are you using AI in your M&A process because it genuinely improves outcomes or because everyone else seems to be?
This article breaks down exactly where AI adds real value across the M&A lifecycle, where it falls short, and what a well-run AI-augmented deal process actually looks like in practice.
AI in Deal Sourcing and Target Identification
Traditionally, deal sourcing in biopharma relied on banker relationships, conference networks, and internal business development teams scanning the same publicly available databases. The process was slow, relationship-dependent, and prone to missing non-obvious targets.
AI changes the sourcing equation in three specific ways.
Scanning at scale.
AI tools can now process patent filings, clinical trial registries, scientific publications, regulatory submissions, and private funding data simultaneously. A deal team using AI-assisted sourcing can identify a company with a novel delivery mechanism in a specific indication months before it appears on a banker's radar.
Signal detection in noisy data.
The more powerful application is pattern recognition across disparate datasets. AI can flag that a small company's Phase 1 enrollment velocity, combined with a recent manufacturing partnership and a specific IP filing, suggests it is preparing for a Series B or strategic partnership discussion. That kind of signal would take a human analyst weeks to piece together manually.
Competitive intelligence.
AI tools can monitor competitor pipelines, alliance activity, and licensing trends in near real time - giving deal teams a continuously updated map of the opportunity landscape rather than a quarterly snapshot.
The limitation here is critical - AI sourcing tools surface candidates, they do not evaluate strategic fit. A list of 200 AI-identified targets is only useful if your team has the operational judgment to filter it down to the three worth pursuing. That filtering requires someone who has actually run a deal - not just modeled one.
AI in Due Diligence - Document Review, Data Rooms, and Risk Flagging
This is where AI is having the most immediate and measurable practical impact on deal execution. According to Acquiry's AI due diligence report published in 2026, AI-assisted document review reduced average due diligence timelines by 30 to 40 percent in complex transactions, while improving consistency in risk identification.
Document review at speed.
A typical biopharma data room contains between 5,000 and 50,000 documents: manufacturing agreements, IP assignments, regulatory correspondence, clinical data packages, employment contracts, CMC documentation, and more. AI tools trained on legal and regulatory language can read and categorize these documents in hours rather than weeks, flagging anomalies, missing documents, and potential liabilities.
Risk flagging in real time.
More advanced systems can cross-reference data room contents against external databases - identifying undisclosed litigation, FDA warning letters, IP conflicts, or regulatory correspondence that a standard review might miss under time pressure. Deal teams using these tools have caught material issues buried in document volume that would have surfaced post-close under the traditional process.
Regulatory diligence.
In biopharma specifically, AI tools can analyze the regulatory history of an asset against current FDA guidance, flagging gaps between a company's development plan and the evidentiary standards regulators are currently applying. The IntuitionLabs pharma M&A IT due diligence guide published in February 2026 confirmed that AI-driven regulatory risk assessment is now a standard component of sophisticated biopharma diligence processes.
What AI cannot do in diligence.
AI can identify that a manufacturing agreement has an unusual termination clause. It cannot tell you whether the counterparty relationship is strong enough that the clause would never be exercised. Context, relationships, and operational history require human judgment - especially in biopharma, where regulatory relationships, manufacturing quality culture, and clinical team capability are often the most important diligence questions.
Across 17 acquisitions, I have seen deals where the most material risk was not in the data room. It was in a conversation with a key employee, a site visit to a manufacturing facility, or a call with a regulatory consultant who knew the agency's informal position. No AI tool replaces those touchpoints.
AI in Valuation and Pipeline Assessment
Biopharma valuation has always been part science and part judgment call. AI is improving the science part, meaningfully, but not completely.
Probability of technical and regulatory success.
AI models trained on historical clinical trial data can now generate more precise probability of technical success (POTS) estimates by indication, mechanism of action, and patient population. These models incorporate FDA approval history, trial design patterns, and competitive landscape data in ways that meaningfully improve on traditional analyst estimates - particularly for novel modalities where historical precedent is limited but growing rapidly.
Comparable transaction analysis.
AI tools can scan transaction databases and identify the most relevant comparables for a specific asset, adjusting for stage, modality, indication, and deal structure. This speeds up the benchmarking process and reduces the risk of cherry-picking comparables to support a predetermined valuation - a common failure mode in banker-led processes.
Commercial forecasting.
AI-driven market sizing and patient identification tools can improve commercial forecast accuracy for assets in development, particularly in rare disease and precision medicine categories where traditional market research methodologies perform poorly.
The judgment layer still matters.
A model can tell you that similar Phase 2 assets in this indication have transacted at a median of USD 800 million. It cannot tell you that this particular asset has a formulation advantage that changes the commercial ceiling - or that the development team has a track record of execution that justifies a 30% premium. Experienced operators bring that layer, and it is not replicable by any model currently available.
AI in Post-Merger Integration
Post-merger integration is where most biopharma deals either succeed or quietly fail. AI is beginning to play a meaningful role here, though the applications are less mature than in sourcing and diligence.
Systems and process mapping.
AI tools can accelerate the mapping of overlapping systems, processes, and organizational structures - identifying integration dependencies and sequencing recommendations faster than manual workstreams. In a complex biopharma integration involving multiple ERP systems, quality management platforms, and regulatory filing infrastructure, this acceleration is genuinely valuable.
Culture and talent risk.
Some platforms now use AI to analyze communication patterns, organizational network data, and employee survey results to identify retention risks and cultural friction points early in integration. The Renovaro/BioSymetrics merger - completed in early 2026 specifically to combine AI-powered biomarker discovery with conventional drug development - explicitly cited AI-driven talent integration planning as a component of their post-close strategy.
Operational continuity monitoring.
In biopharma specifically, manufacturing continuity and regulatory compliance cannot slip during integration. AI monitoring tools can flag deviations in production metrics, supply chain data, or regulatory submission timelines - giving integration teams earlier warning of problems that would otherwise surface as crises.
The integration reality.
The decisions that determine whether a biopharma integration succeeds are almost never process decisions. They are leadership decisions. Which team leads the combined R&D organization. How you resolve conflicting regulatory strategies. Where you consolidate manufacturing. These require executive judgment and accountability - not dashboards. AI can surface the information faster. It cannot make the call.
The Risks and Limitations of AI in M&A
AI in biopharma M&A is genuinely useful. It is also genuinely overhyped in some quarters, and the risks deserve direct attention.
Data quality determines output quality.
AI models are only as good as the data they are trained on. In biopharma, where clinical data can be sparse, regulatory precedents are evolving rapidly, and private company information is limited, model outputs need to be treated as inputs to judgment - not substitutes for it.
AI misses what is not in the data.
A management team's integrity. A key opinion leader relationship. A competitor's unpublished trial results. A regulator's informal guidance on an unresolved CMC issue. These are the factors that often determine deal outcomes - and none of them appear in a data room.
Speed creates new failure modes.
AI-accelerated diligence means deals can move faster. Faster deals mean less time to catch problems that require deliberate human attention. The risk is not that AI misses things. The risk is that deal teams use AI speed as a reason to skip the conversations, site visits, and reference calls that surface what AI cannot find.
Vendor claims outpace validation.
The AI M&A tooling market is crowded and competitive. Many tools are promising capabilities that are not yet consistently delivered at the performance levels marketed. Katten Muchin Rosenman LLP noted in their March 2026 analysis of AI in M&A that vendor due diligence for AI tools has itself become a material diligence workstream in sophisticated transactions. Executive teams need to evaluate tools with the same rigor they would apply to any other material vendor relationship.
What Good AI-Augmented M&A Looks Like in Practice
The best AI-augmented deal processes share a few characteristics that distinguish them from either the purely traditional approach or the AI-maximalist approach.
They use AI to do the work that scales poorly for humans: document processing, data aggregation, pattern recognition across large datasets, and monitoring at volume. They reserve human judgment for the work AI cannot do: strategic fit assessment, relationship evaluation, operational risk judgment, negotiation, and integration leadership.
They build AI outputs into their process as inputs, not conclusions. An AI-generated risk flag triggers a human investigation - not a checkbox. An AI valuation model informs a range - not a number. An AI-identified target list gets filtered by someone who has actually built a business in that space.
They maintain diligence discipline despite AI speed. The 30-day exclusivity window may now allow AI to process the data room in 72 hours. That does not mean the site visit, the management interview, or the manufacturing quality conversation should be compressed or eliminated. The bottleneck in good diligence was never document processing. It was always the quality of human judgment applied to what the documents revealed.
For smaller and mid-sized biopharma companies, the competitive opportunity here is real. Large pharma has more resources - but they also have more process inertia, more committees, and more layers between the AI output and the decision. A nimble executive team with access to the right tools and the right operator-level advisory support can move faster and see further than their size would historically have allowed.
The Bottom Line for Biopharma CEOs
The USD 240 billion in 2025 life sciences deal value is not slowing down. The patent cliff, the pipeline gap at large pharma, and the acceleration of novel modality development are structural forces driving M&A activity for the next decade. AI is now embedded in that process - at the sourcing stage, the diligence stage, the valuation stage, and increasingly in integration.
The question for your executive team is not whether AI will be part of your next transaction. It already is - whether you are using it or your counterparty is. The real question is whether you are using it deliberately, with the right human judgment layered on top.
AI is a powerful tool in biopharma M&A. It is not a replacement for the operator experience, relationship capital, and executive judgment that determine whether deals create value or destroy it.
At Katogen, we bring 35+ years of operator experience - 17 completed acquisitions, USD 4B+ raised, CEO-level accountability across multiple biopharma builds and turnarounds - to every transaction advisory engagement. We help executive teams build deal processes that use every available advantage, including AI, without losing the human judgment that actually drives outcomes.
Katogen is a biopharma advisory and strategy firm helping companies transform ambitious visions into real, sustainable value. Founded in 2016.
