Real-time monitoring and predictive insights facilitate proactive adjustments, potentially leading to more agile and successful collaborations compared to traditional methods reliant on periodic human reviews.
Strategic partnerships, alliances, and collaborations are the lifeblood for growth and innovation in the biopharma and biotech sectors, allowing companies to access complementary expertise, share risk, and expand pipelines. Here too, AI is emerging as a powerful tool to enhance the entire partnership lifecycle, from identifying potential allies to evaluating opportunities, modeling returns, and managing ongoing collaborations.
AI-Assisted Partner Identification and Evaluation
Finding the right strategic partner requires sifting through a vast landscape of potential candidates. AI can significantly accelerate and refine this process by analyzing diverse datasets—including scientific publications, patent filings, clinical trial data (e.g., from ClinicalTrials.gov), company profiles, financial reports, and market intelligence—to identify organizations with synergistic characteristics.6
What are some of the specific criteria that bespoke AI algorithms can use to pinpoint potential partners? This is by no means an exhaustive list but
should include:
Research Focus
identifying companies as well as individual researchers and scientists working on similar or complementary biological pathways, targets, or disease areas.
Pipeline Synergy
finding partners whose pipelines align strategically (e.g., in expertise, manufacturing platforms), offering opportunities for combination therapies or filling portfolio gaps.
Technological Capabilities
locating organizations with specific platform technologies (e.g., mRNA, gene editing, AI-driven discovery) that could enhance the partner’s own capabilities.
Geographic Presence or Market Access
identifying partners that offer entry into new markets or possess relevant commercial infrastructure.
Once potential partners are identified, AI plays a crucial role in evaluation and due diligence. Machine learning and predictive analytics can assess the feasibility and potential value of a collaboration by analyzing factors like the scientific validity of the target’s approach, the probability of clinical success for their assets (based on historical data and predictive models), potential market size and competition, and potential integration risks.11 Beyond that, AI can help evaluate a potential partner’s own AI maturity and data infrastructure, which is increasingly relevant in technology-driven collaborations.66 The cautious deal structures observed in AI-related partnerships, often featuring low upfront payments and significant milestone-based compensation, reflect the use of risk assessment (± AI) in valuing these nascent opportunities.66
Modeling Partnership ROI and Financial Outcomes with AI
Quantifying the potential return on investment (ROI) for a strategic partnership is critical for securing internal approval and negotiating terms. AI and more sophisticated domain-specific models offer sophisticated tools for this purpose:
- Financial Modeling: AI can enhance traditional financial models by incorporating predictive insights. This includes forecasting potential revenue streams from partnered assets, estimating market penetration based on competitive dynamics, modeling the impact of different deal structures, and quantifying potential cost or revenue synergies.67
- Risk-Adjusted Valuation: techniques like probability-adjusted Net Present Value (NPV) calculations or real options analysis, powered by AI-driven predictions of clinical success rates or market adoption, can provide more nuanced valuations of pipeline assets within a partnership context.51
- Incorporating AI Impact: insights from consulting firms like Deloitte suggest AI can significantly boost R&D ROI through accelerated timelines and improved success rates.68 These AI-driven efficiency gains can be factored into partnership ROI models to provide a more comprehensive picture of the collaboration’s potential value.
- Tracking AI Initiative ROI: Given the substantial investment required for AI tools and infrastructure, tracking the ROI of the AI initiatives themselves is also crucial for justifying expenditures and optimizing resource allocation.69
AI’s Role in Managing and Monitoring Alliance Performance
The potential value of a partnership only comes to life through effective execution and management. AI can play a significant role here through, for example:
- Performance Monitoring: AI systems can continuously track progress against agreed-upon milestones, Key Performance Indicators (KPIs), and timelines by analyzing shared datasets, project management updates, communication logs, and operational metrics.59
- Real-Time Insights and Alerts: AI can identify potential deviations, bottlenecks, or emerging risks within the collaboration much faster than periodic manual reviews, allowing for proactive intervention and course correction.45
- Unified View: AI-powered dashboards or “copilots” can consolidate data from various sources related to the partnership, providing alliance managers and leadership with a unified, real-time view of performance and potential issues.16
This capability shifts partnership management from a static, milestone-driven process to a more dynamic, data-informed approach. Real-time monitoring and predictive insights facilitate proactive adjustments, potentially leading to more agile and successful collaborations compared to traditional methods reliant on periodic human reviews.
| Partnership Stage | Relevant AI Techniques | Key Benefits | Key Challenges |
| Identification | NLP, knowledge graphs, ML | Speed, broader search, identification of non-obvious fits 24 | Data access/completeness, defining synergy criteria |
| Evaluation & Due Diligence | Predictive analytics, ML, NLP, knowledge graphs | Deeper insights, risk assessment, success probability prediction 45 | Data quality, model accuracy/explainability, target’s AI maturity 66 |
| Negotiation & Structuring | Predictive analytics, financial modeling | ROI modeling, synergy quantification, risk-adjusted valuation 51 | Forecasting uncertainty, model complexity, justifying valuations |
| Management & Monitoring | AI Agents/Copilots, real-time analytics, NLP (comms logs) | Performance tracking, early issue detection, proactive management 59 | Data sharing protocols, integration complexity, trust in AI alerts |
Some Illustrative Case Studies
Real-world examples highlight how biopharma companies are leveraging AI in partnerships:
Demonstrates a strategy heavily reliant on external partnerships to access cutting-edge AI. Collaborations with Valo Health (using their AI platform for cardiometabolic diseases), Microsoft (for cloud and AI capabilities), and MIT (for research fellowships) showcase how Novo Nordisk leverages external expertise and infrastructure to augment its internal drug development efforts.74
This collaboration focuses specifically on using Partex’s AI platform for identifying and evaluating compounds for potential acquisition or licensing, demonstrating AI’s direct application in the business development function of sourcing external assets.27
This case study illustrates a co-development approach where ZS, AWS, and a biopharma client collaborated to build a custom GenAI tool (Max.AI) to provide faster, deeper commercial insights. This highlights the value of targeted AI solutions co-developed through partnership to address highly company-specific business needs and achieve measurable impacts like 98% time savings and 95% accuracy.75
Other Examples: many other collaborations exist, such as Atomwise partnering with Sanofi 76 and Iktos working with multiple pharma partners 76, indicating a broad trend of established pharma partnering with AI-native biotechs.
These examples suggest that a hybrid approach is often most effective and acceptable. Companies can benefit from consulting specialized external AI platforms and expertise through partnerships, as advised by McKinsey’s ecosystem approach.6 However, successful integration and achieving specific business outcomes often require internal adaptation, co-development efforts, and building internal capabilities to effectively manage and leverage these collaborations, rather than relying solely on external “black box” solutions.
