Note from Editor: This post is adapted from Dr. Rich Sonnenblick’s presentation at the 30th European Biopharma Project, Program, & Portfolio Management Conference in Basel. In his presentation, Dr. Sonnenblick dispelled the hype surrounding AI and agents, while sharing concrete examples of how drug development teams are saving hundreds of hours, de-risking their clinical pipelines, and gaining critical strategic and operational insights through Artificial Intelligence.
If there was one major takeaway from this year’s Basel conference, it was how AI has crossed the threshold from promise to performance in life sciences portfolio management.
The conference was filled with sessions where speakers debated how to connect strategy and execution, how to manage patent cliffs, and how to create transparency across sprawling R&D networks. But beneath all the slides and frameworks, the real story was about how AI is redefining the rhythm of decision-making in drug development.
For Planview, that shift has been years in the making.
Our mission has always been to help organizations turn connected work data into connected intelligence. Today, AI allows us to do that faster, sooner, and with far greater precision.
From Data to Decision: The AI Capability Horizon
In my presentation, “Managing Drug Development Initiatives: Trends in AI,” I described Planview’s model for evolving AI capability.
This model is built on Foundational Infrastructure to enable Early Warning, Simulation and Optimization, Scenario Planning, and finally Generative AI.
Each solution area represents a practical step toward true decision intelligence.
1. Foundational Infrastructure: Becoming a good Steward of Connected Work Data Sources
Every insight begins with connected data.
Drug development generates massive volumes of structured and unstructured information. Information like clinical milestones, resource assignments, dependency maps, risk logs, and regulatory documents.
Planview’s Data Fabric stitches those elements together across products, portfolios, and teams, forming the substrate for machine learning and predictive analytics.
Planview’s Connected Work Graph provides a visual representation of your network of work, resources, and outcomes so you can identify and address dependencies and risks.
The Connected Work Graph provides a powerful visual representation of work, resources, and objectives for instant identification of dependencies, at-risk work, and much more.
2. Early Warning: Seeing Trouble Before It Shows Up
By the time status fields turn red, it’s already too late.
Planview circumvents this by using historical execution data, activity patterns, and even project discussion thread sentiment. Our machine learning models surface early risk signals to identify when work is stalled, resources are overloaded, and collaboration is breaking down.
This early warning capability is like moving from weather reports to a Doppler radar. Project leaders don’t just see what happened – they see what’s brewing on the horizon.
3. System-Enabled Optimization: The AI Co-Pilot for Resource Strategy
Once risks are visible, optimization becomes actionable.
AI models in the Planview platform recommend staffing reallocations, dependency decoupling, and sequencing changes. This transforms portfolio adjustments from painful quarterly debates into continuous, data-driven refinements.
By aligning workloads and capacity to real-time constraints, organizations can gain months of productivity without increasing headcount.
4. Scenario Planning: Rehearsing the Future
Life sciences portfolios are bets involving time, resources, and external factors.
AI-powered simulation and optimization engines let teams test different futures:
What happens if a key trial slips three months?
How does an accelerated submission in Europe affect U.S. launch sequencing?
By quantifying trade-offs in resource, timeline, and probability of success, scenario planning moves decision-making from gut instinct to rehearsed precision.
5. Generative AI: Conversation as the New Interface
The newest layer is conversational.
With Planview Anvi™ Chat, teams can ask natural language questions directly against Planview’s Connected Work Graph:
- “Which studies are showing declining patient enrollment?”
- “Where do we have unassigned work tied to a Q4 objective?”
- “Summarize dependencies between our oncology assets.”
Generative AI turns the portfolio into a living knowledge system capable of explaining itself, teaching new team members, and drafting context-rich summaries in seconds.
Why AI Matters Now
Drug development has always been a data-rich, insight-poor domain. AI changes that equation.
By bringing together structured data, unstructured context, and human judgment, we can identify patterns invisible to conventional analytics – from creeping resource burnout to shifting trial sentiment.
In practical terms, this means:
- Detecting risk six months earlier.
- Redirecting investment toward higher-probability assets.
- Reducing portfolio review cycles from weeks to hours.
- Making project retrospectives continuous rather than postmortem.
When combined with human expertise, AI gives R&D leaders the ability to act on foresight, not hindsight.
Lessons from the Basel Conversations
The broader conference reinforced how essential that transformation has become.
- Jacqueline Poot (IDEA Pharma) showed how “innovation freshness” separates market leaders from laggards – but warned that 2030 patent cliffs could wipe out $200B in revenue without the necessary bets on innovation in today’s pipelines.
- Mahesh Chandra (Novartis) argued for structured decision frameworks that embed data governance into every tollgate, aligning risk, value, and resource allocation.
- Andrea Panariello (IBSA) and Silke Rodrigues (Medac) shared firsthand how digital portfolio tools like Planisware and ONBOARD are professionalizing PPM and creating unified transparency across global sites.
All these sessions reflect the same core truth.
Pharma organizations no longer suffer from a shortage of data — they suffer from disconnected data. AI is what reconnects it, converting noise into a navigable signal.
The Planview Vision: From Connected Work to Connected Intelligence
At Planview, our vision for life sciences PPM is simple, but transformative:
- Predict instead of react: Use machine learning and generative AI to see risk (overloaded teams, work not ready to start) before it user-defined status fields shift from green to red.
- Simulate instead of speculate: Use scenario modeling to compare possible futures, accounting for uncertainty in market size, clinical risk, payer risk, and other critical inputs.
- Converse instead of configure: Let anyone, from scientist to executive, interact naturally with their data to surface insights and best-practice driven recommendations.
When every part of the organization can see the same truth and act on it accordingly, the journey from pipeline to patient accelerates dramatically.
The future of pharma project management will belong to organizations that can connect insight, execution, and adaptation in one continuous loop.
That’s what Planview’s Connected Work Graph and Anvi™ Chat are built to deliver: the intelligence layer that turns project data into strategic advantage.
