Reimagining Planning in Life Sciences with AI Agents

I often reflect on the time, effort, and money poured into planning those global Humira launches. The sheer volume of manual work to build strawman launch plans with realistic timelines, map out cross-functional dependencies, simulate different launch pathways, and prepare for every regulatory, operational, and logistical contingency was mind-boggling.

All that effort just to get directional answers to basic questions like: What's our best path forward? What happens if the timelines shift? What if Europe's approval lags or reimbursement gets delayed? What will we do differently in each market based on how competitors could react?

We relied heavily on internal launch management teams in every country for each indication, as well as expensive consulting support, to model scenarios and stress-test them. It took weeks, sometimes months, to arrive at plans that were robust enough to inspire confidence, only to revise them days later in response to a change in timelines, new data, or a shift in regulatory guidance.

The sprawl of spreadsheets, with scores of tabs for each scenario, felt unmanageable, and the thought of keeping all of them updated with fresh information as situations evolved gave us chills. Even with everyone working flat out, the plans usually trailed reality by a couple of weeks or more.

So, yes, planning was slow. Updates were painful. And even after all that work, most of our decisions still had to be made with incomplete visibility.

That experience shaped my thinking. It's what makes me so certain about the value of an AI planning agent today. If our teams had access to something like this back then, the quality of the plans, the confidence in the decisions, and the sheer clarity across functions would all have been different. And better.

And that's the point. In an industry where the stakes are high and data is abundant but fragmented, planning must be fast, dynamic, and deeply informed. That's precisely what AI enables today.

Why Planning Is the Next Value Unlock for AI in Life Sciences

Planning is foundational to life science operations. From research projects and clinical programs to regulatory submissions and global launches, everything depends on the quality of the plans. Timelines. Resources. Budgets. Dependencies. Risks. Tradeoffs. Everything must come together to inform decisions.

Yet, for all the technological progress across the value chain—from discovery to commercialization—planning remains stuck in the past. It's slow, siloed, manual, and reactive. As I've written before, connecting data isn't enough—what matters is whether that data actually coordinates decisions.

Our AI planning agent changes exactly that. The agent takes on the heavy lift—scenario modeling, milestone mapping, task breakdown, complexity assessment, dependency updates, role mapping, buffer estimation, and even documentation.

Across the board, we're seeing conservative time savings of over 50%, with high-complexity planning tasks experiencing reductions of 60–75% in effort. That isn't a marginal improvement. It represents a fundamental shift in how planning gets done in the life sciences.

Instead of 120 hours per month juggling spreadsheets, diverse tools, and datasets while aligning functions, a planner can now reclaim 86 hours to focus on higher-value work: strategic thinking, cross-asset alignment, competitive intelligence, and scenario planning that genuinely informs decisions, not just documents them.

Tasks to Tradeoffs: How AI Agents Power Scenario-Driven Planning

The agent supports the full lifecycle of project planning. For example, it:

Builds multiple scenarios for a given objective in minutes, aligning them with clinical, regulatory, and commercial milestones, and translates these into structured work breakdowns. You choose the constraints, and it returns end-to-end tactical plans with critical paths.

Creates a single source of truth: Cross-functional deliverables in a single model that updates instantly when any assumption changes. It auto-generates dependencies, assigns resource effort based on complexity, estimates durations and buffers, and merges functional user input while maintaining a unified, coherent plan. And it updates fast. Plans that used to take days or weeks to assemble and review are now generated in minutes and iterated in hours.

Learns from historical data. The agent generates granular Work Breakdown Structure (WBS) structures and estimates effort, durations and buffers based on real-world heuristics anchored in thousands of past activities rather than relying on intuition. It's a living model that adapts, aligns, and surfaces real tradeoffs—so leaders can make real decisions.

Shows and rationalizes its work. Every recommendation is accompanied by a traceable rationale, enabling subject-matter experts to challenge or refine it.

A Day in the Life of a Planner, Revisited

Last month, a respiratory-franchise team used the agent to explore three IND-to-First-Patient-In pathways. The "cost-aggressive" version saved eight weeks but created an uncomfortably thin validation window. The hybrid path, suggested by the agent after it noticed similar patterns in prior monoclonal programs, struck a balance between speed and risk to create an optimal version, which ultimately became the approved go-live plan. What once required a two-week workshop to decide took a morning discussion on Teams.

Real-World Use Cases That Change the Game

With this speed and precision, teams across the value chain can reimagine their approach to R&D operations and execution. A few examples:

IND Planning: You can model what it takes to move a preclinical asset through IND-enabling activities—projected timelines, external spend profiles, and resource demand—all built on relevant precedents.

Clinical Trial Design: The agent enables you to test various trial-leg configurations and their impact on site capacity and drug supply. Evaluating single versus multiple arms with different endpoints and adaptive strategies and contrasting their implications can be done with speed and clarity.

Launch Scenario Modeling: You can simulate execution paths for various launch strategies and observe how timelines shift in response to regulatory pathways, market access challenges, or local requirements. Modeling pathways in Wave 1, 2, and 3 markets and then reordering launch activities allows you to pull forward revenue in priority regions. When done well, this is what enables same-day launches that capture revenue from hour one.

Capacity Planning for M&A: If you're considering an acquisition or in-licensing deal, you can quickly determine what it would take to integrate the asset into your existing pipeline, including resource strain and critical path shifts.

Portfolio-Level Planning: Whether it's a new therapeutic area, lifecycle management for an aging asset, or re-sequencing submissions to align with manufacturing capacity, the agent gives you a structured, data-informed foundation to act. This is particularly critical when multibillion-dollar portfolio decisions can no longer rely on guesstimates.

In all these cases, you're no longer guessing or observing from 30,000 ft. You're seeing what's required, dive into details where the pressure points are, and experiment with what the tradeoffs could look like in different competitive circumstances. The AI Agent redefines how decisions get made: faster, closer to the action, and better informed.

The Strategic Upside: What Teams Can Do With Freed Capacity

What really excites me isn't just the saved time. It's what that time enables.

Planning teams aren't just doing less—they're doing more of what matters. They're stepping into more strategic territory:

• Running risk-adjusted scenarios that previously sat on the wish list.
• Pressure-testing competitor moves with real-time intel and shaping launch strategies accordingly.
• Reallocating budget across the portfolio based on live constraints rather than last quarter's snapshot.
• Partnering earlier with manufacturing and market-access colleagues to shorten the asset's path to patients.

And accelerating value-added execution:

• Driving cross-portfolio optimization across functions and geographies
• Accelerating regulatory planning with better path-to-approval simulations
• Responding faster to change—whether it's a market move or a supply chain disruption

It's the difference between reacting and leading.

For example, across a complex portfolio of late-stage, multi-indication NME pipelines, we are observing firsthand that the cumulative impact of these shifts is substantial: tighter coordination, better-informed decisions, stronger launches, and a measurable acceleration of time to market—1 to 3 months faster per asset is not theoretical. We're seeing it happen.

And we're seeing better launch readiness. Better coverage. Better access strategies. Fewer surprises. The stakes are real: global launch delays cost both revenue and patient access—measured in months and lives.

From Reactive Planning to Strategic Orchestration

This is where the real value lies. Project planners evolve from reactive schedulers to proactive architects of launch excellence. Their insight shapes outcomes, not just Gantt charts. Their time is spent accelerating innovation, not manually updating plans.

In my Humira days, we called that "launch readiness." Today, it feels more like a "launch advantage."

So, when you think of this transformation across large portfolios with dozens of new molecular entities in development, the value unlock is staggering. You are not just creating operational efficiencies. You are unleashing a new kind of intelligence within the enterprise—one that is dynamic, scenario-ready, and intensely strategic.

A Personalized AI Planning Preview—On Me

If you're in the planning function at a life science company, I want to offer you something useful and real.

Just send me the details of an asset you're working on or one from a competitor you're tracking. The basics will do:

• Drug name
• Mechanism of action (MOA)
• Modality
• Therapeutic class
• Indication
• Current phase or stage—or simply how many months from the PDUFA or EMA date

In return, I'll send you a fully AI-generated strawman plan. You'll get:

• A timeline with key milestones and decision points
• Resource demand and external budget forecasts
• Critical path dependencies and bottlenecks
• A plan you can use—or use to challenge your current assumptions

We'll build this using publicly available data sources, such as ClinicalTrials.gov, regulatory filings, and other relevant sources, combined with our proprietary planning datasets.

Of course, when we work with companies in real-world implementations, we take it even further. We fine-tune the agent to operate on internal data lakes—your SharePoint archives, historical project plans, budgets, demand forecasts, and planning systems—so it learns from your actual history and gets smarter with every plan. So, it's not just AI; it's your archived institutional intelligence, finally operationalized.

I want you to experience what becomes possible when you stop spending weeks just getting a baseline plan and start spending your time refining the strategy for proactively accelerating your organization's pipeline.

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