I recently sat down with the CSO and SVP of Portfolio of a Top 20 pharmaceutical company to talk about their goals and struggles for the year ahead. With a $100B+ market cap, $20B+ in annual revenue, and a $6B+ R&D budget, a lot rides on their portfolio of 50+ active clinical trials.
With that many active trials, there’s a lot of data, programs, and pressure on their team. The pressure is twofold: they want to ensure patients have the best chance of getting new medicines and that revenue keeps growing. From my conversation, I thought I’d share a few of their comments that really stuck with me.
Annual review of portfolio decisions
“It’s painful to know that you have to prepare for four months to come to a governance and align.”
With an annual review of 50 to 60 portfolio decisions, the process is not only resource-intensive but also time-consuming. Typically, preparing complex decisions for governance can take up to four months. This lengthy cycle is compounded by the challenge of obtaining comprehensive data from reliable sources and achieving alignment between executive, R&D, and commercial teams on recommendations. The need for data curation and validation further complicates the process.
Complexities of portfolio analysis
“Unless you can drill down on the specifics for each program…it’s not gonna help someone on a project team do their job.”
This comment highlights the dynamic range of a portfolio’s analysis: from C-suite to individual scientists. There’s a critical need for data and discussion that can create value for all stakeholders and tools that provide high-level insights and detailed scientific analysis simultaneously. A significant pain point is the lack of transparency in current portfolio recommendations or PoS, which often fail to provide the necessary depth of analysis required for informed decision-making.
Benefits of AI in portfolio analysis
“We’re trying to go and really understand what level of detail AI can open in this process.”
In response to these challenges, many companies are turning to AI as a potential solution. AI offers the promise of transforming portfolio management by providing explainable insights that can drive more informed decisions. However, the current AI tools, while useful for executive insights, fall short of delivering the “expert AI” needed for Probability of Technical and Regulatory Success (PTRS) calculations and portfolio work.
Tuning the AI model to each portfolio
“Can you mod [VibeOne] to tailor it to align with what we need to make our decisions?”
To truly advance portfolio management in the age of AI, the weights and judgements must be transparent and configurable for each company. Companies need AI solutions that can integrate external data sources with proprietary data—and account for that company’s specific strengths and priorities.
Find AI solutions that deliver
As top pharma companies seek to identify the right AI partners to accelerate the portfolio prioritization decision-making process, the focus is on finding solutions that can deliver on these promises. When thinking about portfolio analytics, here’s a helpful framework to consider:
- Beyond public know-how: choose an AI solution designed with life sciences know-how AND one that has been pressure-tested in relevant capital allocation scenarios.
- Dynamic range: choose an AI solution that offers executive-level concision, but can also go deep credibly with an experienced drug hunter, so you can be confident in your team’s decisions.
- Configurability: customize the AI for your team’s strategy, strengths, and risk framework. Without this ability, you’ll lack the context needed to make informed decisions.
- Explainable: all insights must be attributed back to the original datapoint or source, so when you go deep, you can confirm the recommendations yourself.
See VibeOne in action. Get a demo today.
