Kalleid had the pleasure of attending the largest-ever gathering of the Pistoia Alliance in London, at the Royal Society of Medicine, where more than 300 members gathered from across the biopharmaceutical ecosystem for two full days of learning and networking.
The Pistoia Alliance is a global community of more than 200 member organizations from across the biopharma ecosystem that collaborates to solve common problems, sharing expertise and R&D costs, to move the industry forward and ensure better outcomes for all. Each year, members are invited to convene once in the United States and once in London. The Pistoia Alliance is a leader in collaboration for the life sciences industry and was recently recognized by Bio-IT as an Innovative Practices Award winner as well as an honorable mention for two pivotal projects undertaken by the community.
Kalleid first became involved as a Pistoia Alliance partner around the time of our founding in 2014, and we were pleased to join the London gathering for the first time this year. The theme of the conference was digital transformation and presentations ranged from concrete working sessions on data gathering and governance to ambitious visions for AI and machine learning in the lab of the future. Overall, the venue and general camaraderie of those in attendance cultivated an experience that felt intimate, conversational and forward thinking. Kalleid congratulates the Pistoia Alliance for successfully achieving that delicate balance between plenary sessions and open networking time to ensure everyone walked away feeling more informed, as well as connected to each other.
Shared challenges in applying AI in R&D
While all the participants and presenters agree that there is increasing market pressure to leverage AI to speed up drug development and minimize time-to-market for promising therapies, there are a number of obstacles that we have yet to overcome.
Here are some of the common challenges that came up repeatedly throughout the conference:
The allure of automation vs. the tedium of cleaning up and connecting data sources
We’ve been seeing a lot of “lab of the future” initiatives across our client base and automation is at the center of every one of them. The drug discovery process is long and complex, and the possibility that new therapies could be realized faster and with more precision using integrated data systems has huge profitability potential. But the reality is that automated data collection and analysis cannot happen without the painstaking mapping, cleaning and governance of data sources. The process to establish such a data landscape is tedious, time consuming and often costly. While the goal is to establish a forward-looking data management strategy that prepares organizations to leverage AI as it becomes available for each use case, most companies must undergo significant cleanup before that can happen. Many participants shared similar strategies of isolating particular data streams within their R&D organizations in order to achieve rapid analytical insights in one area and demonstrate its value before trying to tackle end-to-end data governance across the entire scientific ecosystem.
Leadership selling an ambitious vision without having appetite for systemic change
“Artificial intelligence” has become such a buzzword in the media and in marketing that almost every new event and product seems to include this phrase in its description. Life sciences executives are feeling the pressure to leverage available technologies so that their company can be the first mover in a competitive marketplace. But in reality, taking on board artificial intelligence in a way that actually advances the scientific process will require significant upheaval across organizations that many teams are ill-equipped to handle. In addition to the data governance side of things, departments will likely need to create new business processes and operating models to incorporate AI tools into their daily work. This means new job descriptions, taking on board new skills, and making sure the research environment is prepared to accommodate new ways of working.
Helping scientific and IT teams transition to a future with AI
This of course brings us to the obstacle that came up in almost every presentation through the conference: change management. Many of us have lived through seismic organizational shifts that touched many companies and industries–such as the introduction of enterprise employee data management, file sharing software, virtual meeting capabilities, and cloud-based application hosting. But in these cases, we were usually orchestrating change management strategies around a handful of business processes. While it is no doubt disruptive to experience a switch to a completely new tool, the change is manageable when a person’s job remains by-and-large the same. In this new world, where technical capabilities are shifting rapidly, we are expecting that each employee will see many of their business processes changing as a result of AI. And throughout it all, we know that people are living with the latent worry that somehow their skills will be rendered obsolete by this trend. Presenters were careful to emphasize that they don’t foresee AI as replacing today’s science and scientific IT workforce. However, it is widely accepted that new skills and some flexibility will be required of many.
Kalleid’s take: The People Side of Establishing AI
Shepherding life sciences organizations through digital transformation is our specialty, so we have some recommendations for addressing these obstacles as they unfold.
- Build a foundation of two-way communication. We always recommend that clients create pathways of communication that include not only the pertinent information regarding change cascading throughout the organization, but also an avenue for gathering open and honest feedback from impacted employees. A common mistake we see leaders making is to try and anticipate what their people will want rather than asking directly. AI is in its infancy and we cannot know for sure how our organizations will be impacted. That doesn’t mean we should wait until we have a clear picture to start talking with people. On the contrary, it is possible to cultivate excitement about future possibilities if everyone feels connected to the vision and able to voice their views.
- Speak to the underlying fear directly. Job loss is often a fear when organizational change happens but this phenomenon has been exacerbated with AI thanks to grandiose proclamations by prominent C-suite executives and a healthy dose of fear-mongering across media outlets rife with robotic images. None of this transformation is going to happen overnight. It will take time for companies to identify relevant use cases and take on new technologies. Even today, we need skilled workers to validate the results of large language models and to assist in building out these capabilities. The demand for scientific knowledge will always be there. During this period of transition, it is important that team members are reassured of their value in a world with AI.
- Acknowledge the work involved in getting to the future state. Stockholders may clamor for the promise of new revenue streams and revolutionized laboratory operations. But your teams know the reality of such claims: bringing on advanced analytics and automation will take a tremendous amount of work. This means added time, resources, and skillsets in order to prepare the organization to manage its data differently and then apply new technologies. We will only triumph in this environment by emphasizing the value of the journey itself and recognizing the effort involved in reorienting a scientific IT organization around new processes. This means asking the hard questions and getting into the details with the people who understand the relevant processes the best. In the end, life sciences organizations, and their scientific breakthroughs, are powered by people. We bring their humanity into the center of this shift by being honest and transparent about the transition ahead–including its uncertainty.
Our passion for delivering a human-centered approach to digital transformation is at the heart of everything we do, and we are so excited that the folks at Pistoia Alliance share our passion for helping build competencies in change management across scientific organizations. At the start of this year we joined the Pistoia Alliance’s community of practice for change management as a proud member of the steering committee. We look forward to collaborating across alliance members to cultivate an impactful toolbox of change management strategies to meet every scale and budget. Keep an eye out for our upcoming workshop at the 2025 Pistoia Alliance meeting in Boston!
