An ArisGlobal best practice guide, informed by investor insight from Nordic Capital
Life sciences organizations today have little choice but to harness technology-enabled process automation at scale. Against a backdrop of intensifying economic and business pressure, companies need to maintain or increase profit while minimizing the impact of inflation and growing costs. Although the demand for products continues to be high, pharma needs to hone its process efficiencies and increase productivity to sustain margins and fund next innovation.
It is no coincidence that many Boards of Directors are mandating a 50-80% cost savings over the next three years1, particularly in the US as companies brace themselves for new pressure on profits from the US Inflation Reduction Act.
Compromising on drug development or the choice of materials is not an option; ambition and product quality cannot be sacrificed in the pursuit of lower costs and improved efficiency. Rather, the focus now must turn to the performance of R&D services and the resources needed to manage soaring workloads.
It is here that advanced automation offers important potential. McKinsey estimates that pharma companies adopting AI-powered processes could yield a 30-50% reduction in the time it takes to bring drugs to market2. This potential is not lost on the industry. The latest surveys confirm that leading life sciences companies are proactively researching and piloting advanced automation solutions, harnessing technologies such as Generative AI (GenAI) trained on specialist large language models (LLMs).
Those that are not yet looking to next-generation process transformation to optimize and drive efficiencies in workflow processes, meanwhile, have every reason to question the ongoing financial sustainability of their R&D operations.
In the context of the latest economic challenges facing life sciences organizations today, this best practice guide distils some actionable recommendations on how to implement advanced automation including GenAI-enabled process transformation to help rebalance the commercial and financial imperatives of R&D.
Inflation-based impetus for transformation
On top of rising costs, high interest rates, increased global supply chain challenges, intensifying competition, and market-defining changes to the make-up of drug product lines (as blockbuster drugs give way to novel therapies, and as medical devices and personalized data play a greater role), life science companies in the US are now also subject to the national Inflation Reduction Act. This will have a further, direct impact on organizations’ profit potential and the funds available for R&D. (Introduced in 2022, the Act’s aim is to reduce the federal government budget deficit, and lower prescription drug prices, among other priorities.)
All of this means that life sciences companies need to find new ways of becoming more efficient. Nordic Capital’s own research estimates that large pharma companies will need to take out somewhere between 10-15% of their total cost base just to maintain current activity. To support more sustainable business models, meanwhile, they will need to take much more severe action across the value chain, to stay ahead of the rising and converging pressures – potentially by as much as 50-80%. To achieve efficiency gains on that scale, AI becomes not just a nice-to-have, but an essential enabler.
This conclusion is reflected in life science investor strategies for the next 1-2 years. Firms like Nordic Capital are now actively tracking pharma companies that have access to advanced technology and superior data to help alleviate their operational and cost pressures. This could take the form of advanced technology solutions that enable them to capture data in a more effective and efficient way, or AI-powered platforms and tools that mean they can process data in more effective ways.
Another avenue of interest to investors is companies that are harnessing data or technology to help identify scope for new product/therapeutic innovation, diluting an organization’s exposure to current market pressures by taking them beyond the realm of the Inflation Reduction Act.
Where investors are looking to buy and own pharma companies, certainly, the preference is for truly innovative companies that are not impacted by the usual market forces; or those that are serving their markets in a hyper-efficient way.
The imperative to act now
For heads of R&D now, the strategic priority is to maximize the flow of funds to the discovery and development pipeline, and to advance that pipeline cost-efficiently.
This in turn requires optimization of the enabling functions: in other words, everything that supports R&D. And the smartest way to optimize anything is to leverage the latest technology.
Yet to leverage and gain a tangible advantage from new technology, companies need to be prepared to step out in front.
In the case of AI-powered process transformation in an R&D context, this may mean breaking new ground – being among the first to try out emerging solutions, applied to a specific activity for instance, to determine what is possible. Waiting until someone else has broken that ground could mean losing important operational advantage, especially given the pace with which AI technology is advancing.
One way to achieve this in a confident and controlled way is in alliance with a trusted partner – a service organization that understands both the latest technology and the specific requirements of pharma R&D, and which can collaborate closely on defined use cases with clear parameters, to test and demonstrate AI’s potential.
Working with a partner can help crystalize and plan against specific priorities, such as those highlighted in ArisGlobal’s recent research into Regulatory function AI applications.
So what does good look like? How can companies move forward now to secure these desired advantages in a systematic manner, so that they will have made decent headway within the next three to five years?
Prioritizing & planning
Given the conspiring and intensifying challenges, companies need to take action today if they want to see the benefit within an acceptable timeframe and keep pace with the accelerating advancement of technology.
Rather than try to define and seek approval for a “big bang” program of work, and line up resources, emerging best practice suggests targeting key pain points across the R&D value chain and identifying the right partner and the right suite of tools to transform the main ones – taking each in turn, to showcase the benefits and learn from the transition. As long as individual applications and use cases are not tackled in a siloed way, but are supported by an overarching platform, incremental progress is a very manageable and pragmatic way to ease into the era of next-generation AI-powered R&D process automation.
In parallel to identifying priority applications, it’s advisable to network with other life science R&D organizations – peers which are likely to be on a similar journey and have begun their own research and pilots. AI-enabled automation of R&D processes has become a prominent topic at industry events, so attending those sessions and networking afterwards can play an important part in shaping the vision and next plans. This is a valuable chance to learn from other organizations, and gain independent verification of where they are seeking and pursuing value from the latest automation technology. Joining special interest groups and consortia is another way to stay close to developments, hone strategies and roadmaps, and get a first-hand picture of what is working versus what may need a bit more time and maturity.
Identifying economically-sound use cases
Organization’s respective priorities for AI-powered automation will vary, but the key to maximizing the economic advantage will be to remove ‘waste’ from current workflows, and to do this in a way that is directly measurable (something that AI makes very easy). That could be in applying everyday Safety protocols, or in aspects of the Regulatory workload, where (in both contexts) there is considerable potential to simultaneously streamline and tighten sub-processes and also empower busy teams to focus their expertise where it can make the most important difference. Data recorded along the way will clearly show the rate by which workflow is being reduced or accelerated, as time-consuming early stages of a process are reliably and efficiently automated.
Bringing next-generation machine intelligence to critical but time-consuming routine processes gives life sciences R&D organizations the chance to remove cost and increase efficiency and speed, while upholding the stringent quality and safety standards that are essential to high-quality healthcare.
The big leap with advanced AI-based interventions is that the technology is now capable of mimicking and improving upon human intervention at a time when the industry needs the resulting step change to counter significant margin compression.
Funding therapeutic innovation
R&D transformation isn’t only about improving productivity and cost-efficiency, of course. At the same time, all life sciences organizations must heed the call to innovate, to stay relevant and competitive and to buck the downward pressure on the cost of traditional medicine. This is too is a fundamental expectation of investors.
The trend in 21st century life sciences, to target more specific diseases almost on an individual basis, is enabling new product innovation at a micro scale. Smarter use of data meanwhile will enable new economies of scale, through the aggregation of multiple highly targeted diseases to maintain scale from innovation.
That scale is clearly important, to maintain profitability as the cost of drug discovery and development rises in line with the ambition and complexity of novel and advanced therapies. That some 70% of all diseases do not yet have a cure is life sciences’ great challenge, both scientifically and commercially. To ensure sufficient payback (address the “long tail”), companies need to become more cost-efficient in the drug discovery and development cycle. This in turn demands ever smarter use of technology and data.
Positioning operations optimally for life sciences’ new economy
Much is changing permanently in life sciences and in the industry’s business model. The opportunity to pass internal cost pressure into the external market has ended, with the result that R&D organizations which fail to adapt will see their profits decline – an unsustainable situation as far as executive boards and investors are concerned, and therefore a subject for urgent redress.
The top 50 pharma brands have already developed strong strategies here, which they are now refining. Smaller biotechs and mid-size companies are behind that curve, and now need to focus their own technology use to substantially streamline core processes and enhance decision-making.
Again, progress here must start with identifying the problem that advanced automation technology can help solve, ideally hand-in-hand with other initiatives that are in progress. This will ideally help pinpoint opportunities both to streamline key workflows, and to simplify the R&D IT ecosystem – so that data and insights can be exchanged readily, and so that each team becomes exponentially more efficient.
Overcoming objections to drive new dynamism
Once particular process challenges have been exposed, understanding where industry trailblazers are already experimenting with and deploying AI-powered automation across the R&D lifecycle – and where they have started to see strong results – will help companies start to define their own strategies, roadmaps, and timelines.
This will require active networking, and presence at key industry events. The Life Sciences industry’s GenAI Council, which meets regularly around the world, for instance, is actively collaborating on best approaches to apply advanced automation to 14 specific R&D process pain points, following a hierarchy of priorities members have agreed, linked to Safety, Pharmacovigilance, and Regulatory processes initially – specific capabilities that can be transformed relatively swiftly and tangibly. Crucially, though, the premise is that all of these individual applications will be underpinned by the same unifying platform, supporting easy integration and interchangeability of data and insights over time, and a return on investment that keeps building and multiplying.
Taking action
Achieving the kind of radical process efficiency gains that are imperative now (aim for 80% of current workflow “waste” over the next five years, which is optimal for R&D operations to remain viable), it is vital that business owners rather than IT teams own the decision making.
Having set a sufficiently high ambition, senior stakeholders then need to identify what future processes will need to look like, to be operating with just 20% of today’s costs. This is then the reality that a modern technology platform needs to be built for.
The role of the prioritized applications and target use cases is then to deliver rapid results and showcase what’s possible to the wider organization. In this way (module by module, use case by use case), companies can move actively and incrementally toward their goal, rather than waiting 3-5 years to determine whether they are where they want to be.
Only once key processes have been optimized, and cost has been taken out, can companies really focus on advancing the pipeline in a cost-efficient manner.
Returning to McKinsey’s findings, its study estimates that 70% of digital transformations fail due to oversights or problems with managing change. So this essential consideration – taking teams on the journey to R&D process disruption – needs to be front and center to any roadmap, so that they fully appreciate what they stand to gain as part of the transformation, and what the new process map will look like.
Next steps
To progress your own journey to R&D transformation and long-term economic advantage, via AI-powered automation, consider these next moves:
- Focus on pilot projects, choosing high-impact areas for advanced process transformation, supported by a robust, integrated and agile technology platform strategy;
- Partner with an end-to-end service provider for accelerated success aligned with emerging industry best practice. Ideally this should be a company with a deep understanding of AI technology and end-to-end solutions;
- Create a collaborative working arrangement between case processing partners AND the technology provider;
- Network with peers, and talk to industry-specific technology experts; and
- Focus on pilot projects, choosing high-impact areas for advanced process transformation, supported by a robust, integrated and agile technology platform strategy.
This content was distilled from the first in a new podcast series, The Life Sciences GenAI Exchange, hosted by ArisGlobal, in conversation with Daniel Berglund, Partner at Nordic Capital, in late September 2024. The podcast can be streamed here.
