January/February 2025 • PharmaTimes Magazine • 12-13
// AI //
Abstract smart
The economic imperative for AI-powered productivity enhancement
Reflecting on a recent podcast in discussion with a life sciences industry investor, Aman Wasan, CEO of ArisGlobal, considers how pharma R&D organisations should broaden their advanced automation ambitions within everyday operations to ensure a financially sustainable future.
The sustainable success of life sciences R&D organisations depends on their ability to maintain or grow profit while minimising the impact of significant and converging market pressures.
These range from high interest rates and soaring costs, to increased global supply chain challenges and intensifying competition.
They also include market-defining changes to the make-up of drug product lines as blockbuster drugs give way to novel therapies, and as medical devices and personalised data play a greater role.
In the US, the industry is now also subject to the national Inflation Reduction Act. This will have a further, direct impact on organisations’ profit potential and the funds available for R&D.
It is no coincidence that many Boards of Directors are mandating up to 80% cost savings over the next three years.
Compromising on drug development, or the choice of materials, is not an option however. Rather, the focus now must turn to the performance of R&D services and the resources needed to manage soaring workloads.
Nordic Capital 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.
Hence the talk of removing up to 80% of current operating costs.
It is in this context that advanced automation offers a breakthrough; that AI becomes not just a nice-to-have, but an essential enabler.
Track for good
The imperative around AI is now a prominent theme as part of life sciences investor strategies for the next one to two 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 organisation’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 operations that are not impacted by the usual market forces, or those that are serving their markets in a hyper-efficient way.
For heads of R&D now, the strategic priority is to maximise the flow of funds to the discovery and development pipeline, and to advance that pipeline cost-efficiently.
This in turn requires optimisation of the enabling functions: in other words, everything that supports R&D. And the smartest way to optimise anything is to leverage the latest technology.
To gain a tangible advantage from new technology, companies need to be prepared to step out in front. But what does good look like?
‘Where investors are looking to buy pharma companies, the preference is for truly innovative operations that are not impacted by the usual market forces’
Companies’ relative priorities for AI-powered automation will vary, but the key to maximising 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, for instance.
In both of these contexts there is considerable potential to simultaneously streamline and tighten subprocesses, 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.
Crucially, any individual AI/automation applications should be underpinned by a unifying platform, supporting easy integration and interchangeability of data and insights over time, and a return on investment that keeps building and multiplying.
Future-proofing
AI-enabled automation of R&D processes has become a prominent topic at major industry events, so attending and networking at those sessions can play an important part in shaping each company’s own vision and next plans.
Joining special interest groups and consortia is another way to stay close to developments, hone strategies and roadmaps, and get a firsthand picture of what is working versus what may need a bit more time and maturity.
Having set a sufficiently high overarching ambition, business stakeholders should help identify what future processes will need to look like, to be operating with what should ideally be 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 prioritised applications and target use cases is to deliver rapid results and showcase what’s possible to the wider organisation.
In this way (module by module, use case by use case), companies can move actively and incrementally toward their goal, rather than waiting three to five years to determine whether they are where they want to be.AI-enabled automation of R&D processes has become a prominent topic at major industry events, so attending and networking at those sessions can play an important part in shaping each company’s own vision and next plans.
Joining special interest groups and consortia is another way to stay close to developments, hone strategies and roadmaps, and get a firsthand picture of what is working versus what may need a bit more time and maturity.
Having set a sufficiently high overarching ambition, business stakeholders should help identify what future processes will need to look like, to be operating with what should ideally be 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 prioritised applications and target use cases is to deliver rapid results and showcase what’s possible to the wider organisation.
In this way (module by module, use case by use case), companies can move actively and incrementally toward their goal, rather than waiting three to five years to determine whether they are where they want to be.
New order
As already alluded to, R&D transformation isn’t only about improving productivity and cost-efficiency.
All life sciences organisations must also heed the call to innovate, to stay relevant and competitive and to buck the downward pressure on the cost of traditional medicine. This, 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 the vast majority of all diseases do not yet have a cure is life sciences’ great challenge, both scientifically and commercially.
To ensure sufficient payback, 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.
Only once key processes have been optimised, and cost has been taken out, can companies truly focus on advancing the pipeline in a cost-efficient manner.
These insights were distilled from the first in a new podcast series, ‘The Life Sciences GenAI Exchange’, hosted recently by ArisGlobal, in conversation with Daniel Berglund, Partner at Nordic Capital.
Aman Wasan is CEO at ArisGlobal. Go to arisglobal.com
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