The last mile of life sciences innovation
Life sciences has no shortage of innovation, yet one of the industry’s most important challenges remains surprisingly unchanged: how do we translate technological possibility into meaningful real-world impact?
Artificial intelligence is reshaping how researchers identify potential therapies. Digital technologies are changing how clinical trials are designed and conducted. Connected data is enabling faster decision-making, while decentralised approaches are giving patients new ways to participate in research.
But developing or acquiring an innovative technology is only the beginning. The real test comes when organisations attempt to integrate it into existing workflows, scale it across complex portfolios, encourage teams to use it consistently and demonstrate that it has actually improved an outcome.
This is what I think of as the last mile of life sciences innovation.
It is the distance between having access to innovation and successfully operationalising it.
Closing that distance will become increasingly important as the industry moves deeper into an era shaped by artificial intelligence, connected clinical technologies and data-driven decision-making.
Adoption is not the same as utilisation
Life sciences organisations make significant investments in new technology. But an investment does not automatically create value.
There is an important distinction between adoption and utilisation.
An organisation may purchase a technology, deploy it and make it available across the enterprise. That demonstrates adoption.
Utilisation asks a harder question: is the technology meaningfully changing how people work?
Are research teams using the capability across appropriate studies? Has it reduced unnecessary manual activity? Is information moving more efficiently between teams? Are clinical sites experiencing less operational burden? Are patients finding it easier to participate?
These are fundamentally different measures of success.
The industry has historically been very good at measuring implementation milestones. We know when a system goes live, when users are trained and when an agreement has been executed.
But leaders increasingly need to measure what happens afterward.
A technology that is available but rarely used creates very different value from one that becomes embedded into the operating model of an organisation.
For this reason, utilisation should be considered the bridge between investment and impact.
Technology strategy must follow the scientific portfolio
This issue becomes particularly important in drug development because portfolios constantly evolve.
Clinical programs advance, pause or terminate. Therapeutic priorities change. Acquisitions bring new assets into an organisation. New scientific opportunities emerge, while other programs disappear.
Technology strategies therefore cannot remain static.
A capability purchased for one set of assumptions may no longer represent the highest-value use of an organisation’s investment 12 or 24 months later.
Leaders should periodically ask whether their technology portfolio still reflects their scientific portfolio.
Which capabilities are heavily utilised?
Which are underused?
Where has clinical demand shifted?
Can existing technology be redirected towards emerging needs rather than adding another disconnected solution?
This sounds straightforward, but it requires coordination between functions that have traditionally operated separately: research and development, clinical operations, technology, finance, procurement and commercial strategy.
The organisations that connect these perspectives will be better positioned to extract value from innovation.
Measure value beyond the purchase price
Another important change is how life sciences leaders evaluate the economics of technology.
The easiest number to measure is often the acquisition cost.
It is also one of the least complete measures of value.
The true economic equation should consider the operational consequences surrounding the investment.
What is the cost of maintaining fragmented workflows?
How much time is spent reconciling data between disconnected systems?
What is the impact of repetitive manual work?
What does unnecessary site burden cost an organisation?
How valuable is better visibility into a clinical program?
And, perhaps most importantly, what is the cost of delay?
In drug development, time has extraordinary economic and human significance. A technology investment that helps an organisation make better decisions, eliminate unnecessary steps or improve the execution of a clinical program can create value that is not adequately represented by its software price.
This requires organisations to move from a procurement mindset towards a value-realisation mindset.
The question should not simply be: “How much does this technology cost?”
It should increasingly become: “What outcome are we trying to change, and how will we know whether we changed it?”
That shift creates greater accountability for both technology providers and life sciences organisations.
AI will make operationalisation even more important
Artificial intelligence makes the last-mile problem more urgent.
The industry is moving quickly from AI experimentation towards real operational applications across discovery and development. Potential use cases span molecule identification, study design, forecasting, data review, risk identification, patient engagement and clinical decision support.
As these capabilities become more accessible, simply having AI will become less differentiating.
The competitive advantage will increasingly come from how effectively organisations use it.
This means leaders must resist the temptation to treat AI deployment itself as the objective.
Before adopting another capability, organisations should define the problem it is intended to solve.
What decision should become faster?
What process should become easier?
What burden should disappear?
What new insight should become possible?
Who is responsible for measuring the result?
Without those questions, organisations risk creating an increasingly sophisticated technology environment without a corresponding improvement in research productivity.
AI may dramatically accelerate innovation, but it does not eliminate the need for strategy, workflow redesign, organisational adoption and measurement.
In many ways, it makes those disciplines more important.
Keep the patient at the centre of the value equation
The ultimate purpose of life sciences innovation is not better technology.
It is better outcomes for people.
This is particularly important as the industry thinks about patient experience and clinical trial participation.
A patient’s experience of research can be shaped by seemingly operational decisions: where a study site is located, how frequently travel is required, how information is collected, how burdensome the protocol becomes and whether participation can fit into everyday life.
Those decisions can influence more than convenience.
They can influence who is able to participate in clinical research at all.
My doctoral research examining efforts to improve Asian representation in US clinical trials reinforced the importance of looking beyond headline enrollment numbers and considering whether research opportunities are reaching the populations medicines are ultimately intended to serve.
Technology alone cannot solve representation or access.
But thoughtfully implemented technology can remove some barriers, create greater flexibility and help organisations design research around the realities of patients, rather than expecting patients to organise their lives around research.
That is why patient experience should be part of the value discussion from the beginning.
When organisations evaluate an innovation, the questions should include:
- Does this make research easier for patients?
- Does it reduce unnecessary burden?
- Could it expand access?
- Does it help sites spend more time with patients rather than managing administrative complexity?
- Does it ultimately help generate better evidence?
The answers may be just as important as traditional measures of technology performance.
The last mile is a leadership challenge
The next decade of life sciences will produce extraordinary technological capability.
AI will become more deeply integrated into drug discovery and clinical development. Data will become increasingly connected. Research models will evolve. Patients will expect greater flexibility in how they engage with clinical research.
But more innovation will not automatically produce more impact.
Organisations will need leaders who can connect science, technology, economics, operations and patient experience.
They will need to evaluate technology not simply by whether it has been purchased or implemented, but by whether it is being utilised and whether that utilisation is changing meaningful outcomes.
They will need to continually align technology investments with evolving scientific portfolios.
And they will need to expand the definition of value beyond cost savings to include research efficiency, clinical execution, patient access and experience.
Innovation creates possibility.
Operationalisation turns that possibility into impact.
Closing that last mile may ultimately determine which organisations are able to translate the next generation of life sciences innovation into better research, stronger evidence and better outcomes for patients.
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