Mikael Hagstroem: The trend for many years has been to replace the multiple, siloed, and ageing legacy systems in the lab and consolidate them into one modern, centrally-hosted lab information management system.

To anyone outside the sector, however, it might be a surprise to learn that many labs still rely on what are basically analogue systems. In some smaller organisations, that might mean they are still recording observations and results by hand, usually on paper. In others, it might be word processing software and spreadsheets, or homegrown solutions, but still essentially very hands-on and, more importantly, disconnected from the larger digital transformation going on in other parts of the enterprise and beyond.

Two immediate benefits of lab automation are speed-tomarket, and more informed decision-making in the lab. Effective data analysis enables organisations to quickly determine which options have the best chance of success.

AI and ML are well positioned to accelerate those types of critical decisions. Systems utilising the two can be taught to quickly identify patterns in data that require additional investigation, compared to those where both data and human behaviour likely have the least risk.

This is becoming increasingly important due to the robust manner in which digital laboratory systems – such as electronic notebooks and LIMS – can track and store larger volumes of data, including audit records against individual samples or specimens. AI/ML capabilities can monitor lab operations to optimise work plans, and also predict process or system failures, so preventive and corrective actions can be taken to reduce waste and downtime.

New business models accelerate change, and we know consolidation of digitised data provides one system of record for all quality and research data. Digital systems also provide an opportunity to harmonise workflows, testing methods, and policies and procedures, and allow for more complete data-integrity compliance. Once data are in a centrallyhosted platform serving the enterprise, they are accessible to view and can be subjected to algorithms to analyse. So, digitisation is already bringing extensive change to companies that have adopted it in the lab.

AI is going to be, perhaps, even more disruptive. We see that already with the move to autonomous vehicles. Simple digitisation changed the way we interact with our vehicles to some extent. Being able to remove your hands from the steering wheel – or removing the driver from the vehicle entirely – makes us rethink transportation.

I can envision the AI-enabled pharma organisation making much better use of predictive modelling, which can be applied to many different things – maintenance, optimisation, and categorisation – all of which we could use for things like quality failures and, of course, predicting failures to equipment and those kinds of things that may disrupt an ongoing experiment.

Obviously, there’s a lot of value in this for labs. If we can improve drug discovery and diagnostics, then of course we have better outcomes and better workflows. We could improve the decisions we make. Overall, you would get much more output from the lab than is possible today.

A universal digital transformation – from the lab up – is foundational. We also need to consider a new data philosophy; one that allows us to weave the data together into a fabric that is consistent and ready for digital use. Commitment to AI is key. We have seen lots of organisations ‘play’ with AI to this point, but few who have gone all-in. We have to have a proactive strategy. We have to have a plan. You can’t simply play around with AI and think you’re going to achieve great goals. You have to be proactive about it. And I think that’s a critical thought that not all of us have reached.

All the big issues we face in the world today demand digital transformation of the lab to become aligned with the enterprise. We only have to consider the environment – and the role that alternative energy plays in cutting carbon emissions – to appreciate that.

When I think of the digital native lab, I see benefits for industries of all types. Healthcare and diagnostics are certainly examples, because when testing is done companies in these sectors very quickly need to be in a state where there can be augmented decision-making that suggest, for example, the next-best task or test. If they don’t manage the data, they can’t propose the next-best task. Obviously, that’s going to help in many areas, not just when it comes to diagnostics, but also in terms of the healthcare provision itself.

Nothing happens without the lab being involved somewhere, from food safety to healthcare, to fighting pandemics, to all the things we eat and wear and use; everything comes back to a lab. In all the large challenges we have faced as humankind in the age of scientific discovery, the lab has had a significant role to play. In that regard, making use of advanced technology in the lab of the future – still the foundation of discovery – will have far-reaching effects.