AI is set to have a transformative impact on life sciences. But adopting the technology and harnessing its benefits can be challenging for organisations.
We recently hosted a webinar with industry experts, including Lee Maw, Chief Digital & Information Officer at LGC and Subroto Mukherjee, Chief Strategist at FutureGlobalTech to uncover what life sciences leaders can do to ensure successful AI adoption.
Here are our 5 key takeaways:
As ex-Microsoft AI specialist Subroto Mukherjee stated, a significant portion of available data in businesses is often deemed unusable, with estimates suggesting that this could be as high as 80%.
For highly-regulated industries such as life sciences, the added complexity around data sets and compliance exacerbates the challenge. To prioritise data quality, you need to focus on consolidating fragmented and siloed data from across your organisation to create a comprehensive dataset that can be leveraged for AI.
AND Digital’s AI Strategy and Transformation Lead, Leena Pankhania, believes that life sciences organisations are at a distinct advantage in being so data-rich. But it’s ensuring that this data is brought together and managed consistently and effectively, which will help data scientists in using that data for AI.
As you gather your data together, you should be critically assessing it to ensure the data is of high quality, but also that it is diversely representative.
Leena highlights that even before AI, there were inherent biases in healthcare and life sciences that disproportionately impacted (and still impact) women and people of colour, so it is vital that you remove bias from your data sets to avoid amplifying the disparities that already exist and embedding them further.
Introducing ethical AI frameworks that include clear principles for data governance, informed consent, and responsible usage of patient data, will foster a culture of transparency and accountability, and ensure that AI solutions lead to fair and equitable outcomes.
LCG’s Chief Digital & Information Officer Lee Maw believes that life sciences organisations should start by focusing on practical, achievable use cases that deliver tangible value.
There’s no denying that there is so much potential in life sciences for groundbreaking advancements in areas like drug discovery. But for those looking to start out, many of the most effective instances of AI use are found in routine tasks such as managing documents, generating content, automating manual processes, or using GenAI models for chatbots.
By identifying and prioritising more practical use cases, you can start to streamline processes, enhance productivity and operational efficiency, and build credibility within your team’s AI capabilities, laying a solid foundation for more ambitious projects in the future.
Bradley Quinn, Managing Consultant for Data & Insight at AND Digital, highlighted that 70% of life sciences organisations ranked data and AI skills as their highest priority need, whilst at the same time, these capabilities represent the sector’s biggest skills gap, according to a recent ABPI report.
The panel agreed on the need for targeted training programs to help bridge the divide. Initiatives like AI academies and workshops can promote shared learning and can empower teams to understand AI and its terminology, as well as how to use it in their day-to-day roles. It’s important that learning is tailored to individuals so employees across your organisation can understand how AI specifically affects their roles. This approach will increase engagement and reduce any concerns or mistrust about the technology.
Additionally, promoting a culture of continuous learning through mentorship or hands-on experiences will encourage teams to experiment and innovate, helping to drive AI skills and adoption.
To achieve the best return on investment from AI, you need to have a strong understanding of your operations and define the best use cases that align with this and your business goals.
When looking at specific use cases, evaluate how they can improve operations and deliver quick benefits. It's important to have a manageable tech stack and monitor costs related to data storage, transactions and other variables to avoid unexpected expenses.
Additionally, set clear KPIs and track progress against them. This will help you measure the impact of your AI initiatives and ensure sustainable growth in your AI journey.
The final (bonus) takeaway from Lee Maw is; be realistic about your expertise and capabilities, and if it works for your business, pick a good partner to work with, who has the knowledge that you may lack, and can be a trusted ally.
Discover how we work with life sciences leaders to improve patient outcomes and accelerate healthcare innovation here.