• Orchid Jahanshahi, VP Commercial, Life Sciences, ODAIA

Building Your AI-Q

3 Keys to Understanding AI in Life Sciences

I recently interviewed ODAIA CEO Philip Poulidis about the future of artificial intelligence in the life sciences industry.

I wanted to know: What’s the real impact of AI? How much is hype, and how much opportunity? Where should leaders start if they want to make their organizations ready for artificial intelligence?

What’s the real impact of AI?

As it turns out, there is a lot to discuss. Here are three of my key takeaways for anyone who, like me, works at the intersection of pharma and artificial intelligence.

1: Understand the Opportunity

The life sciences industry generates huge amounts of data. This simple fact creates great potential—and great hype. Large stores of unused data, which are a problem for people who want to organize or understand them, are also untapped for new digital technologies like artificial intelligence.

In fact, the life sciences industry has been increasing its use of digital products of various kinds over the last eight to ten years, but this adoption has been inconsistent. In some areas, like clinical research, drug discovery, and some consumer-facing areas, the uptake has been very quick—one study showed a cumulative growth of 35% every year between 2000 and 2018, in clinical research alone. In the last few years, use of AI has truly facilitated this growth.

On the other hand, the adoption of new technologies for commercial or medical marketing uses has been uneven. Most companies have not yet made extensive use of AI, and some functions such as sales and marketing have yet to take full advantage of the opportunities offered by advanced digital tools.

Opportunity is really a key word here. The Pandemic has transformed pharma-physician engagement models from primarily face-to-face to almost entirely digital. This has leveled the playing field for smaller companies with high-value brands (such as in oncology) as the reliance on large sales teams to compete on the ground is no longer relevant.

A smaller niche company can now jumpstart its commercial engine by focusing on its other digital channels, and can probably do this in a more agile way than a large global pharma giant with thousands of field personnel that needs to make large changes to adapt to the new reality.

Companies also have to stop measuring the wrong things. There is a trend to move away from looking at static metrics, insights or actions such as counting call frequency, emails opened, and instead search for more meaningful insights.

More than ever, commercial teams require deep understanding of the value of each interaction they have with their customers, including “sticky” marketing channels that impact individual HCP behaviour.

2: Demystify AI

There are many misconceptions surrounding artificial intelligence. People understand different things when they hear the term, and react to it in different ways. There’s something about AI that can seem ‘sci-fi’, or even sinister, so we really need to educate people about what AI actually is, what it does, and what the strengths and limitations of this new technology can bring to the pharma landscape.

For example, there’s actually a difference between artificial intelligence, which thinks for us, and augmented intelligence, which helps us make decisions. Augmented intelligence is actually more common, and is often used to sort through large amounts of data so that people can understand and make good use of it. Both technologies contain risks, but these tend to be more prosaic than some people fear—the problem might be not that an algorithm could go out of control, but rather that it might reach an incorrect conclusion because it has a built-in bias. One of the responsibilities of people developing or deploying AI in healthcare is to make sure it puts the patient at the centre, and mitigates trust issues such as privacy, bias, and lack of transparency. Healthcare technologies are especially important to be trusted, as the use of AI will touch peoples’ lives in ways that require proper oversight.

The Pandemic has allowed an increased opportunity for technologies, and some including AI, in the life sciences sector. Since March 2020, has been a surge of digital health apps and AI-powered diagnostic and therapeutic tools. As an example, the use of AI for breast cancer screening is becoming a reality and has shown transformative potential.

3: Encourage Brands to Grow their AI-Q

Life science companies are now in a race to adopt new digital tools. Traditional customer relationship management (CRM) software platforms are useful, but can’t handle all of the problems and opportunities brought about by the new landscape. Healthcare professionals are behaving differently in every way when compared to pre-pandemic interactions. This necessitates “smarter” CRMs that recommend personalized actions for fieldforce representatives who need to react to real-time customer behaviour changes. An AI-powered CRM adapts and scales to dynamic business objectives, and allows companies to improve their interactions with customers. This also means that sales and marketing teams will get more feedback about actual customer journeys and will better understand which tools and actions will be most effective.

New tools might not be enough on their own, though. Organizations that want to use AI also need to work on their internal “digital IQs”, adopting new ways of learning and executing for the new technologies. Using lots of data means you need to collect richer data, ideally ingesting as much as you can from across your organization so that the algorithms can do their best work.

The way data is collected also matters. Many seemingly enormous warehouses of data are actually full of “black holes” where no-one can find anything! If there isn’t a consistent method of collection and labelling, it will be hard to sort the data and use it across multiple platforms. Disparate parts of the organization, like sales teams and digital marketing campaigns, need to be speaking the same language and sharing when it comes to data.

Finally, experimentation (and “failing fast”) is key! You might need to try a bunch of different digital tools and platforms before you find the ones that work.

Continuing the discussion

There’s a lot more to talk about, but these were three good places to start. In future posts, I want to dig a little deeper into these topics. AI has a lot of potential for the life sciences industry, and I think it’s important to find ways of communicating that. We’ve got a long way to go, but the future is very bright.

About Orchid:

Orchid Jahanshahi, is the VP Commercial, Life Sciences at ODAIA Intelligence

Over 25 years of experience in Marketing Management, Global Brand Strategy and Sales Management at seven Fortune 500 companies in the biopharmaceutical industry, including Novartis, Merck, Janssen, Sanofi, Bayer, Biogen

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