A Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D

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AI For Commercial Life Sciences: 3 Trends You Can’t Ignore In 2020

   by Rasim Shah, Contributor    500    Comments 0

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As we enter a new decade, our belief in the the impact of Artificial intelligence (AI) is only getting stronger. Supporting the industry to drive the right drug to the right patient at speed is a huge responsibility that we take very seriously. Towards the end of the last decade we have seen great progress made within life sciences and the use of AI, but moving into 2020 the spotlight on commercial teams and gaining competitive advantage with AI will intensify.

Can AI tell us why things are happening? How can we operationalise AI to drive more personalised engagements? How can we support field teams beyond the next best action? How can we predict the future of our brands vs the competition? We need scale with AI, but at the same time we need to be mindful of the different data sets in each market, how can AI help? These are just some of the questions we are asked today by life science commercial teams as we continue to help adoption across the industry.

Supporting commercial teams (sales representatives, sales managers, brand and marketing managers) with AI is not without its challenges. Data access, data privacy, trust and ethics all need to be considered, but utilising data that is already available and providing real world recommendations with full validation and explainability is possible today.

In 2020 we believe the following 3 key trends will drive the use of AI for commercial life sciences leading to scale:

1) Explainability - If your AI is not explainable, ditch it!

AI systems will only make us intelligent if we can action the output, and humans will only be empowered to take action if they can trust the output. In 2020, explainability will need to underpin the outputs made by AI systems. Why has the prediction, suggestion or recommendation been made? Explainability cannot and should not be ignored.

AI is an incredibly powerful tool to drive commercial operations and strategy within life sciences. It is hugely exciting to see AI’s ability to process huge amounts of data, see patterns, spot anomalies and ultimately predict sales, sales opportunities, provide lead recommendations and much more. However, unless an AI system is able to explain the reasons behind its outputs, it will fail to drive the trust we as humans require. Additionally, without the explanations behind the recommendations the user and the system can’t learn together, and it is this learning which is fundamental to the future success of such systems. In Europe for example, where each market has very different data sets, data granularity and privacy laws, the learning process will be critical to driving outcomes over long periods.

Explainability will need to be a fundamental output for all commercially focused AI systems. Our industry is built on evidence and we can not change the rules for AI. At OKRA, we have built an explainability engine powering all insights derived for life sciences commercial teams, and the explanations are driving adoption and trust with users.

2) It’s a two way conversation - AI systems and humans need to hold hands

AI systems and the users of such systems must be joined at the hip. In 2020 we will see more systems that allow the users to feedback directly into the system. For example, there are thousands of sales representatives on the road, having thousands of conversations every day across multiple therapy areas. These conversations can act as new data points and drive new insights that will improve the learning of AI systems over time.

AI systems and humans will need to communicate, and in 2020 commercial teams must apply user knowledge and create feedback mechanisms that generate a rich source of data to feed into future outputs. This combination of technology and human knowledge is essential for commercial AI solutions in life sciences, as the best systems are not designed to replace people but instead empower them to take action with confidence. At OKRA we empower users to feedback into our system, driving incremental improvements in the accuracy of the outputs and the explanations. Both explainability and human input will be critical to driving trust.

3) Rapid AI pilots (8 weeks) - Embrace agile or get left behind

Twelve month, large scale, full country pilots is an approach we hopefully leave behind. In the new decade, validation of your AI pilot should not take longer than 8 weeks. Commercial teams can become the catalyst for life science companies to adapt quickly to the change that AI is bringing. AI can and is fueling competitive advantage within weeks, and commercial teams should seek to engage external AI vendors that can help accelerate the speed of their business through smarter decision-making and faster execution. From creating the business question to the acquisition of data, system design, modelling, feature extraction, prediction accuracy and testing - all of this can be done within 8 weeks if not faster, including the heavy lifting and cleaning of customer data. How do we know? Because this is what OKRA delivers to the industry. Speed will also become increasingly important when scaling one solution across multiple markets. In Europe alone, the UK, Germany and France have a very different data landscape. Data availability, data granularity, privacy laws and finally the needs of sales reps, for example, are different. Hence, rapid, agile pilots are a way to ensure that time and money are not wasted in the hope of driving outcomes through large scale projects.

The growing confidence that life sciences is putting in AI will encourage further innovation in the sector throughout 2020, driving both operational and strategic commercial decisions. Moreover, it will be crucial that AI systems follow these 3 trends in order to be effective in approaching the challenges of the modern market, and in learning at a pace that has not previously been associated with life sciences.

The future of commercial life sciences will increasingly rely on the combination of technology and data. Those who embrace AI will thrive and leap ahead in the intelligence race. Those who don't, will fail to keep up.

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A Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D
 

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