Whilst predictions on the impact of artificial intelligence (AI) vary, they do share a common theme. It’s going to be massive. In fact experts estimate that AI will be contributing $14 trillion to the global economy by 2030. That’s larger than the GDP of any country bar the United States and China. Biotechnology and pharmaceutical companies can benefit from AI too. Having delivered over fifty projects in this area, we have identified the three key challenges they need to address to ensure success.
The rapid growth of AI Is a game changer in practically every aspect of life. And there is no doubt the biotechnology and pharmaceutical industries can benefit hugely from it. Our teams have identified three key challenges that need to be addressed to ensure success:
The article also details how best to formulate a strategy and approach to ensure you successfully integrate AI into your organization.
Novel algorithms, greater computing power and huge datasets will support dozens of applications across the value chain. Pharmaceutical R&D can benefit too, in areas such as:
Commercial and medical units in pharmaceutical corporations can leverage new computational techniques with:
But if you are to successfully tap the true potential of AI then we need to overcome the following three key challenges.
This is understandable. It’s a new and still immature technology. And there are no established conceptual frameworks or strategies to help understand where the value lies. Or, for that matter, how it could fit into an organization.
In our experience, this leads to failure. That’s because IT can be summarized as automating information logistics (storing, sorting and transporting). However, AI automates much more complex cognitive tasks. For example, it’s the difference between an airplane’s autopilot system and the cockpit dashboard. The former is AI, as it is making decisions about direction, speed and altitude. Whereas the dashboard is presenting information and data – that’s IT.
Most AI solutions depend on historical data. And that can often be lacking, or of poor quality. Equally, it may not be available due to legal hurdles. Acquiring quality data requires long-term, sustained and cross-functional effort, and that’s where many organizations struggle. To scale capabilities you need to implement and adhere to data capture points across workflows. And that means your people have to care about this and put the effort in to ensure it happens
The right data may not be available internally.
You may need to consider strategic partnerships, acquisitions or licensing agreements to build up the right data assets. This needs to become a priority for senior management if the organization wants to unlock AI’s true potential.
One common misconception is the belief in an ‘AI expert’. This all too often leads to a costly arms race to equip your organization with the one person who can unlock AI’s value. More often than not this leads to disappointment.
It’s about having a team that can navigate the big picture, based on a thorough understanding of technical capabilities. Many organizations already have what is now called ‘AI’ in-house. They just don’t realize it. Because the underlying enabling technologies are not new. So rather than build new technical silos, empower existing teams to build the foundations that can accommodate AI and integrate it with their industry expertise and corporate vision.
Considering the three limiting factors together will help you create a coherent approach to AI. Remember, reaping AI’s benefits is a long-term effort at the intersection of fast-moving, converging technologies. This calls for a strategy, to manage risks, limit costs and navigate through uncertainty towards real value.
This requires analyzing the AI landscape, considering your data resources and technology against organizational requirements. By doing this your team has the tools to brainstorm, evaluate and map opportunities into three categories:
Avoid focusing on quick-wins or lighthouse projects. These are often chosen for feasibility and distract from the long-term sources of competitive advantage.
There are four phases to implementing immediate opportunities, Proof of Value (PoV), Proof of Concept (PoC), implementation and, once operational, life cycle management. Let’s discuss the first two stages in more detail.
A quick but vital exercise where you explore plausible corridors of technical performance. Question the potential value to the business? What will it depend on? Remember, it’s all too easy to get carried away by new technological possibilities. So it’s essential to look at ROI. Doing PoV allows the Proof of Concept to be conducted in a focused manner. This is because key value drivers often only become clear when looking at ROI in a structured way.
This is the most critical step. It brings science and its methods to data science. PoV establishes the capacity of AI tools to deliver value within the scope of the opportunity. The ideal way of executing the PoC is via sprints that each finish with go/no-go decision gates. This enables rapid re-categorization of ideas that lack feasibility, mitigating risks and costs. If an opportunity cannot be implemented immediately during PoV or PoC, it could be because your organization lacks foundational capabilities to execute it, or AI cannot deliver on the expected use case.
Achieving this requires the orchestration of various lines of work:
Finally, build a communication and scoping bridge between AI specialists and the rest of the organization.
Be disciplined in strategic pathfinding to keep the aim on the strategic horizon. Your biggest opportunities may lie in technologies that are just emerging. Strategic pathfinding integrates AI technology monitoring and evaluation with a roadmap to build enabling and foundational requirements. Through pathfinding, you can prepare your organization to be in pole position for AI applications that blossom in three, five or ten years.
The range of AI use cases in the pharmaceutical and biotechnology industry is rapidly expanding. From the application of old-school decision trees, drug adherence prediction and even learning models that support or reject biomedical hypotheses; opportunities abound. However, to successfully leverage opportunities, make sure you are aware of the challenges and have a plan to overcome them.
Interested in any aspects of AI? Like further information on how you could integrate it within your organization? Then, please get in touch with our team today.
InsightsMore Insights chevron_right