Artificial intelligence has already caught the attention of CEOs and boards of directors as the transformative technology that will upend business models and accelerate new value propositions.1 However, few CEOs sufficiently understand what AI is, and where and how they can gain maximum advantage from it. Chief information officers (CIOs) should take the opportunity to explain the importance of AI to their CEOs and board of directors, separating the “hot” and hype from pragmatic reality.

In fact, Gartner’s “Hype Cycle for Artificial Intelligence, 2019”2 contains over 37 AI-specific technologies, with more than 50% of them predicted to take over five years to reach mainstream adoption.3

While many organizations have experimented with AI proofs of concept, many still struggle with operationalizing AI projects at scale. Three big challenges are:

1. Identifying appropriate use cases: Business leaders often push to accelerate AI projects out of competitive pressures, fearing they will be left behind if they are not investing in AI. Not understanding what AI can be best used for, or worse, developing solutions with limited understanding of business requirements often result in “solution looking for a problem.” CIOs and their business peers should focus on identifying use cases that can best leverage AI’s ability to deal with a large amount of multidimension data and complex systems.

2. AI talent gap: Organizations also need to understand that they don’t have to always build the solution in-house. AI technologies are complex yet still immature, and most life sciences organizations lack those with AI skills. For most, the AI talent gap may never be closed as talented experts continue to be scooped up by the digital natives in masses. Organizations should fully assess their buy vs. build strategy with an understanding that many AI vendors are narrowing their focus to specific business problems and delivering interesting solutions for concrete use cases, rather than just basic AI capabilities.

3. Culture: Appetite for change within the business areas that are impacted by AI decisions, resistance to new ways of working, and existing beliefs are frequently barriers to the adoption of AI. For example, many organizations have deployed AI-based decision engines for their sales and marketing teams that provides data-driven “recommendations.” Yet many organizations cite change management as the greatest barrier to adoption since many “domain experts” do not trust AI models to be as good as or better than themselves.

Organizations should look to exploit use cases offering measurable AI opportunities, such as discovering mechanisms of diseases, finding new biomarkers, and engaging their customers intelligently based on channel, message, and time preferences. Breakthroughs in AI will come in the long run. But for now, life sciences companies should focus on finding practical uses for AI that will have immediate impact.


1. “‘We’re Light Years Ahead of Where We Were’: Novartis CEO Vas Narasimhan Told Us How the Swiss Drug Giant Is Using AI for Everything from Evaluating Managers to Predicting Its Financials,” Business Insider India:

2. “Hype Cycle for Artificial Intelligence, 2019,” Gartner:

3. “Gartner Hype Cycle,” Gartner:


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