Artificial Intelligence – Creating the Environment for Adoption

Part I: The blockers

AI or Artificial Intelligence promises to unlock growth and raise productivity. While some companies started to adopt Artificial Intelligence successfully, many industries remain relatively untouched by AI. How to increase the adoption of AI is now a key element to extend the business benefits of this technology.

The AI opportunity – why some firms are embracing it faster than others

Although most companies are still a long way off from a widespread adoption of AI, the companies that are adopting it are already reaping rewards. A pre-pandemic McKinsey survey pointed to an uptick in revenue in the business areas where it was used—and 44 percent said it had reduced costs ().

Beyond this direct impact on the bottom line, companies that have successfully adopted AI report dramatic improvements in how they serve their customers and run their operations. While companies see the value of AI in optimizing processes, most of them, however, had not yet harnessed AI to drive disruptive innovation—for example, by creating new products or entering new markets. 

What holds business back from adopting AI 

At this point, the key question is: what holds companies back from greater use of AI? And, beyond that: what challenges do sophisticated adopters face when building on their existing leadership in AI? 

Five key areas come to my mind: vision and commitment from executive teams and boards, organizational culture and change management, data readiness, talent, and, finally, standards and regulation. 

The first two barriers show up in different ways at all stages of adoption and, indeed, we mainly see general organization change barriers arising when new ways of working are introduced. 

The other three barriers may depend on the adoption level and are more specific to AI. For example, gathering data into one place is a big challenge when a company first sets out. For intermediate adopters, securing the relevant talent is then a blocker. And for the more advanced and sophisticated adopters, apprehension of negative regulatory response is a barrier. 

Let’s have a look at each one of these blockers:

  1. Lack of vision and leadership from senior executives and boards

A clear vision is needed from senior executives and boards if companies are to get going with AI and their leadership is needed to stay the course. Yet in many companies there’s a lack of understanding of what AI is and where its value and opportunities lie.

  1. Cultural and change resistance 

Deploying AI is for many companies a huge challenge in organizational culture and change management. Companies unfamiliar with the value of AI will have lower buy-in and lackluster implementation efforts. To bridge the unfamiliarity gap, those companies look for external talent, which is often met by resistance in the company as these outsiders have little industry knowledge. 

  1. Data (un)readiness 

In most of the companies, data is distributed across many IT applications, is in a usable format or not correctly classified. This is in many cases a major inhibitor, especially in the early adoption stages. 

  1. Shortages of AI talent

The shortage of talent combining both business and technical understanding of AI is a significant drag on adoption. Companies need data scientists, AI experts, software engineers, and operators (often referred to as DevOps) to turn an AI pilot into a scalable, robust, quality assured operational business tool. 

  1. Regulatory and ethical apprehension 

In quite some cases, valid concerns about algorithms carry risks to the company’s reputation. There’s an opportunity here for widely accepted and clearly articulated standards to control or remove bias, track performance, ensure stability of machine-learning models, and improve the way algorithms’ decisions can be explained. 

These are the main blockers that, from our point of view, are preventing AI to be widely adopted in today’s businesses. In a second post we will put the focus on best practices coming from successful AI adopters, as an example of how to overcome barriers and ensure a smooth AI implementation. Stay tuned! 

Über dselz

Husband, father, internet entrepreneur, founder, CEO, Squirro, Memonic, local.ch, Namics, rail aficionado, author, tbd...
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