A 2018 Gartner study found 25% of companies are experimenting – or ‘just dipping their toes in’ – and 49%, have no AI plans whatsoever. When a company’s decision makers lack technological expertise, it is difficult to craft credible strategies. Management cannot properly weigh risks and rewards, respond to investor expectations, and adequately engage the workforce. In order to avoid abandoning the companies existing business model too early, and unnecessarily risking investor capital, there are several steps managers can take to move into the future.
Early in an AI Transformation, there will not be widespread understanding of the technology. As the company’s AI capabilities grow, knowledge will disperse through the organization and proficiency will grow. Until this occurs, management will need to create a centre-of-expertise (COE) to aid in understanding and decision making. It makes sense for this centre to pull heavily from existing IT and analytics departments as these team members may already have an understanding of AI or will have the baseline skills to quickly get up to speed. If required, the company can pull from external consulting firms in the early stages, but will want to make sure the consultants work with team members so the learnings remain within the company.
Although management may lack the understanding needed to craft a quality AI strategy, they have (hopefully) been crafting high-quality business strategies for years. Management must share their understanding of the company’s business model with their AI COE. In their explanation, management should focus on:
By completing the needs assessment prior to conceptualizing AI applications, the organization can avoid arbitrarily creating problems for preconceived solutions. With this approach, management can remain focused on creating value for the enterprise and improving the functioning of their business model. Once briefed on the areas of opportunity, the AI COE can now conceptualize the ways in which AI can be applied. Once the COE identifies key AI technologies and use-cases for their application, these need to be quickly tested and iterated upon. This will build proficiency of employees and identify which approaches fail to add value and which have promise. Quickly killing poor concepts is just as important as identifying promising ones. The success of the innovation lab is dependent on failing fast.
To get an idea of how to experiment with high-impact AI applications, we can look at the approach of Sompo Holdings. Sompo is a Japanese insurance company who has taken an aggressive approach to their digital transformation. In their needs assessment, Sompo identified machine learning, autonomous vehicles, and cyber-security as key to the future of their business. Sompo went on to establish an innovation lab in Tokyo for Machine Learning, in Silicon Valley for autonomous vehicles, and a lab for cybersecurity innovation in Tel Aviv. By surrounding themselves with world-class thinkers and experts in each separate area of innovation, Sompo has positioned itself to quickly experiment and craft their path forward. By slowly wading in, and testing the waters, managers will be able to create strategies that make a big splash.