Organizations are investing heavily in AI, but only a few have a clear vision for their adoption strategy.
While adoption of AI is not even a question anymore, one of the common mistakes organizations make is to focus on AI’s technical details and ignore other factors such as identifying people and culture that help in successful AI adoption. There are a few critical components that help companies adopt AI successfully.
Look beyond technical expertise
Companies need to provide better training to their resources, especially in central areas like data science and the basic functionality of AI. With adequate insights into data science, innovative AI-driven solutions can be created. Companies need to bridge the gap between technical experts and those with business domain knowledge through AI “translators.” This will result in new AI solutions that talk from a position of understanding business, not only from technical expertise. Companies also need to have a fixed plan to garner as well as nurture AI talent to retain people with the skill and intelligence to create AI solutions that give it a competitive advantage.
Focus on a strong AI strategy
A long-term as well as short-term perspective on AI is essential while creating an AI strategy especially to consider its impact on business models. Several companies treat AI as a solution in search of a problem. Instead, they should identify their top issues that are worth solving with AI. If AI implementation is saving a person’s time, reallocating that time by reengineering the way teams are organized drives tangible value.
Enterprises need to work on building AI solutions in a scalable and reusable manner, which will enable them to apply it throughout the firm instead of specific business units or functions. The SIFMA survey revealed that AI strategy must also focus on developing ecosystems of external partners. In the recent MIT Sloan and BCG 2019 Global Executive Study, over 95% of respondents believe in co-developing AI with other firms. An AI strategy should have a healthy mix of cyber security, information security, and data privacy. Companies need to keep assessing the changes in regulation around ethical and explainable AI, where consumers are affected.
Structuring AI programs is the key
Simply setting up an AI center will not drive innovation for the organizations. They need to focus on building cross-functional teams with those who understand the business, clients, and the industry, and, of course, the AI technology. Organizations that are structuring their AI programs are more likely to lead to genuine innovation. Once a particular team successfully applies AI use cases, they can create a successful model that can be scaled across the organization.