CHICAGO, ILLINOIS – As companies rush to install top-level AI teams, some are accused of using the technology like a hammer without nails; looking for somewhere to apply AI, rather than using AI as a solution to a real business problem.
Kristian J. Hammond, Chief Scientist of Narrative Science and a professor of computer science and journalism at Northwestern University, is one proponent of the business-first approach to AI, and in an op-ed for the Harvard Business Review, implores business leaders not to rush into creating new C-Level role for a ‘Chief AI Officer’.
“In much the same way that the rise of Big Data led to the Data Scientist craze, the argument is that every organization now needs to hire a C-Level officer who will drive the company’s AI strategy. I am here to ask you not to do this. Really, don’t do this.”
“Rushing towards an ‘AI strategy’ and hiring someone with technical skills in AI to lead the charge might seem in tune with the current trends, but it ignores the reality that innovation initiatives only succeed when there is a solid understanding of actual business problems and goals. For AI to work in the enterprise, the goals of the enterprise must be the driving force.”
This is not what you’ll get if you hire a Chief AI Officer. The very nature of the role aims at bringing the hammer of AI to the nails of whatever problems are lying around. This well-educated, well-paid, and highly motivated individual will comb your organization looking for places to apply AI technologies, effectively making the goal to use AI rather than to solve real problems.”
One alternative to hiring a Chief AI Officer, Hammond argues, is to begin with the business problems that need addressing. This places the remit for AI in the hands of the people directly responsible for those problems, which requires equipping them with a framework for understanding where AI can be applied. From there, they are able to recommend where AI solutions might actually lead to the most significant impact on the organization.
Hammond calls on business leaders to firstly examine their data. Whether you have access to ten years of structured transactional data or high volume datasets of image, audio, and text, looking clearly at your datasets will produce natural use cases which “flows directly from the nature of the technologies themselves”.
“If decision-makers throughout your organization understand this, they can look at the business problems they have and the data they’re collecting and recognize the types of cognitive technologies that might be most applicable. The point here is simple. AI isn’t magic. Specific technologies provide specific functions and have specific data requirements. Understanding them does not require that you hire a wizard or unicorn to deal with them. It does not require a Chief of AI. It requires teams that know how to communicate the reality of business problems with those who understand the details of technical solutions.”