How an organisation approaches AI tells you a lot about them
AI is now being deployed on hundreds of use cases by, if studies are to believed, tens of thousands of companies. So its very likely that the organisation you work for, invest in, deal with or seek employment at has, or is in the process of, deploying AI.
So what can the deployment of AI tell you about the state of your firm?
Firstly — do you have a proper strategy aligned with your business model?
(I have written here about sustainable competitive advantage from AI).
Investment in AI should be around a point of serious competitive advantage. It takes a concentration of market insight, data, talent and investment capital to build an AI system. It also takes time, management focus and risk appetite to get it right.
Therefore where you are investing, and the rationale for it, should cut to the heart of your strategic focus — where the value really lies in an existing or emerging business model.
If existing insight and data can be parlayed in to a scale-able AI centric profitable business model then you have a winner — see Google search delivery and advertising or Amazon product delivery as examples.
However, the risk is that if AI is simply being deployed willy-nilly across multiple areas then you’ve probably not figured out what it takes to win. You are simply helping suppliers build depth in their area of competitive advantage.
Secondly — how healthy is your (data) infrastructure?
We recently spent some time with a bank that had invested mightily in data science and AI staff. They had several hundred people hard at work. Smart people, big salaries, long hours. Net result: one tool in production.
This was not because they were not doing a good job. So what was going on? Well — have you ever seen a (typical legacy) bank’s IT infrastructure on a chart?
Decades of transformative acquisitions and strategic imperatives will have left a messy, scarred patchwork of systems — like a plate of spaghetti has been thrown at the wall.
And here legacy is destiny. Competitive advantage in AI is from being in a place to do AI, not from the actual doing of it.
If data infrastructure is a mess then the job for talent is sorting through the mess, retro-engineering and data wrangling. This is not fun, so the best people won’t work here and the returns to talent decline. Beware.
Thirdly — how do you measure up ethically?
Business ethics are increasingly seen in the context of AI. If we want to illustrate sexism we point to Amazon’s recruitment tools, racism we use Google’s image classifier problems and social challenges any number of Facebook examples. The implied mathematical precision of algorithms shows systemic bias better than any number of anecdotes, however much they sum to the same.
So the data that powers your AI shows what historic decisions have been made. And the approach to building new AI shows whether a firm values key attributes such as diversity, combatting bias or dealing with the potential social impact created by their business model. How you build governance, train your teams and think about providing transparency show how you think about the future — what you aspire to be.
Credit: Photo by Jovis Aloor from Unsplash