3 crucial factors when applying Artificial Intelligence in business
Artificial Intelligence is really happening — and it has big implications.
There’s now a collective fascination with the potential for scaling AI in both public and private sectors, and, in the last year, it has gone from being an interesting topic to front and centre of every boardroom agenda.
The big technology vendors jumped on-board very quickly, but we’ve seen a further acceleration; there isn’t a corner of the economy — oil and gas, power and utilities, media and health — that hasn’t been enraptured by the topic.
Given the current hype, I thought it was time to offer some practical advice. So if you’re wondering how to apply AI in business, here’s what you should be thinking about:
AI doesn’t automate jobs, it automates the tasks that add up to jobs and makes work more meaningful by liberating employees from repetitive tasks.
It’s possible to calculate how at risk a job is and plan for automation by retraining, re-skilling and redeploying resources. That’s a far more considered approach than implementing automation and worrying about the implications later.
Unlike software that simply executes a command, AI ‘learns’ to perform certain tasks. The risk is that AI systems acquire some kind of subtle bias that may not be obvious, so one of the challenges of AI is to be able to continually monitor and find potential failures in the learning process, so you don’t end up with unwanted outcomes.
If you know that risks exist, you can plan for them by setting up governance systems and monitoring systems.
Despite the current enthusiasm, many business problems and opportunities do not need AI. The technology introduces complexity and risk, so limiting its use to very specific issues is a good thing.
For example, a simple rules-based chat bot answers customer questions without the need for complex technology and solves a problem without AI. It’s a high value, low-cost solution.
Posted on 7wData.be.