We’ve spent some time over the last few months talking about what Artificial Intelligence (AI) means for businesses, looking at where AI intersects with automation, and explaining why the term AI itself can be problematic.
That last point is a key one, because without knowing what set capabilities a term like Artificial Intelligence encompasses, expectations can easily become out of line with the reality of certain tools. Rather than focus on the underlying collection of technologies and how they’re defined, we focus on measurable output.
However, the same is true when you’re looking at the impact of Artificial Intelligence on the workforce. If you focus on the technological aspects alone, you’re going to miss the wider implications of new technologies being introduced into the workplace, the impact on staff, and how that can be mitigated proactively.
That’s why we use the term ‘Augmented Intelligence’, rather than Artificial Intelligence, to refer to this group of nascent technologies that have the ability to enhance automation solutions. When it comes to the end-user, it doesn’t really matter what’s ‘under the bonnet’, but that’s become the focus of marketing departments.
Unfortunately, with the hype around Artificial Intelligence, businesses sometimes come to the conclusion that these collective technologies, under the banner of AI, are a silver bullet to all their problems, but they’re not.
So, how will Augmented Intelligence help the workforce?
In a similar way that the introduction of back-office (or even physical warehouse) robots is easing the load for human workers, Augmented Intelligence can make automation solutions even more efficient by breaking down incredibly complex operational challenges into understandable steps that are possible to manage.
With better analysis of operations, data, workflows and any other structured entity, businesses – and the people making the decisions in them – will have more accurate information at their fingertips at any time.
Deep learning, image recognition, machine learning, predictive analytics, natural language processing, process automation and a host of other technologies can all augment and improve the automation of complex tasks, but they’re the automation equivalent of a ‘cobot’ – there to enhance existing abilities and remove repetitive, non-critical tasks, not to replace a person.
But what these technologies don’t mean is the end of human decision-makers.
More realistically, this technology will empower middle-management by providing access to tools and data that allows them to both offload low-level outcomes and tasks, and be better-informed for the crucial decisions.