It’s all too easy to conflate the kind of AI being hyped in the industry at the moment with the science fiction notion of machine sentience. We are still a long way from the latter, though, whether you see it as WALL-E or the Terminator.
What’s mostly on offer today from IT vendors and service providers is really just advanced data analytics. With the power and scalability of modern cloud platforms, rules and inferences can be constantly updated and refined as more data is accumulated (machine learning), and applied in near real-time. This can of course create the illusion of intelligence, but sentient computers it isn’t – not yet, at least.
Having said that, we shouldn’t underestimate the potential for today’s kind of AI to have a huge impact on the way some things are done in business. If you work in IT, this is something you need to get to grips with sooner rather than later. Why? Because AI capability is rapidly becoming a lot more accessible, and before long will be pervasive across our application and service estates.
The pace at which things are evolving became clear to me over the course of a number of briefings and conversations I had towards the end of last year. This began with a session at IBM, during which a case study at major oil and gas company was discussed. The Watson ‘cognitive computing’ platform had been used to create a virtual assistant that was transforming IT support by providing users with advice and guidance via a multi-lingual text and speech interface. The results achieved in terms of service level metrics were impressive, but to get there required a substantial professional services engagement – i.e. lots of consulting time and expertise.
In contrast, I then had quite a different conversation with Salesforce.com, which has been acquiring, building and integrating AI capability into its cloud platform for a number of years. In the words of John Taschek, Senior VP of Strategy at the company, “A lot of what we are doing is aimed at making AI a seamless and embedded part of the business process”.
Moving AI into the software stack
One of the examples we discussed was advanced sales forecasting powered by Einstein – the overarching brand name for most things AI in Salesforce.com. The key point here is the notion that you shouldn’t need lots of specialist expertise or coding and integration effort to exploit the potential of AI. It will increasingly be a case of ‘switch on, configure and go’.
More recently, at its January Tech Summit in Birmingham, I heard Microsoft do a pretty good job of spelling out the different routes to AI goodness. If you have the expertise and want to get really ‘down and dirty’, the Azure platform is increasingly going to offer fine-grain AI and machine learning capability, right down to FPGA level. For mainstream developers who need to AI-enable their application without having to worry about the detail, higher-level services are offered so you can access natural-language functionality. For example, a set of APIs can hide all of the underlying AI complexity. Then, further up the software stack, we’ll increasingly be seeing AI smarts embedded seamlessly into Microsoft applications and tools, from Office 365 to its CRM and ERP offerings.
I’ve only mentioned three players here, but technology companies large and small, from Google and Apple to highly-innovative specialist vendors, will be surfacing AI capability in all kinds of different ways. That includes embedding it in the systems and security management tooling used by IT teams.
The upshot is that AI will increasingly find its way into the world of IT professionals – there really won’t be a way of avoiding it. So you need to starting thinking now about the implications in relation to changing user expectations, application design and implementation, service management and support, and not least, security, privacy and compliance.