This summer, KPMG hosted its Executive Symposium on Robotic Innovations in Chicago to discuss how these cognitive and robotic technologies will forever change the workforce’s $9 trillion knowledge work segment. More than 80 senior-level executives attended the event, from industry and large-scale global service providers alike, to hear expert speakers including Cliff Justice, KPMG Partner and U.S. Leader, Shared Services and Outsourcing Advisory. Consulting caught up with Cliff to talk more about the rising tide of “thinking” technology and its potential effects on several industries, including healthcare, financial services and retail, just to name a few.
Consulting: What’s the difference between robotics and cognitive technologies?
Justice: When we’re talking about robotics in the services sense we’re talking about virtual robots, bots that will perform automated tasks that a human would otherwise perform, but doesn’t really require a change to the IT system. There are virtual robots that will sit at the presentation layer of the technology stack where the user sits and carry out repetitive routine tasks. So they’re rules-based. They operate more like a player piano, they don’t think, they’re not smart. Cognitive technologies on the other hand, that would be more in IBM Watson’s class, are built on the principles of artificial intelligence, natural language processing, machine learning, inference models, and hypothesis generation. These are technologies that can capture context from natural language from interactions with people, learn from that context as it receives feedback, and help make decisions that are based in much more abundant data that’s out there through unstructured email and chat data as well as structured data that might sit within an enterprise.
Consulting: How would you characterize the current adoption levels of this technology?
Justice: It’s in its infancy. There are a lot of test cases; there are a lot of pilots going on. IBM gives an example of how Watson is being used at Memorial Sloan Kettering Cancer Center and some of the cancer hospitals to pore through massive amounts of patient records, journal entries and clinical trials and then correlate that to an individual patient’s genetic profile and demographics to give the physician better insights into probabilistic outcomes given a certain mix of surgical and pharmacological treatment methods. There are case studies out there today on how they’re being applied in those types of environments. Businesses are building applications on those platforms to perform tasks and help pore through massive amounts of data that’s not structured and help create hypotheses on certain outcomes given the information it has access to.
Consulting: So how will these technologies benefit industries that adopt them and their customers?
Justice: These types of technologies are probabilistic, not deterministic. It’s not seeking an answer it’s seeking a guess. That’s a lot more how the human mind works, how we shortcut based on the data we’ve seen in the past, our logic and the data we’re presented with and we get to an answer. The difference is these algorithms go through a lot more serial analysis to get to a much more complete answer with a higher level of probability. As these technologies combine, you’re really able to understand and interface with your employees, your customers and the operations of the business in a much more natural way. And with the automation technologies which are able to carry out tasks, you’re creating classes of “digital labor” as some call it in areas of knowledge work, IT, finance, categories like that which are relatively new. We haven’t seen that type of automation before.
Consulting: What’s keeping more companies from diving in?
Justice: You’ve seen very large companies roll out these automation-capable cognitive platforms out and start to build digital services on those platforms. IBM has Watson, Wipro has rolled out Holmes, TCS has rolled out their platform called ignio. Many of these platforms are being offered in the cloud. That’s the IBM model, attracting an ecosystem of partners that are building business-specific applications and those will be offered to small business in the cloud. Chances are the small businesses aren’t going to have the resources to build the platform, they’re going to be the user. It levels the playing field when it comes to smaller companies having access to advanced technologies like this.
Consulting: What effect will these technologies have on the human workforce?
Justice: I’m of the school that this will ultimately create more opportunities for humans because that’s just the history of disruptive technology going back to the industrial revolution. This is different because it automates a level of knowledge work that we’ve ever seen automated before. We’ve seen a lot of classes of muscle be automated but in knowledge work it’s all sort of productivity enhancers with computing technologies. Decision automation, which is what this represents, that’s new. It will be disruptive to some areas but this will create a lot of opportunity for others.
This lowers the barrier to entry for a lot of classes of artificial intelligence which history has shown has opened up new consumer classes, new jobs, new opportunities. There’s also a more pessimistic school of thought that says machines are going to take our jobs. It’s just never happened that way. We’ve had temporary disruptions, protests, Luddites going around burning down factories, but ultimately economies expanded, new customers were reached, prices dropped and new industries were formed. That was the result of a major shift like that.