Why RPA? For that matter, what is RPA — robotics process automation? Jason Kingdon, a man who was working on machine learning 20 years ago, puts it colourfully: “At core, it is trying to be as promiscuous as hell.”
Blue Prism invented the word, but what it does has been the stuff of science fiction for years — it automates processes, it gets different processes that should be incompatible with each other, working together. It speaks machine and human. That’s what Jason Kingdon means by “promiscuous.”
And it automates.
In an ideal world, at least an ideal world from IT’s perspective, everything works smoothly. Every IT system integrates perfectly with every other IT system, everything is just one click of a button away from happening smoothly. But the real world ain’t like that.
As Jason Kingdon says: “It’s no longer IT people that are doing this stuff, it’s actually operations people that sit right next to the work that they have to do.”
IT have been working on solutions for as long as there has been IT: “Using a service-oriented architecture, using the business management systems, using micro IT projects, introducing agile functions, introducing scrums, or various methodologies, lean capabilities and technologies. They’ve been trying to solve the interoperability issue ever since…ever since there was IBM and Apple.”
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The sticking plaster that works
We need plasters. Occasionally we cut ourselves. Critics say RPA is a sticking plaster, Jason Kingdon disagrees, arguing that it provides plasters for wounds that just keep on happening and always will.
“Critics say, RPA is a brief moment between now and when we get — some great all singing solution, but it’s complete nonsense,” he states with aplomb.
Maybe with his tongue ever so slightly in his cheek, he said: “Even if you got a Babbage machine, it’s got a user interface. You can connect to that, but you can also connect to a D-Wave, quantum machine.
So there you have it. RPA can work with a computer designed by Charles Babbage 150 years ago or the latest state of the art in quantum computers — it’s an almost poetic example to illustrate what Jason Kingdon believes is the answer to the question why RPA?
He puts it in a more matter of fact way too. He reckons a ‘digital worker’ – that’s a smart, pre-programmed, software robot, an RPA tool, can do the work of between nine and 50 people.
How do they do this? “In short, these robots are designed to use and access the same IT systems and mechanisms as humans, to perform tasks in the same way as humans – while also working with and learning from humans, other robots – and all past, present and future systems too.”
He said: “Our vision for the future is that an organisation will have one third people, one third for operational infrastructure, and one third for robots – all operating in tandem. And this is already happening.”
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Robots and jobs
Of course, another source of resistance to RPA comes from fears that the technology will take jobs. But the Blue Prism chair doesn’t agree. “RPA will replace the tasks that are terrorising us, are soul destroying, and very difficult for humans to do.”
He gives as an example of a client that had to update its systems every day with the latest currency movements. It was a dull tedious task, but very important. Any error would have had massive consequences. So, the organisation gave this very important but dull task to a senior member of staff — a women in her 50s — who they could trust. It’s a thankless task too, one error and everyone wants to know why. RPA has transformed that, or so he says. “Now, this senior employee can focus on what she is good at. The currency changes can be updated more frequently too, morning and afternoon, every hour, perhaps.”
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Why RPA? It helps scaling up and scaling down
But there is another example of RPA’s benefits, to answer the question why RPA? To illustrate this, Jason Kingdon tells a story about when Blue Prism technology was used when 02 started selling the iPhone. This created a massive operations challenge.
It required a new way of working, a completely new product. Apple was a demanding partner. Things had to be done, ‘just so.’ So the client asks: “What do we do? Do we stop the IT guys doing the work that they’re doing at the moment and start building infrastructure to anticipate demand. Supposing our projections are wildly optimistic? Suppose, it goes the other way and business increases more than we expect?”
In such circumstances, he says, they always get it wrong. Either they invest too much, create too much infrastructure, and get slammed, or they under-invest, holding sales back.
“But put this in a robot context, you show robots, like you would a human, how to perform a new process, and they manage it faster and more accurately than a human ever could. If it turns out that customers go from mildly interested to wildly enthusiastic, what do you do? You add more robots.”
In short, or so goes the argument, RPA can turn the provision of infrastructure to meet unpredictable demand into a variable cost. Why RPA? It’s like the cloud in that sense, you can turn it up, and turn it down.
Jason Kingdon worked on machine learning at UCL in the 1990s, founded Searchspace, a company he says created intelligent transaction monitoring used by the London and New York Stock Exchanges, Lloyds of London and others. He sold the company in 2005, and has been the chairperson at Blue Prism since 2008.
Is RPA overhyped, scalable or a bandaid? Are decision engines next?
Is RPA like a sticking plaster? Is RPA overhyped? Are decision engines the future?
