Table of contentsKey takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology
Automation is changing the way businesses operate. But, automation itself is going through a number of changes. Each of these changes may seem small at first, but when you look at all of them together, you’ll see how far it has come. Today, Arjen Van Berkum discusses the Future of Automation in an interview with us on a new episode of Engati CX.
Interview with Arjen Van Berkum
Well, Automation is a strange beast. Automation is not just one thing. It is a lot of different things. Can be RPA. It can be chatbots. Can be AI. It can be Machine Learning. So, to answer your question, "is automation of vital importance for organizations?", yes. In any way, even if you're a legacy company or if you are a new company, all kinds of automation will help you drive Future Value, and make sure that you can actually focus your workforce or the right stuff, and not on the stuff that can be done by a machine.
So in that sense, information is key. It's critical.
Well, the correlation is that they both are dealing with change. However, for me, innovation is much more incremental. So innovation is about doing what you've always done and trying to do it better. In other ways, in the normative way, disruption is about doing something completely different.
And the difference between the two, is that disruption goes much faster, is usually much more unexpected and comes from angles you usually don't expect it. It’s a fundamental change of your business model. Whilst innovation is something you can see gradually coming towards you and you can prepare for it.
So, in that sense, automation, as a technique or as a way of thinking is not disruptive by nature. It's innovative by nature because we can all see it happening. The only thing is that not everybody is actually dealing with it.
Well, I think you must excel at three things to build machines. And they are creativity, empathy and entrepreneurship. And especially if you look at the 1st one, creativity. In order to create superior customer experiences. You need to be creative. You need to understand what customers want, what their story is, how you can help them. And by bringing that them one step further, that's vital. Now on the empathy part, although machines are getting pretty good at sensing emotions, they're still not as good as humans are.
So, if you're really into the business of having emotionally based customer care. Well, then humans will, at this moment in time, outpace the machine. Not for decades, but for the near future, humans will be better at that.
And then coming to entrepreneurship. No matter what, a machine will never buy a lottery ticket. Because understands the chance. Human beings sometimes do things that are against the law of chance. But in the end it will work out very well, and it requires a certain kind of risk taking. And for me, risk taking is entrepreneurial thinking. So it's not like being an entrepreneur, but it's having the entrepreneurial mindset.
Now all the other stuff that you need for a superior customer interaction, that you can automate. If you use a good local platform, you can rapidly design and develop all kinds of ways of thinking on how to actually improve customer satisfaction.
But if you really want to surprise your customer, you need to be creative and empathetic and entrepreneurial. It's a vital role.
Well, the funny thing is that, if you take it one day at a time, it looks like nothing has happened. But if you look back five years from today, you say, Well, what a lot actually happens. And that is also with AI and machine learning over the next 12 months. It’s all going to be small steps, and every step on its own is not seen as a big one. But if you take them all together, there will be huge steps in artificial intelligence and machine learning.
We will see, in the next period, of course, a lot of focus on everything in the medical world. And it could be based on home diagnostics to algorithms that can easily identify risk groups and can help us identify who should be vaccinated first or not. So we'll definitely see a lot of development in the healthcare industry.
The other industry, where we will see a lot of effort in the AI space is going to be in everything that results around process mining and process identification. Where having huge chunks of data now makes it relatively easy and possible to detect where large processes are actually being done, on what they really are and that will help us make steps forward. But that's all on a really practical level.
If you look on a more fundamental level, what's going to happen in space of especially AI ,we will see that it becomes much easier to start using unstructured data than the current structured data approach that most of the AI machine tooling uses.
Well, in order to understand that, the first step is to understand why companies are hesitant. What I see is that there's a correlation between the age of the organization and its willingness to change.
So older organizations are far less likely susceptible for change than newer organizations. At the same time, automation and especially tools like RPA are, mainly used in organizations that have significant legacy issues and are using automation as a patchwork on their legacy. Now what you see is that actually, there's a world of ‘to speech’ coming into existence. There's the organizations that need automation to keep their old stuff running in the digital transformative society. And are those organizations that actually understand that in order to be truly transforming that they need to get rid of their legacy or continue to build on a new strategy that doesn't involve legacy anymore.
So for me, these old organizations need to make a choice, and if they don't make one of these two choices and do nothing. They're going to be stuck in the middle, because the speed on which automation develops and gives your competitors a competitive edge to actually start producing cheaper make less mistakes, et cetera, means that at the end, if you don't do it, you're gonna be like the rabbit in the headlights of the car. You're gonna get it and show you need to move either left and automate off your legacy or to the right, decide to move away from your legacy into something new.
And I believe that this last one is the valid strategy because if we have seen development, informed them that lookout, we're also seeing, now, that lookout is making approaches into the world off the off the back ends and the data integration layers, which means that also in those areas, going for new technology is no longer a hindrance.
I believe that we are going to see a revival of human values in the next months. Partly, of course, because we will see a significant reappraisal of the public sector in general. And what we see with nurses and doctors and police agents, it will be, on the one hand to drift more wanting real, meaningful, human based contact.
On the other hand, we also know that no off jobs are being done purely for the sake that we have once thought in the process that they should be done. So I believe that the next big step is going to be is that we are going to re-evaluate our processes. We're going to take out the unnecessary steps that have been brought in there for control purposes. But with the coming off AI and machine learning and advanced algorithms, a lot of these processes were there for fault detection. They're going to go away. So I believe that it will start in risk management domains in the finance function where we will see the first big steps and it will move gradually into the customer space. However, in the long run, any company, any organization that does not embrace automation is poised to go bankrupt.
This is a really difficult question because machines are not that good in finding out empathy. If you take, for instance, the domain of speech recognition, most speech recognition tools can go to the 99.5 digits accuracy off, understanding what is being said. But the emotion behind it, they're the best ones are currently between 60 and 65%. Which means that there are too many mistakes. Especially things like humour, sarcasm, irony are very hard for machines to detect. So what I see already is there is a lot of attention on how to identify this.
So, for instance, irony and sarcasm and humour in conversations showed that the machine knows how to deal with it. This will take time because it requires: A. a lot of data for the machines to learn and B. it also requires a certain accuracy in order to get there. Which means that testing it in real life is difficult if you're like 60 to 70% accurate. So what we will be seeing is first in low-end customer care. Like, sure for schools. We will start seeing conversational intelligence, Um, conversationally. I purely for the sake that the risk of having the wrong impression is relatively low there and in order to bring it to high service or even sales coach from a conversational intelligence.
I think that's not going to happen in the next 12 months. It is going to happen in the surface area of the next 12 months, but it's only going to be in those language areas where there's enough data. So it's going to be in, let's say, four or five languages globally, which is, of course, Spanish, English, Chinese and Hindi. And then let's pull me in because even the German market, for instance, is too small to actually develop a language back for that at this stage.
Uh, sure, I think it will take some time, but we’ll definitely see the first in-roads in the next 12 months as a shop in low end surf schools.
Yeah, don't be afraid. Automation is not something you should be scared about. It is actually a blessing. If you're able to see that it will help us focus on human values more. Artificial interfaces and intelligence etc. are not The Terminator. What they are doing is they are giving us the opportunity to reinvent what it's like being human. And if we embrace that opportunity, we will definitely see a lot of potential in the next few years and to come to grasp real human potential.
If we're only going to use it as an optimization strategy and as a strategy to create more profits, then there will be a lot of resistance against this. But if we use it for the good it can bring, which means that not all the benefits should go to shareholders. But some of these benefits should also go to other types of labor, expanding in certain other areas. Yes, then it will definitely be meaningful.
But especially terms like artificial intelligence that we’ve used in a lot in this conversation. It's, of course, a lot of marketing noise because there is no artificial intelligence. The fact is there are very smart machines that have a lot of data, and based on that, they thought they can do really smart things. It's not like it's thinking on its own, as we as humans are doing. And especially when we talk about AI, we should always be aware that also those people that are making the machines for us, if they misunderstand what it is, we will be confronted with regulation that will send technology backward for years.
So, yes, we will make faster decisions. Yes, we'll make better decisions. But we as humans will continue to make them because we are supported by machines. And as long as we can keep on thinking like that and not put every decision into a machine but have the machine, augment our decision progress, then AI moves away from Artificial Intelligence to Augmented Intelligence. And if we're able to really do Augmented Intelligence, we can have the best of both worlds. We can have creativity, empathy, entrepreneurship to get a rich, superior decision making, calculation power, which means that we, as human beings, will get much further than we are right now.