Second half of my conversation with Malcolm Frank about this year’s WEF in Davos. Part 1 ran last week here
There were a couple of themes we discussed with many executives there. One was around where we see digital going and where our clients need to focus their businesses in the next five years. The second is, what about the future of jobs? We presented our research on the 21 Jobs of the Future. We also announced our reskilling initiative with several other technology leaders at Davos on how we aim to retrain a million workers for digital skills of the future. Those were our two big things, and they both seemed to resonate well.
When it comes to digital and its potential impact on jobs, I think the view now is a fairly balanced one: that technology giveth and technology taketh away.
What's fascinating, if you look at today's market, is that have we not seen mass layoffs from digital automation that some have predicted. After all, in the United States we are essentially at full employment. What has actually occurred is digital has now created a huge skill gap, the need for more workers. For example, if you look at data from the Bureau of Labor statistics we're now 500,000 programmers short in the United States, which are great jobs that are not filled. In the next three years, that's going to grow to 1.4 million. That was certainly a big discussion point in Davos.
Will there be automation as RPA (Robotic Process Automation) starts to go at scale, so people who live in Cubeland and push paper to one another, such as in mortgage processing or claims processing, are you going to start to see automation hit jobs in those areas? I think people theoretically believe “Yes.” The extent to which ... is it going to be minor? Is it going to be significant? We don't know yet. I think people believe in that, but it's not happening as quickly as some alarmists are trying portray.
As an interesting aside on the relationship between automation and jobs, the societies that have experienced the highest levels of industrial automation – such as Japan, Germany and South Korea – also have the lowest unemployment levels. Why? Because when one automates, there is also associated innovation and growth. So, it’s yet to be seen if that will also occur with digital automation of white collar labor, but it’s an interesting precedent.
What we do know today, people can't get their hands on enough digital talent. So, if there is a crisis on either side of this jobs issue today, what's interesting is that the crisis is on the fact that we can't fill these roles. We've heard this again and again with clients just saying, "I need people who have these capabilities, and I can't find them quickly enough."
So that’s a summary of the conversations that focused on digital and employment. Regarding digital and its impact on our clients, four themes recurred:
a) How do I change the customer interface? This is something we've talked about for years, that there's the Sunday night - Monday morning phenomenon, by which a social computing experience on a FANG platform is so engaging on a Sunday evening, but then, say, a banking or work experience is so crummy by comparison on Monday morning. We heard that again and again. Clients would ask “As my customers keep looking at their smartphones 100, 200 times a day, how can I make my product just as engaging?” It's a set of new rules for customer engagement and intimacy, with one to one, the combination of physical and virtual, and all those attributes. The customer interface continues to be a big area for digital as clients realize the battleground is not just on products but also on customer experiences.
b) How do I build a smart product? How do I instrument things in my environment, whatever that may be? How do I build the smart place or the smart product by instrumenting things and then harvesting the data and turning it into new customer value propositions?
c) Very specific process areas for RPA and how can we get started? Which process areas make the most sense? Where have others been successful? And what tools, products and vendors can best get me there?
d) Taking advantage of heritage IT. A real understanding now ... it's from an IT perspective ... that IT for most of these firms, I'm speaking of 100 year old companies now, that that IT backbone was purpose built for a different area. It was built for moving and selling widgets, for an industrial business, which obviously gave rise to all the vendors you and I know extremely well, the SAPs and Oracles of the world. Now, nobody is being critical of those decisions because they were the right ones for the time, but now you've woken up, a lot of these firms will recognize it's all about the data. However, that data is trapped in legacy silos. How do they have data as their true north and have the ability to pull that together and manage it in the way that the FANG vendors can? It's not just getting their IT unit cost to an Amazon type unit cost. It's also, how do they view their data in the way that, for example, a Google can view their data?
If you were to bucket the conversations that you have with CEOs and digital, 80% of the conversations would land in one of those four buckets.
As a final point, I was part of a very thoughtful dinner conversation that focused on the intersection of Artificial Intelligence and cultural norms and values. For example, with health care records on artificial intelligence platforms, what do you do with them, and how far should we go? I won't give you the ins and outs of the conversation, but at a high level, Americans were saying, "We really have to keep that as private as possible because you can't have a situation where some individual is wronged by misuse of that information," with preexisting conditions, future insurability, so forth and so on. There were Europeans who were even more strident on that front because of the history that they've had in the 20th century. They pointed to initiatives such as “right to be forgotten” that will only need to be bolstered as AI becomes more prevalent. But then there were some Asians who had a very different view, essentially arguing, "Our view is a much more utilitarian one. We need to determine what's best for the 10,000 and not what's best for the individual." That gives you a completely different perspective. If you could improve the lives of the 10,000, and maybe a couple of unfortunate individual mistakes get made in the process, that may be the better approach. If you just get stuck by focusing on outliers of what could possibly happen with an individual, then you may not get the societal benefit quickly enough. The conversation continued down many other paths with many more examples and a good amount of vibrant, yet civil, argument. The point is that history taught us that in the third industrial revolution – with the advent of the internal combustion engine and assembly line – some societies advanced rapidly with it while others did not. It became clear, to me at least, we will likely have a similar economic lumpiness with the adoption of AI in the fourth industrial revolution.