Let’s not get ahead of ourselves! Balancing the bleeding edge with practical adoption remains a huge enterprise tech challenge

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Julie Sweet

One of the balances that can be trickiest to be struck in the enterprise tech world is that of how vendors square the circle between pushing their bleeding-edge innovation with the practical needs of their users and prospects today. How is your talking about version 10 of your product likely to be received when a customer is still struggling to make version 4 work for them? 

That challenge came to mind again this week during the Davos gathering of the World Economic Forum, where a number of tech leaders were debating the role of technology in creating a more resilient world. It was a conversation that straddled topics including 5G, AI, a dash of quantum computing, ChatGPT and the inevitable Metaverse. 

Setting the scene, Cristiano Amon, President and CEO of Qualcomm, said he sees a trend of “technology as intelligence”:

Technology right now, probably more than ever, especially when we talk about the current economic environment, we see that there is this desire of companies to digitally transform and use technology to become more efficient and become more productive. I do believe that we actually see, right now, an incredible demand for things to become more intelligent, to become more connected.

The evolving nature of technology has been a constant, he reminded the audience, with PCs giving way to phones and mobile devices as the primary computing platform. The pandemic has brought video to the fore and this in turn will develop into “a holographic image render in front of your eyes,” he predicted, with the convergence of physical and virtual worlds emerging alongside in the Metaverse. 

That’s all textbook big vision stuff and again, the immediate question that springs to mind is how relevant this is to enterprise decision-makers and informed buyers today? In a period of economic turbulence and geopolitical uncertainty, where we’ve already seen buying cycles extend as budget holders check and recheck every aspect of the business case, are organizations as keen on next gen tech as the sell side would want them to be? 

Changing minds

ChatGPT may be a good case in point here. Everyone’s excited by it, agreed the thought leaders in the debate, although Accenture CEO Julie Sweet made a good point when she noted: 

You do get the wrong answer for ChatGPT, so all those high school students out there who are trying to use ChatGPT for their exams, just beware – you might get the wrong answer. It’s only as good as the data. 

That said, even as such tech develops and begins to find genuine business cases, there are already practical benefits that can be gained from it in terms of changing perceptions and fostering learnings, she added:

The power of ChatGPT is that it is bringing to life what we’ve been talking to clients about for some time, which is, once you actually have mammoth amounts of data, both internal and external, if the data is good, then you can do amazing things with it. So, we love what’s going on right now with everyone talking about [this], because, in many cases, people have been doubters about why you need to have really clean data connected to external data…it reminds everyone you have to get the data right.

The other thing to bear in mind, she said, is that technology development and adoption doesn’t occur at a predictable constant pace: 

What you see with technology a lot is that it’s gradual, and then sudden. We’re still in the gradual phase [with AI] right now. A big part of it is not the capabilities of the technology, but going back to the people. When we think about the use cases, whether it’s with chips and being at the edge or 5G, the biggest issue is not the technology. It’s that every factory is run by a General Manager. They’re not thought about as platforms. It’s very hard to do the change management, and you’re still convincing people that you need to use not just your data, but other data….the focus has to be how do you get the adoption? That’s what we’re working on. In many of these cases, when you think about the big mega trends, whether it’s Metaverse or cloud or AI, it’s about adoption.

Technophobia

That’s a solid point which inevitably raises issues around the more Luddite tendencies of individuals. Today, the automation and robotics debate always includes the cliched question of whether AI is coming to steal ‘our’ jobs. It’s where ‘technophobia’ begins to raise its head as practical self-interest sets in – and perhaps not without reason, as IBM CEO Arvind Krishna warned that it’s the white collar worker that needs to be more concerned than, for example, a truck driver:

If you talk about truck drivers, in the end there’s a lot of lifting in and out which is not going to get replaced. But clerical white collar jobs will get replaced, a large number of them. So the question is, what else do you create to replace those? When you begin to look upon it, the number of people over 65 in the US was 15-18%; it’s now going to be 30%. What about home healthcare? If you go and look at when farmworkers got displaced, fast food service and restaurants came in to step into half of those jobs. We began to get a service economy. So the question is, what else do societies want? What else do economies want? And that is where those jobs are going to go.

The answer lies in up-skilling and creating a digitally-accepting workforce, argued Accenture’s Sweet, who agreed that the ‘fear factor’ is still in play: 

The labor shortage today has not changed that calculus a great deal. We’re working on the supply chain right now and lots of supply chain disruption, but one of the biggest issues is the lack of supply chain talent to handle those disruptions. So a big driver of the use of technology is, of course, being able to predict and understand your supply chains, but it’s also the ability to automate more and not have to find as many supply chain experts in a world of shortage. So, the calculus around the talent gaps is also changing how people are thinking about investment and adoption.

Attitudes vary as to what the solution is here, she went on :

The reality is, countries have different demographics. Japan is very different in terms of how they’re thinking about labor because of their [talent] shortages than, say, in India with their demographics. But there is a common solution and that is focusing on skills.

If we, as companies and as governments, focus on skills, instead of jobs and roles, and use AI and focus on how you can up-skill…[At Accenture] we have algorithms for our 740,000 people that we can run to say, who can we up-skill for some hot new area? Because everybody’s client-facing, we have an inventory of their skills, and we know how to re-skill them.

So we believe the future for companies, and how do you thrive in the next decade, is you access and you become a creator of talent, you unlock that talent. For governments and schools, focusing on skills is how you’re going to be able to take those workers and and re-skill them. You couple that with the excitement around digital literacy, and the fact that the same clerical workers are using digital in their everyday life, and I think you’ve got real positive momentum for a solution.

My take

Sweet’s arguments around re-skilling have been heard before, inevitably so given that the ‘digital skills crisis/talent shortage’ story has been raging for so long – and probably will continue to do so. That doesn’t undermine the validity of her points, which were articulated very well. This week at Davos there’s going to be a lot ‘Metaverse-ing’ going on. This leadership debate had a thread of that, but did provide a useful reminder of the need to keep your feet on the ground, even as your eyes are staring longingly over the horizon. 

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