Vin stressed that AI should be seen as a tool to complement and not a replacement tool. “AI will make people work much better than they do today,” he explained. The technology is poised to improve productivity and enable employees to deliver greater value to their customers. For example, in call centers, AI is poised to revolutionize the way agents interact with customers.
By automating tasks such as summarizing call transcripts and analyzing sentiment, AI will provide agents with better contextual awareness. This means that call center staff will not only respond to queries more efficiently, but will also anticipate customer needs, shifting the focus from complaint management to improving overall customer success.
Vin acknowledged that the current application of AI is predominantly seen in pre-sale scenarios, but the potential for post-sale interactions is significant. With advances in connected devices, such as smart cars that monitor their own performance, AI could enable businesses to predict problems before they arise and proactively address customer needs. This transition from traditional call centers to customer success centers will reflect a broader shift in the way businesses engage with their clientele, he added.
A key concern about the impact of AI on employment is the potential reduction in the number of jobs due to increased productivity. Vin addressed this issue by suggesting that while AI will improve productivity, it will also create new jobs and opportunities.
The transformation of jobs, from complaint management to customer success management, will require different skills, effectively redeploying the workforce rather than reducing its size. “The number of people won’t change, but the jobs they do will fundamentally change,” Vin said.
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He acknowledged that the rise of AI The rapid pace of technological advancement means that the relevance of skills is shrinking, with the “half-life” of a skill having shrunk from 30 years to just 6 or 7 years. This trend underscores the importance of ongoing education and adaptation in the workforce. Future employees will need to constantly update their skills to remain relevant in an evolving job market, Vin said.
Looking ahead, Vin envisions a future where machines and humans will collaborate closely, each improving the other’s capabilities. As AI takes over more technical tasks, human roles will shift to tasks that require critical thinking, creativity, and nuanced judgment. This hybrid workforce will thrive on mutual improvement, with humans honing AI systems and AI enabling humans to perform at their best.
Below are excerpts from the interview.
Q: One of the things that many people believe is that artificial intelligence (AI) will kill jobs, especially in the IT services sector. Is that true?
Vin: No, I don’t think so. I see AI as a technology that will enhance people rather than replace them.
When I say augment people, what I mean is that it will make people work much better than today, perhaps faster, but it will also provide very different value to the clients we work for.
For example, if we think about call centers, AI will not only help the call center agent to be much more productive because, for example, when he has had a call with you, he will take the transcript of the call, summarize it, and actually automatically get, for example, the sentiment that was exchanged during the call. AI will also help the call center agent to be much more aware of the context. Who is Harrick? What product has Harrick purchased in the past? Is he a satisfied customer?
Q: Is this the person in the call center who has to do it manually right now?
Vin: Yes, today it is done manually by the person and in fact the information is spread all over the place with many data sources within a company. Some of it is in CRM, some of it is in product information, etc. How can all of that be collected and improve my contextual knowledge as a call center agent? In the future, AI will enable the call center agent to be much more proactive. Can I predict or anticipate the need for Harrick to call and contact him, even before Harrick calls, potentially, to help improve customer satisfaction? So it changes this whole model from almost a complaint management to almost a value management.
Q: But is it already being done? Calling before the customer has to?
Vin: To some extent. Today, a lot of this happens before you’ve sold a product or service. The question is, after you’ve bought a product or service, can we actually do it, because the world is changing? There’s so much instrumentation available. I’m talking about a car. Today, the car is very connected, so it collects a lot of information about the car, the driving pattern, wear and tear, and so on. So it’s not that hard to imagine a situation where the car itself can tell you that something is going to go wrong in 30 days. So, in a sense, thinking about contact centers, not as call centers, but almost as customer success centers, is really the kind of transformation that’s likely to happen.
Q: But will you need the same number of people? For example, if you have 100 people and you have these AI applications coming, will you need all 100? Will you need 70? Will you need 50? Because productivity increases, as you said.
Vin: Productivity will increase in a certain class of jobs that they’re already doing, which will allow them to do new jobs that they’re not currently doing. For example, from complaint management to customer success management, there’s going to be a whole bunch of new jobs that they’re going to have to do. So in a sense, as you’re freeing up capacity, you’re using that capacity to change the value that’s perceived by customers. So, in my view, the number of people won’t change, but the jobs that they do will fundamentally change. And the value that customers perceive from that work will also change.
Q: And there are no unmanned customer success centers?
Vin: No, I think we’re probably making a huge mistake in thinking about automation. That’s why, at least personally, I always think in terms of augmentation rather than replacement. There’s a lot of work to be done that’s not being done today. When you free up the work capacity of an existing pool of workers, they’ll start doing things that aren’t being done today.
Q: So there is ongoing training required, right? I mean new training that is required across the industry.
Vin: Yes. In a sense, with AI, people’s roles are fundamentally changing from being doers of work to being sort of trainers and interrogators of intelligent machines, reviewers of the work done by machines, and really owners of critical thinking, creativity, and things like that, which means that people will have to be retrained all the time.
In fact, as machines become increasingly smarter, people’s roles will also constantly change and new jobs will be added, as I said, more related to customer success than complaint management, so continuous training and retraining will be necessary.
In fact, the usefulness of every skill that people are acquiring today – what is often referred to as the half-life of a skill, which is the time it takes for a particular skill that has been acquired to lose half of its value – has been reduced from almost 30 years to 6 or 7 years today, and it continues to shrink. This means that all of our children will need to be reskilled multiple times throughout their careers, so the need to constantly reskill talent or the employee base will become a critical success factor for all organizations.
Q: But has this already happened or has that half-life point you mention been accelerated now thanks to AI?
Vin: Yes. It is accelerating and will probably continue to decline. As technology matures, many of the hard skills and their half-life will continue to decline. Whereas in fact, many more soft skills will be needed, such as the ability to understand, articulate, coherently explain something and critically analyse it. In software engineering, for example, the ability to read code and analyse it will be much more important in the future than writing code, because the writing of the code will be done by a machine. But once the machine has written some code, being able to read it and say it is good, but not great. And this is why it is not great, so let me change that. In fact, this is creating a situation of co-working between machines and people, where machines will constantly improve people and make them better. And people will constantly improve machines. And that is almost like a hybrid workforce, where people and machines constantly improve each other and it will become the norm of the future.
Watch the attached video for the full conversation.
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