EY exec: In three or four years, ‘we won’t even talk about AI’

July was another rough month for the tech sector, with a worse-than-expected jobs report from the US Bureau of Labor Statistics (BLS) and analysis of that data by industry experts.

And on top of that, uncertainties around tech talent remain, according to a recent pulse survey from consultancy and professional services firm Ernst & Young (EY) — uncertainty exacerbated by the arrival of artificial intelligence (AI) tools and platforms. Half of IT leaders expect AI adoption to contribute to a roiling mix of hirings and firings into the fall, the survey found.

Even with hiring plans in place, 61% of tech leaders surveyed say the rapidly evolving technology has made it more challenging for them to source top talent. “One thing is certain: Companies are reshaping their workforce to be more AI savvy,” EY’s report said.

 

Ken Englund, who leads EY’s Americas Technology Growth sector, said companies are now concerned with how they should restructure teams to meet new demands, a restructuring that could mean the end of the most unique hiring market he’s seen in a decade. Englund spends most of his time evaluating and advising up-and-coming companies in IPO and pre-IPO stages — and there are a lot of such firms in the AI, software, and semiconductors space, he said.

Computerworld spoke with Englund about how AI is affecting hiring, re-shaping enterprise restructuring and what employees need to do to stay relevant as the marketplace undergoes dramatic shifts.

Why do you believe the July jobs report was worse than expected, especially for tech? “Two things. When we talk about tech jobs, we’re talking about jobs in tech companies or technical jobs anywhere. I think about jobs in tech, for the most part. I think the other thing to keep in mind, depending on where you look, net, we’re down about 10,000 jobs [in July]. In the scheme of the whole population of tech — there are several millions jobs in tech — what we’re seeing is still very strong demand in technical roles — developers, cyber, data scientists, and lighter roles in service and support and marketing.

“In any given month, we continue to see the workforce in evolution, given AI as a driver of upskilling and reskilling of the employee base. In some months, the net is positive and in some months, negative. I look a lot at layoffs.fyi, that’s sort of the data point I look at out in the market, and the trend line is getting smaller. Aside for a few major restructuring layoffs in tech over the past couple of weeks, the outflow seems to be getting smaller in magnitude.

“We’re seeing a lot of new companies, a lot of new start-ups, angel seed, round A [firms]. So those aren’t hiring that many people, but new company formation is growing.”

What kinds of start-ups are dominating? “AI and analytics, software and cloud — definitely on the digital side of tech versus hardcore infrastructure. In the bigger picture, as we’ve looked at tech over the years, things move from hardware to software over time. We’re still going to need a lot of hard-core infrastructure, semi-conductors, hardware, but more and more will move toward the software and apps layer.”

Can you explain the mixture of hirings and firings that we’ve seen over the past few years? “I actually think we’re starting to get close to a balance. If you go back over the past 24 months, you saw a lot of layoffs that were right sizing. Over-hiring around the time of the pandemic was driving no-regret-hiring as the overall tech sector was moving up, and ZIRP (zero interest rate policy) was allowing the tech companies to hoard talent. Now, there’s much more scrutiny around job requisitions and tying them to specific business needs, goals. Are they really needed? Are they directly aligned to some business initiative or value?

“I think we’re just getting back to what was considered normal behavior before the pandemic.”

How has AI changed the state of tech jobs? “…In general, there’s a very positive view of AI in tech. In a lot of other industries, there’s some uncertainty, some trepidation, some curiosity. But part of our pulse survey said about three out of four tech workers are using AI on a daily basis. So, the adoption in this portfolio of companies is higher than most, and I’d also said most employers and workers have a very good idea that AI is going to improve their business and their work.

“Really, we’re seeing its use mainly in development, software, testing, quality, customer care service as initial use cases. So, it’s slowly getting woven into everyone’s work.”

How are organizations restructuring their employee teams? “Everyone varies a bit. Probably two-thirds of these companies have some sort of reskilling or upskilling program. So, this isn’t about out with the old and in with the new. We did talk about rebalancing the workforce, but a lot of this will be employees being upskilled or retrained. That’s the most critical item going forward.

“I view AI skills as adjacent, additive skills for most people — aside from really hardcore data scientists and AI engineers. This is how most people will work in the new world. Generally, it depends. Some organizations have built whole, distinct AI organizations. Others have built embedded AI domains in all of their job functions. It really depends. There’s a lot of discussion around whether companies should have a chief AI officer. I’m not sure that’s necessary. I think a lot of those functions are already in place. You do need someone in your organization who has a holistic view of the positive sides of this and the risks associated with this.”

Why do you think this has been one of the most unique hiring periods over the past decade or so, and how has AI affected that lately? “I do fundamentally think we’ve had a platform shift. We had this around mobile. We had this around e-commerce. Or, if you go back far enough, we had this shift from mainframes to client-servers. So, I do believe this [AI] is a fundamentally a platform shift.

“From that perspective, the most critical thing when I sit down with clients, I always ask them, ‘How’s your data doing?’ We all know nobody has perfect data. In the AI world, data is going to become even more important. If it was difficult to manage your data before — think about graph databases and vector databases — really we see a lot of investment by enterprises into getting their data right for AI; that translates into ensuring you have the right resources: data architects, analysts, AI engineers and all those sort of positions as driving it.”

A lot of organizations are relying on cloud-based AI services from the likes of Microsoft, Oracle, Amazon and Google. Are you seeing an increase in the use of proprietary small language models based on open source versus these large language models (LLMs) offered through SaaS-style services? “I think it’s both. I think it’s still very early days. I think most enterprises are continuing to work with large language models and I think that will be the trend over the near horizon. Most of the key cloud providers, even frontier [companies], are building small models, too. I believe over time, you will see specialization, verticalization and small models being of distinct value.

“On the small language model size, I think — go back 24 months where large language models were — that’s where small models are now. But for early adoption among enterprises today, most are using large language models and doing RAG work. Not a lot of them are building their own proprietary models. But I do think it’s realistic to believe that most enterprises will have some level of proprietary models build out in the future.

“The thing I always hear is AI is not going to take your job. Somebody using AI will take your job.”

-Ken Englund

“We think about cloud around workloads. I think in the future, when thinking about what models you’ll use — small, large, proprietary, open source — it will be all around use cases. Most of our clients are starting on a single foundational model, but we always tell them to architect in some flexibility, because we think it’ll be models of models in the future.”

What does “models of models” mean? “At the end of the day, to get an answer to an answer to a particular set of use cases, you may need more than one big foundational model; it may be an open-source model…. [or] you’ll go to different models for different needs in the enterprise. I definitely don’t think it’s a one-size fits all. Build for flexibility.

“Most of these big frontier models really have commercial models around APIs. This idea of being fit for purpose for the kind of information or response you need for an inference will be the case going forward. You can think of yourself as … being a smart router for how you direct your AI inferences.”

How can IT professionals ensure they’re not left behind as organizations modernize? “Just start trying these tools, even if it’s in your personal direct to consumer life. People who have some familiarity with these tools off the bat will have a leg up. I think most companies will have a set of certifications, training and upskilling programs in their organizations. A lot of them already have ‘AI 101’ courses. I think as a tech worker, it’s up to you to take advantage of all those resources your company is offering you as a starting point, let alone all the other things out there in the open-source world.

“The thing I always hear is AI is not going to take your job. Somebody using AI will take your job.”

How are organizations upskilling or reskilling, and does that apply differently depending on the worker’s job, like line-of-business vs technologist? “When I think of functions that will use AI, whether that’s marketing or finance or customer care support or product development, they’re very different situations. I think the first three of those, they will have a much more applied use of this.

“My thought is if we fast-forward three or four years from now, we won’t even talk about AI. It will be embedded into the marketing automation software and ERP platforms out there. We’re going to get to a point where it just is.

“I think that’s the case for those line-of-business folks. For me, what’s important for business users, as these models get better — and I like to tell folks today is the worst AI will ever be, everyday it gets a little better — these models, whether they overlay deterministic models on top of probabilistic models, outcome and solution quality will get better. But understanding how that works in the meantime: there will be some judgment for what business workers need. What we’ll see mainly there are assistants — copilots that’ll make recommendations for business users like a marketer, but not probably human-out-of-the-loop at this point.

“Where we’re really seeing much more hard-core work is around software development, testing, quality and those areas where you’re in the nitty-gritty of activities. Those learnings will come out of classical enterprise IT or engineering product teams will flow into other parts of the enterprise.”

But AI is going to affect all of that, correct? “Absolutely. If you think about the most structured language in the world, it’s coding. If you think about these large language models, genAI, they’re really language based. AI’s ability to determine how code gets built, tested, released — that’s ground zero for all this stuff.”

So, more than anywhere else in the IT space, AI is being used to produce software, test it and deploy it, correct? “I think on the corporate IT side, yes. If you think about the rest of corporate IT functions, probably the two areas we’re seeing the most internet in AI is for customer service and customer care. These chatbots have been reasonably good initial products. We think anything that can handle customer care and service requests, we’re seeing move super well.

“Then, [it’s] things around employee workforce experience. So think about how you onboard a new employee — a lot of things that are rules-based and document based are the leading functions for AI.

“I think the last thing I’d add from our pulse poll is that these same concerns around AI adoption are the same ones we continue to see: cybersecurity, privacy, intellectual property are the biggies. The top line in our survey is really around skill-based development around AI expertise. This whole idea around certifications and upskilling is a really critical item.

“I know we’re all focused on the technology part of it, but this will continue — as always — to come down to the people and whether they can use it. That has never changed.”