Employers are getting pickier when looking at tech resumes, but competition for the right skills is still intense. Companies will pay a premium for experience and advanced AI skills.

“The market is no longer defined simply by how many people employers hire, but by how precisely they hire,” said Ger Doyle, ManpowerGroup’s regional president for North America.

In many cases, employers would rather leave a position open than make the wrong hire, Doyle said.

AI fluency and expertise are driving higher salaries, talent consulting firm Robert Half said after surveying more than 2,000 hiring managers, business leaders, and employed workers for its 2027 salary guide. “72% are increasing pay for relevant AI skills, with 42% offering more for AI-related expertise than other technical skills,” the firm said.

The tech job market today

There were 272,040 new IT openings posted in September, boosting the active listings to 625,633, the highest in more than three years, according to a study released by CompTIA earlier this month. About 349,821 jobs listed in September — more than half the active listings — needed AI skills, growing by 32,327 compared to August, CompTIA said.

In a recent survey of tech and IT service employers, AI literacy was the most in-demand technical skill, with 34% of US respondents naming it as a sought-after skill, reported Experis, part of ManpowerGroup, in its fourth-quarter talent outlook. AI modeling and app development followed close behind at 33%.

“We’re seeing it influence hiring across occupations ranging from data science and engineering to project management and operational roles,” ManpowerGroup’s Doyle said.

Employers face a tug-of-war as workflows transform and job requirements change, said Michael Chui, senior fellow at McKinsey.

“You have to have the right talent in place. You have to retrain. You might have to change the incentives. That’s all hard work,” Chui said.

What skills are sought

AI skills in demand are increasingly tied to business priorities and outcomes, consulting firm PwC said in a June study.

Companies want to hire AI-fluent workers who can hit the ground running. They prefer candidates who have used cutting-edge tools and services.

In tech job postings, employers are seeking more experience but fewer skills, said Dean Boerner, a data scientist at Revelio Labs, in a study published last month. Since late 2024, the average number of skills required in tech job postings dropped from about 30 to 21, while required experience rose from about 4.6 to 4.8 years.

Employers particularly want more years of experience in data-related skills such as data organization, data research, and enterprise architecture design. This translates to hiring experienced personnel rather than handing duties to junior hires or AI systems, Revelio said in its study.

Fewer job listings mention broad categories like software and application development, the study said, while a larger number of job listings mention specialized skills such as Java API development and Python development. Other AI-linked skills including networking, cloud development, and machine learning are also in demand.

The AI infrastructure buildout has also led to an increased demand for semiconductor talent, network architects, construction managers, and software developers.

“Data centers are the factories of the AI economy, and every server rack that comes online needs someone who knows how to run it,” said Kye Mitchell, head of Experis US.

Experts said software developer roles are now pivoting toward data and machine learning expertise. AI is automating coding, so software developers are becoming AI engineers with duties that include verifying AI-derived code and ensuring compliance.

“Governance is a thing in which engineers will have a key role to play… AI engineers build contextual guardrails for compliance,” said Deepak Seth, senior director analyst at Gartner.

Salaries

AI engineer will be among the top-paid jobs in 2027, with salaries ranging from $154,500 to $196,750, depending on experience, according to Robert Half.

AI architects get between $145,250 and $204,000. Salaries for AI/ML analysts, machine-learning engineers, and data scientists range from $121,250 to $186,750.

RPA (robotic process automation) engineers, who focus on software and automation, get salaries between $107,000 and $155,250. Salaries also depend on location.

Career ladder

AI is throwing out the traditional way to climb up the career ladder. Hands-on experience and on-the-job upskilling matter more, while academic credentials carry less weight.

Companies are looking for specialized, judgment-heavy skills, “which younger workers are the least equipped to offer,” Revelio Labs said in its study. 

PwC noted that entry-level roles in AI-exposed jobs now demand skills that are traditionally developed through experience, such as leadership and strategic thinking.

“Organizations must rethink how they mentor and train junior staff, helping them step up to complex decision-making much earlier in their careers,” PwC said in its June report.

Fresh graduates combining technical fundamentals with practical experience, AI fluency, and strong communication skills will stand out, Experis’ Mitchell said.

“[Employers] are also building talent from within, expanding talent pools, and using multiple strategies to meet their needs,” Mitchell said.

Proof of AI proficiency in the workplace will become a standard requirement, Gartner’s Seth said. By 2027, 75% of hiring processes will include certifications and testing for workplace AI proficiency during recruiting, he said.

“Anybody in the IT world knows… we were asked for certifications about Java or C++ or whatever. Now suddenly that will shift to workplace AI proficiency,” Seth said.

Skills are also going out of date faster. “The half-life of technical skills will shorten from 8 to 12 years to 2 to 5 years. So adaptability and learning velocity will become very, very key,” Seth said.

“Rehiring is expensive. Reskilling is a better option,” Seth said.

Anthropic has a $100 million plan to help enterprises get more from AI. No, it doesn’t involve agents. It involves people. Lots of people.

How retro.

On October 2, the company announced Claude Frontier Academy, with a goal of training 10,000 frontier deployed engineers by the end of 2027. Participants work through a simulated deployment, including use-case selection, security review, and handover, before undertaking a 12-week residency on a real project at their own company.

For a business selling increasingly capable models, it’s telling that Anthropic recognizes some of the thorniest problems in AI don’t involve AI at all. This, to me, is indicative of real progress, because the heart of any technological shift is always people.

Which people? Well, that’s where this idea becomes particularly interesting. As critical as developers are to the success of AI in any particular organization, we need those developers working alongside the people who understand the business, with authority to turn that understanding into systems that other employees can use. That’s the key to AI success.

Old advice for a new assignment

It has always been thus, however much we try to pretend that this time it’s different. Back in 2013, I quoted Gartner analyst Svetlana Sicular discoursing on the rise of big data technologies like Hadoop: “Organizations already have people who know their own data better than mystical data scientists.” She continued, “Learning Hadoop is easier than learning the company’s business.” Data becomes useful when paired with industry expertise—from experts who understand which data actually matters.

This topic is perpetually relevant. I covered the argument in 2023 and again in 2025. We’ve always needed people who can translate business requirements into software.

AI expands the kinds of knowledge that software can work with. Language models extend that work to material that often sat outside our applications: research notes, meeting transcripts, internal debates, and explanations of why a decision was made. An employee can ask questions across that material without someone first designing a report for every possible question. That opens up useful work that would have been difficult or expensive to automate before.

It also creates new ways to be wrong. A model may find a convincing explanation that was superseded six months ago, confuse a proposal with an approved policy, or combine two departments’ incompatible definitions of a customer. The people who understand those distinctions need to help engineers make them explicit, then build examples that reveal when the system gets them wrong. Companies that figure this out stand to benefit disproportionately; hence, PwC’s April survey of 1,217 senior executives found that companies reporting the strongest AI performance were twice as likely to redesign workflows around AI.

Teach the system what’s good

Bridgewater, the world’s largest hedge fund, has done some interesting work here that substantiates this theory. At AI Engineer NYC on October 13, two representatives of Bridgewater described Vault, its repository and access layer for external and internal unstructured information. Vault isn’t designed to be a dumping ground; instead, the company has shaped the way it stores data to make it useful for analysts.

I attended the talk and watched as Sam Green demonstrated a question about AI infrastructure investment and the Korean won. Left on its own, a web-connected AI agent relied on July material to answer a question posted in October. Hedge funds depend on up-to-the-second information to figure out optimal trades, making this initial attempt a failure. Connected to the human-influenced Vault, however, it searched October 1 through October 8 and drew on seven documents rather than one or two.

Knowing which period matters is part of understanding the investment question; making the right material available consistently is an engineering problem. You need both. A plausible answer assembled from stale research can be worse than an obvious failure because it gives the investor little reason to investigate further. The team also collected roughly 200 questions and manually identified useful documents, including misleading matches. Bridgewater’s Aaron Linsky reported that subsequent improvements lifted the share of relevant documents among the top 10 search results from about 60% to between 90% and 95% on that evaluation.

This method is useful well beyond investing. Someone has to distinguish a document that merely sounds relevant from one that actually helps answer the question. Engineers can then use those judgments to measure whether a change improves the system. Business expertise becomes something the team can test against, rather than advice delivered after the application has shipped. It’s not a case of human intelligence or artificial intelligence. You need both.

Make the expertise usable

Of course, a skeptic could reasonably ask why a model can’t learn the business, too. It’s a fair critique, as AI can learn a great deal of it. After all, that’s the reason Bridgewater (and others) make a point of supplying the right documents, definitions, and examples. This ensures AI can take on more of the analysis, thereby reducing dependence on the few employees who currently carry essential knowledge in their heads.

But access to every document doesn’t settle which policy is authoritative when two departments disagree. Nor does it authorize a change to an approval process or establish who owns a mistake. A model can help expose contradictions and propose resolutions, but someone with responsibility for the business has to decide which resolution the company will adopt.

Of course, most companies don’t have Bridgewater’s decades of codified investment knowledge. They may also have conflicting definitions scattered across spreadsheets and a few employees who know why the official process doesn’t quite describe reality. Nor does it make sense to search for one extraordinary hire or a star employee who knows every model, understands every department, and can persuade everyone to change. (Good luck with that!) After all, someone with deep knowledge of the business may also be the exact wrong person to help an organization evolve, wedded as they may be to how things have always been.

Gartner’s Sicular was right about the value of people who understand the business. AI gives companies more ways to put that understanding to work. Give those people engineers to build with, authority to resolve disagreements, and responsibility for leaving behind a system someone else can operate. We need AI challenging people, and people challenging AI.