In fourteen years from now, nearly one in twelve workers could be out of a job. That’s Indeed’s Hiring Lab’s unemployment forecast for 2040, and (surprisingly) Artificial Intelligence is not the main culprit. The real reason, according to Indeed’s research, is what they call a “mismatch” between the jobs that are growing and the workers available to fill them. The industries adding jobs aren’t always the industries where the workforce already has experience, and the regions adding jobs aren’t always the same regions (within regular travel distance) where that workforce lives.
That mismatch has three trends pushing it along. Baby boomers are retiring, immigration has slowed, and AI is changing white-collar work faster than hiring systems can adjust. If left unaddressed, Indeed predicts unemployment could reach 8% by 2040.
Indeed has its own recommendations to counteract the rise in unemployment. First, employers should reevaluate job requirements. Second, they should invest in training the people they already have. Third, is to expand recruiting candidates in similar fields to encourage the transfer of skills. It is this transfer of skills that has been a sore point in hiring, because most hiring systems have not been designed to consider those candidates in the first place.
Resumes Weren’t Built to Find Transferable Skills
For instance, a self-taught developer, a career switcher, or someone from a military background often has the right skills but the not-so-right titles. These candidates often have the capability an employer needs, but they just don’t have the keyword match to get past the first filter.
It may be time to move away from the old system of resume filtering. Atalef is a hiring platform that matches candidates to roles by skill rather than resume. Its AI powered matching technology, DeepMatch™, rather than assuming, asks: Does this person have the skills the role actually requires? That is the question a title-focused search doesn’t ask, which is why it misses excellent candidates.
Take a company hiring for a DevOps engineer. A keyword search looks for candidates who have held that title before. A skills-based match can highlight someone who has never had the word “DevOps” on a resume but has spent years working with cloud infrastructure, automation, scripting, and CI/CD pipelines, the actual components of the job. That candidate exists in the talent pool today, but most hiring systems miss them.
Skills Aren’t Tied to an Address
Location adds a second layer of complexity to the mismatch Indeed describes. A growing role in one region might have no qualified local candidates, while the right person for that job is waving from a completely different market.
As hiring moves more global and remote, matching on skills rather than limiting potential candidates to those in proximity opens that talent pool further. A team hiring in Germany, for example, doesn’t need to limit its search to Germany if the strongest match for the role happens to be in India, Poland, or Brazil.
The Talent Might Already Be Inside the Company
Indeed’s advice to employers includes investing in the people already on staff, since developing existing talent is faster and cheaper than sourcing new hires. That is the logic behind promoting internal talent marketplaces. Instead of posting a new role externally and waiting months to fill it, a company maps the skills it already has internally and matches employees to open positions before proceeding to look for candidates outside.
This is where skills-based matching is useful in more ways than only recruiting into workforce planning. The same underlying data that identifies external candidates can just as easily identify overlooked capability inside a company’s own team.
Where AI Fits In
With the rise of AI technology, more and more companies are adopting hiring technology that uses AI to filter candidates out faster. However, this approach does not guarantee they will find the right candidates for the role. Using the technology to cut time does not guarantee quality if the existing filters continue to reject good candidates.
Instead, AI can be used to explain matches instead, as in the case of Atalef. The platform provides the justification why a candidate fits a role, where the skill gaps are, and what a realistic development path for that candidate looks like. The goal is to show who actually qualifies and why.
The Bigger Opportunity
Most HR platforms were built to manage applications, resumes, and recruiting workflows. They help companies process candidates, but they do not always help them recognize capability that sits outside familiar titles, keywords, or career paths.
That is the larger problem Indeed’s research points toward. The labor market may not only be facing a shortage of people. It may also be facing a visibility problem.
Qualified candidates are already present in the workforce, in adjacent industries, in different regions, and sometimes inside the same company. But if hiring systems continue to search only for familiar titles and exact-match resumes, those candidates will remain hidden.
This is where skills-based hiring becomes more than a recruitment trend. It becomes a way to make the labor market work better. By looking at what people can actually do, where their skills can transfer, and what gaps can realistically be developed, employers can widen their talent pool without lowering standards.
The skills exist, but the visibility doesn’t.