How Data-Powered Apps Can Connect Jobseekers With Training For Hard-To-Fill Jobs
On the other hand, despite consistent job growth, there are millions of tech jobs in the U.S. that go unfilled and result in slowed economic growth, one company at a time. These technical and skill-based jobs, for the time-being, cannot be exported offshore, nor can a robot do the work here. So, if there are still millions unemployed Americans who need a path to work and there are millions of open positions, what is the problem?
The answer, at least partially, is that we are not using technology to put people back to work or to enable them to get better work. We are letting technology, here and abroad, erode hiring because the current mechanisms to get people trained for unfilled jobs are broken, out of sync with the economy. In other words, we are not fighting automation with automation.
The Missing Link
The critical missing link, literally, is the app that can match an unemployed individual with training (likely at a community college) that is aligned to current difficult-to-fill technical jobs regionally.
This matching is not rocket science. It is done frequently by social media and commercial sites. Whether it is travel for work or pleasure, Expedia, Kayak and others make matches between multiple items – cars, hotels and planes. It’s not too much of a stretch to imagine technology that connects supply with demand in the labor market.
If we indeed want to send many more Americans back to work, we will need to fight technology with technology and innovation with innovation. The shift to smart-technologies and robots of all sorts is well underway. In the time it would take 10 community colleges to put together training programs for a form of advanced welding, which happens to be in high demand, it might make more sense for a robot manufacturer to read employer job specifications and create and market robots that can get the same jobs done.
Welding and assembly line robots are only a small portion of what has been and will be automated in the coming months and years. And the world's leaders in such automation, undoubtedly, will be Americans. Well beyond the assembly line, Uber, AirBnb and Amazon and other companies have and are rapidly displacing traditional workers, and long-standing businesses.
Automation for Finding & Securing Work
The recourse is to create competition between technology that will secure jobs for people and the automated technology that does the jobs of people or finds overseas labor. There is no such industry in the world today to automate finding training for work, yet the seeds for such an industry are all around and so is the funding. While there are compelling job boards like Indeed, Glassdoor, Career Builder, LinkedIn, and Monster and wholesalers of resume and job data from Burning Glass and EMSI, these systems are only as good as the "scraped" resume and open job data currently on the Web.
They don’t serve the people without resumes or LinkedIn accounts. They don't reveal information that can only come from employers about hiring forecasting. They don’t contain the machine-readable data of what is in regional community college training courses.
The automation that is needed are intelligent agents that can challenge the growing need for machine labor and support human jobseekers by helping them build their skills and work aptitude, thereby helping employers meet their hiring needs more quickly.
Unfortunately, those who have the funding to use technology to find jobs or keep people in jobs have not shown the ingenuity, leadership or understanding to fight overseas job outsourcing or in-country automation with technology as sophisticated as the technology that is taking away the jobs.
Creating technology to help people find and keep jobs, especially skilled jobs, requires a whole new approach that includes new players and calculated risk. The billions in funding from federal agencies, the hundreds of millions from significant national foundations and the involvement of the large employers on the national stage have not changed this equation over the last eight years even though many of them are, in fact, technology companies.
As a result, innovative, tech-powered solutions to the jobs crisis are not getting created within education, occupational training and workforce development. Instead, what is getting created are more programs. Unlike the technology we use in daily life, program proliferation does not affect individuals in measurable and sustainable ways, and the individuals who need the assistance are not part of creating the new solutions. For the money that has been expended on many programs, the results are not easily evident, and if there are notable results, they are not showing up in data that can easily be tracked. What is the answer?
The New Prescription: An App in Hand
What is needed is a new way to incubate, iterate and scale technology solutions that truly put modern tools in the hands of the unemployed and under-employed to locate training in real-time that is directly connected to open or upcoming jobs.
It's called an app. This is not the province of foundations, agencies or Federal grants. Unfortunately, it is not the interest of Silicon Valley, Austin or Cambridge, MA. And, if any of these produced the app, socializing it is a whole other issue.
Our nonprofit, the National Laboratory for Education Transformation, along with the University of Chicago Computation Institute and the University of California San Diego Super Computer Center are gearing up efforts to create the underlying data systems and visual representations to build the computational power for that app, and the data visualization to socialize the supply of training in the country, region by region, and the unfilled jobs, region by regions. The center we are building to bring together many specialized organization in the jobs, workforce and employment data is called the National Center for Opportunity Engineering & Analysis.
“Opportunity Engineering” is the term we feel best describes the computational merging of experience, education and employment data to let the matching begin, and to help level the playing field between job-finding and automation – all in the hands of jobseekers and employers. This way we can unleash AI in favor of finding jobs, not automating them.
Gordon Freedman is President of the National Laboratory for Education Transformation (www.NLET.org), a California-based 501(c)(3) non-profit committed to transforming 20th century education into 21st century learning.
The GradsofLifeVoice Forbes team provides thought leadership, research and expert commentary on innovative talent pipelines and related issues such as the skills gap, income inequality, workforce diversity, and the business case for employment pathways. We seek to change employers’ perceptions of young adults with atypical resumes from social liabilities to economic assets. This post was originally featured here.
Hiring & Retention Practices, Innovation, Technology, Workforce Development,
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