America wants more ships, more chips, more advanced manufacturing, and a workforce ready for AI. Who is responsible for preparing the people who will do that work?

We call it the workforce system. But employers, colleges, training providers, workforce boards, and government agencies each own a piece. Who connects them? Who makes sure a training program leads to a job? And who helps workers navigate the distance between the two?

Paul Fain, editor at Workshift and writer of The Job newsletter, joins Ian Davidson to examine what the workforce system actually is, how it is funded, and why so much responsibility falls to states and regions. Paul reports across the boundaries between education, workforce policy, and employer demand. That perspective helps reveal what happens when institutions pursue related goals without the coordination to make them work together.

The conversation starts with the Workforce Innovation and Opportunity Act, or WIOA, the main federal workforce framework. Paul describes a system smaller than many people realize, with funding divided among programs and administered through states and local workforce boards. The question quickly becomes larger: if employers need qualified workers, who should pay to prepare them, and what should employers commit to in return?

AI makes the coordination problem more urgent. Near the end, Paul describes the possibility of labor-market disruption on the scale of industrialization. Ian's conclusion: no one is coming to assemble this for us. States, employers, educators, and workforce organizations will have to build the capacity to respond together.

This is the second episode in Act 3, our continuing investigation into why the workforce system does not function like a system. Next, Human Cloud co-founders Matthew Mottola and Tony Buffum help us examine work spreading across projects, clients, and platforms.

EPISODE CHAPTERS

00:00 - A workforce system built around figuring it out alone

03:00 - What is the workforce system?

06:00 - How workforce funding reaches people

09:00 - Who should pay to prepare workers?

13:00 - Connecting education to jobs that pay off 15:00 - Oklahoma and employer-funded training

]18:00 - What coordinated infrastructure requires

20:00 - Industrial policy and local responsibility

21:00 - Why states are being asked to do more

23:00 - State talent marketplaces and connected data

28:00 - National ambitions meet regional economies

34:00 - AI disruption and support beyond tuition

38:00 - No one is coming to assemble this for us

Read Paul's reporting: https://workshift.org/ Connect with Paul: https://www.linkedin.com/in/paulfain-thejob/ Explore Signal Mapping: https://signollabs.com/blog-signal-mapping/ Learn about Signol Labs: https://signollabs.com/ Working to connect training and employer demand? Contact Ian at ian@signollabs.com or https://signollabs.com/contact-us/. Subscribe to The Signal Lab and share this conversation with someone building your region's workforce.

How do we prepare workers for disruption if we cannot see how work is changing?

Artificial intelligence is already changing how employers operate, organize work, and value different capabilities. Yet much of the workforce ecosystem still relies on job postings to understand employer demand. Those postings can be useful, but they are advertisements. They do not reliably tell us which opportunities are real, who was hired, what work that person performs, what the job pays, or whether it lasts.

Economist Julia Lane has spent decades working on this problem. She is a professor at New York University, and author of Democratizing Our Data.

Julia explains why technological change reaches workers through employer decisions. Companies choose whether to adopt AI, how to redesign their operations, and which workers and capabilities they need. Understanding the future of work therefore requires better information about what employers actually do - not only what they advertise.

This is a conversation about data, but its consequences are deeply human. Bad information wastes employer resources, directs training dollars toward uncertain outcomes, and leaves workers making consequential choices without a clear view of opportunity. Better information could change the narrative from inevitable disruption to informed adaptation.

EPISODE CHAPTERS

00:00 - The Planes That Never Returned: How selection bias distorts what we think we know.

01:10 - Why We Cannot Prepare for Change We Cannot See: The limits of job-posting data.

05:00 - Learning at Work: Why on-the-job training matters across a working life.

06:40 - Labor Supply, Employer Demand, and Technology: Where economic disruption enters the workforce.

08:20 - The Missing Longitudinal View: Why surveys struggle to capture labor-market churn.

10:35 - The Employment Data We Already Collect: What unemployment-insurance wage records reveal.

13:10 - Data Built for Yesterday's Questions: Moving from surveys to administrative records.

14:15 - Industries of Ideas: Tracking how technology shocks move through local economies.

16:10 - What Early AI Adoption May Be Showing: Initial findings from Ohio employers.

17:10 - What JEDx Could Add: Better work locations and faster reporting.

18:25 - What Better Data Means for Learners: Local jobs, earnings growth, and durable pathways.

20:00 - Why Job Postings Are Not Enough: Representation, documentation, and validation.

21:35 - Building the Missing Feedback Loop: Connecting training to actual worker outcomes.

23:05 - A More Optimistic Future of Work: How better signals could help employers and workers adapt.

Learn more about Julia Lane and Industries of Ideas: https://industriesofideas.ai/ Connect with Julia: https://www.linkedin.com/in/julia-lane/ Explore Signal Mapping: https://signollabs.com/blog-signal-mapping/ Learn more about Signol Labs: https://signollabs.com/ Connect with Ian Davidson: https://www.linkedin.com/in/ianwdavidson/ Recommend a future guest: connect@signollabs.com

What do employers actually want?

What skills matter? What credentials will they trust? Can better data improve hiring? After three months investigating how hiring decisions actually get made, Ian Davidson thinks we're starting with the wrong question.

The better question is: Which decision are you trying to change?

Hiring isn't one decision. Job boards decide which opportunities people see. Applicant tracking systems decide who gets surfaced. Recruiters decide who gets a conversation. Hiring managers decide who gets the job.

Together, those decisions allocate one of hiring's scarcest resources: human attention.

And the decisions affecting the most candidates often happen when the system knows the least about them.

Episode 10 brings together insights from Allison Danielsen, Matt Plummer, Andrew Cunsolo, Jordan Franklin, Matt Charney, Brad Robinson, and James Harris.

The investigation began as a search for better signals. It became the Hiring Decision Architecture: a framework for identifying which hiring decision we're trying to improve, what information could make it better, and where to intervene.

EPISODE CHAPTERS

00:00 — What Do Employers Actually Want? Why skills and credentials may be the wrong starting point.

05:00 — What Hiring Managers Really Evaluate Motivation, potential, work ethic, learning, and what resumes miss.

12:00 — Skills-Based Hiring and the Replacement Problem What replaces degree and experience requirements?

14:00 — Application Overload, ATSs and AI Hiring What happens when employers receive 1,000 applications?

16:00 — Hiring Is an Allocation of Human Attention Why consequential decisions happen with limited candidate information.

18:00 — The “Cake Lady” and the Candidates Systems Miss A great hire who never would have survived the conventional process.

21:00 — Better Candidate and Opportunity Profiles Why resumes and job descriptions both leave out critical context.

26:00 — The Hiring Decision Architecture Start with the decision, then identify the information needed to improve it.

28:00 — Talent Marketplaces and Workforce Intelligence What if matching systems understood people's needs and constraints?

33:00 — Fix the System Before the Hiring Manager Improve the decisions determining who reaches a human.

37:00 — AI, Machine-Readable Data and Verifiable Skills Technology can use richer information without overwhelming recruiters.

40:00 — Better Talent Marketplace Matching Matching capability, potential, motivation, needs, and opportunity.

42:00 — Learning and Employment Records + Trusted Career Profile How portable, verifiable data could improve hiring decisions.

45:00 — The Demand-Side Case for Better Workforce Data Employers aren't asking for LERs. They're asking for help with 1,200 applicants.

48:00 — Which Hiring Decisions Should We Fix? Stop starting with data. Start with the decision.

Research supported by the U.S. Chamber of Commerce Foundation and T3 Innovation Network.

Explore the Hiring Decision Architecture: https://signollabs.com/hiring-decision-architecture/

Signol Labs: https://signollabs.com/

Subscribe to The Signal Lab wherever you listen to podcasts.

Missing Context — with Allison Danielsen, CEO of Tallo

Why do hiring technologies struggle to match the right people to the right opportunities?

Job descriptions don't fully capture what employers actually want. Resumes don't fully represent what people can do. Yet we ask increasingly sophisticated matching algorithms—and now AI—to connect the two.

Tallo CEO Allison Danielsen joins Ian Davidson to explore the missing context inside today's hiring systems and what the next generation of talent marketplaces will need to understand about people and opportunity.

They discuss talent marketplaces, job boards, skills-based hiring, degree and experience requirements, defensible hiring practices, resumes, skills and capabilities, early talent, career navigation, AI, and richer career profiles.

Allison explains why hiring managers often struggle to translate qualities like curiosity, motivation, learning potential, adaptability, and lived experience into objective and defensible hiring criteria. They explore why removing degree requirements isn't enough to make skills-based hiring work, why people struggle to identify their own transferable skills, and how talent marketplaces could build a richer, continuously evolving understanding of human potential.

This is the fifth and final interview in The Signal Lab's In Search of Signals investigation into the information that actually shapes hiring decisions.

CHAPTERS

00:00 — The missing context in hiring 03:00 — What is Tallo? 06:00 — Why traditional job boards are changing 08:00 — What job descriptions don't tell us 11:00 — Why employers use defensible requirements 14:00 — The Defensibility Grab Bag 16:00 — Is skills-based hiring actually working? 18:00 — Building a fuller picture of a person 21:00 — What Talent Marketplace 2.0 needs 25:00 — What should we know about every job seeker? 29:00 — Why career navigation matters 35:00 — Why asking people to list their skills doesn't work 38:00 — How talent marketplaces will change 40:00 — Four findings: why better matching needs better context

ABOUT ALLISON DANIELSEN

Allison Danielsen is CEO of Tallo, a digital platform helping early talent move from career uncertainty to confident action.

Tallo: https://tallo.com/

Connect with Allison: https://www.linkedin.com/in/allison-danielsen/

ABOUT THE SIGNAL LAB

The Signal Lab explores the systems that shape opportunity—how people prove what they know, how employers evaluate talent, and how better data can connect learning and work.

The show is hosted by Ian Davidson, Founder and CEO of Signol Labs.

CONNECT & EXPLORE

Signol Labs: https://signollabs.com/

Signal Mapping: https://signollabs.com/blog-signal-mapping/

Connect with Ian Davidson: https://www.linkedin.com/in/ianwdavidson/

Schedule a conversation: https://signollabs.com/contact-us/

What if the biggest problem in hiring isn't finding qualified people—but helping employers make sense of the thousands of applications already sitting in front of them?

Most conversations about the future of hiring focus on skills, AI, resumes, or credentials. But underneath all of those discussions is a much more fundamental question:

How do job boards actually decide who sees opportunity?

Job boards have quietly become one of the most influential recommendation systems in the labor market. Every day they determine which jobs appear in front of millions of people and which candidates employers are most likely to notice. Yet most of us still think of them as digital bulletin boards.

They're anything but.

In this episode, Ian Davidson sits down with Matt Plummer, Chief Product Officer at Appcast, to understand what modern job marketplaces are actually trying to optimize—and why richer candidate data alone isn't enough to change hiring.

One of the biggest surprises from the conversation is that employers aren't asking job boards for more information about candidates.

They're asking for fewer applicants.

Matt explains why job seekers won't tolerate repeatedly rebuilding their professional identity across every platform they use, forcing marketplaces to make recommendations using incomplete information. He also explores how richer signals—skills, credentials, projects, certifications, and verified experiences—could dramatically improve matching, but only if enough people possess them and enough employers can actually consume them.

It's a conversation about incentives, scale, recommendation systems, and one of the largest hidden constraints shaping opportunity today.

Along the way, Ian and Matt discuss:

• Why application overload has become one of hiring's biggest problems
• How job boards balance supply and demand across labor markets
• Why recommendation quality depends on context—not simply more data
• What richer candidate signals could make possible
• Why adoption, not technology, is often the hardest challenge
• How future hiring systems may evolve beyond today's resume

This conversation is also an important milestone in Signal Mapping, Signol Labs' ongoing investigation into the signals employers use throughout the hiring process.

Earlier episodes explored how hiring managers evaluate people, how recruiters scale judgment, and how applicant tracking systems filter candidates. This episode shifts upstream to the marketplaces responsible for connecting people with opportunity in the first place.

Understanding those systems helps answer a much larger question:

If we can improve the information flowing through labor markets, can we improve opportunity itself?

About Matt Plummer

Matt Plummer is Chief Product Officer at Appcast, one of the world's leading recruitment marketing platforms. He has spent years building technologies that help employers reach candidates more effectively while balancing the realities of modern hiring at enormous scale.

About The Signal Lab

Hosted by Ian Davidson, Founder and CEO of Signol Labs, The Signal Lab explores the systems connecting learning and work—from hiring technology and workforce development to digital credentials, AI, and labor market infrastructure.

If your organization is thinking about hiring technology, workforce strategy, Learning and Employment Records, digital credentials, or the future of talent marketplaces, we'd love to continue the conversation.

Learn more at SignolLabs.com

Every hiring system in the world depends on one assumption:

The information applicants submit tells employers something meaningful about what they can actually do.

That assumption is beginning to collapse.

Artificial intelligence now allows job seekers to generate customized resumes in seconds. Employers are using AI to generate job descriptions. Applicant Tracking Systems are using AI to rank candidates. Meanwhile application volumes are exploding.

The result?

Everyone looks qualified.

Recruiters are drowning in applications.

Employers are adding more requirements simply to reduce the number of people they have to review.

And the signals that hiring systems have relied on for decades are becoming increasingly difficult to trust.

In Part 3 of In Search of Signals, Ian Davidson sits down with Andrew Cunsolo, Principal Customer Solutions Partner at Employ, one of the world's largest Applicant Tracking System providers. With more than twenty-five years spent building recruiting technology—including some of the earliest resume parsing systems—Andrew has watched hiring evolve from keyword searches to AI-powered ranking.

His conclusion is surprisingly simple.

The problem isn't AI.

AI is exposing weaknesses that were already built into hiring.

Together, Ian and Andrew explore why applicant tracking systems behave the way they do, how employers actually use resumes, why application volumes are skyrocketing, and what happens when every candidate begins looking equally qualified.

More importantly, they explore what comes next.

Throughout the conversation they discuss how trusted, verifiable information—from digital credentials to Learning and Employment Records to the Trusted Career Profile—could fundamentally change how hiring systems understand people.

Rather than asking recruiters to work harder, the future may depend on giving systems better information.

This episode is part of In Search of Signals, a five-part investigation from Signol Labs examining how employers, recruiters, technology platforms, and marketplaces evaluate human potential.

Each episode follows another layer of hiring infrastructure.

Together they reveal a much bigger question:

If our systems can't reliably understand people today, how will they understand human potential in an AI-driven economy?

Guest

Andrew Cunsolo Principal Customer Solutions Partner Employ

LinkedIn: https://www.linkedin.com/in/acunsolo/

Employ: https://www.employinc.com/

About Signol Labs

Signol Labs helps organizations design the infrastructure of opportunity by connecting learning, work, skills, credentials, identity, and workforce technology.

Learn more: https://signollabs.com

Signal Mapping: https://signollabs.com/blog-signal-mapping/

Connect with Ian Davidson: https://www.linkedin.com/in/ianwdavidson/

If your organization is exploring skills-based hiring, workforce strategy, digital credentials, Learning and Employment Records, talent marketplaces, or trusted hiring data, we'd love to hear from you.

https://signollabs.com/contact-us/

Most hiring systems are designed to answer one question:

Can this person do the job?

Recruiters spend their time answering a very different one:

Will this person thrive here?

That distinction sits at the heart of Episode 6 of The Signal Lab. In the second installment of our five-part investigation into how hiring decisions are really made, Ian Davidson sits down with Jordan Franklin, co-founder and CEO of Stratus Consulting, an IT staffing firm that speaks with hundreds of candidates every week and has placed thousands of professionals into organizations ranging from Northwest Arkansas startups to Fortune 1 companies.

What emerges from the conversation is a fascinating realization: staffing firms aren't simply matching resumes to job descriptions. They're filling in the gaps left by the hiring systems we've built.

Jordan explains that her recruiters certainly evaluate technical skills—but they lean heavily on assessments to validate those. The real work happens somewhere else.

Most importantly, they try to answer a question no résumé can:

What will this person actually be like to work with?

Along the way, Jordan shares why many of her clients never publicly post open positions, why passive candidates often become the strongest hires, and why staffing firms function as trusted advisors for both employers and candidates.

She also introduces the philosophy behind Gumption, the AI-powered platform Stratus is building after years of learning what traditional hiring systems fail to capture. Rather than simply matching people to jobs, Gumption aims to understand the human behind the résumé and match people to the teams where they'll actually succeed.

Signal Mapping Connection

Throughout this series, The Signal Lab is reverse-engineering hiring.

Hiring managers look for trust, character, and judgment.

Recruiters look for motivation, culture fit, and long-term success.

Assessments validate technical capability.

Each stage relies on different signals.

If we hope to build better digital credentials, better learner records, better AI, and better workforce systems, we first need to understand what information humans actually use to make hiring decisions.

Recruiters have quietly spent decades building richer profiles because the systems around them haven't.

Perhaps the next generation of workforce infrastructure shouldn't replace that understanding.

It should make it portable.

Resources

Jordan Franklin

LinkedIn: https://www.linkedin.com/in/jordan-franklin-gumption/

Stratice Consulting: https://mystratice.com/

Signol Labs: https://signollabs.com/

Signal Mapping: https://signollabs.com/blog-signal-mapping/

Connect with Ian Davidson: https://www.linkedin.com/in/ianwdavidson/

Schedule a conversation: https://signollabs.com/contact-us/

If your organization is designing hiring technology, talent marketplaces, digital credentials, Learning and Employment Records, or workforce infrastructure, we'd love to compare notes. This investigation is helping shape a practical framework for understanding the signals that actually drive hiring decisions.

The hiring process is full of advice.

Optimize your resume.

Use the right keywords.

Build your LinkedIn profile.

Customize every application.

But what actually matters once a human being sits across the table from another human being and decides whether to make an offer?

That's the question at the center of Episode 5 of The Signal Lab.

To answer it, Ian Davidson speaks with three employers who hire in completely different environments:

Brad Robinson, District Manager at Mattress Firm, who has hired hundreds of sales professionals and built one of the strongest retention records in the company.

James Harris, CEO of High Impact Analytics, who recruits analysts, consultants, and business leaders in the Walmart ecosystem.

Christy Lamers, founder of Blossom Landscape Design and co-founder of Life Outside, who hires almost entirely through relationships, reputation, and instinct.

The result is an unusually honest look at how people actually make hiring decisions.

What emerges is both simple and surprising.

Resumes matter—but only as a starting point.

The interview matters more.

And what employers are really trying to understand isn't who you were.

It's who you'll become after they hire you.

Will you stay?

Will you solve problems?

Will you work well with others?

Will you ask for help when you need it?

Will you care about the people around you?

Will you make the culture stronger?

Across industries, the same themes keep appearing: honesty, curiosity, coachability, teamwork, resilience, and the ability to do hard things. Employers consistently look for signals that extend far beyond education, certifications, or job titles.

The conversation also reveals a deeper challenge.

Many people possess valuable experiences, skills, and capabilities that never appear on a resume because they don't believe those experiences matter. Yet those hidden signals are often exactly what employers wish they could see.

This episode is the first installment in a four-part Signal Mapping series exploring how hiring decisions are made—and what information actually matters at each stage of the process.

Signal Mapping Connection

If we want digital credentials, learner records, and talent marketplaces to create better outcomes, we first need to understand the demand side of the market.

What information do employers actually use?

What signals do they trust?

What helps them predict success?

This episode starts answering those questions.

Resources

Brad Robinson: https://www.linkedin.com/in/brad-robinson-sales-management/

James Harris: https://www.linkedin.com/in/james-harris-a777055/

Signol Labs: https://signollabs.com/

Signal Mapping: https://signollabs.com/blog-signal-mapping/

Connect with Ian Davidson: https://www.linkedin.com/in/ianwdavidson/

Schedule a conversation: https://signollabs.com/contact-us/

Thank you for listening to The Signal Lab.

The future of opportunity is being built in many different rooms.

Let's connect them.