Work Tech Weekly

Most AI upskilling never reaches the people who need it most. It stays inside corporate training budgets, locked away from the average person.

Michael Ioffe, Co-Founder and CEO of Arist, joins Steve Smith to talk about Make America AI Ready, a new partnership with the U.S. Department of Labor designed to deliver free AI upskilling to every American worker — entirely via text message.

In this episode, Michael walks through how the program works, what the Department of Labor's AI literacy framework actually covers, and why SMS is the right delivery mechanism for education at this scale. They also get into what it takes to build infrastructure for a program that could reach a hundred million people, and what a successful outcome looks like for both the American workforce and Arist.

If you've been thinking about how AI education reaches workers who aren't already plugged into corporate training programs, this one is worth your time.

Every CEO is bullish on AI. Ask the people actually doing the work? The mood is different. Fear. Ambivalence. A vague sense that something important is being skipped.

That gap is what Donald Thompson has been spending his time on. Donald is managing director of the Center for Organizational Effectiveness at Workplace Options, the largest independent provider of holistic wellbeing solutions supporting tens of millions of employees across 200+ countries.

He's also a multi-exit entrepreneur, EY Entrepreneur of the Year recipient, and author of Underestimated and The Inclusive Leadership Handbook.

In this episode, host Steve Smith and Donald dig into why AI adoption fails at work — not because the tools aren't good enough, but because organizations keep skipping the hard parts: trust, work design, and leadership.

What you'll hear:

- Why lack of trust is the biggest barrier to AI adoption right now

- The zero-to-70% frame — and why the last 30% still requires great people

- How to sort AI use cases into automation vs. knowledge work buckets

- The risk conversation that actually works with command-and-control CEOs

- The question most leaders should be asking their teams but aren't

Most workplace technology has historically been built for desk workers. But desk workers represent only a small portion of the global workforce.

In this episode, Steve Smith is joined by Sri Chellappa, CEO and Co-founder of Engagedly, to explore why frontline employees have largely been left out of the HR tech stack and what it takes to build systems that actually work for them.

They discuss the operational reality of mixed workforces, why performance management looks fundamentally different on factory floors and job sites, and how disconnected systems, spreadsheets, and paper processes still dominate frontline environments.

The conversation also touches on a broader shift happening beneath the surface of the AI boom. As automation accelerates across many desk-based roles, skilled trades and physical infrastructure work may become more economically and socially important than many people expect.

Technology will continue to reshape how organizations operate. But the next wave of work tech innovation may come from focusing on the majority of workers the industry has historically ignored.

According to LinkedIn, SEO is dead, answer engines have replaced Google, and the entire go-to-market playbook has supposedly flipped overnight.

In this episode, we’re joined by Helene Jelenc, Director of SEO at Flow Agency, to unpack what the data actually says about how software buyers are discovering brands in the AI era.

We explore how search behavior is fragmenting across surfaces like Google, YouTube, and ChatGPT, why AI traffic is still relatively small but highly valuable, and why the overlap between traditional rankings and AI citations is far lower than most marketers expect.

The takeaway is surprisingly familiar: fundamentals still matter. Brand authority, consistent messaging, and clear expertise are increasingly determining whether your company appears inside AI answers or disappears entirely.

Search may be evolving quickly. The brands that win will still be the ones that know exactly who they are and communicate it clearly everywhere they show up

AI is accelerating faster than most organizations can process.

What feels like progress on the surface also introduces a deeper leadership question: As tasks get automated and outputs get polished instantly, how do we protect judgment, development, and human connection?

In this episode, we’re joined by Dan Riley, Co-Founder of RADICL, to explore the responsibility that comes with building and deploying AI inside the workplace. If humans created these systems, humans still shape their direction.

We talk about AI as a copilot, the risk of over-reliance, the value of creative friction, and why the future of work depends less on the tools themselves and more on the intentionality behind how we use them.

Technology will keep advancing. The real differentiator will be leadership.

"The market just isn't ready for us." After 20 years advising HR tech founders, executives, and investors, Sarah White has heard that line more times than she can count — and she has zero patience for it.

In this episode of Work Tech Weekly, Sarah joins Steve Smith to break down what's really stalling growth, killing deals, and quietly holding HR tech teams back. They dig into why unclear positioning costs you customers and acquisitions, how a $100M company was leaving growth on the table because of internal misalignment, and where AI is reshaping the work tech stack faster than most people realize.

If you're a founder, executive, or investor wondering why you’re stuck in neutral, this conversation will give you a sharper lens on what's actually going wrong — and how to fix it.

In this episode:

• Why "the market isn't ready" is almost always a go-to-market problem

• How messaging mistakes kill acquisitions before a deal ever closes

• The internal alignment gap that's holding more companies back than they realize

• Where AI is quietly reshaping payroll, workforce management, and learning

AI didn’t just speed up work. It changed what matters at work. For decades, talent assessments measured traits, cognitive ability, and technical skills. That made sense when roles were stable and hard skills lasted longer than your iPhone upgrade cycle.

But now? AI handles more of the execution. Technical skills commoditize fast. And the real differentiator isn’t what you know — it’s how you think. In this episode of Work Tech Weekly, I sit down with Dustin Clinard, CEO of Ignis AI, to talk about what happens when the baseline shifts but our measurement tools don’t.

We dig into why traditional assessments may be misaligned with the AI era and whether human skills have quietly become the hardest skills of all. AI can now build a presentation deck in seconds — probably better than you can. But can it decide what story the deck should tell? “The part that [AI] can't do is think, ‘What do I want it to say and in what order?’ That's my judgment call to make,” Dustin says. “Once I decide that, the building-it part can happen really quickly.”

If AI is accelerating execution, judgment becomes the edge. The question is: can we measure it? If you’re rethinking hiring, development, or what performance actually looks like in an AI-first world, this one’s for you.

From an investment perspective, the Work Tech market in 2026 looks… oddly healthy. The charts are trending up, capital is flowing again, and 2025 clocked in at $6.24 billion in funding — the third-biggest year the sector has ever had. On paper, it’s momentum, if you’re brave enough to say it out loud. However, most founders’ lived reality feels so far from OK.

In this episode, we are talking with George LaRocque, the Founder and Chief Analyst at WorkTech (the research and advisory firm, not this podcast … yes, I know it’s confusing.) We break down his 2025 Work Tech investment recap because he has some of the best research and clearest views in the industry on what’s happening beneath the surface of Work Tech: where investors are placing bets, where buyers are actually spending, and which companies may not survive this cycle.

We dig into today’s somewhat paradoxical and often contradictory Work Tech investment dynamics. The disconnect is psychological and structural: Investors see stability, founders feel selectivity. Raising isn’t about having a cool AI story anymore — it’s about adoption, ROI, survival.

Why do hiring processes collapse under volume? Why has “perfect fit” made trust disappear from recruiting? Can AI be both a problem and a solution? Where the hell is Left Miami?

In this episode of the Work Tech Weekly podcast, Steve Smith talks with Claire McTaggart — founder and CEO of SquarePeg — about how modern hiring systems trained candidates to game the process, and why recruiters are now drowning in low-signal applicants.

Drawing on real-world fraud investigations and hands-on work with talent teams, Claire explains how keyword-driven screening flattened resumes into sameness, why asking HR to police fraud is unsustainable and how incentives created today’s mess.

They also discuss where AI has amplified the problem, where it can actually help, and what changes would be required for screening tools to reward authenticity, context and real capability again.

Why do global payroll implementations that were supposed to take 12 months still drag into year two?

In this episode of the Work Tech Weekly podcast, Steve Smith sits down with Jerome Gouvernel — CEO and co-founder of datascalehr — to unpack why global payroll projects keep breaking down, and why the industry keeps selling a model that can’t scale.

Jerome draws on 20 years of experience building global payroll systems to explain why traditional implementations are still waterfall, why promises of “full compliance” are economically fragile, and why most integration work forces teams to learn system languages they’ll never use again.

They also dig into the growing hype around enterprise AI — why compliance can’t be “vibe coded,” why auditability matters more than automation, and why you need a data strategy before you can have a real AI strategy.