The TechWolf Podcast

The AI bills are arriving, and some companies are already asking who should lose access. Meanwhile, the headlines say AI is wiping out entry-level jobs, and the labor data says otherwise.

Susan Youngblood, a former CHRO who rolled out AI across a 9,000-person customer service organization, separates what is happening from what is being reported. She also explains why HR leaders who wait to be invited into the AI conversation will be cleaning up its consequences instead.

Timestamps:

• 03:10; How progressive companies redesign entry-level roles for AI-fluent hires
• 04:32; The hiring data the media keeps getting wrong
• 05:56; What separates companies pulling ahead on AI from those stuck in pilots
• 07:51; Two questions that expose fake AI adoption
• 09:31; The token bill arrives: who keeps AI access, and who loses it
• 13:10; Every agent needs a named human owner
• 15:22; What rolling out AI to 9,000 people taught her about HR’s real job
• 19:17; When the CFO asks if a human is cheaper than an agent
• 23:30; Her prediction: token budgets in your comp package
• 25:02; AI whitewashing and the layoffs that weren’t about AI

Roughly a third of companies now talk about AI as a teammate or an employee, and between a fifth and a third say it has already carried over into the org chart. The argument for doing it has always been familiarity: give people a human reference point and they will use the thing.

Julie Bedard and her co-authors tested that with 1,200 to 1,300 managers in HR and finance, all reviewing the identical document, with only the stated author changed. Adoption intent did not move. Personal accountability dropped, escalations rose, and reviewers who thought an AI employee wrote the work caught fewer errors and passed them on.

Timestamps:

• 03:08 - BCG's 10/20/70, and the 70% nobody budgets for

• 05:44 - The experiment: one document, three different "authors"

• 08:59 - The finding that killed the familiarity argument

• 11:27 - "Our governance systems haven't caught up with agents"

• 17:25 - Reviewers caught fewer errors and escalated more

• 23:34 - Test track versus New York City at rush hour

• 27:56 - Token-based competition, and why usage is the wrong thing to reward

• 36:04 - The first 90 days for a CHRO or a chief AI officer

Every leadership team is now asking which work goes to agents. Almost nobody has answered the harder question sitting underneath it: who owns the answer.

The business owns the process, IT owns the deployment, talent owns the skills data, and total rewards owns the pay bands, which means any one of them acting alone produces a redesign that falls over within a quarter.

Matthew Morgan, Managing Director for Talent Reinvention at Accenture, has spent twenty-five years in job architecture and skills data, and he is watching four client organizations run at this problem right now. He came in with a sharper starting point than most: stop leading with skills, and start with tasks.

Timestamps:

• 01:05 - HR’s moment to lead, and why it is still playing catch-up

• 03:10 - What “talent reinvention” means, and how it differs from transformation

• 06:23 - Why tasks, not skills, are the language your business already speaks

• 09:22 - What has to be true before a skills technology works at your company

• 11:29 - Where to draw the line between redesigning work and just automating tasks

• 14:26 - The four-legged stool: who actually owns work redesign

• 16:36 - Agents in the org chart: what Matt has and has not seen in practice

• 19:58 - Cloudflare’s 20% layoff and the sellers, builders, measurers split

• 30:37 - The three questions to answer before you buy any AI tool

Most companies chase a skills strategy and end up with a portal nobody opens. Ian Bailie has built this twice, once at Cisco a decade ago, the hard way, and now at Synopsys, a 40-year-old company that just doubled in size and absorbed a $35 billion acquisition. His take is blunt: stop turning skills into an HR project. Lead with what AI is doing to the actual work, fix your job architecture first, and give people a reason to care before you ever ask them to touch a profile.

Timestamps

• 00:45 — The Kyle Forrest question: how to actually get HR, IT and finance in one room

• 02:30 — Why “we're HR, and we have the solution” is the wrong way in

• 06:00 — Doing this at Cisco 10 years ago, before the tech existed

• 07:50 — The Synopsys stakes: doubled size, a $35B acquisition, 40 years of legacy

• 11:30 — 3,000 people, one job title: when job architecture is too simple

• 15:00 — Why he refused to buy another skills portal

• 22:00 — One taxonomy, three platforms: Degreed, Avature, BetterWorks

• 27:40 — Automate, augment, human-only: reading the task data

• 31:00 — Giving people the agency over how AI changes their job

• 38:30 — HR's moment, and the risk of missing it

Every vendor at every conference is selling you an agent. Most of them are selling you a workflow with a chat window. Kyle Forrest runs Deloitte’s Future of HR practice and its Next Generation CHRO Academy, and he spends his weeks in the room with CHROs trying to separate the real from the rebranded. In this episode, he lays out the test for a genuine agent, why “make HR strategic” has been the same ask since the 1980s, and what it actually costs a leader to redesign a function that includes their own job.

Timestamps:

• 02:00 - The knowledge walking out the door as boomers retire

• 09:36 - Can HR’s “unique AI moment” and “nothing new here” both be true?

• 15:37 - The three types of HR function, and why “be strategic” is a lazy demand

• 24:00 - Stagility: 75% of workers want stability, 85% of execs want agility

• 28:09 - 42% of workers say no one is measuring AI’s impact on them

• 31:45 - AI assisted, augmented, powered: telling people where their role is headed

• 33:19 - Zero-based work: should this work even exist?

• 40:55 - The BS test for a real agent versus a workflow with a chat window

• 47:10 - The trend Kyle wants you to push back on: pre-built agents

Most skills programs never make it out of the spreadsheet. Cotality's CHRO spent nine months trying to hand-build a skills taxonomy before she killed it and started over.

In this episode, Kendra Angier explains how she rebuilt the workforce-intelligence stack around dynamic data, pulled her COO, CTO, and CFO into the buying decision, and earned an 80% employee NPS on skills along the way. It's a candid look at what actually moves a skills strategy from theory to Monday-morning reality.

Timestamps

• 00:50 - Why she rejects the idea that AI is killing early talent

• 02:43 - The Cotality rebrand, and what it demands from the workforce

• 06:09 - The case for a 12-24 month horizon, not a ten-year plan

• 08:36 - How she got the CFO, COO and CTO to fund skills

• 13:42 - The nine-month taxonomy that nearly broke the team

• 21:22 - “What took us months now takes minutes”

• 25:47 - Why the change-management risk beats the data risk

• 38:17 - From chief HR officer to chief work officer

Everyone wants a “skills-based organization.” Almost no one has built one. 

At AstraZeneca, Global Director of Talent Intelligence Lara Martinez Gonzalez has spent four years connecting a tangle of recruitment systems, development platforms, and job architecture into one language for the workforce, and she’s blunt about how hard it has been. 

In this episode, she argues skills is a data problem, not an HR project, explains why she refuses to say the word “skills” in front of business leaders, and shows where the value has actually landed so far. Plus the trap that cost her almost a year, and the company she’s seen get closest to skills-native.

Timestamps:

• 00:57 — The one skills claim leaders should push back on

• 02:05 — Four years of skills work, told as an Odyssey

• 05:46 — How to pick which problem to solve first

• 10:48 — Why she drops the word “skills” in business meetings

• 16:30 — Reading the market: from skills to tasks

• 20:19 — Who should own tasks:  HR or the business?

• 21:54 — The honest ROI: “still squeezing the lemon”

• 31:32 — Her 90-day advice to peers two years in

Most strategic workforce plans never make it past the CHRO's desk. The horizon is five years, the work is detailed, and the business leader you need in the room would rather talk about anything else.

Felipe Hessel runs Workforce Intelligence at Sandoz, and he's worked out how to flip that dynamic, getting executives to pull SWP towards them rather than dodge it.

In this conversation, he walks through his six-step framework, the contracting move that earns him the right to ask about critical roles, and the agents he built that cut his own SWP cycle time by 60%.

Timestamps

• 01:11 - Why HR's role in AI transformation isn't what most people think

• 04:30 - The three-legged stool of transformation is missing a leg

• 07:33 - The four legs of Workforce Intelligence: analytics, SWP, skills, AI

• 11:02 - The 90-day playbook for a new Workforce Intelligence role

• 12:57 - How to flip a sceptical CEO from push to pull on a 5-year workforce plan

• 18:11 - The six-step SWP framework, from contracting to course correction

• 23:51 - Why investing in workforce tech without integration is a trap

• 28:45 - The AI claim every business leader should push back on

• 32:46 - 2030 prediction: HR as a product organization

When your sales team closes deals 14% faster, the "skills gap" stops being an HR problem and starts being a revenue solution.

In this Special Episode, TechWolf CEO Andreas joins Josh from Workday to discuss the messy, high-stakes reality of transforming a 20,000-person organization. They move past the buzzwords to discuss why 75% of skills profiles usually fail and how Workday used AI to achieve a 91% skill acceptance rate from their employees.

Timestamps:

00:00 - Highlights: why 75% of skills data is usually "dead" on arrival.

01:29 - Fast & Thoughtful: Defining the Workday x TechWolf partnership.

02:20 - The "Less than 20%" Problem: Why most job profiles are missing the point.

04:38 - 3 Years of Mistakes: What I’d tell my past self about data strategy.

06:08 - The AI Pivot: Moving from "Internal Mobility" to "Workforce Planning."

08:18 - Precision Hiring: Identifying the "Critical 12" skills for every role.

08:50 - The 14% ROI: How skills-based hiring directly impacts sales velocity.

09:44 - Scaling to 20,000: The rollout of the Skills Suggestion App.

10:42 - 91% Acceptance: How to actually get employees to own their skills data.

The world is bracing for mass unemployment, but McKinsey Global Institute researchers Sven Smit and Anu Madgavkar suggest we might actually face a labor shortage.

As agents and robots move from theory to the office floor, the real friction isn't just the tech; it's our inability to learn as fast as the machines. This episode challenges the "soft skills" myth and explains why your AI strategy will fail if it lives in the IT budget.

Timestamps

• 07:05 – Why 80% automation might still lead to a labor shortage.

• 11:50 – The "Happy AI" trap: Why adding tools isn't the same as productivity.

• 19:20 – The blurring lines between knowledge workers and physical labor.

• 22:57 – Why "Soft Skills" are a dangerous trap for the next generation.

• 36:53 – Tasks vs. Workflows: The secret to 80% productivity gains.

• 49:08 – The Budget Error: Why AI belongs in the business P&L, not IT.