Decode the AI Shift

AI in HR

Share Your Voice

Explore how artificial intelligence is reshaping HR — from smarter recruiting to predictive analytics, employee experience, and beyond. Share tools, strategies, and lessons learned as we navigate the AI shift together.
IGC
IGC
Bobber

AI saved time. Now what?

That might be the more interesting question coming out of our latest conversation on Making AI Work for People with Dr. Kenneth Matos. ⌛ Because automating a task doesn't automatically create a better job. AI can draft the response faster...but someone still has to check the exception. It can eliminate repetitive work...but leave employees with a day made up entirely of the hardest, most cognitively demanding tasks. It can create capacity...but if the immediate response is simply "great, now do more," did we actually improve work? The data suggests we're still figuring this out: 🔹 Only 26% of organizations surveyed say they're realizing more value from AI than the resources they're investing. 🔹 Just 15% of US HR executives say workflows are consistently being redesigned around the capabilities of people + AI. 🔹 Meanwhile, 37% name employee wellbeing and career insecurity as a priority AI workforce challenge. One idea from the session has really stuck with us: AI should make us as fast as humans can be. It shouldn't create pressure for humans to be as fast as AI. So let's keep the webinar conversation going here. Vote here, then tell us why in the comments. And if your organization has actually redesigned a role or workflow around AI, we'd especially love to hear what changed.
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Anna-Maija Rintamäki

Are we focusing too much on what AI can do for us, and not enough on what AI can help us build?

I've been thinking quite a bit about how we approach AI adoption in HR. There's a lot of encouragement to use AI, automate tasks and become more efficient. But I wonder if we're sometimes looking at it from the wrong direction. We tend to start with questions like "What can Copilot or Claude do for me?" rather than "What's not working in our processes, and how could we improve it?" This really hit me while working on a recent HR project. I ended up building six Power Automate flows and a Power App, with practically no previous experience of either tool. Without AI, I wouldn't even have attempted it. At first, I was just following AI-generated instructions without really understanding what I was doing. But as the project progressed, I started understanding the tools, troubleshooting problems and making more of the design decisions myself. Eventually, I wasn't just asking AI what to do next. I was telling it what I wanted to build and how I wanted to approach the problems we encountered. I'm certainly not a developer now, but I understand much more about what these tools can do. More importantly, the experience has changed how I think about process design. And that's what got me thinking. Just because AI can't perform an entire process for us doesn't mean it can't help us improve it. Maybe parts of the process can be eliminated. Maybe some steps can be automated using tools we already have. Maybe AI can help us learn to use those tools, even if we don't have the technical skills ourselves. In my project, much of the actual automation runs through Power Automate, not AI. AI helped me build it. I think there's an opportunity here that we don't talk about enough: using AI not only to improve productivity, but to expand what employees are capable of doing. Rather than just encouraging people to use AI more, perhaps we should encourage them to look at their everyday work, identify problems and explore what better ways of working could look like—with AI as a tool to help them get there. I'd love to hear how others are approaching this. Are you using AI to help you build solutions with other tools, rather than just using AI to perform tasks directly? And in the spirit of transparency: yes, I used AI to help turn my rather scattered thoughts into this post. 😄 The thoughts and opinions are mine, but AI is definitely better at getting them onto paper (or screen!) in a coherent order.
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Jaya Dubey
Jaya Dubey
Chapter Leader
·People & Culture Leader

Before we ask AI to find the right candidate, have we defined what “right” means?

Last week, while walking back from work, I caught up with someone from my network for a walk-and-talk. She has extensive Talent Acquisition experience across the Nordics and APAC, so I asked her: “Based on your experience, what defines a good hire?” Her answer- It depends on the seniority of the role. For senior-level positions, relevant experience and demonstrated skills tend to carry more weight, although personal attributes still matter. For entry- and mid-level roles, skills and education are important, but so are the qualities that help someone learn quickly, adapt to change, and collaborate with others. As an HR professional, I find this an interesting question, especially as AI becomes more involved in recruitment. How do we define the criteria for a good hire — and, if we use AI, do we know what it is prioritising? I'd love to hear from TA professionals, recruiters, and hiring managers about how you approach this in practice. And if you're in TA, recruitment, or HR and enjoy exchanging perspectives beyond the usual work conversations, let's connect for a walk-and-talk! 😊 #AIinRecruitment #AIinHR #FutureofWork
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Aysegul Bodur

Future of Work

Hi all, I am going to host a Fire Side chat for fundraising purposes. (please delete this message if it is not allowed here). If you are looking for listening discussion how AI may impact our way of working and future work force, please join us 🙂 You can find the link here : https://www.eventbrite.ca/e/2001080982085?aff=oddtdtcreator
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Megan Colombo
Megan Colombo
·Your IGC Community Manager👋🤝

How are you measuring whether AI is actually creating value at work?

In our conversation, Stop Chasing AI ROI: Start Measuring Business Impact, Dr. Kenneth Matos challenges a familiar trap: adoption, license counts, and even hours saved do not automatically equal business impact. A useful question is: what changed downstream? Did AI help your team reduce rework, move decisions faster, improve quality, free capacity for higher-value work, or unlock growth? And importantly, do you have a way to connect that change back to a real business outcome? For HR, this is not about using AI to cut people costs. It is about understanding where people, skills, and AI capacity can work together to create more value. We’d love to hear from you: what is the biggest barrier to proving AI’s impact in your organization? Sound off in the comments on your experience with today's webinar and any new learnings.
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IGC
IGC
Bobber

The Human-Centric AI Council Town Hall is September 9th!

🤖 What does human-centric AI actually look like in practice? We talk a lot in In Good Company about AI’s impact on work, but the people side of that transformation can’t be an afterthought. That’s why we’re excited to partner with Kyle & Co (Kyle Lagunas!) for an upcoming Human-Centric AI Council Town Hall on September 9. This is a chance for HR and People leaders to join the conversation around how organizations can embrace AI while keeping humans, culture, and responsible leadership at the center of the decisions being made. And we don’t want the conversation to end when the Town Hall does. We’ll be continuing it right here in In Good Company afterward, digging into the biggest themes, questions, and practical AI challenges that come out of the session. 🗓️ September 9 at 1pm ET 💻 Virtual Learn more + register → HERE Already planning to attend? Drop the AI-at-work question you most want this group to tackle in the comments. We may send a few of them over to the Town Hall 👇
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Ethics, Fairness & Governance

Share Your Voice

This space is where HR professionals wrestle with emerging AI questions: What's fair? What's legal? What policies actually work?
IGC
IGC
Bobber

The Double-Edged Sword of AI in Hiring

AI has transformed recruiting from a manual, paper-heavy process into something that looks and feels much more streamlined. Résumés can be scanned in seconds, candidates can be ranked based on predictive analytics, and interview scheduling can be automated so recruiters spend more time on high-value conversations. For organizations dealing with high application volumes, this is a game-changer. But speed comes at a cost. Candidates increasingly wonder if they are being fairly judged, or if their applications are dismissed by an algorithm before a human ever sees them. AI also tends to favor candidates who “look like” the data it’s been trained on, which raises real concerns about perpetuating bias. HR teams are now faced with a paradox: AI promises efficiency, but it also risks reducing candidates to data points. Forward-thinking companies are approaching this tension by layering human judgment on top of AI insights. For example, AI might recommend a shortlist, but recruiters still review every résumé to catch non-traditional candidates who could excel. Others are investing in tools with transparent criteria, so candidates can understand what’s being evaluated. Question for the group: If your team is using AI in recruiting, how do you ensure that the process is both efficient and fair—and that candidates still feel seen as people, not just profiles?
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IGC
IGC
Bobber

Beyond the Algorithm: Ethics in HR

AI is reshaping HR in ways we couldn’t imagine five years ago—flagging top performers as “flight risks,” surfacing hidden bias in recruiting, or raising new questions about privacy and policy. These aren’t just tech glitches; they’re ethical dilemmas with real impact on people and workplaces. This space is where HR leaders come together to ask the hard questions: What’s fair? What’s legal? What actually works in practice? Join the conversation to share experiences, challenge assumptions, and explore how to build more transparent and responsible workplaces. 👉 What’s the biggest ethical challenge you’ve faced (or worry you’ll face) as AI takes a bigger role in HR?
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Practical Tools & Applications

Share Your Voice

Every week there's a new AI tool promising to revolutionize HR, but which ones actually deliver? This space is where you get honest reviews from people who've moved beyond the free trials and live with these tools daily. No vendor demos, no affiliate links.
IGC
IGC
Bobber

AI saved time. Now what?

That might be the more interesting question coming out of our latest conversation on Making AI Work for People with Dr. Kenneth Matos. ⌛ Because automating a task doesn't automatically create a better job. AI can draft the response faster...but someone still has to check the exception. It can eliminate repetitive work...but leave employees with a day made up entirely of the hardest, most cognitively demanding tasks. It can create capacity...but if the immediate response is simply "great, now do more," did we actually improve work? The data suggests we're still figuring this out: 🔹 Only 26% of organizations surveyed say they're realizing more value from AI than the resources they're investing. 🔹 Just 15% of US HR executives say workflows are consistently being redesigned around the capabilities of people + AI. 🔹 Meanwhile, 37% name employee wellbeing and career insecurity as a priority AI workforce challenge. One idea from the session has really stuck with us: AI should make us as fast as humans can be. It shouldn't create pressure for humans to be as fast as AI. So let's keep the webinar conversation going here. Vote here, then tell us why in the comments. And if your organization has actually redesigned a role or workflow around AI, we'd especially love to hear what changed.
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Christine Song
Christine Song
Featured Contributor
·Your network truly is your net worth.

The New C-Suite Title Isn't Replacing HR. It's Racing It.

Every HR leader I talk to right now is asking some version of the same question: “Who actually owns AI at my company?” For a while the answer felt obvious. HR owns the workforce, AI touches the workforce, so HR owns AI. That assumption is already out of date (I know, AI is moving that fast). Here's what's actually happening. Two separate moves are underway, and most people are conflating them. Move one: A new seat gets created, and HR isn't automatically in it “Chief AI Officer” has become the fastest-growing title in the C-suite. IBM's 2026 CEO study found 76% of surveyed organizations now have one, up from 26% the year before. Named appointments include JPMorgan Chase, Walmart, Siemens, SAP, Pfizer, GE HealthCare, HSBC, Lloyds, UnitedHealth Group, American Express, Lowe's, Target, CVS Health, and Deloitte, where Joe Atkinson runs the function globally. The part that should get HR's attention: one of the CAIO's standard responsibility buckets is workforce readiness, preparing people to work alongside AI. That's a direct lift from HR's traditional territory. And the reporting lines confirm where the authority is landing: roughly 60% of CAIOs report to the CEO, 25% to the CTO, 15% to the COO. CHRO doesn't show up in that breakdown at all. Fortune's reporting on this puts it plainly: the CHRO seat at the table isn't disappearing, but the authority attached to it is migrating elsewhere, toward finance and operations, which speak the language of margins and workforce economics rather than culture and development. Move two: HR rebrands itself before someone else claims the mandate This is the more direct answer to what people are hearing about "new titles taking over HR." Jacqui Canney at ServiceNow became Chief People and AI Enablement Officer in January. Tracey Franklin at Moderna became Chief People and Digital Technology Officer, merging HR and technology under one seat. And a title has started circulating at HR conferences that hasn't fully landed yet: Chief Work Officer, floated by Databricks' CPO Amy Reichanadter among others, built on the premise that the job is no longer managing people, it's managing work, regardless of whether a human or an agent does it. And these aren't cosmetic changes, they're more pre-emptive. HR leaders who take this move are betting that expanding the title now is cheaper than losing the mandate later. The honest read Neither move has won. Industry advisors tracking this, including Randy Bean and Gartner's Jonathan Tabah, describe the CAIO role as still "transitional". It could become permanent and absorb workforce strategy for good. Or it could get folded back into the CTO, COO, or CHRO portfolio once the AI transformation matures and the novelty of a standalone title wears off. Right now, it’s too early to say, but the pieces are already moving on the chess board. If you're an HR leader, are you waiting to see how this resolves or are you attempting to get in front of it? Bigger question - do you truly want to own it? I’ve had confidential conversations with CPOs who have secretly confided in me that they honestly don’t want to own AI but they are happy to support it. What do you think?
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Kyle Lagunas
Kyle Lagunas
Featured Contributor

Newsflash: Your AI Rollout Isn't (only) Challenging Because of the Technology

In my research at Kyle & Co, I'm observing a trend in HR's AI transformation: Most organizations approach AI the same way they've approached every other software rollout. Find a use case. Procure a tool. Launch it. Hope it sticks. Then wonder why adoption stalls six months in. Here's the thing: It's not a technology problem—it's a people problem… And it always has been. That's the central argument behind our latest ebook AI Transformation and Change Management, a new resource from the Human-Centric AI Council (HCAIC) and lead author Alicia Miller (a seasoned transformation leader with a background in I/O psychology and AI enablement). The guide is practical, grounded, and refreshingly honest about why AI adoption is harder than most change playbooks account for. You can access it as part of our full transformation toolkit! AI Is Fundamentally Different. Your Change Management Strategy Needs to Be Too Traditional software came with a manual. You trained people on the steps, they produced consistent outputs, and you moved on. AI doesn't work like that. The outputs are probabilistic—they vary by user, by context, by how the question is asked, etc. The models keep learning and changing, which means yesterday's best practice can be tomorrow's outdated prompt. And then there's the "black box" problem... when employees can't explain why the system said what it said, trust erodes fast. That's before you get to the harder stuff—the fear that AI is coming for jobs, the blurring of what "my role" even means anymore, the sheer volume of models and tools competing for attention. The guide names these dynamics directly, because you can't design around a problem you haven't acknowledged. The COM Model: A Change Framework YSK (You Should Know) One of the most useful things in the guide is what Alicia calls the COM Model: Capability, Opportunity, and Motivation. It's a behavioral change lens applied to AI adoption, and it reframes where organizations typically get stuck. Capability is the one most teams focus on (training programs, role-based learning paths, L&D roadmaps, etc.). That work matters—but it's not enough on its own. Opportunity is about creating the actual space and conditions for people to learn and experiment. Even when employees want to engage with AI, they often don't have the time, the sandboxes, or the explicit permission to explore. The most experienced people (i.e. the ones whose judgment you most need) are also the busiest. Without protected experimentation time and real access to tools... adoption stagnates. Motivation is where the real complexity lives. People need to understand what's in it for them—not abstractly, but concretely. They need fears addressed directly, not managed around. And they need to see it working before they'll fully commit. Skeptics don't respond to vision statements. They respond to evidence. Surprise! Culture Is the Hidden Variable in Change Management The ebook spends meaningful time on something most rollouts underinvest in: Cultural readiness. AI-enabling cultures share specific traits: a tolerance for experimentation (including failed experiments), continuous learning norms, psychological safety, and genuine knowledge sharing. That last one is interesting. Because AI outputs vary based on how you work with them, the people who've figured out what works have an edge... and organizations need them to share it, not hoard it. If your culture punishes failure, people won't experiment. If it rewards individual performance over team contribution, knowledge won't flow. No training program fixes that. From Pilot to Scale: Where Most Organizations Actually Get Stuck One of my favorite parts of the ebook was the section on pilots. It covers something I’ve been thinking about a lot lately, and I think it’s particularly useful for HR teams being asked to prove ROI before getting broader investment (aka all of us). A good pilot tests more than technical feasibility. It surfaces adoption barriers. It identifies what roles will shift and what capabilities are missing. It gives you the change management intelligence you need to scale responsibly—not just the business case your CFO wants to see. The guide walks through how to structure POCs and pilots to actually evaluate business value... including approaches like randomized control trials and quasi-experimental designs. Suffice to say, this is not typical change management content. It reflects Alicia's background and the HCAIC's commitment to evidence-based practice. I’m really proud of it! A Resource Especially Relevant for the In Good Company Community I think it’s super cool that our friends at HiBob built this community for people-first leaders who believe that how you treat employees is how you build companies worth working for. It's validating for me and for our work with the HCAIC, as it’s exactly the lens this ebook brings to AI. The goal isn't to make your workforce "use AI." The goal is to help your organization evolve into new ways of working where AI is embedded, outcomes improve, and people remain at the center of how value is created. That's not a soft position. It's a strategic one. And it requires HR to lead change, not just support it. The good news is that you don’t have to go it alone—because you’re in good company!
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Kyle Lagunas
Kyle Lagunas
Featured Contributor

Reality Check: Why Human-Centric Change Is the Only Way AI Scales in HR

We’re already one month into the new year, and already I’m seeing renewed focus on AI in HR. I posted on my personal blog the trends I’m tracking for the year, but the biggest lesson we need to learn in 2026: Transformation requires more than cool tools. Follow me on a journey that’s a tale as old as time. HR launches an exciting AI pilot. Your early champions jump on as part of your push past go-live, inspiring an early ramp of adoption… But then things plateau after just a few weeks--until ultimately it fizzles out with only a fraction of the impact you anchored your business case to. The business declines to invest in Phase 2, pointing to a weak proof of concept, and moves onto more reliable outcomes—like cutting costs and offshoring services. If you’ve been there, you know it’s super disheartening--all of that work, all of those calories, all of that momentum… just swept under the rug. It’s the worst. It’s one of the biggest reasons we started the Human-Centric AI Council: We know that lasting, meaningful change is one of the pillars of human-centric AI in HR--and that it’s really hard to affect. So if you’ve been there and felt this (or hesitated because you’re afraid of it), then know this: This isn’t a failure of ambition or technology. It’s a failure of how we approach change. AI is a completely different kind of technology. It’s changing not just how fast work gets done, but how work gets done--and by whom. That’s why adoption feels harder than expected: It requires way more change management than a few training videos and an email campaign. In the HCAIC’s latest change management guide, led by Alicia Miller--Pillar Lead for Change Management--one message is clear: AI adoption succeeds or fails based on people, trust, learning, and judgment--not deployment checklists. We’re publishing the ebook later this month, but I wanted to give you a preview, featuring the practices that stood out most. If you’ve read my blogs here before, you know I get a little long-winded (imagine how precocious I was as an eight-year-old! Lol), so I’ll share some pull-quotes from the eBook first, then offer a deeper dive for my friends with longer attention spans--and/or more interest in the actual best practices we’re purporting in this resource. The Quick Quotes: AI Change Management in 100 Words or Less: AI Isn’t Broken—Your Expectations Are. AI is probabilistic, not predictable. Adoption depends on judgment, trust, and learning—not consistent outputs. Trust Is the Real Output of a Pilot. Pilots test credibility, surfacing human feedback that shapes adoption, risk tolerance, and real-world value. Experimentation Dies When It Competes With “Real Work”. Without protected time and safety, learning stalls and experimentation becomes optional. Scripts Don’t Survive Contact With AI. Static training fails. Frameworks and principles help people adapt as tools and outputs change. Momentum Spreads Sideways, Not Downward. Peer learning and cross-functional networks scale adoption faster than top-down mandates. Fear Is a Structural Constraint, Not a Communications Gap. Unchecked anxiety suppresses learning; psychological safety enables progress. The Real Tea: 6 Key Takeaways from the HCAIC’s Forthcoming AI Change Management Ebook 1) AI Isn’t Broken. Your Expectations Are. One of the most consistent adoption failures comes from treating AI like deterministic technology--stable, predictable, and repeatable. But AI is probabilistic by design. Variability isn’t a flaw; it’s the cost of intelligence. What differentiates organizations that move forward isn’t better tools--it’s comfort with judgment over certainty. Employees who are trained to evaluate, refine, and contextualize outputs build confidence. Employees who are promised consistency experience variance as failure. Our AI Momentum Model research reinforces this directly. Organizations stuck in “Exploring Possibilities” in AI for HR often mistake awareness for readiness, circling AI without developing the evaluative capability needed to move forward. Figure 1. Degrees of organizations use of AI in HR, 2025 Leaders, by contrast, exhibit higher AI literacy and are far more likely to describe themselves as confident practitioners who can enable others. Figure 11. AI and Technology Literacy Across the HR Organization 2) Trust Is the Real Output of a Pilot Many pilots fail not because the technology underperforms, but because they’re designed to answer the wrong question. Technical feasibility alone doesn’t create momentum. Credibility does. But what we're observing in our research (five years of trendspotting, btw) leaders who progress beyond pilots treat them as learning and trust-building mechanisms, not proof points. These organizations use pilots to surface adoption barriers, calibrate risk, and build coalitions across HR, IT, compliance, and the business. Figure 12. Changes in AI Strategy Over the Past Year The data is stark: Nearly half of HR teams remain stuck in exploration, but those that advance to integrated or AI-first models consistently outperform laggards on retention, quality of hire, and workforce agility. Momentum correlates directly with impact, and pilots are the inflection point where trust either compounds or collapses 3) Experimentation Dies When It Competes With “Real Work” Organizations often say they want experimentation, but leave workloads untouched, timelines unchanged, and failure quietly punished. Employees respond rationally… they deprioritize learning a new approach to the same old work. The Momentum Model uncovers why this approach matters: HR organizations that remain too conservative when piloting AI lose influence not because they lack ambition, but because exploration without a clear value proposition for end users signals optionality. The message people hear is, “You should try this” vs. “You can contribute to solution design before we deploy it.” Leaders who build momentum send a different signal: They protect time, normalize iteration, and make learning visible. Experimentation stops being extracurricular and becomes part of how work gets done. That cultural permission is a major separator between Leaders and Laggards. 4) Scripts Don’t Survive Contact With AI The static, step-by-step training we’ve used for decades was designed for stable, static tools. AI breaks that assumption almost immediately. Interfaces change. Models evolve. Outputs shift. Our AI Momentum research outlines why organizations stall when they rely on procedural training alone: Capability gaps--especially in literacy (shared above) and integration (shared below)--are among the strongest brakes on momentum. Figure 16. Integration of HR Systems and Data to Support AI Without shared mental models for how AI should be evaluated and applied, pilots remain isolated and fragile. Leaders invest instead in frameworks that facilitate action. How to define problems, evaluate output quality, manage risk, and escalate judgment calls--these capabilities are more than a wishlist for HR teams that are getting things done. This is why literacy emerges as the single strongest unlock in the model. When people understand how to think with AI, adoption scales even as the technology changes. 5) Momentum Spreads Sideways, Not Downward Some of the most valuable AI learning never comes from formal programs. It comes from peers solving real problems and sharing what worked—or what didn’t work (as expected or at all). One of the clearest paths to lasting change is also one of the cornerstones of corporate culture: Coalitions. Figure 13. Ownership of AI Strategy for HR and Its Functions In our research, Leaders don’t keep AI as “HR’s project.” They share ownership across HR, IT, compliance, and business leaders, creating shared accountability and faster diffusion of learning. Organizations that fail to build these coalitions remain siloed, even when budgets or tools are available. Those that succeed turn individual experimentation into organizational capability — the difference between pilot fatigue and sustainable momentum 6) Fear Is a Structural Constraint, Not a Communications Gap Job anxiety isn’t a side issue in AI adoption—it’s a central one. When people fear automation, they withhold effort, avoid experimentation, and hoard knowledge. We see this often: Organizations with low literacy and weak governance default to risk avoidance, scrutinizing AI use cases into oblivion. Fear fills the gap left by understanding. Figure 15. Presence of HR-specific governance to guide the utilization of AI in the organization Leaders, by contrast, treat governance as a confidence accelerator, not a brake. Clear guardrails reduce uncertainty and give teams permission to move. The data confirms that organizations with visible governance, higher literacy, and active risk postures move faster—not because they ignore risk, but because they manage it deliberately. Psychological safety becomes an enabler of momentum, not an abstract cultural aspiration. Reframing Change Management in AI for HR: Maintaining Momentum with Human-Centricity If there’s a throughline here, it’s this: When AI adoption stalls, it’s usually not because the idea was bad or the team wasn’t capable. It’s because we underestimated how much change we were actually asking people to absorb. So when pilots fizzle, I don’t see failure. I see organizations trying to force a new kind of work through old muscles. The teams that are getting traction aren’t louder or faster. They’re more intentional. They spend time upfront resetting expectations. They protect space for experimentation instead of squeezing it between meetings. They put guardrails in place not to slow things down, but so people feel safe enough to actually use the tools. And they talk openly about fear instead of pretending it’s a comms problem. Momentum doesn’t magically appear after rollout. It’s built (awkwardly, unevenly, over time, etc.) by the choices leaders make about people, not tools. That’s the real work in front of HR right now—and what we’re supporting at the HCAIC. Be on the lookout for the full ebook next month!
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Sofia Lim-Oliver
Sofia Lim-Oliver
Bobber
Featured Contributor
· I explore what it means to live and work with greater purpose, wholeness and humanity - so that together we can thrive at work and at life.

If you're sick of hearing "be more human"...

Every conference. Every webinar. Every conversation. Leadership in the AI age. Leading with AI. Leading Humans and AI. Of course, AI is the big challenge and opportunity of our time. But what's been driving me crazy is all the fluff: - Vague calls to “be more human” - Recycled leadership models with AI copy-pasted - Or just slapping AI on the title to fill seats. I'm a mother of a 4 year old. I don't have time for fluff. I can barely get dinner on the table. So I got fed up. And I went looking for what “leadership in the AI age” actually, practically means. Here’s what I landed on: AI doesn’t change what leadership is — it changes the conditions under which leadership happens: - Speed is higher - Certainty is lower - Leverage is asymmetric (small decisions can have huge impact) - Thinking quality matters more than experience “Be more human” looks lovely on a t-shirt but it's not the answer we need. My favorite litmus test for practicality is: What can we do differently in a team meeting on a Wednesday afternoon? 👓 1. Frame the question before jumping to answers Instead of opening with “Here’s the plan”, start with: “Here’s the question we’re actually trying to answer — and here’s why it matters right now.” 👀 Why? This shifts the meeting from who has the smartest answer --> are we solving the right problem? It also stops AI (and humans) from running ahead with polished answers to the wrong question. 📣 2. Say out loud what you know — and what you don’t “Here’s what we think we know.” “Here’s what we’re assuming.” “Here’s what’s still unclear.” 👀 Why? Doing this lowers false certainty, invites challenge without drama, and models that not-knowing isn’t weakness. It sounds simple but it's surprisingly rare - and powerful when it's said out loud. 🧫 3. Be explicit about what you’re betting on vs. what you’re testing “This is a reversible decision, so we’re going to move and learn.” “This one’s harder to undo, so here’s what we need to validate first.” 👀 Why? This helps teams understand when speed matters more than correctness, when AI outputs are inputs, not answers, and it makes human judgment visible — instead of hidden behind AI-based confidence or tools. 👉 If this was useful, like or share so it reaches other leaders who are also drowning in AI-flavoured fluff. I’ll keep sharing what I’m learning about leading in the AI age — hopefully without the hype!
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Jessica Zwaan
Jessica Zwaan
Featured Contributor
·Author of Built for People: People Ops as a Product

Knowledge will be outweighed by confident judgment...

Reading through Bob's HR trends predictions today and I couldn't help but comment on one I wanted to hear anyone's opinions on! https://www.hibob.com/guides/hr-trends-2026/ "Prediction 4: Knowledge will be outweighed by confident judgment" Two of the suggestions are: Invest in cognitive learning. Shift learning budgets toward building cognitive capabilities: critical thinking, judgment, problem-solving, and decision-making. Use problem-based learning. Incorporate real-world scenarios, cross-functional challenges, and reflection exercises that can strengthen people’s judgment and adaptability. And I'm wondering, what have y'all done (if anything) on workshopping and creating places for practical application of these skills? As someone who prides herself in "knowing a lot about all the little bits and pieces" I've been actively trying to build that skill into something I can tap deeper into using AI, but I'm yet to find a way to really communicate or share those skills at scale. Would love y'all's thoughts!
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Artificial intelligence is reshaping how HR teams work — from automating routine tasks to informing strategic people decisions. Conversations in this space reflect real-world experiences with AI tools, ethical challenges around fairness and governance, and practical lessons on using AI responsibly in talent, analytics, employee experience, and people operations.

Members share honest perspectives on what works, what doesn’t, and how AI impacts daily HR practices, including recruiting, performance management, and workforce planning. Whether you’re wrestling with policy questions or testing new automation tools, you’ll find grounded insights here.

Want to explore more about broader HR topics and peer insights? Head back to In Good Company to browse all discussions, learn about the community, or check the FAQs for curated HR answers.