Future Of HR Analytics

The Learning Needs Analysis (LNA) is an established method of determining and prioritising what people need to learn, which informs the programmes, content and platforms L&D invests in. But here's the problem: We’re not in the business of collecting learning wishlists. We’re here to move the needle on performance. The traditional LNA often leads to vague inputs (“we need help with communication”) that get turned into standardised training or content. Context gets stripped away, relevance disappears, and impact becomes immeasurable. L&D’s role is not to make learning available - it’s to help people do their jobs better, adapt faster, and grow in ways that support the business. I’m afraid AI has the ‘make learning available’ role now. So what should we do instead? 3 things: 1) Start with business goals, not learning goals. - What is the organisation trying to achieve?  - What’s getting in the way?  - Where are the skills gaps or performance bottlenecks? 2) Build a prioritised pipeline Borrowing from Agile, create a dynamic backlog of real business problems - ranked by urgency, risk, and potential upside. This gives you a clear, evolving view of where L&D can make the biggest difference. 3) Introduce an open, structured intake Let stakeholders flag their challenges - but ask the right questions. What’s the performance challenge? What’s the cost of inaction? What outcome are they aiming for? This brings clarity and keeps everyone focused on impact, not activity. This approach does more than improve outcomes. It reshapes how L&D is seen - from content provider to performance partner. If we focus on solving real problems, we’ll have evidence of our impact. If we have evidence of our impact, we’ll stop being the department of training requests - and start being the team that’s relied upon to drive change. By doing what we’ve always done we’ll continue to prove only limited impact. But by being aligned, planning for impact and prioritising based on measurable value, we can do the work that truly matters - and prove that it’s worked. If you want to plan for impact rather than just learning, then my next L&D Office Hours is for you… Sign up for this month's session: https://lnkd.in/e6mdNQeg

One of the activities I've invested considerable time in over the past 6 or so years is Learning Impact Audits. It's an opportunity for organisations to reflect on their learning culture, assess where they are now, where they want to go, and steps to get there, with the help of an external, unbiased view and evidence-based tools. Organisations want an audit for different reasons ranging from 'We want to improve our elearning' to 'We want to change the way we make decisions about learning and performance' to 'We want to align learning strategy with business strategy'. Pinning down the outcome at the start is vital because when it's clear and agreed it's easy to assess the impact of the audit and the applied recommendations later. The process of the audit is similar but the final impact can turn out to be very different depending on who is involved and what's at stake. The experience of involving stakeholders is a strong datapoint in itself. My experience is that when senior people are willing to participate in the audit there's more buy-in later to make relevant and observable change (this isn't rocket science). What is formally reviewed varies including strategies, designs, communications, session plans, resources, evaluations, impact surveys, live sessions, processes, technology etc. But the 'spaces between' provide valuable insights too - the conversations I have with people reveal unrecorded habits, practices and assumptions providing opportunities to dig deeper, challenge and praise. I use the DNA of a learning culture as the entry point for scoping an audit and then apply other evidence-based tools to dive deeper and finally make practical, relevant recommendations for change. I feel an emotional challenge when I recommend that people change what they've been doing because most people are already doing the best they know how, so that's balanced with the evidence about what is working. It's vital to asssess learning culture from where it currently is and not impose an impossible ivory tower solution. It's a joy when people come back to me after they've taken on my recommendations to tell me that: 😀 engagement is up 🏆 they feel more confident and credible 👍 management buy-in has improved 💲 they got more budget 📈 learning is more aligned with the business ⚙️ decision making is easier 🔎 they're better able to measure their own impact. But just a reminder - just like learning - there is no magic wand 🪄 ! After an audit L&D teams need to put in the work to make changes and that takes time, commitment, effort and motivation. 'If you want the rainbow - you gotta put up with the rain' Dolly Parton 🌈

📈 Unlocking the True Impact of L&D: Beyond Engagement Metrics 🚀 I am honored to once again be asked by the LinkedIn Talent Blog to weigh in on this important question. To truly measure the impact of learning and development (L&D), we need to go beyond traditional engagement metrics and look at tangible business outcomes. 🌟 Internal Mobility: Track how many employees advance to new roles or get promoted after participating in L&D programs. This shows that our initiatives are effectively preparing talent for future leadership. 📚 Upskilling in Action: Evaluate performance reviews, project outcomes, and the speed at which employees integrate their new knowledge into their work. Practical application is a strong indicator of training’s effectiveness. 🔄 Retention Rates: Compare retention between employees who engage in L&D and those who don’t. A higher retention rate among L&D participants suggests our programs are enhancing job satisfaction and loyalty. 💼 Business Performance: Link L&D to specific business performance indicators like sales growth, customer satisfaction, and innovation rates. Demonstrating a connection between employee development and these outcomes shows the direct value L&D brings to the organization. By focusing on these metrics, we can provide a comprehensive view of how L&D drives business success beyond just engagement. 🌟 🔗 Link to the blog along with insights from other incredible L&D thought leaders (list of thought leaders below): https://lnkd.in/efne_USa What other innovative ways have you found effective in measuring the impact of L&D in your organization? Share your thoughts below! 👇 Laura Hilgers Naphtali Bryant, M.A. Lori Niles-Hofmann Terri Horton, EdD, MBA, MA, SHRM-CP, PHR Christopher Lind

A few weeks ago, the tech world was buzzing about Zapier's AI fluency matrix. It’s a commendable effort to define AI literacy, but one recommendation stood out to me as particularly dangerous: Under "PEOPLE / HR" for an "Adoptive" skill, it lists: "runs LLM resume screen with bias checks yielding 3x faster shortlist." This sounds efficient, but it promotes a high-risk practice based on a flawed understanding of how these tools actually perform. It mistakes the ability to use a tool for the critical skill of understanding its limitations. My "LLM Reality Check" report provides the data to show why this is so problematic: 🤔 A "3x faster shortlist" of what? My research found leading LLMs agree on just 14% of candidates. A "faster" shortlist is meaningless if it's a different, inconsistent list every time you run it. 🤔 Is the shortlist even complete? We found that LLMs ignored 55% of the talent pool, taking algorithmic shortcuts to meet a quota. You're not getting a faster shortlist of all candidates; you're getting a fast list of some candidates. 🤔 What does "with bias checks" mean? My experiment showed 96% of AI justifications were recycled boilerplate. A superficial "bias check" from a system that doesn't demonstrate deep reasoning is ethics washing, not a robust safeguard. The real "Adoptive" or "Transformative" skill in HR isn't simply running an LLM screener. It's knowing how to critically evaluate it. It's asking the hard questions about reliability, fairness, and transparency before deployment. We need to shift the conversation from "Can we do this?" to "How can we prove this is stable, fair, and compliant?" For anyone building AI literacy frameworks or evaluating vendors, I urge you to look beyond the hype. The data shows we must prioritise governance over speed. ➡️ Check out www.genassess.com for true AI literacy frameworks and assessments. ➡️ Read the full data in my "LLM Reality Check" report: https://lnkd.in/eD3XUkA3 ➡️ And use this to ask the right questions: https://lnkd.in/ejgNgvtP #AIinHR #HRTech #ResponsibleAI #AIethics #LLM #TalentAcquisition #FutureOfWork #Leadership #EunomiaHR #LLMRealityCheck

As AI transforms the workplace, HR leaders are at the forefront of ensuring ethical implementation and human-centric practices. Here are critical areas we must address: a) Inclusion and Collaboration: Implement clear guidelines to ensure AI complements human roles rather than replacing them. Could you create a collaborative environment where humans and AI work synergistically? b) Bias Mitigation: Establish robust safeguards against algorithmic bias. This includes thoroughly vetting AI vendors and ensuring transparency in AI decision-making processes. c) Upskilling and Adaptation: We need to develop comprehensive training programs that empower employees to work effectively alongside AI. Let's promote a culture of continuous learning and technological adaptability. d) Ethical AI Use: Form an AI ethics committee to guide responsible AI adoption and usage across the organization. Develop and enforce clear ethical AI policies. e) Data Privacy and Security: Implement stringent data protection measures to safeguard employee information while leveraging AI benefits. Regular audits and updates to privacy policies are crucial. f) Performance Management Evolution: Rethink evaluation metrics and processes in AI-augmented workplaces to ensure fairness and accountability. g) Diversity and Inclusion: Harness AI to enhance diversity initiatives while implementing checks to prevent algorithmic discrimination. HR professionals have a unique opportunity to shape the future of work. One must proactively develop strategies that maximize AI's potential while prioritizing our workforce's well-being and growth. I'm eager to hear your thoughts: a) What challenges and innovative solutions are you encountering in your organizations regarding AI integration? b) How are you balancing technological advancement with maintaining a human-centric workplace? #FutureOfWork #AIEthics #HRTech #DigitalTransformation #EmployeeExperience #DigitalAgents #AIAgents #DigitalOrganization

As AI tools advance rapidly, it's important for employers to understand where the ethical and legal boundaries lie. The EU AI Act has taken a firm stance: AI systems that infer personality or emotions from biometric data — including face-based personality prediction — are prohibited or classified as high-risk. The legislation recognises the profound risks these tools pose to fairness, discrimination, privacy, and human dignity. In Australia, no equivalent protections currently exist. This means technologies that would be unlawful in Europe could still enter the Australian recruitment market — without the guardrails needed to prevent discrimination or algorithmic bias. As employers explore AI for hiring, screening, or talent management, now is the time to stay alert: —Be cautious of AI tools claiming to “predict personality” or “assess fit” from images or videos. —Demand transparency, validation evidence and bias testing from vendors. —Ensure any AI used in HR aligns with ethical standards — even if legislation lags behind. Until stronger regulation arrives in Australia, the responsibility rests with employers to safeguard their people and their processes from high-risk AI. Join the growing community of multidisciplinary leaders for inclusive and ethical AI at ada.ai.

AI in HR without human oversight isn't innovation - it's irresponsible! I truly believe the human element in AI for HR isn't optional. It's essential and the question isn't just "Can AI do this HR task?" but "SHOULD AI do this HR task, and then, HOW should it be implemented?" - This distinction matters enormously. As HR professionals, we're the guardians of both compliance and human connection in our organisations. When we implement AI, we need to approach it with intentionality and purpose. Here's what I believe ethical, human-first AI in HR looks like: ✅ Enhancing human judgment, not replacing it - AI should handle repetitive tasks while freeing us as HR professionals to apply our emotional intelligence where it matters most ✅ Transparency by default - People deserve to know when AI is being used in decisions that affect them ✅ Privacy as non-negotiable - Employee data requires the highest levels of protection and intentional governance ✅ Reducing bias, not amplifying it -AI systems must be regularly audited to ensure they don't perpetuate existing inequities ✅ Compliance built-in from day one - Not bolted on as an afterthought At humaneer, this approach isn't just a nice-to-have - it's fundamental to everything we build. We believe HR tech should make our  jobs easier while upholding our values, not forcing compromises on either. The most powerful HR tech isn't about removing humans from the equation - it's about augmenting human capability while preserving what makes us uniquely valuable: our empathy, ethical judgment, and ability to navigate complexity. I'd love to hear your thoughts -  what ethical considerations are top of mind for you when evaluating AI in your HR function? And do you agree, is AI in HR without human oversight irresponsible? #HRtech #EthicalAI #FutureOfWork #AIinHR Want to join the conversation, check out our global community - https://lnkd.in/gpsUnm3U

People often ask me what it takes for L&D to create real impact. I think it comes down to six things. The first is 𝗜𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲. L&D needs to be in the room before the solution has been decided. Not just asked to build a workshop after the problem has already been diagnosed. The second is 𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴. That doesn't always mean proving everything in pounds and percentages. Or "People loved the workshop." But it does mean being clear on what should change. Faster onboarding. Fewer escalations. More coaching by managers. Safer decision-making. The third is 𝗣𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽. Learning needs managers, leaders, subject matter experts and learners to help shape it, embed it and make it relevant. The fourth is 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁. A leadership programme should not sit separately from retention, culture or performance priorities. Sales training should not ignore what sales leaders are actually measured on. The fifth is 𝗖𝗼𝗮𝗰𝗵𝗶𝗻𝗴. Because the real work often happens after the session. In the one-to-one. In the team meeting. In the moment a manager chooses whether to revert to old habits or try a better approach. The sixth is 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻. Not a course completed but: Behaviour changed. A system improved. A team working with more confidence, clarity or care. None of this is complicated. But it is often missed. L&D is still too often treated as "the team that creates training". The greater opportunity is to shape the conditions where learning becomes visible, useful and lasting. That's where real IMPACT happens. ____________ PCC Executive Coach & Strategic L&D Consultant. Equipping department leaders in pharma and healthcare to move from siloed brilliance to strategic influence. We treat leadership as a diagnostic science, not a soft skill. Specialising in radical self-leadership, idea advocacy, and diagnostic-led team performance.

The ROI Mirage: L&D Is Yet To Discover Complex Systems Every few months, at conferences or right here on LinkedIn, you’ll hear the familiar questions: “How do we prove ROI?” “How do we show impact?” “How do we measure learning?” After decades of models, from Kirkpatrick to Phillips to now, dashboards powered by AI, the field still can’t agree on what learning impact actually means or how to measure it, beyond L1 & 2. Here’s the uncomfortable truth: traditional ROI thinking fundamentally misunderstands how learning works. L&D believes it operates in the systems domain, but it hasn’t yet discovered complex systems. Having spent over a decade in L&D, I can tell you that most ROI frameworks are built on a Newtonian fantasy of linear cause and effect: Input → Activity → Output → Outcome → Impact. For the past 50+ years, we’ve assumed that learning happens in stable environments, where interventions can be isolated, controlled, and replicated. But organizations and cohorts of learners don’t behave like machines. They’re complex adaptive systems, where outcomes are emergent, not engineered. People learn in context, not in isolation. The same intervention can produce entirely different results depending on timing, culture, and networks of relationships. L&D talks about this all the time, but it rarely acts with that complexity in mind. Trying to measure “learning impact” with control-based metrics is like using a thermometer to measure wind speed. You’ll get a number, but not insight. So what’s the alternative to “What’s the ROI?” A better question is: “What’s the pattern of change this learning is part of?” That shifts things: From Measurement for justification → To Measurement for learning From Linear metrics → To Observation of evolving patterns From Attribution → To Contribution Here is an example: Old linear approach: Tracking “time to proficiency” for new hires, expecting everyone to hit benchmarks at 30, 60, or 90 days regardless of context. Complexity-informed approach: Noticing that new hires who ask more specific questions early, ramp up faster, or that remote-heavy teams struggle with unwritten norms. You start looking across cohorts for patterns: Who’s thriving? Who’s stuck? What conditions are present? The metric becomes, “What’s different about the environments where people succeed, or fail?” Complexity thinking doesn’t make learning unmeasurable, rather it makes measurement meaningful. It helps us look for signals of capability, coherence, and adaptation over time, not tidy numbers that fillup your spreadsheets. L&D doesn’t need to prove impact. It needs to sense it. The 4 Levels of Evaluation were built for a simpler, more stable world. Ours isn’t that world anymore. Until we stop mistaking data for insight, we’ll keep measuring what’s easy, instead of what really matters.

🧠 When was the last time your L&D function had a real audit? Not a survey. Not an LMS usage report. A real audit. One that asks: Is learning aligned to business strategy? Are we applying actual learning science? Does our tech stack enable or obstruct growth? Can we prove impact beyond completion rates? Most companies say they support employee development. Fewer invest in making it work at scale. 📉 According to LinkedIn Learning’s 2024 Workplace Report, only 15% of L&D pros say their programs are fully aligned with business goals. 📉 And even fewer — just 8% — say they consistently measure learning impact beyond participation. So what are we really doing? If learning is meant to drive performance, engagement, and retention, it deserves the same rigor we apply to marketing funnels or product roadmaps. That’s why I created a simple L&D Maturity Audit Checklist for HR and learning leaders to rate their operations and find opportunities to level up. It covers four domains: ✔️ Strategic Alignment ✔️ Program Design (yes, based on actual learning theory) ✔️ Tech Stack & Integration ✔️ Measurement & ROI 💡 Use it with your team, your leadership, or quietly in a corner with a cup of coffee and a look of concern. Either way, it will give you clarity and maybe even a few next steps. 📩 Want the PDF? Drop "audit" in the comments or DM me. It’s time to treat learning like the strategic engine it was always meant to be. #LearningAndDevelopment #WorkplaceLearning #LDCulture #PeopleDevelopment #TalentStrategy #HRLeadership #OrganizationalDevelopment #FutureOfWork