Workforce Planning in Automated Environments

Workforce planning has always been an incredibly complex and difficult task. Despite valiant efforts to improve these models, they have remained relatively static and simplistic, relying predominantly on small teams crunching data or on predictions from the hiring manager community. In an ideal world, we would shift from a static, once-a-year exercise to a dynamic, more proactive model. We would stop reacting to what's happening now and start anticipating what's likely to happen next. Last week, I had the pleasure of spending time with our enterprise data and analytics team, a group that services over 800 customers. The most exciting topic we discussed was three pilots we're running with customers right now that aim to make this a reality: using a digital twin for work planning. It works by connecting vast amounts of external market data with a company's many internal data sources, some they typically wouldn't consider, such as ERP, CRM (sales), LMS, and Time and Attendance systems. This allows us to run scenarios and model future talent needs. Here’s a concrete example: By analyzing Salesforce, HRIS, and ATS data, we can predict that when multiple prospect opportunities reach a specific stage in our customer’s sales cycle, there is a high likelihood of winning at least one of them. We can then analyze the consistent skill sets across all of those prospect opportunities, allowing us to confidently and proactively start a recruitment process for those skills. The goal being that we have candidates at the final stages of the process, before an official requisition has been raised, positively impacting time to hire. We’ve also been able to replicate a similar model based on website sales activity. The question to ask is: what data is generated in what system that allows you to get ahead of the hiring process today. 

Today's thorny topic... "HR Forecasting in the age of AI: Are we just guessing now?" This week, I’ve been elbows-deep in spreadsheets and strategy, partnering with Finance and senior leadership to re-forecast headcount for the rest of 2025. ✅ What did we say we needed at the start of the year? ✅ What’s changed (business priorities, org design, reality)? ✅ Where are we headed next? Sounds like standard workforce planning, right? But it got me thinking, and here’s the thing: How are any of us supposed to forecast for 2026 and beyond… when the ground is shifting this fast under our feet? AI and agentic tools are not “coming soon.” They’re here. They’re being piloted, deployed, and in some cases, replacing entry-level roles right now. We're all reading the same headlines, right? 💥 CEOs are telling their people leaders: “Only backfill if AI can’t do the job.” 💥 Entry-level job postings are down in many industries. 💥 Junior talent is being leapfrogged by automation before they even get a foot in the door. So… tell me again how our existing workforce planning models are supposed to keep up? We’ve built these frameworks assuming: - A reasonably predictable rate of growth - A stable-ish org design - Headcount as the primary unit of work That was pre-AI. Now we’re entering a world where: - Work is modular - Tasks can be automated before roles are redefined - Headcount ≠ capacity - Human potential is multiplied, or sidelined, by how we implement tech Here’s the thorny question we should all be asking: Are we forecasting humans, or outcomes? Because if we keep plugging headcount into spreadsheets like nothing has changed, we’re not planning—we’re pretending. Of course, I asked ChatGPT what might a better approach look like? Here's what it suggested (emoji's and all!): 🔄 Task-based forecasting: Break roles into task clusters and assess what’s automatable, augmentable, and still uniquely human. 🧠 AI fluency audits: How ready is your org to use AI? Not just tools—but mindsets, trust, literacy. 👥 Agile capacity planning: Plan for capability not just bodies. How do you scale outcomes, not just teams? 🎯 Redefine productivity: Move beyond hours worked and roles filled. What did we achieve, and who (or what) helped us do it? Not sure my finance team are going to love some of those suggestions - they're definitely not as easy to plug into the traditional forecasting model!! But, we do have to evolve workforce planning. So here’s a question to my network (especially HR and Finance friends!): * Can your headcount forecast survive a conversation about AI without falling apart? Let me know your thoughts! #WorkforcePlanning #FutureOfWork #AIinHR #StrategicHR #HumanResources #HeadcountForecast

🔧 WFM Doesn’t Need an Update. It Needs a Redesign. After two decades in Workforce Management, across continents and client verticals, I’ve tested countless ways of structuring WFM organizations and optimizing processes. Here’s my conclusion: WFM today is full of inefficiencies, scattered systems, and siloed responsibilities. We don’t need another tool, rather than this we need a systemic redesign. Here’s my vision for the future of WFM: * One Integrated Ecosystem No more disconnected tools. The platform must connect forecasting, capacity planning, scheduling, RTA, and dashboards — in one flow. It should detect changes, trigger alerts, and suggest intelligent fixes. *Role-Based Navigation Each actor (Ops, HR, WFM, IT, agents) should only see what’s relevant, in their language, at their level. The system adapts to the user — not the other way around. *AI-Driven Operations AI should generate scenarios, schedules, risk projections. Humans should decide — challenge assumptions, manage trade-offs (quality vs profitability), and fine-tune plans based on experience and judgment. *Agent-Centered Scheduling Agents submit preferences and constraints. The system auto-generates optimized schedules, taking into account forecasts, learning curves, and shrinkage probability. Schedulers then review and approve. *Mobile by Default Every agent and team leader should interact with the WFM system through a dedicated mobile app. It’s not a luxury, it’s the baseline for agility. *Embedded Ticketing System Every request (staffing change, hiring, exceptions) is tracked via integrated ticketing. This ensures traceability, accountability, and root cause insights. *Clarity Through Simplicity Only 2 to 5 KPIs should be tracked daily. No more dashboard clutter. Focus on what truly drives performance. *Governance with Teeth A clear RACI matrix must be defined — who is responsible for what, why, and to what end. Every WFM role should be redefined to answer a simple question: " What is the purpose of this role, and what do we expect it to achieve?" This is not just a wish list, it’s a blueprint. One that can save millions, unlock capacity, and shift WFM from reactive to strategic. ??What do you think?? What resonates, and what needs to be challenged? Let’s open the conversation. The future of WFM won’t build itself. #ZedWFM #WorkforceManagement #FutureOfWFM #WFMDesign #OpsLeadership #WFMTransformation #WFMBlueprint

𝟲𝟱% 𝗼𝗳 𝗖𝗘𝗢𝘀 𝘄𝗶𝗹𝗹 𝘂𝘀𝗲 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗳𝗶𝗹𝗹 𝘀𝗸𝗶𝗹𝗹 𝗴𝗮𝗽𝘀. 𝗔𝗿𝗲 𝘆𝗼𝘂 𝗿𝗲𝗮𝗱𝘆? Traditional hiring is broken. With 31% of the workforce needing retraining in the next 3 years, you can't solve tomorrow's skills crisis with yesterday's hiring playbook. 𝗧𝗵𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻? 𝗧𝗵𝗲 𝟰𝗕 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: 𝗕𝘂𝗶𝗹𝗱, 𝗕𝘂𝘆, 𝗕𝗼𝘁, 𝗕𝗼𝗿𝗿𝗼𝘄. 𝗧𝗵𝗲 𝟰𝗕 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗕𝗨𝗜𝗟𝗗 🏗️ - Upskill existing talent 𝗪𝗵𝗲𝗻: Skills learnable in 6-12 months 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Core competencies, retention goals 𝗦𝘁𝗮𝘁: 47% of leaders prioritize upskilling as top workforce strategy 𝗕𝗨𝗬 💰 - Hire permanent talent 𝗪𝗵𝗲𝗻: Critical skills needed immediately 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Leadership, specialized expertise 𝗪𝗮𝗿𝗻𝗶𝗻𝗴: Skills change fast - hire for adaptability 𝗕𝗢𝗧 🤖 - Deploy AI and automation 𝗪𝗵𝗲𝗻: Repetitive tasks, 24/7 needs 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Customer service, data analysis 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: AI agents work autonomously AND with humans 𝗕𝗢𝗥𝗥𝗢𝗪 🤝 - Contractors/freelancers 𝗪𝗵𝗲𝗻: Short-term projects, flexibility needed 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Testing capabilities, cost management 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲: Rapid scaling up or down The smartest companies: • Combine all 4 strategies (don't pick just one) • Track capability delivery, not headcount • Start with high performers for BUILD initiatives If you are the CHRO, what is your workforce strategy? Share your thoughts in the comment. Follow Adeline Tiah for content on Future of Work and Leadership Check out my latest newsletter on the Future of Work.

Billions spent. Top talent poached. And suddenly… hiring frozen. Here’s the story behind the freeze… Meta didn’t just pause on a whim. Lavish AI spending turned from power move to investor red flag. Now the freeze isn’t just about slowing down, it’s about regaining control. Meta split its AI empire into four groups,  and any new hire or transfer now needs sign-off from its Chief AI officer. A sharp turn for a company that was leading the AI talent war just weeks ago. Here’s how leaders should rethink AI hiring strategy 👇 ✅ AI Workforce Planning Checklist for Leaders: ☐ 1. Balance speed with sustainability ↳ Scaling AI teams too fast inflates costs w/out clear ROI ↳ Workforce planning keeps hiring aligned with demand ☐ 2. Retain and grow your AI-capable workforce ↳ Recruiting is wasted if internal skills go undeveloped ↳ Upskilling & career pathways reduce dependence on external hires ☐ 3. Forecast future AI skill demand ↳ Identify the emerging skills your business will need to compete ↳ Use workforce analytics to map roles to long-term strategy ☐ 4. Anticipate AI talent supply gaps ↳ Recognize that external talent pools will remain tight for years ↳ Build internal pipelines to reduce reliance on scarce hires ☐ 5. Strengthen governance in AI hiring ↳ Approvals ensure scarce resources go to priority roles ↳ Guardrails protect against reactive, hype-driven hiring ☐ 6. Build workforce flexibility ↳ Hiring freezes are signals to shift into reskilling and redeployment ↳ Scenario planning helps balance external hires with internal growth ☐ 7. Communicate your AI workforce strategy ↳ Show teams how hiring, reskilling, and redeployment fit together ↳ Transparency builds trust when plans pivot or pause The hiring freeze is a headline.  Workforce planning is the lesson. ♻️ Repost if you’re investing in people, not just tech. Follow Janet Perez for Real Talk on AI + Future of Work --- Sources: WSJ, The Telegraph

Strategic workforce planning is broken. AI is changing work faster than companies can keep up, and finance-led SWP isn't cutting it. People Analytics is the BEST team to fix it. McKinsey outlines how SWP is supposed to help companies anticipate workforce needs but most orgs treat it like a budgeting exercise led by finance.... with each team planning their own SWP in silos. The data is a MESS. Every team does SWP differently, finance is left to piece it together. The result is bad decisions, misaligned talent strategies, and hiring mistakes that could have been avoided. ----------------- McKinsey’s research highlights that companies that treat workforce planning like financial planning outperform competitors. Gen AI is projected to automate up to 30% of worked hours by 2030, workforce planning can’t just be about headcount anymore..... it NEEDS to be capability-driven. The article outlines five key SWP best practices: 1️⃣ Prioritize Talent Like a Financial Asset --> Talent is a strategic investment, not just an operational cost. 2️⃣ Balance Capacity & Capabilities --> The right people + the right skills = business success. 3️⃣ Plan for Multiple Scenarios --> AI is unpredictable; companies need adaptable workforce models. 4️⃣ Innovate in Filling Talent Gaps --> Hiring isn’t the answer to every problem; reskilling and internal mobility are crucial. 5️⃣ Embed SWP into Core Business Strategy --> SWP must be continuous and cross-functional, not just an HR exercise. The problem? Most organizations still treat SWP as a financial budgeting exercise rather than an AI-driven, skills-based strategy. ...this is where people analytics comes in. ----------------- Why People Analytics needs to lead SWP 1/ Problem: AI is breaking traditional SWP. Traditional = focus on headcount... missing the critical shift towards skills and capabilities needed to leverage AI. Solution: PA teams KNOW data. PA teams are plugged into skills mapping, forecasting, internal mobility, and incorporate AI-driven labor market insights. 2/ Problem: SWP is often siloed and reactive. Data is fragmented, leading to inaccurate forecasts and missed opportunities. Solution: PA teams can centralize data and drive a proactive, data-driven SWP strategy. 3/ Problem: Data is misunderstood and used incorrectly. Solution: PA teams know talent, know people, and are uniquely positioned to translate SWP results to business outcomes. ----------------- PA team path to ownership: ✅ Build AI-driven workforce forecasting models - start predicting skills gaps. ✅ Create a single source of truth for all data. ✅ Inform internal mobility so companies can build capabilities via internal transfers. ----------------- McKinsey is outlining the new world of SWP but we're ill-prepped to handle it. #peopleanalytics teams are the golden ticket.

𝐑𝐞𝐭𝐡𝐢𝐧𝐤 𝐭𝐡𝐞 𝐑𝐨𝐥𝐞 𝐁𝐞𝐟𝐨𝐫𝐞 𝐘𝐨𝐮 𝐏𝐨𝐬𝐭 𝐈𝐭 We’re not facing a hiring freeze. We’re facing a thinking freeze. Too many companies are stuck in the loop of: Headcount is down, so let’s backfill it. Yet, that loop ignores the larger truth AI and automation aren’t just tools. They’re system-level unlocks. This isn’t about firing people. It’s about not defaulting to hire when the need might already be solvable by the technology and talent you already have. 🔹𝐍𝐨𝐭 “𝐫𝐞𝐩𝐥𝐚𝐜𝐞” -𝐫𝐞𝐭𝐡𝐢𝐧𝐤. 🔹𝐍𝐨𝐭 “𝐞𝐥𝐢𝐦𝐢𝐧𝐚𝐭𝐞” -𝐞𝐧𝐚𝐛𝐥𝐞. 🔹𝐍𝐨𝐭 “𝐬𝐜𝐚𝐥𝐞 𝐮𝐩 𝐭𝐡𝐞 𝐰𝐨𝐫𝐤𝐟𝐨𝐫𝐜𝐞” -𝐬𝐜𝐚𝐥𝐞 𝐮𝐩 𝐭𝐡𝐞 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬. We’re not on the right path if we keep solving future problems with past structures. Ask yourself: What’s the actual goal? Is it to fill seats, or to drive throughput, margin, and performance? If you’re trying to preserve your edge, your domain expertise, customer relationships, and ability to adapt. Hiring into old patterns, becomes a risk, not a strategy. The critical shift is this: 𝐈𝐭’𝐬 𝐧𝐨𝐭 𝐟𝐢𝐫𝐞 𝐚𝐧𝐝 𝐝𝐨𝐧’𝐭 𝐡𝐢𝐫𝐞. 🔹Rewire how you think about roles. 🔹Rewire how work gets done. 🔹Rewire how you create value with what you already have. The companies winning now? They’re not scaling by volume. They’re scaling by velocity. They’re using AI and automation to remove friction and free up their existing workforce to do the work that actually matters. They’re reducing the need for new roles and not the people already delivering outcomes. Over 19 million U.S. jobs are at high risk of automation. That includes white-collar knowledge work, operations, and tech. So the next job opening on your spreadsheet? Don’t treat it like a vacancy. Treat it like a decision point. Can this be solved through automation, augmentation, or GenAI? If yes, then that’s not a job to fill. That’s a system, process, or workflow to build. 🔹This is how you protect your people. 🔹This is how you scale your capabilities. 🔹This is how you move faster with what you already have. #genai #AI #mindsetchange #hr #humanresources #new #hire Forbes Technology Council Gartner Peer Experiences InsightJam.com PEX Network Theia Institute VOCAL Council IgniteGTM IA FORUM 𝗡𝗼𝘁𝗶𝗰𝗲: The views within any of my posts, or newsletters are not those of my employer or the employers of any contributing experts. 𝗟𝗶𝗸𝗲 👍 this? feel free to reshare, repost, and join the conversation!