Job Role Analysis for Training

Lately, I’ve been exploring how AI could reshape specific roles within the federal public service. Instead of debating “replacement vs. no replacement,” I’ve been looking at the task level reality behind the jobs we rely on every day. 🤔 Out of curiosity, I tested the Staffing Advisor PE03 using SmarterX’s JobsGPT because it is one of the most standard HR roles across the Government of Canada and provides a clear, easy to understand baseline for task level analysis.  🔍 What happens when we break this job down into the hundreds of micro tasks it carries: policy interpretation, screening, scheduling, drafting SoMCs, documenting decisions, advising managers, ensuring fairness, coordinating with PSC, and navigating multiple policy instruments? A fascinating pattern emerges. 🔹 Tasks rooted in judgment, equity, and policy interpretation remain firmly human. These are the activities that rely on contextual reasoning, lived experience, and understanding the spirit of the Public Service Employment Act, not just the text. 🔹 Tasks that are repetitive, rules based, or documentation heavy show high exposure to AI augmentation. Drafting templates, screening for basic criteria, scheduling, and extracting data from forms are exactly the areas where automation accelerates timelines without compromising integrity. 🔹 The real shift is not about replacement, it is about role evolution. As AI takes on routine tasks, staffing advisors increasingly become • strategic advisors to managers • fairness and risk mitigation stewards • digital workflow navigators • data informed decision enablers This is where the future of HR feels most exciting. ✨ We are not removing the human element, we are elevating it. What would the public service look like if every staffing function had this kind of task level clarity? How could we redesign workflows, training, or policy to reflect where technology adds value and where human expertise must remain at the centre? I am curious to hear what others in HR, policy, and digital transformation are seeing in their own environments. Where do you think AI creates the right kind of leverage in staffing? 💬 #PublicService #GCJobs #HRTransformation #GCTalent

Where has GenAI quietly become a game-changer in test development? A year ago, I would’ve said item writing without hesitation. Today, though, the biggest ROI is showing up somewhere less obvious: Job Task Analysis (JTA). A JTA is the structured process where subject matter experts (SMEs) break down the tasks, knowledge, and skills required for a job role. It’s foundational for certification, licensure, employment testing, and competency-based assessments. The problem? JTAs are time-consuming and rarely a favorite activity for SMEs. Hours go into identifying tasks and related knowledge and skills, organizing them into domains, and writing survey questions. Necessary work—but not exactly energizing. This is where GenAI really delivers: ✅ Fast job research. LLMs can scan publicly available job information and produce an initial list of tasks and related knowledge and skills in minutes, giving SMEs a strong starting point. ✅ Domain structuring. GenAI can help cluster tasks, knowledge, and skills into logical, defensible domains. ✅ Updating JTAs. AI can compare current and prior JTAs, flagging new or missing tasks that SMEs might miss. ✅ Survey support. GenAI can draft JTA survey content, including demographic questions, speeding up development. A few caveats: 🚧 Works best for well-documented roles. For niche or emerging jobs, a retrieval-augmented generation (RAG) approach using internal job data works better. 🚧 Outputs aren’t perfect—but they’re highly useful as a starting point. 🚧 Keep humans in the loop. SME and psychometric oversight is essential. 🚧 Privacy and security still matter. Bottom line: GenAI won’t replace SMEs or psychometricians, but it significantly reduces the grunt work of JTAs—freeing experts to focus on interpretation, decisions, and better exam design. #AITestDevelopment #AIforJTA #AIInnovations

I published an analysis of job roles in the knowledge graph and semantic engineering job market. I created this survey report as I have worked with companies wanting to set up knowledge infrastructure teams. And in most cases, organizations do not know what to hire for or even the job titles and relevant descriptions needed to build knowledge capabilities. This report does not cover every single role that may exist in an org. But it covers the main roles. In this report, I highlight eight roles: ⚪️ knowledge graph engineer ⚪️ ontologist ⚪️ ontology engineer ⚪️ taxonomist ⚪️ graph database administrator ⚪️ knowledge graph product manager ⚪️ domain expert ⚪️ knowledge graph program manager I detail sample job descriptions, education requirements and pay scales for each role. The survey report can be found on my Substack. Link in comments 👇👇👇

𝐖𝐡𝐞𝐧 𝐈 𝐟𝐢𝐫𝐬𝐭 𝐬𝐭𝐞𝐩𝐩𝐞𝐝 𝐢𝐧𝐭𝐨 𝐦𝐲 𝐫𝐨𝐥𝐞, 𝐭𝐡𝐞𝐫𝐞 𝐰𝐚𝐬 𝐧𝐨 𝐟𝐨𝐫𝐦𝐚𝐥 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞—𝐣𝐮𝐬𝐭 𝐭𝐫𝐢𝐛𝐚𝐥 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐚𝐧𝐝 𝐠𝐨𝐨𝐝 𝐢𝐧𝐭𝐞𝐧𝐭𝐢𝐨𝐧𝐬. No onboarding plan. No job-specific learning objectives. Just “Watch and learn.” I knew that had to change. But where do you begin when there’s nothing in place? I started by assessing the situation and quickly realized a job analysis was the first step—interviewing employees, observing workflows, and gathering information to understand the role itself. But as I dug deeper, a question kept popping up: 𝐖𝐚𝐢𝐭… 𝐢𝐬𝐧’𝐭 𝐭𝐡𝐢𝐬 𝐚 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐍𝐞𝐞𝐝𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬? 𝐎𝐫 𝐚𝐫𝐞 𝐭𝐡𝐞𝐲 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭? That curiosity led to a turning point. A mentor shared two invaluable tools (Thank you Dr Scott Davies): 🔁 A mixed-method approach—combining observation, interviews, and surveys. 📘 And a copy of Applied Measurement Methods in Industrial Psychology. That’s when everything clicked. ✅ A Job Analysis defines what’s required for success in a role—skills, knowledge, tools, and work conditions. ✅ A Training Needs Analysis identifies the gap between those requirements and what employees currently know or can do. You can’t close the gap if you don’t know where the goalposts are. That shift in understanding changed everything for me. It wasn’t just about developing content—it was about creating a targeted, evidence-based training program that prepares people for success, not just compliance. If you’re building training from scratch, start with the job—not the symptom. You might be surprised what clarity that brings. I’d love to hear how others have approached this—what frameworks or tools helped guide your job or training needs analysis? #WorkplaceEngineer #IOPsychology #TrainingAndDevelopment #LearningThatSticks #ManufacturingExcellence #HumanCenteredDesign

Assumptions are expensive. When training is built without a needs analysis, it often solves the wrong problem -- or no problem at all. This wastes time, money, and learner goodwill. A solid needs analysis uncovers the real performance gaps and ensures training addresses them directly. So, how do you do that? 1️⃣ Ask, “What business problem are we trying to solve?” 2️⃣ Talk to managers and frontline staff, not just stakeholders. 3️⃣ Observe the job being done to spot real gaps. 4️⃣ Confirm if training is the solution, or if it’s a process or resource issue. Training without needs analysis solves the wrong problem. How do you uncover the real need before building training? #InstructionalDesign #NeedsAnalysis #LearningAndDevelopment #PerformanceConsulting #AspiringInstructionalDesigner ----------------------- 👋 Hi! I'm Elizabeth! ♻️ Share this post if you found it helpful. 👆 Follow me for more tips! 🤝Reach out if you're looking for an effective learning solution.