Using Data to Drive Innovation in Training Methods

AI is only as smart as the data you feed it. Most HR teams already have the data. But it’s buried in the wrong formats. At Fig Learning, we help HR leaders unlock it. Here’s how to make your data AI-ready. Structured vs. Unstructured: What’s the difference? Structured = ready to use. Labeled, searchable, clean data in tools like LMSs. Unstructured = hidden value. Think emails, transcripts, PDFs, and feedback notes. Structured data is plug-and-play. Unstructured data needs work - but holds gold. Step 1: Audit your data sources Where does learning actually live right now? Start by mapping your tools, folders, and files: - LMS reports? - Post-training surveys? - Feedback forms? - Meeting notes? Inventory what you touch often but never analyze. Step 2: Prioritize what to work on Not all messy data is worth it. Start with content that’s high-volume and high-impact. Focus on: - Post-training feedback - Coaching and 1:1 notes - Workshop or debrief transcripts - Policy docs in unreadable formats This is where insights are hiding. Step 3: Structure the unstructured Use lightweight AI tools to make it usable. Try: - ChatGPT Enterprise to tag and summarize - Otter.ai / TLDV to transcribe and recap - Guidde to turn steps into searchable guides And tag docs with topic, team, and timestamp. Step 4: Train AI on what matters Once structured, your data becomes leverage. Use it to power SOPs, checklists, or internal bots. Let AI write based on your real examples. It will save time and multiply your reach. Good AI starts with good prep. Don’t feed it chaos. Feed it clarity. P.S. Want my free L&D strategy guide? 1. Scroll to the top 2. Click “Visit my website” 3. Download your free guide.

Your instructional designers are wasting their talent building courses nobody asked for. I see it everywhere. Brilliant L&D teams spend months crafting beautiful, interactive modules about "Professional Email Etiquette" or "Workplace Wellness" while the sales team is begging for help with objection handling and the customer success team can't figure out why retention is tanking. We've turned instructional design into an art project instead of a business solution. Here's what's happening: Someone in leadership says, "We need training on X," and your team jumps into action. They research learning theories, build personas, create storyboards, and design gorgeous courses. Six months later, completion rates are 12% and nothing has changed. Meanwhile, the real problems are hiding in plain sight. People are struggling, metrics are declining, and teams are frustrated. But nobody thought to ask the humans doing the work what they needed to learn. Here's where it gets interesting: AI-powered learning platforms finally give us better ways to understand people's needs. Instead of guessing based on annual surveys, these systems can track learning patterns, identify skill gaps through competency mapping, and help you spot where interventions might make a difference. The best instructional designers I know spend more time in the business than at their desks. They're on sales calls, watching customer interactions, sitting with support teams, and asking, "What's making your job harder than it should be?" Now, they can use data from their LMS to validate those hunches and see which learning paths actually correlate with better performance. Stop designing courses for compliance checklists and start creating solutions for real people with real problems. Let the data help you find those problems faster, but remember that correlation isn't causation. Your job isn't to make training. Your job is to make people better at their jobs. There's a massive difference. Want to know if your L&D team is on the right track? Ask them, "What business problem did you solve this month?" If they can't answer immediately, you've got some redirecting to do. L&D leaders, what's the most impactful learning solution your team built by talking to the people who needed it? #InstructionalDesign #LearningAndDevelopment #BusinessAlignment #LDLeadership

I was reviewing quarterly reports with a client last month when they asked me a question that stopped me in my tracks: "Scott, we have all this learning data, but I still don't know which programs are actually improving performance." After 12 years as CEO of Continu, I've seen firsthand how organizations struggle with this exact problem. You're collecting mountains of learning data, but traditional analytics only tell you what happened - not why it matters. Here's what we've learned working with thousands of organizations: The real value isn't in completion rates or assessment scores. It's in the connections between those data points that remain invisible without the power of tools like AI. One of our financial services clients was tracking 14 different metrics across their onboarding program. Despite all that data, they couldn't explain why certain regions consistently outperformed others. When we implemented our AI analytics engine, the answer emerged within days: specific learning sequences created knowledge gaps that weren't visible in their traditional reports. This isn't just about better reporting - it's about actionable intelligence: - AI identifies which learning experiences actually drive on-the-job performance - It spots engagement patterns before completion rates drop - It recognizes content effectiveness across different learning styles Most importantly, it connects learning directly to business outcomes - the holy grail for any L&D leader trying to demonstrate ROI. What's your biggest challenge with learning data? Are you getting the insights you need or just more reports to review? #LearningAnalytics #AIinELearning #WorkforceDevelopment #DataDrivenLearning