Post-Training Performance Metrics

Everyone’s excited to launch AI agents. Almost no one knows how to measure if they’re actually working. Over the last year, we’ve seen brands launch everything from GenAI assistants to support bots to creative copilots but the post-launch metrics often look like this: • Number of chats • Average latency • Session duration • Daily active users Useful? Yes. But sufficient? Not even close. At ALTRD, we’ve worked on AI agents for enterprises and if there’s one lesson it’s this: Speed and usage mean nothing if the agent isn’t solving the actual problem. The real performance indicators are far more nuanced. Here’s what we’ve learned to track instead: 🔹 Task Completion Rate — Can the AI go beyond answering a question and actually complete a workflow? 🔹 User Trust — Do people come back? Do they feel confident relying on the agent again? 🔹 Conversation Depth — Is the agent handling complex, multi-turn exchanges with consistency? 🔹 Context Retention — Can it remember prior interactions and respond accordingly? 🔹 Cost per Successful Interaction — Not just cost per query, but cost per outcome. Massive difference. One of our clients initially celebrated their bot’s 1 million+ sessions - until we uncovered that less than 8% of users actually got what they came for. That 8% wasn’t a usage issue. It was a design and evaluation issue. They had optimized for traffic. Not trust. Not success. Not satisfaction. So we rebuilt the evaluation framework - adding feedback loops, success markers, and goal-completion metrics. The results? CSAT up by 34% Drop-off down by 40% Same infra cost, 3x more value delivered The takeaway: Don’t just measure what’s easy. Measure what matters. AI agents aren’t just tools - they’re touchpoints. They represent your brand, shape user experience, and influence business outcomes. P.S. What’s one underrated metric you’ve used to evaluate AI performance? Curious to learn what others are tracking.

📈 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

Completion rates are one of the most celebrated metrics in corporate learning. They are also one of the least useful. Imagine a company spends $2 million on leadership training. Ninety-five percent of employees complete the program. But leadership behavior does not change. Did the training succeed? Traditional reporting says yes. Strategic instructional design says no. Because completion only tells you that someone finished a course. It tells you nothing about whether performance improved. The metric that actually matters is application. What employees do differently after training. Do managers give better feedback? Do teams make better decisions? Do performance conversations improve? If behavior doesn’t change, the training didn’t work. No matter how good the completion rate looks on a dashboard. Completion measures attendance. Application measures impact. This shift—from measuring participation to measuring performance—is one of the core mindset changes we teach inside IDOL Academy. Because instructional designers who want a seat at the strategy table have to measure what the business actually cares about. Results.

5,800 course completions in 30 days 🥳 Amazing! But... What does that even mean? Did anyone actually learn anything? As an instructional designer, part of your role SHOULD be measuring impact. Did the learning solution you built matter? Did it help someone do their job better, quicker, with more efficiency, empathy, and enthusiasm? In this L&D world, there's endless talk about measuring success. Some say it's impossible... It's not. Enter the Impact Quadrant. With measureable data + time, you CAN track the success of your initiatives. But you've got to have a process in place to do it. Here are some ideas: 1. Quick Wins (Short-Term + Quantitative) → “Immediate Data Wins” How to track: ➡️ Course completion rates ➡️ Pre/post-test scores ➡️ Training attendance records ➡️ Immediate survey ratings (e.g., “Was this training helpful?”) 📣 Why it matters: Provides fast, measurable proof that the initiative is working. 2. Big Wins (Long-Term + Quantitative) → “Sustained Success” How to track: ➡️ Retention rates of trained employees via follow-up knowledge checks ➡️ Compliance scores over time ➡️ Reduction in errors/incidents ➡️ Job performance metrics (e.g., productivity increase, customer satisfaction) 📣 Why it matters: Demonstrates lasting impact with hard data. 3. Early Signals (Short-Term + Qualitative) → “Small Signs of Change” How to track: ➡️ Learner feedback (open-ended survey responses) ➡️ Documented manager observations ➡️ Engagement levels in discussions or forums ➡️ Behavioral changes noticed soon after training 📣 Why it matters: Captures immediate, anecdotal evidence of success. 4. Cultural Shift (Long-Term + Qualitative) → “Lasting Change” Tracking Methods: ➡️ Long-term learner sentiment surveys ➡️ Leadership feedback on workplace culture shifts ➡️ Self-reported confidence and behavior changes ➡️ Adoption of continuous learning mindset (e.g., employees seeking more training) 📣 Why it matters: Proves deep, lasting change that numbers alone can’t capture. If you’re only tracking one type of impact, you’re leaving insights—and results—on the table. The best instructional design hits all four quadrants: quick wins, sustained success, early signals, and lasting change. Which ones are you measuring? #PerformanceImprovement #InstructionalDesign #Data #Science #DataScience #LearningandDevelopment

Assumption #5 to retire in 2026: If completion rates are high, the training worked! You know this one. Senior Leader: "How's that program going?" L&D: "We're at 97% completion!" Everyone nods like we just moved the P&L. Completion is a coverage metric. We keep treating it like an impact metric. What this usually looks like: - Dashboards full of green checkmarks and "100% complete" mentions - Weekly emails pushing people to "finish the module" - Success stories that start and end with, "We hit target!" Meanwhile: - Error rates, rework, and customer pain look the same - Managers can't name one thing their team is doing differently - The business quietly learns that training = extra clicks, not better work We didn't build capability. We proved people know how to get to the Done button. For 2026, the shift is: 1. Pair completion with one behavior signal. If it's worth making mandatory or pushing hard, it's worth asking: "What should we see less of / more of in the work if this actually lands?" 2. Define "what good looks like" before you design. Not "understands x" "Can you do ___ in this moment, under real constraints." 3. Report back on capability, not just coverage. Show leaders: - the metric that moved (fewer escalations, faster handoffs, better first-pass quality), and - the story of how people are working differently Completion proves they showed up. Capability proves it mattered. If you could keep only one metric for your biggest program next year, which would you choose? __ #Leadership #ExecutiveLeadership #BusinessStrategy #SkillsStrategy #LearningAndDevelopment #TalentDevelopment #PeopleAndCulture #OrganizationalEffectiveness #FutureOfWork #CapabilityBuilding

How Do You Actually Measure LLM Performance- A Practical Evaluation Framework for 2025 As LLMs continue to shape enterprise AI, measuring their performance requires more than checking if the answer is “correct.” Modern evaluation spans accuracy, semantics, safety, efficiency, and human judgment. 🔍 1. Accuracy Metrics ◾ Perplexity (PPL) – How well the model predicts text (lower = better) ◾Cross-Entropy Loss – Measures prediction quality during training 📌 Useful for benchmarking probabilistic models. 🔤 2. Lexical Similarity Metrics ◾BLEU – n-gram precision ◾ROUGE (N, L, W) – n-gram recall & sequence matching ◾METEOR – Considers synonyms, stemming, word order 📌 Good for summarization and translation, but limited in capturing meaning. 🧠 3. Semantic Similarity Metrics ◾BERTScore – Uses contextual embeddings for semantic alignment ◾MoverScore – Measures semantic distance 📌 Closer to human judgment than word-based scores. 📝 4. Task-Specific Metrics ◾Exact Match (EM) – Perfect match with expected answer ◾F1 Score – Partial match overlap 📌 Ideal for QA, extraction, and structured outputs. ⚖️ 5. Bias & Fairness Metrics ◾Bias Score ◾Fairness Score 📌 Critical for high-stakes AI use cases: finance, justice, healthcare. ⚡ 6. Efficiency Metrics ◾Latency ◾Resource Utilization 📌 Required for production-grade, scalable systems. 🤝 7. Human Evaluation ◾Fluency ◾Coherence ◾Relevance ◾Toxicity & Bias 📌 Still the gold standard—automated metrics cannot fully capture nuance. 💡 Final Takeaway A robust LLM evaluation framework must combine: ◾Accuracy + Semantic Understanding + Safety + Efficiency + Human Judgment. ◾This multi-layered approach ensures trustworthy, high-performance AI systems that work reliably in production. Reference: “How to Measure LLM Performance,” Analytics Vidhya (document provided). #LLMEvaluation #AIProductManagement #GenerativeAI #MachineLearning #AIEthics #ModelEvaluation #RAG #NLP #ArtificialIntelligence #LLM #AIinBusiness #AIMetrics #DataScience #MLOps #ResponsibleAI

Training isn’t the goal. Impact is ⬇️ Training doesn’t end with the session. It ends with results. Most companies track training attendance. But few measure what really matters, impact. The Kirkpatrick-Phillips Model helps you do just that. It moves beyond completion rates to ask: Did learning change behaviour? Did it drive results? Was it worth the investment? Here’s how the 5 levels break down: ✅ Level 1 – Reaction ↳ Was the training relevant, engaging, and useful? ✅ Level 2 – Learning ↳ Did participants gain new knowledge or skills? ✅ Level 3 – Behaviour ↳ Are they applying what they learned on the job? ✅ Level 4 – Results ↳ Are we seeing improvements in performance, productivity, or quality? ✅ Level 5 – ROI ↳ Did the business gain more value than it spent? To apply this model well: Start with the end in mind ↳ Define clear business outcomes before designing training. Link each level ↳ Show how learning leads to behavioural change and how that drives results. Use real data ↳ Track both qualitative and quantitative outcomes across all five levels. Involve managers ↳ Bring them into the process early, they’re key to learning transfer. Be selective and focused ↳ Avoid tracking everything. Focus on what truly moves the needle. Tell a clear story ↳ Use the data to tell a results-focused narrative that shows the full value of training. 🧠 Remember: Great training isn’t just delivered. It’s measured, proven, and improved over time. Which level do you think L&D teams struggle with the most? -------------------------- ♻️ Repost to help others in your network. ➕ And follow me at Sean McPheat for more.

In L&D, we do a lot of measuring completion and satisfaction rates, but not a lot of tracking our impact. I think a lot of instructional designers would like to have better measures of impact. We know we should do better, but we’re not sure what to do. It’s often tricky to even figure out what to even measure. iSpring’s recent report “The ROI Shift” asked several experts in the field for useful business metrics for L&D. Many of these are metrics that organizations may already track. If you can tie your training initiatives to data that already exists in your organization, that removes a significant barrier. It’s also a sign that you’re aligning your training with business goals and outcomes that the organization values. 1. Time to proficiency 2. First Contact Resolution (FCR) rate 3. Error rate reduction 4. Revenue per employee 5. Employee retention rate 6. Manager-rated behavior change 7. Customer satisfaction score 8. Productivity increase 9. Internal mobility rate 10. Compliance deviation reduction Of course, all of these metrics are affected by many other factors besides training. That’s part of the challenge too. We can’t easily isolate the effects of training on those factors. Manager support, available resources, time to practice, luck, and other factors affect those business metrics as well. But, if you can at least start with proactive alignment with metrics that matter to your organization, you’re already on the right track. For more on these metrics and insight on how to show the value of your training, get the full report here: https://ispri.ng/zxlNV #iSpring #InstructionalDesign #ROI

"You can’t manage what you don’t measure." Yet, when it comes to change management, most leaders focus on what was implemented rather than what actually changed. Early in my career, I rolled out a company-wide process improvement initiative. On paper, everything looked great - we met deadlines, trained employees, and ticked every box. But six months later, nothing had actually changed. The old ways crept back, employees reverted to previous habits, and leadership questioned why results didn’t match expectations. The problem? We measured completion, not adoption. 𝗖𝗼𝗻𝗰𝗲𝗿𝗻: Many organizations struggle to gauge whether change efforts truly make an impact because they rely on surface-level indicators: → Completion rates instead of adoption rates → Project timelines instead of performance improvements → Implementation checklists instead of employee sentiment This approach creates a dangerous illusion of progress while real behaviors remain unchanged. 𝗖𝗮𝘂𝘀𝗲: Why does this happen? Because leaders focus on execution instead of outcomes. Common pitfalls include: → Lack of accountability – No one tracks whether new processes are being followed. → Insufficient feedback loops – Employees don’t have a voice in measuring what works. → Over-reliance on compliance – Just because something is mandatory doesn’t mean it’s effective. If we want real, measurable change, we need to rethink what success looks like. 𝗖𝗼𝘂𝗻𝘁𝗲𝗿𝗺𝗲𝗮𝘀𝘂𝗿𝗲: The solution? Focus on three key change management success metrics: → 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 – How many employees are actively using the new system or process? → 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 – How has efficiency, quality, or productivity changed? → 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 – Do employees feel the change has made their work easier or harder? By shifting from "Did we implement the change?" to "Is the change delivering results?", we turn short-term projects into long-term transformation. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀: Organizations that measure change effectively see: → Higher engagement – Employees feel heard, leading to stronger buy-in. → Stronger accountability – Leaders track impact, not just completion. → Sustained improvement – Change becomes embedded in the culture, not just a temporary initiative. "Change isn’t a box to check—it’s a shift to sustain. Measure adoption, not just action, and you’ll see the impact last." How does your organization measure the success of change initiatives? If you’ve used adoption rate, performance impact, or user satisfaction, which one made the biggest difference for you? Wishing you a productive, insightful, and rewarding Tuesday! Chris Clevenger #ChangeManagement #Leadership #ContinuousImprovement #Innovation #Accountability