Training Resource Allocation

If you’re in Learning and Development… And you’re optimising for "checking the boxes" on training programs… IMO, we’re missing a trick. The likelihood of driving real behaviour change through surface-level programs is low. But when we focus on how people actually learn and grow? Game-changer. So, what should we be optimising for? ✅ Optimise for brain-friendly learning. Understand how the brain processes and retains information. Use spaced repetition, storytelling, and active engagement to make learning stick. ✅ Optimise for emotional engagement. People don’t learn well when they’re stressed or disengaged. Create safe, inspiring environments that spark curiosity and connection. ✅ Optimise for growth, not perfection. Shift the focus from “getting it right” to embracing mistakes as opportunities. Build a culture where learning is continuous, not a one-and-done event. ✅ Optimise for relevance. Every brain asks the same question: “Why does this matter to me?” Design programs that are actionable, personalised, and tied to real-world challenges. ✅ Optimise for habits, not just skills. Skills fade if they aren’t reinforced. Help people build habits that embed what they’ve learned into their daily work. AND DON’T FORGET… 🎉 Optimise for your own development. L&D professionals often pour into others but forget themselves. Stay curious. Seek out trends. Connect with peers who challenge and inspire you. CLO100 If you treat your role as a learning journey—for both yourself and your organisation—then the impact you create will be exponential.

A lot of time and money goes into corporate training—but not nearly enough comes out of it. In fact, companies spent $130 billion on training last year, yet only 25% of programs measurably improved business performance. Having run countless training workshops, I’ve seen firsthand what makes the difference. Some teams walk away energized and equipped. Others… not so much. If you’re involved in organizing training—whether for a small team or a large department—here’s how to make sure it actually works: ✅ Do your research. Talk to your team. What skills would genuinely help them day-to-day? A few interviews or a quick survey can reveal exactly where to focus. ✅ Start with a solid brief. Give your trainer as much context as possible: goals, audience, skill levels, examples of past work, what’s worked—and what hasn’t. ✅ Don’t shortchange the time. A 90-minute session might inspire, but it won’t transform. For deeper learning and hands-on practice, give it time—ideally 2+ hours or spaced chunks over a few days. ✅ Share real examples. Generic content doesn’t stick. When the trainer sees your actual slides, templates, and challenges, they can tailor the session to hit home. ✅ Choose the right group size. Smaller groups mean better interaction and more personalized support. If you want engagement, resist the temptation to pack the (virtual) room. ✅ Make it matter. Set expectations. Send reminders. And if it’s virtual, cameras on goes a long way toward focus and connection. ✅ Schedule follow-up support. Reinforcement matters. Book a post-session Q&A, office hours, or refresher so people actually use what they’ve learned. ✅ Follow up. Send a quick survey afterward to measure impact and shape the next session. One-off training rarely moves the needle—but a well-planned series can. Helping teams level up their presentation skills is what I do—structure, storytelling, design, and beyond. If that’s on your radar, I’d love to help. DM me to get the conversation started.

Most training programs create excitement. Very few create measurable business impact. A few months ago, I worked with an organization that had a very specific challenge. Their frontline teams were attending workshops, feeling motivated, taking notes but when it came to actual performance on the field, their sales conversion was very low. Great energy. Poor execution. Something was missing. So before designing the learning intervention, I asked one simple question: “What’s the real context in which your people operate daily?” Not the role. Not the job description. Not the competencies. The context. What pressures do they face? What conversations are toughest? Where do deals collapse? Who influences decisions? What behaviours matter most on the ground? The organization opened up. We mapped real scenarios. We shadowed calls. We watched interactions. We decoded customer psychology. We understood the reality behind the numbers. Only then did we build the training journey. Not generic content. Not textbook concepts. Not motivational theory. But a program designed exactly around their on-ground realities. The impact. Over the next eight weeks, something changed. Sales conversations became sharper. Objections were handled with more confidence. Teams spoke value, not price. Managers reinforced learning consistently. The conversion saw a huge jump and this was created not by more training, but by the right training. The lesson is simple: Content informs. Context transforms. Workshops don’t create results. Relevance does. When learning mirrors the real world, people don’t just listen they apply. When they apply, organizations grow. What’s one area in your team where you feel content is high but context is missing? If your organization wants training that delivers real, measurable outcomes let’s talk.

"The Secret to Higher Productivity Isn’t a New CNC Machine; It’s a Trained Operator!" Here’s a hard truth I’ve learned after three decades in machining — You can buy the latest 5-axis machine, the best CAM software, or the most expensive cutting tools… But if your operator isn’t skilled enough, your ROI will never add up. Machines are only as efficient as the people who run them. Skill is the real competitive edge. In most machine shops, performance gaps rarely come from the equipment—they come from underutilized potential. An operator trained in setup optimization, tooling selection, and process understanding can outperform expensive automation in terms of consistency and uptime. I’ve seen teams who, with just the right guidance, cut setup times by 25%, extended tool life by 20%, and eliminated rework almost entirely—without changing a single machine. 💡 Training Turns Operators into Thinkers A trained operator doesn’t just press the cycle start button. They think—they notice vibration, temperature rise, tool wear, chip color, or a subtle change in sound. They know when to adjust, when to stop, and when to innovate. That awareness transforms the shop floor from reactive to proactive. ⚙️ Hidden Cost Killers: Untrained Hands Every broken tool. Every reworked part. Every missed tolerance. They’re all small leaks—but together, they sink profits. Operator training plugs those leaks by empowering people with: Process discipline Preventive maintenance know-how Real-time problem-solving skills The result? Less downtime. Less scrap. More output. 💪 The Ripple Effect of Training Training isn’t just about performance—it’s about ownership. When operators are respected as skilled professionals, morale shoots up. Loyalty improves. Attrition drops. And that directly means stability and consistency in production—two things money can’t buy. 💰 The ROI of a Skilled Operator Think of it this way — Every hour you invest in operator training pays you back in: Reduced setup and idle times Better tool utilization Fewer breakdowns Higher part accuracy Technology upgrades can be copied. Skill levels can’t. That’s your real competitive advantage. 🏁 Final Thought “A well-trained operator can make an average machine perform exceptionally". An untrained one can make even the best machine look average.” So before you plan your next capex, ask yourself — Have we fully unlocked the potential of the people running our machines? #MachiningExcellence #SkillDevelopment #CNCTraining #LeanManufacturing #ProcessEfficiency #Productivity #LeadershipInManufacturing

3 most powerful lessons I’ve learned in corporate training as a trainer: After years of delivering corporate training, I’ve discovered a few game-changing lessons that boost results for teams & organizations. Here’s what truly works— and why it matters for your business: 1. Engagement drives results: → Employees forget 75% of new info within 6 days if not actively engaged. → Interactive sessions, real-world scenarios & open discussion keep learning memorable & actionable. 2. Customization beats one-size-fits-all: → Tailored training increases knowledge retention by up to 60% compared to generic programs. → Every team & company is unique— custom content addresses your specific challenges & goals. 3. Measurable impact matters: → Companies that track training effectiveness see a 24% higher profit margin on average. → Setting clear goals & measuring progress ensures your investment delivers real business value. If you’re looking to energize your teams, boost productivity & see real ROI from your training, let’s connect. I design & deliver high-impact programs that make learning stick & drive results.

Most training fails quietly... but not because people did not learn. Because the organisation never created the conditions for learning to survive operational reality. A recent study (Mehner et al., 2025) explored what actually determines whether workplace training turns into meaningful performance improvement. The answer was not course quality alone. It was the social system around the learner. The researchers found that... Supervisor support increased training transfer Peer support increased knowledge sharing Motivation alone was insufficient Volition, persisting through resistance and operational friction, mattered heavily Informal knowledge networks became critical after training One finding stood out to me... Employees who successfully transferred learning often expanded their internal knowledge networks afterwards. In other words: Capability development did not stop when the course ended...It accelerated through workplace relationships. That matters because many organisations still evaluate training as an isolated event: attendance completion satisfaction scores assessment pass rates But performance reliability is shaped afterwards: Can people apply the learning under pressure? Do managers reinforce it? Do peers support it? Is there psychological safety to experiment? Is knowledge shared across the system? Does the environment sustain behavioural execution? This is why two people can attend the same programme and produce completely different outcomes. The training may be identical...The surrounding conditions are not. Capability exists in the individual...Performance emerges from the system around them. Reference: Mehner, L., Rothenbusch, S., & Kauffeld, S. (2025). How to maximize the impact of workplace training: a mixed-method analysis of social support, training transfer and knowledge sharing. European Journal of Work and Organizational Psychology.

A lot of trainers run a great exercise… and then waste the learning moment that follows. The debrief is where performance improvement actually happens. But too often we get generic reflections: “Yeah, that was good” or “Interesting exercise.” None of that helps anyone perform better back on the job. A simple tool I use in almost every session, face-to-face or virtual, is the Feedback Grid. It structures the debrief so delegates can evaluate the outcomes of an exercise, not just how it felt. Here’s exactly how to use it straight after an activity: 1. Set up the 4 quadrants before the exercise Worked Well (+) Needs Change (Δ) Questions (?) New Ideas (💡) By having it visible from the start, delegates know there will be a structured review, not a free-for-all discussion. 2. Immediately after the exercise, ask individuals to add notes Give everyone 2–3 minutes to jot down their thoughts in each category. This stops dominant voices from setting the tone and gives you a broader view of what actually happened. In a virtual room, this is as simple as shared online sticky notes. Face-to-face, use flipcharts or a whiteboard. 3. Analyse the activity, not the activity’s “vibe” This is where most trainers go wrong. We’re not asking whether they “liked” the exercise. We’re capturing what the exercise showed about their skills, behaviours, and decision-making. Examples might include: Worked Well: “Clearer roles helped us move faster.” Needs Change: “We didn’t communicate early enough.” Questions: “How do we apply this under time pressure?” New Ideas: “Create a decision checklist before starting.” These are performance insights, not opinions. 4. Turn the grid into next-step actions Once patterns emerge, summarise 2–3 practical actions they can take into the workplace. This is where the ROI sits. The exercise becomes a rehearsal, and the grid becomes the bridge to real work. 5. Keep the pace tight A structured debrief shouldn’t drag. Five to eight minutes is enough to turn a simple exercise into a meaningful learning moment. When used properly, the Feedback Grid transforms exercises from “fun activities” into performance diagnostics. That’s the whole point of training, to improve what people do, not what they think about the training. What do you use for this? -------------------- Follow me at Sean McPheat for more L&D content and then hit the 🔔 button to stay updated on my future posts. ♻️ Save for later and repost to help others. 📄 Download a high-res PDF of this & 250 other infographics at: https://lnkd.in/eWPjAjV7

A recent study (PMID: 38970765) analyzing 55 studies and 243 effects looked at the dose-response relationship between proximity to failure, strength gains, and muscle hypertrophy. The findings showed important distinctions between training outcomes: - For Strength: Gains remained similar across all rep ranges, indicating that training closer to failure is not necessary for improving maximal strength. Instead, heavier loads were a stronger determinant of strength improvements. - For Muscle Growth: Training closer to failure consistently enhanced muscle growth, with sets ending closer to failure leading to greater muscle size increases. The results suggest that mechanical tension and fatigue accumulation are crucial drivers of muscle hypertrophy. Considerations: - Training to failure isn’t required for strength (it also is more likely to expose one to injury). Progressive overload and heavier loads may be more influential. - Muscle hypertrophy benefits from high effort, but not necessarily failure every set. A balance of fatigue management and volume is important. - Proximity to failure was estimated via repetitions in reserve, introducing variability across studies. Additionally, training status, exercise selection, and individual recovery were not fully controlled. - The exact repetition relationship between training close to failure and strength gain remains unclear. Strength athletes should prioritize load over reaching failure, while those focused on hypertrophy may benefit from higher-effort sets close to failure but, again, fatigue management remains important.

🔴 Knowledge isn’t the goal — performance is. If training doesn’t change what learners do, it’s useless information. To design learning that drives real behavioral change, focus on performance-based outcomes. Here’s how: 1️⃣ Define the desired behavior. Before you create content, ask: "What should learners be able to DO after this training?" ✅ Instead of “Understand conflict resolution” → “De-escalate workplace conflicts using a 3-step framework.” ✅ Instead of “Know safety procedures” → “Complete a safety check before each shift without missing a step.” 2️⃣ Align content to real-world tasks. Cut anything that doesn’t directly impact performance. ✅ Teach skills, not just concepts. ✅ Show learners how to apply the information. ✅ Use realistic examples, not just definitions. 3️⃣ Make practice the priority. If learners only consume content passively, they won’t be ready to act. ✅ Use scenario-based activities. ✅ Have them make decisions and see consequences. ✅ Design realistic practice opportunities. Example: Instead of listing customer service principles, let learners handle a simulated customer complaint -- and refine their approach. 4️⃣ Measure success by actions, not completion. ✅ Set clear, observable performance goals. ✅ Assess what learners can do, not just what they remember. ✅ Provide feedback that helps them improve. Learning should change behavior, not just transfer knowledge. 🤔 How do you design training with performance in mind? ----------------------- 👋 Hi! I'm Elizabeth! ♻️ Share this post if you found it helpful. 👆 Follow me for more tips! 🤝 Reach out if you need a high-quality learning solution designed to engage learners and drive real change. #InstructionalDesign #PerformanceBasedLearning #BehavioralChange #LearningAndDevelopment

Supercharge Your Model Training: Essential Techniques and Tricks 🚀 Are you tired of long model training times and inefficient training process? I have always struggled to understand which techniques can be chained together towards cumulative improvement and the order of magnitude improvement from each. Here is an array of powerful techniques to accelerate training with their effect size. The key in most cases is to know the memory architecture for the GPU  💾 and utilize it optimally by reducing data movement between on chip registers, cache, and off chip high-bandwidth memory. Frameworks like PyTorch make this pretty simple allowing you to do this in a few lines of code at most. - Switch to Mixed Precision: 🔢 Implementing bfloat16 can lead to a potential 3x speedup by reducing the amount of data transferred, thus enabling larger batch sizes. Although GPUs may promise up to an 8x improvement, actual gains could be lower due to memory constraints. Benchmarking is essential! - PyTorch Compile: 🖥️ Experience about a 2.5x speed increase by minimizing unnecessary memory bus traffic. This approach prepares your computations for more efficient execution. - Flash Attention: ⚡ Utilize a fused kernel specifically optimized for attention-heavy models, which can boost performance by up to 40% by enhancing memory hierarchy utilization. - Optimized Data Formats: 📊 Aligning your vocab size to a power of 2 can provide a straightforward 10% speed boost by improving memory access efficiency. - Hyperparameter Tuning: 🛠️ Gain an additional 5-10% speed by tweaking hyperparameters and employing fused kernels for optimizers like AdamW. Bespoke Fused Kernels: 🧩 Push the boundaries with custom kernels designed specifically for your model’s architecture to achieve optimal performance. Leverage Additional Optimizations: ➕ Employ vector operations (e.g., AVX-512) on CPUs or use sparse kernels for pruned models to further enhance memory efficiency. Scale Responsibly: 📈 Before moving to a multi-GPU setup, ensure you've maximized the potential of single-GPU optimizations to avoid inefficiencies. Once your setup is optimized, scaling across multiple GPUs can dramatically reduce training times by parallelizing the workload and minimizing data transfers. You can do this almost trivially by using things like Hugging Face Accelerate. Remember, the effectiveness of these techniques can vary based on your specific model, hardware setup, and other variables. Extensive benchmarking is crucial to find the perfect balance between speed and accuracy. Optimization is a continuous journey. Stay proactive in exploring new methods to reduce training times and remain competitive in the fast-evolving field of machine learning. For more insights, check out Karpathy’s latest video where he replicates GPT-2 on 8x A100s, astonishingly beating GPT-3 on Hellaswag. It’s incredible to see such advancements, allowing what once took months to be accomplished virtually overnight. 🌙✨