At Learn Amp, we're not just adding AI features - we're building intelligence that transforms how people learn and grow at work. This isn't about chasing trends. It's about creating AI that delivers measurable impact.
Here's how we're making it happen.
Building great AI starts with understanding what's actually possible. I spend my time talking with executives from top companies about their AI wins and failures. These conversations show me what really works - and what doesn't.
Keeping up with AI isn't just about reading the latest research. It's about knowing which problems are already solved and which ones are worth tackling. This saves us from building things that already exist and helps us focus on what matters.
Getting your team excited about AI happens when you show them what's possible. I share my own AI wins with the team - the tools that save me time, the insights I wouldn't have found otherwise. When people see real results, they want to try it themselves.
This approach works. When your team experiences how AI can make their work easier or better, they become believers. They start experimenting, sharing their own discoveries, and pushing boundaries. That's how you build a culture that embraces AI instead of fearing it.
All this immersion and experimentation isn't just for show - it's the foundation for building something real. You can't create meaningful AI solutions without first understanding the landscape and getting your hands dirty with the tools yourself.
That's exactly what we've done at Learn Amp. We've taken everything we've learned from the AI community, combined it with our own experiments and discoveries, and started building. Not just adding AI features for the sake of it, but creating something that truly transforms how people learn at work.
Here's the truth: AI is only as good as the data you feed it. While others rush to launch flashy features, we've invested in building a rock-solid data warehouse and ETL pipeline. It's not the exciting part, but it's what makes everything else work.
We organise our customer data properly - clean, structured, and reliable. No shortcuts. This means when our AI makes recommendations or generates insights, it's working with good information. The result? Far fewer mistakes and much better accuracy.
Surface-level AI knowledge isn't enough when you're building real systems. Our AI team (including me) has earned certifications in agentic AI design and MCP. This gives us the technical foundation to make smart choices about our technology and build AI workflows that actually work.
This deeper understanding helps us avoid common pitfalls and build systems that can grow and adapt as AI evolves.
We're building a powerful AI platform from the ground up, entirely in-house. This isn't about bolting on third-party solutions - it's about creating something that deeply understands the unique challenges of corporate learning and development.
Our proof of concepts have exceeded even our own expectations. By combining our structured data approach with sophisticated AI architectures, we've achieved:
Perhaps the most exciting development has been watching our AI agents work together in ways we never initially envisioned. As we add more agents and tools to our AI co-pilot, we're seeing exponential growth in capabilities. These agents don't just work in isolation - they collaborate, sharing context and building on each other's outputs.
The magic happens when these agents can access:
This interconnected intelligence creates a system that understands not just what users need to learn, but why they need to learn it and how it fits into their broader career journey.
We're still in the early stages of what's possible with AI in learning and development. But by building on solid foundations - clean data, deep expertise, and a culture of innovation - we're creating something truly transformative. Our AI doesn't just automate existing processes; it reimagines what corporate learning can be.
The exponential improvements we're seeing with each new capability we add tell us we're on the right track. As AI continues to evolve, so will our platform, always with the goal of creating more personalized, effective, and engaging learning experiences for every user.
And we're just getting started. Keep an eye out for my next post where I'll dive into the technical side - explaining what AI buzzwords “RAG” and “MCP” are, and why we're building these technologies into our stack. Don't worry, I'll break it down in plain English. No computing degree required.
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