This is the third installment of TrainingZone’s series ‘Leveraging AI transformation to tackle L&D legacies’. Part one explored how AI can help with your emergent learning content legacy, and part two looked at AI’s potential to push L&D in untapped spaces. Here, we’ll consider AI’s capabilities in supporting L&D with skills, performance measurement, and metrics.
What if AI could help tackle challenges you’ve struggled to address properly as a learning professional? Those areas where you have the vision, but messy, cumbersome issues have arisen and are difficult to solve.
We’re now reaching a point where AI can be considered your new digital colleague, supporting you with those knottier problems. Here are three emerging AI capabilities to watch and explore:
Performance measurement and assessment: Let AI do the measuring
“Hurray” cried the learners, the “old favourite” multiple choice questions at the end of a course to “certify” your completion will slowly be disappearing! “We will no longer need a photographic memory to complete the quiz. And my skill level will be rated by AI who has given me personalised feedback and coached me through the task in hand”. We’re almost at this stage in AI’s ability.
At the same time learning professionals are whispering anxiously: “But how will we know what to measure to ‘prove’ mandatory training if it is not measured on completion with a knowledge quiz?”. If this sounds familiar, don’t worry – AI will be able to help with that too. You’ll be able to brainstorm performance measures with your favourite large language model (LLM). We aren’t quite there with the technology just yet but these are the sorts of conversations you need to be having.
The master and the apprentice
Performance measurement will be replaced with regular questioning from an AI tutor or coach. They will give employees feedback as they work, with performance support and a chance to learn from that feedback by demonstrating it in practice. What is not to love? It is a bit like a master carpenter and apprentice. The master carpenter knows the capabilities of their apprentice and where they need to guide them to develop their craft. They both play a role for each other.
The AI tutor has a role towards the company and the learner, and that’s where the data will be coming from. As such, it’s time to consider the governance around this to make this vision a reality.
Skills: Let AI provide the connective tissue
We love skills. They are fast becoming the currency that provides a coherent narrative about how personal and business growth intersect and fuel each other. They help us curate and recommend learning content and assess how it’s all going. The dream! Except, different parts of the organisation own different bits of the conversation, and numerous systems are deployed to do some (but not all) of the things you need them to do.
If you needed a(nother) sign, this is it. This is your best excuse yet to connect closer with other parts of the business. It’s time to get properly under the hood of your tech setup so you can have better AI conversations. The technology can help with a lot of the heavy lifting on taxonomies and mapping, with tagging and curating content, and, increasingly, with assessment (see previous point).
Resist the temptation to dwell on the “oh but shouldn’t this better be competencies instead and what about…” conversation. We’ve discussed too much and done too little. Look at your landscape. What you have got to work with? What would take things to the next level? Put colleague AI to work on making headway with this.
Metrics: Let AI connect the dots (note, you need some dots first)
You know the importance of demonstrating business impact, partnering closely and showing the changes you create or hope to create. But finding the time and resources to do this properly is tough.
Let AI be the data analyst that learning never had. AI can not only help with interrogating data better (particularly if spreadsheets/data aren’t your thing), it can now help you talk to your data in natural language. AI can also work across different systems and repositories to help connect the dots (make friends with your data team).
Letting AI help with data also makes qualitative sentiment analysis easier (talk to your Marketing team). This allows you to paint a richer picture by going deeper into your needs analyses and learning intervention evaluations.
Note that AI can help automate or streamline things, but something already has to exist. It’s not unusual for learning teams to find a lot of work on data and workflows needs doing before bringing AI into it. Don’t let this scare you: The second-best time to do this is now.
An invitation for bigger conversations
Take some time as a learning team to think through how AI could help you. Making friends internally will help you think big enough about AI’s capacity to support and accelerate your learning curve.
Put colleague AI to work. Get your use cases and investment proposals sharp for 2026. And why not involve your whole team in this?
