From Builders to Architects
Being able to make a poster with ChatGPT shouldn’t persuade an organisation to let go of their marketing department. Similarly, the ability to use AI tools to assist with learning creation does not mean organisations should dispense with learning professionals.
We’re optimistic about what AI in learning makes possible, and that optimism includes the future of learning professionals.
Let’s be clear though, work will change. Tasks that once occupied a substantial part of a project may require much less time. Organisations will reconsider what they commission, what they handle internally, and where they need support.
We see an opportunity for learning professionals to move more from builders to architects. Many already work that way, of course, but their week just leaves them with considerably more building than they’d like.
What happens to the time saved?
Much of the promise of AI rests on a reasonable proposition: spend less time on production and use that time elsewhere. The question is where.
A shorter development process might make room for a simulation that previously exceeded the budget, it could allow for more ambitious practice, more testing with learners or proper follow-up after launch. It might simply give the learning team time to contribute earlier to decisions that affect their work.
We also know, it could also mean being asked to deliver twice as many courses.
There will of course be situations where more output is useful, where organisations have worthwhile projects they haven’t been able to afford or resource – making those possible is a real benefit. However, higher output shouldn’t be the only measure of what AI has achieved, otherwise we could end up with faster learning teams whose contribution remains constrained in much the same way as before.
For organisations investing in AI-accelerated learning, this is a decision to make deliberately. Some of the saving can reduce costs, some can expand access, some can fund work that improves the learning itself.
At GetSavi, that is part of the conversation about using AI well. Production efficiency gives us choices about the scope and quality of a project; those choices deserve as much attention as the tools.
The draft may arrive before the brief
As more people gain access to course-generation tools, learning professionals may increasingly be brought a draft where they once received a request.
“We’ve made this, can you check it?”
On occasions that will be a useful starting point, where someone has organised their knowledge, explored an idea, and given the team something tangible to discuss. Other times, it will be a substantial piece of work disguised as a quick review.
A polished draft can make a project look further along than it is. People become attached to what they can see, whilst decisions about the audience, scope and approach may already feel settled, even if nobody consciously made them.
Our early lessons in developing our bespoke AI tooling reinforced how easily this can happen – an impressive output can conceal work still to be done, and correcting its direction can absorb the time saved in producing it.
There’s a risk that learning professionals become the final inspection service for an expanding supply of generated material, keeping them very busy while giving them little influence about what gets created.
Moving towards an architect’s role means having a say in how that work begins, using learning design expertise in establishing where specialist input belongs, using tools appropriately, and shaping a project before a draft acquires the status of a nearly finished project.
That matters whether the expertise comes from an internal team, an external partner, or both.
More material makes judgement more valuable
AI used poorly can produce a lot of content quickly, with varying quality. Much of it may be fluent, potentially accurate, and still offer little reason for someone to spend time with it.
As that material accumulates, organisations will need people who can make informed decisions about what to use, improve, or leave published. Learning professionals are well placed to do that.
Whether this translates into greater influence is another question. Expertise can become more necessary without becoming more visible. The finished resource is easy to point to; the decision that prevented unnecessary resource from being built is harder to show.
That gives learning teams and their partners a practical challenge – we need to explain the consequences of our decisions in terms the organisation can recognise: expert time saved, a difficult task better supported, learning that demonstrably propels an organisation forward
It also means being clear about what an organisation is buying when it commissions AI-accelerated learning. The production technology is part of the service, but so is the expertise that directs it and takes responsibility for the result.
Learning professionals have adapted as tools and working environments have changed before, AI is another substantial step in that process. It will continue to alter the balance of our work, and it opens up possibilities that are worth pursuing.
At GetSavi, we want the efficiencies of AI to create room for better learning and a fuller contribution from the people who design it.
“From builders to architects” describes that ambition – the important part is ensuring that efficient production gives those architects room to work.
