A few weeks ago, during a trip to the US, I was lucky enough to find myself standing on the patio of the Stahl House in the Hollywood Hills — a glass-and-steel architectural icon perched high above Los Angeles.
The view from the patio is breath-taking; and as a lover of mid-century minimalist architecture, I found the house equally stunning.
From that patio vantage point, the other striking aspect was the contrast between the hills immediately below the patio and the distant cityscape. Way below, the city stretched out in a perfect grid: flat, predictable, orderly. But right in front of me, the landscape fell away in uneven ridges — sharp, fractured, unpredictable.
I was reminded of the sharp, uneven edges of those hills the other day when I came across the term jagged edge - to describe what the world of AI looks like at the moment.
So, let’s explore the meaning of that term and what it might mean for L&D.
Often, we talk about AI as though it’s spreading evenly across everything; but it isn’t. It’s advancing in fits and starts — powerful in one moment, incredibly clumsy in the next. Researchers have named this unevenness the jagged edge of AI.
Understanding the Jagged Edge
You may well have experienced this jagged edge first hand, when AI is strong and accurate in some areas but weak and unreliable in others.
It can draft a pretty good course outline in seconds. It can generate accurate code that works. Yet still produce nonsense when asked to evaluate a moral dilemma.
This unevenness exists because AI doesn’t understand; it predicts. It excels where data is abundant and patterns are clear — structured writing, number crunching, summarising, and retrieval. It falls short where context, empathy or tacit knowledge contribute significantly to the outcome.
Evidence from the Research
This metaphor for uneven progress is backed by evidence. In 2023, researchers from Harvard Business School and Boston Consulting Group ran a field experiment exploring how AI affected knowledge workers. They called their study Navigating the Jagged Technological Frontier.They found that workers using AI completed 12% more tasks and worked 25% faster when those tasks fell within AI’s frontier — areas where AI already performed well.
But when tasks sat just beyond that frontier, the results flipped: performance declined.
In other words, AI’s progress isn’t smooth or universal. It’s jagged. Full of peaks and troughs that shift depending on the nature of the work.
The Human–AI Division of Labour
At first glance, that jaggedness can seem like a flaw. But if you think of it more like a map or a bunch of helpful road signs — it usefully highlights where humans and machines each add the most value.- AI excels at automating structure: generating first drafts, summarising information, categorising, and identifying patterns.
- We excel at interpreting complexity: spotting anomalies, understanding tone, making ethical calls, and connecting dots in ambiguous situations.
When we understand where the jagged edge runs, we can design smarter systems and workflows that let each party play to its strengths.
What This Means for L&D
For L&D professionals, that insight is particularly useful. It tells us the real opportunity isn’t just using AI generically. It’s about recognising where AI’s edge lies in our own jobs — what can be safely automated, and what still needs human input and decision-making.For example:
- A learning designer can use AI to structure a skills framework (below the edge), but must still apply contextual awareness to fit it to their own organisation (beyond the edge).
- A subject matter expert might use AI to generate examples or case studies (below the edge), but they’ll need to refine and validate accuracy, richness and complexity of those examples (beyond the edge).
It’s going to take us all a while to get used to this ‘new frontier’; and we’ll need to be ready for the jagged edge to shift as AI technology evolves. But recognising and navigating the jagged edge confidently, within our own job roles, could massively transform our productivity and effectiveness.
Reference:
Dell’Acqua, F., Kalliamvakou, E., Kirov, S., Ransbotham, S., & Rock, D. (2023). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Harvard Business School Working Paper 24-013.
