Jobs to Be Done
The jobs-to-be-done framework asks a different question from conventional market analysis. Conventional analysis asks who the customer is and what the customer wants. The jobs framework asks what job the customer is hiring the product to do. The distinction is not semantic: it is the difference between describing purchasing behavior and explaining it. A job is a progress the customer is trying to make in a particular circumstance. The product is the candidate hired to make that progress. Competitors for any given job are not necessarily products in the same category — they are any candidate, in any category, that could do the job. Applied to AI, the framework reveals that the language interface is being hired for a specific job: closing the gap between what a person can imagine and what she can build.
In the AI Story
The classic illustration remains the milkshake case. A fast-food chain wanted to sell more milkshakes. Conventional segmentation by demographics and flavor preferences produced no actionable insights. Researchers then watched who actually bought milkshakes and when. A significant portion of purchases occurred in the early morning, by commuters driving alone. These customers were not buying because they wanted a milkshake; they were hiring the milkshake to make the boring commute more bearable. The milkshake's competitors for that job were bagels, bananas, boredom itself, and podcasts — none of which appeared in any conventional competitive analysis.
Applied to AI, the framework explains dynamics that product-category analysis entirely misses. The job AI is hired to do is not 'write code.' If it were, AI would be a sustaining innovation for the existing software development market, adopted at enterprise procurement speeds within the bounds of the existing developer population. The job is closing the imagination-to-artifact ratio — the distance between a human idea and its realization. This job has been waiting, partially unfilled, for the entire history of computing. Every previous interface narrowed the gap incrementally. The language interface closed it, and adoption occurred at the speed of recognition.
The framework also explains concentrated adoption among non-developers, the population conventional analysis would consider least likely to adopt a software development tool. Non-developers had the job most acutely — the imagination-to-artifact gap was, for them, absolute. They could imagine the tool they needed but could not build it, because building required translation skill they did not possess. When AI abolished the translation requirement, the population that had been entirely unserved became a market.
The framework reveals the unbundling dynamic that applies across professions. Every professional role bundles multiple jobs. The software developer bundles translation work (converting specifications into code) with judgment work (deciding what specifications are worth writing). AI performs the translation job with increasing competence, and the judgment job remains — revealed as the core of professional value, which was always invisible under the bundled role. The same unbundling operates in legal work, medicine, financial advising, and education.
Origin
Christensen developed the framework across his middle career, introducing it in The Innovator's Solution (2003) and formalizing it in Competing Against Luck (2016), co-authored with Taddy Hall, Karen Dillon, and David S. Duncan. The framework has roots in marketing research and the work of Peter Drucker, but Christensen's treatment gave it the causal structure that makes it predictively powerful.
Key Ideas
Products are hired, not bought. Customers hire products to make progress on jobs; the job, not the product category, defines the competitive landscape.
Circumstances matter more than demographics. The same customer hires different products for different jobs in different circumstances.
Non-consumption is the largest market. For most jobs, the population that currently goes unserved is larger than the population served by existing products.
Jobs unbundle under technological pressure. Professional roles that bundle multiple jobs can be decomposed, with different jobs migrating to different providers.
AI's job is imagination-to-artifact. The specific job AI has been hired to do is closing the gap between human ideas and their realization.
Appears in the Orange Pill Cycle
Further reading
- Clayton M. Christensen, Taddy Hall, Karen Dillon, and David S. Duncan, Competing Against Luck: The Story of Innovation and Customer Choice (Harper Business, 2016)
- Clayton M. Christensen, Scott Cook, and Taddy Hall, "Marketing Malpractice: The Cause and the Cure" (Harvard Business Review, 2005)
- Anthony W. Ulwick, Jobs to Be Done: Theory to Practice (Idea Bite Press, 2016)