Sub-shift
“INCOME RESILIENCE”
A useful offer needs to survive a change in the customer’s working life.
Someone does not have to lose a job to reconsider a long contract. Uncertain hours, a change in hiring or a worry about next year can be enough to make flexibility more attractive. The consumer decision arrives before an economist can deliver a settled verdict.
“INCOME RESILIENCE” follows how changing expectations of earnings could affect commitments, spending and willingness to experiment. AI may contribute to those expectations, but the effects will differ by occupation, household and place. Some people could become more confident as assistance improves their opportunities.
For organizations, that makes a customer’s ability to adjust an important part of the relationship. An offer designed around uninterrupted progress may fit less well when work changes.
Follow the household beyond the headline
The ILO’s 2025 index estimates occupational task exposure to generative AI. It is not a count of jobs lost, and it emphasizes transformation rather than full replacement as the more likely outcome. [84]
Stanford’s August 2026 Canaries update reports no widespread economy-wide displacement in its analysis, alongside weaker employment for young workers in more exposed occupations. The authors describe early descriptive indicators, not causal estimates. Controls and data sources affect the picture. [85]
The consumer question is what people do with their own circumstances. Do they shorten commitments, postpone a purchase, seek another source of income or feel able to try something new? Those responses need to be observed directly. An exposure score cannot tell you how a particular customer will behave.
Make room for a changed year
You could let customers pause a non-essential service, preserve work they have created or move between plans without punitive fees. A clearer account of total costs may be more useful than a reassuringly small monthly figure that hides a difficult commitment.
Training and adaptation services have an opportunity too, provided they can demonstrate useful learning. Selling immunity from technological change is a promise no course can reliably make. Customers should understand what a service helps them do and what remains beyond its control.
Individual products cannot supply the employment practices, income protection or public services that shape financial security. They can avoid adding unnecessary fragility. Over the next one to three years, that could become a reason to choose one provider over another.
Households whose earnings and opportunities improve may be ready for more ambitious offers. Lower prices could also give people room to try something they previously ruled out. Flexibility can support that confidence as well as provide protection through a difficult period.
THE SOURCE NOTESFollow the thinking 2 sources · what they show and where their limits lie
Sources and further reading
These sources inform the article. A product announcement describes an offer, not how widely people use it. Arguments and imagined futures are kept separate from observed results.
The index estimates potential exposure of occupational tasks. It does not count observed job losses or predict an individual’s prospects.
These are descriptive employment patterns, not estimates of AI’s causal effect. The analysis did not find broad displacement; the sample and controls affect the picture.
About this research edition
This article draws on selected sources and our interpretation of their implications for consumers. The wider collection contains 50 articles and original illustrations. We have not read everything by everyone on the reading list, calculated the probability of each future or established worldwide adoption. The collection is not exhaustive.
The illustrations are AI-generated, with a visual approach informed by TrendWatching’s 2024 and 2025 reports. They depict ideas rather than observed events. If an article’s central idea changes, its image needs another look. Small edits need not mean a new illustration; automatic replacement is not active.
Each innovation has a source, a review date and an explanation of its relevance and limits. Some are early signs of the change; others offer a useful comparison. None proves the forecast. Monthly monitoring and automatic selection of new examples are not running.