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AI Can Do More. What Does That Make Possible for Us?
Whether those capabilities expand our choices depends on how we put them to work.
Imagine an older woman waking in the night. A robot beside her bed extends a support rail. She steadies herself and stands. What matters to her is being able to get up when she chooses and continue living in a familiar home.
That is a different measure of progress from a model’s place on a leaderboard. The question is what AI enables people to do and choose in their own lives. That depends on resources, judgment, and arrangements that keep the help available.
Even a successful deployment leaves those questions open. A care home could use time saved by robots to give residents more human attention. It could also use that time to justify fewer staff. A machine’s capabilities do not decide which happens.
The gap becomes clearer when AI enters work people already know how to do, with varying degrees of experience.
In a field study, 19 train commissioning technicians completed 173 troubleshooting tasks. An AI assistant provided project-specific information and troubleshooting support, improving performance especially among less experienced participants.
For a newcomer, finding relevant knowledge can mean tackling a task that previously required waiting for an experienced colleague. That is a real gain, even with continued reliance on the assistant. It does not establish that technicians acquired expertise they could use independently; lasting learning remains a separate question.
Giving people advice presents a further difficulty: they must decide whether it fits.
A five-month experiment involving 640 Kenyan entrepreneurs compared access to a GPT-4 business assistant on WhatsApp with standard business guides. There was no statistically significant average improvement in business performance. Relative to the control group, AI access hurt businesses with lower initial performance; for those with higher initial performance, there was tentative evidence of gains.
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The researchers’ analysis suggested that higher-performing entrepreneurs selected advice suited to their circumstances, while lower-performing entrepreneurs more often adopted generic strategies such as price cuts and increased advertising.
A shop owner has to judge those suggestions against cash flow, stock, nearby competitors, and customers’ habits. Advice can be easy to obtain while a workable course of action remains difficult to find. The owner bears the cost of getting it wrong.
These differences should change what we ask of organizations adopting AI. Buying access is only the beginning. People need the means to assess suggestions, act on them, and recover when they fail.
In care, this includes deciding how assistance changes relationships. A Japanese nursing-home panel study found robot adoption associated with higher employment and staff retention, alongside improvements in some care-quality and productivity measures. Workers shifted effort toward tasks requiring human attention and contact.
The findings are observational; they do not establish that robots caused the improvements. They nevertheless point toward questions a demonstration cannot answer. Who maintains the equipment and takes over when it fails? Can a resident refuse robotic help? How will saved time be used?
A robot may support someone’s body. The care arrangements shape how much independence that support gives them.
Useful help also needs a life beyond a single encounter. A technician’s troubleshooting method must be available to the next shift. A shop owner needs records of what worked. A care team needs training and dependable maintenance.
AlphaFold’s open database illustrates how AI outputs can become resources others can retrieve and examine. Smaller workplaces need their own ways to retain useful results, correct errors, and pass knowledge on. Without them, each successful interaction can remain an isolated event.
These arrangements require investment. An OECD report, drawing on evidence from G7 countries and Brazil, identifies barriers involving skills, data, infrastructure, and management. Managers can struggle to identify suitable problems and underestimate the organizational changes adoption requires.
A more capable model does not, by itself, provide those conditions. People and institutions remain responsible for deciding who controls the data, sets work targets, receives the gains, and bears the costs of mistakes — even when AI is involved in those decisions.
We should judge AI by what people can actually do with it in the circumstances they face. Can the technician use the available knowledge to diagnose a fault and decide what to do? Can the shop owner make a decision they can afford to carry through? Can the older woman get up safely, choose how she is helped, and still find human company when she needs it?
The woman at the bedside wants to keep living in her own home. For her, progress means having the support to keep that choice open.
