Can AI Improve the World? Eight Practical Possibilities

Understand what exists and what remains a perspective

The word AI covers very different tools. A system that analyzes sensor data, an editorial assistant, and a model that predicts biological structure do not work in the same way. The success of one does not prove that of the others. The results of GPT-6 Astra, presented in our file dedicated to the model, therefore do not allow us to attribute to it all the progress mentioned here.

For each sector, we separate three elements: a documented use or a clearly identified path, the desired benefit and the necessary condition to obtain it. This reading allows you to be ambitious without presenting a possibility as an acquired result. A technical demonstration can be a first step; Daily use also requires organization, trained users and a way to correct errors.

Health: simplify writing to help teams

The English NHS has published guidance on tools to assist with writing consultation notes. This documents the use of AI in the work of caregivers. This does not prove that any assistant can process medical data or that it automatically improves care. View the NHS publication.

The desired benefit is understandable: reducing part of the writing work to leave more availability to the teams. But an inaccurate rating can have significant consequences. The professional must be able to verify the content and the device must correspond to the healthcare setting. This article presents an organizational evolution; it does not recommend any tool for diagnosing or making a medical decision.

To judge an experiment, a question would be: does the writing time saved remain positive after proofreading and correction? Omissions, quality of information retained and patient experience should also be examined. Real improvement is measured across the entire journey. Removing a task from the screen doesn't help if it reappears elsewhere as errors to be fixed.

Education: offer an alternative explanation at the right time

A student may need to revisit the same idea with different words. An assistant could offer an example, break down reasoning, or help prepare a workout. UNESCO publishes guidance on the use of generative AI in education, with an approach centered on people, their autonomy and data protection. Read the UNESCO guidelines.

The interesting perspective is a help available when the student gets stuck. However, getting an answer is not the same as learning. A good exercise might ask the tool to ask one question at a time, then have the student explain their reasoning. The teacher plays an essential role in choosing the objectives and checking that the help is appropriate for the person’s level.

The useful criterion would be what the student knows how to do again without assistance. We can imagine a session followed by a comparable exercise, carried out alone. If understanding does not progress, the presence of a sophisticated tool was not enough. Age, access conditions and the rules of the establishment must also be taken into account before any use with students.

Disability: making certain information more accessible

Seeing AI from Microsoft illustrates a use already documented: describing images or reading documents to blind or visually impaired people. The editor presents in particular the reading of letters and descriptions of photographs. These are concrete functions of access to information. Discover the Seeing AI presentation.

Human interest is direct: being able to access information with greater autonomy. A description nevertheless remains likely to be incomplete or incorrect. It should not be treated as a guarantee for an action on which safety depends. The evaluation must above all be done with the people concerned: ease of use, real usefulness, errors and situations where other help is preferable.

For companies, this perspective also recalls a very simple responsibility: making their own content readable. Understandable forms, structured texts and described images remain useful. AI does not exempt you from designing a clear and accessible website for its visitors. Improving everyday life sometimes begins with this basic work.

Search: explore more leads without removing the experience

AlphaFold offers a distinct example of chat assistants. The database developed with EMBL-EBI provides more than 200 million protein structure predictions. These are predictions that can support research, not millions of validated clinical findings. View the AlphaFold presentation.

The hope is to help researchers choose the avenues to explore further. This can be compared to a more detailed map before an exploration: it guides the work but does not replace observation of the terrain. Experiments, validation methods and sharing of results remain essential. A promising calculation does not automatically become a drug, a treatment or an industrializable solution.

The potential change concerns access to hypotheses and information previously more difficult to produce. To appreciate the effect, it is necessary to follow the work carried out using these tools, their validations and their limits. Spectacular announcements are a gateway; scientific value is established over time and through collective work.

Agriculture: better understanding the needs of a plot

One avenue of digital agriculture consists of combining field observations, weather data and sensors to help decide when to intervene. The FAO notably mentions controlled irrigation and precision agriculture technologies in its work on agricultural practices. FAO document on agricultural census.

The possible benefit is an intervention better adapted to the observed need. But we cannot deduce from this a water saving or a guaranteed yield for each operation. Crops, soil, equipment and the quality of measurements change the situation. A recommendation is only valuable if it takes these conditions into account and remains understandable to the person working on site.

A serious test would define an area, a period, a goal and comparable observations. The decisions taken, their reasons and the results observed should be preserved. This logic is of particular interest to rural areas: the tool must adapt to the reality on the ground, including connection, maintenance and cost constraints.

Energy: looking for measurable waste

The International Energy Agency is examining the role of AI in optimizing energy systems, including buildings. The possibilities depend on the digitalization and the equipment available. The existence of a potential does not make it possible to promise an identical reduction for all sites. Read the IEA analysis.

For a building, a scenario would be to identify unusual consumption or to better adapt certain settings to the actual occupancy. The comparison should take into account weather, schedules and comfort. Otherwise, a decline due to a softer week could be incorrectly attributed to software. It is also necessary to count the resources consumed by the device itself.

The right objective would be a verified reduction for comparable service. A solution that reduces consumption but significantly degrades the conditions of use does not meet the same need. This idea applies to many AI projects: we must define what must remain of good quality when we seek to save time, money or resources.

Public services: better guidance without alienating people

DINUM presents Albert API as a platform allowing administrations to develop generative AI uses. There is also a user guide for state agents. These measures concern administration; they do not constitute a promise of automatic access for any company. See the Albert API offer.

One of the possible benefits is information that is easier to understand: explaining the steps of a process or helping an agent find a source. For this to truly improve the service, the response must link to the up-to-date official procedure. The citizen must also be able to speak to a person when he does not understand or his situation is out of the ordinary.

A reformulation should never be taken as an administrative decision. The interministerial guide recalls the responsibility of agents. The quality of a service is not only measured by the number of automatic responses: it is also seen in the files correctly directed and the difficulties actually resolved.

Small businesses: making knowledge easier to use

A small team could find a procedure more quickly, prepare a draft or organize requests. The interest depends on the problem encountered. The CNIL brings together practical sheets designed with France Num and the CPME to help small businesss and small businesss understand uses and precautions. Consult these files.

Our practical proposal is to start with a frequent and verifiable difficulty. If the team is always looking for the same information, the file on the documentary AI assistant presents an example method. If the problem is an unclear site, AI is not necessarily the first job. The objective must remain the improvement of the work and the service provided.

Frequently asked questions about useful progress

Are all these uses based on Astra?

No. The examples cover several technologies and organizations. A conversation model, an accessibility tool and a scientific system are not interchangeable. Bringing them together in a reflection on AI allows us to understand the possibilities, but each use requires its own proof and its own evaluation conditions.

How to recognize a real benefit?

Ask which problem was solved, for whom, for how long and with what errors. Also compare the total cost and side effects. An impressive video is not enough. Our guide to check AI answers gives reflexes applicable to common tasks.

What can ISS-AGENCY do in this development?

ISS supports site, visibility and automation projects for businesses. We do not present ourselves as a medical laboratory or an expert in each sector mentioned. Our role is to make digital possibilities understandable and to frame appropriate uses. The contact page allows you to present a concrete need before choosing a solution.