Generative AI for business: use cases, benefits and how to adopt it (2026)

A supplier comparison is waiting for approval. Sales needs a proposal, and the monthly spreadsheet still needs someone to explain what changed. Generative AI can help prepare each piece of work. Its value depends on how useful the result is once someone has checked it.

In 2025, 20% of EU enterprises with at least ten employees used AI technologies, up from 13.5% in 2024, according to Eurostat. This covers several kinds of AI across the business sectors surveyed. It is not a measure of generative AI alone, or evidence that every adopter earned a return.

For a business with twenty people, a useful first project can be a task that already needs doing. Choose the output, gather the information it requires and decide who will approve it.

What is generative AI for business?

Generative AI creates and reshapes text, images, video or code from instructions and reference material. Businesses use it to prepare outputs such as proposals, supplier comparisons and reports, with a person checking the work before it is used.

You can give an instruction in everyday language: “Compare these terms and flag anything we still need to confirm.” A clear purpose, named sources and a requested format make the answer easier to assess.

Some jobs need a formula or an existing business system. Adding up invoices does not require generative AI, while a reliable forecast needs suitable data and validation. An assistant can explain a calculation or write the commentary; fluent commentary alone does not validate the analysis.

Six generative AI use cases for business

The main use cases span information search, content production, conversational assistance, software development, data analysis and process automation. An AI workspace can handle several preparation tasks; persistent access to internal systems and autonomous actions require additional tools and controls.

The following illustrative examples follow an office-supplies distributor with twenty employees. They are not measured customer results.

1. Research and information synthesis

The purchasing manager is comparing warranties from three manufacturers before adding a product range. She collects their public PDF terms and asks for a table of cover, exclusions, deadlines and the source for each entry. Missing information should be marked as missing.

Ilisai supports web research with cited sources and work with PDFs through compatible models. The manager checks the original terms before recommending a supplier. Searching a whole company archive continuously, with different access rights for each employee, calls for a connected document-search service. Such services often use retrieval-augmented generation, or RAG, to find relevant material for an answer. Ilisai does not provide custom RAG.

2. Content creation and adaptation

Marketing starts with an approved product specification and prepares sales copy, a distributor presentation and a French version. Dimensions, materials and terms of sale must stay accurate. Tone and length can change to suit the audience.

The AI document generator produces DOCX, PDF, PPTX and XLSX files to download and review in your usual applications. You can also create assets with the AI image generator and AI video generator. Check that visuals preserve the product's actual features before publishing them. Video generation requires credits from a paid plan or a separate purchase.

3. Conversational assistance and practice

A new employee needs practice handling returns. A colleague provides the approved policy and asks the assistant to play through fictional cases: a late request, opened packaging or a faulty item. The assistant drafts replies and identifies unanswered questions; the colleague removes any promises the business cannot honour.

Ilisai supports this preparation in chat. An assistant on your customer website that reads orders and approves returns needs a connected customer-service product and the relevant controls. Ilisai does not offer that service.

4. Software development support

Supplier exports arrive with inconsistent column names. The distributor's technical contractor can use AI to draft a small program that standardises the files, explain an error and propose tests using fictional data.

A chat assistant can help write and explain code. The contractor must still check which files it changes, how it handles failures and whether it works on the actual formats. Deploying and maintaining an application requires development tools and someone responsible for it; generating code in ilisai does not provide that ongoing service.

5. Data analysis and decision support

The owner exports sales to CSV or XLSX to investigate which product categories have the most returns. She can request totals, an interactive chart and a downloadable report, including an explanation of excluded rows and how each measure was calculated.

Ilisai can analyse these files and display interactive charts in the conversation. The owner checks totals, dates and cancelled orders against the original export. A rise in returns gives her something to investigate; the chart alone cannot establish the cause. The guide to AI for business intelligence and data analysis develops this workflow. For automatically refreshed information and connected dashboards, choose an analytics platform that provides those connections.

6. Workflow preparation, automation and agents

When a supplier sends a new quotation, the purchasing manager can ask AI to extract the terms, compare them with her requirements and draft follow-up questions. She checks the document and sends the reply.

Automatically collecting the email, checking stock and placing an order would require connections between applications and explicit permissions. An agent can choose actions within those permissions, so the business needs to define its limits and how a person can intervene. Ilisai can prepare the analysis and documents in this example. It does not offer autonomous agents or connectors to carry out the process in your business systems.

What benefits should your business measure?

Measure the time it takes to reach an approved result, the quality of that result and whether it can be usefully adapted for other audiences. Include preparation and review: producing a quick draft is only one part of finishing the job.

For the distributor, a fair comparison uses the same supplier documents for a manual table and an AI-assisted one. The reviewer records missing conditions, unsupported entries and the time needed to approve each version.

Personalisation needs an acceptance test too. A proposal for a school and one for an accounting firm might emphasise different needs, while preserving the same correct specifications and commitments. Sales can assess whether each version explains the offer better for its recipient.

AI can also help the marketing team produce layouts or a video script to discuss before committing to a campaign. Those materials support a decision about an idea. Future sales remain an assumption to test.

The total cost of AI ownership includes training, checking and maintenance alongside tool usage. The result of your trial may support wider use, a different task or stopping the experiment.

What makes adoption difficult?

Practical obstacles include incorrect answers, limits on what information the company can share and the work required to fit AI into an existing process. Each use needs adequate source material, an accountable reviewer and a reason to stop if the trial is not working.

The French data-protection authority, CNIL, warns that generative systems can produce plausible but inaccurate answers. In a supplier comparison, an invented delivery deadline could change the recommendation. Ask the assistant to distinguish sourced facts, inferences and open questions, then check the originals.

Before entering customer or employee information, establish which information the company permits in that service and review the relevant processing, retention and reuse terms. Public documents or fictional examples are useful starting material. Assess sensitive information against the actual service and intended use.

Someone also has to prepare the files, review the output and save it where colleagues do their work. Without named responsibilities, the trial can create another unfinished task. A practical AI governance policy helps document who does what.

How to adopt AI in a small business in three steps

Start with a recurring task, test a suitable tool against clear acceptance criteria and adopt the process when the checked result justifies the effort. A first trial should leave you with finished work and instructions a colleague can follow.

1. Define the job and check readiness

“Compare supplier terms” gives you a manageable starting point. Choose someone who knows that work, gather the approved source documents and keep an example of an acceptable result.

Write down the checks: every quotation included, consistent currency, extra charges visible and no invented conditions. Record the time and cost of the current process. If the sources are unavailable or nobody can review the answer, resolve that gap first.

The guide to AI maturity in a business can help you place this trial in context. Your next improvement may be agreeing a procedure for something colleagues already do separately.

2. Choose an approach and run the trial

A ready-to-use workspace suits document preparation, research and occasional analysis. A job that depends on changing internal records needs a service with the appropriate connections and access controls. Custom development makes sense when the requirements justify maintaining it.

Give the options you are considering the same assignment and sources. Compare the checked output, time and cost. A small or fast AI model may be sufficient for an email or straightforward classification; use your own task to decide.

3. Make the process repeatable and review it

Save the instruction, input format and checking procedure. Ask a colleague to repeat the job. Their questions will reveal which steps still depend on the first person's knowledge.

Set a budget and a review date. Record errors and corrections so you can decide what to change, and retest the procedure when you change models or source material. This is a practical way to build AI capability in-house.

In the enterprise

Enterprise procurement needs evidence about access controls, data location and handling, service continuity and the ability to leave a supplier. A successful team demonstration should be followed by testing and contractual review appropriate to the proposed deployment.

Answers must apply to the configuration being purchased. A European billing address or a hosting label leaves other questions about processing unanswered. The sovereign AI guide explains those distinctions; the adoption data review separates AI use, buying preferences and sovereignty.

Include cases where the system should abstain, flag incomplete information or leave a decision to a person. These behaviours belong in the quality assessment alongside successful answers.

Where ilisai fits

Ilisai combines cited research, document generation, images, video and data analysis in a workspace with models from several providers. You can choose from the enabled catalogue and pay through usage credits rather than per-user AI licences.

Its EU-first approach prioritises European routing; it does not guarantee that every model processes every request exclusively within the EU. Check the service and model terms for the information you intend to use.

Begin with a public document and an assignment you know how to check. The Free plan has a limited allowance; review the current plans and pricing before arranging regular use or generating video.

For an example of several tasks across another business, follow a day at an accounting firm, with and without AI.