Generative AI in the Enterprise: What It Changes

Generative AI (ChatGPT, Claude, Gemini) was most companies' first contact with modern artificial intelligence. It writes, summarizes, rephrases and analyzes in a few seconds, but its results are only as good as the framework in which it is used. At Elkano Asset Management, an independent management company with 5 employees, generative AI applied to report production made it possible to go from 100% manual consolidation to investor profiles generated and updated automatically, with PDF export in one click. Here is what generative AI really does well, examples of uses by function, the limits to know before rolling it out widely, and how to structure its use in your company.

What generative AI really does well

Producing a first draft quickly. Email, meeting minutes, report, product description: generative AI excels at producing a usable draft in a few seconds, leaving the human to rework it. At Dana Avocats, this capability was applied to email replies: a complete draft is generated as soon as an email is moved into a dedicated folder, ready to be reviewed and sent.

Summarizing dense information. Summarizing a long document, extracting the key points, condensing several sources into a clear note: these tasks, which take a long time by hand, are among the most profitable.

Generating analyses and visualizations. At Elkano, each investor profile now comes with dynamically generated charts (portfolio evolution, asset allocation, risk indicators), which turn raw data into visuals that can be used directly in discussions with investors.

How generative AI works, in simple terms

A language model has been trained on a very large amount of text. It does not “know” things the way a database does: it predicts, word after word, the most probable continuation of a text given the request. This explains its two sides. It is remarkable at writing, rephrasing and structuring. But it can produce a false sentence with the same confidence as a true one, because it seeks plausibility, not truth. That is why any serious use relies on two safeguards: giving the AI the right sources, and reviewing the result.

Consumer tool or integrated solution: how to choose

A consumer tool is quick to adopt and inexpensive, but it does not know your data and raises confidentiality questions. A solution integrated with your tools and documents requires a project, but produces tailored, traceable answers. A good compromise is to start with the consumer tool on non-sensitive tasks, then switch to an integrated solution for uses that touch customer or financial data, as at Elkano.

The Elkano case: from manual consolidation to automatic report generation

At Elkano, client knowledge is at the heart of every investment decision. But before being able to analyze, the data first had to be consolidated: scattered internal data, calculations redone by hand, reports produced one by one. A time-consuming process for a team of 5 people whose time is the most precious resource.

Qolaig developed the Elkano Investor Profiles Platform, a custom application that centralizes internal data and generates complete investor profiles in real time, with their analyses, charts and associated PDF reports.

Results: 100% of investor profiles generated and updated automatically, zero manual consolidation needed to produce an analysis, and PDF reports generated in one click, ready to be shared immediately.

Concrete uses by function

Marketing and communication: writing content, adapting the same message across several formats, rephrasing a text for another audience. The gain comes mainly from the speed of producing the first draft.

Support and documentation: writing procedures, updating knowledge bases, standard answers to frequently asked questions. Our article on the enterprise chatbot shows how this content can feed an assistant.

Finance and management: generating reports and analyses, as at Elkano, where document production went from a time-consuming manual task to a one-click export.

Sales: preparing proposals, personalizing messages, summarizing call reports. A concrete example is described in our article on the AI copilot in the enterprise, with the Adelya case.

The limits to know before rolling it out widely

The risk of hallucination. Generative AI can produce false information with great apparent confidence. Any factual data must be checked before publication, especially in financial reports like Elkano's, where the accuracy of calculations is non-negotiable.

The question of confidentiality. Any sensitive information sent to a generative AI tool must comply with the company's confidentiality rules. Our article on agentic AI governance and security details the principles to apply, which are particularly relevant in a sector such as asset management.

The lack of context specific to your company. A general-purpose model knows neither your products, nor your procedures, nor your clients. Without access to your data, its answers remain generic. This is exactly what custom projects come to correct.

Common mistakes during deployment

Letting everyone use the tool on their own. Without common rules, each employee chooses their own tool, data and habits. The risks of leaks and the differences in quality pile up without anyone seeing them.

Publishing without reviewing. Content that is generated and distributed as is always ends up containing a visible error. Human review remains the step that protects the company's image.

Measuring enthusiasm rather than results. The number of accounts opened says nothing about the time actually saved. You need to compare the time spent before and after on specific tasks.

Neglecting copyright and attributions. Generated content can resemble existing works. For any published content, check the sources and have sensitive wording validated by the person responsible.

How to structure the use of generative AI in the company

1. Define the authorized uses and the limits. Which types of content can be generated, which require systematic validation, and which data must never be entered into an external tool. A one-page charter is enough.

2. Train teams to write good instructions. The quality of the result depends on the clarity of the request. Knowing the difference between a chatbot, an AI agent and a simple automation also helps choose the right tool for each need.

3. Keep a systematic human review. At Elkano as at Dana Avocats, every piece of generated content is reviewed before being sent or published: the AI prepares, the human validates.

4. Measure the real gain. Time saved, volume of content produced, rate of corrections needed: without indicators, you cannot know whether the tool really changes the teams' work.

5. Update the rules. Tools and uses change quickly. A review of the charter and of the list of authorized uses every three months makes it possible to add new cases and remove those that are not used, without waiting for an incident.

According to the Liferay 2026 Agentic AI Adoption and Governance Report, data analysis and reporting top the list of AI uses in companies with 35% of responses, ahead of customer service at 32%. This confirms the central place that report generation is taking in current AI projects.

FAQ

What is the difference between generative AI and an AI agent?
Generative AI produces content on demand. An AI agent goes further: it can act autonomously over several steps, not just generate a text or a report. Our article on agentic AI governance explains why this autonomy calls for a specific framework.

Can generative AI replace a financial analyst?
The Elkano case shows the opposite: it takes care of consolidation and formatting, while human expertise remains focused on analysis and advice, the highest-value part of the job. Field experience consistently shows that the gains come from removing low-value tasks, not from replacing expertise.

At Qolaig, our Workshop → Build → Run methodology frames this type of project before any technical investment. Let's talk about your project.