Generative AI in business: use cases and deployment

Only 32% of French SMEs and mid-sized companies actually use artificial intelligence, and 26% have taken the plunge into enterprise generative AI (Bpifrance Le Lab, 2025). Yet, 58% of their leaders consider it important for the long-term viability of their business. The gap between interest in these new technologies and their actual deployment is not a question of technology. It's a question of methodology.

This guide doesn't explain why generative AI will transform your organization. It explains how to deploy it in practice, which use cases to start with, and how to avoid costly mistakes.

• 32% of French SMEs and mid-sized companies use AI, including 26% generative AI (Bpifrance Le Lab, 2025)
• Productivity gains are measured primarily on targeted use cases, task by task.
• Data governance is one of the main obstacles cited by CIOs

What does generative AI mean for your profession?

Generative AI refers to the family of artificial intelligence systems capable of producing original content: text, code, image, audio, video. Unlike traditional AI systems that analyze and classify existing data, generative AI creates new content from a natural language instruction called a prompt.

It relies primarily on three families of models. Large Language Models ( LLMs ) process and generate text and code. Broadcast models generate images and videos. Audio models synthesize and transform audio content. In enterprise environments, Large Language Models are the focus of most operational deployments, using tools such as ChatGPT, Copilot, Gemini, Mistral, and DeepSeek.

Breakdown by profession

Generative AI for your profession is not deployed in the same way depending on the function. Here are the most mature applications by domain.

HR teams use generative AI to write job offers, summarize CVs, prepare interview grids and produce personalized training materials.

Legal teams use it to summarize contracts, identify risky clauses, prepare summary notes and assist with regulatory monitoring, particularly on GDPR compliance issues.

Marketing teams use it to generate editorial content, adapt messages to different target audiences, analyze customer data from social networks and digital channels, and automate the production of performance reports.

Development teams use it to generate code, detect bugs, write technical documentation, and accelerate code reviews. GitHub Copilot, Cursor, and the code assistants from major vendors (OpenAI, Anthropic, Google) are currently the most widely used.

Finance teams use it to automate the production of reports, synthesize complex management data, and prepare forecasting analyses.

Public sector organizations use it to improve citizen access to services, automate the processing of routine requests, and facilitate the production of administrative documents.

Priority use cases for enterprise generative AI

Not all tasks are equally suited to automation using generative AI. Here are four areas to prioritize based on their added value and deployment feasibility.

Automation of repetitive tasks

Drafting meeting minutes, summarizing lengthy documents, translating, and formatting reports: these low-value tasks consume a significant portion of employees' time. Generative AI handles them in seconds with a level of quality sufficient for the vast majority of professional uses.

Content generation and synthesis

Writing articles, internal communications, presentations, and responses to calls for tenders. Generative AI doesn't replace subject matter expertise; it accelerates its creation. An expert can produce the same document in a fraction of the time with a well-configured LLM.

Software development assistance

Code generation is one of the most mature and measurable use cases. Development teams using AI assistants report significant time savings on repetitive coding tasks, along with improved automated test coverage.

Customer relations and internal support

Building chatbots capable of answering frequently asked questions, qualifying incoming requests, and directing users to the appropriate resources. These chatbots leverage the organization's knowledge base and integrate with existing customer service tools. The productivity gains for support teams are typically the quickest to measure.

Agentic artificial intelligence: the next step

Agentic artificial intelligence is a major evolution of classic LLM. Where an LLM responds to a single instruction, an AI agent is capable of autonomously chaining actions to achieve a complex objective: searching for information, calling APIs, making intermediate decisions, and producing a final deliverable.

There are many emerging use cases: competitive intelligence agents that automatically collect, synthesize and disseminate relevant information, lead qualification agents that analyze CRM data and produce business recommendations, or project management agents that track progress and proactively alert on risks.

However, governance challenges are more complex than for traditional LLM. An autonomous agent operating within production information systems requires strict safeguards: a clearly defined scope of action, traceability of each action, human validation of high-impact decisions, and regular behavioral audits. Open innovation around these agentic architectures is progressing rapidly, but operational maturity remains to be developed in most organizations.

How to deploy generative AI in a secure way within a company?

The four deployment models

The choice of deployment model determines the level of control over data and the total cost of ownership of an enterprise generative AI project.

Consumer SaaS solutions ( ChatGPT, Copilot, and Gemini in their free or individual versions) offer the fastest setup but the least control over data. Submitted information can be used to train models according to the terms and conditions, which raises data privacy concerns in a professional context. Enterprise versions (ChatGPT Enterprise, Copilot for Microsoft 365, and Gemini for Google Workspace), on the other hand, commit to not using data for training.

The private API (OpenAI API, Mistral API, Gemini API on Google Cloud) offers more control over data with contractual commitments not to use it for training purposes. However, the data still passes through the vendor's infrastructure.

Unlike standard public cloud , sovereign cloud (SecNumCloud hosting, HDS) allows the deployment of open-source models on a certified infrastructure within the European Union. This is the recommended compromise for organizations that process sensitive data without wanting to manage an on-premises infrastructure.

On-premise deployment offers maximum control over data and facilitates GDPR compliance. It requires a suitable GPU infrastructure and a technical team capable of maintaining the model in production.

AI Governance: The Three Pillars

Deploying enterprise generative AI without governance is a risky deployment. Three pillars structure robust governance.

The usage policy defines what collaborators can and cannot submit to the model, the authorized use cases, and the mandatory human validation processes for certain outputs.

Employee training is crucial for adoption and the quality of results. Prompt engineering, even at a basic level, significantly improves the relevance of outputs and reduces the risk of misuse. It has also been a legal requirement since February 2025: the AI Act mandates that organizations using AI systems must train their employees on these tools (Article 4).

Continuous auditing and monitoring make it possible to detect deviations, ensure regulatory compliance and measure the real ROI per use case, to adjust the deployment strategy as learning progresses.

Smile, your partner for deploying generative AI

At Smile, we support organizations in their strategy for deploying generative artificial intelligence from the first experiments to industrial production.

Smile adopts an approach that covers the entire chain: identification of priority use cases, selection of suitable models, deployment on sovereign infrastructure, integration into business workflows and training of teams.

We operate in all sectors of activity, from the public sector to large private companies, with particular expertise in data security, digital sovereignty and regulatory compliance issues.

Our conviction is simple: generative AI creates long-term value when the right use cases are implemented methodically, within validated scopes, and with governance adapted to the organization's maturity.

Do you want to accelerate the deployment of enterprise generative AI in your organization? Discover our expertise and feedback from the public sector .

Frequently asked questions about generative AI in business

What is the difference between generative AI and classical artificial intelligence?

Traditional AI analyzes existing data to classify, predict, or recommend it. It recognizes patterns in structured data. Generative AI produces new content from a natural language instruction.

It not only recognizes patterns, but it also recomposes them to create something new. This creative ability is what makes it particularly useful for writing, summarizing, and generating code.

How to measure the ROI of a generative AI deployment in a company?

Three metrics are the most reliable: time saved on targeted tasks (measurable before/after), reduction in the unit cost of producing a deliverable (content, code, report), and end-user satisfaction with the quality of outputs.

ROI is measured on a use case-by-use case basis, not globally. A successful deployment always begins with one or two pilot use cases where gains are measurable before expanding the scope.

Is generative AI accessible to SMEs or only to large companies?

It is accessible to organizations of all sizes. SaaS solutions like Copilot or ChatGPT Enterprise are available from just a few tens of euros per user per month. Open-source models allow for customized deployments at very competitive costs.

The real barrier is not financial, it is methodological: an SME that deploys generative AI on a targeted use case with appropriate support obtains results comparable to those of a large company.

What are the main risks associated with deploying generative AI in business?

Four main risks must be anticipated. The first is the leakage of confidential data if sensitive information is submitted to a cloud model without sufficient contractual guarantees. The second is hallucinations, meaning incorrect or fabricated outputs from the model, which necessitate systematic human validation of high-stakes content.

The GDPR compliance risks associated with the processing of personal data. And the risks of dependence on a single vendor, which can be mitigated by using open-source models and local deployment.