Generative AI for business: use cases, tools and governance

In short: generative AI pays off for a business when it works on a specific process, with the company's own data and human review: product descriptions, draft quotes, support replies, sales content, analysis of reviews and tickets. Choose it by starting from the process rather than the tool, govern it with written rules and KPIs, and scale only after a measured pilot.

Key takeaways

In 2025 roughly one in ten Italian companies with 10 or more employees already used generative AI.
The best returns come from repetitive, text-based work: product copy, quotes, customer support, sales content, analysis of reviews and requests.
The main risk is quality: every customer-facing text needs a review, and the review has to be real.
Google does not penalise generated text as such, but treats pages mass-produced without value for readers as spam.
Under Italian law, content created with the help of AI is protected by copyright only if it results from a person's intellectual work.
The EU AI Act already requires staff training measures and transparency towards people who talk to a chatbot.

Why generative AI matters now for online stores and B2B companies

According to Istat, Italy's national statistics office, 16.4% of Italian companies with at least 10 employees used at least one AI technology in 2025, double the 2024 figure. Of those, 59.1% used generative AI: about one company in ten overall. Across the EU, Eurostat finds that marketing and sales are the most common purpose among AI users (34.7%). Among firms that considered AI but did not adopt it, the main barriers were lack of expertise (70.9%), unclear legal consequences (52.5%) and data protection concerns (48.8%).

Other Istat figures show where it is used. Among Italian companies using AI, the most common technology is extracting information from text documents (70.8%), followed by generative AI (59.1%) and speech recognition (41.3%); generative AI and text analysis show up mostly in marketing and sales and in administrative processes. The gap between large companies and SMEs is widening: about 20 percentage points in 2023, 37 in 2025. In Italy too, skills are the main barrier: they hold back almost 60% of the companies that considered AI but did not invest.

This guide tackles those barriers: where to use it, how to choose it, how to govern it and how to measure it. For the wider picture, read how AI is changing business models beyond software; more articles are in the AI for business section.

What is generative AI and how is it different from traditional AI?

Generative AI is a family of models that produce new content (text, images, audio, video, code) from an instruction called a prompt. NIST defines it as the class of models that emulate the structure and characteristics of their input data to generate synthetic content. The ones most used in business are large language models (LLMs), neural networks trained on huge amounts of text. Traditional AI classifies or predicts instead: it forecasts demand, flags fraud or reads a document.

RAG (retrieval augmented generation) is the technique that lets the model pull information from company documents before answering: price lists, technical sheets, terms of sale.
Confabulation, often called hallucination, is content that sounds confident but is false. NIST lists it among the 12 risks that are unique to or made worse by generative AI.
An AI agent is a system that uses a generative model to take actions: look something up in a management system, fill in a form, update a record. The more actions it can take, the more it needs narrow permissions and checks. See the articles on AI agents reshaping business software and on AI browser automation.

The practical difference: traditional AI is judged on the accuracy of a prediction, generative AI on the correctness of a text someone will read. That is why human review matters more.

What are the practical business use cases for generative AI?

The processes that work repeat often, start from data you already have and produce output you can check. These are the ones worth starting with.

Use case	Ecommerce example	B2B example	Human check	KPIs to track
Product descriptions	Copy for hundreds of items from catalogue attributes	Readable technical sheets from bills of materials and certifications	Sizes, materials and claims verified	Time per listing, errors, conversion
Quotes and proposals	Replies to large or custom order requests	Draft proposal from the request, price list and customer history	Prices and terms approved by sales	Response time, win rate
Customer support	Answers on shipping, returns and sizing	First-line technical support from manuals	Handover to an agent on exceptions	Tickets resolved per hour, first response time
Marketing and sales content	Emails, campaign copy, ad variants by segment	Application stories, follow-up emails, LinkedIn posts	Tone, data and quotes reviewed	Hours saved, clicks, leads
Analysis of reviews, tickets and requests	Recurring themes in reviews and return reasons	Common customer questions and objections logged by sales	Sample of original texts reread for each theme	Issues found, returns, analysis time
Translation and localisation	Product pages for foreign markets	Catalogues, proposals and documentation for export	Native-speaker review of published text	Time and cost per language, errors
Search across internal documents	Customer service answers on policies and products	Assistant for sales and technical staff on manuals, price lists, specifications	Source shown in every answer	Search time, correct answers
Images and prototypes	Lifestyle shots, banners, creative variants	Packaging mock-ups, presentations, product concepts	Real look of the product left unchanged	Production time, cost per asset

Product descriptions

For an online store with a large catalogue, the value lies in consistency across thousands of listings rather than in creativity. The risk is that the model adds features that are not in the data: the prompt must forbid it and a person spot-checks the output. More examples are in the article on AI in retail.

Quotes and proposals

In B2B, the time between request and proposal affects the chance of closing. AI reads the request, retrieves the price list and history and prepares a draft; prices and terms remain a sales decision.

Customer support

This is the use case with the strongest evidence. A study published by NBER on 5,179 support agents found that a generative AI assistant raised issues resolved per hour by 14%, by 34% for the least experienced, with minimal effect on the most experienced.

Marketing content and personalisation

AI produces variants of emails, ads and posts for different segments, plus draft campaigns to test before committing budget. The value comes from your own data: an email tailored to the customer's industry and purchase history is a different thing from generic copy. In B2B the next step is account-based marketing, with messages built around a single account. Without a check on tone, though, the copy tends to look like everything else, including competitors' copy: NIST calls this effect homogenization.

Analysis of reviews, tickets and requests

Reviews, tickets, emails and sales notes hold market signals that nobody has time to read in full. A model can group these texts by theme (defects, sizing doubts, price objections, feature requests) and summarise them, surfacing trends that sales figures do not show. Before deciding anything, reread a few original texts for each theme: a summary can overstate one problem or miss another.

Translation and localisation

For companies selling abroad, AI speeds up translating and adapting product pages, proposals and documentation. NIST notes that a model may perform worse in languages other than English: published text needs a native-speaker review, especially for technical terms and sales claims. Tools are compared in the guide to AI translation software.

Search across internal documents

An internal assistant connected through RAG to manuals, price lists and specifications answers sales and technical staff while pointing to the document the information comes from. It is a good starting point because the users are internal and can check the source before using it with a customer. Its value depends on how good and up to date the documents are, as the article on AI that uses operational context explains.

Images and prototypes

Generated images work for lifestyle shots, banners, creative variants and packaging mock-ups to validate before production. They must not change how the product looks: colours, sizes or finishes that differ from reality set the wrong expectations and lead to returns. The AI Act requires deep fakes to be disclosed, meaning generated images, audio or video that resemble real people, objects or places and could pass as authentic.

What are the opportunities and risks of generative AI?

Opportunities. Drafts ready in minutes instead of hours, communication tailored to each customer segment, text or image prototypes to test an idea before investing, analysis of large volumes of text that nobody used to read. The NBER study also points to a less visible effect: the assistant passes the practices of experienced colleagues on to new hires.

Risks. NIST's generative AI profile lists 12 risks; for an online store or a B2B company, these are the most concrete:

Confabulation: an invented technical spec, price or return condition.
Privacy: customer data or confidential offers entered into tools that do not guarantee where the data ends up.
Intellectual property: text or images too close to protected content.
Security: prompt injection, meaning instructions hidden in a web page, an email or a file that the model reads and that change its behaviour. OWASP ranks it as LLM01 among the risks of applications built on language models.
Bias and homogenization: stereotypes in images, copy that reads like every competitor's, a tone far from your brand.
Over-reliance: NIST calls it automation bias, the tendency to accept what the machine produces without really checking it. It amplifies the other risks because it hollows out the review.

There is also a management risk: subscriptions bought by individual departments with no shared rules and no idea of their return. The usage policy and the return measurement described below deal with it.

How do you reduce errors and hallucinations?

You cannot remove them completely, but design choices reduce them, before the final review even starts.

Give the model the right sources: with RAG it answers from current price lists, product sheets and policies rather than from its own memory.
Constrain the task: instructions that forbid adding missing data, structured input fields (attributes, prices, terms) and a fixed output format.
Require a source: in internal answers, every piece of information must point to the document it comes from.
Build a test set of real cases with correct answers, and rerun it every time you change the model or the instructions.
Match the review to the risk: full checks on prices, terms and technical data, spot checks on low-impact copy.
Limit permissions for tools connected to company data: an assistant that reads emails or web pages must not be able to place orders or change prices without human confirmation.
Log errors and turn them into new rules in the instructions.

For the security side, see also the article on AI in cybersecurity.

How do you choose the right generative AI tool?

Start from the process, not from the model's name. There are three routes, often combined:

General-purpose assistants in business editions: quick to roll out, loosely connected to your processes.
AI features in platforms you already use (Shopify, WordPress, CRM, helpdesk): low cost and low risk, but limited to what the platform offers. For websites, see the article on AI in CMSs such as WordPress.
Custom integrations via API: the model connected to your catalogue, CRM and documents with tools such as n8n or Make and a database such as Supabase. They need design work but run inside your own workflows.

Four criteria are enough to compare them: data (where it is processed, whether it trains the model, whether there is a GDPR-compliant agreement), integration with existing systems, cost per use estimated on real volumes, and control (request logs, the option to switch models). When the choice leads to a custom integration, that is the work described in the automation and AI solutions.

How do you measure the return on generative AI?

Compare the same process before and after, counting every cost: looking only at the licence price gives the wrong picture. Set the indicators before the pilot, not after.

Item	What to include	How to measure it
Direct costs	Per-user licences, API usage, automation platforms	Monthly invoices against the volume handled
Project costs	Integration, data preparation, instructions and testing	Hours or quote, one-off
Recurring internal costs	Output review, maintenance, training	Hours per week
Efficiency gains	Hours saved, response time, volume handled	Comparison with the baseline
Revenue gains	Conversion, quote win rate, average order value	Comparison with a previous period or a control group
Quality and risk	Errors, complaints, returns linked to the copy	Error log

The full method is in the guide to KPIs for measuring the ROI of AI projects. To lower the upfront investment, also look at public funding for AI projects.

Does using generative AI for content hurt SEO?

Not if the content is useful. Google says its systems reward original, high-quality content however it is produced, and that appropriate use of AI does not break its guidelines. Its spam policies, on the other hand, treat generating many pages with AI tools without adding value for users as scaled content abuse.

For an online store the consequence is practical: generating thousands of product pages from verified, reviewed attributes is a useful application; creating a page for every city and product combination with no new information is a risk. Google also assesses content on first-hand experience (the first E in E-E-A-T), which a model cannot invent: real photos, product tests, customer cases, the view of people who work in the sector. For visibility in AI-powered search engines, see the article on AI search engines and the guide to SEO consulting.

Who owns content created with generative AI?

In Italy, Law 132/2025 on artificial intelligence amended the copyright act: works of human authorship are protected even when created with the help of AI tools, provided they result from the author's intellectual work. Text or images produced with a prompt and published without any human creative input may therefore not be protected. In practice:

for logos, packaging, taglines and other brand assets, keep evidence of the human contribution (drafts, choices, edits);
read the tool's terms on ownership of outputs, licensing and limits on commercial use;
avoid asking for text or images in the style of a specific competitor or author: it raises the risk of resemblance to protected content.

Rules differ from country to country; for trademarks, contracts and unclear cases, get legal advice.

How do you govern generative AI in a company?

With a few written rules, clear roles and regular checks. AI governance is the set of rules, responsibilities and controls a company uses to decide how these tools are used.

What to put in the usage policy

Approved tools, with company accounts rather than personal ones.
Forbidden data: personal data that is not needed, confidential offers, information covered by non-disclosure agreements.
Allowed and excluded uses, for example no decisions on prices, terms or staff without approval.
Review rules for any text that goes outside the company.
Transparency rules: when to disclose that content or a chat is AI-generated.
An owner for each process, a register of active tools and a channel for reporting errors.
A quarterly review of the KPIs and of the policy itself.

The policy works only if the team knows how to use the tools. That takes hands-on training on your own use cases and an experimental mindset: small pilots, each with a hypothesis and a metric, that are allowed to fail without consequences. It is also the moment to check that the uses fit the company's values, for example on transparency towards customers and the handling of data.

What the AI Act requires

Since 2 February 2025, the AI Act (Regulation (EU) 2024/1689) has required organisations using AI systems to look after their staff's AI literacy. With the Digital Omnibus on AI (Regulation (EU) 2026/1744, in force since 27 July 2026), the duty became taking training measures, with no guaranteed minimum level. From August 2026, transparency obligations apply: people talking to a chatbot must be told it is a machine, and some generated content must be labelled. For providers of systems generating synthetic content that were already on the market before 2 August 2026, the duty to mark that content as artificial applies from 2 December 2026. For the principles of trustworthy AI, see the guide on AI governance and explainability; for internal processes, the one on AI, HR and compliance. Specific cases call for legal advice.

How to apply it in your company: first steps

Pick one process that repeats, has an owner and a measurable volume.
Measure the baseline for four weeks: time per task, errors, response times.
Prepare the data: complete catalogue attributes, current price lists, approved standard replies.
Write the rules and train the people who will use the tool: allowed data, review, who to report errors to.
Run a 6 to 8 week pilot with human review of every customer-facing output.
Decide on the numbers: scale, fix or stop, then move to the next process.

A hypothetical scenario: a B2B distributor with 3,000 SKUs and incomplete technical sheets. Instead of a public chatbot, it starts with the sheets: the model writes descriptions from the attributes, a technician approves them in batches, and recurring errors go back into the instructions. Only once the sheets are reliable does a customer assistant make sense, because it will answer from that data. For an online store the order is the same: data first, then automation, as in the approach to business process automation.

Mistakes to avoid

Starting from the tool and looking for a use afterwards.
Publishing generated text without review: an invented price costs more than the time saved.
Approving output without reading it: the review turns into a signature and protects nothing.
Letting every department sign up for tools with personal accounts.
Mass-producing pages with no data of your own and no new information: you get more pages, not more credibility, and risk being treated as spam.
Connecting an assistant to email, the management system or payments with broader permissions than the task needs.

Frequently asked questions (FAQ)

How can a company start using generative AI?

Start with one repetitive, text-based process, such as product descriptions or replies to quote requests. Measure how it runs today for a few weeks, launch a pilot with human review and decide on the results.

What are the main risks of generative AI for a business?

The most common is confabulation: false content written convincingly, such as an invented price or specification. Then come risks to the privacy of the data you enter, to intellectual property and to the security of systems connected to company data. You reduce them with reliable data, review and a written usage policy.

How much does it cost to introduce generative AI?

It depends on the route. Subscription assistants are priced per user, API integrations by usage, plus the integration work and the internal hours spent on review. A pilot on a single process, using tools you already have, lets you estimate the real cost before scaling.

What does the EU AI Act require from companies using generative AI?

Since 2 February 2025, organisations using AI systems must take measures to train the staff who use them. From August 2026, transparency obligations apply: people talking to a chatbot must be told they are dealing with a machine, and some generated content must be labelled. Specific cases call for legal advice.

Can you use customer data with generative AI?

Yes, but only with tools that state where they process data, do not use it to train their models and offer a GDPR-compliant data processing agreement. Enter only the data the task needs and leave out sensitive information.

Will generative AI replace support or sales staff?

The evidence points to support rather than replacement. In an NBER study of more than 5,000 customer support agents, an AI assistant raised issues resolved per hour by 14% on average and by 34% for the least experienced. Decisions on prices, terms and exceptions stay with people.

Does Google penalise text written with generative AI?

Not if it is useful to readers. Google says it rewards original, high-quality content however it is produced, while it treats generating many pages without added value for users as spam. Reviewed text with verified data and real experience falls within acceptable use.

Sources

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