Which AI to Use by Industry?

In short: Generative AI is a technology that creates text, images, or code from natural-language instructions. Which AI to use by industry depends on four criteria: the office suite already in place, the nature of the data being processed, the required confidentiality level, and the maturity of your teams. ChatGPT, developed by OpenAI, the American lab that pioneered generative AI, interprets loosely worded requests with ease; Claude, built by Anthropic, an American company focused on the reliability of its tools, handles long documents; Copilot from Microsoft, the world’s leading publisher of professional software, and Gemini from Google integrate natively with office suites; Perplexity, an American answer engine, cites its sources; Mistral AI, France’s champion of open models, answers Europe’s sovereignty requirements. According to MIT (the NANDA project, 2025), 95% of generative AI pilot projects produce no measurable return — almost always due to a mismatch between the tool and the actual business use case. D&O Partners, a Xerox reseller and print partner, guides this choice together with BEEWA, its IT services partner.

What is generative AI in a business context?

Generative AI refers to a technology capable of producing text, images, sound, or code from instructions phrased in everyday language. Unlike traditional software, which executes programmed rules, these models produce a new response to every request, drawing on the patterns they learned during training.

In a business setting, professional use of this technology spans four broad task families: content production (letters, sales proposals, product sheets), document and data analysis, decision support, and the automation of low-value repetitive tasks. Microsoft’s Work Trend Index (2024) already found that 75% of knowledge workers surveyed were using AI in their work, often with no framework set by their employer.

Two concepts recur constantly in this comparison and deserve a definition. The language model is the engine that generates the output. The assistant is the interface through which your teams access it — a web app, an office extension, or an integration within a business application. A single model can power several interfaces, which is why choosing a vendor shapes the user experience just as much as raw performance.

Why the right AI depends on your industry

General-purpose model leaderboards change every quarter and tell you little about your operational reality. A law firm analysing hundred-page contracts, a retail chain writing a thousand product sheets, and an accounting department checking entries don’t share the same precision requirements, the same confidentiality constraints, or the same relationship to volume.

Four criteria concretely determine the right choice:

The office suite already in place. An organisation running Microsoft 365 will find native integration with Outlook, Word, Excel, and Teams in Copilot. An organisation running Google Workspace benefits from Gemini in Gmail, Docs, and Sheets. Adoption cost drops when the tool lives where teams already work.
The nature of the information being handled. Long, structured documents call for models with a large context window. Short, varied requests favour interpretive flexibility.
The required confidentiality level. Regulated sectors — healthcare, legal, finance, the public sector — impose contractual guarantees that consumer-grade versions don’t offer.
The maturity of your teams. A team with little training gets more out of a tool embedded in its familiar software than from a powerful but isolated platform.

MIT’s 2025 report (the NANDA project, “The GenAI Divide: State of AI in Business 2025”) puts a number on the cost of getting this wrong: roughly 95% of AI pilot projects deliver no measurable return on the bottom line. The cause is rarely the quality of the model chosen, but the absence of fit between the tool deployed and the real business use cases.

Key takeaway: no AI is the best in absolute terms. A solution becomes the right one when it matches the industry, the information being handled, and the tools your teams already use.

The 6 AI tools every B2B buyer should know

Six platforms today account for the bulk of professional use across Europe. Each has a distinct profile, detailed below ahead of the sector-by-sector comparison.

ChatGPT (OpenAI): the AI that understands loosely worded requests

OpenAI, an American lab founded in 2015 and a pioneer of consumer-facing generative models, publishes ChatGPT. This tool is the best-known and most widely adopted generative AI solution. Its distinctive strength lies in its interpretive ability: a vague, poorly phrased, or incomplete request still produces a usable result. That tolerance makes it the best entry point for untrained teams that haven’t yet learned to write precise instructions.

Its memory feature lets it retain a user’s context — their job, their style, their recurring projects — and tailor its responses accordingly. The surrounding ecosystem (custom assistants, image generation, file analysis, voice mode) covers a wide range of needs without any additional tool. Typical uses include brainstorming, day-to-day writing, rephrasing, and creative content production.

Main limitation: the model’s creativity can produce inaccurate statements delivered with confidence. Any figure or factual claim sourced from this tool requires verification.

Claude (Anthropic): the AI for long documents and precise analysis

Anthropic, an American company founded in 2021 and positioned around model reliability and safety, develops Claude. This solution stands out when handling large documents. Its extended context window, documented by Anthropic in its technical specifications (2026), allows several hundred pages to be submitted at once — a full contract, an annual report, a tender specification — and produces a coherent analysis of the whole rather than a fragmented summary.

Feedback from professional use highlights three qualities: analytical rigour, the quality of structured writing, and strong coding performance. The model is more likely to flag its own uncertainty than to invent an answer — valuable behaviour in sectors where mistakes are costly. Preferred use cases include contract analysis, producing long, well-argued content, report synthesis, and software development.

Main limitation: the interface remains plainer than competitors’, with a smaller ecosystem of extensions.

Microsoft Copilot: the AI built into your office suite

Microsoft, the world’s leading publisher of professional software, offers Copilot. This solution occupies a distinctive position: rather than a standalone platform, it settles into the software your teams open every morning. Drafting in Word, formulas and analysis in Excel, inbox triage in Outlook, automatic meeting notes in Teams.

The decisive advantage for organisations concerned about their data: the documents processed stay within the company’s Microsoft 365 perimeter, under the same governance rules as the rest of the information system. No file copy travels to an unmanaged external platform.

Main limitation: relevance depends heavily on the quality of the existing document organisation. A poorly structured workspace produces rough, approximate results.

Google Gemini: the multimodal AI of Google Workspace

Google, publisher of the Workspace collaboration suite used by millions of businesses, develops Gemini. This solution follows a logic comparable to Copilot’s within its own universe: integration with Gmail, Docs, Sheets, Slides, and Meet. Its distinctive feature is its advanced multimodal capability — analysing images, video, and visual files — and its very large context window, among the widest on the market according to Google’s published technical documentation (2026).

Organisations using Google Workspace find in it the functional equivalent of Copilot. Typical use cases include summarising recorded meetings, analysing visual material, preparing presentations, and working with spreadsheets.

Main limitation: outside the Google ecosystem, the differentiating value drops off noticeably.

Perplexity: the research AI that cites its sources

Perplexity AI, an American company specialising in AI-augmented search, falls into a different category: rather than a conversational assistant, the platform works as an answer engine. Every question triggers a real-time web search, and every claim in the response links back to its source, one click away.

Source traceability makes Perplexity the go-to tool for competitive intelligence, factual research, solution comparisons, and current-events questions. Legal, finance, and marketing teams use it ahead of drafting work, to assemble a verifiable evidence base.

Main limitation: writing quality and creativity lag behind the general-purpose assistants.

Mistral AI: the sovereign European solution

Mistral AI, a French publisher, offers its Le Chat assistant as well as models that can be deployed on the client’s own infrastructure. Sovereign deployment in a European data centre, or even on the organisation’s own servers, directly answers the requirements of the most regulated sectors.

Several European public administrations, including the French state in 2025, have adopted these solutions to equip their staff, precisely for this control over where data is hosted. Preferred use cases span the public sector, healthcare, defence, and any organisation whose internal policy forbids sending data outside Europe.

Main limitation: the ecosystem and third-party integrations remain less developed than those of the American players.

Comparison table: strengths, weaknesses, cost, and integration

The table below summarises the criteria most frequently examined during an audit. Prices shown correspond to professional per-user, per-month offers as observed in 2026; as these amounts change regularly, checking with the vendor remains necessary before any commitment.

Tool	Main strength	Weakness	Indicative cost	Integration
ChatGPT	Understands loose requests, memory, rich ecosystem	Can state inaccuracies with confidence	€20–30/user	Web, extensions, API
Claude	Long documents, rigorous analysis, code	Smaller extension ecosystem	€18–30/user	Web, API, code editors
Copilot	Native in Word, Excel, Outlook, Teams	Depends on the quality of your document spaces	€25–35/user	Microsoft 365
Gemini	Multimodal, very large context window	Limited value outside the Google ecosystem	€20–30/user	Google Workspace
Perplexity	Cited sources, real-time web research	Less polished writing	€20/user	Web, browser extension
Mistral	Sovereign European deployment	Less developed third-party ecosystem	Variable by deployment	Web, API, dedicated hosting

Pros and cons of each AI

The table below details, for each tool, what speaks in its favour and what needs to be anticipated before a company-wide rollout.

AI tool	Advantages	Drawbacks
ChatGPT (OpenAI)	Understands poorly phrased requests; remembers user context; very broad ecosystem; fast adoption without heavy training	Can state inaccuracies with confidence; data leaves the company perimeter in the consumer version; personalisation needs governance
Claude (Anthropic)	Handles several hundred pages at once; rigorous analysis; flags its own uncertainty; excellent at code	Smaller extension ecosystem; plainer interface; less geared toward image generation
Copilot (Microsoft)	Native in Word, Excel, Outlook and Teams; data stays within the Microsoft 365 perimeter; unified governance	Relevance depends on the quality of document spaces; limited value outside the Microsoft suite; higher cost per user
Gemini (Google)	Advanced multimodal (image, video); very large context window; native integration with Workspace	Not very differentiating outside the Google ecosystem; features vary by service
Perplexity	Cited, verifiable sources; real-time web research; ideal for monitoring and comparisons	Less polished writing; less suited to creative or long-form content; depends on the quality of indexed pages
Mistral AI	Sovereign deployment possible in Europe; easier compliance for regulated sectors; open models	Less developed third-party ecosystem; fewer ready-made connectors; dedicated deployment is more technically demanding

One observation stands out from this table: price differences between professional solutions remain modest, on the order of fifteen euros per user per month between the cheapest and the most expensive offer. The real financial gap plays out elsewhere — in the number of subscriptions taken out, and in the time actually saved.

The document pipeline: a precondition for any AI use

One question precedes the choice of tool: are your files actually usable by artificial intelligence? A scanned contract with no optical character recognition, an invoice archived as a plain image, a folder that stayed on paper — all remain invisible to any model, however capable.

This is exactly where the blind spot of enterprise AI projects sits. Organisations invest in AI licences while keeping document workflows that prevent these tools from working at all. The Xerox equipment distributed by D&O Partners builds in optical character recognition and automated routing to your workspaces: SharePoint, Google Drive, document management systems. A scanned document then becomes a searchable file that AI can summarise, compare, or analyse.

Three steps structure this pipeline:

Digitise and recognise: turn paper into usable text, with indexing and structured archiving.
Route to the right space: automatically send each file to where teams work, with the appropriate access rights.
Put it to work with the right tool for your industry — the subject of the following sections.

Organisations that reverse this order — buying AI before preparing their data — account for a significant share of the project failures catalogued by MIT.

Which AI should you use by industry?

Eight industries account for most B2B demand. For each, here is the dominant use case, the primary solution, and the recommended combination.

Law firms and legal professions

The legal sector handles long documents where every term carries liability. Claude often takes the top spot for analysing large contracts, comparing versions, and structured drafting. Perplexity covers sourced legal research, essential for verifying a point of law. Specialised solutions round out the toolkit on case law.

The sector’s trap concerns fabricated references. The Mata v. Avianca case, tried in New York in 2023, saw a lawyer sanctioned for citing six court decisions fabricated by a chatbot. Any AI-generated reference must be verified against an official database before citation — a rule now written into the charters of many firms.

Recommended combination: Perplexity to identify sources, Claude to analyse and draft, with systematic human review before anything reaches the client.

Tech companies and software development

Technical teams systematically combine several building blocks. An AI built into the code editor handles day-to-day production — completion, function generation, writing tests. A more powerful reasoning model steps in for architecture, critical reviews, and complex debugging.

Public code-generation benchmarks shift with every major release, which makes tracking them of limited practical use. Mature teams take a different approach: they test candidate models against their own repositories, with their own conventions, before deciding. This empirical approach, recommended by leading coding-assistant vendors in their own technical documentation, produces a decision grounded in the project’s actual context.

Recommended combination: the editor’s built-in AI for daily work, a reasoning model for structural decisions and security reviews.

Retail, commerce, and e-commerce

Retail has three distinct needs. High-volume content production — product sheets, descriptions, seasonal campaigns — favours ChatGPT, whose tolerance for rough briefs considerably speeds up the pace. Sales data analysis runs through Copilot in Excel or Gemini in Sheets, depending on the suite in place. Handling customer queries falls to a supervised conversational agent, backed by a well-managed knowledge base.

One point deserves close attention: an agent in direct contact with customers represents the brand. Tone guidelines, off-limits topics, and escalation procedures to a human must be defined before go-live.

Recommended combination: ChatGPT for content, your spreadsheet’s AI for sales analysis, a supervised agent for customer service.

Finance, accounting, and audit

Here, accuracy trumps creativity. Copilot is the natural fit in Excel for Microsoft-based organisations: building formulas, reprocessing data, consistency checks. Claude adds complementary value analysing long financial reports and spotting inconsistencies between documents. Perplexity covers regulatory monitoring with verifiable sources.

One rule admits no exception in this sector: no figure produced by AI goes into a deliverable without being recalculated or traced back to the supporting document. Generative models excel at structuring an argument, but they don’t guarantee the arithmetic accuracy of a result.

The finance sector perfectly illustrates the document-pipeline issue raised earlier: the value appears the moment scanned accounting records become directly analysable, without manual re-entry.

Public administration and back office

Back-office work lives in office software, and the choice logically follows the suite already in place: Microsoft 365 points to Copilot, Google Workspace to Gemini. Their decisive advantage lies in data scope — files never leave the organisation’s environment.

For public administrations and organisations subject to sovereignty requirements, Mistral is the European option, with deployment possible on controlled infrastructure.

Major regulatory point of attention: Regulation (EU) 2024/1689, known as the AI Act, classifies certain uses as high-risk systems. A key deadline falls on 2 August 2026, with penalties that can reach €35 million or 7% of global turnover. HR uses are among the most exposed.

Marketing, communications, and agencies

Marketing draws on the complementary strengths of these tools more than any other function. ChatGPT serves rapid ideation, tone variations, and short-form content. Claude takes over for long, well-argued content — white papers, in-depth articles, sales arguments. Gemini handles visual assets. Perplexity researches a topic with citable sources.

The point to watch is brand voice: content generated without editorial guardrails produces correct but interchangeable copy. High-performing teams build a style reference — approved examples, house vocabulary, off-limits terms — and feed it into every request as a matter of course.

Recommended combination: Perplexity for research, ChatGPT for the creative angle, Claude for finished long-form content.

Human resources and recruitment

HR teams use AI to write job postings, prepare interview frameworks, summarise annual reviews, and answer employees’ recurring questions. Copilot or Gemini suit these uses well, thanks to their integration with email and internal documents.

This sector calls for the greatest regulatory caution. Automated candidate screening and employee evaluation fall under high-risk systems as defined by the AI Act. These uses require formal documentation, human oversight, and disclosure to the people concerned. HR data is also sensitive personal data under GDPR, which rules out consumer-grade versions.

Industry, logistics, and technical services

Needs here centre on technical documentation: drafting and updating procedures, summarising field-service reports, translating manuals, preparing tender responses. Claude suits the handling of large volumes of documentation; Copilot or Gemini cover everyday office work; Perplexity documents applicable standards and regulations.

Digitising paper field-service reports is frequently the first project — without it, most operational knowledge stays out of reach of analysis tools.

Summary table: which AI for which industry?

Industry	Primary choice	Complement	Key watch-point
Legal	Claude	Perplexity + legal database	Verify every cited reference
Tech and development	Editor’s built-in AI	Reasoning model	Test on your own repositories
Retail and e-commerce	ChatGPT	Copilot or Gemini (data)	Govern tone toward customers
Finance and audit	Copilot (Excel)	Claude, Perplexity	Recalculate every published figure
Public administration	Copilot or Gemini	Mistral if sovereignty required	AI Act deadline of 2 August 2026
Marketing	ChatGPT	Claude, Perplexity	Protect the brand voice
Human resources	Copilot or Gemini	—	High-risk uses under the AI Act
Industry and logistics	Claude	Copilot or Gemini	Digitise paper reports

Combining several AIs: the three-rule method

Few organisations can get by with a single tool. A deliberate combination delivers better results, provided three rules are followed.

One single general-purpose foundation for roughly 80% of needs: the one that integrates with your office suite, so training, budget, and support stay concentrated on a single platform.
One or two specialists where the foundation falls short: sourced research, very long documents, code generation, professional translation.
Routing written into your usage policy: which task goes to which tool, with which categories of data allowed. Without this explicit rule, every employee improvises, and costs and risks quietly pile up.

The most common mistake is stacking up subscriptions out of caution. Two well-managed solutions outperform five barely used ones — a usage audit almost always uncovers redundant licences.

How much does professional AI use cost a business?

The budget breaks down into three line items, the first of which wrongly absorbs most of the attention.

Licences cost €20 to €35 per user per month for a professional offer, based on 2026 pricing published by the vendors (OpenAI, Anthropic, Microsoft, Google, Perplexity) — or €240 to €420 a year per equipped employee. For a team of ten, the annual budget therefore sits between €2,400 and €4,200.

Existing add-on subscriptions are the line item most often underestimated. Meeting transcription, advanced proofreading, assisted writing, professional translation, image generation: these specialised services, subscribed to separately across different departments, frequently add up to €300–600 a month for a small organisation, according to usage audits conducted by BEEWA among Belgian SMEs — or €3,600 to €7,200 a year. A well-configured general-purpose platform covers most of this, turning the project into a source of savings rather than an added cost.

Support and training form the third line item, and the one that determines return on investment. Without training, teams use only a fraction of what’s available and reproduce rough, approximate practices. The 95% of projects with no measurable return catalogued by MIT reflect exactly this imbalance between technology spend and investment in people.

Data security and compliance: the rules to follow

Three documented risks deserve particular attention before any rollout.

The leak of confidential data comes first. A Cyberhaven study conducted in 2023 among 1.6 million employees found that 11% of the data pasted into ChatGPT at work was confidential — contracts, customer data, source code, financial information. The overall volume of data sent to generative applications has also increased sixfold in a year, according to the Netskope report published in 2026.

Shadow AI refers to the use of tools not approved by the employer. According to the 2023 IFOP-Talan survey, 68% of employees using generative AI did not tell their management. IBM’s 2025 “Cost of a Data Breach” report finds that roughly 20% of organisations have suffered a data breach linked to this phenomenon. Banning these tools just makes their use invisible; governing them lets you steer it.

Regulatory non-compliance is the third risk. GDPR governs any processing of personal data, including sending it to an AI tool. The AI Act adds its own obligations for high-risk systems, with the 2 August 2026 deadline already mentioned.

Three simple measures significantly reduce these risks: subscribe to professional versions that offer contractual guarantees, classify data into three levels (public, internal, confidential) with usage rules per level, and formalise a usage policy that everyone knows about.

How to roll out AI in your business: the steps

Step	Content	Expected deliverable
1. Audit	Mapping processes, existing subscriptions, and their costs; detecting shadow AI	Quantified opportunity report
2. Document preparation	Digitisation, optical character recognition, structuring of workspaces	AI-ready documents
3. Tool selection	Choosing the foundation and specialists based on industry and suite in place	Rollout plan
4. Governance	Usage policy, data classification, verification rules	Governance framework
5. Training	Upskilling by role, reusable prompts and templates	Self-sufficient teams
6. Follow-up	Measuring gains, adjustments, technology and regulatory monitoring	Quarterly review

The order of these steps matters as much as their content. Starting with buying licences, without a prior audit or document preparation, is exactly what exposes you to the most commonly observed failure scenario.

D&O Partners and BEEWA: from document pipeline to the right AI

A successful AI project rests on two complementary pillars.

The first is the document side. Without reliable digitisation, optical character recognition, and structured file flows, no tool produces a usable result. D&O Partners, a Xerox reseller and print partner, equips businesses with printing, scanning, and document management solutions — the foundation on which every AI use is built.

The second is technology and usage: choosing the right solutions for each role, integrating them into existing work environments, governing data, and training teams. For this, D&O Partners relies on BEEWA, its IT services partner, which handles usage audits, foundation selection, data security, and employee support.

The complementarity between the document pipeline and IT support avoids the most common pitfall: deploying a capable technology onto workflows that aren’t ready to receive it.

FAQ: frequently asked questions on choosing an AI by industry

The following questions come up most often during audits with businesses choosing their generative AI tools.

What is the best AI for a business?

No solution dominates in absolute terms. The choice depends on your office suite (Microsoft 365 points to Copilot, Google Workspace to Gemini), your file types, and your confidentiality requirements. The most effective strategy combines a general-purpose foundation with one or two specialists.

ChatGPT or Claude: which should you choose for your business?

ChatGPT interprets vague requests better and adapts to its user through memory, which suits teams with little training. Claude handles long documents and produces more rigorous analysis, which benefits legal, finance, and technical writing. Many organisations use both, each on its own ground.

What’s Perplexity for, compared with ChatGPT?

Perplexity works as an answer engine: it queries the web in real time and cites its sources, which serves monitoring and factual research. ChatGPT remains conversational and general-purpose — more creative, but less systematically sourced.

Do you need to pay for multiple AI subscriptions?

Rarely more than two or three. A general-purpose foundation covers around 80% of needs; a specialist is only added when a specific use case justifies it. A usage audit often uncovers redundant licences worth several thousand euros a year.

Which AI best respects GDPR?

Compliance depends less on the model than on the version you subscribe to — professional offers come with contractual guarantees that free accounts lack — and on your settings. For European sovereignty requirements, Mistral offers deployment on controlled infrastructure.

Why start with document digitisation?

Because AI can only analyse what’s usable. A scanned contract with no optical character recognition remains completely invisible to it. The document pipeline therefore directly determines the value the deployed tools can produce.

How much does AI cost for an SME?

Professional licences cost €20 to €35 per user per month, or €2,400 to €4,200 a year for ten employees. This budget is frequently offset by cutting redundant add-on subscriptions, estimated at €3,600 to €7,200 a year for a small organisation.

Is my industry affected by the AI Act?

Uses in human resources, credit, healthcare, education, justice, and biometrics fall under high-risk systems, with a deadline of 2 August 2026 and penalties of up to €35 million or 7% of global turnover. Mapping your use cases lets you determine this precisely.

How do you combine several AIs without creating chaos?

By writing the routing into your usage policy: which task goes to which tool, with which categories of data. Documented routing, more than the choice of technology itself, is what separates real gains from costly duplication.

Do teams need to be trained to use AI?

Training determines return on investment. Without it, teams use only a fraction of what’s available and reproduce rough, approximate practices — which explains a large share of the 95% of pilot projects with no measurable return catalogued by MIT in 2025.

Sources and references

MIT, NANDA project (2025), “The GenAI Divide: State of AI in Business 2025”, Fortune summary — fortune.com
Cyberhaven (2023), analysis of company data pasted into ChatGPT — cyberhaven.com
IFOP and Talan (2023), “Les Français et les IA génératives” survey — talan.com
IBM (2025), “Cost of a Data Breach Report” — ibm.com
Microsoft (2024), “Work Trend Index” — microsoft.com
Netskope Threat Labs (2026), “Cloud & Threat Report” — netskope.com
OpenAI, ChatGPT and enterprise plans — openai.com
Anthropic, Claude for business — anthropic.com
Microsoft, Copilot for Microsoft 365 — microsoft.com
Google, Gemini for Workspace — workspace.google.com
Perplexity AI — perplexity.ai
Mistral AI — mistral.ai
European Union (2024), Regulation (EU) 2024/1689, the “AI Act” — eur-lex.europa.eu

Article published by D&O Partners, a Xerox reseller and print partner, in collaboration with BEEWA, IT services partner — July 2026. As model capabilities and pricing evolve with every release, this comparison reflects professional usage observed at the time of publication; checking with vendors is recommended before any commitment. To audit your document workflows and choose the tools suited to your industry: contact D&O Partners.

Which AI Should You Use by Industry? A B2B Comparison of Generative AI Tools for Business