When an operations director asks «how much have we invoiced this quarter?» to an AI assistant and receives the exact figure from the ERP within two seconds, they are not using ChatGPT. They are using an enterprise copilot: an artificial intelligence layer connected to the internal systems, documents and processes of the organisation. The difference is not cosmetic; it is architectural, and it completely determines what the tool can do for your business.
What exactly is an enterprise copilot
An enterprise copilot (also called a corporate AI assistant or business copilot) is a conversational AI system that operates on the private context of an organisation. Unlike public language models, an enterprise copilot has controlled access to:
- Internal documentation: procedures, contracts, manuals, meeting minutes.
- Operational data: ERP, CRM, production or sales databases.
- Work tools: calendars, email, project management platforms.
- Conversation history and context of the user or department.
The term «copilot» was popularised by Microsoft 365 Copilot, launched in general availability for enterprises in November 2023 and significantly expanded in 2024 and 2025. However, the concept also covers custom solutions built on models such as GPT-4o, Claude or Gemini, integrated via RAG (Retrieval-Augmented Generation) techniques or tool calls (function calling / tool use). What unites them is the same idea: the AI acts as a co-pilot that knows the specific terrain of your company.
If you want to deploy an assistant of this kind tailored to your sector, Summum IA offers a specialised copilot for professional firms and enterprises service that starts with a diagnosis of your information sources and builds the system on top of your existing infrastructure.
How an enterprise copilot works technically
Under the hood, most enterprise copilots combine three layers:
- Information retrieval layer (RAG): when the user asks a question, the system first searches the authorised data sources (indexed documents, databases, APIs) for the most relevant fragments, and injects them as context into the prompt received by the language model. The model does not «remember» your documents: it consults them in real time each time it responds.
- Action layer (tool use / agents): a more advanced copilot can execute actions, not just answer. It can create a calendar event, update a field in the CRM, send an email or trigger an automation flow. This capability makes it an agent, not just a chatbot.
- Identity and permissions layer: the data the copilot can see is bounded by the user's role. A salesperson cannot access HR contracts; a technician cannot see management budgets. This governance is mandatory both for business reasons and under the General Data Protection Regulation (GDPR, EU Regulation 2016/679).
ChatGPT vs. enterprise copilot: the comparison table you need to understand
| Criterion | ChatGPT (public version) | Enterprise copilot |
|---|---|---|
| Knowledge of your company | None. It only knows what you include in the prompt. | Complete, within the assigned permissions. |
| Access to internal data | No. Manual copy-paste or one-off file upload. | Yes. Connected in real time to ERP, CRM, documents. |
| Information freshness | Model knowledge cut-off (may be months old). | Real-time data from your systems. |
| Role-based access control | Does not exist. | Yes, integrated with the user directory (Active Directory, LDAP, SSO). |
| Action capability | Only generates text. Does not act on systems. | Can execute flows, update records, send messages. |
| Where data is processed | OpenAI servers (outside the EU in many cases). | Configurable: private cloud, on-premise or provider with a GDPR-compliant DPA. |
| Traceability and audit | No corporate log. Only personal user history. | Centralised log, exportable, auditable. |
| Cost | Low (personal subscription or pay-per-use API). | Higher, proportional to data volume and integrations. |
| GDPR / AI Act compliance | User's responsibility when entering data. | Designed for compliance from the outset (privacy by design). |
When does investing in an enterprise copilot make sense
Not every company needs its own copilot on day one. The cases where the investment is clearly justified are:
- Large volumes of internal documentation that employees consult frequently: technical manuals, quality protocols, internal regulations, project history.
- Repetitive processes requiring search and drafting: answering customer questions based on contracts, generating periodic reports, summarising meeting minutes.
- Professional firms (lawyers, consultants, engineers) where productivity depends on locating case law, clauses or precedents in the firm's own archive.
- Companies with traceability obligations (ISO 9001, ISO 27001, ENS) that need AI queries to be logged and kept within the control perimeter.
- Sales teams that need quick answers about stock, prices, product sheets or order status without leaving their communication tool.
If your case fits any of these profiles, the logical next step is a preliminary diagnosis. At Summum IA we carry this out in the advisory phase before proposing architecture: see how we approach copilot deployment.
Microsoft 365 Copilot vs. custom copilot: what is right for a Spanish SME
There is a common confusion between «using Microsoft 365 Copilot» and «having an enterprise copilot». Both are valid solutions, but they suit different profiles:
Microsoft 365 Copilot (formerly Copilot for Microsoft 365) is integrated into the Microsoft ecosystem: Word, Excel, Teams, Outlook, SharePoint. From 2024, Microsoft sells it at 30 USD/user/month on top of Microsoft 365 E3 or E5 licences, with a minimum that in practice targets mid-to-large organisations. Its advantages are native integration with Office and security managed by Microsoft under the platform's data processing agreements.
A custom copilot, built on model APIs (OpenAI, Anthropic, Google, or open models such as Llama or Mistral), offers more flexibility: it can integrate with systems Microsoft does not cover (vertical ERPs, proprietary management software, legacy databases) and can be deployed on-premise or with a European cloud provider if data sovereignty is a requirement — increasingly common since the AI Act (EU Regulation 2024/1689, in force since August 2024).
For a Spanish SME with 20–150 employees that already uses the Microsoft ecosystem, the natural starting point is to evaluate M365 Copilot. For companies with heterogeneous systems or strict privacy requirements, the custom RAG architecture is usually more suitable and, often, more cost-effective at scale.
The AI Act and enterprise copilots: what you need to know in 2026
The European Artificial Intelligence Regulation (AI Act, EU Regulation 2024/1689) entered into force on 1 August 2024 and its obligations apply in stages until August 2027. For most enterprise copilots the risk level is low or limited, but there are important exceptions:
- If the copilot influences human resources decisions (selection, promotion, dismissal), it is classified as a high-risk system (Annex III, point 4 of the AI Act) and requires a conformity assessment, registration in the EU database and detailed technical documentation.
- Any copilot that interacts with people without identifying itself as AI must comply with the transparency obligations of Article 50.
- So-called «general-purpose AI models» (GPAI) trained with more than 10^25 FLOPs have additional obligations; this affects providers of the base models, not the companies that use them.
AI risk management from a technical perspective is the territory of Summum IA · AI Act technical; legal and compliance adaptation falls under Summum Consultoría · AI Act legal.
Real use cases in mid-sized Spanish companies
To make the value concrete, these are the usage patterns that generate the most productivity in the projects we have accompanied at Summum IA:
- Employment law advisory: the copilot indexes collective agreements, Labour Inspectorate rulings and the firm's own historical client queries. The advisor retrieves the applicable agreement and the relevant internal precedents in seconds, reducing client response time.
- Industrial distributor: the copilot connects the product catalogue (25,000 references), real-time ERP stock and each client's order history. The salesperson asks «what alternative do I have if product X is out of stock?» and receives three options compatible with the client's previous order.
- Architecture practice: the copilot indexes permit files, PDF drawings and communications with local councils. The technical architect queries the status of each file in natural language without navigating SharePoint folders.
- Industrial maintenance company: the copilot integrates with the CMMS (computerised maintenance management software) and answers questions such as «how many open work orders does machine X have this week?» or automatically generates the work report from the technician's verbal description.
Steps to deploy an enterprise copilot without mistakes
The most common mistake is starting with the technology instead of the use case. The correct process follows this sequence:
- Identify the concrete pain point: what questions do employees ask every day that require searching in multiple places or waiting for an expert? The use case must be specific, frequent and measurable.
- Audit the data sources: where does the information the copilot needs to consult live? Is it structured (database) or unstructured (PDFs, emails)? Is it up to date?
- Define the permissions model: which users see which data. This decision has legal (GDPR) and business implications and must be made before designing the architecture.
- Choose the architecture: M365 Copilot if the ecosystem is Microsoft; RAG over an external or local model if the data is heterogeneous or data sovereignty matters; agents if action capability is needed in addition to response.
- Scoped pilot: one department, one use case, one month. Measure adoption and response quality before scaling.
- Governance and maintenance: documents age, systems change. The copilot needs an owner who reviews the indexed sources and updates the connectors.
Frequently asked questions
Does an enterprise copilot send my internal data to OpenAI or Microsoft?
It depends on the architecture chosen. With Microsoft 365 Copilot, Microsoft processes the data under the data processing agreement (DPA) included in enterprise licences; Microsoft commits not to use that data to train its models and the data remains in the contracted geographic region (EU in enterprise plans). In a custom RAG architecture using the OpenAI API or another provider, data travels to the model provider on each query, so it is essential to review the provider's DPA terms and, if the data includes personal information, to sign a data processing agreement compliant with the GDPR. The option that eliminates this risk is deploying the model on-premise or on a controlled European server — an option Summum IA implements under the sovereign AI service.
How long does it take to deploy an enterprise copilot in an SME?
A functional pilot with a well-scoped use case (for example, a chatbot over the company's quality documentation) can be operational within four to six weeks from the start of the diagnosis. A full deployment with ERP, CRM and multiple documentation source integrations typically requires three to six months. Timelines depend primarily on the quality and accessibility of the internal data sources, not on the technology itself.
What happens if the copilot gives an incorrect answer?
Well-designed RAG systems minimise hallucinations because the model does not «invent» the answer: it generates it from real fragments retrieved from your documents, which can be cited as a source. Even so, no system is infallible. It is therefore recommended to: always show the source supporting the answer; set confidence thresholds below which the system declines to answer rather than improvising; and train users to verify critical responses. The copilot is an acceleration tool, not an oracle that replaces professional judgement.
Do I need a Microsoft licence to have an enterprise copilot?
No. Microsoft 365 Copilot is one option, not the only one. There are alternatives entirely independent of Microsoft built on models from various providers (OpenAI GPT-4o, Anthropic Claude, Google Gemini, Mistral, Llama) and integrated directly with company systems via APIs. These solutions are especially suitable for companies that do not have a consolidated Microsoft environment or that need integrations with vertical software Microsoft does not natively cover.