Copilot alternatives: When companies need their own AI

An client of the industrial sector who already uses Microsoft Copilot in their day-to-day work approached us with a question: “Can Copilot automatically extract data from our suppliers’ PDFs and transfer it directly to SAP?” A valid question – after all, Copilot already handles many everyday tasks. What initially sounded like a simple question led to a closer examination of the process.

Our client’s question led to a solution that went beyond Copilot. You can find out why this was the case in the article.

Copilot is an AI assistant that is integrated directly into the familiar Microsoft applications, where it helps with writing, researching and organising content. It is already available in Word, Outlook and Teams. This is perfectly adequate for standard tasks. However, based on our experience with client projects, we have identified four typical situations where such tools often fall short.

Complex processes: Copilot is designed for general office tasks, such as summarising texts or drafting emails. However, as soon as multi-stage workflows come into play, a standard solution is often insufficient: data must be captured, checked and processed further in another system. This requires its own logic, which a generic solution does not provide.

Integration beyond Microsoft: Most business-critical processes do not run in Word, Outlook or Teams, but in ERP, CRM or industry-specific software. Copilot remains anchored within the Microsoft 365 ecosystem and ends where your actual systems begin. Yet this is often where our customers’ requirements come into play: data needs to flow directly into existing specialist systems, or answers need to be retrieved from the internal knowledge database.

Control over sensitive data: Copilot processes data in the Microsoft cloud. For many organisations, this is sufficient thanks to the EU Data Boundary and GDPR requirements; however, when it comes to patient, financial or confidential business data, additional security measures are often required.

Those who wish to stick with Copilot but have concerns, particularly regarding data protection, can significantly reduce risk through access control models, data classification and policies. This option is particularly suitable where use cases are generic and the actual problem lies not in process complexity or system integration, but in maintaining control over sensitive data.

Open-source models such as Llama or Mistral can be self-hosted with European cloud providers. Instead of subscribing to an AI service, the infrastructure on which the model is installed and operated is provided. This offers greater flexibility in model selection and enables bespoke use cases. However, complex business processes or deep integrations usually require additional customisation.

If AI is to become an integral part of your business processes, a bespoke solution is often the most sensible approach. It can be integrated directly with systems such as SAP, CRM or internal knowledge databases and adapted to your existing workflows. For example, the AI can parse documents, consolidate information from various sources, take corporate knowledge into account and automatically transfer results to other systems. Rather than merely supporting individual tasks, it becomes an integral part of your processes. At the same time, companies retain control over their data and can flexibly adapt models, interfaces and functions to their requirements.

This decision tree uses simple yes/no questions to help you find the right AI solution for your business. None of the three options is inherently better: Copilot is suitable for many standard applications, EU-hosted platforms offer greater control over data location, and your own on-premises AI enables customised logic and deep system integration.

With Copilot, you’re buying a product. With your own AI, you’re building an application.

A local AI solution can be tailored to your data, systems and requirements. Flexible open-source models such as Qwen, Mistral or Gemma are often used for this purpose.

A key principle is RAG (Retrieval-Augmented Generation): the AI specifically accesses internal documents and data, rather than relying solely on its pre-trained knowledge. This produces more up-to-date and traceable answers without the need to retrain the model.

When it comes to deployment, you can choose between on-premises, dedicated servers in a German data centre, or OpenAI models in Azure EU data centres. The most suitable hosting option depends on your security requirements and your infrastructure.

Developing your own AI doesn’t have to be a major project. Our standard starting point is an AI workshop (from €5,000), in which we analyse and refine your use cases, followed by a working prototype (AI MVP from €8,000). Further development only takes place once the prototype has demonstrated that it works. The costs depend on the scope of functionality. This allows companies to assess the benefits at an early stage and make decisions based on a working solution. You can use this free local AI cost calculator to work out the hosting and MVP costs for your integration.

What began as a simple question became the basis for a solution that our industrial client still uses every day: its own on-premises AI that fits seamlessly into its processes.

For many companies, Copilot is a sensible starting point. However, if complex workflows need to be automated or individual systems integrated, a tailored AI solution can offer greater added value.

If you’re facing a similar decision, we’d be happy to advise you. And if Copilot is sufficient for your use case, we’ll tell you that just as frankly.