Questions and answers about AI in business

Using artificial intelligence (AI) effectively starts with your business processes, not with choosing a tool. The most promising areas are recurring, time-consuming or knowledge-intensive tasks where information is processed, assessed, researched, summarised or passed on.

Typical fields include document processing, research, customer service, quotation processes, sales, marketing, internal knowledge systems and supporting employees with complex tasks.

AI Infusion therefore looks at processes and goals first, and only then decides which AI technology fits.

The worthwhile ones deliver a clear economic benefit: automating recurring administrative work, processing large volumes of documents, internal knowledge assistants, sales support, or AI-assisted research and analysis.

The goal is not to introduce as many AI applications as possible. What matters are the applications where effort, technical feasibility and expected benefit are in sensible proportion.

A good AI use case usually sits where high time effort, frequently repeated tasks, available data and a measurable business benefit meet.

Start by analysing existing processes. Potential AI applications can then be prioritised by benefit, feasibility and effort.

AI Infusion helps companies turn general AI ideas into concrete use cases that can realistically be delivered.

No. Companies just starting with AI often do not yet have a comprehensive AI strategy.

Starting with concrete business processes and use cases is usually more useful than an abstract strategy. A company-wide AI roadmap can then grow out of the first prioritised use cases.

Traditional automation is mostly based on predefined rules: when a certain condition is met, a certain action is triggered.

Generative AI – such as a large language model (LLM) – can also process unstructured information like text, documents or natural language, and produce new content or assessments from it.

Solutions become particularly effective when traditional automation and AI are combined.

An AI consultancy helps companies identify, evaluate and implement worthwhile uses of artificial intelligence.

This can include process analysis, use case identification, technology selection, architecture, data protection, proof of concept and implementation.

AI Infusion combines strategic consulting with hands-on technical delivery.

For mid-sized companies, AI consulting is most useful when it goes beyond general strategy recommendations and looks at concrete business processes.

Experience with process automation, existing IT systems, data protection, different AI models and technical integration all matter.

AI Infusion therefore focuses on concrete use cases and how they work in day-to-day operations.

Yes. AI Infusion is based in Munich and works with companies in Munich and southern Germany as well as across Germany, Austria and Switzerland.

Depending on the project, workshops and meetings take place on site or remotely.

AI consulting first answers the question of where and how AI can be used sensibly.

AI implementation begins when a prioritised use case is turned into a concrete technical solution.

AI Infusion covers both, so there is no unnecessary gap between strategic recommendation and technical delivery.

A typical project starts with an analysis of the current situation and the relevant business processes, followed by:

• —identifying potential AI use cases
• —assessing benefit and feasibility
• —prioritisation
• —designing a solution architecture
• —a proof of concept, where useful
• —technical implementation
• —integration into existing systems
• —optimisation in live operation

The exact process depends on the project's complexity.

Processes that are repeated often and in which employees regularly read, check, transfer, categorise or summarise information are particularly suitable. Examples:

• —handling incoming enquiries
• —document analysis
• —preparing quotations
• —research
• —data classification
• —email processing
• —CRM documentation
• —knowledge management
• —internal reporting
• —customer communication

The more clearly a process can be described and the more often it runs, the more attractive automation can be.

First, the manual steps in a process are analysed. Then we assess which tasks traditional automation can handle and where artificial intelligence is needed.

AI can, for example, understand documents, classify content, extract information, draft text or prepare decisions.

The individual steps are then connected through workflows, APIs and existing business software.

Yes. AI can analyse and categorise incoming emails, extract relevant information and draft replies.

Combined with automation systems, further steps can be triggered, such as CRM entries, ticket creation or internal notifications.

For sensitive or business-critical processes, human approval steps can be built in.

Yes. Modern AI systems can extract and process information from contracts, forms, reports, quotations, invoices and other documents.

Depending on the use case, content can be summarised, compared, categorised or converted into structured data.

Yes. Existing systems often do not need to be replaced.

AI applications can be connected to CRM, ERP, document management or other business systems via APIs or other interfaces, so existing processes gain AI capabilities step by step.

An AI agent is a software system that does more than answer a single question: it independently works through defined tasks within a process.

An agent can, for example, research information, analyse data, edit documents, call external systems or trigger further actions.

More complex systems consist of several specialised AI agents that each handle different tasks.

A chatbot typically responds to a user's input with an answer. An AI agent can also carry out work steps on its own and interact with other systems.

For example, an agent can analyse a customer enquiry, retrieve information from a CRM, check documents and then draft a reply.

Yes. An AI assistant can be connected to approved company knowledge and provide information to employees or customers.

Internal documentation, product information, policies or knowledge bases can be included. This usually relies on retrieval-augmented generation, or RAG.

RAG stands for retrieval-augmented generation. An AI model answers a question not only from its training knowledge but also receives relevant information from a defined knowledge source.

This makes internal company information, documentation or product data usable for an AI assistant.

No. Depending on the use case, different AI models and providers can be used – cloud models as well as other commercial or self-hosted models.

The choice depends on requirements such as quality, speed, cost, data protection and technical infrastructure.

It depends heavily on what the agent needs to do.

A simple agent with a few functions differs considerably from a system that connects several business applications, controls complex processes or must meet particular data protection and security requirements.

That is why the concrete process is defined first, before a reliable estimate is made.

It depends on the specific use case: the data processed, the provider, the technical architecture and the organisational measures.

Companies should check in particular which data is processed, where it is processed and which contractual and technical safeguards are in place. A blanket answer for all applications is not meaningful.

No. Depending on the use case, different models, APIs, hosting options and architectures can be used.

For sensitive data, an architecture can be chosen in which data is processed in a controlled way and access rights are restricted accordingly.

In principle yes, but the technical architecture must match the sensitivity of the data.

This includes access controls, data minimisation, logging, permission models and choosing suitable AI infrastructure.

That depends on the provider and the technical solution.

Professional enterprise offerings often provide services and contract models in which customer data is not used for general model training. The specific terms of each service must be checked.

AI security covers both the secure use of AI systems and the use of artificial intelligence to improve existing security processes.

This includes access protection, data protection, monitoring of AI systems and AI-based analysis of security-relevant situations.

AI supports sales teams at different stages of the sales process, for example with:

• —researching prospects
• —preparing customer meetings
• —lead qualification
• —CRM documentation
• —summarising conversations
• —writing follow-ups
• —preparing quotations
• —training sales conversations

The aim is usually not to replace sales, but to reduce time-consuming tasks and give sales staff better information.

Yes. AI can simulate different customer types and conversation situations.

Sales staff practise realistic conversations and then receive structured feedback on questioning technique, argumentation, handling objections or conversation management.

In most applications that is neither necessary nor sensible. AI is best at supporting salespeople with research, preparation, documentation and recurring tasks.

Personal relationships, negotiations and complex decisions remain human tasks.

GEO stands for Generative Engine Optimization. It means preparing content, brands and digital information so that generative AI systems such as ChatGPT, Gemini or Perplexity can understand and classify them, and consider them as a source or recommendation.

It involves technical, content and structural measures.

SEO focuses on making websites visible in traditional search engines. GEO additionally looks at how generative AI systems process information and use it in their answers.

Many foundations overlap, such as technical quality, strong content, clear entities and authority. How visibility is created and measured, however, differs considerably in places.

There is no single technical switch that makes a company appear in ChatGPT automatically.

What matters includes clearly understandable company information, high-quality content, clear topical authority, external mentions, consistent entities and a technically accessible website. GEO improves these conditions systematically.

You can improve the likelihood that AI systems understand and consider a company correctly. A specific answer or recommendation cannot be guaranteed.

Generative systems create answers dynamically and use different information sources depending on the system, question and context.

Defined prompt sets are tested regularly across different AI systems. Possible metrics:

• —brand mentions
• —recommendations
• —citations
• —share of voice
• —visibility compared with competitors
• —prompt coverage
• —topical visibility

A consistent methodology is essential so that changes remain comparable over time.

A brand mention occurs when a brand or company is named in a generated AI answer.

It is worth distinguishing whether the brand is merely mentioned, actively recommended or cited as a source.

Citations are source references within generated answers. Depending on the AI system, websites, articles, studies or other sources are linked or named directly.

For companies, it therefore matters not only whether the brand is mentioned, but also whether their own content is used as a source.

No. Nobody can seriously guarantee that a company will be permanently named or recommended by ChatGPT, Gemini, Perplexity or any other AI system for a particular question.

GEO can, however, improve the technical, structural and content conditions and measure changes in visibility systematically.

It depends on scope and the question at hand. A workshop to identify first use cases differs considerably from a comprehensive process analysis or developing a custom AI solution.

After an initial conversation, it is usually possible to judge which approach makes sense.

The effort depends mainly on process complexity, data sources, required interfaces, the AI models used and security requirements.

That is why the concrete use case is defined first. Only then can you decide whether a small proof of concept or a production solution makes sense.

Yes. A clearly defined use case is often the most sensible starting point.

It lets you test technical feasibility, benefit and acceptance before larger processes or business units are integrated.

That depends heavily on scope. A limited prototype can be delivered much faster than a system that connects several data sources and business applications.

Interfaces, data quality, security requirements and existing IT systems are the key factors.

AI Infusion combines consulting and delivery. Depending on the project, the work ranges from process analysis and use case identification through design and prototyping to developing and integrating production AI solutions.

For larger projects, AI Infusion also works with specialised technology and development partners.

AI Infusion mainly works with mid-sized companies and organisations that want to improve or automate concrete business processes with artificial intelligence.

A particular industry matters less than a clear use case with realistic economic potential.

No. Many companies use only individual AI tools so far or are just getting started.

Collaboration can therefore begin with identifying first use cases or with AI projects that are already well advanced.

It starts with a conversation about your goal, existing processes and current technical setup. First, we clarify whether artificial intelligence makes sense for the use case at all.

If a sensible approach emerges, next steps, required resources and a realistic project scope are defined.