AI representative:
Tasks, competencies & advantages for companies
Many SMEs see AI as an opportunity – and at the same time as a risk: what are the concrete benefits, what does it cost internally, and how can it remain legally and technically clean?
The short answer:
AI consulting helps medium-sized companies to set up AI projects in such a way that they deliver measurable benefits and work during operation. A typical approach consists of use case selection, data and risk checks, piloting and scaling, including team enablement. The decisive factor is not the “best tool”, but a clear process: objectives, data, responsibility, security and KPI measurement.
In the following, you will see which fields of application have proven themselves in SMEs, which challenges are real and how you can introduce AI in a structured way – without tool hype and without compliance debts.
5 key takeaways
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AI consulting is only effective if it is thought through from the use case through to operation. Not as a tool selection or demo, but as an implementable program with clear responsibilities and measurement logic.
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Use cases with existing data and clear KPIs (e.g. documents/back office, service, marketing management, quality/maintenance) are the fastest lever for SMEs. Start small and only scale up once you have a reliable pilot.
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Data access, data quality and ownership are the real bottlenecks: without discoverable, legally usable and stably available data and a specialist owner, AI remains an experiment.
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Compliance is not an “addendum”: GDPR, IT security and (if relevant) AI Act classification must be clarified before the pilot. Otherwise there is a risk of rollout stops, additional costs and loss of trust.
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The AI officer makes AI controllable: he or she combines strategy, risk management, internal guidelines, training, documentation and transparency so that AI initiatives do not end up as a pilot collection, but function permanently.
What is AI consulting and where does it end?
AI consulting is structured support from use case selection, data and risk assessment through to pilot, integration, operation and training. It does not end with the prototype, but where benefits are measurable and responsibility is anchored in everyday life.
AI (artificial intelligence) describes methods with which systems can recognize patterns, make predictions or process texts/images. For SMEs, the definition is less important than the translation into processes: Which decision will be better, which task faster, which error less frequent?
It is important to differentiate: AI consulting is not “buying a tool”. Nor is it synonymous with software development. Good AI consulting combines business, IT, data protection and information security into a feasible plan.
When does AI consulting really pay off for SMEs?
AI consulting is worthwhile if a use case has a clear business impact, data is available and the company is prepared to take responsibility for operation and quality. If objectives, data access or responsibilities are unclear, AI becomes expensive and remains a pilot.
Typical sensible starting points are processes with high volumes, recurring decisions or measurable quality problems. Equally useful: areas where time is short (service, back office) or where data is generated anyway (production, logistics, CRM).
Which use cases work particularly well in SMEs?
Use cases that use existing data and have clear KPIs often work: Automate documents/back office, improve customer communication, optimize quality/maintenance and manage marketing more efficiently. Start with a use case that a department really “owns”.
How does AI automate back office and documents?
AI can reduce repetitive activities: Classifying documents, extracting content, pre-sorting tickets or preparing standard responses. The leverage arises when process steps are clear (receipt, review, approval) and quality is made measurable (error rate, throughput time, escalations).
How does AI improve marketing, sales and customer service?
AI can help to segment customer data, optimize campaigns or process frequent service requests more quickly. The data basis (CRM, web, email, tickets) and the clear separation between “support” and “decide” are crucial, especially when personal data is involved.
How does AI help with production, quality and maintenance?
In production, typical levers are quality inspection, anomaly detection and predictive maintenance. The benefits arise not only from the model, but also from the interaction of sensors/protocols, access, feedback into the daily work routine and stable operating processes.
What prerequisites do you need before the start (data, processes, target image)?
Without access to data, responsibilities and a clear vision, AI consulting becomes a workshop marathon. The minimum is: data is findable, usable, of sufficient quality and regularly available – plus a specialist department that is responsible for KPIs and decisions.
In practical terms, this means appointing an owner in the department, clarifying IT access and identifying whether personal data is involved. Also define how results flow back into the process (approval, escalation, monitoring).
What are the tasks of an AI officer?
The AI officer makes AI controllable within the company. They ensure that AI projects deliver measurable benefits, that risks are minimized and that operations and evidence (documentation/transparency) function in the long term.
- Strategy and compliance: The AI officer translates corporate goals into an AI roadmap and establishes responsibilities. They ensure that ethical and regulatory requirements are taken into account during implementation – especially where personal data or critical decisions are affected.
- Risk management: He carries out regular analyses to identify and minimize potential risks at an early stage. This includes clear approval processes, control mechanisms and escalation paths so that AI does not slip “unchecked” into productive processes.
- Internal guidelines: It creates comprehensible guidelines for dealing with AI in day-to-day work. These include rules for data usage, tool selection, access rights, quality checks and the safe use of generative AI.
- Employee training: The AI officer organizes training courses so that teams can use AI sensibly and responsibly. The aim is practical AI competence: employees should be able to classify results correctly, recognize limits and comply with standards.
- Communication with authorities: He is the central point of contact for regulatory inquiries and audits. In practice, he coordinates information, evidence and internal participants so that the company remains able to provide information.
- Documentation: It ensures transparent documentation and ongoing monitoring of AI systems. This includes ensuring that decisions, data flows, tests, approvals and changes are recorded in a traceable manner.
- Transparency: It promotes the use of explainable AI (XAI) where it is necessary for trust and traceability. This also includes clear communication about where AI provides support, where people have to make decisions and how quality is continuously measured.
AI training for your employees?
What prerequisites do you need before the start (data, processes, target image)?
Without access to data, responsibilities and a clear vision, AI consulting becomes a workshop marathon. The minimum is: data is findable, usable, of sufficient quality and regularly available – plus a specialist department that is responsible for KPIs and decisions.
In practical terms, this means appointing an owner in the department, clarifying IT access and identifying whether personal data is involved. Also define how results flow back into the process (approval, escalation, monitoring).
How does an AI consulting project work - from workshop to rollout?
Short answer: A resilient approach is to prioritize use cases, check data and risks, implement a pilot with KPIs, secure integration and operation and only then scale up. In this way, you avoid creating prototypes that fail in everyday use.
A typical process looks like this: Use cases are evaluated in the AI workshop (impact, feasibility, risk). This is followed by a data and architecture check. In the pilot, a use case is tested close to production, including measurement design. Integration, operation (monitoring, updates, roles) and training are then planned before scaling.
Which technologies are relevant and how do you choose without tool hype?
Many AI projects fail not because of the tool, but because of the selection and operating logic. Technology classes that are often relevant: Machine Learning (prediction/classification), Natural Language Processing (text processing), RPA (automation) and cloud/data platforms for scaling.
Selection criteria are more important than tool names: Does it fit your data? How are access and logging regulated? How do you measure and monitor quality? How do you avoid vendor lock-in? What other integrations are missing?
What risks do you need to clarify (GDPR, IT security, AI Act)?
Before the pilot, clarify at least: data types and legal bases (GDPR), access/logging and protection requirements (IT security) as well as the question of whether your use case falls under AI Act obligations. If you do this later, it will be more expensive and slow down rollouts.
The GDPR becomes relevant as soon as personal data is processed. The purpose, legal basis, access, storage and, if applicable, DPIA must then be checked. From a security perspective, identities, authorizations, data outflow, supplier risks and incident processes are important.
With the AI Act, the classification depends on the application: develop, operate, integrate – and whether the use case is in sensitive areas. In practical terms, this means classifying at an early stage and planning documentation/monitoring. If you need to be more formal, ISO/IEC 42001 can help as a management system framework – but only if governance is really needed.
Do you need help with your AI strategy?
How do you measure the success and ROI of your AI initiatives?
ROI becomes measurable if you define a baseline before the pilot and then compare it using a few KPIs. Useful KPI categories are time, quality, risk and, where appropriate, sales/conversion.
Examples of measurement logic: time savings per process, reduction in the error rate, shorter throughput time, lower escalation rate in service or more stable system availability. Important: Determine who measures, how often measurements are taken and what is considered “good enough” for scaling.
How do you build up AI expertise in a team instead of stacking pilot projects?
AI will only be stable in everyday life if teams know how to evaluate results and understand limitations. This includes training for specialist departments (interpretation, process integration), IT (operation, monitoring) and governance (data protection, security, approvals).
Plan enablement not as “training at the end”, but in parallel with the pilot. This not only produces a result, but also the ability to operate and develop it further.
How do you select an AI consultancy that suits SMEs?
Make sure you have a clear process model with concrete results, a clean data/risk check and real operational capability, not just demos. The right AI consulting makes your team faster and more independent instead of building up dependencies.
Check whether the consultancy works in your reality: limited resources, existing systems, clear priorities. Ask to be shown deliverables (use case assessment, data check, pilot plan, measurement design, operating and role model) and clarify what knowledge transfer looks like.
Practical example 1: Mechanical engineering (DACH) - predictive maintenance
A medium-sized manufacturer reduces downtime by using sensor data and maintenance logs for predictive maintenance. The starting point is a pilot at a critical plant with clear KPIs (e.g. unplanned downtime, response time, spare parts planning). After the pilot, data quality, access concepts and monitoring are stabilized in such a way that the model does not become “obsolete” in day-to-day operations.
Practical example 2: B2B trade (DACH) - manage campaigns & service more efficiently
A retailer improves campaign and service efficiency by creating customer segments from CRM and store data and answering standard support queries in a structured manner. Data protection limits (personal data, consent, retention) and quality metrics (response hits, escalation rate) are defined in the pilot. Only then is scaling carried out – including training the teams so that results remain reproducible.
Conclusion and outlook
AI consulting is valuable when it does not stop at ideas, but brings together benefits, risk and operation. Start small, choose a use case with clear KPIs, clarify data and compliance before the pilot and build up expertise in the team in parallel.
- Oliver Breucker
- December 10, 2025
FAQ
Costs depend primarily on the scope: Workshop/strategy, pilot, integration, operation and training. It makes sense to commission the project in stages so that the budget is linked to measurable milestones.
A first pilot is often realistic in a few weeks to a few months if data access and responsibilities have been clarified. Without these prerequisites, the project usually takes longer due to coordination and rework.
Not essential for the start. However, you need internal ownership in the specialist area, IT operational capability and governance expertise (data protection/security). External support does not replace responsibility, but can accelerate it.
Data must be findable, legally usable, of sufficient quality and stably available. If data is only available in isolation or access is unclear, AI consulting becomes the turning over of stones instead of implementation.
No. Cloud is often pragmatic because of scaling, but on-prem or hybrid can make sense – depending on protection requirements, latency, integration and internal specifications.
As soon as personal data is involved, the purpose, legal basis, access and storage must be clarified; depending on the risk, a DPIA may be necessary. Good projects plan this as a fixed step before the pilot.
This depends on the use case and your role (operator, provider, integrator). In practice, you should classify early on, check obligations and plan documentation/monitoring in order to avoid rollout stops later on.
Through clear data sources, test cases, measurement metrics and approval processes. Critical statements require human control and escalation paths instead of “fully automatic”.
Set a baseline and define a few KPIs: Time, quality, risk and – where appropriate – turnover. Measure in the pilot and only then decide on scaling.
On concrete results (use case scoring, data/risk check, pilot plan, measurement design, operating model) and on empowering your team. Demos without an operating and measurement concept are a warning signal.
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