You run a business, manage a department or own a recurring operational problem. At the same time, artificial intelligence is advancing so quickly that it can feel as though you must act now or risk falling behind. But where should you begin?
AI consulting turns an idea or a business problem into a practical use case that can be tested. It should not start with trying to sell your company a particular AI product. The first questions concern the objective, expected value, available data, risks and effort. Only then can you decide whether AI is the right approach and how it should be implemented.
What does AI consulting mean today?
Artificial intelligence now covers a wide range of capabilities. Large language models can understand questions written in everyday language and formulate responses. Other AI systems can analyse images, support quality assurance, search large knowledge collections or help create documents.
Good AI consulting therefore starts with your work, not a particular product:
- Where do employees repeatedly lose time?
- Which information is difficult to find?
- Which errors or quality issues keep recurring?
- Which idea has been difficult to implement with conventional tools?
- Which data must remain within an environment you control?
These questions lead to an initial assessment. Some problems are well suited to AI. For others, a conventional search function, a better process or custom software may be more reliable and less expensive. Discovering that early is also a useful consulting outcome.
What can Schiemer Software do for you?
You do not need to arrive with a polished AI use case. Bring us a problem, an early idea or simply a question about whether AI could provide an advantage in a particular part of your business.
Together, we examine:
- The objective: What should become measurably better for customers or employees?
- The current process: Where do delays, searching, errors or unnecessary interruptions occur?
- The data: What information is available, how reliable is it and who may access it?
- The risks: Which answers must be traceable, and where is human review still necessary?
- The implementation: Is a small test enough, do you need a custom solution, or would you prefer guidance while your own team implements it?
Schiemer Software can advise you on the decision and support your internal implementation, or develop a suitable custom solution. A useful first step is a free, no-obligation conversation. If it leads to a defined project, we can prepare a tailored proposal.
Example: make company knowledge easier to use
Many companies hold valuable knowledge across an intranet, Confluence, technical documentation, manuals and other databases. The information exists, but employees still need to know where to look. They may read several documents or interrupt an experienced colleague to get an answer.
An internal AI assistant can make selected, approved knowledge sources easier to search. An employee asks a question in ordinary language. The system finds relevant content, produces a clear response and should display the sources it used. The employee can then verify the answer and open the original document when needed.
This is sometimes described as a “company LLM”. In many cases, however, there is no need to train a language model from scratch. A more practical approach may be to connect a suitable model to approved company information in a controlled way. The right architecture depends on the data, security requirements, expected answer quality and budget.
Help new employees become productive
New team members ask many recurring questions: Where is the correct template? How does a particular process work? Which product option meets a requirement? An internal assistant can answer straightforward questions at any time and point to the current documentation.
It does not replace a thoughtful onboarding process or access to real colleagues. It can reduce avoidable waiting, however, and leave experienced employees more time for questions that genuinely require their judgement.
Reduce interruptions between sales and specialist teams
When a salesperson needs a technical product or development detail, they may currently ask support or a specialist department. An internal AI assistant can provide approved standard information more quickly. A subject-matter expert remains involved when the question is new, critical or not clearly documented.
Connect search and documentation
This type of assistant can do more than generate an answer. It can provide the relevant information and its location, saving employees from reading lengthy documentation. It can also draft new documentation using approved background knowledge. A person should review the content, currency and access permissions before anything is published.
What is the business benefit?
Time is a real cost. When employees find information faster, spend less time waiting for answers and interrupt specialists less often with recurring questions, an organisation may save working time. The business case should still be based on evidence rather than a generic promise about AI.
Useful questions for a realistic assessment include:
- How many people search for the same information today?
- How often do questions and waiting periods arise?
- What is the cost of outdated or misunderstood information?
- How much work will data preparation, operation, maintenance and quality control require?
- What improvement would a limited test need to demonstrate before further investment is justified?
Depending on the size and complexity of the company, operating and maintaining the system can become an ongoing responsibility. Knowledge sources change, access permissions need to remain current and response quality must be monitored. This work belongs in the plan from the beginning.
Does sensitive data have to go to the cloud?
Not necessarily. Depending on the use case, an AI system can run within your own infrastructure or another environment you control. Hybrid approaches are also possible, with only clearly defined components using external services.
The hosting location alone does not settle every privacy and security question. You should also assess:
- Which personal or confidential data the system processes
- Who may access each source and response
- Whether inputs or outputs are stored or used for other purposes
- How data is encrypted, logged, updated and deleted
- Which service providers are involved and which agreements are required
- How incorrect or incomplete answers will be detected and handled
We can help you compare the technical options and understand their practical consequences. The legal assessment must reflect the specific use case and should be coordinated with your privacy or legal advisers where appropriate.
AI can do more than answer questions
An internal knowledge assistant is only one example. AI can also inspect image data, identify anomalies or support employees in quality assurance. Other potential applications include classification, document processing and assistance with recurring decisions.
Whether a use case succeeds depends heavily on the quality and quantity of the available data, how clearly the task can be defined and the consequences of an error. The greater the cost or risk of a wrong decision, the more important traceable results, clear boundaries and human oversight become.
How do you start without chasing a trend?
You do not need to wait until competitors have a perfect solution. You also do not need to launch a major project merely to say that your company uses AI. A sensible start is smaller:
- Describe one specific problem or idea
- Assess the value, data and risks
- Plan a limited test with clear success criteria
- Review the result before deciding whether to expand or operate it long term
Our practical advice is backed by technical knowledge in software engineering and artificial intelligence, including relevant study at the Vorarlberg University of Applied Sciences. Our job is to make the difficult technical choices understandable, so you can weigh value, effort and risk with confidence.
Do you have a problem, an idea or uncertainty about where AI could help your business? Email info@schiemer-software.com. In a free, no-obligation initial conversation, we can identify a sensible next step.
Frequently asked questions about AI consulting
Do I need a specific AI idea already?
No. A recurring problem, a slow process or information that is difficult to find is enough to begin. The assessment determines whether AI is suitable or whether another solution would make more sense.
Do we need to develop our own AI model?
Not necessarily. An existing model can often be connected securely to selected company information. A dedicated or locally operated model may make sense where privacy, control, performance or specialised requirements justify it.
Can AI give incorrect answers?
Yes. Responses should therefore point to their sources, important outputs should be reviewed and clear limits should be defined. Human responsibility remains essential for critical decisions.
Is an AI system automatically compliant if it runs in our own infrastructure?
No. A controlled hosting environment may be an important component, but it does not replace an assessment of the data, permissions, security, retention, service providers and legal requirements.