Successfully implementing ChatGPT in your company
Estimated reading time: 9 minutes
Save time, increase efficiency. Using ChatGPT in your company can bring many benefits for employees and managers alike. Those who work with it quickly realise how fast and effective GPT-3 is. Used correctly, ChatGPT can make everyday work in companies more sustainable. But as with any technology, there are opportunities and risks. Read on to find out what these are and how you can use ChatGPT successfully.
While artificial intelligence dominates the IT agenda, its impact is mixed—using AI without a fundamental strategy and clearly defined goals does not automatically solve problems, but rather amplifies existing inefficiencies and drastically increases the volume of digital waste. (MaibornWolff Study on Technology Efficiency, p. 21). Read here to find out how you can still use ChatGPT successfully.
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What are the benefits? ChatGPT for businesses automates time-consuming routine tasks such as email correspondence, customer service responses, and technical documentation.
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Where is it worth using? The use cases are diverse and range from intelligent "personal assistants" to coding support in IT and error analysis in production.
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Why is good data essential? AI is only as good as its input. Unstructured or incorrect data leads to incorrect answers (hallucinations).
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Is my data secure? Be careful with the public version—it is often unsuitable for confidential company data (US server). Isolated environments such as GPT on Azure offer alternatives that comply with data protection regulations.
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Can AI make decisions on its own? No. Despite high text quality, the human-in-the-loop principle applies: subject matter experts must perform a final check of the output.
Our white paper explains how you can use GPT to get the most out of your company data and thus ensure your success.
What exactly is ChatGPT?
ChatGPT is an artificial intelligence-based language model developed by the US company OpenAI. Technically, it is a "Generative Pre-trained Transformer" (GPT), a special form of Large Language Models (LLM). These models use machine learning to conduct human-like conversations, generate text, and solve complex tasks in a dialogue format.
Currently, ChatGPT is primarily used by companies to:
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Develop user-friendly chatbots and voice assistants.
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Respond to customer service inquiries automatically.
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Generate texts and work on problems in a solution-oriented manner.
Our study also highlights the growing prevalence of AI tools: 61% of companies report that the scope of their use will have increased by 2025. (MaibornWolff Study on Technology Efficiency, p. 21)
How can ChatGPT help businesses?
For businesses, using ChatGPT primarily means increased productivity through the automation of routine text-based tasks. Before we get to specific use cases, here are the work processes with the greatest leverage:
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Customer service: Automated response to inquiries and 24/7 support.
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Knowledge management: Internal document searches and preparation of company knowledge.
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Content creation: Drafts for marketing, emails, or reports.
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Technical support: Step-by-step instructions and troubleshooting.
Note:
The quality of the output depends largely on your input data.
42% of respondents in our study already view increased process efficiency as the primary benefit. (MaibornWolff Study on Technology Efficiency, p. 23)
The following applies: Those who view AI merely as a panacea will be disappointed—but those who see it as a specialized tool will reap significant benefits. (MaibornWolff Study on Technology Efficiency, p. 23)
We explain how GPT can be used in practice within companies in our white paper.
Erfolgreiche Use Cases: ChatGPT in Unternehmen nutzen
Wer ChatGPT für Unternehmen einsetzt, zielt meist auf konkrete Effizienzsteigerungen in spezifischen Abteilungen ab. Nachfolgend finden Sie praxiserprobte Anwendungsfälle, in denen KI bereits heute Arbeitsprozesse beschleunigt:
Personal Assistant & Office Management
Scenario: The AI assistant acts as a “second brain” for employees. It not only manages calendars, but also prepares unstructured data for presentations and summarizes long meeting minutes.
Benefit: Information is immediately available in the desired format, which significantly reduces the administrative workload.
Customer Management & Field Service
Scenario: Service technicians or call center agents cannot memorize every technical detail of complex products (e.g., household appliances).
Benefit: An integrated chatbot provides step-by-step repair instructions and suggested solutions on demand. In the future, this bot will also be able to handle end customer inquiries (first-level support) directly.
Shop floor & production
Scenario: In noisy production halls, typing questions into terminals is often impractical.
Benefit: By combining ChatGPT with speech-to-text, technicians can ask the machine status questions verbally. The answer is delivered via text-to-speech directly to the headphones – ideal for hands-free maintenance work.
Engineering & Documentation
Scenario: Engineers spend a lot of time on reports and data sheets. GPT tools automate the creation of technical documentation and, more importantly, the updating of existing instructions.
Benefit: Manual transfers are eliminated; documents are automatically updated based on new data inputs.
Development & Quality Analysis
Scenario: In vehicle or plant development, AI analyzes historical quality databases to identify patterns for new platforms.
AI agents enable existing software landscapes and complex domains to be analyzed and modernized much more quickly. (MaibornWolff Study on Technology Efficiency, p. 23)
Benefits: When clustering and categorizing large amounts of data, ChatGPT is often superior to traditional topic detection methods in terms of speed and accuracy.
Market research
GPT-based analytics platforms link raw survey results with user data to directly derive effective communication strategies.
Challenges and risks in corporate use
Using ChatGPT in your company offers enormous opportunities, but requires awareness of specific risks.
After all, if AI is applied to inefficient processes, it will not result in greater efficiency, but merely in doing the wrong thing faster. (MaibornWolff Study on Technology Efficiency, p. 21)
Before rolling out AI on a large scale, you should evaluate these three hurdles:
Data protection and confidentiality
ChatGPT (in the standard version) processes data on US servers and potentially uses inputs to train future models.
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The risk: Sensitive company data, trade secrets, or personal customer data could be permanently stored in the AI's memory and theoretically retrieved at a later date.
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Recommendation: The public web version is often unsuitable for internal or confidential data. Instead, use enterprise solutions or API connections (such as GPT on Azure) that guarantee data security by contract.
Hallucinations (factual certainty)
Language models are trained to give answers that sound plausible, not necessarily true ones.
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The risk: AI can freely invent ("hallucinate") facts, sources, or technical details and present them in a completely convincing manner.
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Recommendation: Establish a "human-in-the-loop" strategy. Technical content must be reviewed by experts before it is sent to customers.
19% of respondents in our study currently see no tangible business value in AI—a sign of just how crucial a clear strategy and quality assurance are. (MaibornWolff Study on Technology Efficiency, p. 23)
Copyright
Generative AI produces content and code at a pace that is virtually impossible to keep up with manually. (MaibornWolff Study on Technology Efficiency, p. 22)
The legal situation regarding AI-generated content is still dynamic in many countries.
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The risk: It is often unclear whether AI texts can be protected by copyright or whether you are unknowingly infringing the rights of third parties contained in the training data set by using them.
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Recommendation: Label AI content internally and clarify its use for critical assets (e.g., logos, claims) in advance with legal counsel.
59% of respondents in our study fear that digital waste—specifically, unused technical features, dead code, and redundant artifacts—will increase in the future due to AI. Since AI drastically reduces the cost and time required to create code and content, we are not producing less code in less time, but simply much more code overall—without proper governance, we face the risk of an inflation of digital assets.
This trend carries the risk that technical debt will no longer be reduced in a controlled manner but will instead multiply automatically. This points to a classic SaaS sprawl scenario (unchecked proliferation of cloud applications), in which AI tools seep unchecked into business units as shadow IT without any central coordination. (MaibornWolff Study on Technology Efficiency, p. 22)
Should I use ChatGPT in my company?
Whether ChatGPT or GPT on Azure is suitable for your company can depend on several factors. For example:
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Suitable use cases: What are the areas of application for ChatGPT in your company? Where can ChatGPT be used to its fullest potential?
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Good data & quality: If your company data is unstructured, outdated, or incorrect, ChatGPT will provide inconsistent answers or invent facts (so-called hallucinations). A clean, digitally available database is therefore an essential technical prerequisite for success.
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Data protection: The protection of sensitive, critical, and unpublished data must always be taken into account. In this case, it may be advisable to use a GPT solution instead of ChatGPT.
47% of IT managers and professionals feel overwhelmed by the sheer volume and frequency of new AI applications. (MaibornWolff Study on Technology Efficiency, p. 22)
52% of respondents report that the use of inefficient software within their own companies has continued to increase over the past year. (MaibornWolff Study on Technology Efficiency, p. 5)
From experiment to strategy: your next step
The path to becoming an AI-supported company does not begin with large IT projects, but with valid experiments. Instead of immediately rolling out ChatGPT across the entire company, we recommend the following approach:
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Identify: Find a specific process (e.g., first-level support or documentation) that takes up a lot of time.
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Validate: Test feasibility with non-critical data in a protected environment (proof of concept).
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Scaling: For productive use, switch to secure enterprise environments such as Azure OpenAI to guarantee data protection and compliance.
This requires the right foundation: 68% call for a thorough requirements analysis before the project begins. (MaibornWolff Study on Technology Efficiency, p. 19)
In this sense, the key to new competitiveness lies not in adding more tools, but in the ability to eliminate the unnecessary. (MaibornWolff Study on Technology Efficiency, p. 6)
After all, freeing up budgets from the management of legacy systems creates the financial capacity for innovation and a faster time-to-market. (MaibornWolff Study on Technology Efficiency, p. 6)
Technology should always be used where it adds value—not for its own sake. [...] It is important to avoid technological over-engineering and to build the IT systems of the future in a lean manner.
FAQs about ChatGPT in business
Will my company data be used to train the AI?
When using ChatGPT in the standard way: Yes. OpenAI openly states that entered data and interactions can be used to train new model variants. Sensitive or confidential company data should therefore never be entered into the public version, as it could theoretically appear in future model generations.Is ChatGPT suitable for internal corporate use?
The public version of ChatGPT is primarily designed for private users. Since prompts are often logged on US servers and data usage for training purposes may be enabled by default, the tool is often unsuitable for internal or confidential B2B applications from a compliance perspective.
Is there a data protection-compliant alternative for my company?
Yes, Microsoft's Azure OpenAI Service offers a solution here. Microsoft hosts the GPT models in Europe (EU) and operates in compliance with the GDPR and the Azure Data Protection Addendum.What is the difference between ChatGPT and GPT on Azure?
Although the Azure OpenAI Service uses the same language models, it runs completely isolated within your company's Azure infrastructure. No OpenAI APIs are used. Microsoft guarantees that the data will not leave its own cloud environment and—if configured correctly—no inputs will be logged or used for AI training.
Kyrill Schmid is Lead AI Engineer in the Data and AI division at MaibornWolff. The machine learning expert, who holds a doctorate, specialises in identifying, developing and harnessing the potential of artificial intelligence at the enterprise level. He guides and supports organisations in developing innovative AI solutions such as agent applications and RAG systems.