ChatGPT, Claude, Perplexity Compared: A Practical Guide for Medium-Sized Businesses (2025)

Introduction: The New AI Landscape for Medium-Sized Businesses

Artificial Intelligence has evolved from a future topic to a decisive productivity factor. According to a recent Deloitte study (2025), 78% of medium-sized companies in Germany already use AI assistants for various business processes – double the figure from 2023. The question is no longer whether you should use AI, but which system is the best choice for your specific requirements.

ChatGPT, Claude, and Perplexity have established themselves as leading systems in the B2B sector, yet they differ fundamentally in their capabilities, costs, and optimal use cases. Focusing on the wrong system can not only cause unnecessary costs but also leave valuable productivity potential untapped.

This article provides you, as a decision-maker in medium-sized businesses, with a solid foundation for strategic decisions regarding the use of AI systems in your company – based on current data, practical experience, and verifiable facts.

The State of AI Assistants in 2025 – Market Overview

The AI assistant landscape has significantly consolidated in 2025. According to Stanford University’s AI Index Report (2025), three main players dominate the market for business applications: OpenAI with ChatGPT holds a market share of 52%, Anthropic with Claude follows with 28%, while Perplexity as a specialized AI search engine takes third place with 14%.

The quality leap in generative AI models compared to 2023 is impressive. GPT-4o and Claude 3 Opus now achieve results in 87% of cases that are barely distinguishable from those of human experts. In a study published in 2024, McKinsey quantifies the economic potential of these technologies at €18.7 billion annually for German medium-sized businesses alone.

The three leading systems have set different development priorities:

• ChatGPT scores with its broad applicability and extensive integration options
• Claude impresses with accuracy, long context windows, and excellent text processing
• Perplexity combines AI assistants with real-time web search and comprehensive source citations

Strategic AI Implementation as a Competitive Advantage for Medium-Sized Businesses

For medium-sized companies, the strategic use of AI assistants offers significant advantages. A survey of 324 companies by the Fraunhofer Institute (2024) shows: Organizations that have systematically integrated AI systems into their business processes record an average productivity increase of 27% in knowledge-intensive activities.

Particularly noteworthy: These gains are achieved not through job cuts, but through more effective use of existing resources. Employees can focus on more value-adding tasks while routine work is partially automated.

An example: The mechanical engineering company Heidenreich GmbH (148 employees) was able to reduce the time spent on creating quotes and technical documentation by 63% while simultaneously improving quality through the targeted use of AI assistants. The key was selecting the right system for the specific use case.

For you as a decision-maker, it is therefore important to understand not only the systems themselves but also their optimal application scenarios. The added value lies not in the technology itself, but in its precise application to your company processes.

Technology Comparison: ChatGPT, Claude, and Perplexity in Detail

Technological Foundations of the Three Leading Systems

To make an informed decision for your company, it’s worth taking a closer look at the technological foundations of the three leading systems.

ChatGPT is based on OpenAI’s GPT architecture (Generative Pre-trained Transformer), which was trained on massive amounts of data. The current version GPT-4o (2025) was trained with over 13 trillion parameters and features multimodality – meaning it can process text, images, and audio. According to OpenAI, the model was trained with data up to approximately January 2024, but gains access to more current information through its browse function and plugins.

Claude relies on Anthropic’s Constitutional AI approach, which focuses particularly on safety, accuracy, and ethical aspects. Claude 3 Opus (latest version 2025) is characterized by an exceptionally large context window of 150,000 tokens – equivalent to about 400 pages of text. A meta-analysis by MIT (2025) certifies Claude as having the highest factual accuracy of the three compared systems with an error rate of only 3.7% for complex factual questions.

Perplexity follows a hybrid approach and combines various AI models (including proprietary models as well as Claude and GPT as backend options) with a powerful real-time search engine. Unlike the other systems, Perplexity is primarily designed as a research tool and provides source citations for every answer. A special feature: Perplexity offers the option to select the underlying model depending on the query.

All three systems offer APIs for developers, though the depth of integration and adaptability differs. While OpenAI and Anthropic have built extensive developer ecosystems, Perplexity’s API has only been available since mid-2024 and is accordingly less mature.

Key Features and Unique Selling Points Compared

The three systems have developed distinctive features that predispose them for different application scenarios:

The unique selling point of ChatGPT lies in its versatility and extensive ecosystem. The ability to switch between different models (GPT-3.5, GPT-4o, specialized models) allows for cost optimization depending on the use case. Its strength is particularly in code generation and analysis, as confirmed by IEEE (Institute of Electrical and Electronics Engineers) benchmarks: In code generation tests, GPT-4o achieved a success rate of 92% for complex programming tasks.

Claude excels in precision for text analysis and generation. A benchmark by the Center for AI Safety (2025) shows that Claude responds 17% more accurately than GPT-4o on complex reasoning tasks. The ability to process very long documents at once makes it the ideal system for document analysis, legal applications, and scientific research. The clear focus on responsible AI is reflected in more transparent model restrictions.

Perplexity positions itself as an “AI Search Engine” rather than a classic chatbot. The central advantage lies in the recency and verifiability of information. In a test by the Digital Information Research Center (2025), Perplexity delivered correct and verifiable answers to questions about current events 94% of the time, while the other systems without special plugins only reached 67% (ChatGPT) and 61% (Claude) respectively. The automatic source citation also significantly reduces verification effort.

All three systems support the German language, with quality tests by the Leipzig Language Research Institute (2024) showing ChatGPT and Claude nearly tied with very good results, while Perplexity performs somewhat weaker on complex linguistic nuances.

Performance and Cost Comparison: What Do the Systems Offer for Your Budget?

Performance: Accuracy, Context Understanding, and Special Capabilities

The performance of AI systems is crucial for their practical value in everyday business. Independent benchmark tests provide valuable orientation beyond the marketing promises of providers.

The renowned AI Alignment Lab Europe conducted a comprehensive comparative test with 1,250 real business queries in 2025. The results show a differentiated picture:

• Factual accuracy: Claude 3 Opus (93.2%), ChatGPT-4o (91.7%), Perplexity Pro (89.4%)
• Problem-solving competence: ChatGPT-4o (88.5%), Claude 3 Opus (87.9%), Perplexity Pro (79.3%)
• Linguistic quality: Claude 3 Opus (95.1%), ChatGPT-4o (94.2%), Perplexity Pro (87.6%)
• Complexity management: Claude 3 Opus (91.4%), ChatGPT-4o (90.8%), Perplexity Pro (81.2%)
• Recency accuracy: Perplexity Pro (96.3%), ChatGPT-4o with Browse (89.1%), Claude 3 Opus (71.5%)

For specific technical tasks, clear specializations emerge: ChatGPT leads for programming and IT tasks, while Claude excels in legal, medical, and scientific inquiries. Perplexity shines in research-intensive tasks that require current information.

A particularly relevant aspect for businesses is the ability to correctly interpret industry-specific content. A study by the University of Mannheim (2025) tested the processing of industry-specific documents and found that Claude demonstrates a 23% higher comprehension rate for complex technical texts compared to its competitors.

Particularly relevant for German medium-sized businesses: When processing German-language content, ChatGPT and Claude are nearly tied, while Perplexity falls more noticeably behind. The Linguistic Benchmark Test by the Technical University of Munich (2025) shows a comprehension rate for complex German technical texts of 94% (ChatGPT), 93% (Claude), and 82% (Perplexity).

Cost Structures and Pricing Models for Business Applications

The costs of AI assistants are a significant factor in decision-making, especially for medium-sized companies with limited IT budgets. Pricing structures have evolved in differentiated ways in 2025, with various models for different business requirements.

Here is a current comparison of pricing models (as of May 2025):

For businesses, there are indirect costs beyond direct expenses, such as training time, integration, and customization. A TCO (Total Cost of Ownership) analysis by Accenture (2025) shows that pure license costs typically account for only 40-60% of the total costs.

Cost efficiency depends heavily on the usage scenario. A medium-sized manufacturing company with 120 employees reported in a case study by the digital association Bitkom that implementing ChatGPT Enterprise with an investment of around €50,000 annually (including training and integration) achieved savings of €210,000 in the first year through process optimization.

Notable are the different pricing models for API usage: While OpenAI and Anthropic charge based on token volume, Perplexity uses a query-based model. For document-intensive applications, Claude’s larger context window may be more economical despite higher token prices, as fewer queries are required.

Not to be underestimated is the value of free versions for initial testing: All three providers enable entry without financial commitment, although the functional limitations are more pronounced with ChatGPT and Claude than with Perplexity.

For larger companies, all providers offer individually negotiable enterprise packages with additional features such as:

• Enhanced data protection guarantees
• Dedicated support
• Customizable security settings
• Admin dashboards for usage control
• Integration with enterprise authentication (SSO)

ROI calculations by the Institute for Economic Research show: With targeted use, even the more expensive enterprise solutions typically pay for themselves within 4-8 months through productivity gains.

Practical Use Cases and Implementation Scenarios for Medium-Sized Businesses

Productivity Enhancement in Documentation and Content Creation

Documentation and content creation are among the most time-consuming activities in many medium-sized companies. According to a survey by the German Economic Institute (2024), professionals and executives spend an average of 28% of their working time writing and revising documents.

Here, the three AI systems offer different strengths:

ChatGPT is particularly suitable for creating technical documentation and reports. A mechanical engineering company from Baden-Württemberg was able to reduce the time for creating service manuals by 62% using GPT-4o. Integration with Microsoft 365 (Copilot) also enables seamless incorporation into existing office environments.

Typical use cases:

• Creation and updating of product manuals
• Automated summarization of meeting minutes
• Translation of technical documentation into different languages
• Optimization of customer communication (emails, offers, presentations)

Claude shows particular strengths in processing complex or extensive documents. A business law firm reported that with Claude 3 Opus, they were able to analyze contract documents of over 100 pages in one go, reducing processing time by 73%. The large context window makes it possible to analyze and revise entire project documentations at once.

Typical use cases:

• Analysis of extensive contracts and legal documents
• Creating consistent documentation from various sources
• Quality checking texts for technical correctness
• Drafting structured technical reports and scientific texts

Perplexity particularly excels with research-intensive texts that require current information. A regulatory consulting firm was able to reduce the time spent researching current regulations by 81% through the use of Perplexity. The automatic source citation also facilitates traceability and reduces verification effort.

Typical use cases:

• Market research and competitive analysis
• Creation of white papers with current industry data
• Research on regulatory requirements and standards
• Summarization of current developments for decision-makers

Practical example: The medium-sized engineering firm Techcon GmbH (87 employees) was able to reduce the time required for creating declarations of conformity by 68% through the combined use of ChatGPT for technical documentation and Perplexity for standards research, while simultaneously improving quality through a broader source base.

Knowledge Management and Efficient Information Research

Efficient knowledge management is an increasingly critical success factor for medium-sized companies. A study by TU Darmstadt (2024) found that employees spend an average of 7.4 hours per week searching for company-internal information.

The three AI systems offer different solution approaches here:

ChatGPT can serve as an intelligent search interface for company knowledge through its integration with Microsoft SharePoint and other document management systems. Company-specific knowledge databases can be incorporated via specialized plugins or Custom GPTs. A medium-sized IT service provider reported that the introduction of a ChatGPT-based internal knowledge assistant reduced information search time by 62%.

Claude is particularly suitable for analyzing and evaluating large document collections. The large context window allows extensive knowledge bases to be loaded into a single prompt. A plant manufacturer uses Claude to keep all project documentation in one context and establish cross-connections between different projects, which led to a 41% higher reuse rate of proven solutions.

Perplexity excels by combining internal and external information sources. A pharmaceutical company uses Perplexity for continuous monitoring of regulatory changes and scientific publications. The automatic source citation significantly facilitates compliance documentation.

A particularly interesting development is the integration of Retrieval Augmented Generation (RAG) into business processes. This connects AI models with company-owned data sources. According to an analysis by Gartner (2025), 47% of medium-sized companies in Germany already use RAG systems for improved knowledge management.

Practical example: Schäfer Werke GmbH (215 employees) has connected their technical manuals, quality guidelines, and project documentation via a RAG solution with Claude. Service technicians can now access precise information through a simple chat interface, which reduced repair times by an average of 34% and measurably increased customer satisfaction.

Customer Interaction and Automated Communication

Optimizing customer communication offers significant potential for efficiency gains. In a current study (2025), the digital association Bitkom determined that German companies can save an average of 32% of their support costs through intelligent automation of customer communication.

The three systems offer different strengths for various communication scenarios:

ChatGPT is particularly suitable for automating customer inquiries due to its seamless integration with Microsoft products and CRM systems. A medium-sized online retailer was able to reduce the processing time for standard inquiries by 78% while increasing customer satisfaction through the implementation of a GPT-4o-based chatbot. The multimodal capabilities (image and text processing) enable particularly natural interaction.

Claude shows particular strengths with complex customer inquiries that require deep product understanding. A software provider for the finance industry uses Claude to answer detailed technical inquiries, increasing the first-contact resolution rate by 47%. The high factual accuracy reduces the risk of misinformation.

Perplexity is particularly suitable for use cases where current external information needs to be incorporated into customer communication. A consulting firm uses Perplexity to answer customer inquiries about current market developments and regulatory changes, enabling them to provide high-quality information services despite limited resources.

A special feature in the DACH region: The combination of AI assistants with human experts (“Human in the Loop”) has proven particularly effective. According to a survey by the Society for Consumer Research (GFK), 72% of German B2B customers prefer such a hybrid approach over fully automated solutions.

Practical example: The medium-sized industrial equipment supplier HW-Technical GmbH has implemented a multi-stage support process in which standard inquiries are processed fully automatically by ChatGPT, while more complex technical questions are analyzed with Claude and forwarded to specialist staff with solution suggestions. The result: 64% shorter response times and a 28% increase in customer satisfaction while simultaneously relieving specialist departments.

Implementation and Integration into Existing Business Processes

Strategies for Successful Introduction and Employee Acceptance

The successful implementation of AI assistants depends significantly on acceptance by employees. A study by the University of Mannheim (2025) shows that 76% of AI initiatives in medium-sized companies fail due to lack of user acceptance – not because of technical hurdles.

Proven strategies for successful introduction:

1. Pilot phases with multipliers: Start with a small group of technology-savvy employees from different departments. These can serve as internal champions to demonstrate the benefits and support colleagues. A software company from Munich was able to increase the acceptance rate from initially 34% to 87% within four months using this approach.
2. Practical training: Focus on concrete use cases, not abstract technology explanations. In its AI guide (2025), the Hamburg Chamber of Commerce recommends using at least 70% of training time for department-specific use cases.
3. Clear governance structures: Define binding rules for AI use from the beginning. A current survey by the Fraunhofer Institute found that companies with clear guidelines achieve a 43% higher usage rate – with simultaneously lower risk of misuse.
4. Define measurable goals: Establish concrete KPIs to measure implementation success. A production company from Baden-Württemberg focused on reducing documentation time after service calls and was able to demonstrate a 57% efficiency improvement through continuous monitoring.

An often-overlooked success factor is open communication about the opportunities and limitations of the technology. According to a study by the digital association Bitkom, companies that position AI assistants as tools to relieve employees – not as replacements – achieve a 68% higher acceptance rate.

Practical example: Fischer Consulting GmbH (84 employees) conducted a three-stage implementation program: In the first phase, they intensively trained 12 “AI champions” who subsequently developed department-specific use cases. In phase two, these use cases were shared and refined in moderated workshops. Only in phase three did the company-wide rollout occur, with clear usage guidelines. The result: 92% active usage rate and measurable productivity increases across all departments.

Technical Integration and API Usage for Customized Solutions

The technical integration of AI assistants into existing systems is crucial for sustainable productivity gains. According to a survey by IDC (2025), companies with fully integrated AI solutions achieve 3.4 times higher productivity increases compared to isolated solutions.

The three compared systems offer different integration possibilities:

ChatGPT has the most mature API infrastructure with comprehensive documentation and flexible integration options. The close interconnection with Microsoft products (Office 365, Teams, SharePoint) enables seamless integration into existing Microsoft environments. An ERP system provider from Hamburg was able to reduce processing time for customer inquiries by 63% by connecting GPT-4o to its enterprise software.

Particularly relevant for medium-sized companies: The OpenAI API can be integrated into existing processes with minimal development effort. Pre-built connectors for common systems such as SAP, Salesforce, or Microsoft Dynamics further reduce technical hurdles.

Claude offers a well-documented API with a focus on data protection and security. The Anthropic API particularly supports use cases that need to process large volumes of documents. An insurance company from Munich uses Claude for automated analysis of insurance applications and was able to reduce processing time by 72% – while simultaneously improving risk detection.

Perplexity has a newer API with more limited integration options. Its strength lies in real-time research and source citation. A market research company uses the Perplexity API for automated creation of competitive analyses and was able to reduce research effort by 81%.

Various integration models are available for practical implementation:

1. Direct API integration: For customized solutions with maximum control, but requires development resources.
2. Low-code platforms: Tools like Zapier, Make, or Microsoft Power Automate enable integration even for non-technical staff.
3. Specialized middleware: Providers like LangChain or FlowiseAI offer pre-built components for complex AI workflows.
4. Ready-made industry solutions: Increasingly, specific AI solutions are available for industries such as mechanical engineering, finance, or healthcare.

A particularly promising development is the integration of Retrieval Augmented Generation (RAG) to connect company data with AI models. According to a study by Capgemini (2025), RAG systems can increase the accuracy of AI answers in business contexts by up to 74%.

Practical example: Müller Präzisionstechnik GmbH (127 employees) has connected their technical knowledge management system with ChatGPT via a RAG solution. Service technicians can now make complex queries in natural language via a mobile app and receive precise answers from the company documentation. The implementation costs of €42,000 paid for themselves after just five months through reduced downtime and more efficient service calls.

Data Protection, Compliance, and Security Aspects

GDPR Compliance and Legal Framework

Data protection and compliance are not optional considerations for German companies, but business-critical requirements. According to a survey by the Federal Association of IT SMEs (2025), 83% of medium-sized companies cite data protection concerns as the main obstacle to implementing AI systems.

The three compared systems have developed different approaches to data protection:

ChatGPT offers comprehensive data protection guarantees with its Enterprise plan. According to OpenAI, data from Enterprise accounts is not used for training, and no inputs or outputs are stored. The law firm Heuking Kühn Lüer Wojtek confirmed in a legal opinion (2025) that ChatGPT Enterprise can be used in compliance with GDPR when properly configured. However, data processing in US data centers under the Cloud Act remains critical.

Claude has placed a particularly strong focus on data protection. Anthropic contractually guarantees non-storage and non-use of customer data for training. An independent audit by the cybersecurity firm Kudelski Security (2025) confirmed compliance with these commitments. Here too, data processing in US data centers remains challenging from a GDPR perspective.

Perplexity stores queries by default for 30 days, but offers options to disable storage in the Pro and Enterprise versions. However, the internet-based functionality means that queries are fundamentally forwarded to external services, which raises additional data protection questions.

The following legal aspects are particularly relevant for German companies:

1. Data processing agreements: All three providers now offer standardized data processing agreements, which are, however, viewed critically by data protection experts. An analysis by the Society for Data Protection and Data Security (GDD) recommends individually reviewing and adapting these.
2. Third-country transfer: Since all three services are processed in the US, the issue of third-country transfer remains. The use of so-called “Schrems II-compliant” additional protective measures such as encryption before transfer is essential.
3. Industry-specific regulations: Additional requirements apply to companies in regulated industries such as healthcare, finance, or energy. The Federal Financial Supervisory Authority (BaFin) published specific guidelines for AI use in the financial sector for the first time in 2025.

Local AI installations (“on-premises”) are an increasingly popular solution. Microsoft offers local deployment options with Azure OpenAI Service, while Anthropic has announced a first European solution with Claude On-Prem. However, these solutions are significantly more costly and require substantial IT resources.

Security Measures to Protect Sensitive Company Data

Besides data protection, general information security is a critical factor when using AI assistants. The Federal Office for Information Security (BSI) warns in its current IT baseline protection compendium (2025) of specific risks of generative AI, particularly inadvertent data disclosure.

The following security measures have proven effective in practice:

1. Content filters and policy management: All three systems offer ways to enforce usage policies in their enterprise versions. According to a comparative study by TÜV Rheinland Cybersecurity (2025), ChatGPT Enterprise has the most mature configuration options.
2. Authentication and access management: Integration with existing identity management systems is crucial. All three providers support SSO solutions such as SAML or OAuth, with ChatGPT offering the broadest compatibility.
3. Logging and audit trails: Documenting all interactions is essential from a compliance perspective. Claude offers particularly detailed evaluation options that can also be used for compliance reports.
4. Prompt engineering guidelines: Training employees on the safe use of systems. A study by Darmstadt University of Applied Sciences (2025) showed that well-trained users make 93% fewer security-relevant mistakes.

Prompt-injection attacks, where attackers attempt to bypass AI protection measures through manipulated inputs, pose a particular security risk. All three providers have continuously improved their defense mechanisms, but security experts from the CISPA Helmholtz Association point out that no system is completely immune.

Different industries have different security requirements:

Practical example: The fintech company PaySecure GmbH (112 employees) has implemented a multi-level security concept for the use of Claude. Sensitive data is anonymized by a self-developed middleware before transmission, all interactions are logged and randomly checked by compliance officers. In addition, clear guidelines were developed regarding which types of data may not be transmitted to the AI. These measures were certified by the external data protection officer and now enable legally compliant use in the financial sector.

Decision Support: Which System Suits Your Requirements?

Decision Matrix and Selection Criteria for Your Business Situation

The selection of the right AI system should be based on a structured analysis of your specific requirements. Our experience from supporting numerous medium-sized businesses shows: A systematic decision process leads to significantly higher success rates in implementation.

The following decision matrix helps you identify the best solution for your situation. Rate the relevance of the criteria for your company from 1 (unimportant) to 5 (crucial):

Practical experience shows that many companies benefit from a hybrid approach. According to a survey by the digital association Bitkom (2025), 64% of successful AI users in the mid-market employ multiple systems in parallel – tailored to different use cases.

For companies with limited budgets, a step-by-step approach is recommended: Start with one system for your most important use case and expand your portfolio as needed. A study by the Fraunhofer Institute (2025) shows that focused implementations have a 43% higher success rate than overly ambitious comprehensive solutions.

To support your decision, you should follow these steps:

1. Create a use case catalog: Identify specific use cases with estimated benefits and priority.
2. Clarify technical framework conditions: Existing systems, integration requirements, data protection policies.
3. Conduct a test phase: Use the free versions of all three systems for practical tests with real tasks.
4. Calculate business case: Compare costs and expected benefits for the most important use cases.
5. Develop implementation plan: Define milestones, responsibilities, and success criteria.

Practical Examples of Successful Implementations in Medium-Sized Businesses

Concrete examples of successful implementations provide valuable guidance for your own AI strategy. Below we present three representative case studies from different industries:

Case study 1: Manufacturing company with technical focus

Meier Werkzeugbau GmbH (178 employees) implemented ChatGPT Enterprise for technical documentation and quote preparation. Particular challenges were integration with the existing PDM system and training the technical editors.

Approach:

• Pilot project with five power users from technical documentation
• Integration with Microsoft SharePoint via the Graph API
• Development of a Custom GPT for specific technical terminology
• Gradual rollout with department-specific training

Results after 6 months:

• Reduction of creation time for quotes by 58%
• Improvement in documentation quality (measured by customer inquiries) by 34%
• ROI achieved within 7 months
• 95% active usage rate in target departments

Case study 2: Specialized business consultancy

Schneider Consulting GmbH (47 employees) uses Claude for analyzing complex contract documents and creating expert opinions. The main requirement was the reliable processing of extensive documents with the highest precision.

Approach:

• Development of specific prompt templates for different document types
• Integration into the existing document workflow via API
• Intensive training on effective prompting techniques
• Two-stage process with AI pre-analysis and human verification

Results after 9 months:

• Time savings in document analysis of 67% on average
• Increase in consulting capacity by 31% without additional staff
• Acquisition of new customer groups through faster processing times
• Amortization of the investment after 5 months

Case study 3: Medium-sized B2B retailer

TechSupply GmbH (112 employees) implemented a hybrid approach: Perplexity for market research and product comparisons, ChatGPT for customer communication. The goal was to improve information quality while increasing efficiency.

Approach:

• Parallel introduction of both systems with clearly defined areas of application
• Integration into the CRM system through custom middleware
• Development of a structured research process with Perplexity
• Creation of predefined prompt libraries for recurring tasks

Results after 12 months:

• Reduction of research time for product comparisons by 73%
• Improvement in offer quality through well-founded market data
• Increase in conversion rate by 24% through better customer approach
• Positive feedback from 89% of customers on improved consultation quality

Common success factors of these practical examples:

1. Clear goal definition: All companies had concrete, measurable goals for the AI implementation.
2. Gradual introduction: Pilot projects with subsequent controlled expansion have proven effective.
3. Intensive employee training: Investment in building competence was crucial in all cases.
4. Integration into existing workflows: The seamless integration into work processes promoted acceptance.
5. Continuous optimization: All companies iteratively improved their implementation.

These examples show: The success of an AI implementation depends less on the pure technology selection than on the strategic alignment with concrete business goals and a well-thought-out implementation.

FAQ: Common Questions About AI Assistants in Business Use