Why do automation projects fail?
False expectations, lack of piloting, and overly ambitious projects: these are just some of the reasons why automation projects fail in companies, especially when it comes to AI automation. A recent S&P Global study shows how much the problem has grown in the market: 42% of companies surveyed in North America and Europe have completely or partially abandoned their AI projects in 2025, compared to only 17% in the previous year. According to a recent MIT report, over 95% of all generative AI pilot projects fail to deliver real business value, with only 5% of customized AI solutions ever making it into productive use. (MIT, 2025) This shocking finding shows that automation not only offers potential, but also presents many challenges, including:
- High setup costs and time requirements: The high complexity of projects and many dependencies pose a major risk if underestimated
- Underestimated costs: Traditional automation involves significant maintenance costs due to adjustments such as changes to user interfaces and form templates
- Inadequate data quality and data infrastructure: Fragmented, insufficiently processed, or outdated data hinder successful automation
- Organizational weaknesses: A lack of accountability and insufficient internal expertise lead to bottlenecks and poor integration
In the following, we will go into more detail on a few points and highlight the dos and don'ts of automation.
Setting Goals in Automation
When setting goals, it is important to be aware that errors in automation are usually strategic rather than purely technical in nature. This becomes apparent even before the actual project begins: companies think too big and want to automate entire processes or departments right away, instead of proceeding step by step. Ideas such as “We'll automate the entire customer service department” are unrealistic and do not lead to any gains. AI is not a plug-and-play miracle; it requires structured data, comprehensive training, and clearly defined goals and KPIs:
- What should be automated and
- Why should these processes be optimized?
- How can success be measured?
It is important to start small in order to achieve iterative improvements and optimize processes step by step. Success can then be measured based on response time, customer satisfaction, and cost savings.
What Role does Data Quality play?
The database forms the foundation of a successful AI implementation. Poor data availability leads to inadequate implementation. The success of a project depends not only on starting realistically but also on learning from the data as the project progresses. Data quality plays a central role because, as with human employees, scattered or inconsistent information leads to errors and inefficient processes. Clean, centrally available data is a basic requirement for successful automation and efficient collaboration within a company. The choice of use cases is crucial here. Highly complex or sensitive processes are not a good starting point. Tests with small use cases, on the other hand, offer potential for identifying process optimizations and then scaling them up. Organizations with a lower project failure rate are more likely to consider criteria such as risk and data availability when selecting projects (S&P Global, 2025).
Complexity in Automation: Why Customer Service Automation can quickly backfire
Automation takes time and customer feedback. Although companies want to significantly increase their investment in automation, on average less than half of operational processes are automated. (Camunda, 2025) This discrepancy shows that automating customer service processes is more complex than often assumed. Existing automation systems are becoming increasingly outdated and cannot keep pace with the speed of business change. This is particularly critical for companies that have invested in individual solutions to automate specific tasks. Isolated silo solutions provide only limited benefits and make holistic optimization difficult. Automation measures also fail not because of the quality of the AI models, but because of the interaction with existing workflows and organizational structures. Customer service automation is a complex challenge and can quickly become inefficient without careful planning.
An example: An AI chatbot that is expected to take on too many tasks too early without a clean database or sufficient customer feedback quickly fails due to incomplete or contradictory information from different systems. The result? Inaccurate answers, incorrect transfers, and frustration for both customers and employees.
Customers can achieve results with the right use of AI: But how should automation be approached?
Getting Automation off to the right Start
The key to success: Step-by-step automation instead of a big bang approach. Automation is not a sprint, but a marathon. It's not about automating as much as possible, but about pursuing realistic expectations and goals, optimizing carefully selected processes, and improving the customer experience. Here is an overview of the most important aspects to consider:
- Proceed step by step: Start small and scale up after success
- Set realistic goals: Clearly define expectations and set measurable goals
- Carefully select processes: Recurring, clearly structured, yet error-prone processes are particularly well suited at the beginning
- Involve employees: All teams should be informed and involved at an early stage to promote acceptance of AI projects
- Analyze tools and platforms: Comprehensive integration options and long-term scalability must be ensured.
- Introduce fine-tuning and monitoring: Automated processes must be continuously monitored and customer feedback taken into account in order to optimize them in the best possible way
Successful automation is not a large-scale project, but rather a process. For an automation project to be successful, important steps must be taken into account, as explained here using the example of introducing an AI chatbot. First, goals and use cases must be defined. The company determines which tasks the chatbot should perform, e.g., answering standard inquiries, and defines clear performance indicators. As a small start, an initial pilot is first deployed on the website to gain experience with a clearly defined use case (e.g., FAQs in this case). In live operation, the chatbot's performance is then evaluated. Feedback from customers and employees is continuously incorporated into improvements during this step. As a result, the dialogs and automation logic are adjusted until the set KPIs are finally achieved. Only when the chatbot has been successfully tested and approved is it extended to other channels and processes. This could be integration into CRM systems or messengers, for example.
The five phases described help to get started in a structured and low-risk manner and show a rough outline of the automation process:
Why Humans and Machines Should Work Together
AI and automation using technology should be seen as support, not as a replacement for human work. Handing over to humans when necessary, without communication breakdowns or data loss, is crucial for optimizing the customer journey and solving customer problems efficiently. The use of an AI chatbot, for example, achieves sustainable success for companies and provides support by automating the processing of simple to complex inquiries. Nevertheless, human agents are indispensable when the bot cannot help with its knowledge. This ensures that the customer receives support in every case and that the customer's perspective is the focus at all times.
Why Transparency and Testing are crucial
This also applies to the continuous optimization of automation projects. Customer feedback and KPI monitoring allow companies to evaluate results and derive new requirements. Iterative validation and improvement make it possible to identify and correct errors at an early stage. The earlier testing is carried out, the better.
Why Change Management determines Success
The success of automation projects depends crucially on how well change management is implemented. This means, above all, that employees must be involved at an early stage in order to reduce fears about the changes, and training should be offered to increase acceptance. This minimizes potential resistance from the outset. Change management ensures that processes not only work technically but are also accepted by employees. Close cooperation between IT and organizational teams is particularly important: without clear roles and responsibilities, projects often fail due to a lack of coordination or accountability. Highlighting the successes already achieved also helps to communicate the added value and justify the implementation of further automation projects.
Conclusion
Artificial Intelligence is not a perfect tool, but rather a new way of thinking that accompanies companies in all areas. Automation should be understood as a continuous learning process, whereby a realistic start with clearly defined goals and data-based, ongoing adaptation is crucial to achieving success. Anyone starting with automation in their company should therefore not only consider individual use cases, but also clarify at an early stage how processes can be orchestrated holistically. This includes considering data, tools, and responsibilities.
When used correctly, AI can significantly optimize processes and thus reduce the workload on employees. Generic LLM chatbots impress with their ease of testing and flexibility, and showed pilot-to-implementation rates of around 83% in the MIT study. (MIT, 2025)
This is exactly where moinAI comes in: As an experienced partner for AI automation in customer service, moinAI demonstrates in a practical way how an intelligent, customizable chatbot can be profitably deployed and automated in your company. moinAI works with you to implement a concrete, measurable use case that creates real added value for your company.
