Beware of the case: Agent Washing exposes-the marketing problem that endangers its AI projects!
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Published on: June 27, 2025 / update from: June 27, 2025 - Author: Konrad Wolfenstein
Beware of the case: Agent Washing exposes-the marketing problem that endangers its AI projects! - Image: Xpert.digital
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The rapid development of artificial intelligence has led to a remarkable phenomenon that shapes the technology industry and corporate world alike: the so -called agent washing. This marketing problem is one of the most important challenges for companies who want to implement real AI agents and contributes significantly to the confusion and high failure rates in AI projects.
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Understand the problem of Agent Washing
Agent Washing describes a widespread practice in the technology industry, in which providers strategically market existing technologies such as AI assistant, robot-based process automation or chatbots as supposedly agent-based solutions. This renaming takes place despite the fact that these systems often lack the decisive features of real AI agents. Gartner, the renowned consulting company, estimates that the thousands of providers only offer around 130 authentic agent-based AI technologies.
This practice did not arise by accident, but follows an established marketing pattern that has already been observed in other areas. Similar to the Greenwashing, in which the company lend an environmentally friendly image without the appropriate basis, technology providers at Agent Washing try to benefit from the current hype to make AI agent without making the necessary investments in real agent technology.
Fundamental differences between real AI agents and conventional systems
In order to fully understand the problem of the Agent Washing, it is essential to capture the fundamental differences between authentic AI agents and traditional automation solutions. Real AI agents are characterized by several key features that fundamentally distinguish them from conventional systems.
Autonomy and decision skills
While traditional automation tools such as Robotic Process Automation (RPA) follow strictly predefined rules, real AI agents have the ability to make autonomous decision-making. You can analyze huge amounts of data in real time, recognize patterns and make well -founded decisions based on these findings without constant human supervision. This autonomy enables you to react appropriately in unpredictable situations and to adapt your strategies accordingly.
Learning and adaptability
Another crucial feature of real AI agents is their continuous learning ability. In contrast to regular-based systems that remain static, AI agents analyze historical data, recognize trends and draw knowledge from large data sets. This continuous learning process enables you to adapt to new information and refine your performance, which makes you become more and more efficient and more precise over time.
Context understanding and flexibility
While conventional chatbots follow largely regularly-based dialogues and limit themselves to answering predefined questions, real AI agents are able to argue and understand complex relationships. You can not only process structured data such as tables, but also analyze unstructured information such as emails or documents in context. This ability enables you to follow nuanced instructions across longer periods and to achieve complex business goals independently.
Agent Washing's effects on companies
Agent Washing leads to far-reaching negative consequences for companies that want to implement real AI solutions. Practice creates unrealistic expectations for decision-makers who believe that they already acquire mature agent technology, while they actually only receive extended automation tools. This discrepancy between expectation and reality contributes significantly to the high failure rates in AI projects.
Economic consequences and a waste of resources
Gartner predicts that more than 40 percent of all projects in the field of agent AI will be discontinued by the end of 2027. The main causes of this are increasing costs, unclear economic advantages and inadequate measures to control risk control. Anushree Verma, Senior Director Analyst at Gartner, explains that most of these projects are still in an early phase and often have been created as experiments or proof-of-concepts by the current hype.
Technically, the underlying models are often not yet mature enough to provide the promised services. They neither have the necessary ability to act to achieve complex business goals independently, nor are they able to follow nuanced instructions over a long time. These technical limits mean that many solutions advertised as agent -based solutions do not offer a substantial advantage or real return on investment.
Loss of trust and market distortion
Agent Washing not only leads to immediate economic losses, but can also undermine trust in AI technologies in the long term. Companies that have disappointing experiences with supposed AI agents may be more reserved in the adoption of real AI solutions in the future. This can slow down the entire industry development and inhibit innovation.
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Technical demarcation and identification features
In order to identify and avoid agent Washing, it is crucial to understand the technical differences between different automation technologies and to recognize real AI agents.
Robotic Process Automation (RPA) versus AI agent
RPA systems are designed to automate regular, repeated tasks. They imitate human actions to read and process structured data, but can only act in clearly defined situations. As soon as you come across a situation that deviates from the norm, you are unable to adapt automatically and have to alert a human agent.
AI agents, on the other hand, can carry out multi-phase tasks and adapt to unexpected situations thanks to their decision-making ability. They go beyond basic automation and become dynamic, problem -solving units that can continue the process independently, even if things are not as expected.
Chatbots versus real AI agents
Conventional chatbots are only able to respond to the user and forward information to a human agent. Your answer options are often based on prefabricated scripts or natural language processing, which significantly limits your benefits. You can only react, but do not act proactively or make complex decisions.
Real AI agents, on the other hand, recognize problems, find solutions and automatically implement them. You can argue, make context -related decisions and carry out actions independently without having regular dialogues or configurations.
Agentic Process Automation (APA) as a future technology
Agentic Process Automation represents the next evolutionary level of automation. In contrast to conventional automation tools, APA systems can carry out targeted process automation by autonomous AI agents. Several agents perform multi -phase tasks and are coordinated by an orchestration layer, which enables flexible and adaptable automation.
Market dynamics and industry development
The market for AI agents is currently experiencing a phase of intensive growth, which, however, is characterized by uncertainty and exaggeration. A gartner survey under 3,412 participants of a webinar clearly shows the current market situation: 19 percent of the respondents indicated that their company had already significantly invested in agent AGI, while 42 percent reported rather careful investments.
Investment behavior and market maturity
The figures illustrate a split market situation: While a considerable proportion of companies have already invested or is planning investments, 31 percent of those surveyed are either undecided or waiting. This reluctance is entirely justified, given the fact that many of the currently available offers do not provide the promised advantages.
Nevertheless, Gartner predicts considerable growth potential for real agent AI solutions. By 2028, at least 15 percent of all daily business decisions are to be made autonomously by agent AGI compared to zero percent in 2024. In addition, it is expected to have around 33 percent of all company software applications via agent AGI components by 2028, compared to less than one percent in 2024.
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Quality control and market adjustment
The discrepancy between the thousands of providers and the estimated 130 companies with authentic agent -based technologies indicates an upcoming market cleanup. Companies that offer real innovations will stand out from those who only operate Agent Washing.
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Challenges in AI implementation
The implementation of real AI agents brings various challenges that go beyond the problem of Agent Washing. These challenges sometimes explain why many companies use less demanding, but also less effective solutions.
Technical complexity and infrastructure requirements
The integration of real AI agents into existing company systems is technically demanding and can significantly disrupt existing processes. Many companies do not have the required IT infrastructure to effectively manage AI workloads. A Cisco study shows that only a almost almost quarter of companies in Switzerland has flexible networks that are suitable for AI implementations.
Due to limited or lack of scalability, the majority of companies cannot manage new AI processes with their current IT infrastructure. Almost all of them need additional graphics processors (GPUS) to meet the increased performance and arithmetic requirements.
Data quality and data availability
High quality, diverse and accessible data is a basic requirement for all AI activities. However, most companies are weak when it comes to providing such data. The main problem is that corporate data is not distributed throughout the organization in a centrally managed database, but in silos.
These data silos not only make it difficult to implement AI agents, but can also lead to faulty models and false conclusions. Incomplete or inaccurate data undermines the effectiveness of each AI solution, regardless of whether it is a real agent or a conventional automation solution.
Cultural and organizational barriers
The introduction of AI agents is not just a technical, but above all a cultural challenge. Employees must be willing to give up old working methods and accept new technologies. Resistance to changes, lack of understanding for the advantages of transformation and lack of training can significantly endanger success.
The shortage of skilled workers in the IT and digital area represents another major hurdle. Without the right talents, which have both technical know-how and an understanding of digital business models, the full potential of AI technology often remains unused.
Strategies to avoid Agent Washing
Companies that want to implement real AI agents must learn to recognize and avoid agent washing. This requires a systematic approach and the right evaluation criteria.
Identification of real AI agents
Real AI agents are characterized by specific features that distinguish them from conventional automation solutions. They act independently and can handle unexpected situations without constant human intervention. They have the ability to learn from their surroundings and to adapt their strategies in real time.
An important distinguishing feature is the ability to autonomous perception and data collection. Real AI agents continuously collect data from different sources and analyze user behavior as well as text and language information using natural language processing. Building on this analysis, you create plans for action, disassemble complex tasks into sub -goals and prioritize them accordingly.
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Due diligence in the provider selection
When choosing AI solutions, companies should carry out a thorough diligence. This includes the detailed review of the technical specifications, references and case studies by the providers. Companies should ask critical questions: Can the system learn independently and adapt? Does it have real decision skills? Can it cope with complex, multi -phase tasks without human intervention?
Pilot projects and gradual implementation
Gartner recommends using agent AI only where it provides clear added value or a verifiable return on investment. A good start is the use of AI agents for decision-making situations, for automation routine processes or for processing, inquiries before being addressed before more complex use cases are addressed.
Future prospects and market development
Despite the current challenges and the problem of the Agent Washing, agent AGI marks a significant development step in the AI skills and opens up new market opportunities. Technology offers the potential to use resources more efficiently, automate complex tasks and to promote innovations in everyday business.
Transformative effects on industries
AI agents will have transformative effects, especially in marketing and sales. They enable companies based on purchasing samples and preferences with unprecedented efficiency and create personalized experiences. In contrast to traditional marketing automation platforms that work according to fixed rules, real AI agents can react dynamically to customer behavior and adapt their strategies accordingly.
Evolution of jobs
The development of real AI agents will also have a significant impact on the world of work. According to Bloomberg Intelligence estimates, 200,000 jobs could only be eliminated among the largest banks in the world due to the increased use of AI agents. This development underlines the need for companies and society to proactively develop retraining and further education programs.
Regulatory developments
With the increasing spread of real AI agents, regulatory framework will also play a larger role. Companies must take into account data protection, data sovereignty, knowledge and compliance with global regulations as well as the concepts of bias and transparency both in terms of data and on algorithms.
Recommendations for action for companies
In view of the complexity of the Agent Washing problem and the challenges of implementing real AI agents, companies should pursue a systematic approach.
Strategic planning and objective
Companies should first develop a clear digital strategy that defines how AI agents can contribute to achieving the business goals. Vague goals like “We want to use AI” are not enough. Instead, specific, measurable goals should be defined that are tailored to the business strategy.
Competence structure and further education
The promotion of further training is necessary to enable employees at all levels to deal with AI. Companies should invest in further training, data -driven decision -making processes and innovative areas of application in order to implement efficiency increases, process optimization and new business opportunities.
Focus on data protection and security
Ensuring data protection and IT security is essential to minimize risks such as the misuse of data and to build up trust in the technology. These measures not only contribute to the increase in efficiency, but also promote acceptance and sustainable use of AI.
Navigate through the Washing dilemma agent
Agent Washing is a significant challenge for companies that want to benefit from the advantages of real AI agents. The widespread practice of renaming existing technologies to allegedly agent-based solutions leads to unrealistic expectations, a waste of resources and ultimately to high failure rates in AI projects.
In order to be successful, companies have to learn to distinguish real AI agents from conventional automation solutions. This requires a deep understanding of the technical differences, careful Due diligence in the selection of providers and a strategic approach to implementation.
Despite the current challenges, the development of real AI agents offers enormous potential for innovation and increasing efficiency. Companies that now create the right basics and are not fooled by Agent Washing Hype will be able to benefit from the transformative possibilities of this technology in the long term.
The future is not in the simple automation of individual tasks, but in the intelligent cooperation between people and real AI agents who can learn independently, adapt and solve complex business problems. The key to success is to make this future with clarity, realism and strategic foresight.
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