Why 40% of AI agent projects will be cancelled - and how to avoid that outcome
According to a recent forecast by Gartner, more than 40 percent of AI agent projects will be cancelled by the end of 2027. The report cites rising costs, unclear business value, and poor risk controls as the main reasons. (Source: Gartner, June 2025)
This type of technology is often referred to as Agentic AI. These are AI agents designed to independently pursue goals, make decisions, and operate over extended timeframes. Gartner warns that many of these projects are started under the influence of hype instead of being grounded in a solid strategic foundation. In many cases, clear goals, structured planning, and realistic expectations, both technical and organizational, are missing from the start.
This assessment reflects what we see in our own consulting work. As specialized AI experts, we regularly help companies implement AI agents, beginning with goal setting and continuing through to real-world deployment. Many organizations are eager to “do something with AI,” but the gap between initial ideas and meaningful outcomes is often larger than expected.
What we’ve learned from projects
Many companies approach us with the desire to implement AI. However, they often lack clarity on what exactly they want to achieve. The motivation is frequently driven by innovation pressure, market expectations, or internal transformation goals. Without a clearly defined use case, however, the potential value of AI remains vague.
Key questions often remain unanswered. These include: What exactly should be improved? Which processes have real potential for AI and AI agents? And where is the best starting point?
We help our clients gain clarity, set priorities, and create a solid foundation for implementation based on realistic, actionable goals.
Across many projects, we have identified six success factors that consistently separate successful AI agent initiatives from those that fall short.
1. Start with clear objectives, not technology
Successful projects begin with a relevant business challenge. They do not start with a tool or a technology platform. AI agents are most effective when they improve processes, accelerate decisions, or help allocate resources more efficiently. Without a clear objective, any technological implementation quickly loses focus.
2. Be realistic about data
Data is the foundation of every AI agent. However, not all data is suitable for training or deployment. An early assessment can help determine whether the available data is structured, high-quality, and accessible enough for the intended use case. Legal aspects such as data protection and usage rights should also be clarified at the beginning in order to avoid delays later in the project.
3. Involve the right expertise early
Developing and implementing AI agents requires expertise from multiple areas. This includes process knowledge, data strategy, and technical execution. Many companies lack all of these resources internally. We support our clients with targeted consulting and implementation services, supplementing internal teams with our experience to ensure efficient delivery.
4. Start with a focused use case
Trying to build a large, all-encompassing AI system from day one often leads to unnecessary complexity. It is far more effective to begin with a manageable use case that can deliver quick, visible results. For example, AI agents can be used to classify emails or support employees with a smart internal chatbot.
One example is our project with RPP Group. We introduced an internal AI chatbot that now handles complex queries reliably and efficiently within the organization. Read More
5. Bring users into the process early
Even well-designed systems can fail if users do not accept or trust them. It is important to involve employees from the beginning. Clear communication, transparency, and opportunities to contribute to the solution build engagement and long-term success.
6. Build understanding, not just technology
The term “AI” is often used in vague or exaggerated ways. In many organizations, there is little clarity on what the technology can actually do. We help teams build realistic expectations through workshops and strategic discussions. The goal is not just to deliver a working system, but to make sure it is aligned with business value and understood by those who will use it.
In our AI workshop, you’ll learn everything you need for a successful start with an AI project, from selecting the right use case to successful implementation: Read More
AI agents need more than a vision. They need direction
The technology is already available. The potential is clear. But as Gartner’s forecast shows, many AI agent projects do not fail because of the technology. They fail due to lack of clarity, unrealistic expectations, or missing alignment with real business needs.
There is one thing that is often overlooked. AI will not solve problems that are poorly defined or misunderstood. Organizations that begin with vague ideas and high expectations are more likely to be disappointed. Those that start with clear goals, strong data, and the right support can build systems that deliver lasting value.
Helping companies turn technology into real value
We have helped organizations across industries turn AI into something practical and valuable. Our process does not begin with a specific tool or product. It begins with a question. What needs to improve, and how can we build something that truly works and scales from there?
About theBlue.ai
theBlue.ai is a Hamburg-based AI technology company specializing in the development and implementation of tailor-made artificial intelligence solutions. Since 2017, we’ve successfully delivered AI projects for clients worldwide, combining deep technical expertise with practical experience to help organizations identify, validate, and scale AI-based systems.
We are offering:
- End-to-end AI development: from early consulting and prototyping to deployment, integration, and continuous optimization
- Generative AI & LLMs (e.g. GPT apps, document intelligence, chatbots)
- Multimodal AI for text, image, and video understanding
- Scalable systems with cloud/edge deployment, MLOps, and automation pipelines
- Data engineering and integration with existing infrastructure
- Training and workshops to support internal AI adoption and long-term capability building
With over 9 years of experience, our certified team in AI and cloud technologies works closely with clients to develop solutions that align with business goals and existing systems, supported by partnerships with Microsoft, AWS, Intel, and NVIDIA.
Want to explore how AI can support your business? Let’s get in touch.
