Why are Generative AI projects struggling to reach production?

Passio Consulting
vor 3 Tagen
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Generative AI is advancing quickly, and despite significant investments, a substantial number of initiatives fail before reaching production deployment. Let us understand the underlying reasons why this happens, so we can address them with a strategic plan and develop meaningful and useful solutions.

1. Data quality

A Gen AI Project is only as good as its data foundation. Many organisations underestimate the effort needed to gather, clean, and maintain data sets. Issues such as fragmented data sources, inconsistent formats, and outdated information can severely impact projects.

2. Expecting too much from the technology

The hype surrounding this technology often leads to unrealistic expectations. It’s a common misconception that these models can autonomously handle highly complex tasks with little to no oversight. When results fall short, it translates into disappointment and failure.

3. Under-skilled people

Building and maintaining Gen AI solutions requires a rare combination of skills: machine learning expertise, data engineering, prompt design, and deep domain knowledge. Many teams are under-resourced or lack the specialised talent to scale solutions beyond prototypes.

4. Ethical and legal concerns

Generative models can produce inaccurate, biased, or sensitive content. This raises ethical concerns and regulatory challenges, particularly around intellectual property, explainability, and data privacy (e.g., GDPR compliance). Without governance frameworks in place, projects can be blocked or even abandoned.

5. Organisational resistance

Non-technical teams may resist Gen AI initiatives out of fear of job displacement, distrust in AI outputs, or lack of understanding. Without structured change management, communication, and upskilling, internal adoption is often slow, regardless of technical success.

6. Inadequate work methodology

Traditional project methodologies, whether it is agile, waterfall, or hybrid, fall short for GenAI development. Teams need adaptive, experimentation-friendly workflows, cross-functional collaboration and continuous feedback loops.

Conclusion