Overview
Over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls. A new Gartner report confirms what many companies are starting to experience in practice, most AI projects are not delivering the expected return.
According to a survey of 782 infrastructure and operations leaders, only 28% of AI initiatives fully meet ROI expectations, while 20% fail outright.
The findings highlight a widening gap between ambition and execution, as organisations move from experimentation to outcome-driven deployment.
Beinsure analysis suggests this shift is critical for sectors like insurance, where AI adoption is accelerating but operational integration remains complex.
Most agentic AI propositions lack significant value or return on investment (ROI), as current models don’t have the maturity and agency to autonomously achieve complex business goals or follow nuanced instructions over time.
Many use cases positioned as agentic today don’t require agentic implementations.
AI ambition meets operational reality
The data shows that failure is rarely caused by lack of investment or effort. Instead, projects tend to stall when expectations move faster than what the technology can realistically deliver.
Gartner’s findings align with earlier research from MIT, which estimated that up to 95% of generative AI pilots fail to produce meaningful results. The pattern is consistent, early enthusiasm, followed by execution challenges.
Melanie Freeze, Director of Research at Gartner, said many failures stem from overly ambitious or poorly scoped initiatives. AI projects that are not aligned with operational workflows struggle to generate measurable value.
The highest failure rates appear in areas such as auto-remediation, self-healing infrastructure, and agent-led workflow automation. These are complex, unpredictable environments where reliability is critical and current AI capabilities remain limited.
AI Project Outcomes
| Outcome Type | Share of Projects |
| Fully meet ROI expectations | 28% |
| Fail outright | 20% |
| Partial / unclear results | 52% |
As generative AI changes the way companies do business, it is creating new risks and new causes of loss that impact not only the companies themselves but also their business partners such as third-party vendors and digital supply chains.
The analysis highlights AI’s potential to amplify systemic risks, such as through polymorphic malware or AI-targeted data breaches, while also providing a framework to quantify these emerging threats.
Leveraging the cyber kill chain model, the report underscores the urgency for insurers to adapt to AI-driven threats, balancing innovation with robust risk mitigation strategies.
Generative artificial intelligence is considered one of the most important technological breakthroughs of the last few decades. Munich Re Group sees great opportunities for insurers – if they explore the possibilities of the new technology and understand its risks.
Integration, not sophistication, drives ROI
Gartner’s research highlights a clear point, success is not determined by the sophistication of AI models, but by how well they are integrated into business operations.
Among the 77% of organisations that reported at least one successful AI use case, the key driver was embedding AI into existing systems and workflows. When AI becomes part of daily operations, adoption increases and results become visible.
Beinsure analysts note that this mirrors trends seen in InsurTech, where integration into legacy systems remains the primary barrier to scaling AI solutions.
Key Reasons AI Projects Fail
| Failure Factor | Description |
| Poor integration | AI not embedded into existing workflows |
| Unrealistic expectations | Overestimating AI capabilities and speed of impact |
| Data issues | Low-quality, fragmented, or insufficient data |
| Skills gap | Lack of AI expertise within teams |
| Weak business alignment | No clear link to measurable business outcomes |
Generative artificial intelligence is considered one of the most important technological breakthroughs of the last few decades. Munich Re Group sees great opportunities for insurers – if they explore the possibilities of the new technology and understand its risks.
Executive alignment also plays a major role. Around 26% of successful organisations reported full leadership support, while 25% highlighted cross-functional collaboration as a key factor.
Without leadership backing, projects lose momentum. With it, they gain focus.
Factors Driving Successful AI Projects
| Success Factor | Impact |
| Workflow integration | Improves adoption and real operational impact |
| Executive support | Ensures funding, alignment, and prioritisation |
| Clear business case | Links AI to measurable ROI and outcomes |
| Cross-functional teams | Enables smoother implementation across departments |
| Data readiness | Ensures consistent and reliable outputs |
Data and skills remain core constraints
Beyond strategy and integration, two structural issues continue to limit AI performance, data quality and talent.
Gartner found that 38% of organisations experiencing AI setbacks cited skills gaps as a major constraint. The same percentage pointed to poor data quality or limited data availability.
This reflects a broader challenge. AI systems depend on structured, reliable data. Without it, even advanced models struggle to deliver consistent outcomes.
For insurers, this is particularly relevant. Fragmented legacy systems and inconsistent data remain key barriers to AI-driven underwriting and claims automation.
Where AI is delivering most value
Despite the challenges, the report does not suggest AI is failing universally. Many organisations are achieving measurable results, particularly in areas with well-defined processes.
Gartner identified IT service management and cloud operations as the most successful use cases, with 53% of AI wins occurring in ITSM environments.
These areas share common characteristics, structured workflows, clear metrics, and established data frameworks. That makes them more suitable for AI deployment.
Where AI Delivers the Value
| Area | Why It Works Well |
| IT Service Management | Structured workflows and clear KPIs |
| Cloud operations | Mature systems and strong data availability |
| Underwriting (insurance) | Defined risk models and decision frameworks |
| Claims processing | Repetitive processes suitable for automation |
| Fraud detection | Pattern recognition and anomaly detection |
Beinsure forecasts that similar patterns will emerge in insurance, where AI adoption will concentrate in underwriting, claims processing, and fraud detection, areas with defined processes and measurable outcomes.
From experimentation to discipline
The broader shift underway is clear. Companies are moving from exploratory AI pilots toward disciplined, outcome-focused implementation.
High-performing organisations treat AI as a product rather than a side project. They define ownership, measure impact, and integrate AI into core operations from the outset.
They also start with realistic business cases. Instead of expecting AI to transform entire systems immediately, they focus on incremental improvements with clear ROI.
AI Failure vs Success Comparison
| Dimension | Failed Projects | Successful Projects |
| Strategy | Vague or overly ambitious | Clear, realistic objectives |
| Integration | Standalone pilots | Embedded in core systems |
| Data | Poor or fragmented | Clean and structured |
| Leadership | Limited support | Strong executive backing |
| Execution | Experimental | Operational and scalable |
This approach is becoming more important as spending increases. Global markets have weathered trade tensions, but a new anxiety is creeping in – the risk of an AI bubble.
AI bubble concerns intensify with increasing tech valuations and US global impact. Investors see tech valuations climbing so high that earnings might never meet expectations, according to S&P Global Market Intelligence.
AI infrastructure is expected to account for more than 50% of global IT spending by 2026.
That level of investment is drawing greater scrutiny from senior leadership, particularly CEOs and CFOs. Funding decisions are increasingly tied to measurable business outcomes rather than technical potential.
A more grounded AI market
Beinsure analysis indicates that the AI market is entering a more mature phase. The focus is shifting from what AI can do to what it should do within specific business contexts.
This transition is especially relevant for the insurance sector, where AI must operate within strict regulatory frameworks and complex risk environments. The key takeaway is straightforward.
AI does not fail because it lacks capability. It fails when it is disconnected from real business needs.
As the hype cycle stabilises, success will depend less on innovation alone and more on execution, integration, and measurable impact.
The next phase of AI adoption will not be defined by experimentation. It will be defined by results.
FAQ: AI ROI, Failures and Success Factors
Most AI projects fail because expectations exceed practical capabilities. Poor integration into existing workflows, unclear business cases, weak data quality, and skills gaps are the primary reasons initiatives stall or underperform.
According to Gartner, only 28% of AI initiatives fully meet ROI expectations, while around 20% fail outright. The remaining projects deliver partial or unclear results
AI struggles most in complex environments such as auto-remediation systems, self-healing infrastructure, and agent-driven workflow automation. These require high reliability and deal with unpredictable scenarios where AI still has limitations.
Successful AI projects typically share three traits: integration into existing workflows, strong executive support, and clearly defined business use cases with realistic expectations
Data quality is critical. Around 38% of failed AI initiatives are linked to poor or insufficient data. Without clean, structured data, even advanced AI models cannot deliver reliable outcomes.
AI performs best in structured environments like IT service management, cloud operations, underwriting, claims processing, and fraud detection – areas with clear processes and measurable outcomes
Companies are shifting from experimentation to execution. AI is increasingly treated as a core business tool rather than a side project, with a focus on measurable ROI, operational integration, and long-term value.
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QUOTTE: Melanie Freeze – Director of Research at Gartner
by Oleg Parashchak – Editor-in-Chief at Beinsure, Peter Sonner – Lead Tech Editor at Beinsure
