Generative AI has captured headlines. Big models, bold claims, and sweeping promises.
But in practice, many of these projects quietly stall, not because the technology is flawed but because the systems behind them are.
The hype versus outcome gap
According to Gartner, more than 40 percent of agentic AI projects will be cancelled by the end of 2027 due to rising costs, unclear business value and inadequate risk controls. High adoption numbers alone are not enough. Without the architecture, data access, system integration and cost governance, innovation rarely becomes sustainable.
Why these projects fail
Generative and agentic AI initiatives commonly trip over:
- Data silos and weak integration: When AI models cannot access consistent data across ERP, cloud and legacy systems, their insights become fragmented or unusable.
- Infrastructure unprepared for scale: AI workloads demand performance, latency and throughput. A system built for batch jobs may collapse under real time AI demand.
- Cost and governance blind spots: Organisations often treat AI as a new feature instead of a new operating model. Gartner highlights “agent washing” where vendors relabel old tools as new AI.
- Unclear value anchor: Many projects start with ambitious goals but no real KPI for adoption, cost savings or risk mitigation. When the model does not link to measurable impact, the project fades.
From migration to modernisation
Generative AI is not only about models and novelty. It is about the systems that support them. When companies simply lift workloads into the cloud or deploy AI pilots in isolation, they may tick the boxes, but they have not changed how things are done.
Modernisation means:
- Ensuring data flows across systems
- Making infrastructure elastic and reliable
- Building visibility, resilience and automation
- Embedding AI into day to day operations instead of keeping it on the margin
At Britemotion, our value lies in this groundwork. We integrate systems, clean data pipelines and prepare infrastructure, making innovation possible rather than simply visible.
What CIOs and IT leaders should ask now
| Question | Why it matters |
| Is your data unified and accessible across ERP, cloud and analytics? | AI insights require broad and consistent data. |
| Can your infrastructure scale for AI workloads including latency, throughput and cost? | AI at scale exposes underlying system weaknesses. |
| Who owns the pipelines that move data and integrate systems? | Without ownership, integrations lag and projects stall. |
| What measurable outcomes will AI deliver and how will you track them? | Without KPIs, “pilot” never becomes “production”. |
Innovation is not magic. It is methodical. Start by strengthening the infrastructure and integration, and the reality of AI will follow.
Source: Gartner prediction reported by Reuters.
(More than 40 percent of agentic AI projects will be cancelled by the end of 2027.)