Introduction
Executives from every sector are pouring money into AI tools, yet the vast majority of these experiments deliver little lasting value. A report from MIT Sloan and CIO found that 95 %of generative‑AI initiatives fail to produce a positive return because they are not properly integrated into workflows mlq.ai.Riverbed’s survey of 1,800 IT leaders similarly revealed that only12 % of AI projects have been fully deployed, and only 36 % of organisations consider themselves ready to operationalise AI riverbed.com.Even where projects are underway, Kyndryl’s 2025 readiness report shows that 62 % haven’t advanced beyond pilot stages, despite high optimism from leadership kyndryl.com.Bridging this reality gap requires more than bigger models; it demands a systemic approach to data, infrastructure and people.
Why pilots stall
Inadequate data readiness
Many pilots fail because the underlying data estate is fragmented, incomplete or simply not fit for purpose.Studies show that only 6% of organisations consider their data infrastructure AI‑ready techstrong.ai.In Acceldata’s benchmark survey, just one in five data leaders weresatisfied with the accuracy and completeness of their data, and more than half lacked confidence in their ability to measure trust acceldata.io.Without high‑quality, contextual data, AI models produce brittle outputs that cannot be trusted in production.
Immature infrastructure and networks
AI pilots often run on provisional infrastructure that cannot scale. The NetApp/IDC study notes that 84 % of enterprises do not have storage optimised for AI, while nearly half admit their infrastructure lacks scalability businesswire.com.KPMG’s infrastructure survey reports that only 17 % of companies have networks capable of handling AI‑driven workloads, with most describing their environments as only moderately scalable kpmg.com.These limitations cause bottlenecks when organisations attempt to move prototypes into production.
Talent and process gaps
Scaling AI requires cross‑functionalcollaboration, yet many pilots are run in isolation by innovation teams. TheIBM Institute for Business Value found that although 81 % of chief data officers invest in AI, only 26 % believe their data can supportAI‑driven revenue streams aidataanalytics.network. Leaders often underestimate the effort required to re‑engineer processes, establish governance and upskill staff. As a result,pilots become siloed experiments that cannot be replicated at scale.
Lessons from AI leaders
Not all organisations are stuck in pilot mode. NetApp’s study identifies a group of “AI Masters” who have modernised their data platforms and governance. These leaders enjoy 24.1%revenue improvement and 25.4 % cost savings, demonstrating tangible benefits when AI is scaled responsibly businesswire.com.Their success stems from three practices:
- Modernise data and storage – AI Masters invest in unified data platforms that break down silos and provide consistent, high‑quality data. They standardise data definitions, implement strong governance and use automation to maintain accuracy.
- Build scalable, flexible infrastructure – They adopt composable, scalable architectures that can grow with demand. This includes investing in high‑bandwidth networks, modern storage and event‑driven architectures to handle real‑time data.
- Align people and processes – AI Masters integrate AI into core business processes rather than side projects. They upskill staff, embed AI into decision making and ensure security and compliance are addressed from the start.
Bridging the gap: a roadmap
1.Assess readiness holistically
Use a maturity assessment to evaluate data quality, infrastructure, talent, governance and culture. Tools like the RTS Labs checklist outline nine dimensions of AI readiness rtslabs.com.Knowing where you stand helps prioritise investments.
2.Invest in data quality and accessibility
Prioritise improving data accuracy, completeness and consistency. Initiatives should include robust data catalogues, metadata management, data lineage and real‑time integration. Avoid patchwork solutions; instead, build a centralised layer that provides reliable data to all applications.
3.Adopt scalable architectures
Move away from ad‑hoc pilot environments toward scalable, composable infrastructures. This involves modern storage systems, high‑bandwidth networks and event‑driven patterns that allowAI services to communicate without bottlenecks. Containerisation and microservices can help isolate services and improve reliability.
4.Integrate AI into core workflows
Identify processes where AI can deliver measurable benefits and redesign them to incorporate AI from the ground up. This means breaking down tasks into manageable steps, mapping decision points and ensuring there are clear hand‑offs between human and machine. Avoid leaving AI as a bolt‑on layer.
5.Upskill teams and enforce governance
Scaling AI requires collaboration between data scientists, engineers, domain experts and compliance officers. Provide training in data literacy, ethical AI practices and secure coding. Establish governance structures that define accountability, risk management and performance metrics.
Conclusion
The gap between AI pilots and production is not a technology gap but a readiness gap. By addressing data quality, building scalable infrastructure and aligning people and processes,organisations can move beyond proofs of concept and realise the promised returns on their AI investments. AI Masters demonstrate that when these foundations are in place, real business value follows businesswire.com.
