Observations
It has been widely reported that between 70 and 85 per cent of AI projects fail1. However, a deeper review suggests that the majority of these failed AI projects have successfully passed through the PoC stage. The failures most often exist in the transition into a live production environment.
IBRS has identified the following common reasons for the failure of AI PoC to transition into production.
Economics and Cost of Scaling
A significant hurdle in moving AI PoCs into full production deployment lies in the economics of scaling. PoCs are often developed using prototype models on limited infrastructure, which keeps initial costs relatively low. Teams focus on demonstrating functionality quickly, sometimes using quick and dirty methods without fully considering the long-term financial implications of running the solution at scale.
When the time comes to expand the project, organisations frequently discover that the costs associated with the necessary Cloud compute power, data storage, and related infrastructure, as well as ongoing maintenance, are substantially higher than anticipated.
In addition, the consumption-based pricing models typical of Cloud services, often used for AI, mean that successful, widely used applications become progressively more expensive to operate. This escalating cost factor is almost always underestimated during the PoC phase, where budget constraints might favour cheaper, less scalable prototype solutions.
The transition requires not just technical scaling but also associated financial scaling, demanding significant upfront investment and potentially complex adjustments to IT budgets that were not planned for during the initial, often optimistically funded, PoC stage.
In short, failing to conduct a thorough downstream cost analysis and secure a budget for operationalisation before committing to the PoC leads to AI projects stalling due to unforeseen financial impracticality.
Static vs. Dynamic Data Challenges
AI PoCs frequently rely on well-curated, static datasets. These datasets are often cleaned, labelled, and explicitly structured for the PoC environment, allowing the AI model to demonstrate high performance under controlled conditions.
However, this controlled environment masks the complexities of real-world data, which is dynamic, often messy, incomplete, and constantly evolving.
Production systems must contend with live data streams, requiring robust data pipelines, continuous data validation, cleaning, and integration capabilities that are often underdeveloped or absent in the PoC setup. PoC teams frequently export the data needed rather than building connections to live systems, which simplifies the initial build but creates a significant obstacle for productionisation.
The transition to dynamic data requires significant engineering effort to ensure data quality, consistency, and timely processing. For example, models trained on static PoC data may degrade rapidly when exposed to the variability and drift inherent in live production data, necessitating ongoing monitoring, retraining, and adaptation strategies that add complexity and cost. Furthermore, accessing, preparing, and managing the sheer volume and variety of real-world data needed for production AI can be a substantial, often underestimated, undertaking.
Governance Oversights
Governance, encompassing data privacy, security, compliance, model management, and ethical considerations, is often insufficiently addressed during the AI PoC phase.
PoCs may operate in less stringent environments, bypassing rigorous governance checks to accelerate development and demonstrate functionality. However, production systems must adhere to strict organisational policies, industry regulations (like GDPR), and ethical guidelines. Integrating these governance requirements retrospectively can be challenging and costly.
Issues such as data provenance, access control, bias detection and mitigation, model explainability, audit trails, and compliance documentation are critical for production. Still, they may not have been designed into the PoC. For example, ensuring fairness and mitigating algorithmic bias requires careful data selection and model validation processes, which are often absent in PoCs built on limited datasets.
Establishing clear accountability, defining roles and responsibilities for model monitoring and maintenance, and implementing robust security measures for sensitive data are essential governance steps frequently overlooked until the production planning stage, creating significant roadblocks.
The lack of a robust governance framework during the PoC can lead to solutions that are non-compliant, insecure, or ethically problematic when scaled.
AI Safety and Quality in Real-World Deployment
AI models that perform well in the constrained environment of an AI PoC can exhibit unexpected and undesirable behaviours when deployed into the complex and unpredictable real world.
PoCs often test against a limited set of scenarios and data, failing to capture the full spectrum of inputs and edge cases that the system will encounter in production. This leads to issues with model robustness, reliability, and safety.
Real-world deployment exposes models to adversarial attacks, data drift, and unforeseen interactions with other systems or user behaviours.
Ensuring AI safety requires rigorous testing beyond standard accuracy metrics, including stress testing, fairness assessments, and evaluating performance under diverse conditions. The quality assurance processes for PoCs are typically less comprehensive than those required for production-grade systems. Issues such as poor error handling, lack of resilience to noisy data, or difficulties in interpreting model decisions (explainability) become critical failures in production but may be ignored or deemed acceptable during a PoC.
The transition of a PoC into live production requires a shift from demonstrating potential to ensuring reliable, safe, and trustworthy performance in dynamic operational environments.
Integration Challenges
AI PoCs are often developed in isolation from core business systems, such as enterprise resource planning (ERP) or customer relationship management (CRM) applications.
While this simplifies the PoC development, it creates a significant barrier to production deployment, where seamless integration is usually essential for delivering business value.
Retrofitting integration capabilities can be complex, time-consuming, and expensive. It requires careful architectural planning, API development, data mapping, and considering how the AI system will interact with existing workflows and data flows.
Technical debt accumulated during the PoC, such as non-standard interfaces or reliance on data exports, exacerbates these integration challenges.
Without a clear integration strategy from the outset, PoCs may prove incompatible with the existing technology stack or require substantial re-engineering, hindering their path to production.
Skills Gap and Operational Readiness
Successfully moving an AI PoC to production requires a broader set of skills than those typically involved in the initial experimentation phase.
While PoCs may be driven by data scientists and researchers, taking the PoC into live production (productising it) requires expertise in software engineering, DevOps, LLMOps, MLOps, data engineering, security, quality assurance, FinOps, Cloud architecture and management, etc. Many organisations lack the necessary in-house talent or established ‘Ops’ practices to manage the complexities of deploying, monitoring, and maintaining AI models in production. There can also be cultural resistance or a lack of understanding across the organisation about what is required to operationalise AI.
Effective sponsorship, change management, and training are necessary to prepare the ICT workforce and ensure adoption, aspects that are often neglected during the AI PoC focus.
Lack of a Clear Business Case and Benefits Model
AI PoCs often focus on technical feasibility (Can we build it?) rather than demonstrating a clear, quantifiable business case and return on investment (ROI) (Should we build it, and what value will it deliver?).
Without a compelling value proposition that accounts for the total cost of ownership (including scaling and maintenance), securing the necessary funding and executive buy-in for production deployment becomes difficult.
The initial excitement generated by a successful PoC can wane if the path to tangible business benefits, operational efficiencies, or revenue generation is unclear or unconvincing. A rigorous assessment of the potential ROI, aligned with strategic business objectives, should ideally be part of the PoC process itself, not an afterthought.
Next Steps
- Manage AI PoC projects with existing ideation or innovation processes.
- If no such ideation or innovation processes exist, use the excitement around AI to establish these processes and assemble stakeholders to support them.
- Before moving forward with an AI PoC, work through the IBRS Artificial Intelligence Proof of Concept Project Prestart Checklist to validate that the PoC has the potential to deliver real business value and to avoid any future roadblocks to its production.
Download as PDF: AI PoC Project Prestart Checklist.pdf
