From pilot to production: 5 levers to industrialize AI

Barely half of AI proofs of concept ever make it into production. The evidence is clear: although building AI solutions is not difficult, a lack of strong business cases and weak technology foundations often lead to AI prototypes getting shelved.

In this article we summarize common reasons AI pilots stall, then advise on 5 levers to successful AI industrialization.

Common reasons why AI pilots stall

• Lack of alignment to business outcomes: Many organizations approach AI from a technology-first perspective, focusing on what is technically possible rather than what is strategically valuable. Successful AI implementation requires laser focus on quantifiable business outcomes.
• Fragile data infrastructure and pipeline: A study by Precisely found that despite 60% of organizations stating AI is a key influence on data programs, only 12% report their data is of sufficient quality for effective AI implementation.⁽¹⁾
• Mis-aligned operating model: Traditional organizational structures often hinder AI initiatives due to slow decision-making and limited adaptability. Even companies with ample AI specialists slip when operating teams lack a “get to production” mindset, causing hand-offs and re-work.
• Opaque risk posture & governance gaps: A lack of disciplined risk controls can lead to organizations finding themselves operating in a dangerous state of “unknown unknowns”.

Executives who treat AI like any other industrial asset – designed for reliability, governed for risk, and measured for business value – unlock faster pay-back and larger competitive moats.

Five levers to industrialize AI

1. Start with a value-driven use-case framework

Technology leaders should begin by framing every AI prototype with a clearly quantified P&L hypothesis, such as revenue uplift, cost-to-serve reduction, or risk mitigation. Opportunities should be ranked not only by their potential value but also by feasibility, funneling them through a single intake process that ensures resources are allocated to the highest-return projects.

From day one, each model must be instrumented with leading indicators like adoption rates, latency, and data drift alongside lagging business impact metrics such as EBIT and Net Promoter Score (NPS). This approach accelerates informed “go/no-go” decisions before significant investments are made.

2. Build an “AI factory” platform team

For executives aiming to translate AI from isolated experiments into a reliable engine of growth, an “AI factory” can prove quite transformative.

Setting an “AI factory” allows for their most valuable data, engineering, and governance capabilities operating not as bespoke artisan shops, but as a seamless, self-serve production line – one that can spin up and deliver dozens, even hundreds, of AI solutions rapidly and at scale.

3. Institutionalize MLOps & DevSecMLOps

Teams that adopt full-lifecycle automation through MLOps practices can reduce the time it takes to deploy AI models by half or more, speeding up business impact significantly.

MLOps practices allow for production-first mindset – designing models from the outset with considerations for monitoring, scalability, and the ability to quickly roll back if issues arise – that can prevent costly surprises after deployment.

Additionally, automation of repetitive and critical tasks through continuous integration, continuous delivery, and continuous testing (CI/CD/CT) pipelines can enable seamless workflows that reduce human error and frees teams to focus on innovation. Embedding continuous feedback loops allows for detection of model drift or performance degradation in real time, triggering automated retraining or alerts.

4. Rewire the operating model for scale

To scale AI effectively, the operating model itself must be rewired rather than simply overlaying AI capabilities on legacy processes. A federated hub and spoke model work quite well – with the central AI hub responsible for standards, cost governance, and vendor relationships, paired with federated domain squads that build AI products (complete with backlogs, service-level agreements, and sunset criteria) embedded with data scientists.

Organizations should also focus on building a talent flywheel by upskilling business users into “citizen solvers” and rotating engineers across squads to spread capabilities and best practices.

5. Embed responsible & cost-aware governance

Organizations that are looking to successfully industrialize AI must balance risk and cost alongside innovation. Key best practices include:

• Maintaining AI model cards that document lineage, limitations, and ethical review.
• Applying tiered approval for sensitive data and prompt libraries.
• Developing and monitoring real-time cost dashboards covering GPU hours, API calls, and licensing.
• Building incident playbooks linking model thresholds to automatic throttling or rollback.

Strategic pay-off

Gartner warns that 40% of “agentic AI” projects will be abandoned by 2027 if leaders remain trapped in prototype purgatory. The choice is stark: build an assembly line for reliable, governed AI – or accept stranded innovation.⁽²⁾

Last word

CIOs should treat AI like any industrial asset: designed for reliability, governed for risk, and measured for ROI. Start with use cases tied to the P&L, stand up a cross-functional AI platform team, automate delivery with MLOps, rewire your operating model for scale, and embed cost-aware, responsible governance.

Industrializing AI isn’t optional; it’s the only way to avoid innovation gridlock.

⁽¹⁾ Source: https://www.precisely.com/press-release/new-global-research-points-to-lack-of-data-quality-and-governance-as-major-obstacles-to-ai-readiness  (opens in a new tab)

(2) Source: Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027  https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027  (opens in a new tab)