AI & NLP Compliance Consumer Duty

Enterprise AI Implementation Framework: Moving Beyond Pilots to Production

Aug 08
6 min read

The early success of an Artificial Intelligence (AI) pilot often sparks excitement. A strong proof of concept (PoC) can demonstrate clear potential and generate internal momentum. Yet many organisations discover that moving from a controlled experiment to a fully operational, enterprise-wide system is more difficult than expected. Promising pilots sometimes turn into costly lessons that fail to deliver meaningful business results.

This problem is common. Gartner predicts that by the end of 2025, at least 30% of generative AI projects will be abandoned after the PoC stage due to issues such as poor data quality, rising costs, or unclear business value.¹ Scaling AI successfully requires a structured approach that aligns technology, people, and processes.

At Aveni, we’ve guided dozens of enterprise customers through this journey by moving from value-proving pilots into full-scale rollouts that avoid the common pitfalls. Based on our experience, we’ve developed a four-pillar framework that helps organisations transition from pilots to production with measurable success.

Why AI Initiatives Stall Between Pilot and Production

Pilots are often tested in ideal conditions, with clean, static datasets and a dedicated team. Production environments are more complex. They involve real-time, imperfect data, multiple system integrations, and a wide range of users. Understanding these differences is essential to overcoming them.

Challenge 1: Proving ROI

One of the largest barriers is justifying the cost of scaling. While 75% of executives view AI as a top strategic priority, only 25% say they have seen significant value from it, according to Forbes.² Harvard Business Review also reports that only 4% of companies have achieved significant returns from their AI investments.³

The gap often comes from the difficulty of measuring return on investment (ROI). Many benefits, such as improved customer satisfaction or better decision-making, are hard to quantify in financial terms. Productivity leakage is another common problem, where efficiency gains from AI tools fail to produce measurable impact because core processes have not changed.

Challenge 2: Data Readiness

Owning large datasets does not mean an organisation is ready for AI. A 2025 Qlik survey found that 81% of AI professionals say their company has major data quality issues, yet 85% believe leadership is not addressing them.4

Many pilots work because they use manually cleaned, one-off datasets. Scaling to production requires pulling from multiple, often inconsistent, data sources. Without a coherent data strategy, models risk producing unreliable outputs, creating waste and increasing business risk.

A Four-Pillar Framework for Scaling AI

For financial services firms, scaling AI requires a framework that ensures projects are strategically aligned, technically sound, compliant, and adopted across the organisation.

Pillar 1: Strategic Alignment and Value

AI initiatives should start with a clear business problem, not a general interest in technology. Leaders should focus on the most pressing challenges that AI can address, whether that’s reducing customer churn, improving compliance, or increasing efficiency.

Aveni’s experience shows that the foundation for success starts in the pilot phase. We encourage clients to:

Take a baseline measurement before the pilot begins, so gains can be proven against real-world benchmarks.
Define success criteria with users, covering time savings, quality improvements, ease of adoption, and compliance oversight.
Run structured pilots of 3–4 weeks with hands-on feedback loops, ensuring insights aren’t theoretical but directly tied to business outcomes.
Build business cases early, showing how efficiency gains can either increase capacity (serving more clients) or reduce headcount.

By analysing thousands of conversations, Aveni Detect identifies compliance risks, knowledge gaps, and customer friction points. This creates a data-driven foundation for pilots, helping clients demonstrate measurable ROI before scaling.

Pillar 2: Technical Foundation

The infrastructure used in a pilot often cannot handle enterprise-scale deployment. Scaling requires robust Machine Learning Operations (MLOps) to automate and manage the model lifecycle from data ingestion to ongoing monitoring.

Key elements include:

Scalable infrastructure with monitoring, security, and integration with existing systems such as CRMs
Automated data pipelines for real-time data rather than static pilot datasets
Reliable model management with versioning, validation, and deployment processes

Aveni’s architecture integrates with existing communication and CRM systems, providing secure scalability without requiring costly infrastructure changes.

Pillar 3: Governance and Compliance

In financial services, good governance around AI is essential. Rules like the EU AI Act and the Consumer Duty are pushing firms to be transparent, fair, and accountable in how they use AI. And it’s not just a compliance box-tick, the Bank for International Settlements is clear that boards and senior management are ultimately responsible for every AI activity and any fallout that comes with it.

That means governance frameworks need to be baked into the wider way a business manages risk, making sure things like data privacy, explainability, bias, resilience, and human oversight are front and centre. Senior leaders need to understand, question, and sign off on AI use cases, while having the right checks in place to spot and deal with risks like unfair bias, regulatory breaches, or inaccurate outputs before they cause financial, reputational, or customer harm.

Aveni embeds governance into everyday workflows. Aveni Detect records every customer interaction, flags potential risks, and supports compliance reporting, turning regulatory requirements into an efficient, automated process.

Pillar 4: Organisational Readiness and Change Management

AI can surface powerful insights, but their impact depends on whether organisations can translate them into real-world change. The sticking point is rarely the technology itself; it is the shift in habits, processes, and expectations that comes with it.

Clear communication and role-specific training help turn uncertainty into confidence. When people see the direct benefit, such as better coaching, sharper conversations, or more effective decision-making, adoption becomes easier and value compounds quickly.

Aveni supports this transition by stripping away routine analysis and giving teams the headroom to focus on higher-impact work. The insights generated are not generic dashboards; they are precise, contextual, and actionable, making training more relevant and performance improvement more tangible.

Putting the Framework into Action

A readiness assessment across the four pillars quickly shows whether an organisation is equipped to get value from AI or still has groundwork to do. The signals are usually clear:

Strong foundations – when there’s a defined business problem, visible executive sponsorship, reliable data, and a team that cuts across silos, scaling AI becomes a natural next step rather than a leap of faith.
Warning lights – vague objectives, fragmented data, or lukewarm stakeholder buy-in often point to deeper misalignment. These are the moments to step back and tighten the business case before investing heavily.
Back to basics – if the fundamentals of governance, skills, or infrastructure aren’t yet in place, the priority isn’t deployment but building the resilience to support it.

Taking the time to diagnose honestly at this stage saves wasted spend and stalled projects later, while creating the conditions for AI to deliver meaningful, sustainable impact.

From Pilots to Production

Shifting from pilot to production is where many AI initiatives falter. Technical capability matters, but it’s rarely the differentiator. What separates successful deployments is the ability to connect AI directly to business priorities, embed it within a scalable and compliant framework, and prepare people across the organisation to use insights with confidence.

When strategy, technology, governance, and adoption are treated as a single, integrated effort rather than separate workstreams, AI stops being a proof-of-concept exercise and becomes a driver of lasting value. For financial institutions, that shift is the difference between an expensive experiment and a durable competitive edge.

See It in Action

Firms like Key Group and Age Partnership have already used Aveni Detect to prove value at pilot stage and then scale successfully across the enterprise. Explore how they did it:

→ Read the Key Group case study
→ Read the Age Partnership case study