Business Transformation Services

beyond the prototype: scaling AI for real impact

This podcast explores the journey of artificial intelligence from promising pilots to enterprise-wide transformation. Today we’re diving into a topic that’s both timely and deeply relevant: Scaling AI beyond POCs—proofs of concept. Whether you're a business leader, a technologist, or someone simply curious about how AI moves from the lab to the boardroom and floor, this episode is for you.

Podcast Audio Transcript

Mimi: Hello listeners, this is Mimi; thank you for tuning in to yet another exciting and informative podcast from us at Infosys BPM, and today we’re talking about a challenge many organizations face: Scaling AI beyond POCs.

Joining me today is Sourav Ghosh, Senior Industry Principal here at Infosys BPM.

Welcome Sourav. How are you?

Sourav: I’m doing great Mimi. It’s nice to be back in this podcast again.

Mimi: Yes it’s always nice to have you with us. Sourav, let’s start with the basics. Why do so many AI projects get stuck at the proof of concept stage?

Sourav: That’s a great question Mimi. The “POC trap” is surprisingly common. Organizations launch AI pilots with excitement, but months later, they’re still in the sandbox. The issue isn’t technology—it’s the lack of readiness across the business. There’s often no clear ownership, fragmented data, and limited integration with core systems. And sometimes, there’s a cultural hesitation to trust AI.

Mimi: So, when we talk about scaling AI, what does that actually involve?

Sourav: Scaling AI means operationalizing it—which is embedding intelligence into the enterprise. It’s not just about deploying more models. It’s about building robust data pipelines, setting up AI ops for model monitoring and retraining, ensuring governance and compliance, and most importantly, aligning with business outcomes. If your AI doesn’t move a KPI, it’s nothing more than a science experiment.

Mimi: I agree. Any tech intervention should ultimately result in a core business outcome.

Can you share an example of how organizations can successfully scale AI, to provide actual business outcomes?

Sourav: Certainly. One of the most compelling examples comes from a leading retail bank in the UK. They had developed a POC for AI-driven fraud detection using machine learning. The model was highly accurate in identifying suspicious transactions during testing. But it never went live.

Why? Because the fraud detection system was tightly coupled with legacy transaction processing systems, and the compliance team hadn’t been involved early enough.

We helped them reframe the initiative—not just as a tech upgrade, but as a risk and compliance transformation. We brought together data science, IT, fraud operations, and compliance into a unified steering group. We built a secure, real-time integration layer between the model and their transaction engine, and we implemented explainability features to satisfy regulatory requirements.

Within nine months, the model was live across their digital banking channels. It reduced false positives by 30%, improved fraud detection rates, and saved the bank over £15 million annually.

The key takeaway? Scaling AI in banking isn’t just about accuracy—it’s about trust, governance, and integration.

Mimi: Sourav, that’s an amazing outcome for the bank. What are some common mistakes organizations make when trying to scale AI?

Sourav: Great question Mimi. It’s important to understand these mistakes. There are a few recurring ones that I will mention:

• Firstly, over-dependance on technology alone, without business alignment.
• Secondly, ignoring data readiness: Poor quality data leads to poor outcomes.
• Thirdly, lack of stakeholder buy-in: If users don’t trust the model, they won’t use it.
• And fourth, no plan for scale: POCs should be designed with production in mind.

• No alignment with operations: The maintenance team wasn’t involved in pilot design. The AI alerts don’t match their scheduling or resource constraints.
• No integration with business processes: The model outputs aren’t connected to the company’s maintenance ticketing system, so alerts are ignored.
• No ROI clarity: Leadership doesn’t see how the pilot translates into cost savings or productivity gains, so they hesitate to fund a full rollout.

• Early collaboration with operations to define actionable outcomes.
• Clear KPIs tied to business impact (e.g., reduced downtime, cost savings).
• Integration planning from the start—how will the AI fit into existing systems and workflows?

1. Business-first mindset: Tie AI to real outcomes.
2. Platform thinking: Build reusable components.
3. Cross-functional collaboration: Break silos.
4. Responsible AI: Ethics and transparency must be built in.
5. Continuous learning: AI evolves—your strategy should too.