Artificial intelligence holds enormous promise for firms across insurance and financial services. But most pilot projects fail to scale, often stalled by fragmented data, complex workflows, and difficulties in embedding AI into existing business processes. In an exclusive interview, Prasad Prabhakaran, Head of AI Practice at eSynergy, explores how firms can move past AI teething troubles and deliver real-world business value.
The insights come from eSynergy’s latest whitepaper, ‘Wiring AI for Value: Turning Promise into Production in Financial Services and Insurance’, which outlines practical strategies for turning AI prototypes into production-ready solutions that deliver measurable business impact.
Financial institutions have long experimented with AI, from fraud detection and predictive modelling to pricing optimisation. Yet despite the promise, many projects stall. “Pre-GPT, only organisations with deep pockets could build AI models,” Prasad explains.
“You needed data, domain expertise, compute power, and dedicated teams. Even then, most pilots never reached production.”
The arrival of large language models has changed the landscape. “Post-2017, intelligence is just an API away,” he continues.
“Prototypes that once took months can now be built in a week. But speed alone doesn’t guarantee value.” Firms now face a new challenge, potentially even a greater challenge Now they are tasked with moving AI beyond experimentation to integration across business workflows, regulatory compliance, and measurable KPIs.
According to Prasad, the first step is clarity on business value. “Building a prototype is easy. Understanding what it’s actually delivering is much harder,” he says. AI must align with specific goals, whether improving loss ratios, speeding underwriting, or enhancing customer engagement. Without measurable targets, even the most sophisticated AI sits idle, a proof-of-concept on a desk.
The whitepaper highlights three approaches that insurers and financial firms can adopt to overcome these challenges:
1. Intelligent Document Processing (IDP)
Insurance operations generate massive amounts of unstructured data—emails, policies, claims documents. Traditional OCR can extract text, but it struggles with context and language nuances. “Language models can understand the reasoning behind a text,” Prasad explains. A broker sending an Italian policy for a UK client? AI can ingest the document, extract relevant information, enrich CRM data, and update policy systems automatically.
2. Conversational Data Interfaces
For many organisations, business users rely on analysts to retrieve insights—a slow, inefficient process.
Conversational AI interfaces allow users to query data in natural language and receive immediate results. “Ask about sales trends, pricing impacts, or underwriting outcomes, and the system translates it into SQL or Python behind the scenes,” Prasad says.
These tools enable faster decision-making and strategic experimentation. Executives can run “what-if” scenarios, testing pricing changes or margin adjustments in real time. But clean data is essential. “If your data is messy, you get messy output,” he cautions.
Firms that invest in proper data governance see immediate returns in accuracy and adoption.
3. Multi-Agent AI Systems
Complex business processes often require multiple systems to work together—data pipelines, APIs, and operational tools. Multi-agent AI orchestrates these workflows, automating tasks and providing insights across departments. “Think of it as multiple AI agents working together,” Prasad explains. “One ingests data, another analyzes it, a third presents insights to the business team.”
Applications are broad. In insurance, multi-agent systems can speed underwriting decisions, optimise risk assessments, and improve operational efficiency. Beyond efficiency, they also enable consistency and compliance in decision-making.
Underlying all approaches is a critical principle: AI is a business problem, not a technology problem. Prasad emphasises, “Prototypes are plentiful, but value is rare. You need a principle-based AI strategy aligned with measurable KPIs, regulatory requirements, and organisational policy.”
Change management is equally vital. “You can’t just drop AI into a workflow and expect adoption,” Prasad says.
Staff need training, engagement, and trust in the technology. Establishing an AI Center of Excellence—a collaborative space for business, tech, and operations—helps foster innovation while maintaining governance. “The magic happens when AI becomes a business strategy, not just a tech experiment,” he adds.
The whitepaper also provides practical guidance for scaling AI. It outlines steps for evaluating prototypes, designing production workflows, and ensuring risk management and explainability. For firms navigating regulatory requirements, it highlights ways to implement guardrails while still enabling experimentation.
The benefits are already visible. In one case study, a combination of intelligent document processing and conversational AI reduced manual workflows by 60% and sped response times to brokers by weeks.
Multi-agent orchestration enabled faster underwriting, reduced operational bottlenecks, and improved customer satisfaction.
Prasad sums it up, “AI is no longer a pilot project. To deliver real business value, it must be integrated, measurable, and strategically aligned. The whitepaper provides a roadmap for moving from experimentation to production, showing how insurers and financial services firms can unlock AI’s full potential.”
Read the full whitepaper from eSynergy here.
Keep up with all the latest FinTech news here
Copyright © 2025 FinTech Global
