Why Your AI Stack Is Actually Five Products Pretending to Work Together

Walk into any financial services firm and ask about their AI set-up. You’ll hear about tools for recording client meetings, services that summarise conversations, systems that scan for regulatory issues, platforms that assess suitability and engines that generate reports. On paper, it sounds modern and well-designed. Ask how these systems connect in day-to-day use, and the answers grow noticeably softer.

That hesitation points to a familiar problem. Many firms haven’t built an AI strategy at all. They’ve stitched together a row of specialist products that operate in parallel rather than in concert. What looks neatly integrated on a slide deck often behaves more like five separate technologies lashed together by custom integrations and good intentions.

The true costs don’t surface at procurement. They show up months later, when compliance teams discover that the same client conversation is being processed in multiple tools to produce almost identical reports. When security teams realise their audit workload has multiplied. When operations teams count the hours spent maintaining connections between systems that were never designed to share context.

This is the point-solution trap, and most organisations fall into it without noticing.

The Point Solution Trap

In wealth management and advice networks, the pattern is strikingly consistent. A transcription service captures the meeting. A separate product turns it into a summary. A compliance system scans the content for regulatory concerns. A suitability platform checks advice quality. A reporting engine packages the output.

Each product is credible when viewed alone. Each has its own security promises, its own data pipeline, its own formats and its own training requirements. Together, they create complexity that compounds over time.

Five areas cause the most friction:

Perpetual integration work. Every tool needs a connection built and maintained. Updates in one system can break another without warning. A minor software release on Tuesday evening can leave Wednesday’s batch of client meetings stranded between platforms, with compliance teams waiting for resolution.

Fragmented data. A single client interaction often ends up duplicated across multiple databases. Transcripts sit in one system, summaries in another, compliance flags elsewhere. Beyond the storage burden, this creates consistency issues and raises questions about data residency and client deletion rights.

Security overhead. Five vendors mean five audits, five sets of controls and five different architectural models to monitor. Information security teams aren’t overseeing one AI deployment. They’re managing several at once.

Conflicting outputs. One system may flag a remark as high-risk while another marks it as acceptable. Reviewers then spend their time reconciling disagreements between tools rather than assessing the case itself.

Fragmented training. New staff must learn multiple interfaces. Any process change needs to be updated across all tools. What should be a streamlined workflow becomes a patchwork of adjustments.

See why task-specific AI creates integration debt, not integration value →

What Actually Works Better for an Agentic AI Foundation

A platform built around agentic AI takes a different approach. Here, multiple AI agents operate from shared infrastructure and draw on common context. Tasks that once relied on disconnected systems now sit within a single architectural layer.

One model family. Rather than different vendors each applying their own foundation models (the large AI systems that power different tools), everything uses consistent AI architecture. When Aveni’s platform processes a client interaction, whether for transcription, analysis, compliance checking, or report generation, those tasks leverage the same underlying capabilities, trained specifically for UK financial services terminology and regulatory frameworks.

One training dataset. When you refine how the system interprets pension transfer conversations, that learning applies equally to transcription accuracy, suitability assessment, and complaint detection. One improvement, universal benefit.

One security perimeter. You manage a single deployment. One authentication system. One audit trail. One data residency policy. One set of security tests. When regulators want to verify AI governance, they examine one platform, not reconcile security across five vendor relationships.

One audit trail. Every AI decision logs to a unified system. When the FCA asks how you assured an advice interaction, you pull from one evidence chain. The same audit capability that tracks compliance flags also tracks model versions, data origins, and human oversight.

Discover why control beats model size when deploying AI at scale →

When It Actually Matters

Picture this compliance scenario: a customer complains about advice they received six months ago.

With point solutions, your review involves pulling the original recording from the transcription vendor, checking the summarisation tool for what was captured, reviewing compliance flags from the monitoring system, cross-referencing suitability assessments from another platform, then reconciling potentially conflicting outputs.

With a unified platform, you retrieve one interaction record containing the complete chain: original audio, transcript, AI-generated analysis, compliance assessment, and human review decisions, all logged consistently.

When your compliance team identifies model behaviour that needs adjustment, they request one change that propagates everywhere, rather than coordinate updates with multiple vendors on different release schedules.

In Aveni’s collaboration with Lloyds Banking Group documented in the FCA Supercharged Sandbox programme, the shift from manual sample-based oversight (covering 0.2% of interactions) to AI-enabled continuous monitoring (covering 100% of interactions) demonstrates how platform consolidation fundamentally changes what’s possible. This isn’t about AI replacing humans. It’s about eliminating the time humans spend reconciling conflicts between AI systems, so they can focus on the complex judgement calls that actually require human expertise.

Learn how agentic AI transforms fraud detection from reactive to continuous →

What Proper Architecture Requires

Building or adopting an agentic platform requires several critical capabilities:

Task orchestration. A workflow layer coordinates different AI agents for complex processes. When assessing meeting suitability, agents responsible for extracting facts, checking regulatory rules, assessing outcomes, and synthesising evidence all work from shared context rather than passing outputs sequentially through disconnected systems.

Shared knowledge infrastructure. One unified repository of regulatory knowledge, product information, and firm-specific policies that all agents access. When regulation changes, you update once.

Consistent evaluation. The same criteria for assessing AI performance across all capabilities. If you measure accuracy, explainability, and bias in your compliance agent, those same standards apply to your reporting and assistance agents.

Domain specialisation. Platform economics only work when the underlying models understand your industry. AI capabilities trained specifically for UK financial services contexts, like those evidenced by the 90% correlation rates with human expert judgment in our outcome testing automation work with Lloyds Banking Group, eliminate extensive customisation work.

Explore what boards should be asking about enterprise AI deployment →

The Migration Path

For technology leaders currently managing multiple AI point solutions, the path to platform consolidation follows a phased approach:

Map the sprawl. Document actual data flows, connection points, and dependencies across current tools. The complexity is usually worse than remembered.

Identify friction points. Where are teams spending most time reconciling systems, managing inconsistencies, or handling security overhead?

Pilot specific workflows. Rather than wholesale replacement, prove the model with high-value, high-complexity processes like suitability assessment or complaints triage where unified architecture benefits are most apparent.

Calculate true costs. Include maintaining system connections, security overhead, training burden, and data management, not just license fees.

As demonstrated in Aveni’s work through the FCA Supercharged Sandbox programme with Lloyds Banking Group, validating platform approaches in controlled environments allows organisations to prove value before committing to full production deployment. The regulatory alignment gained also reduces barriers to wider rollout.

Read how to measure AI ROI beyond license costs →

The Strategic Choice

The question facing financial services technology leaders isn’t whether to deploy AI, but how to architect AI deployment for scaled adoption over the next decade.

Point solutions made sense when AI capabilities were experimental, deployed cautiously in limited contexts, and treated as research projects. But as AI moves from proof of concept to production, from edge case automation to core process transformation, that architecture model breaks down.

An agentic platform approach treats AI as foundational infrastructure, comparable to core banking systems or customer relationship management platforms. Not a collection of specialist tools requiring constant connection work, but a coherent capability that scales consistently, maintains security uniformly, and evolves cohesively as models and regulations advance.

The firms that will lead in AI deployment over the next five years won’t necessarily be those that adopted AI first. They’ll be those that architected AI correctly, building on unified foundations rather than assembling fragile stacks of point solutions.

See how platform architecture works in practice → 

Frequently Asked Questions about Building an Agentic AI Foundation