The promise of AI-based plug-and-play technology is compelling: instant insights, anomaly detection, and quick time to value. In data governance, particularly within financial services and insurance, the ability to detect data quality issues, inconsistencies, or fraud patterns in near real-time is a game-changer. 

But does quick detection automatically translate to business value? Not necessarily. In fact, the very speed of AI plug-and-play solutions can create friction between perceived value (quick insights) and real value (integrated, actionable improvements).  

In contrast, systems that require configuration and process alignment might take longer to deploy, but often deliver more meaningful, long-lasting business benefits. 

The Appeal of Quick Wins 

Plug-and-play AI solutions deliver fast results by instantly surfacing anomalies within a business’s data landscape. These solutions can be deployed in weeks rather than months, providing immediate visibility into data issues that would otherwise go unnoticed. Insurance companies, for example, have used anomaly detection tools to catch data pipeline bugs, fraudulent claims, or policy errors within days of implementation. 

This quick time to value is critical in highly regulated industries like insurance, where identifying and fixing data issues swiftly can prevent costly errors, non-compliance fines, or reputational damage. These tools promise instant impact, and for many organisations, that’s an enticing proposition. 

The Friction Between Speed and Business Value 

However, while AI-powered anomaly detection is fast, translating those insights into meaningful business improvements is a different challenge. The reality is that detecting an anomaly is not the same as resolving it. In many cases, the detection is just the beginning of the process and businesses still need to: 

• Assess the Anomaly’s Impact: Not every anomaly is a problem. Some could be harmless deviations, others might indicate deeper systemic issues. Without context, businesses risk spending valuable time chasing false positives. 

• Integrate Findings into Existing Workflows: Anomalies need to be funneled into structured processes whether that’s fraud investigations, claims processing, or data quality remediation. If a plug-and-play system operates in isolation, it creates more noise than action so seamless business (operational) integration is a must. 

• It could be governance not quality: The potential hides in the detail where a governance issue or a tolerance based issue isn’t as easily found when specific concepts of ‘error’ are being identified alone. 

• Refine the AI Model for Business Context: AI models work best when they understand the specific nuances of an industry. Off-the-shelf solutions may flag irregularities that aren’t actually problems or miss issues that only a trained business expert would recognise. This is obviously achievable but requires the interaction and feedback that has the potential to take the shine off of “immediate value”. 

Without these elements, the quick value of anomaly detection remains at risk of being surface-level. Businesses may see an influx of alerts but struggle to derive meaningful, long-term action from them. 

Why Configurable Systems Often Win in the Long Run 

On the other hand, configurable systems, those that require process codification and integration into a company’s existing workflows tend to deliver slower initial time to value but greater business value over time. These solutions allow organisations to: 

• Tailor Detection to Business Rules: Rather than generic anomaly detection, configurable systems allow businesses to define their own logic, ensuring that alerts are relevant and actionable. 

• Integrate Seamlessly into Decision-Making: Instead of operating as a siloed tool, a well-integrated system can have or trigger automated workflows, alert the right teams, and drive actual process changes. This has the benefits of getting it right and the opportunities to alter behaviours of the source of issues if required. 

• Ensure Sustainable Value Over Time: A system that understands a company’s data landscape and evolves alongside it provides long term resilience and adaptability, this adaptability allows it to grow with the variables of the changing environment and respond to the changing external environment(often regulated) environment. 

A great example comes from the insurance industry, where investment has taken place in underwriting checks such as limits or policy alignment that integrates anomaly detection with workflow automation and human review processes. The results? Millions in potential error or outside of limits business being written and corrected right up at the beginning of the data journey. 

Striking the Right Balance 

The reality is that companies don’t have to choose between speed and value, they need both. The best approach is often a hybrid model: deploying quick-win AI solutions for immediate insights while simultaneously integrating them into a broader governance and operational framework. 

For data governance leaders in financial services, the goal should be to balance fast detection with structured, actionable workflows and a focus on connecting and integrating with their Operational colleagues.  

Plug-and-play AI can highlight problems quickly, but real value emerges when businesses align those insight with their own processes and decision-making frameworks. 

Quick insights are valuable but only if they drive real, meaningful action. Organisations that recognise this friction and plan accordingly will be the ones that unlock both the speed and depth of AI driven data governance, rather than settling for surface level improvements. 

Am I Biased 

Yes, guilty as charged – I work with an dedicated organisation DQPro that so far have followed one of these routes and we live and breathe these insights daily.  

Balance is the right challenge and finding the right balance is different for everyone.  

But as a route to genuine connected value I believe it is a clear option that starts with connected, specific and unique (configurable) systems that build long lasting value.