A Recordsure perspective, in partnership with Mailock.
In a recent podcast discussion with Mailock (Beyond Encryption), a central theme emerged for regulated firms: how to move beyond limited sampling and build a clearer, defensible understanding of customer outcomes.
At the heart of this shift is not just the use of AI but the right use of AI.
As the discussion makes clear, different types of AI play different roles. In practice, predictive AI and generative AI solve different problems, and understanding that distinction is critical if firms are to scale their oversight effectively.
Why sampling is no longer enough
The discussion highlights a fundamental gap in traditional oversight approaches. While sampling can surface individual issues, it is not designed to provide a complete or defensible view of customer outcomes at scale.
As outlined in the episode, many firms continue to review “only a small fraction of calls, files, and customer interactions” despite increasing regulatory expectations.
This creates risk. Important issues whether linked to disclosures, vulnerable customers or unclear customer journeys can easily be missed, meaning outcomes can fall short of expectations.
Crucially, “randomly sampling can mean key issues get missed and outcomes stray far from ideal.”
Scaling review is essential but doing so effectively requires more than simply introducing generic AI capabilities.
From anecdotes to evidence: where predictive AI matters
A key theme from the conversation is the move from isolated examples to consistent, evidence-based oversight. Small samples may highlight issues, but they cannot prove patterns. As discussed:
“Small samples can spot anecdotes. They struggle to prove themes.”
This is where the role of AI becomes more nuanced.
While generative AI is often associated with summaries or outputs, the challenge here is different: firms need to identify, classify and prioritise risk across large volumes of interactions.
This is particularly important in the context of Consumer Duty, where firms are expected not just to monitor but to “assess, test, understand and evidence the outcomes customers receive.”
For Recordsure, this shift from sampling to evidencing outcomes is where scalable conversation review becomes critical.
The cost of hindsight and the need for ongoing insight
Another key risk discussed is the cost of retrospective review. Where oversight is limited, remediation becomes increasingly complex:
Without the ability to search, classify and prioritise historic evidence at scale, reviews become “slower, costlier, and harder to defend.”
This underlines the importance of continuous visibility.
As Adrian Crean Strategic Partnerships Director at Recordsure notes:
“Make sure you’re doing this ongoing so there are no surprises.”
Achieving this continuous, proactive view again depends on applying the right AI capabilities. Simply generating outputs is not enough firms need to anticipate and identify risk as it emerges.
This is another example of how predictive AI and generative AI solve different problems.
What AI actually changes (and what it doesn’t)
The discussion also addresses a common misconception: that AI alone solves the problem.
In reality, the objective is not technology for its own sake, but better visibility and stronger evidence.
The “big idea”, as discussed, is straightforward: to achieve stronger outcomes, better visibility of conduct risk and greater regulatory confidence, firms need the ability to review far more conversations than manual teams can manage alone.
But scaling review requires clarity on capability:
- Generative AI may help summarise or present information
- Predictive AI enables firms to analyse large datasets, detect risk, and prioritise action
The distinction matters because the goal is not just to ‘review more’ but to review more effectively and defensibly.
Building a complete view of customer outcomes
Ultimately, the discussion with Mailock highlights a broader shift taking place across financial services.
The goal is not simply to review more conversations, but to build a more complete, evidence-based understanding of customer outcomes one that stands up to both internal scrutiny and regulatory expectations.
For Recordsure, this aligns directly with the need to:
- Increase visibility across all customer interactions
- Identify conduct risk earlier
- Provide defensible evidence of good outcomes
- Reduce the cost and complexity of remediation
This is not achievable through sampling alone and not achievable through generic AI approaches.
It requires a clear understanding that predictive AI enables insight and risk detection, while generative AI supports presentation and interpretation.
Closing perspective
As regulatory expectations continue to evolve, particularly under the FCA’s Consumer Duty, firms are under increasing pressure to evidence customer outcomes at scale.
The message from the podcast is clear:
- Sampling is no longer sufficient
- Reviewing more conversations is essential
- And critically, using the right type of AI is what makes that scale meaningful
Because ultimately, predictive AI and generative AI solve different problems – and knowing when and how to use each is key to delivering better customer outcomes and defensible oversight.
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