6 min read
Conversational AI has become one of the most talked-about innovations in recent years. Generative AI (Gen AI) have transformed how people think about interacting with technology, raising expectations for speed, personalisation, and scale. Yet for regulated industries—insurance, banking, healthcare, the excitement is tempered by concern. How do you analyse, audit, and improve conversations generated by an AI that produces different outputs each time?
The answer is far from simple. While Gen AI is powerful, its unpredictability makes it extremely difficult to reconcile with regulatory expectations for consistency, transparency, and accountability.
Gen AI has captured attention because of its ability to generate fluent, human-like dialogue. Customers can ask open-ended questions and receive natural responses in seconds. This flexibility is particularly appealing in industries where customers frequently need guidance on complex products and policies.
Yet the very features that make Gen AI attractive also create risk. Conversations unfold in unpredictable ways, and responses are generated probabilistically rather than through fixed logic. This means no two interactions are ever quite the same. For compliance teams, this variability is a problem. Regulators expect consistent records of advice, decisions, and service outcomes. With Gen AI, it is difficult to prove why a specific response was given or to guarantee that the same question would always receive the same answer.
In short, Gen AI offers promise but introduces unpredictability, an uncomfortable fit for sectors where reliability and auditability are not optional but mandatory.
When organisations attempt to analyse conversations with Gen AI, they quickly run into obstacles. A single conversation can drift across multiple topics, shaped by the customer’s emotions, questions, and choices. The AI’s responses, generated on the fly, make it nearly impossible to forecast every possible outcome.
Scale compounds the issue. Large firms generate millions of conversational records, but these logs resist easy categorisation. Unlike structured data, conversational text is messy, inconsistent, and context dependent. Manual review is impractical, while automated analysis often strips away nuance.
Transparency is another barrier. Gen AI is often described as a “black box.” It can provide an answer but not a clear explanation of how that answer was derived. Regulators, however, demand exactly that: evidence of logic and reasoning. To make matters worse, Gen AI models are dynamic. Small updates or changes to prompts can alter outputs significantly, meaning that what was compliant one week may drift into non-compliance the next.
Improving conversations is equally fraught. What should count as “better”? A faster response? A friendlier tone? Greater adherence to regulation? In regulated businesses, these goals often conflict, creating bottlenecks and uncertainty about how to measure and improve performance.
Some argue that more data will resolve these challenges. By collecting and analysing enough conversational logs, businesses could, in theory, refine their models and identify patterns that improve reliability.
But in practice, the opposite often happens. Conversations are full of noise, typos, slang, half-formed thoughts, that confuse rather than clarify. Training models on larger datasets risks embedding bias or misunderstanding customer intent. For regulated industries, data protection laws such as GDPR further restrict what can be captured and stored, making large-scale collection itself a compliance risk.
Even when datasets are available, more volume does not guarantee more clarity. Regulators are less interested in big data than in explainability and consistency. They want to see why a specific response was given and to ensure that customers in similar situations receive the same guidance. The dynamic nature of Gen AI undermines this goal, leaving businesses with growing datasets but no straightforward path to compliance.
This is where expert systems, such as those developed by Spixii, provide a practical way forward. Unlike Gen AI, expert systems are built on deterministic logic. Every answer follows a defined, auditable path rooted in business rules and regulatory requirements. This means conversations are predictable, consistent, and explainable, exactly what auditors and regulators demand.
For example, if a customer asks about a specific insurance clause, an expert system can guide them through a structured decision tree that ensures only approved information is provided. Each step is logged, traceable, and repeatable. If the same question is asked tomorrow or in six months, the answer will be consistent. This transparency dramatically simplifies compliance and audit processes.
Crucially, expert systems do not sacrifice customer experience. Spixii’s AI can still provide engaging, personalised conversations, but within a framework that guarantees regulatory alignment. This blend of automation and control offers the best of both worlds: the efficiency of AI-driven engagement with the assurance of compliance by design.
Analysing and improving conversations with Gen AI is a daunting challenge for regulated businesses. The unpredictability of dialogue, the scale of conversational data, the opacity of AI reasoning, and the evolving behaviour of models make compliance and audit readiness extremely difficult to achieve. Adding more data may look like a solution but in reality only introduces new risks around bias, privacy, and explainability.
Expert systems provide a way out of this compliance trap. By grounding conversations in deterministic logic, systems like Spixii offer consistency, auditability, and transparency—qualities that regulators demand and customers trust. They show that it is possible to harness the benefits of conversational AI without the risks associated with unstructured generative models.
For regulated industries, the future of AI is not about choosing between innovation and compliance. It is about adopting the right kind of AI, one that delivers customer engagement at scale while ensuring that every interaction is reliable, auditable, and compliant by design. With expert systems, that balance is not just possible; it is already here.