For the past two years or longer, many conversations about AI in business have centred on chatbots. Customer support assistants, internal copilots, and conversational interfaces have dominated the narrative. They are visible, easy to demonstrate, and relatively quick to deploy. They are also the least interesting application of the technology.
Little real value from AI comes from chat session. It shows up in decision-making, workflow automation, and making sense of messy data—when those capabilities are embedded into the operating model as a systems concern.
AI as infrastructure, not interface
Chatbots are fundamentally an interface layer sitting above systems. They do not in themselves radically change how a business operates.
Enterprise AI is most useful when it’s treated as part of the platform. When it:
- Enhances or replaces decision points
- Automates complex, judgement-heavy workflows
- Extracts structure from unstructured data
- Scales expertise across the organisation
Less visible than a chatbot maybe but more valuable.
Two industries serve as examples that illustrate this clearly: wholesale insurance and online travel.
Wholesale insurance: augmenting underwriting, not replacing it
Wholesale and specialty insurance markets—particularly in London—are built on expertise. Brokers submit risks and underwriters assess them. Decisions are nuanced and often based on incomplete or inconsistent data. This is fertile ground for AI.
Submission triage and enrichment
A typical wholesale broker submission arrives as a mix of emails, PDFs, and spreadsheets. Before any underwriting happens, someone has to:
- Extract key risk data
- Normalise formats
- Identify missing information
- Route to the appropriate underwriter
This front-end work is a good fit for automation: ingest, extract, validate, and route—without a human doing file-by-file triage.
In practice, the pipeline looks like this:
- Parse incoming submissions
- Structure the data into a consistent schema
- Flag incomplete or inconsistent information
- Prioritise submissions based on appetite and complexity
This does not replace underwriters. It removes the administrative burden that prevents them from focusing on actual risk assessment.
Risk pattern recognition across the book
Underwriters rely heavily on experience—what they have seen before. But no individual can hold the full history of a book in their head.
A model, backed by the full data set, effectively acts as that memory—queryable at the point of underwriting. With historical submissions, bind rates, and loss performance in the data set, the model supports the underwriting decision by:
- Identifying patterns that correlate with profitability
- Suggesting pricing ranges based on comparable risks
- Highlighting subtle risk signals (e.g. combinations of attributes that increase loss likelihood)
- Highlighting negotiation levers
- Providing context at the point of decision
The goal isn’t hands-off fully automated quoting. Rather it is about shortening turnaround time and improving judgement with better context.
Online travel: managing complexity at scale
Online travel businesses operate in a high-volume, thin-margin environment. Decisions are constant: pricing, availability, fraud, customer experience.
The gains come when models are wired into booking and operations flows, not presented as a standalone feature.
Dynamic pricing beyond simple rules
Traditional pricing models rely on rules and basic demand signals. A learning-based approach supports much more granular optimisation.
In practice, this means:
- Adjusting pricing based on real-time demand, competitor behaviour, and customer segmentation
- Incorporating external signals (events, weather, macro trends)
- Continuously learning from conversion outcomes
The result is not just better pricing—it is adaptive pricing that improves over time.
Fraud detection in booking flows
Travel platforms are frequent targets for fraud—stolen cards, account takeovers, and refund abuse.
Rules-based systems struggle to keep up. A model running in the booking flow can:
- Detect anomalous booking patterns
- Assess risk in real time during checkout
- Reduce false positives while catching more fraud
This directly impacts margin without degrading customer experience.
Operational automation in customer disruption
Flight cancellations, delays, and disruptions create spikes in customer service demand.
At that point you want an automated playbook wired into operations:
- Automatically rebook customers based on preferences and availability
- Prioritise high-impact cases
- Generate consistent, policy-aligned resolutions
This reduces operational strain while improving customer outcomes.
The common thread: decision intelligence
Across both industries, the pattern is the same: use models to tighten the decision loop around:
- Which risks to underwrite
- How to price them
- Which bookings are legitimate
- How to respond to disruption
These are not chatbot problems. They are core business problems.
What this means for enterprise adoption
Many organisations are still approaching AI from the outside in—starting with interfaces and hoping value follows.
The more effective approach is the opposite:
- Identify high-value decision points
- Map the data and workflows around them
- Embed AI directly into those processes
This requires deeper integration. It is more complex than deploying a chatbot. But it is where the real value is.
Moving beyond the hype
Chatbots are easy to demonstrate, which is why they can dominate the conversation. But on their own they rarely change how a business runs.
Real transformation comes from:
- Automating judgement-heavy processes
- Scaling expertise
- Making better decisions, faster
That’s where AI delivers measurable impact.
The organisations that recognise this early—and build for it—won’t just adopt AI. They’ll put it into production across the core workflows.
If you’re planning an AI project or have a tricky business problem to solve, the experts at Systems iO would love to talk it through. Get in touch with Angus Kennedy or Dan McNamara for an informal chat.”
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