AI Robotics in UK Manufacturing, Explained for Insurance Companies in UK

Direct answer: AI-enabled robotics is scaling quickly across UK smart manufacturing and logistics, which means the physical operations insurers underwrite, inspect, and pay claims against are changing faster than most policy wordings, risk models, and back-office systems can keep up with. For UK insurance companies, the practical response is not a new product launch — it's building the software plumbing that lets underwriting, claims, and risk engineering teams actually see what a robotics-enabled factory floor looks like today, in close to real time.

Deloitte UK Tech Trends 2026 names AI-enabled robotics as one of the technologies scaling rapidly across UK smart manufacturing and logistics operations, dated August 2026. That's a directional signal, not a single data point tied to one factory or one insurer, and this post treats it as exactly that. We don't have a precise figure for how many UK manufacturing sites have deployed AI-driven robots, what share of claims now involve a robotic system, or how loss ratios are shifting as a result — those specifics aren't publicly available at this level of detail, so we won't invent them. What we can do is reason honestly from the pattern: when the physical assets and processes an industry insures change materially, the software layer that insurers use to price, monitor, and settle claims against those assets has to change with it, or it quietly falls behind the risk it's meant to cover.

What's Actually Happening in UK Manufacturing Right Now

"AI-enabled robotics" in this context isn't a single product category — it's a cluster of related shifts happening on UK factory and warehouse floors simultaneously. Traditional industrial robots that repeat a fixed motion are being supplemented or replaced by systems that use machine vision, sensor fusion, and onboard models to adjust their behavior in real time: picking irregular items, detecting defects, rerouting around obstacles, or adapting a welding path based on live feedback rather than a pre-programmed script. In logistics operations attached to manufacturing — warehousing, palletizing, last-mile staging — autonomous mobile robots and AI-coordinated conveyor systems are doing work that used to require constant human oversight.

The reason this is scaling now rather than five years ago comes down to three converging factors: cheaper and more capable sensors, cloud and edge compute that can run inference close to the machine, and a labor market that has made automation an economic necessity rather than a nice-to-have for many UK manufacturers. None of that is exotic — it's the same pattern of technology maturing to the point where deployment cost drops below the value it creates. What matters for this post is not the robotics engineering itself but the consequence: the physical risk profile of a manufacturing site with AI-driven robots is materially different from one without them, and that difference shows up in ways that are hard to capture with legacy inspection checklists and static policy schedules.

Why This Isn't Just an IT Story

It would be easy to file this under "manufacturing's problem" and move on. But insurers sit downstream of every one of these deployments. A commercial property and casualty book with manufacturing exposure is, whether anyone designed it this way, now also a book with robotics exposure — new failure modes, new liability questions (is a malfunction a product defect, a maintenance failure, or an algorithmic error?), and new claims patterns that don't map cleanly onto historical loss data. That's an insurance problem before it's an IT problem, and it's arriving on a timeline set by manufacturers, not by insurers' own change-management calendars.

It's also worth being precise about what's not being claimed here. Deloitte UK Tech Trends 2026 describes rapid scaling of AI-enabled robotics across smart manufacturing and logistics — it does not say every UK manufacturer has adopted this, nor does it break out adoption by sector, region, or company size. Reasoning honestly from that pattern means acknowledging the trend is real and directional while resisting the temptation to dress it up with borrowed specificity from unrelated reports. For an insurer building an internal business case for software investment, that distinction matters: the case for acting doesn't rest on a headline percentage, it rests on the fact that the underlying operational reality is shifting under a meaningful and growing share of a manufacturing book, and the systems tracking that book haven't been built with this shift in mind.

Why This Matters Specifically to Insurance Companies in the UK

UK insurers underwriting commercial, property, casualty, or specialty lines with manufacturing exposure are exposed to this shift in at least four concrete ways, and each one has a software dimension.

Underwriting data is going stale faster. A risk survey conducted eighteen months ago, before a client installed AI-driven robotic picking or vision-guided assembly lines, no longer reflects the actual hazard profile of that site. Fire risk, business interruption exposure, and even the liability picture can shift when robots are handling tasks that used to involve manual labor with different failure characteristics. Underwriters need a way to flag when a policyholder's operational footprint has changed enough to warrant a fresh look — and that flagging has to happen through data, not through waiting for the next renewal cycle.

Claims triage needs new categories. When a claim comes in from a manufacturing client with AI-enabled robotics on site, the adjuster needs to know quickly whether this is a conventional equipment-breakdown claim, a product liability question tied to an algorithmic decision, or something in between. Legacy claims systems built around a fixed taxonomy of causes don't have a clean bucket for "robotic system made an autonomous decision that led to a loss," and forcing that claim into the nearest existing category degrades both the claims data and the eventual loss-cost analysis.

Risk engineering visits can't be the only signal. Physical site inspections remain valuable, but they're periodic by nature, and a robotics deployment can change between visits. Insurers that build even lightweight digital channels for policyholders to report operational changes — a new robotic cell coming online, a software update to an existing system — get a continuous signal instead of a snapshot. That's a product and software decision, not a field-underwriting one.

Reinsurance and portfolio reporting need a defensible narrative. As robotics exposure grows across a manufacturing book, insurers will increasingly need to explain to reinsurers and internal risk committees how they're tracking and pricing this specific exposure. "We don't have a system for that yet" is a weak answer in a renewal conversation. Being able to show a structured view of which policyholders have disclosed robotics deployments, and how that maps to loss experience, is a straightforward data and reporting problem with a software solution.

There's a fifth, quieter effect worth naming: talent and workflow inside the insurer's own claims and underwriting teams. Adjusters and underwriters who've spent years building intuition around conventional manufacturing risk — press failures, conveyor injuries, standard equipment breakdown patterns — don't automatically have the same intuition for a claim where a vision-guided robotic arm made an incorrect autonomous decision. That's not a reason to panic, but it is a reason to make sure the software supporting those teams surfaces the right questions and context at the point of claim intake, rather than relying entirely on individual adjuster experience to catch something unfamiliar.

None of this requires an insurer to become a robotics expert. It requires the underwriting, claims, and risk platforms to have a place to capture, structure, and act on information that didn't matter as much three years ago.

What Changes in Practice for an Insurer's Website, Portals, and Internal Tools

This is where the trend stops being abstract and starts touching actual systems that a UK insurer runs day to day.

Broker and policyholder-facing tools

If your submission intake forms, broker portals, or renewal questionnaires don't ask about automation and robotics deployment in a structured way — beyond a generic "describe your operations" free-text field — you're relying on brokers and policyholders to volunteer information that they may not think to flag as insurance-relevant. A well-designed intake flow with a few targeted, structured questions about robotics and automated systems gives underwriting teams searchable, comparable data across the book instead of buried prose in a PDF submission. This is a straightforward addition to an existing digital submission tool, not a rebuild.

Internal underwriting and claims systems

The deeper work is usually in how your internal systems tag, store, and surface this information once it's captured. That means new fields in the underwriting data model, new claim cause codes, and dashboards that let a chief underwriting officer actually query "how many policyholders in our manufacturing book have disclosed AI-enabled robotics, and what's our current exposure concentration." Clear presentation of that kind of operational data matters here — the same instincts behind good dashboard design principles apply directly: a underwriter or claims manager needs to scan a portfolio view and immediately spot the outliers, not hunt through nested menus. This is exactly the kind of work that benefits from custom software rather than forcing a policy administration system's stock reporting module to do something it wasn't built for.

Integration with third-party and IoT data

Some UK manufacturers are already generating telemetry from their robotic systems — uptime, error rates, maintenance logs. Over time, insurers that build the integration pipes to ingest this kind of data (with proper consent and data-sharing agreements) will have a real edge in both pricing accuracy and claims investigation speed. That's a data engineering and API integration project, and it's the kind of work that sits squarely under Custom Software Development rather than something an off-the-shelf policy admin platform vendor will prioritize building for you on your timeline.

Documentation and internal consistency

As new risk categories and product endorsements get created to address robotics exposure, the internal documentation, style, and terminology used across underwriting guidelines, marketing materials, and developer-facing API docs needs to stay consistent — otherwise you end up with three different teams calling the same risk category three different names. This is a smaller point but a real one: the same discipline behind building a brand style guide that developers will actually follow is worth applying internally when you're introducing new terminology across underwriting, claims, and digital product teams at the same time.

Legacy system constraints are the real bottleneck

The honest complication in most of this isn't deciding what data to capture — it's that many UK insurers are still running policy administration and claims systems that were never designed to be extended this way. Adding a handful of fields to a modern, well-architected submission tool is a modest task. Doing the same thing against a decades-old policy admin core, or a claims system with a rigid, vendor-controlled schema, often means the "small" change actually requires a middleware layer, a separate data store, or an API wrapper that sits alongside the legacy system rather than trying to modify it directly. This is precisely the kind of constraint that makes off-the-shelf configuration insufficient and pushes the work toward custom-built integration layers instead.

How to Approach This Without Overreacting

The instinct with any trend headline is either to ignore it or to launch an expensive new initiative. Neither is right here. A measured approach looks like this:

1. Audit your current intake and claims data model for whether robotics or automation exposure is even capturable today. If the answer is "only in free text," that's your starting point.
2. Add structured fields incrementally to submission forms and claims intake rather than attempting a full platform overhaul. Small, shippable changes compound faster than a multi-year re-platforming effort.
3. Build the internal reporting view so underwriting leadership can actually see exposure concentration once the data starts flowing in. Data you can't query isn't useful data.
4. Treat this as an ongoing software investment, not a one-off project. The robotics landscape in UK manufacturing will keep evolving, and the systems tracking it need to be built to extend, not to be finished.

This is also a reasonable moment to think about adjacent digital trust signals. Regulatory and public attention on AI systems affecting real-world outcomes is intensifying broadly — the same pattern shows up well outside insurance and manufacturing, for instance in how governments are approaching platform accountability, as seen in Australia's under-16 social media ban and the 2026 enforcement crackdown. The throughline for insurers is the same: when a technology's real-world impact scales faster than the oversight built around it, being the party with clean data and demonstrable process discipline becomes a competitive advantage, not just a compliance checkbox.

There's also a sequencing question worth thinking through before committing budget: which system should change first, underwriting intake or claims taxonomy? In practice, intake is usually the better starting point, because it's forward-looking and lower-risk — you're adding a question to a form, not restructuring how years of historical claims get classified. Claims taxonomy changes are more valuable in the long run, since that's where the loss-cost data that eventually informs pricing actually lives, but they're also more disruptive to change mid-stream, since adjusters need training and historical claims may need to be reclassified for consistency. A sensible phased approach starts with intake, uses the first six to twelve months of structured disclosures to understand the shape of the exposure building up in the book, and only then tackles a full claims taxonomy revision informed by what's actually been learned — rather than guessing at categories upfront and having to redo the work once real patterns emerge.

What This Kind of Work Typically Costs

Building or extending the software that lets underwriting and claims teams handle robotics-related exposure isn't a fixed line item — it depends on how much of your existing stack needs to change versus how much is net-new. Here's roughly how this kind of work maps to typical engagement tiers:

Tier	Typical scope for this kind of work
Essential — $1,000	A focused addition: new structured fields on an existing submission or claims intake form, a small reporting dashboard update, or a documentation and terminology cleanup pass.
Growth — $2,000	A more involved build: a custom underwriting data model extension, a broker-facing portal update with robotics-specific questions, or a first-pass integration pulling in third-party operational data.
Enterprise — $4,000+	End-to-end work: full claims taxonomy redesign, IoT/telemetry integration pipelines, portfolio-level exposure dashboards, and ongoing extension as the underlying risk landscape keeps shifting.

These tiers are a starting frame, not a quote — the right scope depends on what your current systems already support and how much of this needs to integrate with legacy policy administration infrastructure. A useful way to think about sequencing budget is to treat the Essential tier as the diagnostic and quick-win phase, Growth as the phase where the data model and portal actually get extended in a durable way, and Enterprise as the phase reserved for insurers whose manufacturing book is large enough, and whose legacy systems constrained enough, that a full integration and reporting layer pays for itself in underwriting and claims accuracy alone.

Key Takeaways

• Deloitte UK Tech Trends 2026 flags AI-enabled robotics scaling rapidly across UK smart manufacturing and logistics — a directional signal insurers should plan around, not a precise statistic to cite as if it were.
• Manufacturing risk profiles are shifting faster than most renewal cycles, so underwriting data capture needs structured fields for robotics and automation, not free-text descriptions.
• Claims taxonomies built before this shift likely don't have a clean category for losses involving autonomous or AI-driven systems — that's a data model gap worth closing now.
• Internal dashboards and reporting views need to make robotics exposure across a manufacturing book scannable at a glance, not buried in individual policy files.
• Incremental, well-scoped software investment — intake forms, data models, dashboards, integrations — beats waiting for a single large re-platforming project.
• Consistent internal terminology across underwriting, claims, and digital teams prevents the same emerging risk category from being tracked three different ways.

Getting the underwriting and claims software layer right before robotics exposure becomes a bigger share of your manufacturing book is a lot cheaper than retrofitting it after a difficult claim exposes the gap. If you want help figuring out where to start, book a meeting with our team.

Frequently Asked Questions

What does "AI-enabled robotics" actually mean in a UK manufacturing context?

It refers to robotic systems on factory or warehouse floors that use machine learning, computer vision, or sensor-driven decision-making to adapt their behavior, rather than robots that simply repeat a fixed, pre-programmed motion. This includes vision-guided picking, adaptive assembly, and AI-coordinated autonomous mobile robots in logistics.

Why is this trend relevant to insurance companies specifically, not just manufacturers?

Insurers underwrite the physical operations and liability exposure of manufacturing clients, so any material change in how those operations run — including the introduction of AI-driven robotics — changes the risk being priced and claimed against. The insurer doesn't need to deploy the robots to be affected by them.

Is there a specific statistic on how many UK manufacturers have adopted AI robotics?

No precise, publicly available figure exists at that level of specificity as far as this post can confirm. Deloitte UK Tech Trends 2026 describes the pattern as rapid scaling across UK smart manufacturing and logistics, and that directional signal is what this post is grounded in, not an invented percentage.

How does AI-enabled robotics change underwriting risk assessment?

It changes the hazard profile of a site — different fire risk characteristics, different business interruption exposure if a robotic line goes down, and different liability questions when an autonomous system is involved in an incident. Underwriters need updated intake questions and risk models to reflect that.

Should insurers ask about robotics deployment during the submission process?

Yes, in a structured way rather than relying on a general operations description. A few targeted, specific questions about automation and robotic systems make the resulting data far more usable for underwriting and portfolio analysis than free-text narrative.

What's the biggest gap in most UK insurers' current claims systems for this trend?

Most legacy claims taxonomies don't have a distinct category for losses involving autonomous or AI-driven robotic decisions, so these claims often get force-fit into an existing "equipment breakdown" or "product liability" bucket, which weakens the resulting loss data.

Does this affect commercial property insurance, casualty insurance, or both?

Both, in different ways. Property lines are affected through changed fire, equipment breakdown, and business interruption exposure; casualty lines are affected through new liability questions around who is responsible when an autonomous system contributes to a loss.

How quickly should a UK insurer act on this trend?

There's no fixed deadline, but the underlying risk is already changing on manufacturing floors now, so waiting until it shows up clearly in loss data means acting after the fact rather than ahead of it. Incremental changes to intake forms and claims data models can start immediately at low cost.

What is Custom Software Development, and why is it relevant here?

Custom Software Development means building software specifically shaped to your existing systems and workflows rather than adapting a generic off-the-shelf tool. For insurers, that's the practical route to adding robotics-specific data fields, dashboards, and integrations without waiting on a policy administration vendor's product roadmap.

Can this work be done as an add-on to an existing policy administration system, or does it require a full replacement?

In most cases it can be layered on as an add-on — new intake fields, a reporting dashboard, or an integration pipeline — without replacing the core policy administration platform. Full replacement is rarely necessary just to address this specific gap.

What does a typical first project in this space look like?

A common starting point is auditing the current submission and claims intake forms for whether robotics or automation exposure is capturable in structured form, then adding a small number of targeted fields and a basic reporting view on top.

How much does this kind of software work typically cost?

It varies by scope. Smaller, focused additions — new form fields, a documentation cleanup, a small dashboard update — typically fall in the Essential tier around $1,000. More involved builds, like data model extensions or portal updates, fall around the Growth tier at $2,000. Full claims taxonomy redesigns and IoT integrations sit in the Enterprise tier at $4,000 and above.

How long does a project like this usually take?

Timelines depend heavily on scope and how much needs to integrate with legacy systems. Smaller additions can often be scoped and delivered in a few weeks; larger integrations and full data model changes take longer and are better planned as phased work.

Do insurers need to understand robotics engineering to price this risk correctly?

No. Insurers need to understand the operational and liability consequences of robotics adoption, which is a risk and data question, not an engineering one. The software layer exists precisely to translate operational disclosures into usable underwriting and claims data without requiring deep robotics expertise internally.

What role does IoT or telemetry data play in this trend?

Some manufacturers now generate operational telemetry from their robotic systems — uptime, error logs, maintenance records. Insurers that build the integration pipelines to responsibly ingest this data, with proper consent, can improve both pricing accuracy and claims investigation speed over time.

Is data privacy a concern when integrating manufacturer telemetry data?

Yes, any integration pulling in operational data from a policyholder's systems needs a clear data-sharing agreement and appropriate consent and handling processes. This is a contractual and technical design consideration that should be built into any integration project from the start.

How does this trend affect claims adjusters day to day?

Adjusters handling manufacturing claims may increasingly encounter losses involving robotic or AI-driven systems, and without a clear claims category and investigation process for these cases, triage becomes slower and less consistent across the team.

What's the risk of not adapting underwriting systems to this trend?

The main risk is pricing and reserving against a risk profile that no longer matches reality, discovering the gap only when unusual claims start appearing, and having no structured historical data to explain the exposure to reinsurers or internal risk committees.

How does this relate to reinsurance conversations?

As robotics exposure grows within a manufacturing book, insurers will need to explain how they're tracking and pricing that specific exposure during renewal and treaty discussions. A structured internal view of the data makes that a straightforward reporting exercise rather than a scramble.

Are UK regulators paying attention to AI-driven industrial systems?

Broader regulatory and public attention on AI systems affecting real-world outcomes is increasing across sectors, which is part of the wider context insurers should be aware of even though manufacturing robotics regulation specifically is still developing.

What's an example of a structured underwriting field an insurer could add today?

A simple example is a checkbox or short structured question set on the submission form asking whether the applicant uses AI-driven or autonomous robotic systems in production or warehousing, with a follow-up for scope and function, rather than leaving it to a general operations narrative.

How should an insurer prioritize this against other digital initiatives?

Treat it as one line item within an ongoing underwriting and claims data modernization effort rather than a standalone project competing for a separate budget. Small, incremental additions to existing systems are usually the most realistic starting point.

Does this trend apply equally to all sizes of UK manufacturers?

Adoption patterns likely vary by company size and sector, and this post doesn't have specific data broken down that way. The reasonable assumption is that larger manufacturers with more capital tend to adopt automation faster, but insurers should design intake questions broadly enough to capture disclosure from smaller operations too.

What internal teams need to be involved in this kind of software project?

Underwriting, claims, risk engineering, and the digital or IT team all have a stake, since the data model changes touch intake forms, internal reporting, and claims taxonomy simultaneously. Involving all of them early avoids inconsistent terminology later.

How does dashboard design factor into this work?

Once robotics exposure data starts flowing into underwriting and claims systems, it needs to be presented in a way that lets a manager scan a portfolio and immediately spot concentration risk, which is a direct application of clear dashboard design principles rather than a generic reporting table.

Can existing broker portals be updated to capture this data, or is a new portal needed?

In most cases an existing broker portal can be updated with new structured fields rather than replaced outright. A full portal rebuild is rarely necessary just to add robotics-specific disclosure questions.

What happens if a robotics-related claim doesn't fit any existing category?

It typically gets manually forced into the nearest existing category, such as equipment breakdown, which technically closes the claim but degrades the underlying loss data used for future pricing and trend analysis.

Is this only relevant to insurers with a manufacturing book, or does it touch other lines too?

It's most directly relevant to insurers with manufacturing and related logistics exposure, but the broader pattern — physical operations changing faster than the systems tracking them — applies to other industrial and warehousing-adjacent lines too.

How can an insurer tell if its current systems are already behind on this?

A quick internal audit — checking whether robotics or automation exposure is captured anywhere in structured, queryable form across intake and claims systems — usually reveals the gap quickly if one exists.

What's a realistic first deliverable for a Growth-tier engagement?

A realistic Growth-tier deliverable is a data model extension covering robotics disclosure fields plus a first-pass reporting dashboard, sometimes paired with an initial integration pulling in a manufacturer's basic operational data feed.

Does adopting this kind of software change how underwriters price policies immediately?

Not immediately in most cases. The first phase is usually about capturing and structuring the data; pricing model adjustments typically follow once there's enough consistent data to analyze loss patterns against robotics exposure.

How should an insurer document new risk categories internally?

New categories should be defined once, with consistent terminology, and referenced the same way across underwriting guidelines, claims taxonomy, and any developer-facing documentation, so different teams aren't inventing their own labels for the same emerging risk.

What's the connection between this trend and AI governance more broadly?

As AI systems take on more autonomous, consequential roles — whether in manufacturing robotics or other domains — the surrounding expectations around oversight, disclosure, and accountability tend to tighten. Insurers who build clean data practices now are better positioned as that scrutiny increases.

Can smaller UK insurers realistically act on this without a large budget?

Yes. Starting with small, incremental changes — a handful of new intake fields, a basic dashboard update — is achievable within an Essential-tier budget and doesn't require a large upfront commitment.

What's the risk of waiting until this trend is fully mature before acting?

Waiting means underwriting and claims decisions continue to be made on incomplete data during the period when the risk is actively changing, and retrofitting systems after a difficult claim or renewal surprise is typically more expensive than building the capability proactively.

How does this affect policy wordings and endorsements?

As claims experience involving robotic systems accumulates, insurers may need new or clarified policy endorsements addressing autonomous system failures and related liability questions, which should be informed by the structured claims data discussed throughout this post.

Is there a standard industry framework for classifying robotics-related insurance risk yet?

Not that is uniformly established and publicly documented at this point. This is part of why building internal structured data now, even ahead of an industry-wide standard, puts an insurer in a stronger position once conventions do emerge.

What's the difference between a risk engineering site visit and continuous digital disclosure for this purpose?

A site visit is a periodic snapshot that can miss changes made between visits, while a digital disclosure channel — even a simple one — gives underwriting a continuous signal when a policyholder's robotics deployment changes, closing the gap between renewal cycles.

How should an insurer handle a policyholder that won't disclose robotics deployment details?

That's fundamentally an underwriting policy decision, but having a clear, structured question on the submission form at least creates a documented point where the disclosure was requested, which matters for both risk assessment and any later dispute.

Does this trend increase or decrease overall claims frequency in manufacturing?

There isn't a publicly available precise answer to that at this level of specificity, and this post won't speculate with an invented number. The reasonable expectation is that claim types shift and some frequency patterns change, which is exactly why updated data capture matters.

What's the role of machine vision specifically in this trend?

Machine vision lets robotic systems make real-time adjustments — like identifying defects or adapting to irregular items — rather than following a fixed script, which is one of the core capabilities driving the wider adoption Deloitte UK Tech Trends 2026 describes.

Should insurers build this capability in-house or bring in outside development help?

That depends on internal engineering capacity and how urgently the gap needs closing. Many insurers use a mix — internal teams handle ongoing platform work while bringing in outside custom software development for focused, time-boxed projects like this one.

How does this connect to the Enterprise pricing tier specifically?

The Enterprise tier fits scenarios needing end-to-end work — full claims taxonomy redesign, telemetry integrations, and portfolio-level dashboards — which is the kind of scope larger insurers with substantial manufacturing books are more likely to need.

What's a realistic timeline for seeing value from this kind of investment?

Early value — better-structured submission data, a usable exposure dashboard — can show up within the first project cycle, often weeks. The deeper value, in pricing accuracy and claims efficiency, builds over time as more structured data accumulates.

Are there compliance implications specific to UK insurers here?

General UK regulatory expectations around data handling and fair underwriting practice apply as they would to any new data category being introduced, and any new automated or AI-assisted underwriting logic built on top of this data should be designed with those existing obligations in mind.

How does this trend interact with existing business interruption coverage?

A robotics-dependent production line can have different downtime and recovery characteristics than a manual one, which is relevant to how business interruption exposure is assessed and should be reflected in updated risk questionnaires.

What questions should an underwriter be asking a manufacturing client today?

Practical questions include whether the client uses AI-driven or autonomous robotic systems in production, what functions those systems perform, how they're maintained, and whether there's a documented incident or failure history — captured in structured form rather than narrative.

Is this trend likely to affect premiums in the near term?

It's reasonable to expect some effect over time as loss data accumulates, but a specific premium impact figure isn't available and shouldn't be assumed. The near-term priority is building the data capability that would eventually inform any pricing change.

How should an insurer's marketing or client communications reflect this trend?

Any external communication about this topic should stick to the same disciplined, source-grounded approach used internally — referencing the general pattern from a credible source like Deloitte UK Tech Trends 2026 without overstating specifics that aren't publicly confirmed.

What's the single most useful first step for a UK insurer reading this?

Audit whether robotics or automation exposure is currently capturable in structured form anywhere in your submission and claims systems, and if it isn't, scope a small, focused project to add it before the next renewal cycle.