Why Insurance Companies Can't Ignore AI Robotics in UK Manufacturing Anymore in UK

Direct answer: AI-enabled robotics is scaling fast across UK manufacturing and logistics floors, which means the risk profile insurers underwrite for commercial and industrial clients is changing faster than legacy policy and claims systems can track. Insurance companies that don't modernize their underwriting, claims, and risk-monitoring software now will be pricing risk on outdated assumptions within a year or two. The fix isn't a new insurance product — it's custom software that can ingest, model, and price this new category of operational risk.

Deloitte UK's Tech Trends 2026 report identifies AI-enabled robotics as one of the fastest-scaling categories inside UK smart manufacturing and logistics operations, with adoption moving well past pilot projects into embedded, everyday production use. That's a meaningful shift from where things stood even two years ago, when robotics on UK factory floors was still largely fixed-function automation — arms that welded, packed, or sorted, doing one job the same way every time. AI-enabled robotics is different in kind: machines that adapt to variable inputs, make micro-decisions in real time, and coordinate with other systems and humans on the floor without a person re-programming them for every new task. For insurance companies writing commercial property, casualty, product liability, and workers' compensation policies for UK manufacturers and logistics operators, that shift changes what "risk" actually looks like inside a policyholder's operations. A precise industry-wide figure for how many UK manufacturers have deployed AI robotics isn't publicly available at this granularity, so the honest starting point is the general pattern: adoption is accelerating, it's happening inside your existing book of business, and your underwriting and claims software was mostly built before this category existed.

What's Actually Changing on the Factory Floor

The distinction that matters for insurers is between automation and autonomy. A conventional industrial robot follows a fixed, pre-programmed path — same motion, same tolerances, same failure modes every time, which is exactly the kind of predictable hazard that decades of actuarial data and safety codes were built around. An AI-enabled robot, by contrast, ingests sensor data, adjusts its behavior based on conditions, and in more advanced deployments makes decisions that weren't explicitly programmed by a human — routing a shipment differently, adjusting a grip pressure, flagging or bypassing a quality check. Deloitte's framing of this as a scaling trend rather than an emerging one is the important detail: this isn't speculative future risk. It's live, in production, on lines that UK manufacturers are running today.

Why This Isn't Just an IoT Story

Insurers have already lived through the "connect everything with sensors" wave and built telematics-style products around it — usage-based auto insurance, IoT-monitored commercial property. AI robotics is a different problem because the risk isn't just "more data points to monitor." It's that the system generating the data is itself making decisions, which means liability, causation, and failure analysis after an incident all get harder. If a conventional robot injures a worker, the fault tree is usually mechanical or procedural — a guard was removed, a sensor failed, a lockout wasn't followed. If an AI-enabled robot makes an adaptive decision that leads to an incident, the question of what went wrong, and whose fault it was, may involve a model's training data, a firmware update, a third-party integration, or a decision the machine made autonomously in a way no one anticipated. That's a claims investigation problem insurers' current systems generally aren't built to handle.

The Timeline Problem for Actuarial Models

Actuarial pricing has always depended on enough historical loss data to model frequency and severity with confidence. That's the core tension here: AI-enabled robotics hasn't been deployed at scale long enough for a deep claims history to exist, which means insurers can't simply wait for the data to mature before acting. The manufacturers adopting this technology now are, in effect, generating a new risk category in real time, and the insurers writing their policies are underwriting it whether their systems formally recognize the category or not. Waiting for a clean actuarial signal before updating underwriting logic means several years of policies written against an incomplete picture of the actual risk being carried.

This is a familiar pattern for anyone who has watched how quickly a scaling technology trend moves once it clears the pilot stage — a small number of early deployments becomes a default expectation across an industry within a few underwriting cycles. Deloitte's framing of AI robotics as already scaling, not merely emerging, suggests UK manufacturing is past the early-pilot phase for this specific technology, which shortens the runway insurers have to adapt before it becomes the norm rather than the exception across their manufacturing book.

Why This Specifically Matters for Insurance Companies in the UK

UK insurers underwriting manufacturing and logistics risk sit in an unusual position: the industry they insure is changing faster than the regulatory and actuarial frameworks used to price it. That gap creates real exposure on both sides of the ledger.

On the underwriting side, policies written using pre-AI-robotics risk models are likely mispricing a meaningful and growing share of the book. A manufacturer that installed adaptive AI-driven robotics on one production line looks, on paper, like the same commercial property and liability risk it was two years ago — same SIC code, same square footage, same headcount. But the actual operational risk has shifted: new failure modes, new dependency on software and connectivity, new categories of business interruption if an AI system goes down or misbehaves. Underwriters relying on questionnaires and static risk categories have no reliable way to capture this unless their systems are built to ask for and process it.

On the claims side, the exposure is arguably sharper. When an incident happens on a line using AI-enabled robotics, a UK insurer needs to be able to pull operational logs, sensor histories, and system decision data quickly to determine cause and liability — and increasingly, to differentiate between a policyholder failure and a third-party technology failure (robotics vendor, software provider, integrator) that might trigger subrogation. Insurers whose claims systems are still built around static forms and PDF reports are going to be slow, and slow claims processing in a fast-moving risk category compounds losses and erodes broker and client trust. This is the same underlying dynamic covered in The Real Cost of Building an AI Agent for Your Business — the cost of not building the right internal tooling shows up later, in operational drag, not upfront.

There's also a competitive angle. Brokers placing UK manufacturing and logistics risk are going to gravitate toward insurers who can actually underwrite AI-robotics-heavy operations intelligently — asking the right questions, pricing fairly, settling claims fast — rather than those who either overprice out of caution or underprice out of ignorance. Being the insurer with better software here is a genuine market differentiator, not just an efficiency play.

Consider the two failure modes an insurer can fall into on either side of this gap. Overpricing out of caution — treating any policyholder with visible robotics investment as automatically higher risk without a nuanced model to distinguish adaptive systems from fixed automation — pushes good business toward competitors who've done the work to price it accurately. Underpricing out of ignorance, by contrast, means carrying risk that isn't reflected in premium at all, which shows up later as unexpected loss ratios in exactly the segment of the book that's growing fastest. Neither failure mode is a hypothetical; both are the direct, predictable consequence of underwriting a changing risk category with unchanged tools. The insurers that avoid both outcomes are the ones that treat this as a software and data problem worth solving now, rather than a wait-and-see market development.

It's also worth being honest about scale. Manufacturing and logistics is not a niche corner of the UK commercial insurance market — it's one of the larger sources of commercial property, liability, and workers' compensation premium for insurers active in that space. A risk category scaling this quickly inside a major line of business is not the kind of thing that can be addressed with a footnote in next year's underwriting guidelines. It needs to be addressed structurally, in the systems underwriters and claims adjusters actually use every day.

What Changes in Practice for an Insurer's Systems

This isn't a call to build a new insurance product line called "robotics insurance." For most UK insurers, the more urgent and more tractable problem is upgrading the software that already sits underneath underwriting, risk assessment, and claims — so it can actually see and reason about this category of risk inside policies you're already writing.

Underwriting and Risk Intake

Underwriting workflows and risk questionnaires need fields and logic that capture whether a commercial policyholder is running AI-enabled robotics, at what level of autonomy, on what class of equipment, and with what monitoring and override controls in place. That's a data model and workflow change, not a new product — but it requires software flexible enough to add new risk factors without a multi-quarter development cycle. Static, vendor-locked policy administration systems make this kind of iteration painfully slow; custom-built systems don't.

Claims and Incident Investigation

Claims teams need the ability to ingest structured data from a policyholder's operational systems — sensor logs, robotics vendor incident reports, maintenance records — rather than relying purely on narrative claim forms. Building integrations that can pull this data reliably, normalize it, and surface it to adjusters in a usable form is a genuine software engineering problem, and it's exactly the kind of work that benefits from being purpose-built rather than bolted onto an off-the-shelf claims platform.

Risk Monitoring Dashboards

Some UK insurers are starting to offer or explore ongoing risk-monitoring relationships with larger manufacturing clients — dashboards that give both the insurer and the policyholder visibility into operational risk signals over time, rather than a once-a-year underwriting snapshot. Getting this right matters at the interface level too, not just the backend: a monitoring dashboard that buries critical risk signals under distracting motion or unclear visual hierarchy defeats its own purpose, which is worth keeping in mind — the reasoning in Motion Design in UI: When Animation Helps and When It Hurts applies directly to how alert states and risk indicators should (and shouldn't) animate on a live dashboard used by underwriters and risk engineers under time pressure.

Infrastructure and Vendor Dependency

It's also worth noting that the AI systems now embedded in manufacturing robotics run on real infrastructure with real constraints — a point underscored by the parallel discussion in Why Big Tech Is Betting Billions on Nuclear Power for AI Data Centers, which covers the scale of compute and energy investment now backing AI systems generally. For insurers, the practical takeaway isn't about power infrastructure directly — it's a reminder that the AI robotics your policyholders depend on has upstream dependencies (compute availability, vendor reliability, software update cycles) that are themselves a form of operational risk worth understanding and, eventually, asking about in underwriting.

What Should an Insurer Actually Do About It

The starting point isn't a wholesale platform replacement. It's identifying the specific points in your underwriting and claims workflow where AI-robotics-related risk is currently invisible to your systems, and building targeted software to close those gaps. That usually means:

• Auditing current underwriting questionnaires and risk models for manufacturing and logistics policies to find where AI-robotics exposure is currently unaddressed.
• Building or extending claims intake systems to accept structured operational data from policyholders' robotics and monitoring systems.
• Creating internal tools that let underwriters flag and track AI-robotics exposure across the existing book, so leadership can see aggregate exposure rather than discovering it policy by policy.
• Piloting a risk-monitoring capability with a small set of manufacturing clients before scaling it across the portfolio.

None of this requires predicting exactly how AI robotics will evolve over the next decade. It requires building software flexible enough to absorb new risk factors as they emerge, rather than locking underwriting and claims logic into assumptions that were reasonable five years ago but aren't anymore. That flexibility is itself a design choice — a data model with rigid, hardcoded risk categories will need to be rebuilt every time the underlying technology shifts again, while one designed with extensible risk-factor fields from the outset can absorb the next wave of change with configuration rather than a rewrite. Getting that architecture right the first time is worth the extra care during scoping, because it determines how expensive every future update turns out to be.

This is squarely the kind of work suited to Custom Software Development rather than configuring an off-the-shelf insurtech product — because the exact risk factors, data sources, and workflow integrations differ by insurer, book of business, and existing policy administration stack. A system built specifically around your underwriting logic and your claims process will surface this risk category faster and more accurately than trying to force a generic vendor tool to fit.

There's a practical reason generic tooling struggles here specifically. Off-the-shelf insurtech products are generally built to serve the broadest possible customer base, which means their data models and workflows are deliberately generic — they capture what's common across most insurers, not what's specific to your book, your policy language, or the particular robotics vendors your manufacturing clients happen to use. AI-robotics exposure, by contrast, is exactly the kind of risk factor where the specifics matter: which vendor, what level of autonomy, what monitoring controls, what integration with existing plant systems. A generic product either ignores these specifics entirely or forces you into a rigid taxonomy that doesn't match how your underwriters actually think about the risk. Custom software avoids that trade-off because it's built around your actual workflow from the start, not adapted from someone else's.

Pricing Context: What This Kind of Work Typically Falls Under

Most UK insurers approaching this incrementally — rather than as a single large transformation program — land in one of three tiers depending on scope:

Tier	Typical scope for this work
Essential ($1,000)	A focused build: updated underwriting questionnaire logic and risk-flagging fields for AI-robotics exposure in one policy line.
Growth ($2,000)	Underwriting updates plus a claims-intake integration for structured operational data from a defined set of policyholder systems.
Enterprise ($4,000+)	Full risk-monitoring dashboard, multi-line underwriting integration, and claims data pipeline across the manufacturing and logistics book.

These are starting reference points for scoping conversations, not fixed quotes — actual cost depends on your existing policy administration stack, how many data sources need integration, and how many policy lines are in scope.

Key Takeaways

• AI-enabled robotics is scaling rapidly across UK manufacturing and logistics per Deloitte UK Tech Trends 2026 — this is a present-tense operational shift inside your existing book, not a future scenario.
• The core insurer exposure is that underwriting and claims systems built for fixed-automation risk don't capture the adaptive, decision-making nature of AI robotics.
• Claims investigation gets harder when an AI system's autonomous decision contributes to an incident — insurers need software that can ingest and analyze operational and sensor data quickly.
• Start with an audit of current underwriting questionnaires and claims intake for manufacturing and logistics lines to find where this risk is currently invisible.
• Purpose-built software beats generic insurtech tooling here because the right risk factors and data integrations are specific to your book and systems.
• Treat this as incremental, scoped work — Essential, Growth, or Enterprise tier — rather than a single large platform overhaul.

UK manufacturing isn't waiting for insurers to catch up, and the insurers who build the underwriting and claims software to see this risk clearly will price it better and settle claims faster than those who don't. 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 manufacturing context?

It refers to robotic systems that use sensors, machine learning models, or adaptive control logic to adjust their actions based on real-time conditions, rather than following a single fixed, pre-programmed path. This includes robots that vary grip pressure, routing, or quality-check decisions based on live input rather than a static script.

How is this different from the industrial automation insurers have covered for decades?

Traditional automation is deterministic — the same input always produces the same action, which makes failure modes predictable and well understood actuarially. AI-enabled robotics introduces adaptive decision-making, which means failure modes can be novel, harder to predict, and harder to trace back to a single root cause after an incident.

Why is Deloitte's UK Tech Trends 2026 report relevant to insurance underwriting specifically?

The report identifies AI-enabled robotics as a rapidly scaling category inside UK smart manufacturing and logistics, which is a strong signal that this risk category is moving from a small share of the market to a mainstream one. For insurers, that timing matters for how urgently underwriting models need to adapt.

Does this affect all commercial insurance lines or just specific ones?

It's most directly relevant to commercial property, general and product liability, workers' compensation, and business interruption lines for manufacturing and logistics clients. Cyber liability can also be affected where AI robotics systems are network-connected and create new attack surfaces.

Is this only a concern for insurers with large manufacturing books?

No — even insurers with a smaller share of manufacturing and logistics policyholders are exposed if any of those policyholders have adopted AI robotics, because the risk shift happens at the individual policyholder level, not just in aggregate. A single mispriced large account can matter more than a diffuse trend across many small ones.

What specific new risks does AI robotics introduce for a manufacturer?

New risks include software or model failure leading to physical incidents, liability disputes over whether a robot's autonomous decision or a human process caused an incident, business interruption from AI system downtime, and third-party vendor liability questions when a robotics platform or its software is involved in a claim.

How would a claims team investigate an incident involving an AI-enabled robot differently than a standard one?

Instead of relying mainly on incident reports and physical inspection, adjusters would need access to sensor logs, decision or model output data, firmware and software version histories, and maintenance records, then correlate all of that with the physical facts of the incident. This requires software that can ingest and normalize varied data formats from different robotics vendors.

Can insurers just rely on manufacturers to self-report AI robotics use during underwriting?

Self-reporting through a questionnaire is a starting point but is unreliable on its own, since policyholders may not view every automation upgrade as something worth flagging, and staff filling out forms may not fully understand the distinction between fixed and adaptive robotics. Building better intake logic and prompts, and potentially direct data integrations, closes that gap.

What's the risk of doing nothing and continuing to underwrite with existing models?

The risk is progressive mispricing across the book as more policyholders adopt AI robotics without insurers capturing it, plus slower and more contentious claims when incidents do occur because the necessary data and workflows aren't in place. Over time this shows up as both underwriting losses and reputational damage with brokers.

Is this trend UK-specific or is it happening globally?

The trend toward AI-enabled robotics in manufacturing is global, but the Deloitte UK Tech Trends 2026 report specifically highlights the pace of scaling within UK smart manufacturing and logistics, which is the anchor point for this piece. UK insurers should treat their domestic manufacturing book as an immediate priority regardless of global patterns.

How quickly should an insurer expect to see this show up in claims data?

There's no publicly available precise timeline for when AI-robotics-related claims will become a distinguishable category in industry claims data, so the more useful approach is to build the intake and tagging capability now, before volume makes it urgent. Waiting for clear claims-data signal means building reactively rather than proactively.

What role does a broker play in surfacing this risk to insurers?

Brokers placing manufacturing and logistics risk are often the first to see which clients have adopted AI robotics, since they're closer to the day-to-day operations of policyholders. Insurers that build clear channels and questionnaire prompts for brokers to flag this information will get better underwriting data sooner.

Does adopting AI robotics generally increase or decrease a manufacturer's risk profile?

It can go either way — AI robotics can reduce certain risks (fewer repetitive-motion injuries, more consistent quality control) while introducing others (software failure, novel liability questions, business interruption from system outages). The honest position is that the risk profile changes in composition, not simply up or down, which is exactly why static risk categories fall short.

What is "Custom Software Development" in this context, and why not just buy an insurtech product?

Custom software development means building systems specifically designed around an insurer's existing underwriting logic, claims workflows, and policy administration stack, rather than adapting a generic third-party tool. Off-the-shelf insurtech products rarely account for the specific data sources and risk factors relevant to AI-robotics exposure in a given insurer's manufacturing book.

How long does it typically take to build an underwriting update like this?

Scope-dependent, but a focused underwriting questionnaire and risk-flagging update for one policy line is typically the fastest to deliver, while a full claims data pipeline and multi-line integration takes considerably longer. Starting with a narrow, well-defined pilot is the most practical path for most insurers.

What would an Essential-tier engagement look like in practice?

At the Essential tier, the focus is usually a single, well-scoped update — such as adding AI-robotics risk fields and flagging logic to one underwriting questionnaire — rather than a broad platform change. It's a reasonable starting point for insurers wanting to test the approach before committing to larger scope.

What would a Growth-tier engagement add on top of that?

Growth-tier work typically adds a claims-intake integration that can accept structured operational data from a defined set of policyholder systems, in addition to the underwriting updates. This starts to close the claims-investigation gap, not just the underwriting one.

What does Enterprise-tier scope generally cover?

Enterprise-tier engagements generally span a full risk-monitoring dashboard, underwriting integration across multiple policy lines, and a claims data pipeline covering the manufacturing and logistics book as a whole. This suits insurers treating AI-robotics exposure as a portfolio-wide priority rather than a single-line pilot.

Should insurers build a new AI-robotics-specific insurance product?

Not necessarily as a first step — for most UK insurers, updating existing underwriting and claims systems to properly capture this risk inside current policy lines is the more urgent and tractable priority. A dedicated product can follow once the underlying data and risk models are solid.

What data sources would a claims-intake integration need to connect to?

Depending on the policyholder, this could include robotics vendor incident and maintenance logs, factory floor sensor systems, and internal quality-control or safety-management software. The specific integrations depend heavily on which robotics platforms and monitoring tools a given policyholder uses.

How does this connect to cyber liability underwriting?

AI-enabled robotics is typically network-connected and software-dependent, which means it can introduce new cyber attack surfaces — a compromised robotics control system could cause physical incidents as well as data exposure. Underwriting AI-robotics exposure and cyber exposure increasingly need to be considered together rather than in separate silos.

What happens if an AI robotics vendor's software is at fault in an incident, not the policyholder?

This is exactly the kind of scenario where claims data quality matters most, because determining whether liability sits with the policyholder or a third-party vendor requires access to system logs and decision data that a standard incident report won't capture. Better claims software makes it possible to pursue subrogation against a vendor when warranted, rather than absorbing the full loss.

Is this relevant to smaller, regional UK manufacturers or mainly large ones?

It's relevant across the size spectrum — AI-enabled robotics has become accessible enough that mid-sized and even smaller UK manufacturers are adopting it, not just large industrial players. Insurers shouldn't assume this exposure is concentrated only in their largest accounts.

How should an insurer prioritize which policy lines to update first?

A reasonable approach is to start with the policy lines and client segments where manufacturing and logistics exposure is highest and where claims frequency or severity data already suggests elevated risk. That focuses the initial software investment where it will have the clearest impact.

What internal teams need to be involved in a project like this?

Underwriting, claims, and IT/engineering all need to be involved, since the change touches risk questionnaires, claims workflows, and the underlying systems that support both. Actuarial input is also valuable for translating new risk categories into pricing adjustments over time.

Can this work be done incrementally, or does it require a full system overhaul?

It can and generally should be done incrementally — starting with a narrow underwriting or claims update and expanding based on what's learned, rather than attempting a full platform overhaul upfront. This reduces risk and lets the insurer validate the approach before committing larger budget.

What's the risk of moving too slowly on this compared to competitors?

Slower-moving insurers risk mispricing a growing share of their manufacturing book while faster-moving competitors and brokers steer well-informed, well-priced business elsewhere. Claims handling speed and accuracy also become a competitive differentiator as AI-robotics-related claims become more common.

Does this trend affect commercial insurance brokers as well as insurers?

Yes — brokers placing manufacturing and logistics risk benefit from understanding which insurers have modernized their underwriting for AI-robotics exposure, since that affects both pricing accuracy and claims experience for their clients. Brokers who understand this shift can better advise clients on coverage gaps.

What should an insurer ask a manufacturing policyholder to understand their AI robotics exposure?

Useful questions cover what level of autonomy the robotics systems operate at, what monitoring and human-override controls exist, whether the systems are network-connected, and what vendor support and update processes are in place. These questions need to be built into underwriting workflows rather than asked ad hoc.

How does business interruption coverage relate to AI robotics risk?

If a manufacturer's production line depends on AI-enabled robotics and that system fails or is taken offline for a software issue, the resulting downtime is a business interruption exposure that traditional BI models may not fully account for. Insurers need to understand how central these systems are to a policyholder's actual production capacity.

Is regulatory guidance available yet on how UK insurers should handle AI robotics risk?

There isn't a single, settled regulatory framework specifically addressing AI-robotics risk in commercial insurance underwriting as of now, which means insurers are largely working from general principles and their own risk assessment rather than a prescribed standard. That makes internal capability-building even more important in the near term.

What's the difference between a risk-monitoring dashboard and a standard underwriting review?

A standard underwriting review is a point-in-time snapshot, typically annual, while a risk-monitoring dashboard offers ongoing visibility into operational risk signals over time. The latter is more resource-intensive to build but gives both insurer and policyholder a clearer, more current picture of risk.

Should risk-monitoring dashboards be offered to all policyholders or select ones?

Starting with a pilot group of larger manufacturing clients is a more practical approach than a broad rollout, since it lets the insurer refine the data model, integrations, and user experience before scaling. Wider rollout can follow once the approach is proven.

How does UI and dashboard design factor into this kind of risk-monitoring tool?

Risk-monitoring dashboards are only useful if underwriters and risk engineers can quickly identify what matters under time pressure, which means visual hierarchy and how alerts or changes are presented directly affects decision quality. Poorly designed motion or cluttered layouts can bury the exact signals the dashboard exists to surface.

What's a realistic first project for an insurer just starting to address this?

A realistic first project is usually a targeted underwriting questionnaire update for one manufacturing-heavy policy line, paired with basic risk-flagging so leadership can start seeing exposure across the book. This is scoped enough to deliver quickly while establishing a foundation for larger work later.

How does this connect to the broader theme of AI infrastructure dependency?

AI-enabled robotics on a factory floor ultimately depends on upstream infrastructure — compute, connectivity, vendor software reliability — which is itself a form of operational risk worth understanding, even if it's not the primary underwriting focus. Recognizing this dependency chain helps insurers ask better questions during underwriting.

Are there existing insurance products for robotics that insurers could just adopt?

Some robotics-specific insurance products exist in the broader market, but for most UK insurers the more immediate and tractable priority is updating existing commercial lines to properly capture AI-robotics exposure, rather than launching an entirely new product. Product innovation can follow once underlying risk models and data capability are solid.

What's the cost of not modernizing underwriting and claims systems for this risk?

The primary costs are progressive underwriting mispricing, slower and more contentious claims handling, and losing well-informed business to competitors with better-adapted systems. These costs tend to compound quietly rather than showing up as a single obvious loss event.

How should an insurer think about the timeline for this kind of software investment?

Given that AI robotics adoption in UK manufacturing is described as scaling rapidly rather than nascent, treating this as a near-term priority — starting within the current planning cycle — is more appropriate than treating it as a longer-term strategic consideration. Incremental delivery means value can start showing up quickly rather than only after a long build.

Does this apply to logistics and warehousing risk as well as manufacturing?

Yes — the Deloitte UK Tech Trends 2026 report specifically groups AI-enabled robotics scaling across both smart manufacturing and logistics operations, and the same underwriting and claims considerations apply to warehousing, fulfillment, and distribution risk. Insurers with logistics-heavy books should treat this with the same urgency as those focused on manufacturing.

What kind of team is needed to build this software internally versus with a partner?

Building this internally requires underwriting and claims domain expertise combined with software engineering capacity that many insurers' internal IT teams aren't resourced for on top of existing priorities. A dedicated custom software partner can move faster on a well-scoped project without pulling internal teams off other work.

How does data privacy factor into pulling operational data from policyholders' systems?

Any integration that pulls sensor or operational data from a policyholder's systems needs clear data-sharing agreements and appropriate handling practices, particularly where that data could reveal proprietary process information. This needs to be designed into the integration from the start, not added afterward.

What's the biggest mistake an insurer could make in responding to this trend?

The biggest mistake is treating this as a future problem to revisit later, when the underlying shift — as described in Deloitte's UK Tech Trends 2026 report — is already happening inside current books of business. Waiting for clearer signals in claims data means responding after mispricing has already occurred.

Can existing policy administration systems be extended, or do they need replacing?

In most cases, existing policy administration systems can be extended with targeted custom modules for risk flagging and claims data intake rather than replaced outright, which is faster and less disruptive. Full replacement is rarely necessary just to address this specific risk category.

How should success be measured after implementing these changes?

Reasonable measures include the percentage of manufacturing and logistics policies with AI-robotics exposure properly flagged, average claims investigation time for incidents involving these systems, and underwriter confidence in pricing accuracy for this segment. These metrics can be tracked incrementally as each phase of the software rollout completes.

Does this affect reinsurance considerations as well?

As AI-robotics exposure becomes a more distinct risk category, reinsurers will increasingly want visibility into how primary insurers are identifying and pricing it, which makes having clear internal data and flagging systems valuable for reinsurance conversations as well. This is a secondary but real reason to build this capability now.

What's a reasonable way to pilot this without committing to a large budget?

Starting at the Essential tier with a single policy line's underwriting update is the most budget-conscious way to test the approach, learn what data is actually available from policyholders, and refine the model before expanding. This avoids over-investing before the specific data and workflow needs are well understood.

How does this trend interact with existing workers' compensation risk models?

AI-enabled robotics can change the nature of workplace injury risk — potentially reducing some repetitive strain injuries while introducing new interaction risks between adaptive machines and human workers on the same floor. Workers' compensation underwriting models built purely around historical automation-related injury data may not capture this shift accurately.

How does this trend affect product liability underwriting for equipment manufacturers versus manufacturers using robotics?

Product liability exposure differs depending on whether a policyholder manufactures AI-enabled robotics or simply uses it in production, since the former carries direct liability for the robot's design and decision logic while the latter's exposure is more about operational integration and oversight. Underwriting questionnaires need to distinguish clearly between these two roles rather than treating "robotics involvement" as a single undifferentiated category.

Who should an insurance company talk to about starting this kind of project?

Insurers ready to start should scope a specific, narrow pilot — one policy line or one claims workflow gap — and bring that scope to a software partner experienced in building for regulated, data-sensitive environments like insurance. Book a meeting with our team to talk through what a first phase could look like for your book.