Artificial intelligence for insurance marketing

AI offers a smarter way forward. McKinsey estimates that AI and advanced analytics could unlock up to $1.1 trillion of annual value for insurance by improving pricing, distribution and customer engagement. And we’re already seeing results: insurers using AI are cutting CAC, improving lead quality and increasing lifetime value.

In this guide, we show how AI is transforming insurance marketing — especially across digital channels and website optimisation. We draw lessons from other industries, highlight proven case studies (including Fresh Egg’s own CRO wins), and set out a practical roadmap to help insurers acquire, convert and retain more customers.

Paid media and digital advertising

Paid media is one of the most fertile testing grounds for AI because results are measurable and immediate. Platforms like Google Ads and Meta have long used machine learning to optimise bids and creative delivery — but leading insurers are now going further, developing their own AI-driven capabilities to push beyond what the platforms provide.

Programmatic ad buying

AI removes the need for manual targeting and bid management. Algorithms analyse thousands of data points in real time to place the right ad in front of the right prospect at the lowest possible cost. This is especially valuable in competitive insurance categories where CPCs are high.

For example, one insurance client achieved a 50%+ reduction in CPA by applying AI-driven supply-side intelligence to optimise premium inventory and refine audience targeting. And this isn’t unusual: programmatic advertising now makes up more than 90% of global digital display ad spend (Statista) — with AI powering that efficiency.

AI generated creative and Dynamic Creative Optimisation (DCO)

Producing enough fresh creative to keep campaigns performing is a constant challenge in insurance marketing. AI-driven tools and dynamic creative optimisation (DCO) are transforming how brands meet this demand.

Our partner agency Kinase uses platforms such as Flow to generate high-quality video creative. A single ad concept can now be adapted into multiple audience-specific versions in minutes by adjusting visuals, captions, and calls to action. This approach has delivered a 14% higher click-through rate (CTR) and 6% higher conversion rate, while enabling the team to produce five times more creative variants in the same timeframe. That additional volume helps campaigns avoid fatigue and sustain performance.

Once creative is produced, platforms like Google Marketing Platform and Adobe Target use AI to automatically test combinations of copy, imagery, video, and calls to action — serving the best-performing variation to each audience segment. For insurers with multiple products and customer personas, feeding fresh creative into these AI-led optimisation loops can unlock meaningful gains in both CTR and conversion.

AI-first campaign types

Google’s Performance Max and Meta’s Advantage+ paved the way for AI-led campaigns, delivering ads across varied inventory based on set parameters and KPIs.

Now Google has gone further with AI Max for Search — adding an AI-driven layer to traditional search campaigns. It can generate ad copy, identify search terms, and select landing URLs, all aimed at optimising performance against your chosen KPIs.

Intelligent lead scoring

AI takes lead scoring far beyond static rules like “+10 points for opening an email.” Modern models analyse hundreds of demographic, behavioural, and contextual signals to assign a real-time probability of conversion and expected value to each lead.

A powerful extension of this is lookalike modelling. By enriching first-party customer data with third-party signals, AI can uncover entirely new segments that mirror high-value customers. This is particularly effective for niche or specialist products, where traditional targeting often wastes budget.

Case study: Kinase and Purplebricks

A standout example comes from Kinase’s work with Purplebricks. Faced with rising lead acquisition costs and uneven lead quality, Kinase built a bespoke model that combined field data (like postcode clusters, property value, and agent availability) with smart bidding automation in SA360. By weighting geographic and operational variables, the model optimised bidding not just for volume, but for value.

The results were compelling:

This case shows how intelligent lead scoring — powered by AI and fuelled by real-time data integration — doesn’t just make media more efficient; it makes every downstream step more effective. While the context here is property, the principle translates well to insurance, where regional agent availability, risk appetite, or even quote volume constraints could shape scoring logic.

CRM, email and marketing automation

While intelligent lead scoring optimises who insurers target, AI also shapes what happens next — helping brands engage, nurture, and convert leads more effectively through their CRM and automation platforms.

Predictive scoring and behavioural journeys

AI helps insurers identify high-value leads and trigger the right journeys at the right time. Predictive models evaluate hundreds of attributes — from demographics to browsing behaviour — to estimate each lead’s likelihood to convert.

This score shapes the strategy: high-propensity leads can be prioritised for call-back triggers or immediate sales outreach, while lower-scoring leads move into longer-term nurture tracks.

Once scored, platforms like Salesforce Marketing Cloud and HubSpot adapt sequencing across email, SMS, and app channels. They respond dynamically to behaviours such as an incomplete quote or a re-engagement after dormancy — adjusting cadence, channel, and content automatically.

In one example, IntelliArts helped an insurer cut 6% of low-quality leads and increase profits by 1.5% in just months by prioritising sales follow-up based on AI-driven scoring. This type of predictive and behavioural orchestration delivers both precision and responsiveness — reducing waste and driving conversion.

Churn prediction

AI can spot customers at risk of lapsing by analysing behavioural and transactional signals. For example, a policyholder who stops engaging with communications or frequently calls about pricing may be flagged as high-risk.

Predictive models then trigger targeted retention campaigns — loyalty incentives, renewal offers, or proactive outreach — before churn occurs. This approach helps increase retention and protect long-term customer value.

Subject lines, copy and delivery timing

Natural language models can quickly generate and test multiple subject line and message variations, ensuring content resonates with each audience segment. Machine learning also analyses past engagement data to personalise send times at the individual level, increasing open and click through rates.

These techniques deliver measurable uplift. Case studies show that AI assisted subject line optimisation and send timing alone can drive double-digit increases in open rates. Insurers can often integrate these capabilities directly into existing platforms like Salesforce Marketing Cloud, Adobe Campaign, or HubSpot to scale results without additional resource strain.

Natural language models can generate and test multiple subject line and message variations, ensuring content resonates with each audience segment. Machine learning also analyses past engagement data to personalise send times at the individual level, boosting open and click-through rates.

Content marketing and SEO

For insurers with complex, information-heavy websites, keeping content visible in both search engines — and now in AI-driven search interfaces — is a moving target. AI is reshaping how SEO is prioritised, with innovations that reach far beyond traditional keyword research.

AI-assisted SEO: beyond keyword clustering

Traditional SEO tools like SEMrush and Ahrefs now use AI for advanced keyword clustering, content scoring, and topic modelling. But the most forward-thinking insurers are going further — using AI to reverse engineer search engine algorithms at scale.

• Bulk SERP scraping and pattern analysis – AI models can analyse thousands of ranking pages across insurance and related categories to identify the common traits of top-performing content: average word count, backlink patterns, topical breadth, and technical signals. This enables teams to design content that better aligns with what search engines reward. Visual idea: mosaic of lots of search engine result screenshots to do with ‘car insurance’ (for example)

• Technical SEO insights – AI can crawl sites and surface issues faster than manual audits. Tools like Screaming Frog, enhanced with machine learning, can detect themes that perform well, diagnose recurring site errors, and even suggest on-page improvements such as meta titles and descriptions.

Optimising for AI driven search results and LLMs

As search engines roll out AI-powered summaries (e.g. Google’s Search Generative Experience (SGE) and Bing Copilot), the SEO landscape is shifting. It’s no longer enough to rank in the “10 blue links” — insurers must ensure their content is visible to large language models (LLMs) that generate answers directly on the search results page.

• Structuring content for AI visibility – AI search relies heavily on structured data and authoritative third-party references. Insurers can boost visibility by adding schema markup (e.g. FAQ, HowTo, Product schemas) and by publishing well-sourced, factual content more likely to be pulled into summaries.

• Entity-based optimisation – LLMs like ChatGPT and Gemini build knowledge graphs around entities (e.g. “auto insurance,” “life insurance quotes”). Insurers can map entity relationships and ensure their brand is semantically linked to core topics, with breadth as well as depth to improve coverage.

• Proactive LLM optimisation – Advanced SEO teams are even fine-tuning open-source LLMs on their own industry content to understand how models prioritise sources, exposing gaps they can close.

Early research from BrightEdge shows that AI Overviews in Google are already diverting clicks from traditional listings — with brands optimising structured content and FAQ formats more likely to be cited as sources.

Competitive intelligence powered by AI

AI is raising the bar on competitor analysis:

• Content gap analysis – Tools like MarketMuse and Clearscope use machine learning to identify topical gaps between your content and competitors’. This helps insurers create more comprehensive content that covers all relevant subtopics.

• Backlink intelligence – AI can analyse competitor backlink profiles at scale, revealing which domains and content types drive authority. This insight enables smarter citation campaigns and highlights opportunities to emulate — or surpass — competitor strategies.

Conversational chatbots and content activation

AI-powered chatbots and virtual assistants are becoming a core part of the content marketing stack. NLP-driven chatbots allow insurers to surface content dynamically, tailoring responses to user questions.

For example, Allianz Benelux launched a Landbot chatbot that delivered 90% positive feedback while reducing support volumes. These tools don’t just provide service — they guide customers toward helpful content that educates and nudges them closer to a quote.

Website optimisation with AI

For most insurers, the website is the nerve centre of the digital customer journey — where prospects compare products, request quotes, and decide whether to convert. Yet the cost of driving traffic keeps rising: ContentSquare’s Digital Experience Benchmark found cost per visit increased ~9% year on year, making every session more valuable and intensifying ROI pressure on conversion optimisation.

AI is changing how insurers approach this challenge. By combining real-time personalisation, rapid experimentation, and predictive analytics, teams can adapt experiences to each visitor’s intent, reduce funnel friction, and compound performance gains over time.

Real time personalisation: from static pages to dynamic journeys

Modern personalisation engines (e.g. Webtrends Optimize, Optimizely, Fresh Relevance) allow websites to tailor messaging, CTAs, and offers using first-party and behavioural data. The business case is clear: McKinsey research shows personalisation can drive 10–15% revenue uplift (ranging from 5–25% depending on execution).

In financial services, Synchrony used Dynamic Yield to run personalisation and UX tests that increased credit card application conversions by 7% — proving these tools can improve regulated, high-stakes forms such as insurance quote flows. Similarly, UK motor insurer Geoffrey Insurance introduced AI-powered personalised videos, boosting customer retention by 12%.

Experimentation at scale: faster learning, compounding lift

AI is turning website experimentation from a slow, manual process into a faster, smarter tool for conversion rate optimisation (CRO). Traditional A/B testing can take weeks and typically tests only one or two variables at a time. AI-assisted experimentation changes this by auto-generating variants, prioritising hypotheses, and dynamically allocating traffic to the best-performing experiences.

This shift helps insurers move beyond incremental gains to achieve optimisation at scale. High-volume testing leaders like Booking.com have long shown how continuous experimentation drives sustained growth — and AI now makes this approach accessible without heavy engineering investment.

In one insurance-specific example, German broker CLARK used AI-generated banner copy in an A/B test and achieved a 15.8% uplift in clicks compared to human-written control content. This shows how AI can surface the highest-impact ideas and put them in front of customers faster.

At Fresh Egg, we’re developing AI-assisted tools to audit websites and accelerate test throughput. These tools identify friction points in quote forms and other high-value pages, draft evidence-based hypotheses, and generate on-brand test variants in minutes. When combined with robust CRO processes, insurers can run more effective experiments without overloading teams.

The result is a faster learning cycle: more experiments launched, more winners identified, and more revenue recaptured from the same traffic. As acquisition costs rise and customer expectations grow, AI-enhanced experimentation should be a strategic priority for insurers seeking stronger conversion performance.

Predictive analytics & proactive interventions: catching abandonment before it happens

AI can detect drop-off risk before it occurs by analysing micro-behaviours such as cursor movement, scroll depth, and hesitations in form fields. This enables timely interventions — like a contextual chatbot prompt or a simplified pathway to complete the quote.

This isn’t theory: peer-reviewed studies confirm that cursor signals predict attention and engagement, which is why behavioural analytics tools feed these features into models. Platforms like FullStory now combine rich interaction data with data warehouses and machine learning to support predictive analysis and intervention.

Fresh Egg insurance CRO proofs (site optimisation at work)

Fresh Egg’s own insurance and FS programmes demonstrate the tangible impact of structured CRO underpinned by data and testing:

Offline & integrated channels

AI isn’t limited to online interactions — insurers are now using it to enhance offline touchpoints and link them seamlessly with digital campaigns.

AI-powered call centre optimisation 

Real-time speech analytics and agent guidance can transform call handling. In one healthcare example from Inizio Engage, AI-based call routing reduced manual triage by 50% and significantly improved efficiency. These platforms also boost first-contact resolution, reduce average handle times, and automate quality assurance across calls.

AI-enhanced direct mail

Direct mail remains effective for reaching older demographics or high-value prospects. Platforms like Lob and Postie use machine learning to score audiences and trigger personalised, event-driven mailings. This reduces wasted spend and increases response rates by focusing on those most likely to convert — while integrating seamlessly with digital journeys such as quote abandonment.

Learning from Retail and E-commerce

Retail has been using AI to deliver personalisation at scale for over a decade. Amazon’s recommendation engine is credited with driving up to 35% of total revenue by suggesting products customers are most likely to buy. The same concept applies in insurance: a customer shopping for car insurance could be nudged to bundle home or pet cover.

E-commerce brands are also leading the way in AI-driven content production and campaign speed. Zalando, for example, uses AI to auto-generate campaign visuals and copy, cutting production timelines by 90% while reducing costs. For insurers, this approach could be applied to personalised landing pages, policy documents, or quote follow-up campaigns — freeing teams from manual workflows and boosting agility.

Learning from fintech and challenger banks

Fintechs and challenger banks are ahead of insurers in AI adoption, particularly in onboarding and lifecycle communications. Brands like Monzo and Revolut use machine learning to send tailored nudges at key moments in the journey — boosting activation and retention. Insurers could apply the same approach to remind prospects about incomplete quotes, highlight policy benefits, or pre-empt churn risk.

Another proven playbook is risk-based pricing and credit scoring. Banks have long used predictive models to segment customers by creditworthiness. Insurers can mirror this by predicting lead quality, focusing marketing spend on prospects with the highest conversion potential and lifetime value. In one case, IntelliArts found that predictive lead scoring reduced low-quality leads by 6% and lifted profit by 1.5% in just months.

Learning from travel and hospitality

Travel and hospitality is one of the most data-driven industries, with dynamic pricing as a core capability. Airlines like Delta and hotel groups such as Marriott and Hilton use AI-powered revenue management systems to adjust fares and room rates in real time. Insurers could apply the same models to create usage-based products or micro-policies — for example, offering lower auto premiums to drivers who travel fewer miles.

Travel brands also lead in multi-channel orchestration. A customer browsing a flight on Expedia who doesn’t book may see a retargeted ad within hours and receive an email reminder soon after. The same approach works in insurance: prospects who abandon a quote can be automatically prioritised for call-centre follow-up, guided by AI insights into the likely cause of drop-off.

Advanced customer acquisition: triggering on life events

AI-driven predictive modelling helps insurers identify customers at pivotal life stages — such as marriage or buying a home — when insurance needs are at their peak. By combining structured and unstructured data (property transactions, social signals, spending patterns), life-event models can significantly improve marketing efficiency.

In financial services, one predictive life-event model delivered a 5× increase in accurately identifying customers experiencing major life changes. Insurers can use similar triggers to reach prospects with personalised offers at exactly the right time.

Conversion optimisation beyond the website

AI-powered proposal builders are helping insurers simulate cost–benefit trade-offs in real time, allowing customers to explore scenarios and select the right level of cover. Some firms report that AI-driven proposal generation enables them to close deals up to twice as fast, while maintaining consistent formatting and branding. This speed and accuracy reduce errors, streamline agent workflows, and keep prospects engaged at key decision points.

At the same time, next-best-action (NBA) algorithms are being integrated into CRM systems and agent workflows. These models recommend the most relevant upsell, cross-sell, or incentive based on each customer’s profile and behaviour. Already common in banking, NBA is now showing similar benefits in insurance — driving higher mid-funnel conversion rates and increasing average policy value.

Product & offer personalisation: behaviour driven premiums

AI-enabled usage-based insurance (UBI) lets insurers personalise premiums based on real driving behaviour. Telematics programmes use devices or apps to monitor mileage, braking, and speed — rewarding safer drivers with lower premiums compared to standard policies.

A LexisNexis survey of UK motorists found that 8% would take UBI policies today, scaling to 12.8 million drivers if discount benefits were clearer. AI-powered telematics models like these improve fairness, retention, and cross-sell opportunities, while building trust through transparent, behaviour-linked pricing.

Voice & Conversational AI

AI-driven chatbots and voice assistants are becoming more sophisticated, capable of handling full quote journeys, answering complex product questions, and even completing sales.

For example, ServisBOT’s Quote-to-Sale chatbot helped AA Ireland achieve an 11% uplift in quote-to-policy conversion immediately after launch. Broader industry research suggests well-deployed conversational AI can drive up to a 20% increase in policy conversions by providing real-time support during decision-making.

Core metrics and causal targeting

Insurers still rely on foundational metrics — customer acquisition cost (CAC), lifetime value (LTV), return on ad spend (ROAS), churn rate, and incremental lift — but AI strengthens how these are tracked and interpreted.

Incremental lift is especially powerful, as AI models determine how many conversions were genuinely driven by a campaign versus those that would have happened anyway. This moves beyond simple response rates and reveals true marketing impact.

A key technique here is uplift modelling, which segments audiences into:

• Persuadables – convert only if targeted

• Sure Things – would convert anyway

• Lost Causes – won’t convert regardless

• Do Not Disturbs – negatively influenced by targeting

By focusing only on persuadables, insurers can eliminate wasted spend and increase ROI. In financial services, Intuit used uplift modelling to improve retention targeting, cutting waste and boosting ROI. In insurance, StatCore applied the same approach to historic campaign data for a UK general insurer — achieving a 68% reduction in CPA by targeting only those with the highest causal response potential.

Research supports this impact at scale: a Springer study on insurance retention found uplift modelling could deliver 80–110% sales increases while reducing offer volume by up to 80%, compared to standard supervised learning.

AI also advances multi-touch attribution (MTA), which is vital in complex, multi-session quote journeys. Techniques such as Shapley value and recurrent neural networks (RNNs) help distribute credit fairly across touchpoints based on actual influence. A practical overview of these methods and their application to marketing measurement using deep learning is outlined by Strong.io

Data infrastructure

Effective AI marketing starts with clean, connected data. Many brands are working toward a unified customer record — a single source of truth that brings together intelligence from quote engines, websites, CRM systems, and call centres to build real-time customer profiles for AI decision-making.

This isn’t just theory. A leading UK life insurer partnered with Visionet to implement an AI-powered CDP accelerator, consolidating thousands of records into 360° customer views. The result: a 5% uplift in new customer acquisition and up to a 10% increase in policy sales, driven by smarter segmentation, churn prediction, and cross-sell modelling.

Without this backbone, AI tools lack the context for accurate predictions — and marketing teams lose the ability to activate them meaningfully.

People & processes

Successful AI implementation depends on more than technology — it relies on people and governance.

• Upskilling – Marketers need training in AI tools, analytics, and experimentation frameworks so they can interpret outputs and design appropriate experiences. Peer programmes, sandbox testing, and cross-functional learning are effective ways to build capability.

• Governance – Ethical AI must be central to planning. Policies around bias mitigation, transparency, and explainability are critical — especially under GDPR and emerging AI regulations. Companies should establish data steering committees, define decision thresholds, and use monitoring dashboards to ensure outcomes align with both ethical standards and regulatory expectations.

Technology stack

Modern insurers are converging on a leaner, more integrated AI tech stack. Instead of standalone tools, platforms are increasingly hybrid — offering experimentation, personalisation, and automation under one roof.

When selecting platforms, it's crucial to focus on integrability, explainability of models, and vendor support for regulatory audit trails.

Generative AI in Personalised Journeys

Generative AI (GenAI) is set to reshape customer interactions — from dynamically written emails and real-time chatbot dialogues to personalised policy documents.

According to PwC’s GenAI trend report, 31% of insurance CEOs say their companies have already changed technology strategies because of GenAI. These tools can craft tailored messaging, answer policy queries, and even generate prevention advice for property and health — enabling hyper personalised experiences at scale, and crucially, across touchpoints.

AI-Driven Product Innovation

AI is set to shape product strategy by enabling micro-policies, modular coverage, and on-demand pricing. Real-time signals — such as mileage, travel behaviour, or weather risk — can feed dynamic pricing models that adjust premiums instantly.

This shift supports digital-first customers while also powering creative marketing offers that adapt to customer context in the moment.

Regulatory Scrutiny: GDPR and AI Act Impacts

Regulators are trying to move quickly to keep pace with AI innovation. The EU’s AI Act, in force since August 2024, mandates strict governance for high-risk AI systems — including underwriting, pricing, and claims automation in insurance. Non-compliance can mean fines of up to €35 million or 7% of global turnover.

The UK has taken a principles-based, pro innovation stance, encouraging ethical AI adoption under existing regulations. Still, expectations such as bias mitigation, explainability, and algorithmic reviews are increasingly aligned with EU standards.

Insurers must also navigate GDPR obligations around automated decision-making rights and data subject transparency. Beyond Europe, Canada’s Directive on Automated Decision-Making and new U.S. state-level AI laws reinforce the need for rigorous ethics and accountability in AI-driven insurance.

Why AI governance matters in insurance

Insurance is one of the most heavily regulated and data-sensitive industries, with heightened obligations around fairness, transparency, and accountability. As AI adoption expands across marketing — from predictive targeting to generative content — the need for robust oversight grows rapidly.

In the UK, there is growing public scrutiny of opaque or complex pricing and renewal practices.

For example:

Fairer Finance found that insurers are using up to 400 data points to price car and home insurance policies — including email domain, birth location, or time of day of application — many of which have little clear link to underlying risk. Their report argues this leads to a “poverty premium,” unfairly penalising vulnerable groups.

The Financial Conduct Authority (FCA) has proposed measures to tackle “complex and opaque pricing practices” in general insurance — including concerns about ensuring renewal pricing aligns with the Consumer Duty, preventing loyal customers from paying more simply because they stay, and enhancing transparency in how renewals are priced.

• Discriminatory targeting (e.g. excluding vulnerable groups)

• Inaccurate profiling from outdated or incomplete data

• Lack of consent when enriching CRM data with third-party sources

• Over-reliance on black-box systems, particularly generative AI

Key regulatory frameworks (recap)

Insurers and their marketing teams operate in a fast-evolving regulatory environment.

Three frameworks are particularly relevant:

• EU AI Act – Classifies many insurance applications (profiling, pricing, decision automation) as “high-risk,” requiring enhanced governance, documentation, and human oversight

• GDPR – Article 22 gives individuals rights around automated decision-making, including explanation, contestation, and human review. Marketing teams must ensure data use is lawful, transparent, and fair.

• UK AI Regulation Roadmap – Follows a principles based model rather than one binding framework, with regulators issuing joint guidance on transparency, explainability, and fairness. Fresh Egg - Accelerating measurable impact

What’s evolving most rapidly is not only the letter of the law but also the expectations of both regulators and consumers. The Financial Conduct Authority (FCA) has made clear it is monitoring how AI shapes consumer outcomes — not just in underwriting and claims, but also in marketing, pricing, and retention strategies.

Core compliance risks in marketing AI

These are not abstract risks — each has a measurable regulatory and reputational cost if left unchecked.

Managing risk: practical steps for insurers

To minimise regulatory, reputational, and ethical risks, insurers should treat AI in marketing like any other regulated function — ensuring oversight, traceability, and accountability at every stage.

Key safeguards include:

• Establish a cross-functional AI governance board – Include compliance, data science, marketing, and legal. This group should review new use cases, approve model deployment, and set escalation procedures.

• Document AI models and data pipelines – Keep clear records of training data, segmentation logic, and performance benchmarks. Documentation supports explainability, regulator engagement, and an auditable trail.

• Run bias and fairness testing – Use tools like IBM AI Fairness 360, Google What-If, or Microsoft Fairlearn to test outputs for demographic bias in targeting, pricing, or messaging.

• Embed human-in-the-loop oversight – For high-impact outputs such as pricing, eligibility, or customer communications, ensure human review — especially when large language models (LLMs) generate content.

• Conduct vendor due diligence – Assess vendors for compliance certifications, data protection standards, and model transparency. Contracts should include auditability, monitoring, and update provisions.

• Maintain strong data consent practices – Regularly review consent mechanisms to ensure clarity and compliance, particularly when combining first-party data with third-party enrichment. This protects trust and reduces exposure.

What’s evolving most rapidly is not only the letter of the law but also the expectations of both regulators and consumers. The Financial Conduct Authority (FCA) has made clear it is monitoring how AI shapes consumer outcomes — not just in underwriting and claims, but also in marketing, pricing, and retention strategies.

Looking ahead: responsible AI as competitive advantage

Compliance is not a checkbox. As AI becomes core to acquisition, conversion, and retention, insurers that embrace transparent and responsible practices will gain not just regulatory goodwill but also customer trust and long-term reputational equity.

The UK’s forthcoming Data (Use and Access) Act (DUAA), alongside global frameworks such as the OECD AI Principles, signals a growing consensus: innovation and ethics must advance together.

Insurers that embed these principles into their martech stack, marketing operations, and vendor strategy will be positioned to lead in an AI-enabled future — turning responsible AI from a regulatory obligation into a genuine competitive advantage.

Drop us an email on enquiries@freshegg.com or talk to us here.

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