Customers are looking for more than just speed in financial advice through AI, they expect a new value proposition where AI benefits are shared with them. Our research into the mortgage advice process shows that a hybrid Human-AI model, combined with a clear shared-savings discount, achieves the highest customer satisfaction and most positive brand perception, outperforming fully human advice. In this blog, we share the findings from our research.
Key insights:
- Eighty per cent of customers feel more positive when advised by a human compared to AI alone.
- Seventy-five per cent perceive AI as a tool to cut company costs and boost profits, leading to negative views if these benefits are not visibly shared with their customers.
- When customers notice a discount linked to AI, the ‘cost-cutting’ image fades, and brand perception positively increases by over ten per cent.
- A hybrid AI-human team with a discount boosts customer positivity by twelve per cent compared to a full human team without a discount. This highlights that customers want mortgage advice companies to use AI for the right activities and share its benefits with them.
- AI-only advice scores significantly lower (around minus ten per cent customer satisfaction) than human or hybrid models.
Why this matters: reinventing the business model
Mortgage advice plays a pivotal role in financial services and society, shaping both individual lives and the business models of banks, insurers, and intermediaries. For many, buying a home is one of the biggest financial decisions, while for financial institutions, it is a key source of income. AI is transforming how value is created, delivered, and perceived, beyond just speeding up financial advice processes.
Efficiency alone will not drive AI adoption. Firms need to rethink their service models and value propositions, ensuring that customers see the benefits AI offers and trust its application. This transformation allows for lower prices with sharper brand and service differentiation, attracting new customers, and driving growth. It requires a fresh approach to delivering advice, engaging clients, and structuring offerings, with technology at the heart of a customer-focused transformation.
Study overview
PwC experts Pieter Verheijen en Sil Duckers, in partnership with Eindhoven University of Technology, explored AI’s impact on financial mortgage advice. Our research included three surveys with over one thousand Dutch participants. Each participant experienced a simplified mortgage advice scenario from human advisors, AI agents, or hybrid Human-AI teams. In the hybrid scenarios, we varied the sequence (AI-human versus human-AI) and sometimes included a visible discount on the price linked to AI use in the mortgage advice process. After each scenario, participants assessed their satisfaction, brand perception, and attitudes towards the provider and AI usage.
What customers shared with us
In the financial services sector, AI often steps in to replace human customer service with chatbots and virtual assistants. While early versions have room for improvement, prices typically remain unchanged, leading customers to feel the company gains more than they do. Our findings align with this: seventy-five per cent of the respondents think AI is used to cut company costs, eighty per cent express higher satisfaction when a human is involved compared to AI alone, and AI-only advice scores about ten per cent lower in satisfaction. This fuels hesitation and negative views towards AI agents.
Yet, AI holds immense potential. It can work around the clock, manage administrative and repetitive tasks, and in mortgage advice, tackle the heavy lifting, from gathering and validating documents to analysing and drafting recommendations, allowing human advisors to confirm results, engage with clients, and offer follow-up care. The solution for this perception issue around the use of AI agents is to create shared benefits in your value proposition to customers and involve humans at key moments.
Two effective strategies
First, involve humans at the right times. Companies should not fully replace humans; instead, they should use AI for suitable tasks and form a hybrid team to deliver advice. In the more successful hybrid model (AI-human), AI manages heavy lifting such as document gathering, validation, analysis, and draft advice, while the human advisor confirms results, interacts with the client, and provides follow-up care. Customers rate satisfaction with this hybrid model as high as with fully human advice. The sequence matters: when reversed (human-AI), where the human does the analysis and the AI interacts with the client and provides follow-up care, customers are less positive.
Second, share AI-driven savings with customers. Although this shared-savings strategy might seem counterintuitive, it tackles the core perception issue. A visible discount linked to AI use reframes automation as shared value rather than cost-cutting, boosting positive perception by over ten per cent for customers. This approach balances benefits between the company and the customer, enhances brand sentiment, and allows for lower price points that can boost the company’s market competitiveness and growth.
The winning combination
Bringing together hybrid Human-AI teams with a shared-savings discount creates a powerful synergy. Participants were twelve per cent more positive when served by an AI-human team offering a shared-savings discount compared to a fully human team without a discount on the price. This human-led, AI-powered strategy reduces service costs while boosting customer satisfaction and brand image.
Options to reinvent your value proposition
Our study did not test specific pricing or service setups, but several strategies could be effective based on your brand, audience, and regulatory environment. Treat these as hypotheses to test and refine with customer feedback and performance data.
For example:
- Tailor service levels by segment: Some customers prioritise speed and cost, while others value human reassurance.
- Offer a tiered service model, fully human (premium), hybrid AI-human (shared-savings), and AI-only for straightforward digital-first cases (lowest price), giving customers clear choices on service and price.
- Clarify roles in the hybrid model: AI manages document collection, analysis, and draft advice; human advisors confirm results, explain recommendations, and provide follow-up care.
- Make savings visible, such as a separate line showing shared savings or digital processing credits, so customers see their benefits.
- Add trust-building features like on-demand explainability, optional human callbacks at key decision points, and outcome commitments (for example, faster decision windows with fee credits if missed). These can be standard in certain packages or offered as add-ons.
- Expand offerings with additional AI-enabled services: For example, advice-as-a-service (subscription with ongoing monitoring and nudges around rate changes, affordability shifts, and renewal windows) and instant pre-qualification and scenario exploration (real-time ‘what if’ simulations on borrowing power, monthly payments, and buffers).
How to make it happen – think big, start small
You can start with a focused pilot in one mortgage product, linking it to a visible shared-savings discount, while maintaining a fully human option as a premium choice. Set clear hypotheses for adoption, satisfaction, conversion, complaint rates, price sensitivity, and tier uptake, then measure rigorously.
Simultaneously, test pricing and service models by segment. Use a shared-savings model to set a lower price point for hybrid and AI-only journeys where suitable, and A/B test the language, so customers understand their benefits. Be transparent in customer communications, show how AI is used, where humans step in, and why the price reflects shared efficiencies. Involve risk, legal, and compliance from the start: Align AI use, pricing, and disclosures with policy and regulation; implement model-risk management, fairness and robustness testing, audit trails, and clear thresholds for human review.
Scale from pilot by updating the operating model. Redesign advisor roles towards verification, communication, and aftercare; refresh training and scripts to reflect the hybrid flow; and strengthen governance and quality controls around AI’s role in analysis and recommendations. On the technology side, integrate end-to-end components, document automation, validation, analysis, and workflow orchestration, with human-in-the-loop checkpoints, monitoring, and compliance gates before deployment. Make shared savings visible in proposals, digital flows, and invoices to reinforce fairness.
As confidence grows, extend the approach across related advice processes and consider new AI-enabled services where they fit your brand and segments, such as advice-as-a-service or instant pre-qualification with real-time ‘what-if’ scenarios. Treat these as options to explore and refine based on customer feedback, performance, and compliance reviews. Publishing a simple ‘How we use AI’ and ‘How you benefit’ statement can further strengthen trust as you scale.
This is how a human-led, AI-powered, and compliant transformation takes shape: think big, start small, prove the value, and scale by embedding reinvention across pricing, roles, governance, and technology, so the value created by AI is visibly shared with customers, driving adoption, trust, and growth.
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