The Role of AI and Hyper-Personalization in Non-Retail Digital Commerce

In today’s digital-first world, the expectation for seamless, personalized commerce experiences is no longer confined to retail. Sectors such as healthcare, banking, energy, and travel are rapidly embracing AI-driven personalization to transform how they engage customers, deliver value, and drive growth. As digital commerce matures beyond its retail roots, organizations in these industries are discovering that hyper-personalization—powered by artificial intelligence—can be a powerful differentiator, but it also brings new challenges around trust, transparency, and data privacy.

The State of AI Adoption in Non-Retail Commerce

The digital revolution, accelerated by the pandemic and evolving consumer behaviors, has fundamentally changed how people interact with brands and services. Today, only 46% of consumers globally are satisfied with their digital commerce experiences, highlighting a significant opportunity for non-retail sectors to innovate and lead. Banking and financial services currently lead in digital commerce satisfaction, with 62% of consumers expressing contentment, while sectors like transportation and energy lag behind, signaling room for improvement and disruption.

AI adoption is accelerating across industries, but consumer attitudes remain mixed. While Millennials and Gen Z are more likely to embrace AI-powered features and crave experiences tailored to their preferences, older generations are less familiar with or enthusiastic about these capabilities. Organizations are investing in AI to deliver hyper-personalized experiences, but must also address skepticism and demonstrate clear value to win consumer trust.

Hyper-Personalization: Moving Beyond Segmentation

Hyper-personalization goes far beyond addressing customers by name or offering generic recommendations. It leverages AI and advanced analytics to anticipate individual needs, preferences, and behaviors in real time, creating experiences that feel uniquely relevant to each user. In non-retail sectors, this shift is unlocking new opportunities:

Healthcare: Patients increasingly expect digital-first, customized care journeys. AI enables providers to offer personalized health recommendations, tailored content, and seamless telemedicine experiences based on patient history, demographics, and health goals. Younger generations, in particular, value self-service tools and proactive engagement that mirror the best of digital retail.
Banking and Financial Services: Modern consumers want more than online payments—they seek intuitive, omnichannel experiences and personalized financial advice. AI-driven tools empower banks to deliver real-time insights, product recommendations, and educational content, deepening customer relationships and driving loyalty.
Energy and Utilities: As the sector shifts toward smart grids and renewable energy, AI-powered predictive analytics help utilities forecast demand, optimize distribution, and engage customers with personalized insights on energy usage and sustainability. Consumers can now manage home energy production, monitor usage, and optimize EV charging through unified digital platforms.
Travel and Hospitality: Travelers demand hyper-personalized, seamless digital experiences. AI enables brands to deliver tailored recommendations, eco-conscious options, and dynamic offers based on individual preferences and travel history, increasing customer lifetime value and loyalty.

Practical Use Cases: AI in Action

Across these sectors, AI is driving tangible improvements in both customer experience and operational efficiency:

Predictive Analytics in Energy: Utilities use AI to anticipate demand fluctuations, optimize grid management, and provide consumers with actionable insights to reduce costs and carbon footprints.
Personalized Health Recommendations: Healthcare providers leverage AI to analyze patient data and deliver individualized care plans, reminders, and wellness content, improving outcomes and engagement.
Dynamic Insurance Pricing: Insurers harness data from connected devices (like telematics in vehicles) to offer usage-based or behavior-based policies, rewarding responsible behavior with lower premiums and greater transparency.
Real-Time Financial Advice: Banks deploy AI-powered chatbots and recommendation engines to guide customers through complex decisions, from loan selection to investment planning, in a personalized, accessible manner.

Consumer Attitudes: Promise and Skepticism

While the promise of AI-driven personalization is clear, consumer attitudes are nuanced. Many appreciate the convenience and relevance of tailored experiences, but concerns about data privacy, transparency, and the true value of AI features persist. For example, a majority of consumers have never used conversational AI tools to make purchase decisions, and many remain unconvinced of their benefits. Data privacy is a top concern, with 33% of consumers citing it as a source of friction in digital commerce.

To overcome skepticism, organizations must:

Clearly communicate how AI enhances the customer experience.
Offer tangible value, such as exclusive discounts or faster issue resolution, in exchange for data sharing.
Ensure transparency around data usage and privacy policies.

Challenges: Data Privacy, Trust, and Transparency

The success of AI-driven personalization hinges on robust data strategies and consumer trust. Key challenges include:

Data Privacy: Consumers expect personalized experiences but are wary of sharing personal information without clear benefits and assurances. Transparent data policies and robust security measures are essential.
Trust: When technology becomes a barrier rather than a bridge, trust is at stake. Organizations must prioritize intuitive, user-friendly design and proactive support to minimize friction.
Transparency: Clear communication about how data is collected, used, and protected builds confidence and encourages engagement.

Guidance for Responsible AI Implementation

To exceed customer expectations and unlock the full potential of AI and hyper-personalization, organizations should:

Prioritize Customer-Centric Enhancements: Focus on delivering real value—such as hyper-personalized offers, seamless self-service, and quick resolution of issues.
Strengthen Trust and Transparency: Be upfront about data practices, offer clear privacy controls, and demonstrate a commitment to security.
Break Down Data Silos: Integrate data across channels and touchpoints to enable holistic, real-time personalization.
Continuously Measure and Optimize: Use data-driven insights to refine AI models, personalize experiences, and address emerging pain points.
Act Fast, Iterate Often: Pilot new capabilities, gather feedback, and evolve quickly to stay ahead of changing expectations.

The Path Forward

AI and hyper-personalization are redefining digital commerce across non-retail sectors, offering unprecedented opportunities to engage customers, drive loyalty, and unlock new value. The leaders will be those who harness AI responsibly—balancing innovation with trust, transparency, and a relentless focus on customer needs. As digital commerce continues to evolve, Publicis Sapient stands ready to guide organizations through this transformation, helping them not just meet, but exceed, the expectations of today’s digital-first world.