Businesses are turning to AI and data-driven insights to create hyper-personalized, seamless experiences in the evolving landscape of Customer Experience Managment (CXM) and Customer Communications Management (CCM).

Our research shows that 69% of financial service customers believe personalization is an essential factor in establishing trust. Financial institutions can leverage AI to refine risk assessment, and SaaS-driven Just-in-Time financing solutions can ensure customers receive precisely what they need when they need it, improving efficiency and satisfaction. Additionally, Just-In-Time modeling could impact both the means and frequency that organizations are going to need to engage with their customers, including more frequent digital communications and updated data collection.

With AI making it possible for insurers to analyze vast amounts of data, they can look to offer personalized policies that lower costs and increase accuracy. For example, usage-based models for auto insurance calculate premiums based on actual driver behavior and risk analysis rather than factors like age, gender and location, which can help personalize customers’ experiences and build trust.

For healthcare, AI is transforming patient engagement by tailoring communications, enhancing accessibility through multilingual support and streamlining personalized treatment plans.

AI-driven personalization is no longer at the bleeding edge of the tech stack — it’s a necessity for organizations looking to stay competitive, improve customer loyalty and ensure regulatory compliance. Companies in all industries can look to integrate elastically scalable solutions for cost-effective access to real-time data, insights and tools to improve customer responsiveness and satisfaction with personalized communications.

We know that data is key to digital transformation, especially in regulated industries where data quality and governance are critical. Yet, according to Gartner, the average cost of poor data is still an eyewatering $15 million annually.

The acceleration of AI adoption exacerbates this issue. Many businesses are still burdened with issues around data silos, hidden biases, poor metadata and weak governance frameworks. These issues compromise the effectiveness and fairness of AI models, which could be detrimental to the customer experience as these features are rolled out.

Success in AI for customer communications and interactions depends on having clean, consistent and up-to-date data. To ensure the information companies collect from customers is accurate and complete, they must start with secure and simple processes.

For insurers, data management processes and strategies should include strong governance, data audits, real-time validation and continuous monitoring to ensure not only better policyholder experiences but also to ensure their AI projects effective. Similarly, in the healthcare industry, providers with strong data governance can better minimize risk by protecting sensitive personal health information and streamlining essential processes, such as claims processing, by keeping data accurate and up to date.

Ensuring high-quality, integrated data can help deliver exceptional customer experiences, especially as AI models become increasingly sophisticated. Companies must recognize that data quality and strong data governance are pivotal for unlocking the potential of AI and advanced analytics across industries. This is why strengthening governance and modernizing data collection with a single, centralized data collection solution that’s easy to use will help organizations boost data quality and accuracy with their AI initiatives.

While 47% of consumers agree that GenAI has the potential to improve the communications they receive, many are concerned about security and ethics. The massive amount of sensitive data AI that models require increases the potential for risk, raising customer concerns about how that data is being used.

As a result, companies must reassure customers about security, fairness and how their data will be used, with a well-defined AI policy to outline that their data won’t be misused. This can also help organize information so the right data can be accessed securely and provide a higher-quality customer experience.

On top of customer concerns, regulatory challenges remain a top hurdle for AI adoption, especially in industries with strict oversight, such as insurance, healthcare and financial services, where new regulations are constantly rolling out. In 2025, companies should expect to see more changes as regulatory bodies take a stricter look at how enterprises interact with customers and expectations rise around AI transparency.

The rise of consumer data privacy concerns and these stricter regulations necessitate careful data handling in 2025. Organizations must stay ahead of regulatory trends with proactive, well-documented AI governance that allows them to respond flexibly to changing compliance demands. Non-compliance and lack of transparency can lead to severe consequences, including hefty fines, legal actions and reputational damage to customers.

Establishing a strong data governance framework will enable organizations to build trust with their customers and help to create a clean audit trail for AI use, document provenance and AI disclosure requests.

As AI continues to influence customer interactions and business models in 2025, data remains essential to unlocking its full potential. Companies must prioritize data governance and quality to ensure accuracy while establishing robust governance frameworks to stay aligned with rapidly evolving regulatory requirements. They should turn to automation to assist with tracking, audits and explainability while also improving customer experience quality and operational efficiency.

Regardless of the industry, these strategies can help organizations enhance customer relationships, build resilience in a complex regulatory environment and drive growth.