We’re in the midst of a fundamental shift in the banking industry. Forces such as rising customer expectations and ever-changing regulatory requirements coupled with new, non-traditional competitors in the market have made it imperative for banks to rethink their business and client engagement models.

While banks are strategizing how to invest for the future, they are still tasked with remaining competitive. Not surprisingly, bankers say that attracting new customers is one of their top challenges over the next two years. At the same time, banks recognize the need to nurture their relationships with current customers through increased engagement and personalized communications. With these issues combined, we’re looking at a global trend of banks placing customer service as the number one investment priority for the future.

But customer service expectations are changing – people are increasingly seeking high quality, personalized, and ‘always on’ service similar to what they receive outside the banking industry – so banks must adjust their traditional communication models. Presently, engagement strategies are centered on targeting new and current customers based on product or channel, like a home mortgage blast to all applicable customers in the 25-35 age range, or a client portal experience wrought with complex charts and graphs, requiring clients to interpret and wonder what it all means in terms of their financial goals.

The bottom line is — customers want things explained to them in a way that they can immediately understand and take action on. It’s time for banks to tailor their offerings to meet each and every customer’s needs and ultimately, become a valued partner.

Fortunately, the technological advances that have contributed to increased customer expectations also hold the solution: personalization through automation. Artificial intelligence now makes it possible for organizations to craft personalized, automated communications at scale – exactly what banks need to deliver the cost-effective, high quality personalized services their customers are seeking.

There are specific sectors of the industry already taking advantage of such technologies to improve customer service. For example, fund managers are utilizing AI-powered natural language generation (NLG) to automatically write portfolio commentary, saving literally weeks of time. Wealth managers are using similar technology to prepare for client meetings by automatically generating investment portfolio reviews, freeing up their time to conduct in-depth analysis ahead of the meeting and offer strategic advice on future investments. Another wealth firm is using the technology to directly correspond with clients, explaining their progress towards goals with pointers on how to improve.

Not only does the technology enable scale, it makes compliance easy. From a regulatory perspective, machine-generated analysis and reporting provides an audit trail that captures every step of the process, which can then be detailed out alongside the report. Once issues of validation, quality, and consistency are established, they become a standard part of the system’s performance and reporting.

The reality is that NLG technology can be used anywhere structured data exists and contains information that people need to know. This technology can provide a genuinely personal level of communication at scale that is also compliant and consistent.

High-touch customer service delivered by a machine? I hope my bank takes note.

Kris Hammond is the chief scientist of Narrative Science.