What's Holding Banks Back From AI—And How To Move Forward

Forbes

09-06-2025

Assaf Baciu is President and cofounder at Persado, which provides specialized generative AI used by some of the world's largest brands.
The financial services industry is justifiably risk-averse. With consumers' and businesses' livelihoods on the line and stringent regulations in play, tech changes often aren't worth the risk.
It's no surprise, then, that banks and other financial institutions have been slow to adopt AI. Still, customers are starting to notice banking's slow pace of innovation: In a 2024 Persado study, more than half of Generation-X, Millennials and Generation-Z consumers surveyed said they would consider switching banks for one that provided more personalized digital experiences. Since it costs banks, on average, $561 to acquire a new retail customer, maintaining loyalty is critical for stability and revenue.
A cohort of trailblazing global financial institutions has heeded customers' call for personalization, driving billions in revenue using AI to generate high-performing marketing content. These results are promising, but many banks are still holding back.
The largest U.S. banks realize more than $200 million annually in additional revenue, on average (with mid-size and smaller banks realizing between $20 million and $50 million in additional revenue each year), using purpose-built AI to generate and optimize marketing content, according to my company's research cited above.
Retail banking customers are on board with AI as well: My company also found that 75% of consumers consider their banks' use of "AI to help understand their needs and preferences" to be acceptable.
So, what's stopping banks from taking advantage of AI?
AI can quickly make a significant, measurable business impact for banks and card issuers. Still, in highly regulated industries that handle the sensitive data of millions of customers, marketing teams face headwinds on the road to implementation.
Banks want to be certain when adopting new technology like AI that there will be demonstrable ROI, their teams will be able to easily navigate and audit the solution, they'll remain compliant and customer data is safe.
Let's assess the common barriers to AI adoption and how to address them:
Your teams should be aligned on the goals of an AI implementation and how you will measure said goals.
Identify the use cases you're looking to optimize with an AI solution and get signoff from key stakeholders on the most critical success metrics (e.g., demonstrable ROI, time savings or increased customer engagement) for each use case.
From there, identify your baseline for a given use case, then use AI to generate alternative language so that you can test it with a select cohort of customers. Your marketing teams can then compare results and understand how to use AI to prompt higher customer engagement and conversions.
A major distinguishing factor for meeting your AI goals is the data an AI solution is trained on. A specialized AI solution for marketing, for example, should be trained on (non-personally identifiable information) real customer interactions and can be fine-tuned to reach target customer segments. This ensures your AI has an immediately relevant foundation, knows its audiences and can refine copy to keep driving impact.
Specialized AI solutions are not only grounded in a relevant knowledge base of customer interactions but also have guardrails to mitigate hallucinations or biased outputs.
Banks can fine-tune these marketing solutions by uploading their brand guidelines and historical campaign data to ensure the AI is generating and optimizing content that is both relevant and aligns with content performance goals and brand voice. In turn, the AI, which continuously learns from the bank's own campaign insights—not broad models—serves as a companion for marketing to generate personalized content at scale.
Marketing and legal teams will still need to check that AI outputs are compliant, relevant and on-brand, but this reserves 'humans in the loop' for final reviews and auditing the data that trains the AI.
Legal and compliance reviews are critical elements of a review cycle for any piece of customer-facing content, but they often create bottlenecks for marketing campaigns, slowing time to market and delaying results. Generic AI solutions run the risk of exacerbating lengthy review processes, as they're designed for quantity—not quality.
When identifying an AI solution, auditability, compliance and privacy should be top of mind. Analyze potential tools based on criteria like transparency, explainability, fairness, security and regulatory compliance throughout the entire AI system, from training data to evaluating outcomes.
Advanced AI solutions have AI agents for each financial regulation (such as the FTC Act, UDAAP, ECOA, FSRA, C.A.R.D. and TCPA) and enable banks to upload and configure risk profiles and governance guidelines, trimming the number of review cycles and getting campaigns in front of customers faster. As regulations emerge or change, teams can upload new guidelines to keep the AI up-to-date. Capabilities like advanced encryption, granular access controls and automated threat detection can assuage privacy and security concerns.
However, humans are integral to ensuring on-brand, compliant messaging, even when using specialized AI. These tools can facilitate greater collaboration between marketing and legal teams but, even in the absence of AI, consider how your organization can diminish the siloes between marketing and legal teams—and expedite reviews. Are there common compliance mistakes marketers are making? If so, explanations of these mistakes can help your marketing team learn and avoid regulatory trip-ups in the future.
Paradigm-shifting advancements in technology will always inspire a mix of emotions: fear, curiosity and excitement. Printing presses were destroyed out of protest when they were first invented, but just like AI, the change they brought on was inevitable.
Now, aware of the potential benefits of AI and tips for overcoming barriers and risks to adoption, bank marketers are at a crossroads. Success will come not from embracing AI with thoughtful, measured steps—balancing innovation with compliance, and experimentation with proven best practices.
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