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What are the trends for financial services, CX and technology in 2025? It will likely be another year of AI taking “all the air out of the room,” according to Alfred (Chip) Kahn, the CEO and founder of OvationCXM. He sat down for a Q&A on his predictions for the coming year for banking, AI, customer experience, data management and beyond.
Q: What do you predict as trends for financial services in 2025?
Kahn: The big thing has to be the change in the regulatory environment next year. Executives in top 25 banks tell us they are really bogged down in compliance-related activity. The reality is there’s been a heavy-handed approach to banking regulations over the last four years.
Bank stocks went up 10% the day after the election, which shows there is anticipation that there might be some loosening of that in 2025. This could allow executives the mind space, time and energy to focus on innovation again.
Over the last 18 months, we’ve seen projects get delayed, rescheduled because there’s another compliance-related steering committee meeting. I think this regulatory shift could open up innovation, which is ultimately good for consumers and small businesses.
2025 isn’t about a groundbreaking new contraption but about continuing to execute on what we know needs to be done—driving better customer experiences and delivering new products to market.
AI and Customer Experience in 2025
Q: What do you see for AI and customer experience in banking in 2025?
Chip Kahn: The use of AI for customer experience in banking really depends on the segment of banks. The top five or six banks are in a league of their own. They have the budget to develop proprietary AI models hosted in their own data centers.
For them, it will be all about internal development. They're going to build a lot of it, and they will look to partner on some of it. There will need to be very specific differentiation provided [by partners] for any opportunity to participate in those efforts.
The next 45 banks or so appear to be figuring out the framework. They’re focused on compliance frameworks and guardrails. Hopefully, 2025 will be the year they start implementing models.
These models will likely be more internally focused than externally focused, such as using co-pilot-type solutions to empower bankers rather than deploying generative AI models in customer-facing use cases. The risks—both real and perceived—along with potential fines and reputational damage, make that leap risky for now.
Community banks are in a different world. In many cases, chatbots might conflict with their emphasis on personalization and community. I will be curious to see how that plays out.
AI for Agentic Workflows
Q: What is agentic AI?
Chip Kahn: Agentic workflows involve more complex, multi-step processes that trigger third-party systems and may require rationalization from an AI model. There are a lot of workflows in banks that are very time-intensive and involve significant manual effort.
Q: What are your thoughts on the use of AI in agentic workflows in banking?
Chip Kahn: Agentic workflows involve more complex, multi-step processes that trigger third-party systems and may require rationalization from an AI model. There are a lot of workflows in banks that are very time-intensive and involve significant manual effort.
If AI is applied internally, there’s definitely potential here. For example, streamlining these workflows could save time and reduce the manual handoff of tasks. However, outside of the top five banks, I don’t think we’ll see widespread implementation of AI in agentic workflows by 2025.
Customer Experience Trends
Q: What are the overall trends in customer experience you're hearing about for 2025?
Chip Kahn: The main trend for 2025 is AI—it’s taking the oxygen out of the room on almost all topics. Regardless of where banks are on their journey, everyone is thinking about AI. The key questions are: where are the banks in developing frameworks for it and how will they partner, build, or deploy AI?
That said, there's still a lot that needs to be done outside of AI. Using the term "digital transformation," even though it’s a bit outdated, there's significant work required in enabling banks to collaborate with fintechs, driving innovation and achieving deposit primacy. This includes introducing innovative products, whether built internally or sourced from third-party partners.
A lot of foundational infrastructure still needs to be established to achieve these goals. Hopefully, 2025 will see the industry make progress in these areas while AI frameworks are being sorted out.
Q: How do ecosystem connectors power innovation?
Chip Kahn: There’s opportunity in integrating financial services into third-party products. Banks have always relied on third-party partners to deliver products, like issuing and accepting credit cards.
For that reason, the bank’s first-party customer data often resides in third-party systems. Delivering a seamless, holistic customer experience requires real-time access to that data—for compliance and customer satisfaction.
We address this through ecosystem connectors. These connectors create real-time data pipelines for customer experience (CX) data—not transactional data—bringing it back to the bank in real time. This eliminates the “1-800 ping pong” of backend troubleshooting when something goes wrong with a customer handled by a third party.
It ensures the bank stays compliant, can answer customer questions in real-time without escalation and provides a consistent brand experience. These connectors are part of a network that feeds data back into the bank's systems of record in real time.
For instance, you could run a customer summary report from the data in your CRM, but that only tells you what's happening within the walls of the banking institution. When you bring in data from all the other places your customer interacts with, you get a true view of how they feel—understanding their sentiment and potential issues that could lead to churn. This is part of the enabling infrastructure that will ultimately power better AI use cases in the future..
AI and Data Concerns in Banking
Q: What is your position on AI and data privacy and data sovereignty in banking?
Chip Kahn: There is concern in banking around data privacy and data sovereignty, and rightly so. There are numerous compliance requirements and federal and state regulations to ensure data, particularly PII, is protected.
A significant amount of information about individuals and businesses, including their financial data, must be safeguarded. This is why the AI conversation is likely moving slower in banking and financial services.
The challenge is how to put the necessary guardrails in place for AI. It’s hard to use Gen AI when the models' workings aren’t fully transparent, especially when it's operating in the cloud. To address this, AI compute and workloads need to be moved locally to the bank’s infrastructure, often on an IBM Z system.
This hybrid cloud approach allows banks to safeguard their data and avoid relying on third parties, ensuring that data never leaves their systems. Along with model explainability, this approach begins to address some of the challenges the market faces in rolling out data-driven solutions.
Addressing Data Silos and Enhancing Customer Journeys with AI
Q: How does data become siloed, and what is the solution to overcoming it?
Chip Kahn: Data often gets siloed across systems of record, leading to technology debt. To overcome this, customer data platforms (CDPs) are becoming essential, offering a single customer record across these silos. However, the challenge grows when considering ecosystems. Third-party partners are also siloed, making it significantly harder to address data silos.
What is needed is a data orchestration layer that overlays across internal systems and business ecosystems, allowing for rules-based data reading and writing. This harmonized data layer brings together disparate data from enterprises in a way that is human-understandable.
It integrates into existing systems and provides a solid foundation for AI-driven analytics, offering insights across all customer touch points, not just one or two. As time goes on, this will become even more important.
Having the equivalent of open banking for CX data, where information is fed back to the bank, is becoming critical. In our case, we focus on building orchestration layers to bridge data silos, enabling access to data from both internal systems and partner networks. This is essential for creating AI use cases like customer health metrics or predictive analytics.
Q: How can generative AI support more seamless customer journeys?
Chip Kahn: Generative AI is instrumental in driving seamless customer journeys because it can aggregate and analyze data in real time. In business and commercial banking, customer journeys typically involve multiple companies and touchpoints, both internally and externally. Providing context for the next best step is a major challenge.
Gen AI excels at synthesizing disparate data quickly and delivering actionable insights. For example, it can guide the next steps in a customer journey or solve problems in real time—tasks that would take much longer for a human to accomplish. This capability is transformative for enhancing customer experiences by reducing friction and ensuring timely, context-aware interactions.
Q: What is Ovation CXM’s strategy for using AI to power customer experiences?
Chip Kahn: At Ovation CXM, our strategy begins with focusing on internal use cases. For example, we help team members improve product fluency, which is crucial in areas like treasury, where understanding product details and pricing can directly impact customer support. By focusing on internal scenarios, we mitigate risks associated with unsupervised AI interactions.
We have also partnered with IBM, enabling us to run AI workloads locally on mainframe chipsets. This ensures data stays within the bank’s firewalls, avoiding cloud-based security concerns. Additionally, we adopt a “bring your own model” (BYOM) approach. This allows banks to deploy multiple models tailored to specific customer segments, products and workflows, all within the goal of enhancing the customer journey.
Q: How does Ovation CXM support banks of all sizes in their AI initiatives?
Chip Kahn: Our role extends beyond technology; we aim to be consultative partners. We guide banks in selecting the right models for their use cases, whether they are enterprise banks, super-regionals or smaller institutions. This involves helping them align specific large language models (LLMs) or machine learning tools with their customer journey objectives.
By offering thought leadership and hands-on support, we ensure that banks can effectively leverage AI to address their unique challenges, from regulatory compliance to improving customer interactions. This consultative approach is vital in a landscape where every institution’s needs and resources differ.
Q: How should banks choose companies to help with AI and CX use cases?
Chip Kahn: Banks should prioritize partners who deeply understand the financial services industry. Horizontal software platforms that cater to multiple industries may lack the depth needed to address the unique challenges of banking—from regulatory requirements to delivering accessible financial products.
Financial services often involve complex interactions with multiple internal teams, customers and third-party providers. Unlike direct-to-consumer models, these interactions require specialized solutions to manage intangibles and create transparency in what can feel like a black box to customers.
Banks should look for partners who provide secure, hybrid cloud solutions but also excel in orchestrating these multifaceted workflows. This focus ensures alignment with the unique opportunities and challenges of the financial sector.
Watch this interview with Chip Kahn.
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