AI in financial services has moved from lofty claims about AI's use to being at the center of the growth conversation, shaping how you compete, run, and stay trusted. Margins are under pressure as customers benchmark you against their best digital experiences, whether they are opening a checking account, redeeming loyalty points, disputing a card charge, or seeking hardship help.
This article looks at AI through two lenses that matter most to financial‑services CEOs: how it drives internal growth (how you operate and enable talent) and external growth (how you win and keep customers).
Analysts and boards are already asking a direct question: where does AI show up in the way you grow, not just in how you cut costs. McKinsey’s latest State of AI research notes that financial services leaders are pulling ahead by using AI for both efficiency and revenue, while laggards struggle to demonstrate impact beyond pilots. A CNN report on Scale AI’s work with platforms like Meta shows how quickly AI investments are turning into revenue and cost advantages for firms that move beyond experiments and embed AI within core businesses.
In that environment, “we have some AI use cases” is no longer enough. CEOs need holistic, yet simple and defensible narrative about how AI improves both the economics and the experience of doing business.
On the internal side, AI is changing how efficiently you operate, how quickly people ramp up, and how attractive you are to the talent you need next.
Operational efficiency and risk controls
AI is already helping banks streamline KYC and AML checks, automate document intake, and strengthen fraud and transaction monitoring across cards and payments. Models that triage alerts and highlight truly suspicious behavior reduce false positives, take pressure off compliance teams, and make it harder for bad actors to slip through.
Onboarding and the learning curve for staff
Relationship managers, underwriters, and contact‑center agents face steep learning curves in complex product and regulatory environments. AI‑powered guidance, coaching tools, and knowledge retrieval platforms can shorten time‑to‑competency by surfacing the right policies, product details, and suggested next steps in the flow of work, rather than relying on static manuals and shadowing alone.
Attracting and enabling top talent
For technologists, data scientists, and digitally savvy commercial leaders, a modern AI environment is a signal of how serious a firm is about innovation. Institutions that offer real datasets, contemporary platforms, and a path to ship AI‑enabled features into production have an easier time attracting and retaining the people needed to keep up with the market.
When you connect these capabilities to metrics you already track, such as cost‑to‑serve, operational losses, time‑to‑competency, error rates, they become part of a tangible internal growth story, not just back‑office experimentation.
Externally, AI is starting to separate institutions customers describe as “easy to deal with” from those that feel dated and frustrating. The most direct path from “AI strategy” to revenue and loyalty runs through a series of customer journeys where you can remove friction and make interactions feel more personal.
Onboarding that feels almost invisible
Account opening, card issuance, and loan applications still create a lot of drag, with customers re‑entering data, uploading the same documents across channels, and waiting while manual checks happen behind the scenes. AI can recognize intent earlier, pre‑populate and validate data from trusted sources, and route cases based on risk profile instead of blanket rules, leading to more digital account openings completed in one sitting, faster card approvals and instant issuing where risk allows, and a smoother first experience.
Personalization that goes beyond a first name
Most personalization in financial services still looks like broad segments and generic cross‑sell, regardless of what is happening in a customer’s financial life. AI lets you use real behavior, balances, and interaction history to tailor offers, advice, and timing, recommending next‑best actions to bankers and advisors, adjusting digital prompts based on how customers respond, and aligning pricing or rewards with demonstrated preferences, which shows up in product penetration, primary‑relationship share, and retention.
Beyond individual customers, AI can scan market “winds” and competitive moves, such as how peers are pricing, which products are gaining traction in which segments, to inform product and portfolio strategy. That helps leadership decide where to lean in, where to pull back, and how to position offers before trends are obvious in lagging indicators.
Service, disputes, and collections that adapt in real time
Customers experience your institution most often when something is wrong: a declined card, a suspicious transaction, a disputed charge, a hardship request. These moments carry disproportionate weight in how they describe you and how long they stay.
AI can triage intent in digital and voice channels, surface the right answers, and guide agents based on context instead of rigid scripts, so card disputes, fraud alerts, and claim calls get resolved with less back‑and‑forth. In collections, AI can help tailor outreach channels, timing, and repayment options to preserve relationships and improve recovery, rather than pushing every account down the same path.
If you focus on just a handful of these journeys and link improvements to revenue, cost, and risk metrics you already report, AI becomes part of a clear external growth story rather than a buzzword.
It is tempting to treat AI as a technology topic and hand it off to a specialized team, but in financial services it touches trust, fairness, and your license to operate in ways that go far beyond model accuracy. A mis-targeted product offer can quickly become a reputational issue, and a biased or opaque model can trigger regulatory scrutiny, not just customer dissatisfaction.
CNN’s reporting on how firms like Scale AI monetize data‑labeling and model‑training work for major platforms shows how much money and influence now sit behind AI systems most customers never see, while business commentary warns that companies that fail to integrate AI thoughtfully into operations risk being out-executed even if individual employees are doing their best work. Inside your institution, relationship managers, advisors, underwriters, and service leaders will only adopt AI if it clearly helps them have better conversations and make better decisions; if tools create awkward customer moments or undermine their judgment, they will be quietly worked around.
This is why visible CEO‑level sponsorship now matters: where AI will be used, how it will be governed, what “fair and helpful” means in your context, and how human judgment remains central. This is not about owning every technical choice; it is about setting the guardrails and priorities within which your teams build.
Most institutions have proofs of concept scattered across functions; far fewer have a straightforward narrative that connects those efforts to outcomes that matter to boards, regulators, and employees. You do not need a long list of use cases to tell that story; you need a short list of priority journeys where AI‑enabled friction removal and personalization can change your trajectory.
Three questions are a practical place to start:
Where, specifically, is friction in our customer and employee experience costing us growth or loyalty and which of those moments could AI realistically improve in the next 12–24 months?
How will we govern AI‑driven personalization, fraud controls, and risk models so they feel helpful and fair to customers, and defensible to regulators and leadership?
What sponsorship, talent, and partners do we need so AI is embedded in how we serve, price, and advise customers, not just in one innovation program?
If you can answer those with clarity, AI stops being a side conversation about technology and becomes a concrete part of how you grow: by making banking more personal, less frustrating, and easier to choose again.