Financial services incumbents play a cautious game when it comes to adopting emerging technology. The playbook is simple: wait, watch, and only act once the technology has been proven and de-risked.
AI is upending the playbook.
Instead of waiting on the sidelines, financial institutions are diving in headfirst—leveraging AI to drive efficiency, improve decision-making, and unlock new competitive advantages.
Why Now? The Catalysts for AI Adoption
Several factors are accelerating AI adoption in financial services:
- Powerful technology: The foundational large language model (LLM) technology is remarkably powerful.
- Explosive growth in data parameters: The volume and variety of available data are growing exponentially, including images, audio (and video).
- Scalability and cost-efficiency: Training AI models and drawing inferences is becoming faster and more affordable.
- Breakthrough user experience: AI has become more intuitive and accessible, making integration into workflows easier than ever.
But perhaps the biggest driver? Financial institutions no longer see AI as a threat. They see it as an imperative.
From Caution to Confidence: Incumbents Are Ready
The old narrative suggested that legacy financial firms were too slow, risk-averse, and bureaucratic to capitalize on technological shifts. That’s not the case with GenAI.
Today, banks, wealth managers, and insurers are doubling down on AI because they have the assets that matter most:
- Data: There is no AI without data, and incumbents have it in abundance. They sit on vast interaction and transaction datasets, coupled with deep contextual insights like customer sentiment and life
- Brand and relationships: Trust is a currency, and established firms have decades of credibility with customers.
- Talent: AI engineers and data scientists increasingly see financial services as an attractive career path. As The Wall Street Journal recently pointed out, AI jobs in banks are now some of the most desirable in tech.events.
AI isn’t just another technology trend for incumbents—it’s a defining shift that leaders and shareholders are demanding investments in. The commitment is coming from the top: Boards and CEOs are all-in.
The Real AI Opportunity is in E&E: Efficiency and Experience
AI and machine learning have been embedded in financial services for years. But generative AI has unlocked a new dimension: relatability and accessibility.
Incumbents are leveraging trained models to perform tasks – and some reasoning – and the rapid improvements in AI training and inferencing costs continuously expands the scope of scalable use cases.
Don’t let me overstate it; models still require a lot of fine tuning.
Nevertheless, incumbent firms are balancing innovation with risk, starting with use cases that deliver efficiency and are low-risk, low visibility and high-explainability e.g., meeting summarization, enterprise search, data transformation, deepfake identification. They are being cautious and experimental with high-stakes applications like credit decisions.
…But it is no longer just about backend automation—it’s about reshaping client interactions and workflows in a way that feels seamless, dynamic, and participatory.
Take chatbots, for example. In the past, many banks dismissed them as frustrating and ineffective. But with generative AI, chatbots are evolving into highly interactive, intelligent assistants that enhance customer engagement.
Consider portfolio allocation and rebalancing. What was once a rigid, one-sided process using pre-defined inputs can now become a conversational, interactive experience where clients collaborate with AI-powered advisors to make informed decisions.
As AI-powered agents become more sophisticated, adoption will only accelerate—creating a flywheel effect that drives expansion into more use cases.Leadership at technology firms is already asking: Before asking for headcount, demonstrate why using AI cannot get you what you want. Leaders in financial services are thinking on similar lines. It is only time that this thinking shows up in their resource allocation.
The Biggest Challenge Isn’t the Tech—It’s Change
AI is already reshaping competitive advantage in financial services. But success won’t be determined by who has the best technology—it will be determined by who can adapt the fastest.
That means firms must rethink talent, incentives, and working models.
- Breaking down silos: AI-driven efficiencies and enhancements require teams with customer context and the domain knowledge to collaborate across functions, products, and customer interactions.
- Managing ethical, regulatory, and compliance risks: From prompt hygiene to validating outputs, AI adoption brings new challenges in governance and security. Model risk is now elevated to a principal risk category. AI governance needs to be woven into business governance, with the agility to support innovation and robustness to protect the firm.
- Redefining success metrics: Speed, continuous improvement, and collaboration will become the defining KPIs for AI adoption. Firms will need to measure speed-to-scale, streamline decision-making, and increase throughput in product and service delivery.
The Future: Who Will Execute Best?
The era of AI skepticism in financial services is over. Incumbents are no longer playing defense—they’re actively investing, experimenting, and scaling AI-powered solutions.
The question isn’t if AI will transform financial services. That’s a given.
The real question is: Who will execute best?
Want to hear more? Check out Atul Kamra’s full webinar with Purdue University’s Mitch Daniel’s School of Business here: link
