If you work in the financial services industry, you already know: the pressure is on. Every day, you hear about competitors using AI to gain an edge and you see the headlines about incredible efficiency gains. Your board is asking, “What’s our AI strategy?” and your team is wondering how to get started.
We get it. It can feel like a high-stakes game of catch-up. But here’s the good news: the race isn’t over. In fact, it’s just beginning.
We recently spoke to some of the best minds in the finance industry during our webinar, AI in FinTech: Learn Growth Strategies and Explore the Rise of AI Agents. Panelists included Vishwas Srivastava, Senior Vice President of Software Engineering at JPMorgan Chase & Co.; Sarah Lackey, Vice President of Strategic Projects at Open Lending; and Guy Ellis, Chairman at Trigger. Their insights confirmed a core truth: the most successful companies are those that move with purpose.
At GAP, we believe in growth acceleration. Based on the insights from our panel, here is a practical blueprint to help you cut through the noise and deliver measurable progress.
The New Competitive Edge: The “Amazon-Type” Experience
Consumer expectations have changed. We’ve all grown used to the instant gratification of an “Amazon-type” experience, one where a decision is made in seconds, not days. This demand for speed and personalization is what’s driving AI adoption. Our clients know that to stay competitive, they must start adopting these tools to deliver what consumers expect.
For a long time, a lack of education and a deficit of trust slowed things down. But that’s changing. With more transparent success stories and a growing maturity in the technology, companies are realizing AI isn’t just a shiny new tool; it’s a necessary one.
The question is no longer if you’ll adopt AI, but how. The simple truth is, before AI can become a true business engine, it has to be grounded in operational reality and that starts with your data. As our panelists emphasized, unlocking AI’s potential isn’t just about selecting the right tools; it’s about doing the foundational work that provides AI with valuable input.
Overcoming the Data Bottleneck
You can have the most advanced AI models in the world, but if your data is a mess, you’re going nowhere fast. As our panelist Ellis put it,
“In the battle of bad data versus great AI, bad data always wins.”
This is the biggest bottleneck we’re seeing. It’s not about the technology; it’s about the foundation you build on. Boards and leaders are realizing that having clean, usable data is not a “nice-to-have” — it’s a critical prerequisite for unlocking AI’s full value.
As both Ellis and Srivastava pointed out in the webinar, AI in FinTech, it all comes down to data. It’s a classic “garbage in, garbage out” situation. So, before you even think about building a model, you need to make sure your data is clean and ready to use. That fundamental data governance work is what truly makes the difference between a project that succeeds and one that falls apart.
Focus on ROI, Not Just the Hype
Boards are rightfully focused on ROI, but often get lost in the noise of public tools like ChatGPT. The key is to shift the conversation from what AI is to what it can do.
The most successful projects solve a real business problem with clear, measurable KPIs. For example, JPMorgan Chase isn’t just using AI for the sake of it; they’re generating $1.5 billion to $2 billion in annual business value, largely by containing fraud costs and saving 360,000 legal hours a year. That’s an outcome you can put on a spreadsheet… and share with your board.
This is where good governance comes in. The core principle is that accuracy always beats speed every time. As Lackey explained, regulators don’t care about how fast you make a decision; they care about fairness and accuracy.
You have to uphold your fiduciary duties and build a culture of accountability from the get-go. As Sarah said, whether it’s under five seconds or not, you have to ensure it’s traceable and that you can explain every detail, monitoring for potential bias. It’s about putting the right guardrails in place, a practice Open Lending has done since day one by asking, “What does this look like from a regulatory perspective?”
The Human-in-the-Loop Model
AI isn’t coming for your job; it’s here to be your co-pilot.
While we’re seeing incredible progress with multi-agent tools that can automate complex, multi-step workflows, the human element remains essential.
JPMorgan’s “Ask David” tool, which automates investment research, is a perfect example. It handles the repetitive, time-consuming tasks of parsing SEC filings, summarizing research and generating draft valuations with about 90% accuracy.
This is possible through a sophisticated, three-layered architecture. First, a supervisor agent acts as an orchestrator, taking user input and breaking it down into smaller tasks. This is then handed over to the second layer of data agents, which are highly trained to handle both structured data from databases and unstructured data from the web and internal sources.
The final layer focuses on modeling and quality checks, running financial models and benchmarking the outputs before the results are sent to the user. But at the end of the day, a human banker still has to review the output, interpret the results within the market context, and make a final judgment call.
For high-stakes decisions, the human loop is still required. AI handles the rote work, freeing up your people to focus on what matters most: strategic thinking, critical judgment and building client relationships.
90-Day Action Plan
We know you need to show progress fast. So, what’s one thing you can do in the next 90 days that will actually move the needle?
The advice from our panelists was clear: fail fast and take one step at a time. As Srivastava said, chase one win that you can prove in 90 days by running a narrowly scoped pilot with measurable KPIs.
Don’t try to do everything at once. Instead, pick a business pain point and aim for a quick, measurable win. Here are a few ideas:
- Implement an AI assistant for Your Developers: This is a no-brainer. It’s the fastest way to get a quick lift and see measurable results easily.
- Automate a Single Workflow: Look at your budgets and find a workflow to automate. This is low-hanging fruit that can help you show real value creation.
- Host an Internal Hackathon: While this isn’t necessarily a quick win, it’s a great way to inspire your teams and get them digging in. And it can generate great use cases and ideas that actually lead to fast solutions.
The key is to start with a measurable business outcome and not to rush into a full transformation. It’s about securing a tangible win that builds momentum and proves the value of AI to your entire organization.
By focusing on clean data, measurable outcomes and a culture of governance, you can turn the pressure to adopt AI into a powerful engine for growth.
Watch the full on-demand webinar, “AI in FinTech: Learn Growth Strategies and Explore the Rise of AI Agents,” to hear more from our expert panelists.
We’d love to help you build on this blueprint. Explore our AI in Finance Technology page for insights tailored to your industry, and let’s connect to discuss your organization’s AI journey.
