Jordon Wolfram

Jordon Wolfram’s leadership has been shaped by startup customer service, financial services and Amazon. Those experiences taught him to understand customer needs, make data-driven decisions and build for scale. At Symple Lending, he has carried those lessons into a broader view of data leadership, where strong foundations, shared definitions, experimentation and capable people determine how useful data can become.

Moving Data into the Decision

Wolfram distinguishes between “running data as a service” and “running data as a product.” The first delivers dashboards and analysis for others to interpret. The second puts data directly into systems that can help make decisions in real time. At Symple Lending, he has applied that understanding by positioning data as its own vertical rather than placing it within marketing, sales or technology. The structure allows Wolfram’s team to work across the customer journey while helping individual departments make better-informed decisions. A real-time recommendation engine, for example, uses customer information to identify relevant financial products and route customers to appropriately trained sales agents.

As Wolfram says, “AI only works well if your data environment is rock solid. Our team therefore puts significant effort into collecting and curating information so employees without specialized data expertise can use it confidently.”

Decisions Require Shared Meaning

In democratizing data, Wolfram sees a leadership challenge as much as a technical one. Broader access creates little value if different teams interpret the same metric differently.

His response is a centralized, code-based ontology that gives commonly used terms shared definitions, version control and reproducibility. Access is governed by responsibility, including protections around personally identifiable information, while stakeholder feedback helps expand the vocabulary over time.

Wolfram stays cautious against optimizing a metric without considering its downstream consequences. A message may increase customer responses, for example, while ultimately damaging the experience if it becomes too aggressive.

Customer conversations give him another way to test whether the organization understands what customers need. Using LLMs, GraphQL and vector databases, his team compares conversations that resulted in loans with those that did not, surfacing pain points and effective responses that help shape agents’ sales scripts.

Good data leadership, in his view, requires asking not merely whether a number improved, but whether the decision behind it produced the right outcome.

Let Production Challenge Assumptions

For Wolfram, experimentation is where an idea earns credibility. Production testing gives a hypothesis the chance to prove itself against evidence rather than intuition. It’s this mindset that has made A/B testing central to his approach. Ideas from employees closer to a product have sometimes produced stronger results than options he initially considered more logical, reinforcing his belief that evidence should have the final say.

The same idea influenced how Wolfram built Symple Lending’s data architecture. Recognizing that the first version would not support long-term growth, he planned for 10x, 100x and 1,000x scale. The architecture has since supported roughly 1,000x growth, with dynamically scaling cloud infrastructure and CI/CD allowing changes to be tested, monitored and rolled back when necessary.

AI can Extend Judgment but Not Replace IT

Where Wolfram’s approach to AI becomes especially clear is in compliance, where scale cannot come at the expense of judgment. For him, the greater value lies in creating the consistency and transparency needed to operate responsibly as the organization grows.

His team uses AI to review customer conversations for required information, unrealistic promises and context. Compliance officer feedback informs the patent-pending process, while performance standards are tied to compensation across the organization. Wolfram estimates the system performs work that would otherwise require roughly 100 people to listen to calls. Symple Lending is now moving toward real-time monitoring so managers can address potential issues as conversations unfold rather than correcting them afterward.

Building people is part of the same discipline for Wolfram. He wants data skills to spread across the business, but not at the cost of the careful judgment needed to use them responsibly.

“I try and make myself redundant,” he says.

For Wolfram, developing people means teaching them not only how to access data, but when the evidence, time or context is insufficient to support a conclusion. As AI makes the technical “how” easier, he believes data leaders must focus more on why a decision should be made and what it should ultimately achieve.