Artificial intelligence (AI) is everywhere in today’s conversations about secured finance - and for good reason. But talk is one thing. The real question is: how do we move beyond the hype and into something that actually works?

That’s exactly what we explored in a recent Secured Finance Network webinar, which was presented by SFNet’s Data and Tech/AI Committee. I joined a panel of fellow technology leaders to discuss where AI is making a difference right now, and what might be around the corner. The session, moderated by ABLSoft’s Nancy Lee, brought together a range of views from across the industry, including  those from Paul Bower of FGI Technologies, Solifi’s Eldon Richards, Finley Technologies’ Jeremy Tsui, and myself.

As someone focused on product strategy and innovation at Lendscape, I found the conversation a great opportunity to reflect on where AI truly makes an impact - not just what’s theoretically possible, but what’s already happening

Here are some of the ideas that stood out from the discussion.

Rewiring the ABL Landscape

To date, a lot of today’s AI activity in secured finance is happening in familiar pain points: credit and risk management, collateral monitoring, and compliance. These areas are data-heavy and have labor-intensive processes - and naturally lend themselves to automation. But many firms are still asking: where do we start?

Start small, scale fast was something we all agreed upon. Targeted pilots, focused on specific outcomes, let teams learn quickly and build momentum before rolling things out more widely.

Of course, AI is only as effective as the data behind it, and a key theme in the discussion was the importance of high-quality, real-time data. AI is only as good as what you feed it. The better the quality and quantity of your data, the more accurate and useful the results. Large organizations might have enough internal data to train their own models. But for others, working with providers who can securely pull together anonymized data from multiple sources is a much more realistic path.

When choosing those partners, it’s not just about who has the biggest dataset. You want someone with deep technical expertise and a proper understanding of the secured finance world - not just a generic AI vendor.

For me, one of the most promising developments is how AI helps users interact with complex systems in more natural, intuitive ways. Instead of delivering abstract outputs or technical scores, newer platforms enable clear, plain-language responses to everyday questions. That kind of clarity builds trust. And when people trust the system, they’re far more likely to act on what it tells them.

Lowering the Technical Barrier

Another encouraging shift is just how much easier these systems are to use. Interfaces are becoming more intuitive. Conversational tools - the kind that let you type in a question and get a useful response - open things up to a wider group who no longer need technical expertise to benefit from advanced systems.

In short, AI is lowering the technical barrier to adoption. That means more people across the business can dig into the numbers, ask better questions, and make faster decisions. AI starts to feel less like a bolt-on and more like a natural extension of expertise, empowering more people to analyze, contribute, and drive innovation.

Anticipating the Risks

That said, AI adoption isn’t without risk and there are critical caveats to bear in mind. We all agreed that human oversight is non-negotiable. AI can support decision-making, but it shouldn’t remove accountability.

This is especially important in a highly regulated environment like secured finance. You’ve got to understand how the model arrives at its conclusions - and you need to be testing for bias, fairness, and transparency from the start.

From my perspective, I think one of the biggest risks is wasting time. It’s easy to get pulled into an AI project without having a clear objective or a solid data foundation. That’s a fast track to frustration. Starting with a sharp problem statement and clean, structured data makes all the difference to make real progress.

An Unimagined Future

We spent some time talking about what’s next - and it’s not just about efficiency. AI has the potential to unlock new business opportunities. Smarter prospecting, better cross-selling, more personalized client experience - it’s all on the table.

But perhaps the most important takeaway? AI isn’t here to replace people. It’s here to augment them. By taking care of repetitive work, it creates space for people to focus on higher-value thinking and more impactful work.