AI in Practice: Bits in Glass applies automation in financial services. Source: Shutterstock

Celia Wanderly, chief innovation officer, Bits In Glass, shares how her global team is driving innovation through intelligent automation and responsible AI.

With a focus on financial services, she explains how generative AI, low-code platforms, and data governance are reshaping operational models and why diverse talent is crucial to building AI that serves everyone.

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“We work with data, we work with process, and we work with AI and all of that comes together as we build solutions for our clients,” says Wanderly.

The intelligent automation company, a long-time Appian partner, has grown from Canadian roots into a global force, with 350 people working across North America, Europe, and India. Now, Wenderly is leading the charge to embed data and AI horizontally across all lines of business.

For financial services, where efficiency, compliance, and customer experience collide, this approach is reshaping how institutions operate.

From vision to impact

Wanderly’s journey into tech began with a fascination for artificial intelligence back in her school days in Brazil. “I always loved math and science and thought I was going to be an engineer,” she recalls. “Then a university professor came and talked about artificial intelligence, and I was fascinated.”

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Though early AI studies felt overly theoretical, she eventually circled back to the field bringing decades of experience in data architecture with her. In the last six to seven years, she’s focused on using AI not just for innovation’s sake, but to solve real business problems.

One key area is what Wanderly calls “smart ingestion” a solution that tackles a common, unglamorous bottleneck: unstructured data intake.

“Clients still receive a large amount of information via email, with attachments and no standard format,” she explains. In banking and insurance, where underwriting or client verification involves this kind of data, the process can be slow and error prone.

“The perception is that putting constraints on intake like a portal could reduce competitive advantage. But with generative AI and large language models, we can mimic what a human would do with that information. We automate the ingestion, extract insights, and trigger the next step in the workflow.”

Responsible AI starts with responsible data

In a regulated industry, AI must be more than efficient, it must be ethical. “Even though we’re talking about responsible AI more now, AI governance starts with data governance,” Wanderly asserts. “And data governance is a journey, not a destination.”

Bits in Glass leverages platforms like Appian to build on trusted, compliant environments. But Wanderly is quick to emphasise that real responsibility goes deeper. “You need business principles for fairness and bias, and training for your development teams.”

She gives a powerful example: “We all think of zip codes as benign data. But in fraud detection, you might start to associate certain areas with higher fraud risk. That can then bias loan approvals, perpetuating social inequities.”

The solution? A dual-layer approach. Business leaders must define ethical standards, while technologists need tools and training to recognise unintended consequences. “It has to be AI for good and not for evil,” she adds.

In her view, AI needs checks across both business and technical teams. “You need to ask: who’s going to be using this, what decisions will be made, and who could be impacted?”

Rethinking enterprise design

Bits in Glass is also embracing the next frontier: agentic AI. “Historically, we’d map out every step in a rules-based way,” Wanderly says. “But agentic patterns let the system adapt and make decisions in context. That’s a fundamentally different paradigm.”

In financial services, this shift could be transformative from customer onboarding to regulatory compliance. “We’ve developed a regulatory change management solution in Appian. Instead of manually tracking updates and feedback across lines of defense, we now explore how AI can proactively scan for new regulations, summarise internal feedback, and even make recommendations still with a human in the loop, of course.”

The result is efficiency without sacrificing oversight.

The gender gap in AI

Asked about women in AI and tech leadership, Wanderly speaks candidly.

“We have to be mindful even as we are recruiting,” she says. “You get a lot more male candidates than female candidates. And then you have to watch for unconscious bias just because you see more of one doesn’t mean it’s better.”

In areas like machine learning and data science, the gender gap is even starker. Wenderly is working to change that through mentorship, recruitment practices, and advocacy in groups like Women in AI North America.

“I was awarded Innovator of the Year in 2020, and I was overwhelmed at the ceremony to see how much it meant to young women,” she says. “Representation matters. We need diverse voices not just for equality, but because it leads to better AI.”

Her call to action is simple but urgent: “It’s active work. If we let it happen on its own, it won’t happen.”

Staying ahead in a fast-changing world

With the AI landscape evolving at breakneck speed, Wenderly remains grounded through constant learning. “It’s daily reading. I feel like if I miss a day, I miss a lot.”

Interestingly, she finds the academic and commercial cycles converging. “Twenty years ago, I wouldn’t have imagined using academic research in client settings within weeks of publication. But now, that’s happening.”

That connection between advanced research and practical results is a distinguishing feature of Bits in Glass.

“We want to see organisations use the innovation we’re building,” Wanderly says. “Not just to be impressed by it but to see it transform the way they work.”