Recently, I had the privilege of speaking at Boston University’s TechConnect event hosted by the Questrom School of Business. My talk focused on how artificial intelligence is transforming financial services—not in the abstract, but in the real workflows, technologies, and people that make this industry move. In this post, I recap key points from the session, including trends in intelligent automation, the rise of AI-native workers, and what it means to lead a data science function in today’s financial ecosystem.
From Quant to AI: A Career Evolution Reflecting an Industry Shift
I opened by sharing a bit of my journey—starting as an engineer-turned-quant in an era when intuition ruled investment desks. Back then, quantitative analysts were tucked away in the back, their models viewed with skepticism. The financial crisis was just about to occur, with quants ultimately bearing some of the blame (quant models and tools played a contributing role in the systemic vulnerabilities that led to it).
Today, the tables have turned.
AI-powered models are front and center, helping firms scale insights, reduce bias, and accelerate decisions. The rise of quants and data scientists reflects a broader truth: data and algorithms are reshaping finance from the inside out.
Disruptive Technologies Driving Transformation
My talk centered on three technologies that are rewriting the playbook:
- Intelligent Automation: Combining robotic process automation (RPA) with AI capabilities to handle everything from invoice processing to trade settlement exception handling. Generative AI and the promise of Agentic AI are playing a role in strategic discussions and roadmap design.
- Natural Language Processing (NLP): Extracting insights from documents, news, and conversations to support client servicing, compliance monitoring, and research. Again, early tests show that Generative AI techniques can support these use cases. Time and testing will tell.
- Generative AI: Beyond buzzwords, generative AI is enabling new modes of interaction and creativity, from summarizing financial disclosures to enhancing customer experience through personalized content.
Each of these technologies is already embedded in financial services. They’re not pilots. They’re in production.
Where AI is Delivering Value in Finance
We explored several functional areas where AI is delivering measurable improvements:
- Client Servicing: Chatbots and virtual assistants that reduce friction in client support.
- Portfolio Management: Models that distill news, earnings reports, and sentiment into investable signals.
- Trading: Adaptive algorithms that adjust strategies in real-time.
- Post-Trade Processing: Automated reconciliation, exception resolution, and settlement accuracy.
- Compliance: Real-time surveillance and automated regulatory reporting.
My message was clear: AI isn’t a future opportunity—it’s a present asset.
Augmenting the Workforce, Not Replacing It
A recurring theme in the Q&A was whether AI will replace human jobs. I emphasized that the most effective use of AI isn’t substitution—it’s augmentation. AI frees up human capacity to focus on what machines can’t do: critical thinking, creativity, empathy, and ethical judgment. Specifically, as it relates to the hype around Generative AI, LLMs are big prediction engines that don’t add new knowledge. They can bring existing knowledge to our fingertips, but they can’t expand the universe.
We’re seeing the emergence of a hybrid workforce where humans and machines co-pilot outcomes. And with that comes the need for a new kind of worker: the AI-native—someone fluent in collaboration with intelligent systems.
Culture, Leadership, and the Human Side of Change
Adopting AI at scale isn’t just about data pipelines and compute power. It’s about culture change. I discussed the importance of reskilling, change management, and building trust in AI systems. Organizations that lead with transparency, ethical design, and employee empowerment will thrive.
This led to a closing point that resonated with many students in the room: A human should not do what a machine is best suited for. A machine should not do what a human is best suited for.
Final Thoughts
BU TechConnect was an energizing event. I walked away, impressed by the depth of curiosity and the quality of the questions. Students today are entering a financial services industry that looks nothing like the one I joined—and they’re doing it with a mix of optimism, technical fluency, and a desire to shape a more collaborative future between humans and machines.
If you were there, thank you for engaging. If you missed it, connect with me on LinkedIn, and let’s keep the conversation going.
Disclaimer: All views are my own and do not reflect those of my employer. No confidential information is disclosed here.