Been getting a few requests to do a Q&A recently and things are a bit quiet, so thought I’d open it up. Quick background:
- Industrial Engineering degree + MSc in Systems Engineering
- MBA from Kellogg (Northwestern)
- Started out at Accenture in systems/strategy consulting
- Moved to Citigroup CIB (Oil & Gas + Transportation)
- Worked at Ellington Capital (LATAM ops for a large MBS hedge fund)
- Led LBOs at AIG Southern Cone Fund (retail/food sectors)
- Time in London at Capital Partners (IB boutique) and Seymour Pierce PE (managed 2 VCTs)
- Part time consultant trainer at Training the Street during the summer
- Representing a South American IB boutique in London
Pretty open to questions — happy to talk careers, IB/PE, academia, or anything else. Ask away.
Current impact of AI in your line of work and how do you see it progressing through the broader industries in the next few years?
In my line of work — spanning investment banking, private equity, and now training analysts — AI is already having a measurable impact, but mainly as an augmentation tool rather than a replacement.
On the execution side, we’re seeing AI significantly improve efficiency in areas like financial modelling, data extraction from large datasets, and initial due diligence. Tasks that used to take analysts hours — such as cleaning data, building first-pass models, or screening investment opportunities — can now be done much faster and with fewer errors.
In training junior bankers, I also see AI changing how skills are developed. Analysts are increasingly relying on AI tools to support modelling, valuation, and even drafting investment memos. This shifts the emphasis from pure technical execution to judgement, interpretation, and critical thinking, which are becoming more valuable.
However, in core areas like deal structuring, valuation judgement, and negotiations, AI still plays a limited role. These require experience, context, and an understanding of market dynamics that are not easily automated.
Looking ahead, I see AI progressing in two main ways across the industry. First, it will further commoditise routine analytical work, putting pressure on traditional junior roles. Second, it will enhance decision-making by integrating more real-time and alternative data into investment processes — particularly in private equity and credit.
Overall, I don’t see AI replacing finance professionals, but I do see it raising the bar. The professionals who combine strong financial expertise with the ability to leverage AI effectively will have a clear advantage.
Thank you for the complete response. I agree with and appreciate your expertise here.
Quick follow up:
1. what are some higher value add tasks/workflows junior bankers should focus more on now that “grunt work” is likely to be commoditized in the future.
2. Furthermore, I’ve heard it argued that junior bankers cannot develop properly if they don’t build up the tacit knowledge that comes, for example, building a model or putting together a CIM from scratch; do you agree?
Great questions — and very relevant given how quickly workflows are evolving.
1. On higher value-add tasks for junior bankers:
As more of the mechanical work becomes automated, I think junior bankers should deliberately focus on areas that build judgement and context, not just execution.
In practical terms, that means:
Understanding the story behind the numbers
Not just building a model, but being able to explain why revenue grows, what drives margins, and what the key sensitivities are.
Industry and business model analysis
Developing a real view on sectors — competitive dynamics, pricing power, and how companies actually make money.
Interpreting outputs rather than producing them
AI can generate a model, but it cannot reliably tell you whether the assumptions are realistic or what the key risks are.
Client communication and materials with insight
Moving beyond formatting slides to contributing to the narrative — what is the equity story, why this deal makes sense, why now.
Commercial awareness
Understanding how deals are originated, negotiated, and structured — which is often underexposed at junior levels but increasingly important.
So the shift is from “can you build it?” to “do you understand it, challenge it, and communicate it?”
2. On whether juniors still need to ‘learn the hard way’:
I do think that concern is valid — and important.
There is a layer of tacit knowledge that comes from building things from scratch:
how a model breaks, how assumptions link together, how small changes cascade through outputs. That intuition is very hard to develop if you only rely on AI.
So in that sense, I would say yes — the foundation still matters.
However, I don’t think the answer is to resist AI. The answer is to use it selectively:
Early on, juniors should still build models and materials themselves, even if it’s slower
Then use AI to check, stress-test, and improve their work
Over time, shift toward reviewing and refining AI-generated outputs
In other words, AI should accelerate learning — not replace it.
If juniors skip that foundational phase, they risk becoming dependent on tools without understanding, which is quite dangerous in a deal environment where mistakes are costly.
So my view is:
AI will change how juniors learn, but it shouldn’t eliminate the learning process itself. The best analysts will be those who combine first-principles understanding with the ability to leverage AI efficiently.
Veritatis nostrum exercitationem omnis repellat. Quidem consequatur perspiciatis vero nihil nobis quam.
See All Comments - 100% Free
WSO depends on everyone being able to pitch in when they know something. Unlock with your email and get bonus: 6 financial modeling lessons free ($199 value)
or Unlock with your social account...