Stop Trying to Be the Smartest in the Room
- Sam Uddin
- Jul 16
- 8 min read
Capital Markets Advisor | Strategy, Risk & Executive Leadership
March 5, 2026
AI Has Changed What Expertise Looks Like
Let me start with the conclusion: AI is the most significant professional shift I have witnessed in my career.
After more than two decades advising regulators, advocacy groups, and CEOs across North America, Europe, and the Middle East, I have developed a reasonably good instinct for separating genuine disruption from noise. This is not noise. Not because of what it might do someday. Because of what it is already doing, to how I think, how I work, and how I see the road ahead for everyone in our field.
AI is already reshaping how professionals think, analyse, and communicate. In this article, I want to share three things:
what AI actually is in practice,
what it means for financial services and advisory professionals, and
why early-career professionals may benefit from it more than anyone expects.
AI Is Not One Thing — Even to the Same Person
AI means different things to different people. I know this firsthand: the topic came up at a management meeting recently, and everyone in the room had a different view. But here is what's underappreciated: it can mean different things to the same person, depending on when they use it, how they use it, and for what purpose.
Some days it is a research engine helping me synthesise regulatory frameworks across jurisdictions. Some days it is a sounding board for a strategic narrative I am shaping for a CEO or a board. Some days it retrieves a risk principle or a market-structure argument I learned years ago but cannot quite pinpoint. It is the advisor who knows your thinking, holds your context, and never judges a rough draft.
At its best, AI today is not a single assistant but a panel of experts, each with a different area of depth, available to you simultaneously. One might stress-test your risk logic. Another sharpens your executive narrative. A third challenges your regulatory assumptions. And as this panel becomes more sophisticated, it increasingly understands you. Not just your query. Your reasoning patterns. Your professional vocabulary. Your standards.
That distinction matters enormously. Because once you understand it, you stop asking "will AI replace me?" and start asking the far more productive question: "how far can this take what I already do well?"
We Have Seen Structural Shifts Before - But Not Like This One
Every generation has its defining structural shift. The Industrial Revolution mechanised physical labour. The internet democratised information and restructured entire industries. Each time, the pattern was similar: disruption, adaptation, a new equilibrium.
AI follows that arc but breaks from it in one critical respect. Previous revolutions automated specific tasks or categories of work. AI is a general substitute for cognitive work. It does not replace one skill set; it improves at everything simultaneously. When factories automated, displaced workers retrained as office workers. When digital platforms disrupted financial intermediaries, professionals moved into advisory and relationship roles. But AI is improving at advisory reasoning too. And analysis. And writing. And risk assessment. And regulatory interpretation.
Matt Shumer, an AI entrepreneur whose recent essay I found unusually clear-eyed on this moment, observed that the experience tech workers have had, watching AI move from a helpful tool to something that outperforms them on core deliverables, is the experience the rest of us are about to have. In capital markets, institutional investing, legal services, and every other knowledge-intensive profession. The question is not whether this arrives. The question is whether you are positioned when it does.
What This Means for Financial Services and Advisory Professionals
In our world, we sell expertise, judgment, and trust. Those things are not going away. But the delivery of those things is changing quickly, and the professionals who understand that will be the ones who define the next era of the industry.
AI is already performing work that once required significant senior time: synthesising regulatory filings, drafting investment narratives, building risk scenario models, reviewing governance documentation, and generating first-cut stakeholder communications. The implication is not that these roles disappear. It is that the expectation of what a professional can deliver, in depth, speed, and precision, is rising sharply.
In my work as a senior advisor, the demands on executive communication, regulatory alignment, and capital-markets positioning are high and unforgiving. I have begun using AI to sharpen that work: drafting frameworks faster, pressure-testing messaging against the audience, and drawing on regulatory parallels across the markets I have worked in. It does not replace my judgment. It extends my reach.
That is the model worth understanding: AI as a force multiplier for people who already know what they are doing. Knowing how to maximize that is one of the most important professional skills in the current environment.
A Word to Junior Professionals: This Is Your Leapfrog Moment
The dominant narrative around AI focuses on displacement. I want to make a different argument, one that matters especially for early-career professionals in finance, consulting, and advisory roles.
Expertise in our field has always been a function of time: years of exposure to market cycles, regulatory changes, client relationships, and the slow accumulation of pattern recognition. AI does not eliminate that journey. But it can reshape and compress it. A junior analyst who uses AI seriously can engage with the complexity of an investment risk problem, a regulatory question, or a stakeholder communication challenge at a level that would previously have taken years to reach.
Think of it as the professional equivalent of leapfrogging. Just as some markets skipped fixed-line infrastructure and went straight to mobile, early-career professionals who embrace AI can accelerate their development in ways their predecessors simply could not. The learning curve is not gone, but it is changing shape. The education system and the workplace both need to adapt alongside it.
The expectation of what junior staff can and should deliver is rising. That is not a threat to those who engage — it is an invitation. What deserves more honest attention, however, is the short-term dislocation this is already creating: new graduates are finding it harder to secure the entry-level roles that traditionally served as their on-ramp into the profession. That is a real problem, and it requires a real response from governments and educators.
Seven AI Terms Every Financial Professional Should Understand
If you are going to participate meaningfully in this shift, a few terms are worth adding to your professional vocabulary:
SLM (Small Language Model): A leaner, faster alternative to the large AI models most people are familiar with. Unlike tools like ChatGPT that run on massive external servers, SLMs can be deployed within a firm's own infrastructure — meaning sensitive client data, portfolio information, and internal communications never leave your environment. For regulated industries where confidentiality and data sovereignty matter, this is not a technical detail. It is a strategic one.
AI Agent: An AI system that does not merely respond to prompts but takes autonomous, sequential actions: conducting research, drafting documents, executing multi-step workflows. This is where the real transformation of financial services workflows is already beginning.
RAG (Retrieval-Augmented Generation): A technique that allows AI to draw from specific, current documents or proprietary databases rather than relying solely on its training data. Highly relevant in regulated industries where accuracy, recency, and source attribution matter.
Agentic AI & Agentic Workflow: Agentic AI refers to systems that can set their own goals, make independent decisions, and act autonomously over extended periods — going well beyond simply answering questions. An Agentic Workflow is what that looks like in practice: AI handling a complex, multi-step process end-to-end with minimal human input at each stage. Think of it this way — Agentic AI is the autonomous vehicle, and the Agentic Workflow is the specific route it drives. For firms in financial services, this means entire operational and analytical processes being executed autonomously, from research and reporting to client communications and compliance checks. It is closer than most organisations have planned for.
Context Window & In-Context Learning: Think of the context window as the size of the meeting room, how much information AI can actively hold at once. In-context learning is what happens when the right people and information are in it: AI adapts and sharpens its responses based on everything you've shared in that conversation, without any formal retraining. The more relevant context you bring, for example, a client brief, a regulatory document, or a strategic objective, the more tailored and useful the output will be. For advisory professionals, this is the difference between a generic answer and one that actually reflects your mandate.
The Opportunity Nobody Is Talking About Loudly Enough
There is no shortage of alarm in the AI conversation. What is missing is an honest account of the professional upside, and it is substantial.
AI can elevate what you do. It can make you faster without making you shallower. It can help you explore more scenarios, stress-test more assumptions, and bring more to every client or board conversation. For those of us in advisory roles, where quality of thinking and clarity of communication are the product, AI is genuinely additive.
The professionals who will struggle are not necessarily those whose roles are most exposed to automation. They are the ones who refuse to engage at all: who dismiss this as hype, who feel that using AI diminishes their expertise, or who assume that their seniority, their regulatory environment, or their sector will insulate them. It will not.
But insulation was never the right goal. Integration is.
Where We Actually Land
Nobody can tell you exactly what the new equilibrium looks like. Maybe AI becomes the invisible infrastructure of how knowledge work gets done — as unremarkable as the spreadsheet or the Bloomberg terminal. Or maybe agentic automation reshapes institutions more fundamentally than most are currently planning for. Honestly, both are plausible.
What we can say is that the transition is already happening, it's accelerating, and waiting for certainty before engaging is itself a choice — and not a good one.
The adjustment isn't really about the technology. It's about how institutions, regulators, and individuals respond to it. Workplaces will need to adapt. Education will need to adapt. And risk and governance frameworks — something I've spent a large part of my career building — will need to evolve to account for AI's growing role in decision-making, model risk, and organizational accountability.
We do not yet know what the new normal looks like. But we can predict, with reasonable confidence, the direction of travel. In my experience, the people who navigate uncertainty best are not the ones who wait for clarity. They are the ones who engage early, adapt deliberately, and use what they already know as a foundation rather than a comfort zone.
AI is a companion to what we do, not a threat to who we are. The risks are real, and they deserve serious attention from everyone in risk management and governance. But so are the opportunities, and right now, they are available to any professional willing to engage with genuine curiosity and without ego.
The future will not belong to the smartest person in the room. It will belong to the person who knows how to use the intelligence now available to everyone. I would rather be the person who figured that out early.
What has your experience been with AI in financial services or advisory work? I am genuinely interested in how others across institutional investing, capital markets, and regulatory roles are navigating this — whether you are sceptical, convinced, or somewhere in between.
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