The following guest article was written by Jorge Alvarez, Founder and CEO of evolveIQ, a New York-based firm that builds AI-driven marketing and sales systems that boost revenue for B2B companies through automation, targeted campaigns, and optimized content strategies.
Artificial intelligence is no longer a niche experiment in finance, it’s shaping the way deals are evaluated, risks are managed, and strategies are executed. But the jargon can make even seasoned professionals tune out. Let’s strip it back, start with the basics, and work our way up to the newer, more advanced concepts that are quietly redefining workflows.
From AI to Machine Learning to LLMs
At its core, artificial intelligence (AI) is about teaching machines to do things that usually require human intelligence: recognizing patterns, making decisions, and solving problems.
Machine learning (ML) is a subset of AI where systems learn from data rather than being explicitly programmed. The more quality data they consume, the better they get at predicting or classifying future outcomes.
Large language models (LLMs) take ML further, training on massive volumes of text to understand and generate human-like language. Models like these can interpret prompts, answer questions, summarize information, and generate content with a fluency that, until recently, seemed out of reach.
Generative AI: Reactive Brilliance
Generative AI uses models like LLMs to create new content: text, images, code, audio, even video, based on learned patterns. It’s reactive by nature: you give it a prompt, it gives you an answer or an output.
In finance, generative AI has already made waves for its ability to synthesize reports, draft communications, or surface insights from unstructured data in seconds. It’s impressive, but it’s not the end of the story.
Enter AI Agents: From Reactive to Proactive
The next leap is agentic AI: AI agents that don’t just wait for a prompt but can plan, decide, and act toward a goal with minimal human input.
Think of the difference this way:
- Generative AI is like a skilled analyst waiting at their desk for you to hand them a task.
- An AI agent is more like a self-directed associate who identifies the work that needs doing, finds the data, runs the analysis, and sends you the deliverable, all without being told step-by-step.
This autonomy comes from combining reasoning, memory, and orchestration capabilities. Agents can chain multiple steps together, adapt when new information comes in, and trigger follow-up actions without another human nudge.
Why the Difference Matters
More than a tech distinction, it’s a shift in how work gets done. Research shows that 73% of insights from traditional, reactive AI never get translated into action. Agentic AI closes that gap by not only producing the insight but also executing the next step.
In finance, where timing and precision matter, that’s a big deal. It means workflows can move from “insight → action” without the delays of human bottlenecks.
Adoption by the Numbers
The shift is already underway:
- Around 70% of users actively engaging with AI agents are on the buy side, with 30% on the sell side.
- Many of these users aren’t deep technical experts, but they’re domain specialists using AI in high-value workflows.
- Power users run hundreds of agents daily, processing billions of tokens of information.
- Roles most impacted include analyst functions, with estimates that up to 75% of traditional tasks in areas such as modeling, reporting, and compliance are now partially automated.
The Tech Stack Behind It
Under the hood, most of these systems blend familiar building blocks: ML models, LLMs, and generative AI, with new capabilities:
- Memory: So agents can learn from past actions.
- Reasoning engines: To decide the best next step.
- Integration layers: To connect with data sources, analytics tools, and execution systems.
The result isn’t a replacement for human judgment but an acceleration of decision-making and a rethinking of how workflows are structured.
Where It’s Going
For buy-side firms, the appeal is speed and precision in research and portfolio decision-making. For sell-side teams, it’s about scale, repeatability, and faster turnaround for clients. For the tech vendors supporting both, the opportunity lies in building the connective tissue that makes AI not just smart, but actionable.
The fundamentals: AI, ML, LLMs, and generative AI are the building blocks. Agentic AI is what happens when you arrange those blocks into a system that doesn’t just respond, but drives the work forward. And in a market where every basis point counts, that shift might be the most important AI trend yet.