Having spent the last few years on the front lines of AI in finance, deep inside workflows, use cases, and conversations with real operators, I have an important update: the game has changed.
We’re well past the point where the latest model architecture (GPT-4, Claude, LLaMA, Deepseek, etc.) is the differentiator, or even ‘deep research’. These models are astonishing in capability, yes, but they’re also increasingly commoditized. The edge has shifted.
Context is now the competitive advantage.
Agents, like specialized regions of the human brain, focus their computational resources on specific areas of expertise. This specialization enables high proficiency and efficiency, much like dedicated neural circuits drive complex cognitive functions. By concentrating their scope, Agents utilize targeted knowledge for more effective and insightful results within their domain.
They speak the language of their verticals, know the flow of real decisions, and operate with the confidence that only comes from being tightly integrated into the surrounding ecosystem.
What’s emerging as the real frontier is orchestration: the infrastructure layer that surrounds these Agentic models and turns general intelligence into specific, trusted outcomes. That means everything from memory systems and data pipelines to decision logic, compliance rails, and personalized prompts. In other words, not just “what” the AI says but why, how, and with what awareness of the environment it’s operating.
Agentic AI is about embedded intelligence, referring to agents that reside within broader company workflows and earn trust through context.
Each implementation (we do many with some of the largest regulated Asset Managers) starts with a use case that takes time to mature. Organizations deploying AI systems are learning to embrace the reality that every tweak and evolution makes the system disproportionately better - this is a journey rather than a destination. This compounding intelligence is where real value is built.
Let me be clear: trust, not speed, is the gating factor in regulated or high-stakes environments. Finance, legal, healthcare - they don’t reward novelty. They demand observability, determinism, explainability, and compliance by design. This is where the focus lies, not on flashy demos, but on building durable systems that can be trusted.
Agentic AI is about embedded intelligence, where agents reside within workflows and earn trust through context.
And that’s the point. The orchestration layer is where context lives. It’s where signals from structured and unstructured data come together, where rules are enforced, where specific history is remembered. Where reasoning happens.
With GREAT context, foundation models, amazing as they are, are finally able to deliver reliable output.
If you’re building or adopting AI right now, here’s what I’ve learned in a nutshell:
- Models alone don’t win. Specific systems, data and workflows do.
- Context isn’t fluff. It’s the new infrastructure.
- Patience isn’t a delay. It’s a strategy.
- The corporate winners are not chasing virality. They’re earning trust.
This AI “renaissance ”is just beginning, and the companies that understand this shift from generation to orchestration, from “wow” to “how”, will define the next decade of AI in their verticals.
Build for the long term - this is how we can ensure a safe future for all of us in this new world.
Taking inspiration from Confucius, my learning is this: “Your AI journey of a thousand miles progresses one step at a time.”
