By Sarah Hoffman, AlphaSense
As enterprise AI systems become more advanced, they are moving beyond task automation toward workflow intelligence. On Wall Street, this evolution is playing out where milliseconds can mean millions and decisions can ripple across markets. Financial institutions are beginning to embed agentic AI into core operations to surface insights and accelerate decision-making.
The firms leading the next wave aren’t just adding tools, they’re rethinking workflows to achieve greater speed, precision and accountability.
Why Finance Needs Agentic AI Now
Financial institutions are under constant pressure to make faster decisions in increasingly complex environments. According to an IDC study, 26 percent of CFOs cite decision velocity as their top strategic challenge. With analysts and compliance teams overwhelmed by regulatory filings, market volatility and shifting risk indicators, the need for AI that reduces manual effort and accelerates intelligence delivery is more urgent.
Agentic AI, while still in the early stages of deployment, is showing promise for moving into decision-critical workflows. As these agents become more embedded, they have the potential to dramatically reduce the time to move from signal to strategy.
Agentic AI has the potential to make workflows more proactive, running continuous background analysis and alerting teams before events break, effectively buying decision-makers time and helping them capitalizing on opportunities.
Redesigning Workflows
Isolated automation can yield short-term gains, but the payoff will come from end-to-end redesign. According to McKinsey’s 2025 research, the most significant EBIT gains come from companies that embed AI comprehensively into processes. Yet only 1 percent of enterprises operate at true AI maturity, where use cases are vertically integrated into functions like financial analysis, fraud detection, and customer onboarding.
For finance executives, this is a call to action: rewire research, risk and reporting functions to allow AI agents to ingest, remember and contextualize data continuously. When agents combine the power of language models with institutional memory, they become valuable collaborators, identifying market shifts, flagging compliance concerns and enabling faster, higher-confidence decisions across the organization.
Governance and Auditability
Embedding agentic AI into workflows is not just a technical challenge, it’s a cultural one. For AI to work alongside traders and researchers, institutions will need to build trust in the AI’s recommendations without eroding healthy skepticism. And trust depends on governance.
Yet according to IBM’s 2025 Cost of a Data Breach Report, only 34 percent of organizations with governance frameworks audit for AI misuse, and 63 percent of breached organizations lacked any formal AI governance at all.
In finance, where regulatory scrutiny, audit trails and reputational risk are central concerns, leaders must ensure that agentic systems have embedded explainability, continuous oversight and transparent data sourcing.
The Bottom Line
AI’s next frontier isn’t about replacing analysts or automating isolated tasks. It’s about reimagining the workflows that power them. While full-scale deployment is still ahead, agents can connect dots across silos, spot market shifts sooner, flag compliance concerns faster, and ultimately give decision-makers more time to act.
Agentic AI’s greatest promise is what it makes possible for people: the freedom to focus on judgment, strategy and creativity in an environment where every second counts.
Sarah Hoffman is Director of Research, AI at AlphaSense, a market intelligence and search platform.
