AI Agents in Finance: What They Are, Why They Matter, and How to Benefit

You’ve probably noticed AI agents in finance popping up everywhere, from trading desks to audit teams. But what exactly are AI agents, and why do they matter for a career in FP&A? 

Imagine a digital teammate constantly watching data, spotting issues instantly, deciding next steps, and acting without your input at every step. With tighter deadlines and huge datasets to process, finance professionals who can use AI agents spend less time on repetitive tasks and more on strategic analysis.

This guide breaks down AI agents in finance: what they are, where they’re already being used in finance, how they’re reshaping career paths, and the skills you’ll need to stay ahead.

What are AI Agents in Finance? 

An AI agent is software that independently senses data, evaluates it, makes decisions, and executes actions with minimal human guidance. It runs continuously through a clear three-step loop:

• Sensing: Gathering and interpreting data.
• Thinking: Evaluating possible actions and outcomes.
• Acting: Taking action based on what it has learned.

How are AI Agents Different From Other AI Technologies?

Unlike traditional AI tools, AI agents handle complex tasks autonomously and can adapt in real-time. Here’s how AI agents differ from other types of AI technologies:

• Machine learning models: Perform specific predictions but can’t independently determine next steps. They require human interpretation and action.
• Large Language Models (LLMs) like ChatGPT: Require explicit instructions to act and can’t autonomously continue processes.
• Expert systems: Depend on rigid, pre-programmed rules and cannot easily adjust to unexpected changes.

In contrast, AI agents proactively plan and execute multiple actions. They integrate seamlessly into financial platforms, such as:

• Trading software.
• Enterprise resource planning (ERP) systems.
• Real-time analytics dashboards.

This enables finance professionals to dynamically manage rapid market shifts and internal workflows more effectively.

High-Impact Use Cases of AI Agents in Finance

Finance teams are already seeing concrete benefits from AI agents in finance across several high-impact workflows:

• Financial process automation: Deloitte’s Zora AI agents autonomously gather, reconcile, and summarize critical financial data, significantly accelerating finance workflows. This reduces manual errors and frees finance teams to focus on strategic analysis and insights.
• Enhanced auditing: PwC’s Next-Gen Audit platform deploys AI agents to proactively identify financial irregularities, flag compliance issues, and enhance overall audit accuracy, significantly reducing risk exposure.
• Real-time risk monitoring: MSCI’s AI agent identifies changes to the risk and performance characteristics of client portfolios and shares insights with investment managers for proactive risk management.

Emerging Capabilities to Watch

As AI agents in finance evolve, they’re gaining powerful new capabilities that directly enhance your workflow:

• Personalized wealth-advice chat: AI agents integrate client data with portfolio performance and real-time market data, delivering timely, customized investment ideas directly to clients. This allows human advisors more time to engage in strategic financial planning.
• Goal-oriented planning: AI agents autonomously map out complex tasks, anticipate required steps, and make proactive decisions without ongoing guidance.
• Agent-to-agent collaboration: Agents securely share data and insights across multiple finance functions, such as treasury and FP&A, to streamline processes and strengthen coordination.
• Autonomous data preparation: AI agents independently gather, clean, and integrate real-time market and financial data. This significantly reduces your data preparation time and increases the accuracy of your financial analyses.

Opportunities and Challenges for Your Career

Finance roles are evolving quickly with the adoption of AI agents in finance, creating meaningful changes in the daily work and career trajectories of professionals.

By automating repetitive tasks, AI agents free up time to focus on activities that make a bigger impact, such as scenario analysis and strategic decision support. To successfully capture these opportunities, you’ll need new skills, including:

• AI agent orchestration (managing and coordinating different AI workflows).
• Prompt engineering (writing clear, effective AI instructions).
• Multi-agent collaboration (understanding how multiple AI agents interact across finance processes).

However, these advancements introduce some career risks as well:

• Tasks involving highly structured routines might become partially automated, potentially reducing demand for purely transactional roles.
• Professionals remain accountable for AI agent outcomes, making strong oversight critical.

Here’s a clear snapshot of these considerations:

To successfully navigate these shifts, proactively developing AI-related skills and embracing oversight responsibilities will become critical for your ongoing professional growth.

Take the Next Step to Level Up Your AI Skills

Mastering AI agents in finance positions you for oversight roles critical to finance teams adopting automation. Staying competitive as finance evolves means proactively building your AI skills, especially in areas like prompt design, data governance, and multi-agent collaboration.

Ready to excel in AI? CFI’s AI for Finance Specialization equips you with foundational and advanced skills to integrate AI into modern finance workflows. By the end of the program, you’ll be ready to thrive in finance roles that demand AI innovation and technical expertise.

Start Learning Now!

Additional Resources

AI and Finance Jobs: How AI is Reshaping Careers in Finance

Why Explainable AI is Critical for Financial Decision Making

AI and Financial Statement Analysis: Tools and Techniques

See all AI resources