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If generative AI was the breakthrough technology of 2023 and 2024, agentic AI may be the next big step, and it’s already getting serious attention in consulting, finance, and accounting. In the past year, many professional services firms have begun experimenting with AI systems that can do more than generate text or analyze data. These systems can plan tasks, take actions, and execute multi-step workflows with limited human supervision. So, what exactly is agentic AI? How are firms using it, and why should students and professionals in consulting, finance, and accounting care?
What Is Agentic AI?
Agentic AI refers to artificial intelligence systems that can automatically plan, reason, and carry out tasks to achieve a specific goal. Unlike traditional AI tools that simply respond to prompts, agentic AI systems can determine how to complete a task, access tools or data sources, and execute multiple steps in a workflow all on their own.
To break it down into simple terms, traditional automation follows a fixed script, while generative AI produces outputs like text, images, or code. Agentic AI plans, decides, and performs actions. For example, an agentic AI system might be given a goal such as: “Analyze this company’s financial performance and prepare a presentation.”
Here, instead of simply generating text, the AI might retrieve the financial data, run analysis, create charts, draft slides, and finally, refine the output based on feedback. These agentic AI systems can even work together, forming networks of specialized AI agents that collaborate on complex problems.
Why Professional Services Firms Are Interested
Consulting, accounting, and financial advisory firms operate on the premise of delivering expertise and analysis, and agentic AI could dramatically change how that work gets done. Agentic AI is particularly good at tasks that involve research and data gathering, document analysis, financial modeling, report generation, and workflow automation.
In many cases, these are the same tasks traditionally performed by junior staff. Now, this doesn’t necessarily mean AI will replace entry-level roles, but it may change how these roles work.
How Consulting Firms Are Using Agentic AI
Many major consulting firms are already experimenting with agent-based AI systems internally and for client work. For example, several large firms have built internal AI platforms that help consultants research topics, analyze data, and draft presentations more quickly. Some systems are even designed to orchestrate multiple AI agents working together.
Check out these real-world examples of agentic AI in action:
Supply Chain Optimization
Consultants have deployed agentic systems that automate demand forecasting, optimize logistics, and reduce costs. One project reportedly reduced inventory costs by 18% by automating forecasting and operational decisions.
Sustainability and Climate Strategy
AI agents can analyze emissions data, model decarbonization scenarios, and generate recommendations for companies that are pursuing net-zero goals. Before agentic AI, this sort of work would require months of manual analysis.
Enterprise Automation Platforms
Some consulting firms have created platforms that allow multiple AI agents to “collaborate” across systems, connecting tools from providers like cloud platforms, analytics software, and CRM systems.
Applications in Finance and Accounting
Agentic AI also has many uses in finance and accounting, as those industries are built around complex workflows and large volumes of data. Here are some examples of potential applications:
Financial Analysis and Forecasting
AI agents can monitor market conditions, analyze financial data, and update portfolio strategies or forecasts in real time.
Audit and Compliance
Agentic systems can automatically review documents, check regulatory compliance, and flag anomalies for human auditors.
Workflow Automation
Finance teams often spend hours transferring data between systems. Agentic AI can automate these processes, improving accuracy and reducing delays.
The Challenges of Agentic AI
Despite all the excitement surrounding agentic AI systems, they’re still far from perfect. As with most workplace technologies these days there are concerns around data security, and since professional services firms handle sensitive client information, AI governance is absolutely critical.
On top of that, agentic AI raises questions about accountability. For instance, if an AI agent produces a flawed analysis or recommendation, who is responsible? Agentic AI systems not only require high-quality data, but careful human oversight. Without those elements, projects can fail to deliver expected results.
What This Means for Early-Career Professionals
For students and young professionals entering consulting, accounting, or finance, the rise of agentic AI means AI literacy is becoming a core skill. Future consultants and analysts may spend less time gathering information and more time interpreting results, advising clients, and overseeing AI.
Moving forward, it will be incredibly important to learn how to work alongside AI. Set aside some time to research how AI fits into your role, and how you can use it to bring value to your employer (or potential employers). Agentic AI may still be in its early stages, but judging by how quickly generative AI became a staple of the modern workplace, it’s a safe bet that agentic AI will reshape how work is done in the professional services industry.
Rob Porter is an editor at Vault.
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