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Over the past two years or so, most conversations about AI in professional services have focused primarily on efficiency. Firms are using generative AI tools to complete tasks such as drafting memos, summarizing documents, analyzing spreadsheets, and assisting with research, but what if AI tools could actually "think"?
Unlike generative AI tools, agentic AI systems are designed to act more independently. They can plan multi-step tasks, make decisions (with defined parameters), interact with other systems, and execute workflows with limited human intervention. In essence, these agentic AI systems are digital teammates, and for the professional services industry, this is significant.
From Assistance to Autonomy
In most professional services environments today, AI functions as a tool. For example, a consultant might use it to summarize interview notes, an auditor might use it to flag anomalies in transaction data, and an investment banking analyst might use it to draft a pitch outline. Now, agentic AI expands upon that role.
Imagine a consulting engagement where an AI agent not only analyzes client data but also identifies gaps, requests additional datasets, runs models, and prepares a preliminary recommendation deck, all before the human team meets to refine the strategy. In accounting, an AI agent could monitor transactions continuously, flag compliance risks, request clarifications from internal systems, and draft documentation all on its own.
In banking, agentic AI systems could analyze portfolio risk, execute predefined strategies, and generate reports without waiting for manual input. Of course, this technology is still evolving (rather quickly), but AI systems are moving on from being reactive tools to proactive participants—yikes.
What This Means for Consulting
Essentially, consulting firms sell expertise, analysis, and judgment. Much of their value lies in structuring ambiguous problems and delivering actionable recommendations. Agentic AI could compress timelines significantly—multi-week processes might shrink to days, and data gathering, benchmarking, and financial modeling could become largely automated.
This won’t eliminate the need for consultants, but it will change where they create value. If AI agents can conduct large portions of quantitative analysis, consultants will be judged less on how fast they build slides and more on how well they frame questions, interpret AI-generated outputs, challenge flawed assumptions, and communicate strategic implications to senior executives.
For early-career professionals, this could alter the apprenticeship model. Historically, analysts learned by grinding through research and modeling. If AI handles more of that foundational work, firms will need new ways to train talent in critical thinking and business intuition.
Implications for Accounting
Accounting has already seen automation reshape workflows, and agentic AI could accelerate that trend. Instead of periodic audits, firms could move closer to continuous AI-driven monitoring systems. Along with this, agentic AI tools could track compliance in real time, identify unusual patterns, and escalate risks automatically.
All of this has some major implications. First, efficiency gains could reduce time spent on routine testing and documentation. Second, the responsibility for oversight intensifies. AI agents can flag issues, but they cannot ultimately assume legal or ethical accountability. Licensed professionals remain responsible for validating outputs, exercising skepticism, and ensuring regulatory compliance.
That means future accountants may spend less time manually checking numbers and more time evaluating system integrity, managing AI governance frameworks, and communicating findings to stakeholders.
Banking and Autonomous Decision Systems
In banking, agentic AI has the potential to reshape risk management, trading, and even client advisory functions. Algorithmic trading is nothing new, but agentic AI systems could adjust strategies dynamically, and coordinate across risk, compliance, and portfolio management systems in ways that are, in essence, autonomous decision making.
Financial institutions operate in heavily regulated environments. The more independent AI systems become, the more firms must ensure transparency, explainability, and of course, control. Regulators are unlikely to accept “the algorithm decided” as a sufficient explanation for real financial outcomes.
As a result, banking professionals may need fluency in not just finance, but in understanding how AI systems function and where their limitations lie.
Career Implications
For students and early-career professionals, ignoring agentic AI would be a pretty big mistake. Moving forward, it will be important to understand how agentic AI systems differ from basic generative AI, and how those systems can augment your role rather than threaten it.
Along with this, research where agentic AI systems are being deployed in your industry, and what risks and governance issues they might raise. In interviews, expect more questions about technology literacy and adaptability. On the job, those who proactively experiment with AI tools and demonstrate responsible, thoughtful use, may position themselves ahead of their peers.
It’s important to keep in mind that agentic AI isn’t some distant, abstract concept. It represents the next stage in automation’s evolution within professional services (and many other industries). Going forward, the firms that thrive will likely be those that integrate with these systems thoughtfully while doubling down on human judgment. The professionals who thrive will be those who can oversee, interpret, and strategically leverage agentic AI.
Rob Porter is an editor at Vault.
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