Many finance and accounting leaders complain that their work is increasing at a time when labor is critically constrained. Data from the American Institute of Certified Public Accountants finds that roughly 75% of all licensed CPAs reached retirement age by 2019, creating a talent shortage that hasn’t recovered in the years since.

Simultaneously, artificial intelligence is raising questions around staffing and capacity. Organizations are slowly coming to a consensus that they don’t need to backfill headcount for manual data entry, reconciliation or similar repetitive tasks. Instead, they can actively redesign the work they want finance teams to do. That means prioritizing analysis, strategy, exceptions and insights. Rather than simply keying data into spreadsheets, teams are increasing their focus on financial planning and analysis.

In manufacturing and distribution, the role of finance goes far beyond cash management, invoices and cash application. These teams are ultimately responsible for overhead rates, evaluating production investments, compliance, inventory costing and in-transit inventory ownership, all in the face of growing volatility.

This leads to complex audits and unique challenges across the P&L. As such, modern teams are increasingly turning to AI and automation. Adoption of generative AI in tax, accounting and audit firms jumped from 8% in 2024 to 21% in 2025, representing the sharpest increase of any professional services segment. 

Some of the most powerful use cases center around automating low-value, repetitive work such as flagging variances before month-end, automating reconciliation, detecting data anomalies across systems, predicting cash flow swings, and surfacing insights in an instant that used to take teams days or weeks to assemble. With the help of AI, finance professionals gain the insight and advance notice to make adjustments early, before problems escalate. 

It’s important to emphasize that AI isn’t replacing finance professionals. Rather, it’s providing the kind of modern tools they’ve needed for years now. 

As a result, both sides of the business are improved: Finance roles become more engaging and impactful, while organizations benefit from faster, better-informed decisions.

AI can certainly be transformational, but it isn’t magic. It’s important to set the right groundwork in order to realize the full value of AI automation across finance and accounting.

Following are what teams need to prioritize:

Data readiness and process clarity. Many organizations are hesitant to get started because their data ecosystem isn’t ideal. But even imperfect data can provide a useful starting point. What’s more, AI can be used to clean, enhance and map that data. This accelerates the entire process.

At the same time, successful AI-powered automation requires a solid understanding of existing and even ideal business processes. Teams must be able to articulate what their current process entails, what the process actually should look like, and why or when individuals should deviate from it.

This clarity is what allows AI to operate safely, effectively and at scale. While neither are prerequisites preventing you from starting, investing in either data quality and connectedness or process documentation and mining can significantly increase your ROI.

AI agents in addition to automations. Traditional automations have their place in supply chain operations, but agentic AI represents the next evolution. AI agents operate across systems, orchestrating workflows, handling exceptions and continuously learning from outcomes. They’re able to reason and make flexible decisions, instead of just executing prewritten code.

In finance and accounting, this increasingly means that agents span enterprise resource planning, production systems, inventory data, forecasting models and compliance workflows, enabling strategic, coordinated action. Reducing “swivel seating” and double-entry alone can deliver quick results.

As more traditional automations work to drive efficiency and build dashboards, AI agents create decision-making systems that operate continuously and intelligently.

Buy-in and oversight. To ensure success, teams need to align on enablement. This starts with executive team agreeing on business objectives and the AI budget. The execution team, whether cross-function or IT-led, must work through what technology to use, how to validate and test, and ultimately how to govern and control models, agents and AI solutions.

The best outcomes tend to come from a structured approach, moving deliberately from alignment to prioritization, rapid prototypes, production deployment and finally ongoing continuous improvement, governance and controls. Throughout the process, leadership must also express excitement and drive organizational changes proactively.

As organizations look to navigate volatility, labor constraints and growing complexity, AI offers a powerful opportunity to up-level finance and accounting teams. Staff can begin to focus on more strategic, embedded and proactive decision-making, provided the right automations and agents are in place.

As a result, organizations become better-positioned to understand, protect and grow their margins — an increasingly critical goal in the face of greater complexity and globalization.

Dag Calafell is director, technology innovation with MCA Connect.