A CFO’s Guide to Responsible AI

The modern CFO is responsible for far more than budgets and financial reporting. Finance leaders are expected to prioritize technological investment to provide real-time guidance.

But middle-market CFOs increasingly face challenges from relying on outdated systems and not prioritizing strategic implementations. However, as demand for AI and automation grows, there are ways for CFOs to adapt with resilience and agility.

Today’s Technology Landscape

The tech environment is rapidly changing, and many CFOs are simply trying to stay afloat.

Trends include:

• A rise in spending. Research by Gartner shows that 75% of CFOs plan to increase technology by at least 4% this year.
• A focus on AI. Gartner found that over half of CFOs (59%) are planning to significant increase AI spend by 10% or more this year. However, 12% have not started due to lack of AI literacy and lagging systems.
• Tech debt is creating issues. 60% of IT leaders say legacy systems have a negative impact on the overall business, according to Forbes.

The issues are heightened in the middle-market, where businesses are caught between being too complex for small firms and too nuanced for enterprise models.

AI is making advanced analytics and efficient workflows more accessible, driving significant investment among middle-market companies. In fact, over half plan to invest in AI tools soon.

While many companies are realizing greater efficiency through AI, core business goals remain: making better decisions, reducing manual work, and scaling growth.

Here’s how CFOs can strategically lead AI initiatives in middle-market organizations.

Data Readiness over AI Hype

An AI pilot’s success hinges on access to reliable, high-quality data. Flawed data, insufficient governance, or a weak security posture can undermine any AI initiative.

To make your data work for AI, follow these steps:

• Collect Raw Data: Bring together historical and real-time information from every corner of your organization—transaction records, customer exchanges, and data from connected devices.
• Maintain Data Hygiene: Correct errors, eliminate duplicates, and address inconsistencies to keep your data trustworthy.
• Integrate Your Data: Created a unified data approach to give your AI comprehensive access to essential information.
• Manage Your Data: Conduct routine quality assessments, uphold privacy practices, and restrict sensitive data access.
• Label and Structure: Clearly mark your data with descriptive tags so AI systems can learn efficiently.

After cleaning and structuring your data, ensure integration with key systems like ERP platforms. Use AI to spot financial anomalies, automate workflows, and ensure smooth data exchange.

Concentrate on Improving Business Outcomes

AI doesn’t need to be flashy to add value. Leading organizations focus on clear, actionable use cases that drive measurable ROI.

We have found mid-market businesses benefit most by automating routine tasks like invoice approvals or syncing customer data.

For example, a mid-sized manufacturing client previously dealt with slow, manual quoting procedures prone to mistakes, which hindered sales teams and made scaling up difficult. Integrating AI into their existing operations led to fewer manual tasks, greater accuracy, and a more scalable approach to quoting.

Prioritize Governance and Maturity

Organizations don’t instantly transition from having no AI to being fully AI-enabled. Instead, they progress through stages of AI maturity: moving from specific use cases to developing business-wide capabilities that are measurable and consistent.

While each stage offers new ways to create value, each also brings additional demands related to data management, risk control, and organizational preparedness.

Rapid progress without sufficient governance increases risks, too much control without enabling adoption slows progress, and focusing solely on efficiency without considering growth limits long-term benefits.

In the middle market, AI readiness is inseparable from overall business readiness — without clear data, governance, and alignment, AI increases risk instead of reducing it.

Use Cases for AI in Finance

AI has proven effective for mid-market organizations by:

• Automating tax regulation monitoring and calculations
• Streamlining anomaly detection, workflows, and data sharing
• Enhancing cybersecurity through real-time threat response
• Improving financial analysis via automated outlier detection
• Using chatbots to efficiently handle routine customer inquiries

How to Move Forward with AI Adoption

Research has shown that by 2030, 70% of finance functions will use AI for real-time decision making and 15% of daily decisions will be fully autonomous, according to Gartner.

A solid AI strategy includes:

• Clean, centralized data
• Solid systems integrations
• Defined AI use cases and goals
• A governance model

The mid market companies that thrive in AI implementation aren’t the ones that will implement the fastest. They’re the ones that prepare through visibility, strategy, and a solid data foundation.

We help mid-market businesses across accounting, advisory, and technology initiatives. Whether you’re ready to implement AI or need help understanding your data and governance model, we’re here to help.