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But middle-market CFOs increasingly face challenges from relying on outdated systems and not prioritizing strategic implementations.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

AI is also not a turnkey solution you install once and walk away from. When you invest in AI, you’re really investing in a technical foundation. The tools will improve, capabilities will expand, and vendors will iterate faster. Companies that succeed are not looking for a single “final” state. They’re building a layer on top of their existing systems that can evolve as technology matures. This mindset matters because organizations expecting immediate perfection often lose patience before the real benefits begin to appear.

Just as critically, AI is not a substitute for ERP discipline. If approvals are loosely enforced, or core processes are routinely bypassed, AI will not fix those problems. It will amplify them. Automating weak processes simply accelerates bad outcomes.

How CFOs Should Think About AI

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.

The most successful implementations follow a simple path. They start small, with one workflow that has clear pain and clear metrics. Early pilots focus on proving value, not building perfect systems. Governance and KPIs follow quickly because further investment in deeper integration and broader rollout needs to be based on measurable, quantifiable results.

While clean data is incredibly important, data perfection is not a prerequisite. If you don’t have the means to invest in something like a data warehouse, make sure you have clear ownership. Every AI-supported process needs a human accountable for its outcomes.

This requires clear governance and accountability.

Organizations need to define:

• Who owns AI-supported processes
• Who is responsible for validating outputs and how decisions influenced by AI are documented
• What controls are in place to manage risk.

This is particularly important for finance teams. Even when AI contributes to analysis or recommendations, the responsibility for financial accuracy remains with the user. With those guardrails in place, the next step is to decide where to focus first.

How to Choose the First AI Use Case

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.

Rather than starting with tools or features, finance leaders should start with a focused set of questions:

• Where is AI already being used within our systems?
• Which financial outcomes matter most right now?
• Where are we experiencing the greatest inefficiencies or risks?
• Do we have consistent data to support improvement?
• Who will own the results?

From there, identify one or two high-impact use cases. Pilot those use cases with clear metrics. Measure results. Refine the approach. Then expand into adjacent areas. This phased approach reduces risk, builds confidence, and ensures AI investment is driven by demonstrated results.

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.