Author: Lori Love, Eide Bailly Outsourced Accounting Senior Manager | Nick J. Mortensen, Eide Bailly Partner
A version of this article first appeared on EideBailly.com.
Today’s business leaders are responsible for far more than managing finances and operations. They’re expected to prioritize technology investments, navigate change, and provide real-time guidance that supports growth.
This growth mindset causes significant friction in the middle market, where businesses are caught between being too complex for small firms and too nuanced for enterprise models. Many organizations are increasingly challenged by outdated systems, disconnected data, and competing priorities.
And while over half of CFOs (59%) are planning to significantly increase AI spend, 12% have not started due to lack of AI literacy and lagging systems.
For business leaders, responsible AI is about making innovation measurable, governed, and scalable. Their role is to ensure AI investments improve business performance without weakening data integrity, controls, security, or stakeholder trust.
Think Beyond Technology
A survey of our clients found that while many businesses are implementing AI, almost half (43%) are experimenting with individual tools without a formal strategy or governance.
Leaders should think about AI as an operational capability, not a technology initiative. AI doesn’t replace financial judgment; it amplifies it.
Consider the following four dimensions for AI implementation:
- Data foundation: Are your systems integrated, accurate, and governed?
- Business value: What specific financial outcomes will AI improve?
- Operating model: How will finance, operations, and technology align?
- Governance and control: How will outputs be validated and risks managed?
Organizations that lead with data discipline, clear governance, and outcome-focused investment will extract the most value from AI while managing risk.
Focus on 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.
In finance, AI often creates value by improving forecasting and planning, automating reporting and analysis, accelerating operational processes, and strengthening risk management and controls.
Regardless of its impact, we see many leaders treat AI as a turnkey solution. 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.
The real barrier appears when you review your alignment and processes. 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.
Build a Strong Foundation
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.
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.
Start Small and Scale Strategically
The mid-market companies that thrive in AI implementation aren’t the ones that implement the fastest. They’re the ones that prepare through visibility, strategy, and a solid data foundation.
Rather than starting with tools or features, business 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.
Turning AI Into a Competitive Advantage
AI is redefining how organizations operate and make decisions.
The most successful business leaders view AI as an operational capability and decision-support tool rather than a standalone technology initiative or decision-maker. Leaders who focus on data quality, align AI to business outcomes, and build strong governance will turn AI into a competitive advantage rather than a risk.
Whether you’re ready to implement AI or still evaluating where to begin, success starts with a clear strategy, strong governance, and a commitment to measurable business outcomes.