Your artificial intelligence (AI) pilot was successful. You had executive sponsorship, a clear use case and results promising enough to warrant expansion. However, when it came time to move toward enterprise deployment, timelines began to slip, outputs became less reliable, and the business case became more difficult to defend. Now, your AI initiative has stalled.
As a finance leader, justifying a significant investment in a capability that fails to deliver measurable business outcomes is difficult.
AI pilots rarely stall because the tools themselves don’t work. They stall because the organization has not clearly defined how AI will be funded, governed, measured and embedded into daily operations. That definition is what separates a successful demonstration from a deployed capability, and it belongs at the start of the initiative — not at the point of scale.
Cherry Bekaert's 2025 Middle Market CFO Survey found nearly half of finance leaders reported using AI or machine learning-based tools, yet many were still struggling to translate adoption into measurable gains in forecasting accuracy, reporting speed or strategic insight. Finance leaders aren’t ignoring AI; adoption is moving faster than the operating discipline needed to convert AI into measurable value.
What Has Changed in 2026
The AI conversation has shifted from experimentation to accountability. In 2025, many leadership teams were still asking where AI could be tested. In 2026, they are asking whether AI is improving margins, accelerating decisions, reducing manual effort, and creating a repeatable advantage.
AI adoption has become easier, while enterprise scale has become more complex. Generative tools, embedded software features, and AI agents have lowered the barrier to entry, but they have also raised expectations for ownership, governance, data controls, cost transparency and measurable business value.
Why the Pilot Succeeded in the First Place
Understanding the stall starts with being honest about why the pilot performed well. Most pilots are appropriately engineered for success through:
- Curated Data: Pilots typically run on a clean, controlled dataset assembled specifically for the test. Production runs on everything else.
- Narrow Scope: Pilots focus on one workflow and one set of assumptions within a single team.
- Motivated Users: Early participants are usually volunteers who want the tool to work and will correct it when it does not.
- Hands-on Support: A small group monitors output daily, catches errors quickly and adjusts as needed.
While a pilot can prove technical feasibility, it does not always prove the business has the operating model required to sustain value.
Why AI Initiatives Stall at Scale
Five conditions account for most stalled initiatives. Each is an operating model gap rather than a technology gap.
1. Business Success Was Never Defined
Many pilots are evaluated on whether the technology performed well. Far fewer are evaluated on whether the output changed a decision, removed a step, or improved a metric the business already tracks.
When success is defined only as "the model works," leaders have no clear basis for deciding whether to increase investment. The initiative may remain active, but it stalls in review because the organization cannot demonstrate why the capability matters to the business.
Defining the business outcome, accountable owner, and measurement on day one is what allows a pilot to move into production. Leaders should be able to answer three questions before the pilot launches:
- What measurable business outcome is the initiative expected to improve?
- Who is accountable for achieving that outcome?
- What evidence would justify expanding, adjusting or ending the investment?
Without those definitions, technical success may generate activity without proving business value.
2. No One Owns the Workflow
AI initiatives often cross finance, technology, risk, legal and operations. When accountability is distributed among those functions without a clear structure, it effectively belongs to no one.
Decisions about data access, output review, exception handling, funding, and process changes can then stall in committee. Technology or data science teams may be responsible for the platform, but platform ownership is not the same as accountability for the business outcome.
Establishing a defined AI ownership structure, with a single point of final accountability, resolves this ambiguity before it becomes a barrier to deployment. That owner should be responsible for the workflow change and resulting business value, while technology, risk, and compliance functions play clearly defined supporting roles. and compliance functions play clearly defined supporting roles.
3. Data Readiness Was Treated as Cleanup
According to our CFO survey, data accuracy and integration issues remain the top two barriers to modernization overall, ahead of budget, talent and technology selection. Yet many organizations still treat data readiness as a cleanup exercise that follows deployment rather than a design decision that precedes it.
Most modernization roadmaps start with the technology decision, including platform, vendor and model. Data readiness rarely receives the same attention because it is less visible and harder to demonstrate in a boardroom. A dashboard is easy to show. A governed data pipeline is not.
AI built on fragmented data, disconnected systems, and undocumented processes will expose the same gaps it was meant to solve, only faster. Many governance models and enterprise resource planning (ERP) systems were designed for transaction processing, not for feeding AI models clean, consistent, well-labeled data over time. Without that foundation, even well-funded AI initiatives can underdeliver because the business cannot trust, apply, or sustain the outputs at scale.
Organizations that get this right treat data readiness as a design decision before the AI initiative launches, not as cleanup after it stalls. That means:
- Assigning clear ownership for each data source
- Standardizing definitions across systems so numbers tell one consistent story
- Creating a refresh cadence that keeps information current
This data readiness foundation treats the data layer as infrastructure, not an afterthought.
4. The Work Itself Was Never Redesigned
More than one-third of our surveyed CFOs said their current systems are insufficient for reporting and analysis, and only about one-third rated their planning and forecasting tools as highly effective. Those findings suggest many organizations are still operating with workflows, systems and decision-making processes that were not designed for AI-enabled work.
AI applied on top of an existing process generally produces a faster version of that process, including its inefficiencies. Creating that value may require removing reconciliation steps the technology makes unnecessary, changing review and approval points, redefining exception handling, and resetting how teams make decisions.
Organizations that scale AI successfully redesign the workflow itself rather than simply layering technology onto existing processes. This work frequently receives less attention than the implementation effort because process redesign, training, and change management are more difficult to measure than software deployment. Without that redesign, AI may reach production without ever changing the behaviors or decisions that matter most to the business.
5. Governance and Controls Never Caught Up With Adoption
In 2026, the risk profile has expanded as employees can access powerful AI tools with little friction and software vendors are embedding AI into platforms the business already uses. These capabilities can unlock value, but they also introduce:
- Shadow AI risk
- Inconsistent outputs
- Unclear data permissions
- Unmanaged vendor exposure
- Limited visibility into the information used to generate recommendations
If governance follows adoption instead of guiding it, leaders may inherit a control issue that is harder to unwind. A successful pilot may demonstrate technical viability, but that does not automatically establish the governance framework required to scale AI responsibly across the enterprise.
The answer is not to slow AI adoption but to create guardrails that help responsible adoption move faster. Leaders need an operating model that defines approved use cases, permitted data, output review, cost monitoring, and accountability when AI influences customer, employee, financial reporting or operational decisions.
Those guardrails should reflect the significance of the decision the AI output will inform. A tool that helps draft internal content does not require the same oversight as a capability that informs financial reporting, operational decisions or customer-facing activities. Organizations that treat governance as a design decision rather than a post-deployment fix are better positioned to scale AI while maintaining trust, transparency and control.
The CFO’s Role: Turn AI Projects Into a Managed Investment
For CFOs and operators, the question is no longer, “Can we use AI?” The better question is, “Which use cases are worth scaling, governing and funding?” meaning AI should be managed less like a technology experiment and more like an investment portfolio.
Before scaling a use case, leaders should be able to define the measurable value, confirm whether the data is reliable enough to support the decision, assign ownership for the workflow change, and determine what controls are needed before the output informs customer, employee, financial reporting or operational decisions. Without that discipline, organizations may generate activity without proving return on investment (ROI).
This changes how AI programs should be prioritized. The strongest opportunities are not always the most impressive demonstrations. Instead, they are the use cases tied to recurring work, measurable pain points and decisions where faster, more consistent insight can improve financial or operating performance.
A managed investment approach gives CFOs a practical decision framework:
- Value: Define the measurable business outcome, baseline, and threshold that would justify further investment
- Feasibility: Confirm the data, workflow, and operating environment can sustain the use case beyond a controlled pilot
- Ownership: Assign accountability for the business outcome and the workflow change, not only the technology
- Risk and Controls: Determine the privacy, security, approval, and validation requirements before outputs influence business decisions.
- Funding and Measurement: Sequence investment against evidence of ROI, adoption, and operating impact, with clear points to expand, adjust or stop
Managing AI this way moves the decision beyond whether a pilot worked. A managed investment approach asks whether the organization can operate the capability in production and whether it changes behavior that matters to the business.
How Cherry Bekaert Can Help
AI readiness is an operating capability that connects strategy, data, process, controls, adoption and measurement. Cherry Bekaert’s Analytics & Automation Services team helps business leaders distinguish AI activity from AI value. Our professionals identify the use cases most likely to improve performance, assess readiness gaps that could slow progress, and define the data, process, and governance work required before organizations increase investment.
From there, we develop an AI Value Realization Roadmap that:
- Prioritizes use cases by value, feasibility and risk
- Sequences foundational fixes before broader rollout
- Establishes governance and ownership
- Creates a practical measurement model for ROI, adoption and operating impact
For business leaders ready to move beyond the pilot phase, start with a clear assessment of whether the business has the foundational discipline to turn AI into a governed, measurable and repeatable capability. The companies that create lasting value from AI in 2026 will not be defined by how many pilots they launch, but by how effectively they convert AI into durable business performance.
If your initiative has stalled — or you want to make sure it does not — contact us to assess what is standing between your pilot and measurable business performance.
