August 20 Executive BriefingCFO AI Round Table

From AI Experimentationto Enterprise Adoption

Executive briefing summarizing the August 20 CFO AI Round Table poll results and breakout discussion insights.

Breakout discussion insights have been synthesized and anonymized. Examples have been generalized where appropriate to protect participant confidentiality.

The Gap Between Experimentation and Scale

The August 20 CFO AI Round Table revealed a clear gap between AI experimentation and enterprise-scale adoption.

A quick poll was conducted at the beginning of the CFO AI Round Table, with 28 CFOs and senior financial leaders participating, primarily from mid-market companies. Nearly two-thirds of respondents — 64.3% — described their organizations as Exploring or Experimenting with AI, while only 7.2% said their organizations are Scaling or Transforming.

Yet there is an important counterpoint: 53.6% of CFOs reported that their organizations are already realizing measurable value from AI.

That combination may be the most important finding from the session.

Organizations do not need to be highly mature in AI to generate value. CFOs are already seeing practical results from individual use cases. The challenge is determining which successes should be expanded and how to build the capabilities needed to do so responsibly and economically.

Headline Findings

64.3%

of organizations describe themselves as Exploring or Experimenting.

53.6%

are already realizing measurable value from AI.

7.2%

identify as Scaling or Transforming.

60.8%

of finance leaders place themselves in Learning or Experimenting.

CFO AI Round Table infographic showing survey results on enterprise AI adoption with headline findings, charts, and key themes from discussion insights

What CFOs Are Seeing Today

Participants described AI creating value across a variety of finance and business workflows, including:

Financial reporting and monthly narratives
Variance analysis and commentary
Data gathering and reconciliation
Dashboard generation and recurring reporting
GL review and anomaly identification
Product profitability and margin analysis
Accounting documentation
Scenario analysis and executive decision support
Contract and document review
Repetitive workflow automation

Participants shared several tangible examples of AI reducing time and cost across reporting, analysis, document review, and other professional workflows. These examples reinforce that meaningful AI value often begins with a specific business problem rather than a large-scale transformation initiative.

The Scaling Gap

The poll results show that broad enterprise-scale adoption remains limited, even as AI use and measurable value are becoming more common.

Enterprise AI maturity

Exploring21.4%

Experimenting42.9%

Applying28.6%

Scaling3.6%

Transforming3.6%

While more than half of respondents report measurable value, relatively few describe their organizations as Scaling or Transforming with AI.

The breakout discussions surfaced several factors that may influence broader adoption.

CFOs identified considerations involving data quality, security, privacy, compliance, hallucinations, ERP access, governance, cost, employee adoption, and ownership.

Taken together, the poll results and discussion point to an increasingly important question:

“How do we consistently identify the right opportunities, prove their value, manage the risks, and scale what works?”

The CFO Role Is Evolving Alongside AI Adoption

The poll also showed considerable variation in CFOs' own levels of AI experience.

60.8% of finance leaders described themselves as Learning or Experimenting with AI, while 39.3% placed themselves at Applying, Leading, or Integrating.

This is significant because CFOs are increasingly being asked to play a broader role in AI — evaluating investments, challenging business cases, assessing ROI, controlling costs, managing risk, and helping determine which initiatives deserve to scale.

AI is becoming as much a leadership and operating-model issue as a technology issue.

What Appears to Separate Experimentation from Scale

Based on the poll results and discussion, a practical path from experimentation to broader adoption appears to involve seven disciplines:

Prioritize opportunities → establish the business case → prepare the data → manage risk → prove value → drive adoption → scale what works.

Human oversight also remains critical. Participants consistently emphasized the need to validate AI-generated outputs, particularly when financial, regulatory, or sensitive information is involved.

The emerging model is not fully autonomous finance. It is AI-assisted work with clear human accountability.

The Emerging CFO Agenda

Taken together, the poll results and breakout conversations suggest that the CFO AI agenda is increasingly moving beyond awareness and experimentation toward questions of value, governance, adoption, and scale.

The next questions are becoming more strategic:

How should we measure AI ROI beyond time savings?
Who should own and coordinate the enterprise AI agenda?
What governance and controls are necessary?
How do we determine which pilots deserve to scale?
How do we prepare our data, processes, and people for broader AI adoption?
How do we determine which successful AI use cases should become repeatable organizational capabilities?

That last question may be the most important takeaway from the August Round Table.

What This Means

AI experimentation is becoming increasingly common. The next challenge is determining where AI can create meaningful business value and building the data, governance, adoption, cost discipline, and measurement capabilities needed to scale what works.

Continue the Conversation

If one of these issues is particularly relevant to your organization, we welcome the opportunity to continue the conversation around the AI decisions, opportunities, or challenges you are navigating.

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