The ROI Gap Edition

AI adoption is no longer the exception.

Organizations are introducing new tools, employees are becoming more productive, and models designed for professional work are becoming faster and more affordable.

But adoption alone does not guarantee meaningful results.

Recent research points to a growing gap between organizations that use AI and those that have redesigned their workflows, data, controls, and decision-making processes to create measurable value from it.

The opportunity is not simply to add more AI.

It is to become more intentional about where AI belongs, what it should improve, and how success will be measured.

- Brianne

What Happened This Week

AI Adoption Is Rising Faster Than Financial Returns

• Why it matters: McKinsey’s September 2026 AI insights report that nearly nine in ten survey respondents say their organizations regularly use AI and eight in ten say AI has improved individual productivity, yet only 37% say it has contributed to organizational EBIT. For accounting professionals, this highlights the difference between making an isolated task faster and creating measurable value across the organization. If AI saves time but the surrounding workflow, capacity, or client experience remains unchanged, the organization may never realize the full benefit.
• What to do: Choose one AI use case and define the expected business outcome. Instead of measuring only time saved, consider whether the tool improves turnaround time, accuracy, capacity, client experience, or decision-making.
• Source/Further Reading: McKinsey

KPMG Examines Where AI Is Creating Value In Finance

• Why it matters: KPMG’s AI in Finance Report 2026, based on a survey of 1,013 senior finance leaders across 20 countries and 13 sectors, reports that active AI use across finance has more than doubled in two years while a gap is emerging between organizations achieving performance at scale and those investing without comparable results. The report reinforces that successful AI adoption in finance depends not only on the technology, but also on governance, controls, data quality, human oversight, and the organization’s ability to connect adoption with measurable performance.
• What to do: Review one AI-enabled finance or accounting process and identify:
  • What business outcome it is expected to improve
  • How the output is reviewed
  • What evidence should be retained
  • How errors or exceptions are handled
  • Who is ultimately accountable
• Source/Further Reading: KPMG's AI in Finance Report 2026

Microsoft Brings Governed Business Context into Copilot

• Why it matters: Microsoft's Fabric IQ annnouncement highlights one of the biggest challenges of using AI in accounting and finance: a model needs more than access to data; it also needs consistent business definitions and reliable organizational context. Questions about revenue, margin, cash flow, utilization, or forecast performance can produce unhelpful results when teams rely on different definitions or sources, so governed data and shared reporting logic become essential as AI is integrated into business analysis.
• What to do: Identify three metrics your firm or organization relies on regularly. Confirm that each has:
  • a consistent definition
  • a designated source
  • a clear owner
  • documented reporting logic
• Source/Further Reading: Microsoft

One Action This Week

Choose one AI use case currently being tested or used in your organization. Write down:

1. The task: What is AI helping accomplish?
2. The outcome: What should improve because of it?
3. The measure: How will you know whether it worked?
4. The review: Who is responsible for checking the output?
5. The next step: What happens to the capacity AI creates?

If the team cannot answer those questions, the firm may be using AI without knowing whether it is creating value.