Artificial intelligence (AI) remains a strategic priority for organizations worldwide, but new research suggests that readiness varies significantly across industries and regions.
The Association of International Certified Professional Accountants (AICPA) and the Chartered Institute of Management Accountants (CIMA), in collaboration with North Carolina State University’s Enterprise Risk Management (ERM) Initiative, recently released a global study examining executive perspectives on the opportunities and risks associated with AI.
The survey gathered responses from 1,735 executives representing eight geographic regions and eight industry sectors, offering a broad view of how organizations are navigating AI adoption.
The findings suggest an increasingly uneven AI landscape. Some respondents report real strategic gains from AI, while others are still struggling to build the talent, systems, and governance needed to use it effectively.
The global survey suggests many organizations see AI as strategically important but run into real-world hurdles when it comes to putting it into practice. Leaders are increasingly pointing to AI as a competitive differentiator, yet gaps continue to show in three core areas:
Put simply, the profession broadly agrees that AI matters, but not every practice has the right foundation in place to roll it out responsibly and effectively.
The report’s focus on talent gaps gives organizations a useful check on their own readiness. Even though the research reflects executive perspectives across industries, the takeaway lands close to home for accounting professionals, both inside their practices and in the work they do with clients.
One theme shows up again and again: workforce capability matters. AI tools, including generative AI, machine learning systems, and predictive analytics, take more than just curiosity and a few test prompts. They require real training and clear expectations.
Many accounting professionals are already using generative AI for things like quick research tasks, drafting emails, or summarizing technical guidance. This kind of experimentation is useful, but it does not reflect the level of readiness required to deploy AI consistently and reliably across an entire practice.
The implication is clear: AI literacy must evolve from optional exploration to structured professional development.
Talent readiness gets most of the attention, but for many organizations, infrastructure is the bigger constraint. You can have curious, motivated people, but if the underlying systems are not ready, AI projects quickly stall.
Effective AI implementation usually depends on setting a few basics into place:
Practices operating on fragmented legacy systems may find AI tools difficult to deploy meaningfully. AI outputs may be unreliable if data resides in disconnected silos or is inconsistently formatted.
For accounting practices advising clients, the same dynamic shows up outside the firm. Many small and mid-sized businesses are interested in AI driven forecasting or automation, but they do not yet have solid data governance or connected systems. That gap creates a real advisory opportunity for practices that can help clients strengthen their digital foundation before layering on AI tools.
The survey’s takeaway is straightforward. AI cannot compensate for a disorganized data environment.
One of the most significant concerns highlighted in the executive insights report relates to governance. As AI becomes more involved in financial analysis, reporting, forecasting, and operational decisions, it naturally raises some practical questions:
Governance in this context refers to the policies, oversight structures, and risk management frameworks that ensure AI systems are used responsibly and ethically.
Accounting professionals have a clear role to play here. The profession already lives in a world of internal controls, auditability, and compliance. Those skills translate well to AI governance, especially as firms and clients look for ways to adopt new tools without creating new risk.
Instead of treating AI as only a technology initiative, many practices will be better served by treating it as a control environment issue. That means documentation, monitoring, and ongoing evaluation become part of the AI conversation from the start.
For practices in the CAS space, the adoption gap creates both risk and opportunity.
Firms that put off structured AI planning may find themselves at a competitive disadvantage. Automation in areas like transaction processing, anomaly detection, and forecasting is moving quickly, and client expectations are moving with it. More and more, clients will assume their advisory partners understand these tools and can help apply them in practical ways.
At the same time, moving too fast without the right guardrails can create real operational and reputational risk. AI adoption works best when it is intentional, documented, and aligned with how the firm manages quality and compliance today.
A measured approach may include:
Creating a cross functional team to evaluate tools, risks, and training needs helps ensure AI strategy is not siloed in IT or limited to an innovation group.
Before rolling out AI tools, firms can take a clear look at data quality, system integration, and security controls so they know what will support reliable results and what will not.
Well defined policies should cover:
Professional development that focuses on AI risk, ethics, and practical application can help practices build internal confidence and lead stronger client conversations.
Many practices are still in the experimentation phase, using AI tools for drafting, summarization, and research support. The next step is moving from informal use to structured adoption, backed by:
Practices that close the readiness gap tend to treat AI as an operational capability, not a novelty. That means it is governed, aligned to firm goals, and assessed based on performance and risk.
For accounting professionals, this shift is happening in what feels like a Strange New World. Rapid technology change, evolving client expectations, economic uncertainty, and shifting regulatory environments are all converging at once. AI is not showing up on its own. It is part of a broader transformation in how practices operate and how value is delivered.
Thriving in this Strange New World requires more than curiosity and experimentation. It requires clarity, structure, and intentional growth. This theme is central to the profession’s ongoing dialogue, and it will be a focus at Scaling New Heights 2026, where accounting professionals will explore how to scale responsibly, adapt with confidence, and build resilience amid constant change.
This article was written with the assistance of AI and edited by a human.