Jason Marx is the CEO of Wolters Kluwer Tax & Accounting.
AI adoption is accelerating at a remarkable pace. But many organizations are mistaking AI activity for AI transformation.
They track the number of users, prompts, hours saved and pilots launched. True transformation occurs when AI changes how decisions are made, how expertise is distributed, how work moves through the organization and how value is delivered to customers.
Wolters Kluwer’s 2025 “Future Ready Accountant Report” found that 72% of accounting firms globally are now using AI at least weekly, including 35% that use it daily. AI adoption rose to 41%, up from just 9% in 2024, and 77% expect to increase AI spending over the next three years.
The direction is clear. The bigger question for leaders is whether their ways of working are evolving with it.
Start With The Decision, Not The Technology
Adoption adds AI to existing ways of working. Transformation redesigns how work gets done because AI is now part of the operating model.
A transformed organization doesn’t just work faster. It operates differently.
In a transformed organization, expertise is available when and where it is needed. Decisions are informed by relevant information without requiring people to hunt for it. Work moves with fewer handoffs and less rework. Professionals spend less time gathering context and more time applying judgment.
Here’s a simple test: If AI disappeared from your organization tomorrow, which decisions would become harder, slower or more dependent on a small number of experts? If the answer is none, you’ve likely adopted AI, but you have not transformed your operating model.
Before applying AI, leaders need to understand precisely what they are trying to improve: where decisions are delayed, where judgment is inconsistent, where expertise is concentrated in a few individuals, or where too much time is spent gathering and evaluating the information needed to reach a sound conclusion.
Technology doesn’t fix broken workflows. It often accelerates them. In many organizations, the greatest inefficiencies aren’t found in the work itself. They’re found in what I call “the work before the work” -searching for information, reconciling data, routing requests, chasing approvals and assembling context.
These activities rarely create customer value, yet they consume enormous amounts of time and expertise. They also introduce delays, inconsistencies and unnecessary friction.
AI Can Reduce Friction, But It Cannot Own Accountability
AI can inform decisions. It cannot own them. Organizations need much clearer decision rights. What information should AI prepare or monitor? When should it surface insights, make recommendations, route work or escalate issues? And where should human judgment remain the final authority?
In high-stakes professions like accounting, a CPA cannot simply say, “Well, AI said so.” AI can perform much of the heavy lifting, but accountability for the outcome remains with the professional. To exercise sound judgment, professionals need to understand not only what the AI recommends, but why. Recommendations should be transparent, explainable and grounded in verifiable data and authoritative guidance.
The Future Ready Accountant data show the leading concerns “include privacy and security risks (41%), lack of staff experience with AI (39%), and data quality issues (38%).” Nearly a third cite the need for “continuous monitoring and updates.”
Governance is often framed as a tradeoff against innovation. In practice, the opposite is true. The organizations that will move fastest are those that establish enough trust, transparency and accountability to scale AI confidently.
Redesign The Workflow, Not Just The Technology
Through our work with thousands of tax and accounting firms, we see breakdowns occur in the gaps between people, teams and systems: unclear handoffs, disconnected data and misaligned priorities. Applying AI or agentic workflows on top of broken processes may create speed, but not clarity. You simply get to the breakdown faster.
AI-enabled operating models can help by surfacing relevant information sooner, routing it to the right people, monitoring progress and clarifying next steps. But the greater opportunity lies in redesigning how work moves through the organization, so people spend less time coordinating activity and more time applying judgment where it matters most.
Firms are already applying AI across a broad range of work, with 40% of firms globally report using advanced AI for tax, accounting and audit research; 38% as a productivity tool or AI assistant; and 37% for bookkeeping automation. The transformation question is whether firms are connecting these capabilities through redesigned workflows or simply adding them to the way work has always been done.
Redefine Roles Around Value Creation
As intelligent systems take on more routine processing and coordination, organizations need to redefine roles around value creation rather than task completion. What we do not want is for humans to become traffic controllers for AI, simply routing information and monitoring the system. The goal is higher-value work: applying judgment, advising clients, coaching teams and making the decisions that drive quality outcomes. Professionals must own both the decision and the outcome.
Leaders should carry that logic through the organization. We can’t simply ask, “Did my department complete its piece of the workflow?” We need to ask, “Did we deliver the right outcome for the customer?” Customers don’t experience your AI adoption. They experience the quality, speed and confidence of the outcomes you deliver.
Leadership Has To Go Where The Work Happens
Leaders need to go where the work happens. In my experience, where the organizational chart says work happens and where it really happens are often two different things.
Go to the front lines. Ask where the friction is. The people closest to the work and the customer usually know where the problems are.
Then create a culture where identifying those problems is encouraged and experimentation to solve them is supported.
Otherwise, AI creates a false sense of progress: higher AI adoption and very little change. Firms should instead aim for faster work paired with clearer decision-making, stronger accountability and greater trust.
Effective leadership in the intelligence era will be less about supervising activity and more about designing systems where better decisions can happen at scale. The ultimate measure of success isn’t how much AI you deploy. It’s whether your people become better equipped to decide, act and deliver value. That is the difference between adoption and transformation.
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