Welcome to the first installment of Ellen’s three things, where I reflect on some of the most pressing issues and opportunities for the accounting profession. This month, I want to talk about the “gotchas” that can derail even the most promising AI initiatives.
AI holds tremendous potential for the accounting profession, from automating routine tasks to uncovering insights buried deep in client data. In fact, we discussed this in Aiwyn’s recent white paper here on “The Basics of Automation and AI in Accounting”, with the three Ps framework - People, Processes, and Product.
Getting AI to fulfill its potential is another story. Based on my experience, I’ve seen firms stumble over the same issues repeatedly. Addressing these challenges early on can save a lot of time, money, and frustration—and ultimately lead to much more successful outcomes.
The first gotcha: Poor data quality
AI needs data. The higher the quality of the data, the more powerful and effective the AI will be. The old adage applies here: garbage in, garbage out.
Many in the accounting profession underestimate how critical data quality is. I’ve encountered firms excited about implementing AI only to discover that much of their data is incomplete, disorganized, or trapped in disconnected systems. Worse, some data isn’t digitized at all—it lives in the minds of individual professionals or in handwritten notes.
The below tips will help you create a strong foundation for AI applications.
💡 Tip:
- Conduct a data audit to identify gaps.
- Digitize, standardize, and centralize data to make it more accessible.
- Prioritize quality over quantity.
The second gotcha: Understand processes
Even when data quality is addressed, unstandardized workflows can be another stumbling block.
Inconsistent processes across teams, service lines, or even within the same team (how many different ways can partners generate a bill? Answer: as many as the number of partners) cripple initiatives that involve AI solutions. If every group has its own tools and methods, the data collected becomes fragmented, and the workflows themselves become nearly impossible to automate.
Automation thrives on consistency. AI less so, but it is still highly dependent on easily interpretable data. Without process standardization, you’ll see only a fraction of the total benefits of what AI can do for you.
💡 Tip:
- Standardize key processes to ensure consistent data collection.
- Focus on workflows that can be scaled and automated.
- Engage stakeholders early to create alignment across teams.
The third gotcha: Lack of leadership buy-in
One of the biggest challenges to AI adoption in the accounting profession is cultural resistance, often stemming from a lack of leadership alignment.
Let’s imagine this scenario, unfortunately all too common. A firm enthusiastically forms a “digital transformation” team, tasked with bringing AI, innovation, and improvements to the firm. When push comes to shove, the tax leader pushes back, saying she doesn’t want her team to be “bothered” in critical production periods. This gets us back to the old scenario of service lines choosing their own tools, creating a fragmented approach.
Without organizational leadership buy-in, AI initiatives remain siloed and stalled, limiting their effectiveness and adoption.
💡 Tip:
- Create an org structure that is both “bottoms-up” (embedded accountant within each function that is tasked with ensuring implementations are successful) and “top-down” (a centralized team that owns the firm-wide coordination and strategy).
- Educate leaders on the specific benefits and ROI that their team will see with AI adoption.
- Create clear goals and KPIs to demonstrate the value of AI initiatives.
AI isn’t a magic wand—it’s a tool, and like any tool, its effectiveness depends on how it’s used. Poor data, fragmented workflows, and a lack of alignment can all turn a promising AI initiative into a frustrating failure. But with clean data, standardized processes, and unified leadership, the accounting profession can unlock AI’s transformative potential.
