AI In Tax: Common Pitfalls That Keep Projects From Taking Off

ByAleksandra Bal,

Contributor.

 Aleksandra Bal covers indirect tax and technology developments.

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Sep 16, 2025, 09:40am EDT

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Wrecking Ball Approaching AI

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The promise of AI in the world of tax is compelling: streamlined compliance, predictive insights, and newfound efficiency. Yet for all the enthusiasm, many tax departments find their ambitious AI projects grounded before they ever reach cruising altitude. The reasons for this often have less to do with the technology itself and more to do with the realities of data, people, and processes.

Starting Smart, Not Big

The journey from understanding AI concepts to actually implementing them is where the first stumbles often occur. A common misstep is starting too big. Tax leaders sometimes try to redesign entire processes at once, hoping to deliver an end-to-end transformation right out of the gate. The result is usually the opposite: projects drag on, resources are stretched thin, and momentum is lost.

Another common trap is picking the wrong first project, jumping straight into high-stakes initiatives that require heavy integrations, while ignoring smaller wins like data extraction. The safer bet is to start with a narrow, low-risk pilot like automating some spreadsheet workflows. It’s the kind of pilot you can complete in a month or two, and if it doesn’t work out, nothing’s lost and you simply fall back on your manual process.

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There’s also a tendency to focus on the tool instead of the outcome. AI gets a lot of attention, and some teams feel pressure to use it even when a simpler automation approach would do the job. The label “AI-powered” shouldn’t matter as much as whether the solution solves the problem effectively.

In short, the common mistakes are clear: trying to boil the ocean, chasing perfection too soon, or letting the hype around AI dictate decisions. The smarter path is to start small and scale thoughtfully from there.

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Too Many Projects, Not Enough Progress

With all the buzz around generative AI, many tax teams fall into the trap of running pilot after pilot. For example, a tax team might launch pilots for AI-driven invoice scanning, chatbot support for tax queries, and predictive analytics for audit risks. Each pilot sounds promising, but with limited staff and budget, none of them gets the attention needed to succeed. Six months later, the team has three unfinished projects, no live solution, and a frustrated leadership asking why AI hasn’t delivered. This flurry of activity creates the illusion of progress but results in a trail of half-finished experiments.

This “pilot fatigue” often comes from top-down pressure to be seen as innovating with AI. Leaders want momentum, but without focus, the energy gets diluted. Instead of proving value, the department ends up with scattered efforts and no clear win to point to.

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The way forward is prioritization. Not every idea deserves a pilot, and not every pilot should move ahead at the same time. The most successful teams pick a few feasible projects, give them proper resources, and see them through beyond the prototype stage. In the end, it’s better to have one working solution in production than a stack of unfinished experiments.

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From Prototype to Production

A common stumbling block for tax teams is underestimating the leap from prototype to production. Some estimates place the AI project failure rate as high as 80%, which is almost double the rate of corporate IT project failures. Building a proof of concept in a few weeks is one thing but turning it into a tool people rely on every day is something else entirely. This is where many AI projects stall and why so many never make it beyond the pilot stage.

The problem usually isn’t the technology itself. It’s the messy reality of moving from a controlled demo into a live environment. A prototype might run smoothly on a clean sample dataset, but in production the AI has to handle the company’s actual data that may be incomplete, inconsistent, or scattered across systems. Cleaning, organizing, and integrating that information is often most of the work, yet it’s rarely factored into early pilots.

Integration poses another challenge. A model that runs neatly in a Jupyter notebook isn’t enough. To be production-ready, it must plug into existing workflows, interact with legacy systems, and be supported with monitoring and error handling. That typically requires a broader team of engineers, operations specialists, even designers. These are roles many tax departments don’t have readily available. Without them, promising pilots get stuck in limbo.

The lesson is simple: tax teams need to plan from day one for data readiness, system integration, and long-term ownership. Without that preparation, pilots risk becoming one-off experiments that never make it past the demo stage.

Building on a Shaky Data Foundation

AI projects succeed or fail on the quality of their data. For tax teams, that’s often the first and toughest hurdle. Information is spread across different systems, stored in inconsistent formats, and sometimes incomplete. In many cases, key details are still buried in PDFs or email threads instead of structured databases. When an AI model has to work with that kind of patchy input, the results are bound to be flawed.

The unglamorous but essential part of AI is cleaning data and building reliable pipelines to feed information into the system. It’s rarely the exciting part, but it’s the foundation and without it, no model will perform consistently in production. The challenge is that, in the middle of all the AI hype, executives are often more willing to fund the “flashy” AI projects than the “boring” data cleanup work that actually makes them possible.

The takeaway is simple: treat data readiness as a core step in your AI journey, not an afterthought. A few weeks spent getting the data right can save months of wasted effort later.

Automating a Broken Process

A common pitfall for tax teams is dropping AI into processes that are already complex or inefficient. Automating a clunky workflow doesn’t fix the problems but it just makes them harder to manage.

AI adoption isn’t about layering a shiny new tool on top of old habits. It’s about rethinking the process as a whole. If AI takes over Task A, then Tasks B and C may need to change too. Reviewing the process upfront makes it easier to spot redundancies and cut steps that no longer add value.

The takeaway is simple: don’t just automate what you already do. Use AI as a chance to simplify and modernize. Otherwise, you risk hard-wiring inefficiency into the future of your operations.

The Trap of 100% Accuracy

Tax professionals are trained to value precision, so it’s no surprise many are reluctant to trust an AI tool unless it delivers flawless answers. The problem is, that bar is unrealistic with generative AI. These systems don’t “know” facts the way a database does. They predict words that are statistically likely to follow each other, which makes them great at generating fluent text but prone to confident-sounding mistakes, often called hallucinations.

Tax leaders need to understand this isn’t a bug that will soon be patched. It’s the nature of how these models work today. That doesn’t mean they’re unusable, but it does mean the goal shouldn’t be perfection. Instead, the focus should be on managing the risks and setting up safeguards that make AI outputs reliable enough for practical use.

On the technical side, tools like retrieval-augmented generation (RAG) can help by grounding AI answers in trusted documents instead of letting the model make things up. On the process side, though, there’s no way around human review. If the output involves regulations, case law, or financial figures, a qualified professional still needs to check it.

The real shift is in how we think about AI. Waiting for a system that’s 100% accurate isn’t realistic. The smarter approach is to design workflows where AI handles the heavy lifting and humans handle the judgment calls. When you set it up that way, AI doesn’t have to be perfect but reliable enough to speed things up without taking control out of human hands.

The Human Side of AI

For all the talk about data and algorithms, one of the biggest obstacles to AI adoption in tax departments may be people. Employees often view new technology as a threat, either to their jobs or to the way they’ve always worked. Fear of being replaced, or simple distrust in an unfamiliar tool, can stall an AI initiative before it even begins.

AI projects are often pitched as a way to save time and reclaim capacity by shifting people from repetitive, low-value tasks to higher-impact “strategic” work. In theory, that sounds ideal. But here’s the reality: not everyone naturally transitions from manual tasks to strategic ones. Can every compliance specialist suddenly become an advisor? Does the company actually need five more people in strategic roles instead of five handling tax filings?

When a department frees up dozens of hours of compliance work, there has to be a clear plan for how that capacity will be redeployed. Without one, employees are more likely to see AI as a threat than as a tool that supports them. For adoption to succeed, teams need to believe the technology will make their work more valuable and not make their roles redundant.

Pragmatism Over Hype

The promise of AI in tax is real but so are the pitfalls. Projects rarely stumble because the technology is broken. They stumble because of human, process, and data challenges that get overlooked.

Starting too big. Spreading resources across too many pilots. Ignoring data quality. Clinging to inefficient processes. Chasing perfection. Failing to bring people along. Any one of these can stall progress.

The way forward isn’t about shiny labels but about small wins that build trust and momentum. And it’s about shifting expectations. For tax departments, success won’t come from doing everything at once. It will come from doing the right things, in the right order, with the right support.

The opinions expressed in this article are those of the author and do not necessarily reflect the views of any organizations with which the author is affiliated.