Why tax codes and legal jargon can trip up your AI model
Tax codes and legal systems are notoriously intricate, with subtle distinctions that often determine outcomes. For example, something as simple as how pumpkins are taxed—whether they’re sold for decoration, food consumption, or flavoring in products—show just how nuanced these systems can be.
These distinctions are not limited to the United States; globally, tax laws differ across countries, regions, and even municipalities, with varying regulatory frameworks, filing processes, and interpretations. AI models need to grasp these complexities to be effective.
When tasked with parsing such details, AI must rely on well-curated and deeply specific data. Without proper training on datasets that cover the full breadth of these intricacies, models risk generating incorrect or incomplete results.
For businesses, these errors can lead to costly mistakes, from misclassifying products for tax purposes to misunderstanding legal precedents. This makes the curation of high-quality, context-rich data a priority for any company using AI in legal or tax-related functions.
The dangers of overlooking tiny legal and tax details
Legal and tax codes don’t just vary by jurisdiction—they can change depending on the context of a transaction or event. In the case of pumpkins, for instance, the tax treatment changes based on whether the pumpkin is used as decoration, baked into a pie, or added to a latte.
AI models that don’t account for such subtle distinctions can easily produce inaccurate recommendations or decisions. These nuances are found across many industries and are particularly pronounced in areas like healthcare regulation, international trade, and financial services.
A model that hasn’t been trained on the right datasets may overlook critical elements, such as exemptions or specific interpretations of a tax law.
This kind of oversight is both an academic problem and a practical one that can result in fines, regulatory non-compliance, or business disruptions. AI must be able to interpret data at this granular level, which requires a deep focus on domain-specific training and data curation.
Why generic AI won’t cut it for specialized business tasks
Generic large language models (LLMs) like GPT-4, Llama, and Mistral are designed to handle broad, general knowledge tasks, but they struggle when required to tackle highly specialized, domain-specific challenges.
This becomes especially apparent when companies try to apply these models to areas requiring deep expertise, such as interpreting legal precedents or managing local tax regulations.
Without fine-tuning and custom training on curated, domain-specific datasets, these models can’t provide the precision and reliability needed for complex business tasks.
In sectors like law, healthcare, and finance, where accuracy is key, relying on generic models can lead to poor outcomes. Custom, industry-focused AI models are the solution for these cases. Businesses that invest in creating specialized AI tools tailored to their unique data and requirements will see the best results.
Accurate AI needs excellent data curation
Effective AI solutions depend on the quality and scope of the data they’re trained on. AI models, particularly those handling sensitive or complex areas like tax research or regulatory compliance, must pull from a wide array of data sources—typically including local and hyperlocal tax codes, regulatory filings, legal interpretations, court rulings, and scholarly analyses.
Data is often presented in a variety of formats, such as PDFs, spreadsheets, memos, and even video or audio files, adding to the challenge of making it usable for AI.
Given that these sources are often unstructured and constantly changing, the process of transforming raw data into something usable requires continuous attention and updates.
Without constant processing and curation, AI models will fall behind, rendering their outputs less accurate or even obsolete. For AI to stay relevant and deliver accurate insights, the underlying data needs to be fresh, standardized, and readily accessible.
Your AI is only as good as the data you feed it
AI thrives on diverse, high-quality data. To accurately parse something as complicated as the U.S. tax code or summarize key issues in regulatory compliance, an AI model must pull from numerous sources.
These might include court documents, federal and local tax codes, legal analyses, and relevant news coverage. Each of these sources changes frequently, with new rulings, interpretations, and laws being introduced regularly.
The data must be processed in a way that makes it accessible to the AI, typically involving standardizing documents that come in different formats—such as PDFs, policy memos, or even audio files—so they can be analyzed effectively.
Without careful handling of this data, AI models risk producing subpar outputs that don’t reflect the most current or relevant information.
Keeping data fresh is key for AI performance
Data curation isn’t a one-time process. For AI models to be reliable, they must be updated in real-time with the latest information from all relevant sources. Tax codes and regulations, for instance, can change overnight.
If an AI model isn’t consistently updated with this new information, its outputs become outdated and potentially harmful. A model that was accurate a few months ago could suddenly provide incorrect advice or analysis simply because it hasn’t been fed the latest data.
To prevent this, businesses must invest in ongoing data stewardship—regularly sourcing, processing, and integrating new data into the AI’s architecture. Being diligent here makes sure AI stays effective and trustworthy over time, especially when dealing with dynamic fields like law and finance.
Niche AI models will outperform the big names – Here’s why
Large language models that aim to cover everything often fall short when applied to specific, high-stakes tasks. Generic LLMs might excel at processing general datasets, but they lack the depth required to handle specialized areas like legal precedent analysis, regulatory compliance, or hyperlocal tax regulations.
Businesses that require precision and expertise in these areas can’t rely on off-the-shelf solutions.
That’s why many companies are moving toward developing industry-specific AI models. Gartner projects that by 2027, half of all generative AI models used by enterprises will be tailored to specific industries or business functions, compared to just 1% in 2023.
This shift points to the growing recognition that specialized tasks demand highly tuned, niche AI solutions. Companies that focus on creating these tailored models will have a competitive advantage in the market.
Garbage in, garbage out
The quality of an AI model’s output is directly tied to the quality of the data it processes. Inaccurate, outdated, or incomplete data will inevitably lead to poor AI results. This principle, often summed up as “garbage in, garbage out,” holds especially true for industries where precision matters, such as law and tax.
AI tools can only be as good as the data they are trained on. When developing AI for business-critical tasks, making sure the data is both accurate and representative of all relevant information is key.
To train a smarter AI, you need data from everywhere
AI models can’t function properly without pulling from a wide range of sources. For tasks like analyzing tax codes or legal precedents, the model needs to access court documents, federal and local laws, expert opinions, and even news coverage.
These sources can come in formats as varied as PDFs, spreadsheets, policy memos, and multimedia files. A robust AI model must be able to process and integrate data from all these diverse formats to generate useful insights.
How to prepare data for AI models
Preparing data for AI involves more than just collecting it. Data must be standardized and organized into a structure that the AI can easily digest, typically through transforming a mishmash of unstructured data formats—like scanned documents, policy memos, and spreadsheets—into a cohesive and usable dataset.
The challenge lies in integrating this data and in continuously updating it to reflect new information, rulings, and regulations. Businesses that successfully manage this process will build AI models that are reliable, accurate, and up-to-date.
The two steps you must get right to make AI-ready data
To make data usable for AI, two key steps are a must: grounding and human oversight.
Grounding refers to the process of augmenting a large language model with specialized, domain-specific knowledge that isn’t part of the core model. This typically involves using retrieval-augmented generation (RAG), where the AI accesses external data sources on demand.
The second step involves human expertise. While AI can process vast amounts of information, it still requires human subject matter experts to make sure the model’s outputs are accurate and contextually relevant. Experts provide the domain-specific insights needed to fine-tune the AI for complex business tasks, making sure the model doesn’t miss critical nuances.
Why AI still needs humans to reach its full potential
AI alone can’t replace human experts, especially in industries that demand deep knowledge and judgment, like law, healthcare, or finance. Human subject matter experts play an irreplaceable role in making sure AI models are aligned with real-world professional needs.
While AI can sift through massive amounts of data, it’s the human experts who guide the model in understanding which data is most relevant and how it should be applied. Human oversight is what elevates AI from a useful tool to a truly transformative asset for businesses.
Don’t be fooled, AI’s real power is just beginning
The success of AI models like ChatGPT passing the bar exam has led to an oversimplified view of AI as an all-powerful tool that can replace human expertise.
While AI’s ability to excel in structured environments is impressive, it’s only the foundation of what it can achieve.
The real power of AI lies in its future potential to handle unstructured, professional-grade tasks. This future depends heavily on the quality of the underlying data and the expertise applied in curating and updating it.
