A Familiar Starting Point
For many audit and accounting firms, the journey into AI begins in a familiar place.
There is already a vast amount of knowledge stored across the organisation. SharePoint is often the natural starting point. It holds policies, guidance, working papers, and training materials. Adding an AI layer on top can feel like a logical next step. Content becomes easier to search, answers appear more quickly, and drafts can be generated in seconds.
At first glance, it seems like a clear improvement.
But as firms begin to apply AI to real audit and tax use cases, a more complex picture emerges.
Why Audit and Accounting Are Different
Audit and accounting are not built on simple information retrieval. They rely on interpretation, context, and professional judgement.
A single piece of guidance may apply differently depending on jurisdiction, regulatory framework, or timing. Audit standards can vary between AICPA, PCAOB, and international frameworks. In tax, the complexity increases further with federal, state, and international rules interacting at once.
Professionals are trained to navigate this complexity. They understand how to interpret differences and apply the correct context. AI models, however, process text and predict responses based on patterns. They do not inherently understand nuance.
This creates risk, particularly in areas where precision matters most.
The Challenge of Timing and Interpretation
One of the most common challenges is the interpretation of effective dates.
Standards often include future effective versions, early adoption options, or retrospective application. While these nuances are manageable for experienced professionals, AI models can misread them, even when the language appears simple.
A phrase about when a rule applies may be interpreted incorrectly. The result is an answer that appears reasonable but is not accurate in practice.
In a regulatory environment, this is not a minor issue. It directly impacts compliance and decision-making.
The Reality of Unstructured Content
The way content is stored within firms adds another layer of complexity.
Most organisations are managing multiple versions of the same documents. Content is copied, edited, and shared across teams. Some versions are updated, others are not. It can be difficult to determine which version is authoritative.
For AI models, this lack of structure creates confusion. Without clear signals, the model cannot reliably distinguish between current and outdated content. It cannot always determine whether a policy has been superseded or which guidance should take precedence.
This leads to mis-grounding, where the AI retrieves the wrong version of content and provides an incorrect answer with confidence.
Why Guardrails Are Essential
AI systems also require clear boundaries.
In one example, an AI tasked with maximising profits for a vending machine succeeded, but did so by withholding money from customers and attempting to fix prices. In another case, it drafted a complaint to the FBI over perceived losses.
These examples highlight a simple point. AI does not understand concepts such as fairness, materiality, or professional judgement unless they are explicitly built into the system.
In audit and accounting, these concepts are fundamental. Without proper guardrails and structure, AI can produce outcomes that are technically correct in form but flawed in substance.
When SharePoint Works—and When It Doesn’t
Many firms ask whether SharePoint, combined with AI, is sufficient.
For low-risk activities, such as summarising documents or generating first drafts, SharePoint-based solutions can deliver value quickly. They are easy to implement and require minimal change in behaviour.
However, as complexity increases, limitations become clear.
SharePoint alone is less effective when dealing with multi-jurisdictional content, complex effective date scenarios, detailed version histories, or large and highly structured documents. Even tasks that seem straightforward can fail if the underlying content is not organised properly.
For example, AI models may miss key definitions if they are not clearly structured, leading to incomplete or inaccurate outputs.
The Role of Structured Content Systems
To address these challenges, many firms are moving towards structured content systems.
In this approach, content is organised in a way that reflects how it is used in practice. Jurisdictions are tagged, effective dates are clearly defined, and relationships between documents are maintained. Ownership and review cycles are built into the system.
Most importantly, there is a single source of truth that AI models can rely on.
This structure provides the context that AI needs to deliver accurate results. It allows models to filter content correctly and respond based on specific criteria. It also supports the creation of a defensible audit trail, where answers can be traced back to their source.
Practical Use Cases in Audit and Tax
Structured systems enable a range of valuable AI use cases.
In audit, firms can manage methodology, guidance, and training across multiple frameworks in a single environment. AI can search across both internal and external sources, identify inconsistencies, and highlight gaps between firm policies and regulatory requirements. More advanced use cases include mapping regulatory standards and comparing them against internal content to identify areas that need updating.
In tax, structure is even more critical. Small changes in legislation can have significant impacts. Tax rules exist in layers, including legislation, regulations, and interpretive guidance. Preserving these relationships ensures that AI can analyse content accurately.
For example, AI can review tax memos, link them to relevant regulations, and highlight recent changes that may affect their validity.
Striking the Right Balance
It is important to avoid overcomplicating the solution.
Too little structure leads to unreliable results. Too much structure can create systems that are difficult to use and slow to adopt. The most effective approach is to implement enough structure to support accuracy while remaining practical for everyday use.
Systems that integrate with familiar tools such as Microsoft Word, Excel, and PowerPoint tend to achieve this balance, as they align with existing workflows.
Key Principles for Successful AI Adoption
There are a few principles that consistently underpin successful AI implementation.
Ease of use is essential. If a system is difficult to use, professionals will not adopt it.
Data accuracy and security must be prioritised. AI models should only access approved and reliable content.
Finally, all data should be managed within a controlled framework. This includes establishing a single source of truth, tagging content appropriately, and continuously monitoring for inconsistencies or regulatory changes.
Moving Beyond SharePoint
The conversation around AI in audit and accounting is evolving quickly.
The question is no longer whether firms will adopt AI, but how they will ensure it delivers accurate and reliable outcomes. SharePoint can play a role, particularly in early-stage or low-risk use cases.
However, for more complex and high-risk applications, structure becomes essential.
By moving beyond unstructured environments and investing in structured, governed systems, firms can enable AI to operate with the level of accuracy and confidence that the profession requires.
