Data Management for AI Implementation
CONTRIBUTORS
Justin Pulgrano, SVP of Strategic Growth, Crunchafi
THE ACCOUNTING AI PLAYBOOK
accountingaiplaybook.com
FIRST EDITION
2025
Justin Pulgrano, SVP of Strategic Growth, Crunchafi
accountingaiplaybook.com
2025
When it comes to AI in accounting, data quality is essential. AI tools rely on clean, complete, and well-structured data to produce accurate and trustworthy results. If flawed data goes in, flawed insights come out. For firm leaders exploring AI, building a strong data foundation is a strategic necessity.
Whether you are automating a tax workflow or piloting an audit analytics tool, the success of your AI initiative starts with how well you collect, prepare, and manage your data. Let’s dive into some of the practical strategies you can use to overcome data management challenges.
Many firms encounter similar obstacles when attempting to prepare their data for AI. These barriers compromise data quality, introduce inefficiencies, and hinder the success of AI initiatives.
As firms accumulate more data from various sources, such as client systems, third-party platforms, and internal tools, the sheer volume can become overwhelming. This data often arrives in different formats and structures, making it challenging to manage effectively. Without proper organization, valuable insights may be missed, and the risk of errors increases.
Most firms still rely on manual data handling to prepare financial data, including downloading reports, combining datasets in spreadsheets, and then manually cleaning up these spreadsheets. These tasks are time-consuming and increase the chance of mistakes that can compromise analysis.
Even small errors, such as duplicated entries or misapplied account codes, can produce misleading results once the data is fed into AI tools. Without automation, data reliability remains fragile, and the risk of downstream issues increases.
Accounting data often comes in various formats, including Excel spreadsheets, Word Documents, and PDFs, and lacks a standard structure. Each team at the firm and each client may use varying naming conventions, field types, or data formats, which makes consolidation difficult and slow. Given the varying expertise of team members and clients involved in producing the data, numerous factors influence the final output.
This inconsistency delays analysis and reduces the effectiveness of AI tools, which depend on clean, well-structured input. Without standardization, every engagement becomes a one-off data cleanup project.
In many firms, no one is formally responsible for the quality of the data being collected, stored, or analyzed. This is especially true across departments or service lines, inside the firm. Without assigned ownership or governance policies, inconsistencies go uncorrected, and data issues compound over time.
This lack of accountability also puts the firm at risk for compliance gaps, especially when handling sensitive client data or managing retention and audit trail requirements.
When data is scattered across disconnected platforms, including GL systems, reporting tools, file drives, and email threads, it becomes difficult to access, align, or secure. Teams often spend hours manually reconciling information.
This fragmentation creates data silos, hinders collaboration, and slows down AI tools that rely on centralized, real-time access to structured data.
To overcome these common challenges, firms can apply a set of proven best practices. These approaches improve data reliability, streamline workflows, and help prepare your firm for AI-driven analysis.
Manual collection slows down engagements and increases the risk of error. Automating this process improves accuracy and frees staff to focus on more valuable work.
For years, engineers have utilized tools like CData and FiveTran to extract data directly from source systems and databases, powering software products. Today, accountants have access to purpose-built tools like Crunchafi’s Data Extraction product that connect directly to key accounting platforms, such as QuickBooks, NetSuite, and Sage, to automatically extract complete, reconciled datasets. This tool eliminates the need for back-and-forth emails, screen sharing, or manual reformatting of client data.
Another helpful tool is Microsoft’s open-sourced MarkItDown, which transforms a variety of file formats into clean, structured Markdown text. These standardized datasets are ideal for training and fine-tuning large language models (LLMs).
Automated extraction also brings consistency to your process. Every time data is pulled, it comes in the same structure and includes the same fields. This repeatability enables faster onboarding, improved quality control, and seamless integration with downstream tools.
Most importantly, this approach ensures your firm starts with accurate, up-to-date information. When the data accurately reflects the source system, your audit or analytics work begins with a foundation that is both trust worthy and AI-ready.
Standardization enables scale. Without it, every engagement requires unique cleanup and formatting, which reduces efficiency and increases the risk of errors slipping through. In repetitive work, such as audits and tax returns, a lack of standardization and dependence on tribal knowledge can slow teams down, especially as staff changes occur year after year.
A best practice for client-facing teams at accounting firms is to create standard templates for key workpapers and deliverables, then use data mapping rules to translate incoming client data into that format. These rules should cover field naming, account classifications, and acceptable formats for items such as dates and currencies.
For more complex workflows, use tools like Workato or Integrate.io to create automated pipelines that apply your mapping rules consistently. These tools can also validate transformed data to ensure nothing is missing or misaligned during theconversion.
When all datasets follow the same internal structure, your team can build repeatable workflows, and your AI tools can operate without the need for custom logic per client. This is another area where purpose-built accounting software tools, such as Crunchafi, can be beneficial. This level of structure is what makes advanced analytics possible across multiple engagements.
Good data requires clear ownership. Without someone responsible for maintaining data integrity, even the best tools can’t prevent errors from spreading across systems and reports.
Appoint a data steward or governance team with both accounting and data fluency. This can start within a specific department at the firm (i.e., Audit) and then expand to others. This group should define standard operating procedures for data collection, validation, and review. They should also manage access rights and monitor for policy compliance across departments.
Governance also means establishing policies around retention, privacy, and change control. For example, every dataset should have a defined lifecycle—when it is created, how long it is kept, and how changes are tracked and approved.
Clear governance builds trust in the data. When staff understand expectations and know where to go for answers, data becomes a strategic asset rather than a liability. It also helps your firm stay compliant with regulatory standards and client confidentiality obligations
Data should be both protected and easily accessible. When files are spread across desktops, email threads, or on-premise servers, they are vulnerable to loss, breach, or corruption.
Shift to cloud-based storage platforms that support role-based access, audit trails, and encryption. Tools like CCH Axcess, Caseware Cloud, and Sharefile enable secure collaboration and real-time access for staff.
A centralized data environment reduces confusion, prevents duplication, and provides a reliable source of truth for AI-driven analysis.
Even clean data requires ongoing oversight. Without regular validation, errors can creep in and undermine the accuracy of AI outputs.
Firms should schedule recurring data audits and implement automated validation rules to flag issues such as duplicates, missing fields, or unexpected values. These checks help maintain consistency and reliability.
At the same time, invest in staff training to reinforce data entry standards, raise awareness of common pitfalls, and ensure that everyone understands their role in maintaining data integrity.
This combination of human oversight and automated controls keeps your data reliable. When staff are trained and systems are monitored, you reduce risk, strengthen compliance, and maximize the accuracy of AI-driven analysis.
Maxwell, Locke & Ritter is the largest locally owned and managed firm in Austin, Texas, and their audit firm was experiencing some challenges when working with privately held clients using a wide range of general ledger systems. The team’s efficiency was often hindered by delays and inconsistencies in receiving necessary financial data from clients, resulting in wasted time, a decreased work-life balance, and project bottlenecks. The lack of a standardized data retrieval process meant auditors frequently waited for clients to pull reports and then spent hours combining, reconciling, and reformatting the data provided.
After implementing Crunchafi, the audit team experienced a significant improvement in workflow. This tool enabled auditors to directly and securely extract detailed general ledger data from clients’ systems without requiring full system access or additional logins. This self-service capability reduced downtime, improved project management, and allowed team members to answer their own questions or ask more targeted ones, streamlining the audit process. The solution also enhanced client relationships by shortening request lists and making the audit experience less burdensome for clients. Importantly, Crunchafi provided a standardized format for all collected data, regardless of the client’s accounting system, allowing the team to build repeatable processes and leverage analytics and other tools on top of consistent, reliable data outputs.
The experience at Maxwell, Locke, and Ritter highlights that investing in the right data management tools can transform audit efficiency, team morale, and client satisfaction. Crunchafi not only saved valuable time and improved work-life balance for auditors but also allowed the firm to deliver a more client-friendly, technologically advanced service. The case highlights the importance of solutions purpose-built for accounting firms' data needs and supported by responsive, client-focused service teams. The takeaway: Tools like Crunchafi Data Extraction help clients organize their financial data in a structured, consistent, and efficient way. With better data quality, firms can gain greater confidence in the accuracy and reliability of the outputs produced by their AI tools.
In addition to Crunchafi Data Extraction, these platforms help firms streamline audit data workflows and prepare for AI integration:
● Validis – Automates client data extraction and formatting
● Inflo – Provides data ingestion along with collaborative audit workflows
● MindBridge – Combines data integration with AI-driven risk analysis
All relevant data sources are identified and accessible
Data is complete
Data is structured (notburied in PDFs or free text)
Consistent formats used across all sources
Automated tools are inplace for data extraction
Incoming data is mapped to a standard format
Transformation rules are documented and tested
Data flows are clearly defined and repeatable
A data owner or steward is assigned
SOPs for data handling and validation are documented
Retention and privacy policies are being followed
Change tracking orversion control is in place
Role-based access controls are implemented
Data is encrypted instorage and transit
Backups are scheduled and tested
Storage environment isscalable and cloud-accessible
Staff are trained ondata tools and best practices
Data entry standards are communicated
Support contacts areavailable for troubleshooting
Regular data audits or validation checks are scheduled
To generate reliable results with AI, firms must first ensure their data is trustworthy, structured, and accessible. This chapter explores the most common data management challenges accounting firms face—including data overload, manual processes, inconsistent formats, and poor governance—and explains how these issues limit the effectiveness of AI tools. It then outlines best practices to improve data quality, such as automating data extraction, standardizing data structures, assigning ownership, and strengthening security and governance. A real-world case study shows how one firm dramatically improved audit efficiency using Crunchafi Data Extraction, while additional tools and a practical readiness checklist help firms take the next step toward AI integration. With the right data foundation in place, firms can unlock more accurate insights, reduce risk, and gain greater confidence in their AI outputs.
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