Building a strategic roadmap for AI implementation in accounting firms
Alex Anthony, Director of Innovation, GBQ
Brent Pruim, Director of Innovation & Change Management, Rehmann
accountingaiplaybook.com
2025
The path to AI adoption isn't always clear. Jumping in without a roadmap can lead to wasted spending, stalled progress, and firm-wide confusion. But waiting too long carries its own risks, as competitors begin streamlining operations and reducing costs with AI tools.
This chapter provides step-by-step guidance to help you build a clear, effective AI strategy. You'll learn how to form a task force, identify high-impact use cases, evaluate AI tools, implement those tools, and measure ROI. Whether you're starting from scratch or refining an existing plan, the resources in this chapter will help you move forward with confidence.
Even when leaders recognize the need for AI adoption, common barriers can slow progress.
Firm size and structure greatly influence how quickly and deeply AI can be adopted. While large firms often have the capacity to build dedicated innovation teams and explore custom AI solutions, smaller firms may have tighter budgets and fewer staff.
That doesn't mean smaller firms are at a disadvantage. In many cases, their leaner structures allow them to move faster, avoid bureaucratic slowdowns, and respond to feedback more quickly. The key is to build a strategy that fits your firm's realities. With the right mindset, strong vendor support, and a clear plan, even firms with limited resources can implement AI successfully.
To successfully implement AI and weave it into your firm's DNA, you'll need a plan. Here are some best practices that can help you ensure your firm has a clear strategy for leveraging AI tools.
Start by assigning clear ownership of the firm's AI strategy by forming a dedicated AI Task Force. This group will be responsible for developing your AI roadmap, evaluating opportunities, managing risk, guiding firm-wide implementation, and providing a governance function.
To build an effective task force, think cross-functional and cross-departmental. Try to include representatives from the following areas
Senior manager or partner with deep workflow insight
Tax director or senior staff member familiar with process efficiency and compliance
Advisory lead to identify AI opportunities that drive client value
Director of technology or IT lead to assess integration and security feasibility
Someone from your internal risk or quality control team (e.g. a risk officer or quality partner) to ensure AI governance aligns with standards like PCAOB or GDPR
Operations leads or individuals with project or process management training who can streamline firm-wide adoption
If available, include anyone responsible for internal tooling, process improvement, and/or emerging technologies
Optional but valuable—these reps can help pilot AI use in non-technical departments and bring insight into how AI can enhance client-facing deliverables
People officer, learning and development team, and/or HR subject matter expert will provide valuable training and implementation insight for associates
Legal counsel or representative to advise on data usage, privacy compliance, and AI policy implications, including regulatory and contractual considerations
Aim for a mix of early adopters and practical skeptics to balance ambition with accountability. The task force should include a range of experience levels — from staff to senior leaders — to reflect the different ways AI will be used across job functions. Appoint a senior sponsor, such as a partner or firm executive, to champion the initiative and ensure firm-wide buy-in.
This group's first assignment is to assess the firm's AI maturity level. This includes understanding where the firm is in its transformation roadmap. Once this is assessed, identifying resources to educate the task force on AI's capabilities and limitations, including areas like machine learning, natural language processing, and generative AI will provide the most value to the firm. From there, define what AI means for the firm. What are the specific use cases? What risks need to be addressed? What outcomes are you aiming to achieve?
Ultimately, this group will build the firm's broader AI Implementation Plan, which will include governance, training, and implementation (see our Governance and Training chapters for more detail). This framework must be holistic, apply to the entire firm, and not be limited to specific AI tools.
Not all AI use cases will deliver the same value for your firm. To identify the most impactful opportunities, you need a systematic approach to evaluating potential applications. Consider factors like task repetitiveness, data availability, integration potential, and compliance requirements.
The following checklist will help you evaluate whether a task or process is a good fit for AI, and assess potential AI tools before adoption. Use it to prioritize your AI initiatives and focus on the areas that will deliver the greatest return.
Use this checklist to evaluate whether a task or process is a good fit for AI, and to assess potential AI tools before adoption.
Is the task repetitive, high-volume, or rule-based?
Is the task currently time-consuming or resource-heavy?
Does automating this task free up staff for higher-value work?
Does automating this task deliver organizational benefits beyond individual teams?
Can the task be clearly defined with consistent logic or inputs?
Do we have access to clean, structured data for this task?
Is the data volume sufficient to support AI functionality?
Can this data be securely shared with the tool/vendor?
Can the AI tool integrate with our existing systems or workflows?
Will the output from the AI tool be easy for staff to act on?
Is there a clear owner or team responsible for managing this use?
Will it be easy to update the integration if our systems change?
Can we pilot this tool on a small scale or sample dataset?
Are success metrics (e.g. time saved, accuracy) easy to define and measure?
Can the tool's results be validated or reviewed by humans?
Does the use case involve sensitive client data?
Has the vendor confirmed that data won't be used to train external models?
Are there controls in place for compliance, audit trails, or human oversight?
Are bias, fairness, explainability, and interpretability adequately understood, actively monitored, and with governance processes in place?
Can metrics for ROI and impact be easily measured and reported?
Will this tool meaningfully reduce costs or increase output?
Will it improve accuracy, consistency, or turnaround time?
Is there a clear benefit to the team using it?
Throughout the rollout, track how AI is being used and what impact it is having. Develop detailed adoption reports to track usage trends and the impact on business processes and productivity. Most vendors now offer tools to help with this, including Microsoft Viva Insights, OpenAI's enterprise dashboard, Google Workspace analytics, or vendor-supplied admin platforms.
Use this tool to estimate the value of your AI investment. Input a few key metrics, such as time saved, labor costs, and AI pricing, to calculate your firm's net savings, ROI, and payback period. It's a fast way to build your business case and prioritize high-impact AI initiatives.
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These numbers are for illustrative purposes only. Download the ROI Calculator to make your own calculations.
With a framework in place, your next decision is whether to build your AI tools or buy them. Most small and mid-sized firms will choose to buy, given the time, technical expertise, and budget required to develop AI solutions in-house. Choosing to build might be viable for firms with Big 4-level resources, but for most, buying is the more practical and less risky path forward.
Before selecting a vendor, conduct an internal audit of your current platforms and vendors. Many accounting software providers—such as Intuit, Thomson Reuters, Wolters Kluwer, and Caseware—have already integrated AI into their platforms. If you're already using Microsoft 365, upgrading your plan to include Copilot can be a natural next step that provides enterprise-level security, smoother adoption, and access to built-in training resources.
However, choosing a vendor is not without risk. The AI marketplace is expanding rapidly, and not all tools will be maintained or supported long-term. A poor vendor selection can leave your firm locked into a platform that becomes obsolete or unsupported. That's why it's critical to thoroughly vet vendors—review their roadmaps, evaluate their customer support models, understand their security controls, and assess whether their vision aligns with your long-term goals.
Beyond mainstream vendors, firms with a higher appetite for experimentation can also consider partnerships with smaller AI developers. These vendors may offer unique capabilities or early access to innovative tools. Some even offer free or low-cost custom AI builds through partnerships with major platforms like Microsoft and OpenAI, giving smaller firms an opportunity to test advanced functionality with minimal upfront investment.
A comprehensive list of AI tools available for accounting firms across different categories.
Risk Assessment & Anomaly Detection
Data Extraction
Financial Statement Disclosures
Audit Quality Review
Audit Workflow & Coordination
Tax Research & Q&A
Tax Law Analysis & Planning
Tax Return Delivery
Client Impact Alerts
Bookkeeping Automation
Accounts Payable Automation
Expense Management
Document Management & OCR
Practice Management / Admin Assistants
Forecasting & FP&A
Financial Data Analysis / Decision Support
Contract & SOC Review
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With a framework in place and vendors selected, you can move to the rollout stage of adoption. However, it is best to use a phased approach to ensure smooth adoption and minimize disruptions.
0-6 Months
6-12 Months
12-24+ Months
Start with a small-scale pilot to validate the potential of AI in a low-risk, controlled environment. The goal is to identify quick wins, surface challenges, and establish a baseline policy for responsible use.
Select a single team or function with minimal regulatory exposure such as marketing, knowledge management, or internal operations. You can also pilot AI tools for non-client-facing tasks like content drafting, document search, or summarization.
Expand AI adoption to a full department, industry, or service line. Begin shifting AI from an experimental initiative to a functional asset within day-to-day workflows.
Roll out to a department already showing interest or pilot success such as a tax team using AI for research, or an audit team exploring AI-assisted documentation. Teams with clearly structured processes are ideal at this stage.
Operationalize AI across the organization. At this stage, AI becomes a normalized part of daily work, embedded into standard operating procedures, supported by training programs, and monitored for ROI and compliance.
Extend AI access to all applicable teams — especially those with repeatable workflows, such as audit, tax, advisory, or back-office operations. Prioritize teams that already had champions or exposure in earlier phases.
When enacting this phased rollout approach the AI taskforce should set an attainable goal for when they plan to achieve full rollout. Clearly communicate the timeline and rollout plan within the firm in order to set expectations and create buy-in.
Throughout the rollout, track how AI is being used and what impact it is having. Develop detailed adoption reports to track usage trends and the impact on business processes and productivity. Most vendors now offer tools to help with this, including Microsoft Viva Insights, OpenAI’s enterprise dashboard, Google Workspace analytics, or vendor-supplied admin platforms.
Gather the insights from your reports and refine your adoption process as you go. Strive for continuous improvement in your rollout. Even so, not all staff will embrace AI. If staff aren't using the tools, take note. Consider removing access after a set period of inactivity. AI licenses can be expensive, and you want to prioritize active users.
To measure ROI, your AI taskforce should establish KPIs tied to your firm's goals. Whether it's reducing operating costs, increasing net revenue, or increasing time to market for new products, you should set clear benchmarks to strive towards.
GBQ and Rehmann began exploring AI to offload administrative tasks. Both firms already used Microsoft 365 and decided to adopt Copilot, citing its seamless integration, enterprise-grade security, and clear vendor and adoption roadmaps.
Copilot significantly improved document search and analysis in SharePoint. Instead of navigating through folders or relying on keyword searches, employees could ask natural-language questions about specific documents. Copilot also suggested relevant files based on recent activity, accelerating collaboration across teams.
With Copilots integration with the MS Office Suite users were able generate emails faster, and utilize meeting note takers within the MS Teams platform. Additionally, using Loop and other copilot enabled MS tools users gained the ability to effectively and efficiently recap meetings, create action items, and reorganize their OneNote pages for clear and concise documentation.
To support adoption, the firms used Microsoft's Copilot Academy, an on-demand learning platform. Employees followed role-specific learning paths and short video modules, making it easy to learn and apply AI features.
Microsoft also provided their Copilot Adoption Playbook, which guided the firms through their phased rollout—from pilot testing to departmental deployment and firmwide integration. These roadmaps reduced friction, set clear milestones, and gave firms confidence that they were implementing AI in a secure, scalable, and strategic way.
By leaning into Microsoft's ecosystem, these firms gained access to enterprise-grade resources that supported them at every stage of AI adoption. The takeaway: with the right vendor, any firm can roll out AI tools with the strategy, support, and success of a much larger organization.
This chapter provides a step-by-step roadmap for developing a firmwide AI strategy—one that aligns with your size, resources, and business goals. It covers key decisions such as strategy ownership, whether to build or buy AI tools, how to select the right vendors, and how to execute a phased rollout. Readers will also learn how to monitor adoption, measure ROI, and drive continuous improvement. A real-world case study highlights how mid-sized firms can successfully implement tools like Microsoft Copilot using structured rollout plans and built-in training resources.
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