As AI continues to spread across the tax and accounting profession, confusion about how to use it effectively is slowing progress for many tax and accounting firms. Clear distinctions exist between traditional, generative, and agentic AI — each with its own strengths and use cases across tax workflows. Yet these terms are often used interchangeably, making it difficult for decision-makers to determine what they actually need. The result is a delay in the adoption of technology that has become a competitive necessity for today’s tax and accounting professionals.
According to the Tax Firm Advisory Services Report from Thomson Reuters Institute, clarity around AI adoption matters. The report highlights how AI and automation are already reshaping tax advisory delivery through capabilities such as automated scenario modeling and predictive analytics. These tools allow firms to move from reactive compliance work to proactive, insight-driven advisory services — but only when the underlying technology is carefully selected and applied with intent.
By understanding the distinct capabilities of each AI type and applying a practical decision-making framework, your organization can build an AI stack that aligns the right technology to specific workflow challenges. Rather than pursuing AI for its own sake, this approach enables you to implement AI with clarity and unlock measurable efficiency gains, strengthen advisory relationships, and realize maximum impact. Let’s take a look.
Understanding the AI landscape
AI is often discussed as if it were a single technology. AI is actually an umbrella term for multiple technologies, each designed to solve different problems. The most successful organizations don’t choose one form of AI. They intentionally build an AI stack that applies the right type of intelligence to the right task.
Generally, AI technologies fall into three categories. Each plays a distinct role and delivers value in different ways:
- Traditional AI excels at automating structured, repetitive tasks.
- Generative AI (GenAI) adds value to knowledge-driven work such as tax research, drafting, and synthesis by creating content from vast datasets.
- Agentic AI autonomously executes tasks across multiple steps or systems, using decision-based logic to achieve specific goals.
Those that see the greatest impact from AI combine these capabilities rather than relying on a single approach. This layered strategy enables the technology to support everything from back-office efficiency to higher-value advisory work, without sacrificing accuracy or professional judgment.
Traditional AI
What is does best:
- Applies predefined rules
- Analyzes historical data
- Identifies patterns to improve consistency and efficiency
Common use cases in tax and accounting:
- Workflow and data entry automation
- Anomaly detection
- Audit sampling
- Predictive analytics
- Pricing and margin analysis
Generative AI
What it does best:
- Creates new content based on existing knowledge
- Uses natural language to interact with users
Common use cases in tax and accounting:
- Drafting emails and client communications
- Generating financial reports, invoices, or other accounting documents
- Summarizing lengthy financial reports, tax law, or guidance
- Explaining complex concepts
- Assisting with tax research
- Fraud detection
Agentic AI
What it does best:
- Executes tasks autonomously, often across multiple steps or systems
- Uses decision-based logic to achieve a goal
Common use cases in tax and accounting:
- End-to-end workflow execution
- Document processing and routing
- Multistep compliance tasks
- Proactive task management
Many organizations are already moving beyond experimentation and seeing tangible returns from AI investments. According to the Thomson Reuters Institute Future of Professionals Report, 53% of organizations surveyed are already seeing ROI directly or indirectly tied to their investment in AI. This ROI is showing itself in a variety of ways, from improved efficiency and productivity to enhanced response time and a reduction in errors.
How to choose the right AI solution for your organization
Before selecting an AI solution, pause and ask yourself a simple but critical question — what problem are we trying to solve? Once you know the answer, you can align your firm’s needs with the best type of AI to address them.
Step 1: Clarify the nature of the work
Start by identifying whether the task is structured, knowledge-based, or workflow-driven.
- Ask yourself: Is the task highly repeatable and rules-based?
If the work follows consistent logic, relies on defined thresholds, or involves pattern recognition across large data sets, traditional AI is often the right fit. These systems excel at enforcing consistency, reducing manual effort, and surfacing insights from historical data.
- Ask yourself: Does the task involve understanding, summarizing, or communicating information?
When professionals need help drafting, explaining, or synthesizing complex tax and accounting information, generative AI is most effective. It augments human expertise by accelerating research, improving clarity, and reducing time spent on first drafts.
- Ask yourself: Does the task span multiple steps or systems?
If the goal is to complete an end-to-end process — such as moving information from intake through review and delivery — agentic AI is best suited. These systems are designed to orchestrate actions, make decisions, and progress work with minimal handoffs.
Step 2: Assess risk, oversight, and accountability
Not every task requires the same level of human control.
- Where accuracy and compliance are paramount, firms often start with traditional AI, which operates within clearly defined boundaries.
- Where professional judgment is still required, generative AI works best as an assistant rather than a decision-maker.
- Where efficiency gains outweigh manual coordination, agentic AI can automate steps while keeping humans in the approval loop.
Understanding where human oversight is needed helps firms deploy AI responsibly and with confidence.
Step 3: Match the solution to the outcome
The final question to ask is, what outcome matters most?
- If the priority is speed and consistency, traditional AI delivers dependable results.
- If the goal is insight, explanation, or responsiveness, generative AI adds immediate value.
- If the objective is scale and operational leverage, agentic AI unlocks the greatest long-term impact.
In practice, many tax workflows benefit from a combination of all three forms; traditional AI to validate data, generative AI to interpret findings, and agentic AI to move work forward automatically.
5 Considerations for implementing AI in the tax and accounting space
Successful AI adoption requires more than experimenting with the latest tools or following industry trends. It demands a deliberate, structured approach. Firms that move too quickly either leap to the most advanced AI without establishing a foundation, or select tools based on hype rather than fit. These firms often face implementation challenges, missed efficiencies, and unnecessary risk.
Let’s look at five critical considerations for implementing AI with clarity, confidence, and measurable impact.
1. Data first, AI software second
Every organization, whether it’s a tax and accounting firm, government agency, or corporation, generates vast amounts of data. Yet many firms lack visibility into where that data resides, how consistent it is, or whether it can be trusted. Without that understanding, even the most advanced AI tools will fall short.
Successful AI initiatives almost always begin with data readiness, not software selection. As stated in the above decision-making framework, start by clearly defining the problem you’re trying to solve — whether it’s improving workflow efficiency, reducing risk, or enhancing advisory insights — then identifying the data sets required to support that outcome. This often involves addressing data hygiene issues, such as standardizing formats, eliminating duplicates, and correcting errors, as well as establishing governance and processes to ensure new data is captured accurately going forward.
When data is organized, accessible, and reliable, AI becomes far more effective. Clean data enables traditional AI to automate repetitive tasks, generative AI to deliver meaningful insights, and agentic AI to coordinate workflows with confidence. In short, understanding and preparing your data is the foundation of any successful AI strategy.
2. Apply AI intentionally
The most successful firms follow a progression-based approach to AI adoption, starting with traditional AI to deliver quick, tangible wins. At this stage, AI algorithms also analyze large datasets to identify patterns, anomalies, and potential risks at a speed no human team could match. This enables tax and accounting professionals to redirect their time toward interpretation and higher-value analysis.
Once a strong foundation is in place, firms can layer in generative AI to accelerate knowledge work. In tax workflows, this includes researching complex regulations, drafting client communications, summarizing guidance, and supporting advisory conversations. Generative AI also enhances predictive analytics, helping you anticipate risks, trends, and upcoming tax events based on historical and real-time data.
Agentic AI represents the next stage of evolution. Rather than performing a single task, agentic AI can coordinate multiple steps — monitoring regulatory changes, triggering analyses, initiating client outreach, and escalating decisions to human professionals when judgment is required. Successful firms pilot agentic AI in controlled use cases, refine governance and oversight, and then scale it across the organization.
This progression helps firms avoid overreach while still moving forward with confidence.
3. Develop your AI “muscle”
Successfully adopting a technology requires changes to routines and workflows which can be disruptive and is likely to be met with some resistance.
Remember, AI is not a turnkey technology that you can simply install. Humans are required to instruct the technology and human processes will need to be adjusted to incorporate it into the daily workflow. The capacity for change management doesn’t just happen; it is a “muscle” that organizations must develop.
Building that muscle starts with leadership. When partners and managers use AI themselves, experimenting with tools, learning what works, and sharing both successes and mistakes, they signal that this technology is worth the learning curve. Staff are far more likely to adopt AI when they see leaders wrestling with the same questions they are. Create space for hands-on exploration. AI literacy doesn't come from presentations or training decks; it comes from repeated use, small wins, and building confidence through practice.
The goal is not perfection, but steady progress. Encourage your team to start with low-risk tasks like:
- Summarizing documents
- Drafting client emails
- Researching technical questions
As individuals gain experience, their comfort with more complex applications grows naturally. Over time, what once felt uncertain becomes routine.
Aside from changes to established workflows, the implementation of advanced technology likely entails working with new types of professionals — data analysts, process engineers, pricing specialists, and other allied professionals. The ability of organizations to integrate those new professional roles into tax and accounting practices — and to recognize and compensate them properly — can result in a competitive advantage.
4. Align AI with client needs
AI has the potential to significantly strengthen collaboration between your firm and your clients, but only when it is applied with client needs in mind. While AI can transform your internal processes, the most valuable use cases often address what clients want — transparency, responsiveness, and proactive guidance. Your clients can be active partners in this work as they often hold the data needed for AI solutions and can help define what success looks like.
When applied thoughtfully, AI enables more frequent, informed, and timely client interactions. AI-powered tools can:
- Surface insights in real time
- Support scenario modeling during client conversations
- Automate routine communications such as document requests, status updates, and deadline reminders
This thoughtful application reduces friction in the engagement process and creates space for more strategic, high-value discussions focused on planning and decision-making rather than data gathering.
For this reason, AI initiatives should not be developed in isolation. The greatest impact comes when firms work alongside clients to align data, workflows, and goals, creating shared value across the engagement. When AI is deployed to solve real client challenges, it enhances collaboration, strengthens client relationships, and allows human expertise to take center stage.
5. Choose the right AI for the job
One of the most common missteps firms make when adopting AI is choosing technology based on hype rather than fit.
With so much attention on the newest and most advanced AI capabilities, it’s easy to assume that more sophisticated technology automatically means more effective technology. In practice, the opposite is often true. Firms typically fall into one of three patterns, each reflecting a mismatch between the technology chosen and the problem at hand.
The traditional AI trap
Some firms overlook rules-based automation and traditional AI because it feels less transformative than generative AI. Yet traditional AI remains one of the fastest ways to deliver measurable value. Automating invoice processing, data categorization, reconciliation, and validation tasks can dramatically reduce manual effort and create immediate capacity for higher-value work.
The GenAI overreach
While generative AI excels at synthesizing research, drafting content, and supporting advisory conversations, it is not designed for mathematical accuracy, deterministic calculations, or strict rules enforcement. Using GenAI where traditional AI is more reliable can introduce risk, inconsistency, and additional review overhead.
The Agentic AI premature leap
Finally, some organizations attempt a premature leap into agentic AI without establishing the foundation it requires. Agentic AI depends on clean data, well-defined processes, and experience managing AI-driven outputs. Firms that skip earlier stages often struggle with governance, oversight, and trust in the system. Agentic AI should build on a proven base of automation and generative capabilities, not replace them.
The objective is not to deploy the most advanced type of AI, but to deploy the right AI for the job at hand. By matching the technology to the task, you can reduce risk, accelerate adoption, and ensure AI delivers meaningful, sustainable value across your tax workflows.
Bringing clarity and trust to AI adoption
While many firms struggle to know which AI technologies to adopt and how to achieve meaningful outcomes, you don’t have to be one of them. Partnering with a trusted AI technology provider brings clarity to AI adoption so that you fully understand which tools are right for your workflows.
Why does trust matter?
Without trust, AI adoption stalls. Teams hesitate to rely on outputs they don't understand. Clients grow uncomfortable when they can't see how recommendations were formed. Leadership avoids committing to technology that introduces regulatory or reputational risk.
Trust changes the equation. When you work with a provider who adheres to rigorous industry standards — and who builds transparency into every workflow — AI becomes something your teams actually use, your clients value, and your firm can scale with confidence. Trust turns AI from a pilot project into a strategic advantage.
The most successful firms aren't building complex AI stacks from scratch. They're leveraging integrated solutions that intelligently apply the right AI for each task.
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