Agentic AI and the future-ready accounting firm: What comes after automation?

The rise of agentic AI

While most leaders first experienced AI as a chat assistant – you ask, it responds – AI has evolved since November 2022, when ChatGPT was introduced to the world. Agentic AI introduces something more operational - systems that can plan and execute actions toward an outcome, often across multiple tools and datasets.

Unlike rigid, script-based automation (including traditional RPA) that can break when screens, inputs, or handoffs change, agentic systems are designed to adapt and self-correct – within defined guardrails – when workflow conditions shift.

The critical guardrail in tax and accounting remains having experts in the loop. Agents can accelerate, suggest, and coordinate, but qualified professionals review, approve, and apply judgment. That expert-in-the-loop oversight is what protects quality, compliance, and client trust as AI influences more steps and decisions.

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Consider agentic workflows along this spectrum:

• Taskers automate low-value, repetitive tasks like document classification.
• Automators run entire processes end-to-end, such as flowing categorized transactions into trial balances.
• Collaborators provide intelligent guidance during complex workflows, such as routing or posting.
• Orchestrators coordinate multiple agents to deliver an outcome, such as moving a 1040 workstream from intake to a first-pass, return-ready draft, with professional review and approval at key judgment points.

For today’s firm leaders, the ROI from agentic workflows depends on the architecture of their tech stack. Agentic AI doesn’t thrive in silos. If your tech stack is fragmented, agents will spend most of their time figuring out the answers to these questions:

1. Where the right data lives
2. Who/what is allowed to access it
3. The current state of the work
4. How to trigger the next action

Orchestrators need clean endpoints, reliable identity and permissioning, and integrated data flows. In other words: agentic workflows depend on integrated systems that share context, plus clean, governed data and secure controls.

What the data says: Integration + cloud maturity are growth accelerators

This year’s Future Ready Accountant findings consistently point to the same conclusion: that digital maturity drives performance.

In 2025, 52% of firms adopted or expanded cloud-based solutions, and 87% of professionals with highly integrated technology (75%+ integrated) experienced revenue growth.

Meanwhile, high-growth firms are 53% more likely to have highly integrated systems and 38% more likely to be fully cloud-based.

Why does this matter?

To maximize ROI and deliver outcomes, agents need a connected platform, with cloud as the backbone, integration to reduce rework, and responsible AI embedded where work happens. The closer firms get to a single source of truth – with consistent data models – the more reliable and explainable AI outputs become.

The architecture behind agentic workflows

If you want agents to deliver more than demos, treat agentic AI as an architecture program. These are the elements that consistently separate pilots from production.

What’s holding firms back, and how leaders remove the blockers

In real-world implementation and use, hiccups happen across the spectrum, from people to processes and technologies.

• People: AI maturity and adoption is uneven. Solve for this by creating tiered training (a baseline and something more advanced), clear policies that communicate guidelines, and institutionalize sharing. Structured curiosity beats isolated experimentation and helps protect sensitive data.
• Processes: Don’t just "add AI" to yesterday’s steps. Redesign end-to-end workflows to take advantage of AI’s strengths and remove handoffs, rework, and manual routing in the team’s daily work.
• Technologies: Many tech stacks weren’t built for orchestration. Modernize core systems, connect tools via APIs, and standardize data models so agents can act with context.

Keep in mind that “just block it” can backfire. When firms restrict AI without providing approved alternatives, teams still under pressure to move fast may turn to whatever tools are available — creating shadow IT and increasing data-handling risk. Responsible adoption – clear data rules, vetted tools, and repeatable training – scales more safely than blanket bans.

The entire firm needs to understand that this is not an IT side project. It’s an organizational change program that needs leadership messaging, governance, and measurement.

A practical roadmap: Small steps to drive compounding returns

There’s no need to boil the ocean with a grand, multi-year plan to get meaningful results. Pinpoint a workflow where the friction is obvious and the value is measurable, and start there.

Pick a "thin slice" use case. Choose something document-heavy where people lose time to handoffs and rework: think intake → extraction → review. Then decide what "better" means before you change anything: faster cycle time, fewer rework loops, stronger realization, happier clients.

1. Get the basics right; fix the foundation first. Agentic workflows dislike disconnected systems; if your core data lives in islands, agents will too. A little integration work up front will pay dividends, as the agent spends more time moving work forward and less time finding context.
2. Add guardrails early. While it isn’t glamorous, it’s what turns hesitation into a "yes." Define who can do what, where review is required, and how decisions get logged. Without being able to explain what happened and why, scaling isn’t possible. Treat governance, audit trails, and explainability as the price of admission for production.
3. Bring AI to the work, not the other way around. The easiest wins come when intelligence is built into the workflow tools your team already uses, keeping staff from copying, pasting, and context-switching all day.AI delivers more value when it’s embedded in everyday applications, not bolted on as a separate destination.
4. Level up. Once a single task is stable, connect the dots. That’s how firms move from "AI helps here" to "this workflow runs end-to-end with fewer handoffs."
5. Keep experts in the loop. Use AI to accelerate, not to abdicate. The goal isn’t to hand judgment to machines. It’s to offload the busywork so staff can spend more time on the parts that actually require expertise.

As Future Ready Accountant data suggests, firms that invest in integration, cloud maturity, and AI embedded in workflows are more likely to see growth, and they create the conditions for agents to deliver real, repeatable impact.

Bottom line

Agentic AI is the next stage after automation: moving from isolated assistance to coordinated execution across the firm’s workflow. But agentic only delivers when the foundation is real: integrated systems, governed data, secure access controls, and embedded intelligence that operates within defined guardrails.

For firm leaders and technology executives, the opportunity is bigger than efficiency. It’s a chance to modernize the firm’s operating model, building a connected platform that improves accuracy, speed, transparency, and the capacity to deliver higher-value advisory.

The firms that lead will be the ones that treat trust, integration, and governance as first-class design requirements, build system accountability into the core, and then scale from curiosity to outcomes with experts in the loop.