How to use AI in accounting with confidence & why human overview is non-negotiable
Key Takeaways
Use AI to augment professional judgement; never to replace accountability.
Treat “human in the loop” as non-negotiable for compliance, tax, and client outcomes.
Deploy AI first in low-risk, repeatable workflows to unlock safe productivity gains.
Prioritise data quality, governance, and system integration over prompt experimentation.
Recognise that AI increases information speed — and therefore increases the value of trusted human advice.
AI has moved from “interesting experiment” to everyday reality at remarkable speed. In the space of a few years, tools like ChatGPT have become embedded in how people work — and that’s changing expectations across the accounting profession. Clients are using AI as a starting point for questions. Teams are exploring note takers, drafting tools and automation. And firms are asking the big question: how do we harness AI’s productivity gains without increasing risk?
The good news is that the AI we have today is already “good enough” to make a meaningful impact — when it’s deployed thoughtfully and responsibly. The less good news is that AI can be fluent, confident and completely wrong. It doesn’t understand what matters, it doesn’t grasp consequences, and it doesn’t carry professional responsibility. That still sits with you.
Summary
This blog pulls together what’s changing, where AI is most useful, where it creates risk, and how to build an AI-enabled practice that stays human-led.
AI is the next wave — and it’s amplifying everything
Every major leap in technology arrives in waves. Mainframes delivered computing power. PCs put that power on every desk. Mobile made connectivity constant. Cloud made it scalable. AI builds on all of that — internet-scale knowledge, cloud compute, connected systems, mobility — and when applied correctly, it becomes transformational.
What’s different about AI is that it unlocks something we’ve never really had before: “intelligence as a utility”, which is something you can access as and when you need it. That’s why so many workflows are being rethought, from internal operations to client service.
And it’s happening fast. Adoption has accelerated to the point where AI isn’t a niche tool used by a few enthusiasts. It’s increasingly part of the modern financial workflow. In practice, this means clients may expect quicker answers, more proactive insight, and smoother digital experiences.
Why benchmarks don’t tell you what you actually need to know
AI performance has improved so quickly that many traditional tests are now saturated. Models can answer complex questions and score highly across common benchmarks, so those tests are no longer useful for measuring real progress.
The more relevant question is: how do we test the edge cases: ambiguity, nuance, and unfamiliar situations? The kinds of scenarios where people apply judgement, not just recall.
That matters for accounting because so much of the value you provide lives in the grey areas:
“What’s the right treatment here?”
“What’s the risk if we do it this way?”
“What follow-up questions should we ask before deciding?”
The core risk: confidence without accountability
One of the most important takeaways is this:
AI is powerful, but it isn’t accountable.
AI can sound fluent and authoritative even when it’s wrong. It doesn’t know when something matters. It doesn’t understand the consequences. It doesn’t hold professional responsibility.
That is exactly why the mindset shift matters. The question shouldn’t be:
“Can AI do this for me?”
It should be: “How can AI help me do this better?”
Used properly, AI becomes a thought multiplier. Used blindly, it becomes a risk amplifier.
For accountants and bookkeepers, where compliance, trust and accuracy are central, that distinction is everything.
Where firms are already using AI (and why it’s working)
Three practical use cases stand out as “already delivering value” in firms:
1) Communication and content
AI is being used to draft emails, create internal policies, build help guides and support documentation. This is low risk when outputs are reviewed and kept away from client-critical decisions.
2) Task automation in bookkeeping workflows
Automation is most effective where tasks are rules-based, repetitive and high volume — precisely the areas where specialist tools shine. This is where solutions built for accounting and finance workflows can reduce manual work while improving consistency.
If you’re looking to streamline capture and reduce admin, Dext’s document capture options (mobile, email-in and WhatsApp) are designed for speed and accuracy. Combine that with bank feeds and statement extraction and you remove a significant chunk of manual processing from the month.
3) Meetings and note takers
This came through strongly: AI note takers are becoming exceptionally powerful. Capturing meeting notes automatically:
saves time
improves accountability
protects you if client expectations or next steps are later disputed
For many practices, this is one of the easiest “first wins” — as long as you remain mindful of data security and where those transcripts are stored.
Why “AI-enabled practice” still means human-led
An AI-enabled practice is not a practice where machines replace people. It’s a practice where AI supports end-to-end workflow efficiency — and humans remain in control.
A useful model from the transcripts is that practices should focus on the workflow “factory”: the right people, the right processes and the right product moving work from start to finish efficiently. AI fits into that factory by increasing speed, improving consistency and creating capacity for higher-value conversations.
At the heart of it, an AI-enabled practice should be:
Human-led relationships: clients still want conversations and proactive advice.
AI-supported workflows: turn around queries faster, surface anomalies earlier.
Consistent processing: reduce manual input and variability.
Capacity creation: free up time for advisory work, not data entry.
The tax reality: AI can help, but it mirrors the quality of the user
A particularly important point raised in the transcripts is how AI performs in tax and technical areas.
AI can be useful for direction, helping you explore possibilities, identify what to check, or work through an example so you can validate it. But the output often mirrors the quality of the input and the user’s understanding. If the user lacks context or asks the wrong question, the tool can confidently return misleading answers.
This is where risk appears:
Too little context leads to wrong conclusions
The model “fills in gaps” in a plausible way
Users may treat outputs as authoritative when they shouldn’t
The practical conclusion is simple: professional judgement isn’t going away. You still need governance, review processes, and accountability for the work you deliver.
When clients use public AI instead of asking you first
One of the biggest shifts described is behavioural: clients are using public AI tools as a starting point. Previously, “your mate in the pub” might have been the unreliable source. Now, it’s an AI chatbot.
The challenge isn’t that clients ask questions. That’s good. The challenge is when they:
assume AI answers are correct
act on them
then challenge professional advice when it doesn’t match what the AI said
This is exactly where the profession’s role becomes more valuable, not less. As AI increases the speed and volume of information, it also increases risk, and risk drives demand for trust.
In a world saturated with AI-generated content, credibility is currency. And credibility is still human.
The rise of AI agents (and why they matter for accounting)
Beyond chatbots, the transcripts highlight a major shift: agents.
A simple way to think about AI agents is that they’re tools you can delegate to — not for judgement calls, but for:
repetitive checks
routine follow-ups
applying known rules consistently
surfacing exceptions for review
The crucial boundary is this:
Don’t give agents tasks that require judgement
Do use agents for consistent, repeatable work
Keep humans in control of review and decisions
This is where the next evolution of automation sits, particularly when tied to bookkeeping workflows where the same edge cases appear repeatedly across clients.
In Dext, that’s the direction behind Dext’s AI Agent. We’re moving beyond static rules to guided automation that can help handle edge cases, while keeping users in control.
The practical checklist: how to use AI with confidence
1) Start with clarity, not tools
AI is not a hammer. Start with the problem you want to solve and define a clear outcome first.
2) Prioritise data quality over “clever prompts”
AI amplifies what you give it, good or bad. Structured, complete data matters more than prompt-writing skills.
This is why strong capture and data management foundations are so valuable: accurate inputs create reliable automation outputs.
3) Fit AI into systems that can actually support it
AI works best when it plugs into connected, compatible systems — ideally cloud platforms with integrations and APIs. Fragmented tools create inefficiency and risk.
4) Manage change, or adoption will fail
Don’t try to do everything at once. Pick one workflow where you’ll see the biggest benefit and focus there. Adoption takes dedicated time, and “overwhelming the team” is a common failure point.
5) Keep oversight non-negotiable
AI can assist but cannot take responsibility. Build review and approval into your processes — especially for anything affecting compliance, tax treatment, or client outcomes.
6) Do due diligence on compliance and security
If you’re sending data to any AI system, you need to know:
where it’s stored (UK, EU, US, etc.)
what the provider does with it
whether it’s used to train models
what controls exist for separation and confidentiality
As a general principle: when a tool is free, there’s usually a reason, and that reason may involve data. Read the terms, understand the data pathway, and choose providers you can trust.
How to choose the “best AI” for your firm
There isn’t a single “best” general AI model — because they change constantly. A practical approach is to build an internal evaluation set:
Collect real questions your team and clients ask regularly
Run them through a few tools
Compare output quality and usefulness
Review as a team
Reassess every few months as models evolve
General tools can help with summarisation, drafting and brainstorming. Specialist tools should handle specialist workflows, particularly where consistency, auditability and control matter.
That’s the logic behind using a platform built for bookkeeping automation — where capture, extraction, categorisation, supplier rules and bank connections are designed around accounting outcomes, not generic text generation.
Bringing it together: AI shouldn’t replace accountants. It should empower them.
AI is already changing how work gets done. The firms that win won’t be the ones that adopt the most tools; they’ll be the ones that adopt AI thoughtfully with:
clear outcomes
good data
connected systems
managed change
human oversight
strong governance
When you get that right, AI does what it should do: reduce manual workload, improve consistency, create capacity, and let your team spend more time advising and less time processing.
If you want to see what AI-powered bookkeeping automation looks like in practice, explore Dext Solo, calculate the ROI with the pricing calculator, or speak to our team about your workflow goals.
Start your AI-powered practice journey with Dext
AI is reshaping accounting — but the firms that succeed will be those that adopt it intentionally, safely, and strategically.
With accounting-specific AI, built-in controls, and automation designed for real-world workflows, Dext helps you embrace innovation without increasing risk.
Book a demo or explore how Dext can support your AI-enabled practice, and start your free 14-day trial today.
