© 2026 tech+bash. All rights reserved.
AI tools are changing how accounting and finance work gets done. tech+bash helps teams understand where AI genuinely helps, where it doesn't, and how to build the skills to use it well.
Every accounting team is encountering AI right now — in the tools they already use, in guidance from leadership, in conversations with clients. The question most teams are actually trying to answer isn’t “should we use AI?” but “how do we use it without making expensive mistakes?”
This guide explains how tech+bash approaches that question and what working with us looks like in practice.
AI tools are widely available and improving rapidly. The practical barrier for most teams isn’t getting access to them — it’s developing the judgment to use them well.
That means knowing:
These aren’t questions that resolve themselves through trial and error. They require someone with both accounting domain knowledge and a genuine technical understanding of how AI systems work. That combination is rare, and it’s where tech+bash is different.
Not every task is equally suited to AI assistance. The ones that are well-suited tend to share a few characteristics: they’re language-heavy, they have clear criteria for what good looks like, and errors are relatively easy to detect.
Drafting and summarising — AI is strong at producing first drafts of client communications, summarising long documents, and restructuring notes into a clear format. The output usually needs review and editing, but it’s a good starting point.
Research and explanation — asking an AI to explain an accounting standard, summarise recent guidance, or describe how a specific tax treatment works is often faster than searching manually. The important caveat: AI models have knowledge cutoffs and can be confidently wrong on specifics. Verify anything that matters.
Formula and code generation — for accountants using Excel, Python, or SQL, AI is a genuinely useful tool for writing and debugging. It’s faster than documentation lookups and handles the syntax so you can focus on the logic.
Data analysis tasks — summarising a dataset, identifying patterns, generating charts and tables — AI tools can assist with these meaningfully, particularly when the data is well-structured and the question is clearly defined.
AI tools make certain kinds of errors that are hard to catch because the output is confident and well-formatted. The specific risks for accounting and finance teams:
Numerical errors — AI language models are not calculators. They can make arithmetic mistakes, apply incorrect logic to financial calculations, and produce figures that look right but aren’t. Any AI-generated output involving numbers should be verified against a reliable source.
Hallucinated specifics — AI models sometimes produce plausible but incorrect details: a reference to a standard that doesn’t exist, a tax rate that changed last year, a regulatory requirement that applies in a different jurisdiction. In accounting, these specifics matter. Confident-sounding wrong information is a genuine hazard.
Inconsistency across sessions — AI tools don’t have persistent memory in the way humans do. A process you’ve successfully automated with AI assistance once may produce different results the next time if the inputs or prompts aren’t controlled carefully.
Overreliance leading to skill atrophy — this is a slower-moving risk but a real one. Teams that offload judgment to AI tools without maintaining the underlying skills become dependent on them — and less able to catch the errors those tools produce.
We work with teams to build practical AI capability, grounded in accounting context. The focus is always on judgment alongside mechanics — not just how to use a tool, but when to use it, when not to, and how to verify what it produces.
We start by mapping where your team is and what you’re actually working with. Which AI tools are already in use? Which workflows have people tried to improve with AI? Where have things worked, and where have there been problems? This gives us a clear picture of what’s already happening and where the gaps are.
We work through specific accounting and finance tasks — drafting reports, analysing data, reviewing documents, writing formulas — and explore AI assistance for each. The goal is to build a clear, practical understanding of where AI adds value for your work, not AI adoption in the abstract.
Getting useful output from AI tools reliably requires knowing how to structure your inputs. We teach the specific techniques that matter for accounting and finance context: how to give AI the right information, how to constrain its outputs, and how to frame questions to get useful responses rather than generic ones.
For any AI-assisted work that touches financial figures, client communications, or regulated output, we help teams build verification habits that catch errors before they propagate. This isn’t about distrusting AI — it’s about using it in a way that holds up under scrutiny.
Many AI capabilities are now embedded in tools teams already use — Microsoft 365 Copilot, Excel’s AI features, AI-assisted PDF tools. We help teams understand what these actually do, what they’re reliable for, and how to integrate them into existing workflows without disruption.
This coaching is designed for accounting and finance teams who are taking AI adoption seriously — not as a box-ticking exercise, but as a genuine question of how to work better.
It’s particularly valuable for:
You don’t need a technical background. You need a specific context — the work your team does, the tools you use, the pressures you’re under — and we build the coaching around that.
The most useful starting point is a conversation about where your team is and what you’re trying to achieve. From there we can propose something concrete.
Get in touch and we’ll take it from there.
The tech+bash Add-in works in Excel Desktop (Windows) and Excel Online. Install takes under two minutes.