Frequently Asked Questions

AI today is reliably useful for repetitive, time-consuming tasks: preparing and formatting reports, analysing and summarising contracts or regulatory documents, answering tax and accounting questions, drafting management commentary, and building templates or checklists. The biggest gains tend to come where your team spends hours on work that doesn't require expert judgement — just attention and time.

AI can't read directly from your ERP or other internal systems without a technical connection. It can't guarantee 100% accuracy in tax calculations, and it can't replace the professional judgement of a qualified accountant or tax adviser. AI outputs always need to be reviewed — especially where the accuracy of a number or a legal interpretation matters.

It depends on the tool and how you use it. The key questions are: where is data processed, whether it's used to train the provider's model, and whether the tool meets GDPR requirements. Enterprise versions of tools (such as ChatGPT Enterprise or Microsoft Copilot for M365) generally offer stronger data protection guarantees than free versions. Defining clear rules about what data enters AI tools is part of sound AI governance in any finance team.

No. Most AI tools for finance teams work without any technical implementation — they're accessible via a browser or app. More advanced integrations (such as connecting AI to your ERP or automating workflows) do require technical support, but basic use of AI for document analysis, writing, or research is available without IT involvement.

The best starting point is identifying one specific, painful process — somewhere your team regularly spends hours on routine work. Then check whether the data for that process is accessible and in the right format. Only then choose a tool. Starting with the tool before you've defined the problem is the most common reason AI pilots fail.

It depends on the scope. First usable results from a simple use case — such as contract analysis or drafting commentary — can come within two to four weeks. A broader rollout across multiple processes, with governance and team training, typically takes three to six months. The critical factor isn't technology — it's adoption: the team's willingness and ability to change how they work.

An AI readiness assessment is a structured review of how prepared your team and processes are for AI. It covers process mapping, data readiness, security requirements, and the team's capacity to adopt new tools. It's not a prerequisite for every AI project, but it helps avoid the most common mistakes — particularly investing in tools that the team ultimately doesn't use.

A successful AI pilot has a measurable outcome: time saved, fewer errors, faster report preparation, or another concrete indicator. If a pilot ends with a presentation of results but no change in how the team works day-to-day, it isn't working. A good pilot is small, specific, and has a clear success criterion defined before it starts.

There's no single answer that fits everyone. It depends on what the team works with every day and what they need to solve. Teams in the Microsoft ecosystem (Outlook, Excel, Teams) typically start with Copilot. For document analysis, tax research, or producing structured written content, ChatGPT or Claude are strong options. For teams in Google Workspace, Gemini is the natural fit. Before buying licences, we recommend testing a specific use case first.

ChatGPT is the most widely used, strong in document analysis and professional writing. Copilot is embedded directly in the Microsoft tools most finance teams already use — the main advantage is that the team doesn't need to switch between applications. Claude is particularly recognised for working with long documents and producing precise, well-structured outputs, making it well suited to more analytically demanding tasks. All three have enterprise versions with stronger data protection.

No. NextChange doesn't sell tools and doesn't receive any commissions from vendors. We help clients choose and implement what makes sense for their specific team and processes — regardless of who makes the tool.

We work with finance and accounting teams of various sizes — from smaller companies with a handful of accounting staff to large corporations with complex financial processes. What matters isn't the size of the company, but whether there's genuine motivation to actually implement AI — not just run a pilot.

Most engagements start with a short introductory workshop, where we map your processes together, identify opportunities for AI, and outline the first practical steps. From there, we propose a concrete way of working together — from one-off advisory to ongoing support through an AI rollout.

The simplest way is to write to us or book a short introductory call. We're happy to look at your situation with no commitment. Contact us →