It's the question we get asked more than any other by finance directors and by other practices: which tool should we be using to produce management accounts?
The honest answer is that the question is slightly wrong. Syft, Power BI and Claude are not three competing products. They solve different parts of the same job, and most of the arguments you see online are people comparing a hammer to a spirit level.
So before picking anything, it helps to split the job into its three parts.
The three parts of a management accounts pack
Extraction. Getting a trial balance and the underlying transaction detail out of the ledger, on time, every month, without someone re-keying it.
Presentation. Turning that data into a consistent pack: P&L against budget and prior year, balance sheet, cash, aged debt, the four or five KPIs that actually matter to this business, laid out the same way each month so the reader can compare at a glance.
Interpretation. The bit people pay for. Why gross margin fell three points. Whether the debtor days movement is a collections problem or a mix problem. What the board should do about it.
Different tools are strong in different columns. In short: Syft and Xero Analytics are strong on extraction and presentation, partial on interpretation. Power BI depends on your pipeline for extraction, is very strong on presentation once built, and weak on interpretation. Claude is weak on extraction on its own, moderate on presentation, and strong on interpretation.
Option one: Syft, and now Xero Analytics
Xero bought Syft Analytics in 2024, and in January 2026 rolled out Analytics powered by Syft as a native capability inside Xero itself. In the UK it sits on Comprehensive and higher plans. Standalone Syft still exists as the fuller product for accountants and bookkeepers, and it connects to QuickBooks and Sage as well as Xero.
Where it wins. There is no build. You connect the ledger and you have a defensible pack the same afternoon. Reports can be scheduled, so recurring management accounts become a background process rather than a monthly task. Consolidation works across different ledger platforms, which matters if you have a group with one company on Xero and another on QuickBooks. Benchmarking against sector data gives you a conversation opener that a standard pack does not.
Where it stops. You work inside somebody else's model. Layout customisation has a ceiling, and you will hit it if your client wants their pack in a particular house format. More importantly, it only knows what the ledger knows. If the interesting number is revenue per fee earner, utilisation, jobs won, headcount, or anything living in a CRM or an operations system, Syft cannot see it.
Who it suits. Firms producing packs for a lot of clients where the underlying question is genuinely financial, and consistency matters more than bespoke layout. For most practices, this is the right first move.
Option two: build it in Power BI
We run our own reporting on Power BI, so it would be easy for us to oversell this. We won't.
Where it wins. Power BI is the only one of the three that lets you blend ledger data with everything else. One model, one set of measures, one portfolio-wide view. If the answer requires job costing data, practice management data, payroll headcount and the nominal ledger in the same visual, this is the option. It also gives you a single model across many entities rather than a per-entity report you regenerate.
Where it costs you. Almost nobody underestimates the report build. Almost everybody underestimates the pipeline. Getting data out of the ledger, into a warehouse, refreshed reliably, with the right journal type filters and a stable account mapping, is where the work is. Then somebody in the firm has to own it: know DAX, understand why a measure broke, and be there in eighteen months.
There is also a live commercial change worth knowing about. On 2 March 2026 Xero retired its 15% app store revenue-share model and replaced it with five tiers priced on the number of connected organisations and monthly data egress. There is no grandfathering, and existing apps have been migrated across. In plain terms, pulling everything out of every client ledger nightly now has a price attached. If you are architecting a warehouse pipeline in 2026, incremental refreshes and filtered queries are no longer a nicety.
Who it suits. Firms with a genuine data capability in-house, or clients whose key numbers are not in the ledger. It is a poor first move for a practice doing management accounts for fifteen clients.
Option three: Claude and the other LLMs
This is where most of the noise is, and where the claims are furthest from the reality.
Where it wins. Interpretation, and the working-paper grind around it. Give Claude a nominal ledger extract and a prior-year comparative and it will write a genuinely usable first draft of variance commentary, produce a sensible query list for the client, spot postings that look out of place, and turn a stack of numbers into board-pack narrative in the tone you asked for. On the review side it is very good at the tedious checks: does the balance sheet cast, do the comparatives tie, has the same cost been coded three ways across the year.
Where it must not be trusted. It does not connect to your ledger by itself, it does not reconcile anything, and it will produce a number that looks plausible if you let it. The rule we work to is simple: the model never sources a figure. Figures come from the ledger, through a controlled extraction. The model works on figures you have given it, and everything it produces gets reviewed by a qualified person before it leaves the building.
There is a governance point too. Xero's revised developer terms, effective from March 2026, prohibit using data obtained through its APIs to train AI or machine learning models. If you are running client ledger data through any AI tool, you need to know what that vendor does with it. For a practice handling client confidential information, that is a question to answer before you start, not after.
Who it suits. Everybody, as a layer on top of whatever produces the numbers. Nobody, as the thing that produces the numbers.
So what should you actually do?
Work down this list and stop at the first one that describes you.
You produce packs for clients and the numbers are all in the ledger. Use Syft or Xero Analytics. Add an AI layer for the commentary. Do not build anything.
You produce packs for clients and the interesting numbers are not all in the ledger. You need a data layer. That is Power BI or an equivalent, and it is a project with an owner and a budget, not an afternoon.
You are an FD inside one business. Xero Analytics probably covers the standard pack. Spend the saved time on the commentary and the forecast, which is the part the board actually reads.
You are a practice deciding what to productise. Do not sell “management accounts”. Sell the interpretation. The extraction and presentation layers are commoditising fast, and the price of both is falling. What is not commoditising is a qualified person who can look at the pack and tell you what to do about it.
The uncomfortable bit
None of these tools fixes the real constraint, which is nearly always the bookkeeping underneath. A beautiful dashboard built on a ledger where the bank has not been reconciled since April, stock is a plug and the director's loan account is a dumping ground is worse than no dashboard, because it looks authoritative.
Before you spend anything on tooling, spend a fortnight getting the ledger clean and the coding structure right. That single piece of work will improve the quality of your management information more than any of the three options above.
If you want a second opinion on how your management reporting is put together, or on whether a build is justified in your case, get in touch. We are happy to tell you that you do not need one.
Common questions
Is Syft the same as Xero Analytics now?
Related but not identical, and the distinction matters when you are choosing. Xero acquired Syft in 2024 and Analytics powered by Syft is now native inside Xero, on Comprehensive and higher plans in the UK. Standalone Syft remains the fuller product for accountants and bookkeepers, and it also connects to QuickBooks and Sage. That is the version you want if you are consolidating a group across different ledgers.
Can an AI tool produce my management accounts?
Not on its own, and we would treat any vendor claiming otherwise with caution. An LLM does not connect to your ledger and does not reconcile. Where it is genuinely strong is the layer above: variance commentary, query lists, spotting odd postings, drafting board narrative. We use it daily under one firm rule: the model never sources a figure. Figures come from the ledger, and a qualified person reviews everything before it leaves the building.
Is it safe to put client ledger data through an AI tool?
Only if you have checked what the vendor does with it, and most people have not. Xero's developer terms, revised in March 2026, prohibit using data obtained through its APIs to train AI or machine learning models. We settled that question for our own stack before we used it on a client file, and we would expect any practice handling confidential information to do the same.
How much does a Power BI build actually cost?
The report is the small part, about a day off a good template. The pipeline is where the money goes: extraction, a warehouse, reliable refreshes, journal filters, stable account mapping. Then someone owns it in eighteen months' time. We run ours, so we price from experience rather than optimism, and we tell you the ownership cost up front.
We are a small practice. Where should we start?
Syft or Xero Analytics, with an AI layer for the commentary. That gets you a defensible pack this month rather than next year. Come back to a data layer when a client's key numbers stop living in the ledger. That is the signal, and until it arrives a Power BI project will absorb a year you could have spent on the advisory work.
What if our bookkeeping is behind?
Start there. It is not a delay. It is the highest-return fortnight available to you. No tool on this list improves a ledger where the bank has not been reconciled since April. Get the records clean and the coding structure right and your management information improves immediately, before you have spent anything on software.
