Finance.
28 real jobs AI can take on for a finance team - most of them inside the licence you already pay for. The colour on each card is the kind of AI; the line at its foot is what it takes to get it.
THE COLOUR IS THE KIND OF AI — AUTONOMY RISES LEFT TO RIGHT
The line at the foot of each card is what it takes to get it - a free chatbot, the licence you already pay for, or a build.
If these words are new, the homepage walks the whole spectrum →
Risk management & compliance.
Data silos and lack of scenario planning characterized by delayed responses to market changes, leaving the organization vulnerable to unforeseen financial threats and compliance breaches.
Internal guidance documents are refreshed quickly whenever rules change, with costed impact and a stakeholder summary.
Try it in your own AI
- Context: I look after [area, e.g. procurement policy] at [company], and [the regulation that changed] changed this week. Our internal guidance still shows the old rules and teams are quoting it - the longer it's wrong, the more decisions get made on stale numbers.
Objective: Work through the guidance below and flag every section the change affects, with updated wording and a note of the cost impact where spend limits move. [Paste the current guidance and a summary of the rule change.]
Style: A two-column table - current wording, proposed wording - then the cost impacts as a short list.
Audience: The stakeholders who own the guidance - they need to approve changes fast, so lead with the biggest impact.
✓ In the licence you already pay for
Working papers are reviewed against the latest standards and updated accurately, with a clear recap sent to the manager.
Try it in your own AI
- Context: I'm in the finance team at [company]. [The new standard] has just landed and our working papers were written under the old treatment. If they drift out of line we fail audit questions we should never fail.
Objective: Compare the working paper below against the new standard and list what needs to change, section by section, with the reasoning. [Paste the working paper and the relevant standard text.]
Style: A numbered list ordered by materiality - each item two sentences, what changes and why.
✓ In the licence you already pay for
Suspicious vendor activity, duplicate invoices and abnormal spend are flagged and risk-scored continuously for fast review.
✓ In your licence - assembled once, then it runs. We set these up →
Routine finance questions are answered instantly from approved sources, with complex cases escalated to the right person.
Try it in your own AI
- Context: Our finance team at [company] fields the same routine questions every week - expense limits, approval routes, coding queries - and each one interrupts someone's real work. I want the answers written once, properly, from the policy itself.
Objective: Using the policy below, draft clear answers to the ten questions teams ask most, and flag any question the policy doesn't answer so we know what to escalate. [Paste the policy and your question list.]
Style: Q&A format, each answer under 80 words, quoting the policy section it came from.
Tone: Plain English - the readers are not finance people.
✓ In your licence - assembled once, then it runs. We set these up →
Staff get consistent, policy-aligned finance answers from live data, with complex queries routed and logged for the team.
✓ In your licence - assembled once, then it runs. We set these up →
Procure to pay.
Inefficient invoice processing and procurement approvals, inconsistent policies, lack of spend intelligence resulting in higher costs.
Contract terms are interpreted into a clear accounting position, refined with colleagues and packaged for stakeholders.
Try it in your own AI
- Context: I'm a [role] at [company]. We've signed [type of contract] and I need to land the accounting position before the close - the terms below carry a couple of judgement calls and I want them argued properly, not asserted.
Objective: Read the key terms and set out the accounting position you'd take, the judgement calls involved and the strongest case against your own view. [Paste the terms.]
Style: The position first in three sentences, then the judgement calls as a short list, then the counter-case.
Audience: Finance colleagues who'll challenge it - give them something to push against.
✓ In the licence you already pay for
Multiple contracts are compared, risks surfaced and recommendations agreed, then shared as a concise decision pack.
Try it in your own AI
- Context: I manage [supplier/customer] contracts at [company] and I've got [two or more] agreements covering similar work on different terms. The differences are where the risk hides, and nobody has time to read them side by side.
Objective: Compare the contracts below and surface every material difference - payment terms, liability, termination, renewals - with the risk each difference creates and what you'd recommend we do about it. [Paste or attach the contracts.]
Style: A comparison table, then recommendations as a short ranked list.
Audience: A decision meeting - it should read in five minutes.
✓ In the licence you already pay for
Regulation-driven amendments are compared in a clear table with recommended changes and a reusable onboarding guide.
Try it in your own AI
- Context: A regulation change has produced two versions of our [agreement type] - the standard amendment and the one [counterparty] marked up. I need to see exactly where they diverge before we sign, because the differences are the negotiation.
Objective: Compare the two amendments below clause by clause, flag every divergence and recommend which wording we hold on and which we can concede. [Paste both versions.]
Style: A clause-by-clause table with a hold/concede column and one line of reasoning each.
✓ In the licence you already pay for
Key terms and revenue implications in a new contract are extracted, an accounting view agreed and summarised for the deal owner.
Try it in your own AI
- Context: I'm in finance at [company]. A new [deal type] contract has just come in and the deal owner needs our accounting view this week - the revenue treatment depends on terms buried in the document.
Objective: Extract the commitment, fees, invoicing terms and licence period from the contract below, then set out the revenue implications and anything that changes how or when we can recognise it. [Paste the contract or its key sections.]
Style: The terms as a short table, then the revenue view in a paragraph.
Audience: The deal owner - commercially minded, not an accountant, so translate the treatment into what it means for the deal.
✓ In the licence you already pay for
Invoices are matched to purchase orders and receipts, discrepancies and duplicates flagged, and corrective actions recommended.
✓ In your licence - assembled once, then it runs. We set these up →
Overdue accounts are ranked by risk and given tailored recovery actions and personalised follow-ups to improve cash flow.
✓ In your licence - assembled once, then it runs. We set these up →
Contract, spend and supplier data is pulled together quickly so buyers negotiate from current facts and rated assessments.
Try it in your own AI
- Context: I'm negotiating with [supplier] at [company] - renewal on [date]. The contract, spend history and any ratings are below; the leverage is in the detail I haven't had time to read.
Objective: Build the negotiation pack: what we spend versus what we committed to, where usage diverges from contract, their performance record, and the three strongest asks with fallbacks. [Paste the contract and spend data.]
Style: Facts as a table, asks ranked, the walk-away noted.
✓ In the licence you already pay for
Outstanding invoices are surfaced, calls scripted, reminders sent and payment plans logged back to the ledger, with legal kept informed.
Built for your business - the work you'd hire us for →
Record to report.
Having data stored in disparate environments can slow decision making and reporting.
Balances are reconciled across systems, gaps and mismatches diagnosed, and adjusting entries proposed for manager review.
✓ In your licence - assembled once, then it runs. We set these up →
Actuals versus budget are analysed, large variances explained and findings shared with stakeholders in a clear summary.
Try it in your own AI
- Context: I run budgets for [team/area] at [company]. Month-end actuals are in and the variances below need explaining before the review meeting - the ones over [threshold] will get questioned line by line.
Objective: Analyse actuals against budget, identify the variances that matter and draft the likely explanation for each from the notes provided, flagging where I need to dig further. [Paste the variance data and any context notes.]
Style: One line per variance - size, direction, probable cause - biggest first.
Audience: Stakeholders in the budget review - no jargon, and no excuses dressed as analysis.
✓ In the licence you already pay for
Customer and supplier accounts are reconciled, discrepancies resolved and a queryable close knowledge base shared with the team.
Try it in your own AI
- Context: We're closing the period at [company] and I've got customer and supplier accounts that don't agree - the usual mix of timing differences, missed credits and duplicates. Every unexplained line delays the close.
Objective: Work through the two ledgers below, match what matches, and give me the unmatched items grouped by likely cause with a suggested next step for each. [Paste both extracts.]
Style: A table of unmatched items - amount, likely cause, next step - largest first.
✓ In your licence - assembled once, then it runs. We set these up →
Day in the life.
See how people can use your AI assistant to perform common tasks throughout their day to save time, generate value, and improve their wellbeing.
See how an audit, risk and compliance data lead uses AI to manage reviews, evidence and reporting across a working day.
Try it in your own AI
- Context: I lead audit and compliance data at [company]. The review below is in flight; the evidence lives in ten places and the write-up is due [date].
Objective: From the material below, build the evidence index - what we have, what's missing, who owes it - draft the chase list, and start the findings write-up from what's already supported. [Paste the review scope and evidence list.]
Style: Index as a table, chases as one-liners, findings drafted with each claim's source cited.
✓ In the licence you already pay for
See how a vendor engagement manager uses AI to prepare, track and strengthen supplier relationships day to day.
Try it in your own AI
- Context: I manage supplier relationships at [company]. The QBR with [vendor] is on [date]; the material below holds the quarter's tickets, invoices and promises.
Objective: Prep me: what they promised versus delivered, the issues we raised and their status, spend against contract, and the three things to press them on, with evidence. [Paste the material.]
Style: A one-page brief, press-points last.
✓ In the licence you already pay for
See how a financial analyst uses AI to speed up analysis, surface insight and support better decisions.
Try it in your own AI
- Context: I'm a financial analyst at [company]. [Stakeholder] wants a view on [question] by end of day; the data is below and the blank sheet is where the day dies.
Objective: Structure the analysis: the sub-questions to answer, what in the data answers each, the analysis run with working shown, and the caveats a reviewer would raise - before they raise them. [Paste the data and the question.]
Style: Question tree, then findings, then caveats.
✓ In the licence you already pay for
Follow a finance manager using AI to run reporting, analysis and team tasks through a typical working day.
Try it in your own AI
- Context: I'm a finance manager at [company]. Month-end week: the close checklist, the review queue and the team's questions are below, and all of it claims priority.
Objective: Sequence my week: what's on the critical path for close, what can be delegated with a note (draft the notes), what waits, and the reviews batched by type so I do them in runs. [Paste the checklist and queue.]
Style: Day-by-day plan, delegation notes ready to send.
✓ In the licence you already pay for
See how an income tax compliance manager uses AI to manage filings, deadlines and review work across the day.
Try it in your own AI
- Context: I manage income tax compliance at [company] across [entities/jurisdictions]. The filing calendar and open items are below; the danger is a quiet deadline in a loud month.
Objective: Build my control view: everything due in the next 60 days with status, the open items blocking any of them, who owes what, and the chase notes drafted. [Paste the calendar and open items.]
Style: Deadline table, soonest first; chases underneath.
✓ In the licence you already pay for
See how a finance manager uses AI to support analysis, planning and decisions throughout a working day.
Try it in your own AI
- Context: I'm a finance manager at [company]. Beyond the close, my job is the 'what does this mean' questions - and [stakeholder]'s question below is one of them: [paste it].
Objective: Answer it in stages: what the data shows, the two or three plausible explanations, what would distinguish them, and the plain-English answer I'd stand behind, with my confidence stated. [Paste the relevant data.]
Style: The plain answer first, the working underneath.
✓ In the licence you already pay for
Planning & analysis.
Lack of insight into customer and market trends can lead to reactive rather than proactive processes, reducing the ability to capitalize on market opportunities.
Routine data collection and admin move much faster, so analysts spend more time on high-value analysis and due diligence.
Try it in your own AI
- Context: I'm an analyst at [company] covering [sector]. I lose the first hours of every week to routine gathering - market news, indicator updates, the same summaries - before any real analysis starts.
Objective: Build me a reusable weekly brief on [sector/market]: what moved, why it matters to [what you cover] and what deserves a closer look. Use the sources below as the starting point. [List your trusted sources or paste this week's material.]
Style: One page - three sections, headline first, no more than five bullets each.
Tone: Neutral and factual - this feeds my own analysis, not a client note.
Works in a free chatbot
Historical sales data is cleansed and modelled to forecast the year ahead, with results refined and shared with the team.
Try it in your own AI
- Context: I'm forecasting next year's revenue for [company] from the historic sales data below. The data's messy - inconsistent formats, gaps - and the forecast will be challenged, so the working needs to be visible.
Objective: Clean the data, show me revenue by category over the past three years, then build a category-level forecast for the next twelve months with your assumptions stated plainly. [Paste or attach the data.]
Style: The forecast as a table plus one chart, assumptions as a short list underneath.
✓ In the licence you already pay for
Teams query financial data in plain language and assess regulatory risk, turning findings into briefings and updated policy.
Try it in your own AI
- Context: I'm in finance at [company] and the data below holds answers to questions people keep asking - but the people asking can't query it.
Objective: Let me ask in plain language. I'll ask about [e.g. spend, margin, trends]; you answer from the data with the caveats visible - what's excluded, how recent it is, what to double-check before the number goes in a paper. [Paste the data extract.]
Style: Each answer - the number, then the caveats in one line.
Tone: Precise. If the data can't answer, say so.
✓ In the licence you already pay for
Clear self-serve training and FAQs are produced fast so staff adopt a new finance tool with minimal friction.
Try it in your own AI
- Context: We're rolling out [tool] to the finance team at [company]. Adoption is the whole game - if the first week feels hard, people quietly go back to the old spreadsheet.
Objective: From the documentation below, draft a getting-started guide covering the five tasks people will do most, plus the questions they're likely to ask with plain answers. [Paste the documentation and the five tasks.]
Style: One page per task, numbered steps, no step longer than a sentence. FAQs as Q&A.
Audience: Busy finance staff who didn't ask for a new tool.
Tone: Encouraging, not chirpy.
✓ In the licence you already pay for
Rolling cash flow is analysed for seasonality and variances, with a pre-meeting brief and shared next steps.
Try it in your own AI
- Context: I watch cash for [company]. The rolling cash flow below has moved in ways I only half understand, and I've got a review meeting on [day] where I need to lead the conversation rather than react to it.
Objective: Analyse the data for seasonality and genuine variances - separate the two - then draft a pre-meeting brief: what moved, why it probably moved, and the three questions we should settle in the room. [Paste the cash flow data.]
Style: One page - variances as a table, questions as a short list.
✓ In your licence - assembled once, then it runs. We set these up →
An investment case is built from research and financial data, presented to leadership and revisited against actual results.
Try it in your own AI
- Context: I'm building the case for [investment] at [company] - roughly [size], payback expected through [mechanism]. Research and financials are below; the exec will remember the numbers I got wrong, not the ones I got right.
Objective: Build and stress it: the case as leadership will read it, assumptions with sources, sensitivity on the two that matter most, and the honest kill-criteria - what result at [checkpoint] would mean stopping. [Paste the research and financials.]
Style: The case on two pages, sensitivities as a table, kill-criteria unmissable.
✓ In the licence you already pay for
Most of these run on the licence you already pay for.
The gap is that nobody's pointed it at the job yet. That's the work we do, and it starts with a half-hour call - bring the one that looked most like your week and we'll tell you whether it's a quick win or a proper build. No licence yet? We'll help you pick one first.
← All twelve teams