AI Clarity
You've invested in AI.
Your finance team hasn't felt it yet.
IQSS works with Indian mid-market finance and AP teams — ₹100Cr to ₹2,000Cr — to find where AI will cut the most time from your finance operations, build it alongside your team, and leave you equipped to keep going.
The problem
Most Indian finance teams have a paid AI subscription — Claude, ChatGPT, or both. Very few are using it for anything that matters.
Not because the tools are not good enough. Because no one has mapped the tools to the actual work — the reconciliations, the vendor follow-ups, the month-end close, the exception handling that consumes your team's best hours every week.
The result: your organisation has made an AI investment. Your finance function has not felt it.
That is the gap we close.
Where are you now?
Four levels of AI maturity in finance. Most teams are stuck at Level 1.
Someone on the team uses their AI tool to draft a vendor email or summarise a report. Personal time is saved. Nothing in the finance function actually changes. Close time, error rate, AP cycle — all flat.
AI is embedded into specific finance workflows — reconciliation prep, exception flagging, vendor follow-ups. Teams get measurable time back. The routine is handled. Judgement is reserved for what requires it.
AP processing, compliance monitoring, and month-end close run with AI managing the routine and escalating only the exceptions. Your team controls outcomes. Output scales without scaling headcount.
Closed feedback loops let agents monitor outcomes, detect anomalies, and refine their own logic. The CFO manages decisions, not data. Every month compounds on the last. Finance intelligence becomes a structural advantage.
The false summit
Most Indian finance teams are stuck at Level 1 — and they know it. The paid AI subscription is active. A few people use it. Someone drafted a vendor email with it last week. The finance controller uses it to summarise board packs. Productivity is genuinely higher — and yet the close cycle is the same length, the exception queue is the same size, and the month-end is just as painful as it was twelve months ago.
That is the false summit. You have climbed far enough to feel like progress — but the view has not changed. Individual wins at the person level are real. The problem is that they stop at the person. The process beneath them is unchanged. The ERP still exports the same files. The reconciliation still runs the same way. The AP team still spends Tuesday morning doing what AI could do in four minutes.
The longer an organisation sits at Level 1, the harder the transition becomes. Teams get comfortable with the workaround. Managers stop asking why the close still takes ten days. The AI licence becomes a line item that nobody questions and nobody can explain.
Getting from Level 1 to Level 2 is not a technology problem. It is a deployment problem — knowing which workflow to target first, how to map it, and how to build it in a way your team will actually use on a Monday morning. That is the work we do.
How we work
Three engagements. Each one builds on the last.
AI Diagnostic
Find where AI will have the most impact in your finance function.
We map your finance workflows, identify where your team is spending time on work AI can handle, and surface the one or two places where deployment will have an immediate, measurable impact.
You walk away with a prioritised opportunity map and a clear recommendation for what to build first — whether you work with us or not.
AI Sprint
Build it. In your environment, with your team, in 30 days.
We take the highest-priority opportunity from the Diagnostic and build it into a working workflow. Not a prototype. Something your team uses on Monday morning.
Covers workflow design, tool selection, connector setup, team training on the specific workflow, and a measurement baseline so you know what changed.
AI Retainer
Keep compounding. Every iteration builds on the last.
Monthly engagement. We identify the next workflow, build it, train the team, measure the output. The operating model improves continuously rather than stalling after the first deployment.
Every iteration compounds on the last.
Common questions
What finance leaders ask before booking the Diagnostic.
We already have a paid AI subscription. Why do we need this?
Having the subscription is not the same as having a deployed workflow. A paid Claude or ChatGPT licence gives your team access to a model — it does not tell you which finance processes to target, how to design them, or how to build them in a way your team will actually use. The Diagnostic does that. If you already have the subscription, the Diagnostic costs you two sessions and gives you a clear deployment plan. If nothing changes after that, you have lost two hours.
Is this a software product or a consulting engagement?
Neither, exactly. We do not sell software you configure yourself, and we do not deliver a report and disappear. We build working AI workflows inside your environment — using tools you already have or tools we select with you — and we train your team to run them. The output is a live workflow your team uses, not a deck about what you could build.
Does our IT team need to be involved?
For the Diagnostic — no. It is two structured conversations with the CFO or Finance Controller, no technical setup required. For the Sprint, it depends on what we build. Most workflows in the first Sprint run on file exports from your ERP — no API integration, no IT project. If something requires IT involvement later, we scope it explicitly before committing.
What does the Diagnostic actually produce?
A prioritised opportunity map — the two or three finance workflows where AI deployment will have an immediate, measurable impact, ranked by effort and return. A clear Sprint recommendation: which workflow to build first, what it will require, and what you should expect to see change. You can take that plan and execute it yourself, hand it to your IT team, or work with us. The Diagnostic output belongs to you regardless.
We are a ₹500Cr company. Is this the right scale for us?
Yes. We work with finance teams at companies between ₹100Cr and ₹2,000Cr in revenue. At that scale, you have enough complexity to make AI deployment worthwhile — a vendor base large enough to benefit from automation, a finance team large enough to feel the time cost — but decisions move fast and you can see the impact of a single workflow change within weeks, not quarters.
Your finance team is leaving time and accuracy on the table every week.