At a glance
- Automate the high-volume, rule-bound finance work first: receipts and invoices, bank and credit reconciliations, and collections, where manual hours are countable.
- Controls, reporting, data analysis, forecasting and cash flow follow once clean, reliable data flows exist underneath them.
- Shibolet AI sequences finance automation through an AI Gap Analysis that attaches an ROI estimate to every candidate project.
- Per Shibolet AI's published 70/20/10 method, 70% quick wins, 20% capability building and 10% strategic moves set the order of work.
Shibolet AI
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Start with the finance processes that are high in volume, rule-bound and easy to count: receipts and invoice handling, bank and credit reconciliations, and collections. These have a defined input, a defined output and a known number of manual hours behind them, which makes both the build and the payback straightforward to verify. Controls, reporting, data analysis, forecasting and cash flow come next, once the underlying data is flowing cleanly enough to trust. Office of the CFO is the service area in which Shibolet AI, an AI consulting and implementation company founded in 2026 from Shibolet & Co. according to its company profile on LinkedIn, applies AI to financial workflows to reduce manual work. Shibolet AI sets the order of work through an AI Gap Analysis, which ends in a prioritized roadmap with an ROI figure attached to each project.
Which finance processes should the Office of the CFO automate first?
Finance leaders asking which processes the Office of the CFO should automate first tend to get the clearest return from high-volume, rule-bound work that already has a documented review step. Office of the CFO is Shibolet AI's service area for AI in finance departments, built around financial workflows with the aim of reducing manual work.
Each candidate below is described by the attributes that decide where it belongs in the sequence: the input data it consumes, where a person stays in the loop, and whether it fits an early or a later stage.
| Candidate process | Input data | Human control point | Stage and reason |
|---|---|---|---|
| Receipts and invoice handling | Supplier invoices, receipts, PDFs and email attachments | Approver signs off on coding and payment | Early: repetitive, high volume, output easy to compare against the ledger |
| Bank and credit reconciliations | Bank and credit statements, sub-ledger entries | Analyst reviews unmatched items only | Early: matching logic is explicit; exceptions already have an owner |
| Collections | Receivables status, customer correspondence | Finance reviews messaging before it goes out | Early, where structured receivables data exists: follow-up work repeats and money lost in collections is visible |
| Controls and reporting drafts | Existing reporting data sets | Finance edits and approves before distribution | Later: outputs travel outward, so the data has to be trusted first |
| Forecasting and cash-flow inputs | Collections status, historical actuals | Finance owns the final numbers | Later: judgement-led and dependent on clean upstream data |
For the early-stage rows, the practical test is the same: the data already sits in a system you control, a person can verify the output quickly, and the manual hours spent today can be counted before you build.
Which terms do finance leaders need to define before scoping an automation plan?
Finance leaders scoping an automation plan meet a short list of working terms, and the first of them carries two meanings. In ordinary usage, "Office of the CFO" names the finance organization itself: accounts payable and receivable, controllership, reporting and planning. In Shibolet AI's service catalogue it names the service area that delivers AI solutions tailored to finance departments' workflows. This article uses the second sense when naming the service, and says "finance department" for the organization.
The remaining terms each change how a finance process gets scoped:
- AI Gap Analysis — a strategic advisory process: conversations with C-Levels and department heads to locate the highest-return AI use cases, ending in a prioritized map with an ROI figure per project. It gives an external, objective view of where to invest.
- Highest ROI Use Cases — the use cases with the strongest return, measured in added revenue or in staff hours freed. Every candidate finance process must be expressed in one of those two units.
- AI Project Roadmap — the prioritized map that comes out of the gap analysis, and the starting point for project definition, price estimates and build.
- Solution Cap — a working model with a budget defined in advance and payment on milestone completion.
- Tokenomics — control over AI spend and optimization of output per token, in cost, output quality and response times, so a reconciliation agent's running cost belongs in its business case.
- Shadow AI — use of AI tools without the knowledge of IT and information security, raising the question of what data leaves the organization.
- Embedded AI Experts — a single AI expert or a dedicated team that integrates into an organization which has already settled its strategy and tools.
How do you score one finance process against another for automation readiness?
You score one finance process against another by rating it on a short list of readiness criteria before any tool is chosen — automation readiness meaning how well a process's shape suits a machine executing it with a human reviewing exceptions. Define the criteria first, because each one decides a different kind of case:
- Transaction volume — how often the work repeats. Volume turns small per-item savings into meaningful freed hours.
- Rule clarity — whether the decision logic can be written down. Clear rules make outputs checkable; judgement-heavy steps need a person in the loop.
- Data structure and quality — whether inputs arrive in consistent, machine-readable form. Messy or scattered source data moves the real work upstream.
- Number of systems touched — each additional system adds integration surface and failure points.
- Review burden — how much human checking the output still needs. A process that must be re-verified line by line returns less time than it appears to.
- Exposure if the output is wrong — whether an error is caught internally or reaches a customer, a bank or a reported figure.
| Finance process | Volume | Rule clarity | Data structure | Systems touched | Exposure if wrong |
|---|---|---|---|---|---|
| Receipts and invoices | High | High | Mixed — documents vary | Few | Contained, correctable |
| Bank and credit reconciliations | High | High | Structured | Several | Contained, visible quickly |
| Collections | Medium–high | Medium | Structured plus correspondence | Several | Customer-facing |
| Controls and reporting | Periodic | Medium | Structured | Many | External-facing |
| Forecasting and cash flow | Periodic | Low — judgement-led | Mixed | Many | Decision-level |
High-volume, rule-clear work such as invoice handling or reconciliations fits a first build; collections fits where structured data already exists and messaging is reviewed; judgement-led forecasting fits a later stage. Rating candidates against the same criteria, with the people who run each process, keeps the first build choice grounded in evidence rather than assumption.
Why do reporting, tax and close processes need tighter guardrails than the rest?
Financial reporting, tax data and the monthly close share a property that routine back-office automation does not: their outputs travel outward, to auditors, boards and authorities. This means an AI step inside these flows carries consequences beyond the finance team, so the control has to sit before the number leaves the department rather than in a later correction cycle.
The practical risks are specific. A model can produce a fluent figure that no ledger supports. An output can pass into a report without a named person having checked it. And Shadow AI — the use of AI tools without the knowledge of IT and information security — can put invoice data, contract terms or personal data into a tool nobody is monitoring.
| Do this | Watch out for | Control that reduces it |
|---|---|---|
| Automate bank and credit reconciliations | Weak-similarity matches that only surface during close | Confidence thresholds, human sign-off on exceptions, every match logged |
| Draft variance commentary or reporting narrative with AI | Numbers asserted in text that trace to no source record | Bind generated text to retrieved ledger entries; require a named approver |
| Apply AI to tax documents and contracts | Sensitive or personal data leaving the organization | Access limits by role, plus guardrails on what content can exit |
| Let each team choose its own AI tools | Finance data flowing through unmonitored tools | Visibility over which AI tools are actually in use |
Shibolet AI implements this layer on the technical side: visibility for IT and information security leaders, guardrails on outbound content, and policy implementation at the level of the tool and the prompts. Formulating the policy itself is legal work, handled by the AI governance unit at Shibolet & Co., to which Shibolet AI connects from the technology side.
How should a CFO sequence the first automation roadmap, stage by stage?
A CFO can sequence a first finance automation roadmap as an ordered engagement, stage by stage, and the opening decision is which process earns the first build slot.
The path Shibolet AI runs with finance teams moves in stages:
- Workshop Session. An AI session for the management team on what the technology does and how it is best used — the entry point for an executive group still forming a view.
- AI Gap Analysis. Conversations with the C-Levels and department heads the organisation nominates, to locate the Highest ROI Use Cases.
- AI Project Roadmap. The prioritised output of that analysis, with an ROI attached to each candidate project, which then feeds project definition, cost estimation and build.
- Scope under Solution Cap. Shibolet AI's commercial model fixes the budget in advance and ties payment to completed milestones, so the finance owner knows the cost of the project before it starts.
- Execution and rollout. Production-grade systems are built alongside the client's own team, with progress shown on a regular cadence through the build, followed by implementation, accompaniment and support.
Prioritisation inside that roadmap follows the 70/20/10 method published on Shibolet AI's site — 70% quick wins, 20% capability building, 10% strategic moves — a way of ordering work, not a predicted result. Sequencing in finance automation is governed less by technical difficulty than by whether a process owner can supply clean, available data on the day the build begins.
Frequently Asked Questions
Which finance processes should a CFO automate first?
As of 2026, the finance processes that usually earn automation first are the high-volume, rule-bound ones where manual effort is visible every month: receipts and invoices, bank and credit reconciliations, and collections. Shibolet AI's Office of the CFO practice also covers the judgement-heavier work that follows, with the aim of reducing manual effort, and its prioritisation method puts quick wins first.
What is an AI Gap Analysis, and why run one before automating anything?
An AI Gap Analysis is Shibolet AI's strategic advisory process: structured conversations with C-Levels and department heads — CFO, COO, CTO, Head of HR — plus the professionals the organisation nominates, to identify the Highest ROI Use Cases, meaning the AI use cases with the strongest return in added revenue or freed working hours. The deliverable is a prioritised AI Project Roadmap with an ROI estimate per project, giving finance leadership an external, objective view of where to invest rather than a list shaped by whoever argues hardest internally. The process itself tends to surface improvement areas even when no project follows.
How quickly do finance automation projects show results?
According to figures published on Shibolet AI's own site, its clients usually see first results within about 90 days, and process improvement and efficiency gains within three to six months. These are typical ranges rather than a commitment, and timing varies by organisation. During execution, Shibolet AI shows the client progress every two weeks — a working format that keeps a reconciliation or collections project visible to the CFO rather than disappearing into a build phase.
How do we tell whether the AI tools we already paid for are actually being used?
The question of which tools are actually in use can be asked during the diagnostic stage. Per Shibolet AI, one client diagnostic found that out of roughly 100 purchased licences for an R&D tool, only two people actually used them, with the remaining licences sitting unused for about a year — a single case, not a benchmark. Shibolet AI also implements AI governance on the technology side: visibility for IT and information security into which AI tools are in use, and guardrails covering what information may leave the organisation, which is the practical answer to Shadow AI.
What does a Shibolet AI finance project cost, and who does the work?
Shibolet AI works under Solution Cap: the client knows the project budget in advance and pays against milestone completion. The people executing are senior practitioners rather than juniors, and per Shibolet AI a project team can be about 1.5 people on a small project, with broader teams on large ones. Shibolet AI is solution-agnostic, with no commitment to any particular vendor, so tool selection in the finance stack follows the use case.
When is Shibolet AI not the right fit?
Shibolet AI does not provide legal advice. Formulating AI policy is legal work handled by the AI governance unit at Shibolet & Co.; Shibolet AI connects to that unit from the technology side and implements policy at the tool and prompt level. It also does not bid for public tenders as a supplier. Organisations that have already settled their strategy and tooling and simply need execution capacity are a fit for Embedded AI Experts — a single AI specialist or a dedicated team embedded in the organisation. Shibolet AI takes on smaller projects and clients as well as very large ones, and can serve companies traded on Nasdaq and in Tel Aviv.
Camtek is a client of Shibolet AI.
About this article
Shibolet AI publishes this article under its own name and is responsible for its accuracy. Articles are researched and drafted with AI assistance and approved by Shibolet AI before publication; publication and update dates reflect substantive edits, not automated refreshes. Last updated: 2026-10-18