The short answer
AI helps when the hard part is understanding messy input: reading an email, pulling fields from a PDF, sorting a request, drafting a reply. It is less useful, sometimes risky, for applying clear rules (VAT, approval thresholds, payment terms) or final decisions that move money. What works: AI reads and suggests, automation applies the rules, a person approves anything consequential, and everything is logged.
Where AI genuinely helps
| Task | Why AI fits | Control to keep |
|---|---|---|
| Reading supplier invoices, receipts, forms | Varied layouts suit extraction models better than templates | Confidence thresholds, review queue |
| Shared inbox triage (payables, HR, orders) | Free-text requests need classifying | People reassign; misroutes tracked |
| Drafting replies and summaries | Less typing on routine mail | A person edits and sends |
| Suggesting matches (payments to invoices, lines to purchase orders) | Handles near-miss references and names | A person confirms each |
| Flagging unusual items | Spots what rules miss | Flags go to a person, never auto-block |
| Searching policies and past records | Fast answers from long documents | Links to the source |
AI turns unstructured material into a structured suggestion; a person or rule takes it from there. Our document automation guide goes deeper on extraction.
Where AI is the wrong tool
| Situation | Use instead |
|---|---|
| Clear, stable rules (VAT, withholding tax, approval limits, payment terms) | Rules: ERP workflows route multi-level approvals by role, identically every time (ERPNext documentation) |
| Releasing payments | AI may prepare a batch or flag a changed bank account, never release funds; fraudulent change requests are a common route to misdirected transfers, so a person verifies bank-detail changes through a known contact |
| Exact calculations (payroll with PAYE, pension and other deductions, tax returns, financial statements) | Deterministic calculation and review, not a model's best guess |
| A broken process | Fix it first, or AI automates the confusion faster (which workflows to automate first) |
| Poor data | AI cannot match invoices to purchase orders never entered |
Controls to keep
Accountability for decisions rests with the organisation, not an automated system: the NIST AI Risk Management Framework treats governance, accountability and human oversight as core, and the NDPA gives people rights over decisions based solely on automated processing. Back-office documents are also full of personal data: employee records, addresses, bank details.
Owner and accuracy
A named owner per workflow samples outputs monthly; quality does not stay constant.
Human approval
Wherever money, access or someone's rights are affected.
Logs and fallback
Log inputs, suggestions, decisions and overrides; keep a manual fallback for outages or odd behaviour.
Only what is needed
Limit data to the purpose; a model routing invoices does not need payroll.
Processing location
Many AI services run outside Nigeria: check the NDPA transfer safeguards and tell individuals.
Vendor terms
Check retention and model training; business plans often differ from consumer ones.
Under the Nigeria Data Protection Act 2023 (NDPA), enforced by the NDPC, you need a lawful basis and should process only what the purpose requires; high-risk processing may call for a data protection impact assessment. Sector regulators, such as the Central Bank of Nigeria for banks and payment service providers, may add expectations. General information, not legal advice.
What usually goes wrong
| Mistake | What happens |
|---|---|
| Starting with the tool | A product hunting for a problem; start with the workflow and its measured cost |
| No review queue | Low-confidence results block the process or slip through |
| Accuracy drift | New suppliers, layouts or model updates change results; nobody samples |
| Shadow AI | Staff paste invoices or employee details into personal chatbots when the approved route is slow; offer a governed option |
| Dollar-priced surprises | Usage billed in US dollars costs more than planned when the naira weakens; estimate volumes and check pricing first |
Hypothetical accounts payable inbox: a distributor gets supplier invoices by email in many layouts.
- Extract (AI)Supplier, invoice number, date, amounts and tax from each PDF.
- Check (rules)Supplier exists, invoice number not a duplicate, totals add up.
- Match (ERP)To purchase order and receipt, with AI-suggested matches.
- Approve (person)Payment through the existing ERP approval workflow.
Clean matches wait for normal approval; low-confidence or out-of-tolerance items go to a review queue. Whether money moves stays with rules and people.
Rather talk it through? If you are being asked to "use AI" in finance or operations, we can review your workflows and tell you where it pays off and where plain automation is the better choice. Talk to a Promatics specialist
When to bring in help
Start with an AI feature in software you already use, a person approving output; chatbots are fine for learning. Bring in a professional when AI touches financial records, personal data or approvals, connects to your ERP or accounting system, or auditors or a board must see the controls. Our AI solutions and intelligent automation service assesses fit honestly, designs rules, review steps and logs, connects it through workflow automation and stays accountable for how it runs.
Readiness checklist
- The workflow is measured today (volume, time per item, error rate).
- The rules part and the "reading messy input" part are separated.
- Every point where money, access or rights are affected has human approval.
- A confidence threshold and review queue are defined.
- Inputs, suggestions and decisions are logged.
- Personal data sent to the AI service is limited to what is needed.
- Data location and vendor retention and training terms are reviewed.
- A named owner samples accuracy on a set schedule.
- A manual fallback is documented and tested.
An AI readiness assessment scores your candidate use cases; our AI agents and document processing services build the ones worth doing.
Sources and further reading
Product capabilities and guidance change. These are the primary sources this article relies on, checked on the review date above.
- Nigeria Data Protection Commission (NDPA, GAID 2025 and guidance for data controllers and processors), Nigeria Data Protection Commission
- AI Risk Management Framework, National Institute of Standards and Technology
- Workflows, ERPNext documentation (Frappe)
This article is general information, not legal, accounting or security advice for your specific situation. Examples are hypothetical unless stated otherwise.