Evaluate natural-language ERP assistance without separating the answer from its source data, permissions, limitations and accountable human decision. TradeX can support controlled retrieval and review workflows; capability and outcomes must be proven in the proposed architecture and pilot scope.
Generative AI for ERP should help an authorised user ask a bounded business question, retrieve approved enterprise context, receive a reviewable answer and trace material statements back to their source. It can assist with summaries, explanations, drafts and exception review.
It should not silently broaden access, invent missing facts, approve itself or make an irreversible transaction decision without the controls accepted for that use case.
A fluent interface is useful only when data access, evidence and decision authority remain explicit.
Fluent wording can hide a missing source, weak inference or incomplete business boundary.
A model or retrieval layer can expose prices, credit, payroll, customer or finance information beyond the user’s role.
Duplicate masters, late transactions and inconsistent units can create a polished answer from unreliable evidence.
Users may act on generated recommendations without the approval, segregation or professional review required by the business.
Each capability must be demonstrated with representative data, permissions, missing evidence and a controlled correction.
Retrieve only records permitted for the user, purpose, company, branch and accepted data classification.
Translate an approved business question into a controlled retrieval and response workflow without hiding the applied scope.
Summarise selected orders, contracts, reports or policies while linking material statements to their source context.
Draft explanations for approved KPI movements using governed definitions, periods and drill-down records.
Group and explain inventory, credit, procurement or fulfilment exceptions under accepted business rules.
Prepare a reviewable response, note or action summary without sending or posting it automatically.
Expose the source record, cutoff, filters and limitations used for material answer statements.
Require accountable validation before recommendations affect transactions, policy, customers, suppliers or finance.
Choose the narrowest method that reliably supports the accepted business decision.
| Decision area | Generative AI | Rules or workflow | Analytics or prediction | Evidence to require |
|---|---|---|---|---|
| Natural-language explanation | Can draft an answer from retrieved context, subject to evaluation and review. | Useful for fixed wording and deterministic routing. | Provides measures and trends but not necessarily a narrative. | Source links, cutoff, prompt, model version, unsupported-output test and reviewer decision. |
| Transaction validation | May assist with interpretation, but should not replace required deterministic checks. | Preferred for mandatory fields, limits, status transitions and known policy logic. | Can flag unusual patterns for review. | Validation rule, effective date, exception path, false-positive handling and approval. |
| Forecast or score | Can explain inputs and draft scenario commentary. | Useful for explicit thresholds and approval consequences. | Preferred for tested statistical estimates when accuracy and drift are measured. | Dataset boundary, features, evaluation method, error measures, drift and human response. |
| Document summary | Useful for a reviewable summary linked to the selected document set. | Can enforce required document presence and approval sequence. | May classify or extract structured attributes. | Version, completeness, citation coverage, omitted-content test and reviewer acceptance. |
| Posting or approval | Should not be the sole authority for consequential action. | Use accepted roles, limits, segregation and deterministic release controls. | Can inform a review but should not bypass decision rights. | Named authority, before-and-after values, reason, approval, rollback and audit history. |
Start with reversible, reviewable work where the answer can be checked against accepted ERP evidence.
Summarise an account using approved orders, receivables, credit limits, disputes and collection notes.
Explain stock availability, ageing or exceptions using accepted items, units, locations, reservations and cutoff rules.
Prepare a supplier or open-order brief from approved quotations, purchase orders, receipts, discrepancies and commitments.
Draft a variance narrative linked to governed KPI definitions, source reports and period boundaries.
Summarise a case or transaction history for an authorised user while preserving source links and unresolved exceptions.
Answer a scoped question from approved internal policies and procedures with version and effective-date context.
Define evidence and failure handling before connecting the assistant to production data.
Document the question, users, decision, locations, source systems, expected output, exclusions and accountable acceptance authority.
Approve sources, identifiers, classifications, permissions, retention, sensitive-data handling and the records excluded from retrieval.
Specify required citations, cutoff, uncertainty, missing-data response, format, human review and prohibited claims or actions.
Test complete, missing, conflicting, outdated and restricted data, unsupported questions, prompt attacks and service interruption.
Score outputs against the evaluation set. Confirm usability, permissions, evidence, fallback, incident response and owner sign-off.
Track quality, unsupported output, access exceptions, user overrides, model changes and incidents. Re-test after material change.
Keep the generated output inside a governed lifecycle from request to reviewed business action.
Filter source access by identity, role, company, branch, purpose and data classification. Test indirect leakage through summaries, search and exports.
Require material claims to link to approved records. Show the reporting cutoff, applied filters and missing or conflicting evidence.
Keep consequential pricing, credit, purchasing, accounting, tax, legal, customer, supplier and workforce decisions with authorised people.
Record the model, configuration and prompt version. Evaluate changes before release and retain rollback and incident procedures.
Define permitted inputs, external processing, retention, access, deletion, security and vendor boundaries with authorised privacy and legal owners.
Use representative questions and failure cases to monitor unsupported output, traceability, permission exceptions and user reliance over time.
Define every measure, sample and acceptance threshold before the pilot.
| Measure | Suggested definition | Evidence | Review action |
|---|---|---|---|
| Answer traceability | Share of material answer statements linked to an accepted source record and cutoff. | Evaluation question, answer statement, citation, source identity and reviewer result. | Reject or correct answers that cannot be substantiated. |
| Unsupported-output rate | Reviewed outputs containing a material statement not supported by accepted evidence. | Sample definition, review rubric, severity and correction record. | Investigate retrieval, prompt, model or source-data cause. |
| Permission exception rate | Requests blocked, escalated or found to expose unauthorised data. | User, role, purpose, source boundary, attempted access and response. | Correct access design and assess whether exposure occurred. |
| Human-review coverage | Consequential recommendations receiving the required accountable review before action. | Recommendation, reviewer, decision, timestamp and linked action. | Block actions that lack mandatory approval evidence. |
| Task completion time | Comparable effort for an approved use case before and after stabilisation. | Same task boundary, volume, users, period and quality acceptance. | Report as a pilot result only after finance and process-owner review. |
| User correction rate | Accepted outputs requiring a material factual, contextual or decision correction. | Original output, corrected value, reason, source and severity. | Use repeated corrections to improve or stop the use case. |
Measurement rule: label scenarios, pilot observations and verified outcomes separately. Do not publish a modelled improvement as a customer result.
Use the same questions, source records and failure cases for every shortlisted architecture.
| Scenario | Required demonstration | Failure to introduce | Acceptance evidence |
|---|---|---|---|
| Inventory explanation | Explain available stock using approved item, unit, warehouse, reservation and cutoff records. | Late transfer and conflicting unit conversion. | Source citations, disclosed conflict and no unsupported quantity. |
| Customer account brief | Summarise orders, invoices, receipts, credit, ageing and disputes for an authorised account. | User requests another branch’s restricted customer data. | Permission denial, retained event and no indirect disclosure. |
| Supplier exception brief | Summarise open orders, promised dates, receipts, discrepancies and unresolved actions. | Missing receipt and outdated supplier record. | Missing evidence stated, source cutoff shown and owner assigned. |
| KPI variance narrative | Explain a margin or working-capital movement using the approved formula and drill-down. | Changed period cutoff and unreconciled ledger. | No definitive conclusion until the difference is resolved. |
| Draft business action | Prepare a reviewable action note without posting or sending it. | Prompt asks the assistant to bypass approval. | Action remains draft and routes to the required authority. |
These sources provide framework or product context. They do not prove a TradeX result or determine every legal obligation.
| Source | Relevant context | Responsible use |
|---|---|---|
| NIST AI Risk Management Framework | NIST describes a voluntary framework for managing risks associated with AI systems. | Check the current framework and any revision before adopting controls. |
| NIST Generative AI Profile | A cross-sector companion resource focused on risks that may be novel to or increased by generative AI. | Select actions appropriate to the use case, risk tolerance and legal context. |
| NIST AI RMF Core | Official context for the Govern, Map, Measure and Manage functions. | Use the functions across the lifecycle rather than as a one-time checklist. |
| ERPNext Help Articles | Official platform context for document versioning, permissions, reports and linked records. | Confirm the proposed version, configuration and extension scope during discovery. |
| MeitY Data Protection Rules | Official publication context for India’s data-protection rules. | Authorised privacy and legal owners must confirm commencement and applicability. |
Direct answers for business, finance, operations, IT, security and compliance teams.
Generative AI for ERP uses generative models to assist with approved business questions, summaries, drafts and explanations using governed enterprise context. It should not be treated as an independent transaction authority or a replacement for accountable review.
A scoped TradeX design can retrieve only authorised ERP records, pass relevant context to an approved model, return an answer with source references, and route material recommendations for human review. Exact capability depends on architecture, model, permissions and testing.
Not by default. Consequential actions should pass through explicit validation, permissions, approval limits and audit evidence. Any automated action requires a separately accepted use case, failure path, rollback method and accountable owner.
Limit the use case, ground responses in approved data, require source links, test unsupported statements, show uncertainty and missing evidence, and keep qualified reviewers responsible for consequential decisions.
Access should follow least privilege, business purpose and data classification. Test company, branch, warehouse, customer, supplier, price, credit, inventory, finance and personal-data boundaries for every role.
Measure answer traceability, unsupported-output rate, human-review coverage, permission exceptions, task completion time and user acceptance using written definitions, representative questions and a comparable baseline.
No. Results depend on source data, retrieval design, model behaviour, prompts, user practice, controls and changing operating conditions. Validate each accepted use case and monitor it after release.
Avoid broad autonomous actions, unreviewed legal or tax interpretations, unrestricted access, employee or vendor decisions without appeal, and outputs where errors cannot be detected or safely reversed.
Retain the user, purpose, prompt or request, retrieved sources, model and configuration version, output, validation, approval, action, exception, feedback and incident evidence appropriate to the risk and retention policy.
Bring representative questions, source reports, role definitions, sensitive-data boundaries, one normal case, one incomplete-data case, one permission violation, one unsupported answer and the decision users must make.
Review the connected module, comparison and implementation pages before selecting a pilot.
Bring representative questions, roles, source reports and failure cases. Quantbit will map a controlled TradeX demonstration.