Using Natural Language Queries to Replace Complex SAP Report Building

Building a custom report in SAP has never been quick. Between navigating transaction codes, configuring selection screens, choosing the right layout variant, and exporting results into something a stakeholder can read, a moderately complex report takes an experienced analyst 45 minutes to two hours. A new analyst unfamiliar with the correct T-codes might take twice that.

Natural language interfaces change that equation entirely. Instead of navigating SE16N, SQVI, or Report Painter, a user types a plain-English (or plain-French) question and receives a formatted answer drawn from live transactional data. SAP Joule, embedded in S/4HANA Cloud, already supports natural language queries across finance, procurement, and supply chain modules, and the capability set has expanded rapidly through 2025 and into 2026.

McKinsey’s 2025 State of AI report found that organisations deploying conversational AI within ERP systems reduced reporting cycle times by 35–50%, with the largest gains in ad hoc and exception-based reporting. This post demonstrates what that looks like in practice — real query-to-report examples, an honest look at limitations, and a roadmap for Canadian enterprises considering adoption.

How Does Natural Language Reporting Actually Work in SAP?

Natural language reporting layers a large language model on top of your existing SAP data model. The user submits a question in conversational language, the system translates it into a structured query against live transactional data, and the result comes back as a table, chart, or summarised narrative. No knowledge of tables, transaction codes, or report variants required.

In SAP’s ecosystem, this works through three mechanisms:

• SAP Joule (embedded copilot): Available natively in S/4HANA Cloud, Joule interprets natural language prompts and queries transactional data across modules. Users ask questions directly within the SAP Fiori launchpad.
• SAP Analytics Cloud (SAC) with AI assistants: SAC supports natural language search across connected data models, translating questions into visualisations and tabular reports.
• Custom BTP integrations: For organisations needing domain-specific query capabilities beyond what Joule offers natively, SAP Business Technology Platform allows custom natural language layers built on foundation models from providers like Anthropic, OpenAI, or Google.

The critical point: the AI does not invent data. It translates a question into a structured data retrieval operation against your authorised SAP dataset. Role-based access controls still apply — a user without payroll authorisation will not receive payroll data, regardless of phrasing.

What Do Real Query-to-Report Examples Look Like?

The best way to understand the shift is through concrete examples. Each pair below shows the traditional SAP approach alongside the natural language equivalent.

Example 1: Open Purchase Orders by Vendor

Traditional approach: Navigate to ME2M (Purchase Orders by Vendor). Configure selection criteria: purchasing organisation, document type, date range. Execute. Export to spreadsheet. Sort and format for stakeholders. Approximate time: 15–25 minutes.

Natural language query: “Show me all open purchase orders over $50,000 CAD for the past 90 days, grouped by vendor, with total committed value.”

Result: A formatted table with vendor name, PO count, total committed value in CAD, and oldest open PO date. Delivered in under 30 seconds.

Example 2: Month-over-Month Revenue Variance

Traditional approach: Run FBL3N (G/L Account Line Items) for the revenue accounts. Export two months. Build a pivot table. Calculate variances manually. Format for the CFO. Approximate time: 30–45 minutes.

Natural language query: “Compare revenue by product line for January 2026 versus December 2025. Show the variance in dollars and percentage, and flag any product line with a decline greater than 10%.”

Result: A variance table with colour-coded flags for declining product lines, plus a one-paragraph narrative summary highlighting the largest movements.

Example 3: Vendor Payment Aging

Traditional approach: Run FBL1N (Vendor Line Items) with open items selected. Configure aging buckets. Export and format. Approximate time: 20–30 minutes.

Natural language query: “What is our current vendor payment aging? Show totals for current, 30-day, 60-day, and 90-day-plus buckets, and list the top five vendors by overdue amount.”

Result: An aging summary with bucket totals and a ranked list of overdue vendors, formatted for immediate sharing.

Example 4: Inventory Slow-Movers

Traditional approach: Run MC46 (Slow-Moving Analysis) or build a custom query in SQVI. Configure material type, plant, movement period. Export. Approximate time: 25–40 minutes.

Natural language query: “Which materials in our Toronto distribution centre have had no goods movement in the past 180 days? Show the total inventory value in CAD and sort by value descending.”

Result: A table of slow-moving materials with quantities, values, and last movement dates, ready for a write-down review.

In each case, the savings are structural, not marginal. The analyst who previously spent 30 minutes building a report now spends 30 seconds refining a question. That compounds quickly across a team running dozens of ad hoc reports per week.

What Are the Current Limitations?

Natural language SAP reporting is powerful but not unlimited. An honest look at the boundaries matters.

• Complex multi-step calculations: Queries requiring custom formulas, multi-table joins across unrelated modules, or non-standard accounting logic still need Report Painter, BW queries, or ABAP-based reports. The AI handles standard scenarios well but struggles with edge cases unique to your chart of accounts.
• Historical data depth: Joule queries live transactional data. If your requirement involves archived data in HANA warm storage or third-party warehouses, the natural language layer may not reach it without additional configuration.
• Precision of language: Ambiguous queries produce ambiguous results. “Show me our best vendors” could mean highest spend, best on-time delivery, or fewest quality defects. Training users to ask precise questions improves output quality significantly.
• Audit and reproducibility: A traditional SAP report variant is a saved, reproducible artefact. A natural language query is ephemeral unless the system logs query history. For CRA audit purposes, ensure that any report supporting tax filings or financial statements is saved in a reproducible format.

Gartner’s 2025 survey on AI in enterprise applications found that 62% of organisations deploying conversational AI in ERP reported at least one instance of imprecise queries producing inaccurate results. The solution is user training and validation protocols, not avoidance.

How Should Canadian Organisations Approach Adoption?

A phased approach works best. Organisations that try to replace their entire reporting stack overnight encounter resistance and accuracy issues. Those that start small see faster ROI.

Phase 1: Identify high-frequency, low-complexity reports

Survey your teams to catalogue which reports they build most often. Prioritise those that:

1. Use standard SAP data (GL balances, open items, purchase orders, inventory positions)
2. Are requested ad hoc rather than scheduled
3. Currently take 15+ minutes to build manually
4. Do not require custom calculations or cross-module joins

Most organisations find 40–60% of their ad hoc reporting volume falls into this category.

Phase 2: Pilot with a small user group

Deploy to 5–10 power users in one function. Measure three things:

• Time savings: How long did the report take before versus after?
• Accuracy: Does the natural language output match the traditional report output for the same query period?
• Adoption: Are users actually using it, or reverting to T-codes?

A Deloitte Canada study on AI adoption in Canadian mid-market enterprises found that pilot programmes with structured measurement were 2.4 times more likely to scale to full deployment than those without defined success criteria.

Phase 3: Expand and integrate

Once validated, extend access to broader user groups and additional modules. Integrate query outputs with SAP Analytics Cloud dashboards or export APIs that feed downstream tools. For guidance on connecting SAP outputs to enterprise workflows, see our API integration services.

Compliance considerations

Natural language queries must respect the same access controls, audit requirements, and data residency rules as traditional reports. Key considerations for Canadian organisations:

• PIPEDA: Ensure queries involving employee or customer personal information are restricted by role-based access controls. The AI layer must not bypass SAP’s authorisation model.
• Data residency: If your natural language layer uses cloud-based AI services, confirm data processing occurs within Canadian-hosted infrastructure. SAP BTP offers Azure Canada Central as a deployment region.
• Audit trails: Configure query logging so that every request, data retrieval, and output is recorded. This is essential for reports supporting financial filings with the CRA.

For a deeper treatment of governance requirements, see our guide on AI governance in regulated industries.

Mini Case Study: A Calgary Energy Services Firm

A mid-market energy services company based in Calgary with 800 SAP users was spending an estimated 320 hours per month on ad hoc reporting across finance, procurement, and field operations. Most of that time went to standard queries — open PO reports, cost centre variance analysis, equipment maintenance history — that experienced analysts could build in 20–30 minutes but junior staff took far longer to produce.

After enabling SAP Joule across their S/4HANA Cloud environment and running a 12-week pilot, they measured:

• Ad hoc report generation time dropped from an average of 28 minutes to under 2 minutes for queries within Joule’s supported scope
• 73% of ad hoc reporting requests were handled entirely through natural language queries by the end of the pilot
• The FP&A team redirected approximately 60 hours per month toward variance analysis and forecasting work that had been perpetually backlogged
• Estimated annualised productivity gain: $280,000 CAD in reallocated analyst time

The firm is now expanding access to field operations managers, who previously waited 24–48 hours for head office to fulfil report requests.

Key Takeaways