AI Question Matching: Auto-Fill ESG Questionnaire Responses

What AI question matching is (in plain terms)

AI question matching is the process of taking a new question from a customer’s ESG questionnaire and linking it to the best existing information you already have—usually one of three things: a structured datapoint (a number), a policy/control (a document or statement), or an approved narrative answer you’ve used before.

For example, a customer might ask, “Do you track Scope 1 and Scope 2 emissions and what methodology do you use?” Another might ask, “Provide your GHG inventory approach and boundaries.” A good question-matching system recognizes these are the same underlying request and routes both questions to the same sources: your emissions datapoints, calculation methodology, and evidence attachments.

The key is that AI is not ‘writing marketing text’. It’s doing information retrieval and mapping: finding the right existing answer components and presenting them in the format the questionnaire expects.

Why AI ESG questionnaires feel so painful without matching

Most suppliers don’t struggle because ESG data is impossible to find. They struggle because it’s scattered across Finance, HR, Operations, and shared drives—then reassembled again and again under deadline.

Without question matching, every questionnaire becomes a mini-project: someone pulls last year’s spreadsheet, someone else hunts for the latest policy PDF, numbers get re-keyed into a portal, and a reviewer catches inconsistencies late (or not at all). The result is predictable: follow-up questions, rework, and internal frustration.

AI question matching removes the biggest source of waste: repeatedly translating slightly different wording into the same underlying ESG datapoints.

• Less manual copy/paste across spreadsheets and portals
• Fewer inconsistencies across customers and reporting cycles
• Faster review and sign-off because answers start closer to ‘approved’
• Lower risk of missing evidence requests because sources are attached

The question matching workflow (how suppliers should run it)

AI question matching only works when it’s paired with a repeatable workflow. In practice, the most effective supplier workflow looks like this:

1) Centralize your ESG data (datapoints + policies + evidence) 2) Upload a new customer questionnaire 3) AI matches each question to the best existing sources 4) A human owner reviews and approves the suggestions 5) Export a consistent response package (PDF/Excel/portal-ready) This sequence matters. If you skip step 1 and treat AI as a magic typing tool, you’ll get superficial answers that are hard to defend. If you skip step 4, you’ll move fast but increase risk.

Typical Complezy workflow: centralize → upload → auto-fill → review → export

Complezy is designed for suppliers who answer many ESG questionnaires. The system is built around a ‘single source of truth’ ESG workspace that your team maintains on a simple cadence (monthly or quarterly).

Step 1: Centralize ESG datapoints. Store the numbers customers repeatedly request—energy consumption, GHG emissions, workforce metrics, incidents, training hours—using consistent units and definitions.

Step 2: Centralize policies and evidence. Attach supporting documents (invoices, certifications, policies, audit reports) directly to the relevant datapoints so you can prove provenance quickly.

Step 3: Upload the questionnaire. When a customer sends a PDF, Word file, Excel sheet, or portal export, you upload it into Complezy.

Step 4: AI question matching suggests answers. Complezy maps each question to the best matching datapoints and narratives and proposes an answer draft.

Step 5: Human review and export. Owners verify the suggested responses, adjust wording where needed, and export a consistent deliverable—reducing follow-ups and speeding customer approval.

Auto-fill ESG responses: what you can safely automate (and what you shouldn’t)

The safest approach is to treat AI as a co-pilot: it proposes the first draft, and your team remains accountable. In practice, some types of content are ideal for auto-fill, while others require careful review.

Auto-fill works best for quantitative and repeatable requests: metrics, yes/no questions about whether a policy exists, and standard narratives that don’t change often.

Where you should be cautious: forward-looking claims (“we will reduce emissions by X%”), legal interpretations, and customer-specific contract statements. These can still be supported, but they should always be reviewed and tied to evidence.

• Best for auto-fill: emissions totals, energy use, headcount breakdowns, incident rates, policy existence and links
• Needs review: targets, materiality statements, supplier risk processes, claims about audits or certifications

Concrete benefits for suppliers (time, accuracy, and trust)

Suppliers adopt AI ESG questionnaires tooling for one reason: it saves time under deadline. But the best outcomes are broader than speed.

Time saved: When AI question matching starts from a reusable dataset, response cycles shrink dramatically—especially when multiple departments contribute. Many teams move from ‘weeks of back-and-forth’ to ‘hours of review’ once their workspace is set up.

Fewer errors and inconsistencies: Matching reduces the chance that one customer gets a number that differs from what another customer received last month. Consistency is a procurement trust signal.

Better audit readiness: Evidence attachments and provenance reduce the ‘numbers without sources’ failure mode. That matters as assurance expectations increase and customers ask for backup.

• Reduce turnaround from weeks to hours for repeat questionnaires
• Lower rework by standardizing metrics, units, and narratives
• Cut follow-ups by attaching evidence to answers from day one
• Improve confidence in customer onboarding and renewals

How to keep AI audit-safe (the controls that matter)

Audit safety is a workflow problem, not a model problem. The goal is to ensure every answer has a clear owner, a traceable source, and a version history.

In practice, suppliers keep AI audit-safe by enforcing four controls: human review, ownership, evidence attachments, and change tracking.

Human review: AI suggests; a named owner approves. This keeps accountability clear.

Ownership: Assign data owners by domain (Finance for energy/emissions inputs, HR for workforce, Operations for incidents/policies). Owners validate updates on a cadence.

Evidence attachments: For key datapoints, attach the source (invoice, report, certification) so reviewers and customers can verify quickly.

Version history: Track changes so you can explain why a number changed between cycles (new invoices, updated boundaries, corrected factors).

• Never publish AI outputs without human approval
• Store calculation notes and definitions alongside metrics
• Attach evidence to the datapoint, not just the questionnaire
• Keep a simple update cadence so data stays current

How suppliers can get started with AI question matching in 5 steps

You don’t need a full ESG team to start. What you need is a minimum dataset, clear owners, and one real questionnaire to test the workflow.

• Choose one ‘representative’ customer questionnaire you want to answer faster.
• Build a minimum ESG dataset (10–15 metrics) and define units and boundaries.
• Upload 3–5 key policies/evidence files (certifications, energy bills, code of conduct).
• Upload the questionnaire and review AI-matched suggestions with the right owners.
• Export the response, track follow-ups, and expand your dataset based on what customers ask next.

Common pitfalls (and how to avoid them)

Pitfall 1: Treating AI as a typing tool. If you don’t centralize your dataset first, you’ll still spend time hunting for sources.

Pitfall 2: Inconsistent definitions. Two teams using different units or boundaries will produce conflicting answers. Standardize once.

Pitfall 3: Missing evidence. Customers increasingly ask for proof. Attach evidence early and reuse it across questionnaires.

Pitfall 4: No owner. If ‘everyone owns it’, nobody owns it. Assign owners by metric category.