Enterprise AI / Project note
Sustainability Pro
A specialist knowledge assistant helping business users interpret sustainability requirements and identify practical next steps.
- Stage
- Specialist workflow · Concept
- Primary users
- Sustainability teams / Procurement teams / Category managers
Evidence first
What is proven, what I changed, and what remains open.
A 90-second evidence map for hiring review. Synthetic product views explain mechanics; they never count as outcome proof.
- Verified now
- A documented specialist-product direction connecting approved guidance, procurement context, practical next steps, and expert escalation.
- Next evidence gate
- A source-governed prototype tested on high-frequency questions with specialist review.
User / workflow evidence
Supported synthesisThe concept addresses occasional users who must translate distributed sustainability requirements into a supplier or business action without assuming policy expertise.
Product decision
Supported synthesisGuidance, not authority: expose controlling sources, applicability, missing evidence, and the point where a sustainability specialist must take over.
AI / system boundary
Designed · not yet measuredApproved source sets, effective dates, jurisdiction, response structure, and escalation rules constrain AI retrieval and explanation; experts retain policy interpretation and approval.
Evaluation → change
Designed · not yet measuredThe release contract checks source, effective date, applicability, evidence gaps, uncertainty, and escalation for every high-impact guidance response.
Outcome / boundary
Open evidence gapNo shipped knowledge base, production usage, legal interpretation, or measured business result is claimed.
AI product decision record
The choices behind the product, architecture, and release gate.
Use grounded AI to scale access to guidance; never transfer policy authority from specialists to the model.
AI fit
Supported synthesisUse an LLM to interpret the question, explain applicable guidance, identify missing evidence, and prepare the next step.
Occasional users need contextual help, while specialists should focus on exceptions and consequential interpretation.
Alternative consideredA search portal leaves applicability work to the user; an open chatbot overstates authority.
Knowledge & context
Designed · not yet measuredUse RAG only over approved, versioned policy and guidance with jurisdiction, effective date, and citation metadata.
The system must show which rule controls the answer and whether it is current for the scenario.
Alternative consideredFine-tuning or model memory would make changing policy harder to update, inspect, and withdraw.
System architecture
Designed · not yet measuredUse a bounded retrieval-and-escalation workflow with expert ownership; expose it to the Hub after source and permission contracts are approved.
The core capability is governed guidance, not autonomous execution.
Alternative consideredMCP is useful only when reusable access to approved resources is safer and simpler than a direct governed integration.
Evaluation & release
Designed · not yet measuredRequire source, effective date, applicability, missing evidence, uncertainty, and escalation status for high-impact responses.
Factual wording is insufficient if the answer applies the wrong rule to the wrong context.
Alternative consideredGeneric helpfulness or similarity metrics do not test policy decision safety.
Evidence I still need from the private project record3 open items
- 01
Top user questions and their source artifacts.
- 02
Specialist-reviewed prototype sessions and failure themes.
- 03
Evidence that guidance improved task completion or reduced expert rework.
The product bet
Contextual guidance across policy, requirements, and supplier questions
A specialist knowledge product that translates requirements into practical next steps while preserving expert authority.
Sustainability knowledge is complex, distributed, and difficult for occasional users to translate into day-to-day decisions.
- Primary user
- A procurement or business stakeholder interpreting a sustainability requirement in a supplier decision.
- Trigger
- A policy, reporting, supplier, or evidence question appears inside day-to-day work.
- Inputs
- Approved policies and guidance, requirement context, supplier scenario, jurisdiction, effective date, and expert escalation rules.
- Product action
- Retrieve the controlling guidance, explain its applicability, identify missing evidence, and recommend a bounded next step.
- Output
- A cited requirement brief, evidence checklist, uncertainty note, and expert escalation when needed.
- Decision enabled
- What evidence or action is required now, and when a specialist must take over.
Public-safe product demonstration
A complete workflow, without protected data.
The names, records, amounts, dates, scores, and thresholds inside this surface are fictional. The product logic is the point.
A fictional packaging buyer must assess a recycled-content requirement before issuing a tender.
- Primary user
- Packaging buyer
- Decision enabled
- Turn a requirement into traceable evidence and an accountable exception.
Translate policy language into a decision-ready requirement.
The workbench separates mandatory criteria, requested evidence, geography, and effective date so teams do not evaluate different questions.
Supplier evidence must support the material claim, custody path, and applicable region.
Requirements, suppliers, documents, and recommendations are synthetic. The workflow is a public-safe product reconstruction.
Product decision and trade-off
Users need fast practical guidance, but a fluent answer can be mistaken for policy authority.
- Product choice
- Design the product as guidance with sources, applicability, missing evidence, and explicit expert escalation.
- Rejected alternative
- An open-ended sustainability chatbot that returns an answer without an accountable boundary.
- Product consequence
- Trust and escalation become first-class product states rather than footnotes.
The answer cites an outdated requirement or treats a conditional rule as universally applicable.
Effective-date checks, jurisdiction and scenario clarification, source citations, uncertainty disclosure, and expert handoff.
100% of high-impact guidance shows source, effective date, applicability, missing evidence, and escalation status.
The leadership pattern in its appropriate form.
This case is not retroactively enlarged into a Director mandate. It shows which product-lead behaviors were already present at this scope.
Defined a user decision contract around a complex and risk-sensitive knowledge domain.
Protected expert accountability while creating a scalable first line of support.
Director-ready portfolio leadership means deciding where AI can scale access and where authority must remain with specialists.
Product portfolio leadership connecting specialist knowledge to repeatable enterprise workflows.
Evidence boundary
Impressive because the boundary is clear.
The workflow framing, procurement connection, and specialist-agent pattern are established product direction.
- Established a reusable pattern for specialist knowledge agents.
The policy record, supplier scenario, citations, and quality benchmark are fictional.
No production knowledge base, legal interpretation, user adoption, or measured business result is claimed.