Enterprise AI

Described conceptually. No confidential employer material appears here.

Large organizations generate enormous volumes of information that has to be read, interpreted, reconciled, and reported — regulation, emissions factors, supplier disclosures, operational data from across business units. Most of it arrives in inconsistent formats from sources never designed to be aggregated.

Enterprise AI work fails for reasons that have nothing to do with model capability.

The output has to be defensible. A sustainability disclosure is a reported figure with governance behind it, not an interesting estimate. That means every step needs provenance: where a number came from, what was assumed, who validated it, and what happens when the source changes.

And the data arriving is genuinely uneven. Intake workflows receive submissions of widely varying quality, and the difference between a usable figure and an unusable one is often domain judgment rather than a validation rule.

A figure that cannot be traced cannot be reported, and a workflow that produces untraceable figures quietly creates risk rather than capacity.

Apply AI to the parts of the work that are genuinely synthesis — reading widely, organizing findings, surfacing what changed, drafting a first pass — while keeping validation, judgment, and accountability with people who can be responsible for the answer.

Design the workflow so the AI step is always inspectable. If a figure cannot be traced, it does not ship.

Regulatory intelligence and monitoring.

Emissions research and factor synthesis.

AI-enabled knowledge synthesis across large document sets.

Sustainability data intake and validation workflows.

Decision support for operational and reporting teams.

Automation and agent-based workflows within enterprise constraints.

Described conceptually — outcomes remain with the organization.

The hard part of enterprise AI is governance, not capability. The question is never “can it produce this?” It is “can we stand behind this in a disclosure, and show our work?”

AI changed the size of problem I was willing to attempt. Work that would previously have required a team and a budget became something I could scope, prototype, and evaluate myself. That changed which problems seemed worth starting.