From AI to Everyday Impact: How NeuralOps Can Put ESG into Practice✎ Edit

Excess inventory, repeated shipments, and time-consuming manual work show up as costs in e-commerce. They also give businesses a practical place to start with ESG. Each one comes down to decisions teams make every day about what to buy, how to ship, and where to spend their time.

At AINNA, we built NeuralOps to help teams develop applications and automate workflows. Its Smart Routing architecture selects AI models based on what each task requires. We’ve used NeuralOps to support system development for two factories and a state government. That experience reinforced a principle I apply to both AI and ESG: start with a specific operational problem and decide how you’ll measure progress.

Environmental | Match resource use to the task

In inventory management, an early alert about slow-moving stock can prompt a team to review a purchase before excess inventory builds up. The same principle applies to AI. If a lightweight model can reliably classify records, Smart Routing can assign the task to that model.

In roughly its first month, the NeuralOps architecture handled a workload of about 32 billion tokens, with AI model costs of around RM100. The token figure reflects the workload across the overall architecture. Assessing the environmental impact would require separate measurements of energy use and emissions.

Social | Give people more time for work that needs their judgment

Routine data checks, reports, and notifications can take up a significant part of the working day. Automating some of those steps gives teams more time to investigate stock discrepancies or resolve customer issues. Processing time and error rates can help show whether the change is working for the people who use it.

Governance | Make decisions easy to review

When AI recommends an action that affects inventory, spending, or customers, the team should be able to see what data informed the recommendation, what triggered it, and who approved the action. For example, NeuralOps could flag slow-moving stock and suggest a smaller reorder. The purchasing team could then review the recommendation before changing the supplier order.

For me, the practical starting point is one process with a clear baseline. If excess inventory is the issue, measure the value of slow-moving stock before and after the change. If manual reporting is the issue, track the time it takes and how often errors occur. Those measures give the team a basis for deciding whether to keep, refine, or expand the workflow.

If you could improve one process in your organisation with AI, which would you choose and how would you measure the result?

#ESG #NeuralOps #AINNA #DigitalTransformation #AIAutomation