AI-enabled operations
What is the Altura AI Runbook for governed NetSuite operations?
How to use AI in NetSuite operations without turning finance, integrations, or approvals into ungoverned experiments.
Written by Altura Innovation · Reviewed by Altura Innovation · Updated
The short answer
What is the Altura AI Runbook?
The Altura AI Runbook is a governed operating method for using approved AI tooling inside NetSuite, Celigo, and back-office workflows. It defines structured context, risk tiers, data contracts, human approval, measurement, and fallback ownership so AI assists work without quietly taking control of risky decisions.
Key takeaways
- The durable value is the method, not a specific public AI tool claim.
- AI candidates need structured context, risk tiers, data contracts, and approval points before build.
- AI should help prepare, classify, summarize, and route work before it is trusted with higher-risk decisions.
- The AI Accelerator teaches the runbook and packages governed pilot candidates.
Who Altura is
Altura Innovation is an AI operations partner for multi-channel ecommerce brands running on NetSuite.
Who it is for
Finance and operations leaders at $5M–$250M+ multi-channel ecommerce brands.
Related Altura offer
AI Accelerator teaches teams the Altura AI operations runbook.
01 · In depth
Why does AI need a runbook in NetSuite operations?
AI needs a runbook because NetSuite operations involve money, permissions, customer commitments, inventory, and audit evidence. A runbook makes the use of AI explicit: what context it can use, what output is expected, what risk tier applies, and where a human approves.
Ungoverned AI starts with a prompt and hopes the work is safe. Governed AI starts with the operating workflow and asks where AI can assist without weakening control.
The runbook also keeps the method tool-agnostic. Approved AI tooling can vary by environment, but the governance pattern should stay stable.
02 · In depth
What belongs in a governed AI candidate?
A governed AI candidate should include the job to be done, the source data it can read, the output contract it must meet, the risk tier, the approval point, the fallback owner, and the measurement path. Without those pieces, the candidate is still an idea.
This is why the AI Accelerator packages pilot candidates with decision cards, data contracts, and risk tiers. The artifact matters because it turns a promising workflow into something a team can build, govern, and review.
Candidate workflows often include exception triage, reconciliation support, data cleanup, close-package preparation, and integration monitoring decision support.
- Decision card: what decision or assistive step the workflow performs.
- Data contract: what the AI step reads and what it must return.
- Risk tier: what can go wrong and how tightly the step is governed.
- Approval point: where a person reviews before risk increases.
03 · In depth
Where should AI stay out of the way?
AI should stay out of autonomous ledger changes, unchecked customer-impacting decisions, permission changes, and any workflow where the source data or approval path is unclear. In those areas, AI may help prepare evidence or summarize exceptions, but a person should retain judgment and sign-off.
The practical rule is simple: the closer the workflow gets to money, controls, customer commitments, or audit evidence, the stronger the approval path should be.
If the team cannot explain how a result was produced or who approved it, the workflow is not ready for governed AI operations.
Decision matrix
How is governed AI different from generic AI use?
Generic AI use can help individuals move faster. Governed AI operations are built to survive inside controlled business workflows.
| Decision point | Generic AI use | Governed AI operations |
|---|---|---|
| Starting point | A person asks a tool for help with a task. | The team maps the workflow, risk, data, and approval path. |
| Context | Context is often pasted manually and inconsistently. | Structured context defines what the AI step can read and use. |
| Risk control | Risk depends on individual judgment in the moment. | Risk tiers define how tightly each candidate is governed. |
| Outcome | Useful output may stay personal and hard to repeat. | The workflow can be measured, reviewed, improved, or retired. |
Glossary
What terms matter in this guide?
Altura AI Runbook
Altura's method for governing AI-enabled workflows with structured context, risk control, human approval, and measurement.
Decision card
A short definition of the AI-enabled job, inputs, output, approval point, risk tier, and owner.
Data contract
The expected shape of the data an AI-enabled step reads and returns, so output can be tested and reviewed.
Risk tier
A classification that determines how much review, approval, and monitoring an AI-enabled workflow needs.
Keep reading
Where should you go next?
Questions
Frequently asked questions
Ready to turn AI use into governed operations?
Altura can help establish the baseline, teach the runbook, and package the first candidates worth building.