Human Review by Design
Responsible AI & What We Do Not Automate
AI should assist bounded work with approved data, source context, human review, and clear escalation—not silently own consequential decisions.
Quick Answer
The answer before the details.
Tensor Garden evaluates whether a workflow is suitable for AI before selecting a model or automation tool. Good candidates have clear inputs, bounded outputs, source context, review, and fallback. Consequential decisions, unsupported claims, sensitive actions, and ambiguous exceptions remain human-owned unless an accountable and appropriately authorized process says otherwise.
What this page establishes
- Define the approved task, data boundary, source context, reviewer, and fallback.
- Use human review for outputs that affect people, money, rights, safety, or material commitments.
- Label demonstrations and AI-generated examples so they are not mistaken for customer evidence.
When this matters
- Employees are already using general-purpose AI with company information.
- A workflow drafts customer, legal-adjacent, financial, health, insurance, or personnel content.
- Leadership wants internal knowledge retrieval, agents, voice workflows, or automated routing.
What to avoid
- AI does not replace accountable professional, legal, clinical, financial, insurance, employment, or safety judgment.
- A model output is not treated as source evidence when the underlying source can be reviewed.
- High-impact actions do not proceed silently without appropriate authorization and escalation.
What buyers can verify
- Inspect the allowed task, prohibited data, model access, sources, reviewer, and fallback.
- Test ordinary cases, ambiguous inputs, missing sources, unsafe requests, and escalation behavior.
- Review logs, user feedback, model or prompt changes, and known failure patterns over time.
Make the approach inspectable before the work begins.
Start with the decision boundary
The workflow must identify what AI may draft, classify, summarize, retrieve, or route—and what requires responsible human judgment.
Control data and sources
Approved tools, data minimization, permissions, source references, and prohibited-data rules should be defined before broad use.
Design review and fallback
Users need to know when to verify, reject, escalate, or stop the workflow, and owners need a way to review errors and changes over time.
Questions and evidence before commitment.
Inspect the allowed task, prohibited data, model access, sources, reviewer, and fallback.
Test ordinary cases, ambiguous inputs, missing sources, unsafe requests, and escalation behavior.
Review logs, user feedback, model or prompt changes, and known failure patterns over time.
What work should not be fully automated?
Work involving consequential judgment, unclear authority, sensitive exceptions, unsupported claims, or material commitments should retain appropriate human ownership.
Does human review mean reading every AI output?
The review design depends on impact and risk. Some workflows require approval for every output; others may use sampling, thresholds, source checks, and escalation.
Can AI use internal company knowledge?
Yes, when sources, permissions, data boundaries, retention, access, citations, and ongoing ownership are designed and reviewed explicitly.
Inspect the owners, boundaries, process, evidence, and handoff.
Trust should come from what buyers can review: scope, decision ownership, security boundaries, delivery process, test evidence, documentation, and clear labels around demonstrations or future outcomes.
Map the whole stack
We look at infrastructure, users, vendors, phones, websites, custom software, data, security, and AI opportunities in one operating map.
Stabilize the risk first
The first plan separates urgent IT/security gaps from longer-term automation so the business is not building AI on top of unstable systems.
Build the workflow layer
Once the foundation is clear, we connect CRM, documents, support, reporting, intake, follow-up, and AI into repeatable operating workflows.
Connect the trust boundary to the work.
Turn trust questions into a scoped, reviewable roadmap.
The assessment identifies owners, systems, vendors, data, risk, workflow friction, evidence gaps, and the boundaries that should shape the first approved phase.
Current-state map
Systems, vendors, users, workflows, data, risk, and recurring manual work captured in one operating view.
Risk and stability callouts
What has to be fixed before automation: access, backup, security, handoffs, custom software, or undocumented infrastructure.
Automation candidates
The repeat work that is ready for AI or software once the foundation and review path are clear.
30/60/90 roadmap
A sequenced plan across IT, custom software, business operating systems, AI automation, and AI governance — so the next step is obvious instead of scattered.
The page describes the approach and boundaries for this topic. The engagement scope remains the source for what is included in a specific project.