Does the assessment guarantee a positive ROI?

No. It uses supplied evidence and labels assumptions and unknowns. Calculate ROI only when volume, time, costs, exception rates, and pilot outcomes are supported by evidence.

Can it determine whether an integration or API is available?

It identifies what must be checked, but does not claim an integration exists unless current, authorized evidence confirms it.

Can it approve or execute high-impact actions?

No. It can recommend safer autonomy and controls; consequential approvals and actions stay with authorized people.

Automation Feasibility Pro — Assess AI Fit

Example session with this skill installed

Assess this automation idea using only these facts: our team processes around 400 supplier invoices monthly; staff copy vendor, amount, due date, and PO number from PDFs into an ERP; mismatches are sent back for review. We don't know OCR accuracy, exception rate, API availability, or current labor time. We want the system to approve and pay invoices automatically. Give a verdict, evidence/unknowns table, safer target autonomy, validation pilot, and economic data needed. Do not invent ROI or integration access.

Read your context and instructions
Compiled the automation feasibility pro

Feasibility decision: PILOT FIRST; do not auto-approve or pay yet

The workflow may support document extraction and routing, but the evidence provided is not enough to justify autonomous approval or payment. Start with a read-only extraction pilot and keep financial authorization with an accountable human.

Evidence and unknowns

Area	Known	Unknown / required evidence
Process	About 400 invoices/month; PDFs are copied into an ERP; mismatches go to review	Invoice variation, required fields, current steps, owner, duplicate handling, and true end-to-end cycle time
Inputs	Vendor, amount, due date, PO number are needed	Scan quality, languages, layouts, missing/ambiguous fields, data rights, representative test set
Quality	Mismatches are reviewed	Baseline field accuracy, false matches, false negatives, exception rate, and acceptable error thresholds
Integration	An ERP is used	Supported API/connector, permissions, sandbox, idempotency, audit logs, retry behavior, and payment controls
Risk	Proposed automation includes approval and payment	Fraud exposure, duplicate/altered invoices, segregation of duties, approval policy, privacy/security, and recovery process
Economics	Approximate monthly volume only	Staff minutes, fully loaded labor cost, rework, tool/integration cost, review cost, payment loss exposure, and maintenance burden

Safer target autonomy

Extract fields into a draft record with page references and confidence/validation flags.
Match vendor and PO against authorized ERP records using deterministic rules where possible.
Route missing fields, mismatches, duplicates, and low-confidence cases to staff.
Require authorized human review for invoice approval and payment.
Log source document, extracted values, edits, reviewer, and final action.

Bounded validation pilot

Use a representative, permissioned historical sample that includes clean, scanned, unusual, duplicate, and mismatched invoices.
Run in shadow mode: compare extraction and routing against staff-reviewed outcomes; do not approve, post, or pay.
Measure field-level accuracy, mismatch detection, missed exceptions, staff correction time, throughput, latency, and reviewer workload.
Test access controls, duplicate prevention, idempotent retries, audit logs, failure recovery, and retention.
Define pass/fail thresholds with finance and security owners before testing; stop on any missed high-risk discrepancy.

Economics needed

Collect current labor minutes and cost per invoice, correction/rework rates, monthly seasonality, software and integration costs, human review time, exception handling, ongoing monitoring, and expected risk-loss reduction only if it can be supported. Calculate ROI only after pilot results and full operating costs are known.

Conditions to proceed: verify ERP integration in a sandbox, document finance approval controls, establish a representative test set and thresholds, and obtain security/privacy review. No production access or payment automation is confirmed by this assessment.

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About this skill

What this skill does

Automation Feasibility Pro evaluates whether a specific business process can be automated reliably, safely, economically, and operationally in the user’s real environment. It separates a polished demo from production readiness, AI capability from system capability, and technical possibility from defensible business value.

How it works

The agent maps the actual process, inputs, outputs, systems, volume, accuracy needs, latency, consequences of error, exception burden, human roles, privacy/security constraints, and economics. It tests whether AI is necessary or deterministic automation is better, identifies integration and data gaps, and proposes validation experiments. It gives an honest verdict such as GO WITH CONTROLS, PILOT FIRST, AUGMENT, REDESIGN, BLOCKED, or INSUFFICIENT EVIDENCE, with explicit conditions to proceed.

Evidence and risk discipline

It never invents API access, permissions, model performance, benchmarks, process volume, costs, compliance status, deployment results, test results, or ROI. It labels facts, assumptions, estimates, and unknowns separately. High-consequence actions retain proportionate human review and approval.

Limitations

A feasibility assessment is not proof that a system will perform in production. Results depend on representative data, current integrations, operating constraints, and tested controls. Validate the highest-risk assumptions in a bounded pilot before committing to deployment.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Automation Feasibility Pro — Assess AI Fit