Workflow discovery
Can the vendor explain the current process, handoffs, exception paths, owners and measurable operating outcome before proposing a build?
Vendor due diligence
Evaluate automation vendors by production readiness, not presentation polish.
Enterprise AI automation vendors prove they understand the workflow, controls, systems, evidence, support model and business value before a build is approved.
Buyer review lens
A credible partner can explain the operating model before choosing the automation method.
Workflow and exception paths are understood
Access, data and AI controls are explicit
First release has acceptance criteria
Support ownership is clear after launch
Due diligence areas
Review the vendor against the work they will have to own.
System and data access
Have they mapped APIs, portals, CRMs, ERPs, inboxes, files, databases, reporting tools and access constraints?
AI control model
Do AI actions have approved data sources, confidence thresholds, human review points and escalation rules?
Security and permissions
Are credentials, least-privilege access, data boundaries, audit logs and environment separation addressed before launch?
Implementation evidence
Can the vendor show a first-release plan, acceptance criteria, validation gates, rollback path and handover model?
Operational support
Are monitoring, alerts, runbooks, ownership, change review and improvement cadence part of the production plan?
Commercial fit
Does the estimate connect scope, risk, reliability, integration depth and support expectations to measurable operating value?
Change readiness
Will the workflow be adopted by the teams who own exceptions, approvals, fallback steps and ongoing improvements?
Evidence to request
Ask for proof that maps to operations, controls and launch risk.
A polished demo is useful only after the vendor shows how the production workflow will be scoped, validated, controlled and supported.
01
Workflow map with systems, users, handoffs, decision points and exception paths
02
First-release scope with clear exclusions and measurable acceptance criteria
03
Security summary covering access, credentials, data boundaries and audit evidence
04
AI governance notes for prompts, knowledge sources, review thresholds and escalation
05
Integration plan for APIs, portals, databases, files, inboxes and reporting outputs
06
Production support model with monitoring, alerting, runbooks and change ownership
07
Commercial assumptions tied to volume, time savings, risk reduction and support
08
Comparable case context or delivery evidence for similar operational workflows
Review process
Vendor evaluation narrows toward a controlled first release.
01
Prepare
Define the workflow, business outcome, stakeholders, current systems, current pain and decision criteria before vendor conversations.
02
Validate
Ask vendors to explain the workflow back to you, identify constraints and separate the practical first release from the future-state wishlist.
03
Compare
Score each vendor on workflow understanding, controls, implementation discipline, security, support and value rather than demo polish.
04
Decide
Choose the partner that can launch a controlled production workflow with clear ownership, not just a prototype or tool configuration.
Risk signals
Early warning signs usually show up before the contract.
Tool-first recommendation
The vendor proposes a platform or agent pattern before understanding the workflow, systems, data and support model.
Unclear production owner
No one can explain who monitors runs, handles exceptions, updates rules or responds when a source system changes.
Weak control language
The proposal lacks practical answers for permissions, data boundaries, audit evidence, human approval and rollback.
Loose ROI assumptions
The business case depends on vague productivity claims instead of volume, handling time, rework, delay or quality measures.
No acceptance criteria
The vendor cannot describe what must be true for the first release to be accepted by operations and leadership.
Vendor review questions
Evidence to verify before selecting an automation partner.
What does enterprise AI automation vendor due diligence include?
A useful review covers workflow understanding, system access, data quality, AI controls, security, implementation plan, acceptance criteria, production support, commercial assumptions and evidence from comparable operational work.
How do we compare AI automation vendors?
Compare vendors by their ability to understand the workflow, design controls, integrate with existing systems, define a practical first release, support production operations and connect scope to measurable business value.
What is a warning sign in an automation proposal?
A warning sign is a proposal that starts with a generic tool, chatbot or demo before documenting the workflow, data sources, permissions, exception handling, support ownership and acceptance criteria.
Should vendor due diligence happen before an RFP?
A light due diligence pass before or during RFP preparation helps the buying team ask better questions. The vendor evaluation page turns those questions into comparable procurement criteria.
Start with the workflow
Find the first automation worth building.
Send one messy process, report or system handoff. We will help define the practical next step.