This checklist is for the moment before an executive funds a specific AI workflow. It is not a vendor interview script. It is a go or no-go screen for the buyer, sponsor, and implementation partner.
The central question is simple: do we know enough to fund this workflow responsibly, and do we know what evidence would make us expand, narrow, or stop?
McKinsey’s State of AI research should make buyers more demanding before they fund. Regular AI use is widespread, but enterprise-wide scale and EBIT impact remain much less common, and the high performers are the ones redesigning workflows, assigning senior ownership, and defining human validation points. The funding meeting should therefore test whether the partner can name the workflow, owner, control points, and expansion evidence before the build starts.
Metacto’s Opportunity Mapping turns that into a funding artifact: a 2-3 week Phase 1 with a ranked opportunity map, stakeholder and systems review, context and risk assessment, value case, target workflow, and first-build recommendation.
Funding should follow a workflow decision, not AI enthusiasm
If the team cannot name the owner, metric, source systems, review path, and pilot exit criteria, the next step is scoping, not implementation.
The 15 questions
- What exact workflow are we funding?
Name the trigger, user, handoff, output, and system update. “Operations automation” is not specific enough.
- Who owns the business metric?
The owner should be a real executive or operating leader, not a steering committee.
- What is the current baseline?
Capture current cycle time, cost, error rate, throughput, backlog, revenue leakage, or quality before the AI workflow changes anything.
- Why is this workflow worth funding now?
The answer should connect urgency, value, volume, risk, or strategic importance.
- Which users will work with the first release?
A pilot needs named users or roles. Anonymous “teams” do not create adoption.
- What internal context does the workflow need?
List the documents, records, policies, messages, databases, and business rules required for a useful output.
- Which systems must the workflow read from?
Examples include CRM, ERP, ticketing, document stores, email, Slack, spreadsheets, and data warehouses.
- Which systems can it write to, and under what approval?
Write-back rules are a funding decision, not a late technical detail. Metacto’s Agents and Workflows page is the reference when AI moves from advice to action because the production design needs integrated source systems, human review, write-backs, evals, monitoring, dashboards, and runbooks.
- What data is restricted?
Name sensitive fields, regulated data, customer records, internal notes, and information that should stay outside the workflow.
- Where must a human review or approve?
Define the decision points. Do not settle for a vague “human in the loop.”
- What does a successful first release do?
Describe the smallest useful workflow, not the end-state platform.
- What is out of scope for the first release?
A good implementation partner should help protect the pilot from expansion pressure.
- What will we measure weekly?
Use business and operating measures: usage, quality, cycle time, review burden, exception rate, and owner confidence.
- What would make us stop?
Stop criteria are a sign of serious governance. They prevent weak pilots from becoming permanent obligations.
- Who operates the workflow after launch?
Someone must own monitoring, incidents, user feedback, improvements, and expansion recommendations. Metacto’s Continuous AI Operations is the right reference for this part of the checklist because post-launch operations include evals, tuning, runbooks, monthly reviews, and incident handling rather than passive support.
Funding readiness screen
Use this screen in the funding meeting. Any not-ready answer becomes a scoping task before implementation starts.
Readiness area: Business case
- Ready to fund
- Owner, baseline, metric, and urgency are clear
- Not ready yet
- The team is funding AI activity without a workflow economics case
Readiness area: Context
- Ready to fund
- Source systems, rules, permissions, and restricted data are known
- Not ready yet
- The partner is expected to discover access constraints during build
Readiness area: Pilot scope
- Ready to fund
- First release, out-of-scope items, and acceptance criteria are explicit
- Not ready yet
- The pilot is a broad prototype with no decision threshold
Readiness area: Operations
- Ready to fund
- Monitoring, support, incidents, and improvement ownership are assigned
- Not ready yet
- Launch is treated as the finish line
The partner’s role in the funding decision
A good implementation partner should not push every workflow into build. They should help the sponsor understand whether the workflow is ready, what can be learned quickly, and what should be deferred.
That is one reason an accountable delivery model such as Lightning Pods can help. The partner’s job is not to produce a larger roadmap. It is to get the first funded workflow to a measurable release or reveal why it should stop.
flowchart LR
A["Funding question"] --> B["Owner and metric"]
B --> C["Context and access"]
C --> D["Pilot boundary"]
D --> E["Operate plan"]
E --> F{"Fund, narrow, or stop"} How to run the meeting
Put the COO or business owner, CTO or technical sponsor, security lead, finance owner, and implementation partner in the same conversation. Walk through the 15 questions. Mark each one green, yellow, or red. Green means ready. Yellow means scoping needed. Red means do not fund yet.
That discipline saves the company from the worst version of AI implementation: a pilot that is technically impressive, politically popular, and operationally homeless.