Which AI Project Should We Do Next?

A practical checklist for choosing defensible AI investments—not merely impressive demos

Start with easy integration

Companies have dozens of plausible AI ideas. The difficult question is where AI can create measurable business value, how much autonomy is appropriate, and whether the implementation effort is justified.

Share your work email to receive a practical scoring worksheet that helps you compare use cases and route them to one of nine Size × Agency project types.

Your demo backlog and operations backlog should meet

Prioritize the problems worth solving after the presentation ends

The AI backlog is full of impressive demos. The operations backlog is full of people correcting records by hand.

The worksheet brings those lists together. It helps identify where scarce engineering capacity can remove a meaningful operational bottleneck—and where a plausible idea would create more maintenance than value.

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Score more than technical possibility

Evaluate the value, operating burden, and appropriate level of autonomy

Score each use case across three practical questions:

Business bottleneck: What measurable constraint, cost, delay, or customer problem does it remove?
Maintenance burden: What integrations, monitoring, documentation, and ongoing changes will it create?
Human oversight: Who reviews decisions, handles exceptions, and remains accountable?

An AI-generated summary and an integration that writes payment records into your ERP should not follow the same approval process. Finance tends to have follow-up questions.

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Pressure-test the 200 OK

Technical success does not guarantee business correctness

An external endpoint returns 200 OK with half the expected records. What happens next?

Does the integration detect the missing records?
Can it prevent incomplete data from flowing downstream?
Does it notify someone who can investigate and reconcile the result?
Is ownership clear when the dashboard is green but the numbers are wrong?

If the answer is unclear, the project plan is incomplete. The worksheet helps surface those operational questions before another demo quietly creates work for the spreadsheet cleanup team.

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Balance enterprise controls with speed to evidence

Three perspectives in one practical worksheet

The checklist combines the questions that determine whether an AI project deserves a real commitment:

Enterprise view: P&L impact, scale, ownership, reuse, organizational change, and governance.
German management view: GDPR, information security, employee participation, documentation, and human accountability.
Startup view: The largest bottleneck, customer value, speed to evidence, and scarce engineering capacity.

The result is a more grounded discussion about project size, appropriate agency, and the controls required in production.

Leave with fewer demo winners

Make engineering commitments that remain credible after go-live

Use the worksheet to build a shortlist with:

A real business bottleneck to remove
A measurable reason to act
An implementation size the organization can support
A level of AI agency matched to the risk
A maintenance burden you understand
A named owner for exceptions and business-level correctness

This is the case for sharing your email: you receive a reusable decision tool designed to improve an actual prioritization meeting, rather than another report about AI potential.

Plan for the integration lifecycle

Fast AI-generated work still needs monitoring, reconciliation, and ownership

Plumbed helps turn AI-generated integration work into governed, observable integration operations. That includes monitoring, reconciliation, controlled repair, and clear lifecycle ownership.

AI can build the first version quickly. A defensible project plan also explains how the business will detect incomplete data, handle external changes, repair failures, and trust what happens after the integration runs.

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Start with easy integration

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