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First AI Factory POC Checklist

Is this use case strong enough to be the first module of your AI Factory?

How to score: For each statement, tick one option: Yes / Partly / No. If most answers are Yes, this is a strong candidate for your first AI Factory module. If many are Partly/No, treat it as a later use case or a pure experiment.

Answered: 0 /30

1. Business value & ownership

1.1.The problem is clearly defined in business language (not "let's test LLMs").
1.2.The use case is tied to concrete KPIs (e.g. cost reduction, NPS, cycle time, risk).
1.3.There is a named business owner who is accountable for outcomes.
1.4.The impacted process is important enough to matter, but not so mission-critical that it blocks experimentation.
1.5.Success criteria for the POC are measurable and agreed upfront (we know what "good" looks like).

2. Data & feasibility

2.1.The required data already exists in the organisation (we don't need to create it from scratch).
2.2.Data is at least partially accessible via existing systems or pipelines (not only in ad-hoc files and inboxes).
2.3.Data quality issues are manageable within a POC scope (we can clean/shape enough to learn, without a 12-month data programme).
2.4.The use case can start with "good enough" data and improve over time (no perfection required on day one).
2.5.For LLM/RAG-style cases: we can reasonably limit the context to a clearly defined corpus (documents, knowledge base, domain).

3. Governance, risk & compliance

3.1.The use case can be assigned a risk tier (low/medium/high) consistent with EU AI Act classification, with a named AI/model owner.
3.2.It is realistic to pass basic compliance checks (privacy, security, regulatory) within the POC timeline.
3.3.We can register this POC — including any agents, tools, and third-party integrations — in a simple AI system registry from day one.
3.4.We can implement observability as a minimum: logging prompts/responses, tracking usage and cost, basic monitoring of failures.
3.5.Any use of sensitive data can be handled through anonymisation, masking, or alternative data where required.

4. Reusability & platform fit

4.1.The use case can be implemented using the same building blocks we want in our AI Factory (e.g. orchestration layer, agents, RAG, existing data pipelines).
4.2.Components we build (connectors, prompts, agents, workflows) can be reused in future use cases with limited effort.
4.3.The interface pattern (chat, API, workflow step, internal app) is something other teams could also use.
4.4.We can expose the solution as a service on the platform (not just as a one-off script or local prototype).
4.5.The POC will help us test the delivery model (intake → design → build → deploy → monitor), not only the model's intelligence.

5. Complexity & time-to-value

5.1.The scope is small enough to deliver a working POC in roughly 8–12 weeks.
5.2.The process and stakeholders are clear – we don't need to redesign the whole organisation first.
5.3.Technical complexity is moderate: challenging enough to be meaningful, but not so complex that it blocks learning and delivery.
5.4.There is a clear "first version" we can ship, even if the long-term vision is broader.
5.5.The POC can demonstrate visible value to users and sponsors quickly (even if impact scales later).

6. Adoption & change

6.1.There is a target user group (team or function) willing to try a new AI-powered way of working.
6.2.We can integrate the POC output into an existing workflow or system (not just "yet another separate tool").
6.3.We have capacity to provide basic enablement: short training, onboarding materials, FAQ, support contact.
6.4.We have a plan to collect user feedback during the POC and use it to iterate.
6.5.There is at least one executive sponsor ready to champion the solution if it works.

How to interpret your results?

Answer all 30 statements to see your interpretation — 30 to go.

Mostly YES

What this means

  • Your use case is a strong candidate to become the first module of your AI Factory.
  • You seem to have the key ingredients in place: clear business value and ownership, "good enough" data, realistic governance and observability, reasonable complexity, and potential for reuse as a platform pattern.

What you can do next (on your own)

  • Turn this checklist into a simple decision one-pager:
    • 1–2 lines describing the business problem (no AI jargon),
    • KPIs and expected impact,
    • named business owner and sponsor,
    • POC scope for the next 8–12 weeks,
    • which components are meant to be reusable for future use cases.
  • Use that one-pager to align stakeholders and formally label this initiative as your first AI Factory module, not "just another POC".
  • Define from day one:
    • the minimum metrics you will track (value, usage, cost, quality),
    • the minimum platform elements that must be in place (registry, logging, monitoring) so that future use cases can plug into the same pattern.

How DS Stream can help

If you're at "Mostly YES", you're at the perfect point for an AI Factory Maturity Review:

  • We'll walk through your answers and your AI Readiness one-pager together.
  • We'll help you shape this POC explicitly as a Factory pattern (architecture blocks, governance, observability, reuse).
  • We'll co-create a 90-day plan that ensures this POC doesn't end as a one-off hero project, but as a reusable blueprint for the next 3–5 use cases.

If your result is "Mostly YES", consider booking the review before you lock in scope and architecture – it's the best moment to avoid building something that won't scale.

Mix of YES and PARTLY

What this means

  • You're moving in the right direction, but some critical aspects are under-defined (typically KPIs, ownership, data access, governance, reusability).
  • This use case can become your first Factory module, but you should tighten a few areas before you commit.

What you can do next (on your own)

Focus first on the areas where you see the most Partly:

  • If KPIs are fuzzy → work with business/finance to put real numbers behind "value" (e.g. time saved per case, volume per month, cost per ticket).
  • If ownership is unclear → name a single business owner and write it down; share it with all stakeholders.
  • If data is "kind of" available → list concrete sources, owners, and the minimum "data plumbing" needed to get started.
  • If governance/observability are vague → define a minimal standard: risk tier, model registry entry, basic logging, and a simple monitoring view.
  • If reusability is questionable → decide which components you deliberately design as reusable (e.g. agent, RAG pipeline, integration pattern).

After tightening these, revisit the checklist and see if some Partly answers can now become Yes.

How DS Stream can help

In this scenario, the AI Factory Maturity Review works well as a short sharpening workshop:

  • We'll review your Partly answers with your team and turn them into clear decisions (e.g. KPI targets, risk tier, POC boundaries, technical pattern).
  • We'll help you decide whether this use case should indeed be your first Factory module, or whether it is better positioned as the second or third case in your portfolio.
  • If needed, we can suggest a short discovery sprint (e.g. 1–2 weeks) focused on solidifying scope, governance and platform fit before you start building.

If you see a lot of Partly, use the review to "tighten the bolts" upfront rather than discovering misalignment halfway through the POC.

Mix of NO and PARTLY

What this means

  • This use case may be interesting, but as the first module of your AI Factory it looks risky:
    • weak or unclear KPIs and ownership,
    • problematic data access or quality,
    • difficult governance or compliance profile,
    • limited reusability for future use cases,
    • high complexity and unclear adoption path.
  • It's more of a candidate for a later wave or a controlled experiment than for your foundational Factory module.

What you can do next (on your own)

You essentially have two realistic options:

  1. De-risk this use case
    • See if you can simplify it: narrow the process, limit the data domain, drop non-critical integrations from phase one.
    • Look for a very similar use case in the same business area but with better data, clearer ownership, or lower governance friction.
    • If you can quickly fix a few critical items (ownership, KPIs, data access, basic observability), rerun the checklist and check if the profile improves.
  2. Choose a different first POC as your Factory module
    • Prioritise use cases that:
      • have a committed business owner and explicit KPIs,
      • reuse existing data and pipelines,
      • can be governed with a minimal but clear control set,
      • can serve as a template for multiple teams or regions.
    • Use this checklist as a filter: run 2–3 candidate use cases through it and see which one naturally scores higher as a first step.

How DS Stream can help

A weak score doesn't mean "AI is not for you" – it means you need a smarter starting point. In the AI Factory Maturity Review we can:

  • Show you clearly why this use case is a fragile foundation for an AI Factory (using your own answers as evidence, not opinion).
  • Help you identify 1–2 alternative use cases from your context that match the "first Factory module" profile much better.
  • Co-design a mini portfolio of starting use cases, such as:
    • one simple, highly reusable "platform-shaping" case, and
    • one more advanced case to demonstrate the Factory's power in a second wave.

If your result is "Mostly No / Partly", the review is a good way to avoid burning your first 90 days on a POC that was never set up to scale.

Next Step: Book Your AI Factory Maturity Review

Regardless of your score:

  • Mostly YES – you come with a strong candidate; we help you turn it into a true Factory pattern.
  • Mix of YES and PARTLY – we help you close critical gaps before you invest time and budget.
  • Mostly NO / PARTLY – we help you choose a better first step and build a short roadmap instead of guessing.

If you received this checklist as part of the AI Factory campaign, use the link in your email to book your free AI Factory Maturity Review with a DS STREAM AI Strategy Advisor.

During this 45–60 minute session, we will:

  • Walk through your answers in this checklist and your AI Factory Readiness Canvas.
  • Challenge and refine your choice of the first POC as a Factory module.
  • Highlight key risks and dependencies around strategy, governance, data, and platform.
  • Outline a pragmatic 90-day roadmap to move from POC-hell to a minimum viable AI Factory in your organisation.
Make an appointment →
Carlos Navarro, AI Strategy Advisor at DS Stream