Saturn Five AI Readiness Diagnostic

Tell us about you

So we can send you the result and follow up if you’d like a deeper read.

About your company

Helps us slice your result against the right peer set.

1. 1

   What's your role?*

   CEO / FounderCOOCFOCRO / RevenueVP Customer Success / ServicesVP / Head of Data or AIOther
2. 2

   Where would you place your AI/outcome services today? (TSIA DARE level)

   Explorer — proving feasibility, free pilotsAdopter — consumption pricingOperator — subscription/ARR, the customer runs itPartner — we guarantee the outcome
3. 3

   Our customer and operational data is clean, connected, and accessible enough to power AI.*

   Why it matters: AI is only as good as the data beneath it. Fragmented, low-quality data means every initiative starts from scratch, stalls in cleanup, or produces outputs no one trusts — the #1 reason pilots never reach production.

   1Siloed across systems; AI would start from scratch2Some integration, but quality and access are inconsistent3Mostly unified and reliable for priority use cases4Governed, real-time, AI-ready data foundation
4. 4

   We know the lineage and consent status of the data we'd feed to AI.*

   Why it matters: You can't govern what you can't trace. Unknown lineage and consent expose you to compliance, IP, and privacy risk the moment that data feeds a model — exactly the exposure that surfaces in diligence at the worst time.

   1No tracking of source or consent2Known for some sources, gaps elsewhere3Documented lineage and consent for key datasets4Full lineage, consent, and egress controls enforced
5. 5

   We have a prioritized set of AI use cases tied to business outcomes — not experiments.*

   Why it matters: Curiosity-driven experiments burn budget without moving the business. Without a prioritized, outcome-tied portfolio, AI becomes scattered pilots competing for attention instead of a few bets that change the P&L.

   1Ad-hoc experiments driven by curiosity2A list of ideas, loosely prioritized3A prioritized portfolio tied to specific metrics4A funded roadmap tied to P&L, reviewed regularly
6. 6

   AI has an accountable executive sponsor and dedicated funding.*

   Why it matters: AI without an owner and a budget stays a science project. No accountable sponsor means no hard calls, no funding for the unglamorous foundations, and initiatives that quietly die when the novelty fades.

   1No clear owner or budget2Informal champion, ad-hoc funding3Named sponsor with a real budget4Board-visible sponsor; AI is a strategic line item
7. 7

   Our newer AI- and value-based services can grow the business on their own — not just our traditional implementation and support revenue.*

   Why it matters: In TSIA's survey, zero companies believed their portfolio would grow without legacy implementation and support. If your AI services can't stand on their own, the old land-and-expand motion is a liability in the AI era, not an asset.

   1No — growth still depends entirely on traditional services2Barely — new services contribute little to growth3Partly — they help, but legacy still drives growth4Yes — AI & value services sustain growth on their own
8. 8

   AI solutions are deployed with monitoring, ownership, and retraining — not fire-and-forget.*

   Why it matters: Models decay. Fire-and-forget AI silently degrades — accuracy drifts, edge cases pile up, trust erodes — until a quiet failure becomes a public one because no one owned the lifecycle.

   1No monitoring once shipped2Manual spot-checks3Defined owners with monitoring in place4Full lifecycle: monitored, retrained, SLA'd
9. 9

   We measure AI output quality and drift, and improve it continuously.*

   Why it matters: If you don't measure output quality, you're blind on the one thing that decides whether AI helps or hurts. Undetected drift and errors compound into bad decisions and lost confidence.

   1We don't measure output quality2Anecdotal feedback only3Tracked metrics with periodic review4Continuous evaluation tied to outcomes
10. 10

    We have an AI use policy and a maintained risk register.*

    Why it matters: Without a policy and a live risk register, AI risk stays invisible until it's an incident. You can't manage exposure you've never named — and a buyer or regulator will ask you to name it.

    1Neither exists2Informal guidance, no register3Policy + register exist and are used4Governed, reviewed, board-overseen
11. 11

    Controls cover IP exposure, data egress, and customer-facing AI disclosure.*

    Why it matters: Uncontrolled tool use leaks IP and customer data out the door, and undisclosed AI erodes trust. These are the exposures that become deal-blocking findings in diligence.

    1No controls; uncontrolled tool use2Some informal rules3Defined controls for key risks4Enforced controls with auditing & disclosure
12. 12

    We're prepared for an AI failure — incident response and accountability are defined.*

    Why it matters: AI will fail at some point — the question is whether you're ready. No incident plan turns a contained problem into a reputational and legal one, with no clear accountability when it counts.

    1No plan2Ad-hoc, would improvise3Documented response plan4Tested response with clear accountability
13. 13

    Our teams have the AI literacy and tools to use AI in their daily work.*

    Why it matters: AI you can't use is shelfware. Without literacy and access, even the best tools sit idle, the value case never lands, and skeptics get their proof that "AI doesn't work here."

    1Few skills; little access2Pockets of capability3Broad literacy with role-based enablement4AI-fluent org with continuous upskilling
14. 14

    AI is embedded in real workflows, and adoption is measured — not just licenses bought.*

    Why it matters: Buying licenses isn't adoption. If AI isn't embedded in real workflows with measured usage, you pay for capability you never capture — the gap between AI spend and AI value.

    1Tools bought, little real use2Used by a few enthusiasts3Embedded in some core workflows, adoption tracked4Embedded org-wide with measured value
15. 15

    There's trust and psychological safety around AI — people aren't hiding usage or fearing replacement.*

    Why it matters: Fear drives AI underground. When people worry about being replaced or judged, they hide usage and resist change, and the org never builds the muscle — adoption stalls no matter how good the tools are.

    1Fear and shadow usage2Mixed; uneven trust3Generally open, managed change4High trust; AI framed as augmentation
16. 16

    We can price, sell, or deliver against outcomes (not just seats/effort) where AI enables it.*

    Why it matters: If you can only sell seats and effort, you can't capture the value AI creates. Staying effort-priced caps your margin and cedes the outcome-based premium to competitors who can prove results.

    1Purely seat/effort-based2Exploring outcome-based ideas3Some outcome-based offers in market4Outcome-based commercial model at scale
17. 17

    We can baseline and track the business value our AI delivers — before, during, and after — not just usage.*

    Why it matters: Only 5% of services orgs have a formal value-engineering capability. If you can't baseline and prove value, you can't price to it, defend renewals, or move to outcomes — it's the single gap that caps premium pricing. TSIA calls it the highest-leverage build.

    1No — value isn't systematically quantified2Informal — depends on individuals3Partial — done in pockets (sales or CS)4Formal value-engineering function & methodology, owned end-to-end
18. 18

    What's your biggest barrier with AI right now? (select all that apply)

    Don't know where to startData isn’t readyNo clear ROISkills gapGovernance/risk fearsAdoption — people won't use itStuck scaling past pilots