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Sprint Studio — sprintstudio.co Free Resource

AI Readiness
Checklist

The complete self-assessment for business leaders. 36 questions across 6 critical dimensions — with scoring, action items, and a clear verdict on where you stand.

36 questions

6 dimensions

Scoring system included

~15 min to complete

How to use this checklist

2 Yes Fully applies

1 Partially Work in progress

0 No Not in place yet

For each question, click your answer. Scores are calculated automatically and saved — you can close the page and come back anytime. Answer honestly — there are no wrong answers, only accurate ones. The goal is clarity about where you are, not a perfect score.

01

Process Clarity

The single biggest predictor of AI project success. If you can't describe the process clearly, an AI agent can't execute it. Before automating anything, you need to know exactly what you're automating.

max 12 pts

#	Question	Yes (2)	Partly (1)	No (0)
1	Can you describe every step of the process you want to automate — in order, with no gaps?
2	Do you know how long each step takes and who is responsible for it?
3	Have you identified the specific bottleneck — or are you still guessing where the problem is?
4	Is this process documented somewhere — even informally (a flowchart, a SOB, a shared doc)?
5	Do you know the volume — how many times per day or week this process runs?
6	Can you define what "success" looks like if this process were automated — with a measurable outcome?

If you scored 0–6 on this dimension

Map the process before thinking about AI — interview the people who do it daily and write every step down.
Identify the single most painful, repetitive sub-step. That's your starting point, not the whole process.
Define one measurable KPI that would confirm the automation is working (time saved, error rate, volume handled).

02

Data Readiness

You don't need perfect data. You need accessible data. AI agents learn from historical patterns and make decisions based on structured inputs. If your data lives in emails, sticky notes, or someone's memory — there's nothing to work with.

max 12 pts

#	Question	Yes (2)	Partly (1)	No (0)
7	Is the data you'd feed to an AI agent digitized and centralized — not scattered across spreadsheets and inboxes?
8	Do you have at least 6–12 months of historical data for the target process?
9	Can someone on your team explain what each data field means and how it's used in practice?
10	Is the data consistently formatted — no mix of free text and structured fields for the same type of information?
11	Can you export the relevant data in a standard format (CSV, JSON, or via API) without manual effort?
12	Do you have a process — even informal — for handling missing, incorrect, or duplicate data?

If you scored 0–6 on this dimension

Start with a data audit: list every system where relevant data lives and assess whether it's machine-readable.
Prioritize centralization over perfection — move data to one place (even a spreadsheet) before worrying about quality.
Assign a data owner: one person responsible for knowing what each dataset contains and keeping it current.

03

Team & Skills

You don't need an in-house AI team. You need a process owner who understands the workflow, has authority to make decisions, and is willing to invest time in the project. The cultural dimension matters more than the technical one.

max 12 pts

#	Question	Yes (2)	Partly (1)	No (0)
13	Is there a clear owner for the process you want to automate — someone who lives and breathes it daily?
14	Does your team see AI as a tool to remove tedious work — not as a threat to their jobs?
15	Do you have someone who can evaluate AI outputs — not build the model, but judge whether it's working?
16	Is leadership visibly supportive of the AI initiative — not just tolerant of it?
17	Can the process owner dedicate at least 20% of their time during the pilot phase (typically 3–5 weeks)?
18	Has your team already used any AI tool in their daily work — even something as simple as ChatGPT?

If you scored 0–6 on this dimension

Identify and empower a champion: one person who is curious about AI, not scared of it, and has authority over the target process.
Run a short internal session to explain what AI agents actually do — reduce fear by replacing vagueness with specifics.
Get leadership to explicitly sponsor the pilot in a team meeting. Visible support changes team dynamics significantly.

04

Technical Infrastructure

You don't need a cutting-edge stack. But your systems need to be connectable. Smaller companies using modern SaaS tools often have an advantage here — APIs and integrations are built in. Legacy systems can kill a project before it starts.

max 12 pts

#	Question	Yes (2)	Partly (1)	No (0)
19	Are your core tools (CRM, ERP, ticketing, email) accessible via APIs or do they support structured data exports?
20	Can you extract data from your systems without calling IT or submitting a ticket every time?
21	Is your IT environment flexible enough to add a new SaaS tool without a 6-month procurement and security review?
22	Do your main tools support integration via Zapier, Make, webhooks, or a REST API?
23	Is your business data backed up regularly and stored in a recoverable format?
24	Can you create a test or staging environment — separate from production — to run an AI pilot safely?

If you scored 0–6 on this dimension

Audit your core tools: check if they have native integrations or API access. If not, consider whether modern alternatives exist.
Start with tools that already connect — HubSpot, Notion, Slack, Google Workspace — rather than forcing legacy systems.
Map the integration complexity of your target process before committing to a build.

05

Budget & Expectations

Budget isn't just money. It's time, attention, and tolerance for iteration. A focused pilot can be completed in 3–5 days. The real investment is your team's time and your willingness to iterate on imperfect v1 outputs.

max 12 pts

#	Question	Yes (2)	Partly (1)	No (0)
25	Do you have budget allocated specifically for an AI experiment — not borrowed from another initiative?
26	Are you willing to invest 3–5 days of your team's time in a discovery and validation process before building?
27	Are your expectations tied to a specific, measurable KPI — not a vague feeling that "we should do something with AI"?
28	Have you estimated the cost of NOT solving this problem — time wasted per week, error rate, customer impact?
29	Are you prepared to iterate — accepting that v1 won't be perfect and committing to refine it over 2–4 weeks?
30	Is there executive sponsorship that actively protects the pilot from being deprioritized when other demands arise?

If you scored 0–6 on this dimension

Before allocating budget, calculate the current cost of the problem: hours per week × hourly cost × 52 weeks. Make the ROI case explicit.
Define success in writing before starting: "The pilot succeeds if [specific metric] improves by [X%] within [Y weeks]."
Start smaller — a 3-day Discovery Sprint to validate feasibility costs less than 6 months of a stalled project.

06

Governance & Compliance

Often ignored until it blocks everything. AI agents need access to business data. Depending on your industry, that data may be subject to GDPR, industry regulations, or internal policies. Know your obligations before you start building.

max 12 pts

#	Question	Yes (2)	Partly (1)	No (0)
31	Do you know what data an AI agent would need to access — and whether you're legally permitted to share it?
32	Do you have a data processing policy in place — or at least a clear understanding of your GDPR obligations?
33	Is there a decision-maker who can approve AI tool usage without requiring 12 layers of sign-off?
34	Have you considered how GDPR or sector-specific privacy regulations apply to your specific AI use case?
35	Do you have a contingency plan for what happens if the AI makes an error with sensitive or customer data?
36	Can you explain to customers or stakeholders — in plain language — how AI will be used in the process it touches?

If you scored 0–6 on this dimension

Map the data flow: list every type of data the AI would access and classify it (personal, financial, health, public). Then check your existing policies.
Consult with a legal advisor or DPO before starting if the process involves personal data of EU customers or employees.
Draft a one-page AI usage policy — even a simple one — that explains what AI is used for and what data it touches. Transparency builds trust.

Your Readiness Score

Scores are calculated automatically as you answer each question above.

#	Dimension	Score / 12
01	Process Clarity
02	Data Readiness
03	Team & Skills
04	Technical Infrastructure
05	Budget & Expectations
06	Governance & Compliance
Total AI Readiness Score		/ 72

54 – 72

You're ready. Start now.

Strong foundations across most dimensions. You have the conditions for a real AI project with measurable outcomes. Don't over-plan — pick a use case and start.

36 – 53

You're close. Close the gaps.

Solid in most areas with 1–2 clear weaknesses. Focus improvement efforts on your lowest-scoring dimensions before committing to a full build.

18 – 35

Groundwork first.

Multiple dimensions need attention. The investment in foundations now will directly determine whether your AI project succeeds or fails. Don't skip steps.

0 – 17

Step back before stepping forward.

Several critical prerequisites are missing. Starting an AI project now would likely fail and create skepticism. Lay the foundations first — you'll move faster later.

Red flags & green flags

Regardless of your score, these signals matter.

Stop. Address these first.

You can't describe the process you want to automate in under 2 minutes.
Your data lives primarily in people's heads, not in systems.
You expect AI to replace strategy, not execute it.
You're investing because competitors are — not because you've identified a real, painful problem.
Nobody on the team actually has bandwidth for this right now.
There's no single decision-maker for this initiative.

Go. You have what it takes.

You have one specific, painful, repetitive process — and you can describe every step.
You have a champion: one person who is curious, not scared, and has authority.
You're comfortable starting small and proving value before scaling.
You scored 8+ on at least 4 of the 6 dimensions.
You can tie the success of the project to a number — not a feeling.
Leadership is actively supportive — not just allowing it.