Audit

AI Readiness & Data Gap Assessment

Interactive, structured audit to identify high-value AI pilots, map available data, score data quality and integration readiness, surface privacy/compliance gaps, and create a short pilot playbook you can save and iterate.

Interactive Tool

AI Readiness & Data Gap Assessment

Use this guided audit to prioritize practical AI and automation pilots that are supported by data, clear owners, and appropriate governance. Work through each section honestly — the goal is to create a realistic, low-risk pilot you can run, measure, and learn from. Save responses so your team can compare sites, locations, or progress over time.

Give the candidate AI or automation use case a short, clear name (e.g., 'Demand forecast for dinner shift').
Rate the likely impact on measurable outcomes (revenue, waste reduction, labor efficiency, guest experience). 1 = low, 5 = very high.
1.0 10.0
Rate how easy it will be to pilot (data availability, engineering effort, vendor fit, change required). 1 = hard, 5 = easy.
1.0 10.0
Summarize key assumptions, data sources needed, and what success would look like.
Optional: add a second candidate use case.
1.0 10.0
1.0 10.0
1.0 10.0
1.0 10.0
Check systems or data sources you have access to today. Use the 'Other' field for anything missing from the list.
If you selected 'Other', describe the source and access method.
List who owns or controls the key data sources (names, roles, teams). This helps plan access and governance.
Rate the POS data quality for fields you need: item mapping, timestamps, modifiers. 1 = poor, 5 = excellent.
1.0 10.0
Rate current inventory/receiving data accuracy. Consider cycle counts frequency and reconciliation practices.
1.0 10.0
Rate how reliable your scheduled vs. actual labor data is (time clocks, schedules, exceptions).
1.0 10.0
1.0 10.0
Describe the biggest gaps (missing fields, inconsistent item codes, late uploads, manual spreadsheets).
Can your POS, inventory, or other systems export data automatically (API, SFTP, scheduled CSV)?
Do you already use an integration platform, ETL tool, or data warehouse?
Select the best description of your current technical capabilities.
Mention specific connectors, known blockers, or systems that will be hard to access.
If yes, additional governance, consent, and retention work may be required.
For guest-profile use cases confirm consent or policy alignment.
Describe any anticipated legal or compliance work (contracts, DPA, vendor review, HR/employee-data considerations).
Pick the pilot type that best matches your highest-scoring, easiest use case.
Be specific: which locations, timeframe, and exact data sources will the pilot use?
List 2–4 measurable success criteria the pilot will be judged on (e.g., % reduction in waste, % forecast accuracy, minutes saved per ticket).
How long will you run the pilot to collect meaningful results? Typical pilots run 4–12 weeks.
Rough budget including any vendor fees, engineering time, and paid analysis. Keep it realistic to avoid scope creep.
Name and role of the person responsible for the pilot. Clear ownership reduces risk.
List concrete next actions (data access requests, test extract, baseline metric capture, vendor kickoff).
Considering data, integration, privacy, skills, and budget, how ready are you to run the proposed pilot? 1 = not ready, 5 = ready to start.
1.0 10.0
Anything else the team should know before starting?
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