AI Systems Checklist
Is Your Business Ready for AI? Score Yourself in 5 Minutes.
Most companies think they're 'not ready for AI' when they actually are — or think they're ready when they're missing critical foundations. This checklist gives you an honest assessment.
How This Works
32 Questions Across 4 Categories
Rate your organization on each item. Be honest — this isn't a test, it's a diagnostic. The goal isn't a perfect score, it's identifying the gaps that will determine whether AI succeeds or fails in your business. Companies that score above 50% are typically ready for AI implementation. Below 50% means you'll benefit from our Discovery audit to close the gaps first.
Category 1
Data Readiness (8 Questions)
Centralized Data Storage
Your core business data lives in structured systems (CRM, ERP, database) — not scattered across personal spreadsheets, email threads, and desktop folders.
Consistent Data Entry
Your team follows consistent data entry standards. Fields are filled out completely and formatted uniformly. You don't have 5 versions of the same customer name.
6+ Months of Historical Data
You have at least 6 months of historical data in your core systems. AI learns from patterns — more history means better performance.
API Access to Core Systems
Your primary tools (CRM, helpdesk, ERP) offer API access or integration capabilities. This is how AI connects to your existing workflow.
Data Privacy Framework
You know what data you collect, where it's stored, who has access, and what regulations apply (GDPR, HIPAA, SOC2, etc.).
Regular Reporting Process
Your team produces regular reports (weekly, monthly, quarterly) from your data. This means the data is structured enough to extract insights from.
Single Source of Truth
For any given metric (revenue, customer count, pipeline), there's ONE agreed-upon source — not three departments with three different numbers.
Digital Process Records
Your key workflows leave digital trails — tickets are logged, emails are tracked, deals are recorded. AI needs digital breadcrumbs to work with.
Category 2
Workflow Clarity (8 Questions)
Documented Core Processes
Your most important workflows are documented somewhere — even if the docs are outdated. You can describe the steps to a new hire.
Identifiable Bottlenecks
You can point to the 3-5 biggest time sinks in your operations. You know WHERE the pain is, even if you don't know the solution yet.
Repeatable Processes
Your core workflows follow roughly the same steps each time. They're not completely different every day — there's a pattern AI can learn.
Volume of Transactions
You process meaningful volume — 50+ support tickets/week, 100+ leads/month, 500+ invoices/quarter. AI shines at scale, not on 5 items/week.
Clear Success Metrics
You can define what 'better' looks like. Faster response time, fewer errors, more revenue, less time spent — you have a target, even if it's rough.
Cross-Department Handoffs
You have processes that cross department boundaries — sales-to-ops, support-to-engineering, marketing-to-sales. These handoffs are where AI creates the most value.
Manual Data Transfer
Someone on your team copies data between systems — from email to CRM, from spreadsheet to ERP, from helpdesk to tracking sheet. This is pure automation opportunity.
Quality Inconsistency
Output quality varies depending on who does the work. Some people are great, some are new, and the results show it. AI standardizes quality at the top performer's level.
Category 3
Organizational Readiness (8 Questions)
Executive Sponsor
At least one C-level or VP-level leader is actively interested in AI adoption. Change needs a champion at the top.
Team Openness to Change
Your team is generally receptive to new tools and process improvements. They may be cautious, but they're not hostile to change.
Available Point Person
Someone on your team can dedicate 2-3 hours per week to work with an AI implementation partner — answering questions, testing, giving feedback.
Budget for Tools
You can allocate $200-$1,000/month for AI platform costs (separate from consulting fees). This covers the AI infrastructure your systems run on.
Tolerance for Imperfection
You understand AI is 90-95% accurate, not 100%. You're OK with human oversight catching the exceptions rather than demanding perfection from day one.
Timeline Flexibility
You can commit to a 3-month implementation window. AI isn't overnight magic — it takes weeks to build, test, deploy, and train.
Prior Tech Adoption Success
Your team has successfully adopted at least one new tool or process change in the last 2 years. This shows the muscle for change exists.
Pain > Inertia
The pain of your current manual processes is greater than the comfort of doing nothing. You're motivated to change, not just curious.
Category 4
Technical Foundation (8 Questions)
Cloud-Based Core Tools
Your primary business tools are cloud-based (SaaS) rather than on-premise legacy software. Cloud tools have APIs that AI can connect to.
Modern CRM
You use a CRM from the last decade — Salesforce, HubSpot, Zoho, Pipedrive, etc. Not a paper Rolodex or an Access database.
Email on a Major Platform
Your team uses Google Workspace or Microsoft 365 for email. These platforms have robust APIs and integration ecosystems.
Team Communication Tool
You use Slack, Microsoft Teams, or similar for internal communication. This becomes the interface for many AI notifications and interactions.
Existing Integrations
You already use some integrations — Zapier, native connections between tools, or API-based automations. This means the infrastructure mindset exists.
Admin Access Available
You have admin access to your core tools (or can get it). AI integrations need API keys and configuration permissions.
IT Support (Internal or External)
You have someone — internal IT, an MSP, or a technical co-founder — who can help with tool configuration if needed.
Stable Internet & Infrastructure
Your team has reliable internet and their tools work consistently. AI adds a layer on top — it needs a stable foundation.
Your Score
How to Interpret Your Results
Count how many items you checked:
25-32 (80-100%): You're highly AI-ready. Jump straight to implementation — start with Growth or Enterprise tier. Your infrastructure and culture can support rapid deployment.
17-24 (50-75%): You're ready with some prep needed. Start with our Discovery audit ($7,500) to identify and close specific gaps. Most companies are here — and most see ROI within 6 weeks of starting.
9-16 (25-50%): Foundation work needed first. Our Discovery audit will map exactly what needs to happen before AI implementation succeeds. The audit itself is valuable — it's a systems architecture review even without AI.
0-8 (0-25%): Focus on fundamentals first. Centralize your data, document your processes, and adopt modern tools. We can advise on this through a Strategy Call, but implementation would be premature.