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Pro AI Solutions LLC

AI Readiness Scorecard

20 questions. 5 categories. The same diagnostic I use with clients in $15K deployments — at no cost to you.

Question 0 of 20

Category 1: Process Documentation

AI can only automate what's clearly defined. This section evaluates how well your core workflows are documented and repeatable.

1. How are your most important business workflows documented?

They're not — they live in people's heads Some are written down, but inconsistently Most core workflows are documented and accessible All critical workflows are fully documented, versioned, and up to date

2. When a new employee handles a key task, how much does the output vary from what an experienced employee would produce?

A lot — it depends entirely on the person Somewhat — some steps are clear, others are guesswork A little — most steps are defined, edge cases vary Minimal — the process is defined well enough that output is consistent

3. How would you describe your lead follow-up process today?

There's no consistent process — it depends on who's available We have a rough process but it breaks down regularly We have a defined sequence that gets followed most of the time We have a fully documented, consistently executed follow-up system

4. If a key employee left tomorrow, what would happen to the workflows they own?

Major disruption — no one else knows how to do what they do Significant slowdown — we'd figure it out eventually Some disruption — the basics are documented but nuances would be lost Minimal impact — their workflows are fully documented and transferable

Category 2: Measurable Outcomes

If you can't measure it, you can't improve it — and AI can't optimize it. This section evaluates how well you track performance across your operation.

5. How do you currently track whether your sales or service processes are working?

We don't track this in any structured way We track revenue but not the underlying process metrics We track a few key metrics but not systematically We have defined KPIs tracked consistently across key processes

6. Do you know your lead-to-close conversion rate for the last 90 days?

No — we don't track this Rough estimate only — nothing data-backed Yes, approximately — pulled from memory or rough records Yes, exactly — it's in a dashboard or report I can pull right now

7. When something breaks down in your operation, how do you typically find out?

A customer complains or revenue drops — we find out late A team member notices and tells us — inconsistent We have some alerts or check-ins that catch most issues We have monitoring in place — issues surface before they become problems

8. How clearly can you define "success" for your top three operational workflows?

Not clearly — success is subjective or undefined Somewhat — we have a general idea but not specific criteria Mostly — we can define it but it's not always tracked Exactly — success is defined, tracked, and reviewed regularly

Category 3: Technology Foundation

The tools your business runs on determine what AI can integrate with. This section evaluates your current tech stack and data infrastructure.

9. How would you describe your current CRM or customer management system?

We don't use one — contacts are in spreadsheets, email, or memory We have one but it's inconsistently used We use it regularly but it's not fully set up for our workflows We have a properly configured CRM that our team actually uses every day

10. How do your business tools communicate with each other today?

They don't — everything is manual or copy/paste between systems Some basic integrations but mostly disconnected Key tools are connected — some automation in place Well-integrated stack — data flows automatically between core systems

11. Where does your customer and operational data live?

Scattered — email, paper, personal phones, spreadsheets Partially centralized — main records in one place but gaps exist Mostly centralized — some exceptions but the core is organized Fully centralized — structured, accessible, and clean

12. How comfortable is your team with adopting new digital tools?

Very resistant — any new tool takes months to get adopted Mixed — some embrace it, others don't Generally open — with training, adoption usually happens Strong adopters — the team picks up new tools quickly

Category 4: Operational Leverage

AI creates leverage only where time is already being spent on repeatable, lower-judgment work. This section identifies where that potential is highest.

13. What percentage of your team's week is spent on work that follows a consistent, repeatable pattern?

Less than 20% — most work is custom or unpredictable 20–40% — some repeatable tasks but not dominant 40–60% — a significant portion is patterned and predictable 60%+ — most of what we do follows a predictable workflow

14. How often do high-value tasks (closing deals, client relationships) get delayed because of administrative work?

Constantly — admin work takes priority by default Often — it's a recurring problem but we manage Sometimes — we've improved it but it still happens Rarely — high-value work is protected from admin drag

15. If you could remove one bottleneck in your operation today, what would it be closest to?

Everything — the whole operation is the bottleneck Follow-up and communication — things fall through the cracks Scheduling, intake, or reporting — time-consuming but patterned Scaling delivery — we have a model that works, we just need more throughput

16. How do you currently handle after-hours inquiries or leads that come in outside business hours?

We don't — they wait until the next business day Auto-reply only — no real engagement until we're back Someone checks periodically but it's not systematic We have a system that captures and responds to leads 24/7

Category 5: Leadership Readiness

AI implementation succeeds or fails at the leadership level. This section evaluates your organization's readiness to make and sustain meaningful AI investments.

17. How would you describe leadership's current mindset toward AI?

Skeptical or uninterested — we'll wait and see Curious but uncertain — we're not sure where to start Interested and exploring — we're evaluating options Committed — we're ready to invest in the right implementation

18. When your business has invested in a new system or process in the past, what best describes what happened?

It usually fails — adoption doesn't happen and we revert Partial adoption — some people use it, most don't Mostly successful — rough start but it eventually sticks Successful — we've built a culture of adopting what works

19. How much budget has your business realistically allocated (or is willing to allocate) for AI infrastructure in the next 6 months?

Nothing — we'd need to see ROI before committing anything Under $500/month — we're testing the concept $500–$2,000/month — we're serious about building something $2,000+/month — we're committed to building real infrastructure

20. What is your primary goal for AI in the next 12 months?

We don't have one yet — still figuring out if it applies to us Explore and experiment — try a few things and see what sticks Solve a specific problem — automate one core workflow that's costing us time Build infrastructure — create systems that run while we focus on growth

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Pro AI Solutions LLC · [email protected] · (980) 990-3628