Your organization's AI readiness, assessed across six critical dimensions.

About 30 minutes for a leader who knows their business — less if you move quickly through the operational questions.

7 focused dimensions covering the full scope of your AI readiness
40+ questions calibrated to surface your real gaps — not generic ones
Personalized recommendations specific to your organization, industry, and constraints

$27

Early access pricing

One-time payment. Instant access. No subscription. Yours to keep.

🔒 Secure & private. Your responses are never shared or sold. Payment processed by Stripe.

What Happens Next

Complete the assessment — approximately 30 minutes
Receive your personalized AI readiness report
Walk into your next leadership conversation with clarity

No follow-up sales call. No consultant pitch. Just your results.

Continuing where you left off — your progress has been restored.

Section 1 of 7 0% complete

1. Business 2. Strategy 3. Execute 4. People 5. Process 6. Technical 7. Governance

Section 1 of 7

Your Company Profile

This input will help set context on priorities, size, and target business value.

Welcome. You've made a good decision to spend 30 minutes here. What follows is a structured assessment of your organization's AI readiness across seven dimensions. Your answers will be used to derive recommendations that are specific to your business — your process, your team, your constraints. The more specific your answers, the more useful your results. There are no right or wrong answers. Answer as honestly as you can about where your organization actually is today.

Company name *

The organization being assessed.

Company name is required.

Your role in this organization

Who is completing this assessment?

C-Suite (CEO, COO, CTO, CFO)

VP / Director

Manager / Team Lead

Board Member / Investor

Individual Contributor

Consultant / Advisor

Industry

How old is your organization?

Less than 2 years (startup)

2–5 years (growth stage)

5–15 years (established)

More than 15 years (mature)

Ownership type

Privately held

Publicly traded

Private equity / VC-backed

Non-profit / NGO

Government / Public sector

Primary operating region

Team size (full-time employees)

1–10

11–50

51–200

201–1,000

1,001–5,000

5,000+

Annual revenue range

Pre-revenue

Under $1M

$1M–$10M

$10M–$50M

$50M–$250M

$250M+

How would you describe your revenue trajectory over the last 12 months?

Declining — revenue is down year over year

Flat — revenue is roughly stable

Moderate growth — up 1–20% year over year

Strong growth — up 20–50% year over year

Accelerating — up more than 50%, or pre-revenue scaling rapidly

We don't track this formally

Primary business model

B2B SaaS / Software

B2C / Consumer

Professional Services

E-Commerce / Retail

Manufacturing / Operations

Marketplace / Platform

Other

Section 2 of 7

Goals & Strategy

Where your business is trying to go and how AI fits into that plan. The clearer your business objectives, the more targeted your recommendations will be.

How do you define AI success (or AI-enabled business transformation) for your organization?

Be specific. What would success look like in 12 months?

What customer outcome would successful AI transformation most directly improve?

Think about the experience your customers have today. Where is AI most likely to change what they feel, see, or receive from your organization? Select all that apply.

Faster response times or service delivery

More consistent or reliable customer experience

More personalized products, services, or communications

Lower prices or better value through operational efficiency

New capabilities or offerings we can't currently provide

Reduced errors or improved accuracy in customer-facing work

Self-service options that don't currently exist

Customer outcome is not a primary driver — our focus is internal efficiency

What's driving AI urgency right now?

Select all that apply.

Competitive pressure (peers are deploying AI)

Operational inefficiency (too much manual work)

Cost reduction mandate from leadership

Growth bottleneck (can't scale without AI)

Customer demand / expectations

Exploratory (no immediate pressure)

Investment budget for AI-enabled initiatives (next 12 months)

Not yet defined

Under $25K

$25K–$100K

$100K–$500K

$500K–$2M

$2M+

How mature is your AI strategy?

Who is accountable for AI-driven business outcomes in your organization?

CEO / Founder

CTO / Technical leader

COO / Operations leader

Chief AI / Data Officer

Distributed across teams (no single owner)

Multiple and/or no distinct owner

When does your business plan to begin deploying AI-enabled capabilities?

Already in progress

Within 3 months

3–6 months

6–12 months

12–24 months

Still exploring (no timeline set)

What's your organization's history with AI-enabled projects?

None — this is our first attempt

Informal pilots (no production deployment)

1–2 production deployments, limited scope

Multiple deployments, mixed results

AI is part of our core product / operations

Section 3 of 7

Organizational Ability to Execute

Your track record with change and your organization's capacity to see AI initiatives through to business results — not just start them.

How would you describe your organization's openness to change?

Your track record with major technology initiatives

None — this would be our first major tech transformation

1–2 successful (cloud migration, ERP implementation, etc.)

Multiple successful transformations — strong delivery track record

Mixed — some successes, some initiatives didn't stick

Previous major tech initiatives have mostly failed or stalled

Who approves technology spending at the scale of this initiative?

I approve it directly — founder or owner authority

Requires one additional sign-off (CFO, board, or partner)

Requires committee or multi-stakeholder approval

Budget authority is unclear or not yet established

How long does a typical technology decision of this scale take in your organization?

Under 2 weeks — we move fast when the case is clear

2–6 weeks — standard review and approval cycle

6–12 weeks — deliberate process with multiple stakeholders

More than 3 months — significant governance overhead

We haven't made a decision like this before

What's standing between your organization and successful AI deployment?

Select all that apply.

Lack of budget

Lack of technical skills or talent

Poor data quality or restricted data access

Employee resistance to adopting new ways of working

Unclear ROI or business case — hard to justify the investment

Leadership hasn't made AI a priority

Security or compliance concerns about AI tools

Legacy systems that are hard to integrate with

No clear plan for where to start

How would you rate your organization's track record for completing major initiatives?

Section 4 of 7

People & Skills

Your team's capability to adopt and sustain AI-enabled ways of working. Leadership commitment and internal readiness are among the strongest predictors of successful AI outcomes.

How many technical staff (engineers, data scientists, analysts) do you have?

What AI/ML experience exists in your organization today?

None — no AI experience on the team

Some — a few people have experimented with AI tools

Moderate — deployed basic AI/ML models or automations

Strong — active AI development team building AI-enabled products

How would you describe your team's overall comfort with AI-enabled tools and ways of working?

How engaged is your leadership in pursuing AI-enabled business improvements?

Training and upskilling budget for AI-enabled work

No budget allocated

Small — under $10K

Moderate — $10K–$50K

Significant — $50K+

Who is the business champion for the target project?

This is the person internally accountable for driving this initiative to completion.

Yes — a named owner is accountable for this project

Informal — someone is interested but not formally accountable

Not yet identified

Access to external AI expertise

Select all that apply.

Board member or advisor with AI background

External AI consultant or agency engaged

Vendor relationships only (software vendors)

No external AI expertise

Section 5 of 7

Process Deep-Dive

The quality of your recommendations depends on how specifically you can describe the business process you want to improve. The more detail you provide here, the more actionable your output.

Which business functions would benefit most from AI-enabled improvements?

Select all that apply.

Sales & Business Development

Customer Support

Marketing & Content

Finance & Accounting

HR & Recruiting

Operations & Supply Chain

Software Engineering / R&D

Legal & Compliance

Executive & Strategic Planning

What is your highest priority process to automate?

Our recommendations will be specific to your target process and target business objectives here.

Process name

Which team runs this process?

How many people are involved?

How often does it run?

Time per cycle (per person)

What triggers the process?

What are the inputs?

What are the outputs?

What goes wrong (failure modes)?

Do you have documented Standard Operating Procedures (SOPs)?

Yes — SOPs exist for most key processes

Partial — some processes are documented

No — processes are mostly tribal knowledge

How would you describe your current level of automation and tool leverage?

Think about the tools, systems, and workflows your team relies on today.

Fully manual — work is done by hand with minimal tool support

Basic tools — standard software (Excel, email) but no automation

Some automation — a few workflows automated, most still manual

Moderate automation — automation handles routine tasks in key areas

Highly automated — most workflows are systematized or tool-driven

Do you use any workflow automation tools?

No automation in use

Exploring but not deployed

Yes — Zapier / Make / n8n or similar

Yes — custom built automation

Section 6 of 7

Technical Readiness

How well your data, systems, and infrastructure can support AI-enabled improvements to your target process.

What tools and technology does your organization currently use?

Select all that apply.

PRODUCTIVITY & COMMUNICATION

Microsoft 365 (Word, Outlook, Teams)

Google Workspace (Docs, Gmail, Drive)

Slack

Zoom / Microsoft Teams (video)

CRM & SALES

Salesforce

HubSpot

Other CRM

FINANCE & OPERATIONS

QuickBooks / Xero

SAP

NetSuite

Asana / Monday.com / Jira

CUSTOMER & COMMERCE

Shopify

Zendesk / Freshdesk

DATA & INFRASTRUCTURE

Cloud data warehouse (Snowflake, BigQuery, Redshift, etc.)

Internal databases

On-premise servers

AI TOOLS IN CURRENT USE

ChatGPT (OpenAI)

Microsoft Copilot

Google Gemini

Claude (Anthropic)

GitHub Copilot

AI features built into existing software (Salesforce Einstein, HubSpot AI, etc.)

Image generation tools (Midjourney, DALL-E, etc.)

Custom or internally built AI tools

None — we don't use AI tools yet

OTHER

Microsoft Excel / Google Sheets (standalone)

Other tools not listed

Where does the data for your target process primarily live?

Select all that apply.

Cloud CRM (Salesforce, HubSpot, etc.)

Cloud ERP (SAP, NetSuite, Oracle, etc.)

Cloud data warehouse (Snowflake, BigQuery, etc.)

Internal databases

Spreadsheets / Excel / Google Sheets

File storage (SharePoint, Google Drive, etc.)

On-premise servers

Siloed across departments (no central store)

How would you describe the data available for your target process?

Data is scattered, incomplete, or hard to access

Data exists but is inconsistent or unreliable

Data is mostly reliable but not well-organized

Data is clean, consistent, and reasonably accessible

Data is well-governed, structured, and ready for analysis

Does the technology supporting your target process have API or data export capabilities?

Yes — most systems have accessible APIs

Partial — some APIs available, others locked

No — minimal API access

I'm not sure

How well are the systems supporting your target process connected to each other?

Siloed — systems don't talk to each other, all handoffs are manual

Minimal — one or two connections exist, most handoffs are manual

Partial — some systems are integrated, others require manual effort

Mostly connected — most data flows automatically between key systems

Fully integrated — systems share data seamlessly with minimal manual work

Does your organization handle sensitive or regulated data?

Select all that apply.

Personal data / PII

Healthcare data (HIPAA regulated)

Financial / payment data (PCI regulated)

EU resident data (GDPR regulated)

Intellectual property / trade secrets

No sensitive or regulated data

Cloud infrastructure maturity

Cloud-first — all major systems cloud-hosted

Hybrid — mix of cloud and on-premise

Primarily on-premise

Minimal IT infrastructure

Do you have a dedicated data or engineering team?

Yes — dedicated data/ML/engineering team

Partial — some technical resources, not dedicated

No — relying on external vendors or non-technical staff

Section 7 of 7

Governance & Risk

How your organization approaches risk, data protection, and vendor oversight. Many smaller organizations are just beginning to build these practices — this section helps us understand your starting point so we can build a practical plan.

How does your organization approach deploying new technology before all risks are fully understood?

We wait — we only move forward once risks are well-understood and mitigated

We're cautious — we prefer proven approaches with limited exposure

We're balanced — we accept some risk when the business case is clear

We're open — we'll move fast if the opportunity is compelling

We're aggressive — we move quickly and address issues as they arise

Have your vendor contracts been reviewed for AI data usage restrictions?

e.g. Restrictions on vendors using your data to train AI models.

Yes — contracts explicitly restrict AI data training use

Under review — legal team is currently auditing vendor agreements

No — this hasn't been addressed yet

I'm not sure

Technology Evaluation

Do you have established processes to review, qualify, decide, and implement software tools or products?

Formal — documented security and compliance checklist with sign-off required

Semi-formal — ad hoc review, no standard checklist, sign-off varies

Informal — teams evaluate and adopt tools with minimal central oversight

No formal evaluation process — tools are adopted as needed

How would you describe your organization's awareness of AI-related regulations and compliance requirements?

This may include data privacy laws, sector-specific regulations, or broader frameworks. It's okay if you're just getting started.

Not on our radar — we haven't looked into this

Aware but not acting — we know regulations exist but haven't assessed our exposure

Assessing — we're currently reviewing which regulations apply to us

Partially addressed — we've made progress but gaps remain

Fully addressed — we have documented controls in place and review them regularly

How mature is your approach to data protection in AI implementations?

No formal controls — AI tools can access most business data freely

Basic controls — some data classification, informal access rules

Defined policies — data classification and access controls are documented

Enforced controls — technical and procedural safeguards actively maintained

Privacy-by-design — data protection built into AI systems from the start

How does your organization protect proprietary information when using AI tools?

For example, do employees have guidance on what company data can be shared with external AI systems?

No policy — team members use AI tools without restrictions on what they share

Awareness only — employees know to be careful, but nothing is documented

Basic guidelines — informal rules exist about what can and can't be shared

Formal policy — a written policy covers AI tool usage and data sharing

Enforced controls — policy, employee training, and monitoring are all in place

Risk management approach for AI projects

How do you identify and manage risks specific to AI deployments?

No formal process — we move fast and address issues as they arise

General risk management — same framework used for any IT project

AI-specific checklist — bias, drift, and explainability reviewed before deployment

Continuous monitoring — post-deployment performance and risk tracking in place

Mature framework — NIST AI RMF or equivalent applied to all AI initiatives

Your organization's AI readiness, assessed across six critical dimensions.

Where should we send your results?

Your Company Profile

Goals & Strategy

Organizational Ability to Execute

People & Skills

Process Deep-Dive

Technical Readiness

Governance & Risk