AI Readiness Assessment & Enterprise AI Adoption Operating Model

An AI readiness assessment evaluates an organization's preparedness to adopt and scale artificial intelligence across six dimensions: technology infrastructure, people and skills, processes and workflows, governance frameworks, data assets, and organizational culture.

Human Agency conducts AI readiness assessments for enterprises to identify where they stand, where the gaps are, and what it takes to move forward with confidence rather than guesswork.

Why AI Readiness Matters

Most enterprises don't fail at AI because of bad technology. They fail because they don't know where they actually are.

A company that thinks it's ready for AI agents when it hasn't solved basic data governance will waste months and millions before discovering the foundation isn't there. A company that thinks it needs another year of planning when it already has the infrastructure for quick wins will watch competitors pull ahead.

The gap between "we're doing AI" and actually being ready is where most enterprise AI investment gets wasted. A rigorous assessment closes that gap.

The Six Dimensions of AI Readiness

Human Agency assesses AI readiness across six dimensions. Weakness in any one of them creates a bottleneck that no amount of investment in the others can fix.‍

1. Technology

What we assess‍

• Infrastructure
• Compute
• Integration capability
• Existing AI tools

‍Common gaps‍

• Legacy systems that can't integrate
• Insufficient compute for scale

2. People

What we assess‍

• AI literacy across roles
• Technical talent
• Leadership understanding

‍Common gaps‍

• Executive team can't evaluate AI strategy
• Workforce lacks basic AI skills

3. Process

What we assess‍

• Workflow documentation
• Automation readiness
• Change management

‍Common gaps‍

• Processes aren't documented well enough for AI to augment
• No change management capacity

4. Governance

‍What we assess‍

• Policies
• Risk frameworks
• Compliance readiness
• Oversight structures

‍Common gaps‍

• No AI-specific governance
• Compliance team excluded from AI decisions

5. Data

‍What we assess‍

• Quality
• Accessibility
• Security
• Labeling
• Organizational data culture

‍Common gaps‍

• Data is siloed, unstructured, or too messy for AI to use reliably

6. Culture

‍‍What we assess‍

• Openness to change
• Trust in technology
• Innovation history
• Leadership buy-in

‍Common gaps‍

• Workforce fears AI as a threat
• Leadership sees AI as IT's problem

The Five Stages of AI Readiness

After assessing the six dimensions, we place the organization on a five-stage maturity model. This framework tells you where you are, what "good" looks like at your current stage, and what it takes to move to the next one.

Stage 1: Awareness

The organization knows AI exists and sees competitors using it, but hasn't started in any meaningful way.

• Characteristics: No AI tools in production. Conversations about AI happen at the leadership level but haven't translated into action. Data infrastructure was built for reporting, not AI.
• What to do here: Education and AI literacy for leadership. Assessment of data assets. Identification of 2-3 high-value, low-risk use cases.
• Biggest risk: Analysis paralysis. Organizations at this stage often study AI for months without shipping anything.

Stage 2: Experimentation

Individuals and small teams are using AI tools — ChatGPT, Copilot, department-specific solutions — but there's no coordination, no governance, and no organizational strategy.

• Characteristics: Shadow AI usage is common. Some teams see results; others are experimenting without clear goals. No policies around AI use, data sharing, or vendor selection.
• What to do here: Inventory existing AI usage. Establish basic governance guardrails. Identify which experiments are producing results and formalize them. Build a small AI team or embed external engineers.
• Biggest risk: Ungoverned experimentation that creates security vulnerabilities or compliance issues before anyone notices.

Stage 3: Integration

AI is embedded in some workflows with organizational support. Governance is emerging. Some teams use AI as a regular part of their work, but it hasn't spread across the organization.

• Characteristics: 2-5 AI-powered workflows in production. A small team manages AI deployments. Basic policies exist. Data infrastructure is partially ready for scale.
• What to do here: Scale what's working. Build a repeatable deployment process. Invest in enablement so more teams can adopt independently. Strengthen governance for higher-risk use cases.
• Biggest risk: Staying comfortable. Organizations at Stage 3 often plateau because the early wins feel sufficient.

Stage 4: Optimization

AI is an operational capability, measured and governed across the organization. Multiple teams use AI fluently. The organization has processes for evaluating, deploying, and monitoring AI tools.

• Characteristics: AI is in the budget, the org chart, and the strategic plan. Governance is mature. Data infrastructure supports AI at scale. Adoption is broad, not just in tech-forward departments.
• What to do here: Optimize for ROI. Build custom AI assistants tailored to your institutional knowledge. Explore AI agents for more autonomous workflows. Benchmark against industry leaders.
• Biggest risk: Complacency. The operational model is working, which can reduce urgency to innovate.

Stage 5: Transformation

AI fundamentally shapes how the organization operates. New roles, new business models, new capabilities that weren't possible before AI. People and AI are deeply collaborative.

• Characteristics: AI influences strategic decisions, not just operational ones. The organization creates AI-driven products or services. Competitive advantage is tied to AI capability.
• What to do here: Lead your industry. Share what you've learned. Continue investing in people — transformation is a moving target, not a destination.
• Biggest risk: Losing the human element. Organizations that optimize entirely for AI efficiency risk the same trap as the automation-first approach: technology serving itself rather than the people it was built for.

The AI Adoption Operating Model

Knowing your stage is step one. Moving to the next stage requires an operating model — a repeatable system for making progress.

Human Agency's adoption operating model follows six phases:

1. Assess: Conduct the six-dimension readiness evaluation. Identify current stage. Map the specific gaps between current state and target state.
2. Roadmap: Build a 90-day plan (not a 12-month fantasy). Prioritize by impact and feasibility. Identify quick wins that build organizational confidence alongside longer-term strategic investments.
3. Quick Wins: Ship 1-3 high-visibility, low-risk AI deployments within the first 30-60 days. These create momentum, demonstrate value, and give the organization proof that AI works in their context.
4. Scale: Take what worked in quick wins and extend it. Build repeatable processes for AI deployment. Train more teams. Expand governance to cover new use cases.
5. Govern: Formalize policies, monitoring, and oversight. Ensure compliance with relevant frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001). Build internal capability to manage AI as a permanent operational function.
6. Optimize: Measure outcomes against goals. Identify underperforming deployments and fix or sunset them. Continuously assess new AI capabilities against organizational needs.

This isn't a one-time journey. The model cycles — each optimization phase feeds back into a new assessment as technology and organizational needs evolve.

How Human Agency Conducts Assessments

Our assessment process takes 2-4 weeks depending on organization size and combines quantitative analysis with deep qualitative understanding:

• Stakeholder interviews across leadership, management, and individual contributor levels
• Technology audit of existing infrastructure, data assets, and integration capability
• Process mapping of high-value workflows that are candidates for AI augmentation
• Governance review of existing policies, compliance posture, and risk managemen
• Culture survey measuring AI sentiment, change readiness, and trust levels
• Competitive benchmarking against peers and industry leaders

Frequently Asked Questions

How do we know what stage of AI readiness we're in?

Look at three signals: Are AI tools in production (not just being tested)? Is there organizational governance around AI use? Do people across the organization — not just the tech team — use AI in their daily work? If the answer to all three is no, you're likely at Stage 1 or 2. If some are yes, you're at Stage 3. If all are yes and AI is measured as an operational capability, you're at Stage 4 or beyond. A formal assessment provides a precise picture across all six dimensions.

What does an AI readiness assessment involve?

It's a 2-4 week process that evaluates your organization across six dimensions: technology, people, process, governance, data, and culture. It includes stakeholder interviews at multiple levels, a technology and data audit, process mapping of high-value workflows, a governance review, and a culture survey. The deliverable is a readiness scorecard, gap analysis, and a prioritized 90-day action plan — not a generic report.

How long does it take to move from experimentation to integration?

For most organizations, 3-6 months with focused effort. The key accelerators are: executive sponsorship (not just buy-in), basic governance guardrails in place, at least one quick win in production that demonstrates value, and an AI literacy program that gives people beyond the tech team the skills to participate. The key blocker is trying to be comprehensive too soon — it's faster to go deep on 2-3 use cases than shallow on 10.

Should we assess before we start using AI, or after?

Both work, but the assessment serves different purposes. Before adoption, it prevents wasted investment by identifying gaps early. After initial experimentation, it provides structure — it takes the experiments that are working and builds a path to scale them, while identifying risks in the experiments that aren't governed. If your organization is already at Stage 2 (experimentation), an assessment is the fastest way to move to Stage 3.