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iteriaAI Strategy. From Vision to Value.
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iteriaAI Strategy. From Vision to Value.

AI Readiness Assessment

Are You Ready for AI — Really?

Many organizations are eager to invest in AI, but few have a clear understanding of whether they are actually ready to succeed with it.

Start Your Assessment

"The iteria AI Readiness Assessment helps leadership teams move past assumptions and get an objective view of where they stand — and what to do next."

This is often the fastest way to replace uncertainty with clarity.

The Iteria AI Readiness Assessment helps you:

Audit your Oracle environment for AI opportunities

Comprehensive review of your data landscape and infrastructure readiness

Identify 3-5 high-ROI use cases

Prioritized opportunities ranked by business value and feasibility

Build a pragmatic roadmap with clear timeline

12-24 month execution plan with phased approach and milestones

What AI Readiness Means

AI readiness is not about having the latest tools. It is about having the foundations in place to turn AI initiatives into real business outcomes.

Business & Strategy

Are AI efforts clearly tied to business priorities and outcomes?

Data

Is your data accessible, reliable, and usable for AI applications?

Processes

Are workflows defined and stable enough to benefit from automation?

People & Talent

Do teams have the skills and incentives to adopt AI?

Governance & Risk

Are security, compliance, and trust built in from the start?

Readiness gaps in any one of these areas can stall even well-funded AI initiatives.

What We Assess

Our assessment covers four critical areas to ensure your organization is truly ready for AI implementation.

Data Readiness

Is your Oracle data accessible, structured, and tagged for AI?

Technologies we evaluate:

Oracle DB, Autonomous DB, Cloud ERP, APEX, etc.

Technology Fit

Which AI technologies match your use cases?

Technologies we evaluate:

OpenAI, Claude, Grok, Azure AI, AWS, Google Cloud, etc.

Infrastructure Readiness

Can your systems support AI workloads?

What we evaluate:

Oracle Cloud, multi-cloud setups, on-prem constraints, etc.

Team & Skills

Do you have the right people and processes?

What we evaluate:

Internal capabilities, training needs, governance readiness, etc.

How the Assessment Works

1

Stakeholder interviews across business, IT, and operations

2

Review of existing data, processes, and AI initiatives

3

Identification of strengths, gaps, and risks

4

Alignment on what success should look like

The process is collaborative and grounded in real operational context — not theoretical maturity models.

What You Receive

At the conclusion of the assessment, leadership teams receive:

  • A clear snapshot of current AI readiness
  • Identified gaps and risks that could limit ROI
  • A prioritized set of AI opportunities
  • Strategic recommendations for next steps

The output is executive-ready and designed to support informed decision-making.

How This Fits Into Your AI Strategy

For many clients, the Readiness Assessment is the first step in a broader AI strategy. It provides the foundation for:

Prioritizing high-impact use cases
Building a realistic roadmap
Designing governance models
Reducing risk before investment

When combined with iteria's AI Strategy approach, it ensures effort is focused where it will matter most.

Who This Is For

Executives

Evaluating whether and how to invest in AI

Stuck Teams

Organizations stuck in pilots with no path to scale

Leaders

Seeking alignment across business, IT, and operations

ROI Seekers

Teams that want returns, not experimentation

Frequently Asked Questions

Common questions about the assessment process and outcomes.

Scope & Timeline

The assessment looks at five areas of AI readiness: business strategy and use cases, data quality and coverage, data pipelines and integrations, infrastructure for AI workloads, and governance/security/compliance. It focuses on what is required to move from ideas and pilots to AI that is reliable, scalable, and safe in production.
The assessment looks at five areas of AI readiness: business strategy and use cases, data quality and coverage, data pipelines and integrations, infrastructure for AI workloads, and governance/security/compliance. It focuses on what is required to move from ideas and pilots to AI that is reliable, scalable, and safe in production.
The assessment looks at five areas of AI readiness: business strategy and use cases, data quality and coverage, data pipelines and integrations, infrastructure for AI workloads, and governance/security/compliance. It focuses on what is required to move from ideas and pilots to AI that is reliable, scalable, and safe in production.

Use Cases & Business Value

The assessment looks at five areas of AI readiness: business strategy and use cases, data quality and coverage, data pipelines and integrations, infrastructure for AI workloads, and governance/security/compliance. It focuses on what is required to move from ideas and pilots to AI that is reliable, scalable, and safe in production.
The assessment looks at five areas of AI readiness: business strategy and use cases, data quality and coverage, data pipelines and integrations, infrastructure for AI workloads, and governance/security/compliance. It focuses on what is required to move from ideas and pilots to AI that is reliable, scalable, and safe in production.

Data, Pipelines & Architecture

The assessment looks at five areas of AI readiness: business strategy and use cases, data quality and coverage, data pipelines and integrations, infrastructure for AI workloads, and governance/security/compliance. It focuses on what is required to move from ideas and pilots to AI that is reliable, scalable, and safe in production.
The assessment looks at five areas of AI readiness: business strategy and use cases, data quality and coverage, data pipelines and integrations, infrastructure for AI workloads, and governance/security/compliance. It focuses on what is required to move from ideas and pilots to AI that is reliable, scalable, and safe in production.

Governance, Security & Risk

The assessment looks at five areas of AI readiness: business strategy and use cases, data quality and coverage, data pipelines and integrations, infrastructure for AI workloads, and governance/security/compliance. It focuses on what is required to move from ideas and pilots to AI that is reliable, scalable, and safe in production.
The assessment looks at five areas of AI readiness: business strategy and use cases, data quality and coverage, data pipelines and integrations, infrastructure for AI workloads, and governance/security/compliance. It focuses on what is required to move from ideas and pilots to AI that is reliable, scalable, and safe in production.

Relationship to Other Projects

The assessment looks at five areas of AI readiness: business strategy and use cases, data quality and coverage, data pipelines and integrations, infrastructure for AI workloads, and governance/security/compliance. It focuses on what is required to move from ideas and pilots to AI that is reliable, scalable, and safe in production.
The assessment looks at five areas of AI readiness: business strategy and use cases, data quality and coverage, data pipelines and integrations, infrastructure for AI workloads, and governance/security/compliance. It focuses on what is required to move from ideas and pilots to AI that is reliable, scalable, and safe in production.

Start an AI Readiness Assessment

If you’re considering AI, questioning current initiatives, or looking for a clear path forward, we should talk.

To start, email us at:

Briefly include your role, industry, and the AI challenges you’re facing, and we’ll take it from there.

Clarity comes before confidence.

Readiness comes before results.