AI Readiness Assessment
Evaluate your current infrastructure and digital maturity to determine AI readiness, identify quick wins, and prioritize next steps for implementation.
Objective
Evaluate your current infrastructure and digital maturity to determine AI readiness, identify quick wins, and prioritize next steps for implementation.
What's Included
Infrastructure & Cloud Audit
Multi-cloud, on-prem, and hybrid environment evaluation (AWS, Azure, GCP)
Data & Pipeline Review
Data lakehouse readiness, data access flows, latency issues
AI Gap Analysis
Identify maturity gaps in MLOps, tooling, and AI model lifecycle
Compliance Check
GDPR, NIS2, and upcoming AI Act alignment
Cost Optimization Plan
Review of GPU, storage, and compute usage for FinOps recommendations
What You Get
Maturity heatmap and risk matrix
Visual assessment of your current AI readiness across all infrastructure components.
Detailed gap analysis
Across 6 platform pillars: infra, data, MLOps, security, governance, compliance.
Actionable roadmap
Quick Wins (0–3 months), Mid-Term (3–6), Strategic (6–12).
Key metrics baseline
Cloud costs, model deployment time, infra utilization tracking.
Benefits
Cost Savings
Identify savings of up to 30% in AI cloud spending
Risk Reduction
Reduce risk exposure on privacy and compliance
Scale Faster
Build the foundation to scale AI initiatives faster
"I don't just do IT consulting."
I bring a productized assessment that has helped companies like yours reduce cloud costs and deploy models faster. Want a quick diagnostic to see if this is a priority for you?
Schedule Your Assessment CallFree AI readiness quick score
10 questions, 2 minutes, no email. See where you stand and which assessment tier fits.
Strategy
Executive sponsor identified for AI initiatives
Use cases prioritised by business impact AND feasibility
Data
Data inventory completed — you know what data you have and where it lives
PII and sensitive data classified and tagged
Infrastructure
CI/CD pipelines support model deployment (not just application code)
Monitoring covers ML-specific metrics (drift, latency, quality)
Governance
AI risk classification in place (aligned with EU AI Act risk tiers)
Regulatory mapping completed (AI Act, GDPR, DORA, NIS2 — which apply?)
People
ML engineering talent in place (not just data scientists)
AI literacy programme available for non-technical staff
0/10 answered — your score appears when all questions are answered.
AI Readiness Assessment Pricing
Fixed-fee, senior-led, no time-and-materials. Quoted after a 30-minute scoping call.
from €12k
Single platform · 2 weeks
- • Infrastructure audit (one platform)
- • AI gap analysis
- • 15-page report
- • 60-minute walkthrough
€18–22k
Multi-platform · 3–4 weeks
- • Multi-platform infrastructure audit
- • AI gap analysis & cost optimisation
- • DORA / GDPR / EU AI Act roadmap
- • 20-page report + 90-min walkthrough
€28–38k
Multi-BU · 4–6 weeks
- • Multi-business-unit assessment
- • Executive narrative & funding case
- • 12-month transition roadmap
- • 25-page report + exec readout
All prices excluding VAT. Travel and onsite workshops invoiced at cost.
Frequently Asked Questions
How much does an AI readiness assessment cost?
Fixed-fee from €12,000 for a focused single-platform review (2 weeks), €18,000–€22,000 for the standard multi-platform assessment (3–4 weeks), and €28,000–€38,000 for enterprise-wide multi-business-unit assessments (4–6 weeks). All prices exclude VAT and are quoted after a 30-minute scoping call. No time-and-materials.
What is the pricing model — fixed fee or hourly?
Always fixed fee. We quote a single price after the scoping call and stick to it. If scope changes mid-engagement, we re-quote in writing before any extra work starts. We don't bill for travel time, status meetings, or report revisions.
What does an AI assessment actually evaluate?
We assess five dimensions: data readiness (quality, accessibility, governance), infrastructure maturity (compute, networking, security), team capabilities (skills gaps, training needs), process alignment (how AI fits existing workflows), and compliance posture (GDPR, AI Act, industry regulations). The output is a prioritized roadmap, not just a report.
How is this different from a generic IT audit?
A generic IT audit checks boxes. An AI assessment evaluates your organization's specific readiness to deploy and operate AI workloads in production — including MLOps maturity, data pipeline architecture, model governance, and organizational change readiness.
What do we need to prepare before the assessment?
Minimal preparation is needed. We'll request access to your current architecture documentation, interview key stakeholders (typically 5-8 people across IT, data, and business teams), and review existing data pipelines. Most of the discovery happens during the engagement itself.
Continue Your AI Journey
Scoring AI use cases? →
The AI opportunity assessment prioritises use cases by value, risk and compliance fit.
Next Step: Platform Design →
Design a production-ready AI platform tailored to your needs.
See Case Studies →
Explore real-world platform engineering outcomes.
Learning Resources →
Books and courses on AI, DevOps, and automation.