AI Readiness
Assess. Align. Accelerate—so your first (or next) AI initiative lands ROI, not technical debt.
Market Proof
78 %
Orgs using AI in ≥ 1 function (↑ from 55 % last year)
McKinsey & Company
$184 B
Worldwide AI spend 2024; forecast $526 B by 2027
IDC
8 %
Only 8 % of firms have scaled AI across business
Gartner AI Maturity 2024
38 %
Skills shortage is #1 barrier to adoption
IBM Newsroom
25 %
Data complexity blocks AI roll‑outs
IBM Newsroom
70 %
Pilots never reach production due to readiness gaps
MIT Sloan Review
Key Benefits
De‑Risk Investment
Align AI goals to value pools and data reality.
Data Confidence
Quality, lineage, and governance pipelines ready for ML.
Talent Blueprint
Upskill map + hiring plan to close AI skills gap.
Process Fit
MLOps & model‑governance baked into SDLC.
Responsible AI
Bias, privacy, and ethics frameworks from day one.
Faster Time‑to‑Impact
Clear roadmap lets you jump from POC to production 50 % faster.
Services & Solutions
Readiness Assessment
data, infra, org, governance scorecards
Value‑Pool & Use‑Case Prioritization
ROI matrix, quick‑win backlog
Data Foundation
pipelines, feature stores, quality & lineage tooling
MLOps & Platform Blueprint
model registry, CI/CD, monitoring, incident run‑books
Talent & Operating Model
CoE setup, squad archetypes, reskill pathways
Responsible‑AI Framework
bias tests, privacy guard‑rails, policy as code
Success Stories
Retail
Fragmented data, stalled POCs
6‑wk readiness → recommender live in 4 mo, sales +12 %
Bank
Skills gap stalled gen‑AI chatbot
Talent roadmap + MLOps stack → pilot to prod in 60 days
Healthcare
PHI governance concerns
Responsible‑AI framework → FDA‑ready model, breach‑risk ‑35 %
Industry Use-Cases
Financial Services
credit‑risk scoring, AML anomaly detection
Healthcare
imaging triage, personalized treatment paths
Retail & DTC
demand forecasting, dynamic pricing, gen‑AI copy
Manufacturing
predictive maintenance, quality vision systems
Energy & Utilities
load forecasting, grid anomaly alerts
Logistics
route optimization, real‑time ETA, drone vision
Telecom
churn models, network fault prediction
Public Sector
benefits fraud detection, document summarization
Insurance
straight‑through claims, telematics risk scores
Media & Streaming
content recommendation, ad‑inventory pricing
Travel & Hospitality
demand‑based room pricing, voice assistants
EdTech
adaptive learning paths, plagiarism LLM detectors
Engagement Models
Readiness Sprint (4 wks)
AI Blueprint Program (10–12 wks)
Fractional Head of AI
AI Center‑of‑Excellence Setup
Delivery Accelerators
AI Maturity Scanner: 60‑item survey + data‑profiling → score vs. peers
Use‑Case Catalog: 150 + proven AI patterns by industry
Data Health Dashboard: quality, drift, missingness KPIs
Responsible‑AI Policy Pack: OPA rules, bias test suite, model cards
Evidence & Quality
ROI business‑case for each shortlisted use‑case
Data‑quality and lineage reports (Great Expectations, OpenLineage)
Model risk & bias assessment docs (model cards, factsheets)
Executive‑ready roadmap with 30‑60‑90 day milestones
Tooling Ecosystem
Data
Snowflake, BigQuery, Databricks
MLOps
MLflow, KubeFlow, Vertex AI, SageMaker
Governance
Great Expectations, Feast, OpenLineage, OPA
Collaboration
Jira Align, Confluence, Slack, Miro
Certifications & Partnerships
What We Know
AI Readiness Guild — data scientists, architects, and change‑pros who dissect every new LLM, tooling shift, and regulation (EU AI Act) weekly, updating readiness scorecards.
MLOps Sandbox — monthly hack‑weeks pressure‑test fresh stacks (LangChain Agents, Kubeflow 2, Vector DB benchmarks) before client recommendations.
Modern AI Readiness Stack
Data
Snowflake, BigQuery, Delta Lake, Kafka + Debezium
MLOps
MLflow, SageMaker Pipelines, Vertex AI, Feast
Governance
OpenLineage, DataHub, Great Expectations, OPA Policies
Responsible AI
Fairlearn, IBM AI Fairness 360, Model Cards Toolkit
Readiness today means success tomorrow—build the foundation before the models.
Ready to Prime Your Org for AI Success?
Book a 30‑minute AI readiness consult and turn hype into a concrete roadmap.
Book Your Consultation →FAQ
Why an “AI readiness” project before a pilot?
How long does a readiness assessment take?
What data quality do we need?
Skills gap? Build or buy?
Which cloud or stack?
How do you ensure Responsible AI?
Can we start with Gen‑AI?
What about data privacy?
How is ROI tracked?
Kick‑off timeline?