Services
Advanced Analytics
Before you invest in AI, understand where you stand. We assess your data maturity, identify the highest-impact opportunities, and build a practical adoption roadmap — not a theoretical one.
Key Takeaways
An AI readiness assessment evaluates your organization's data quality, infrastructure, team skills, and use cases to determine where AI can deliver real value. Most mid-market companies have 3-5 high-impact AI opportunities they haven't identified yet.
- Comprehensive assessment of data quality, infrastructure, and team readiness
- Prioritized AI opportunity roadmap ranked by ROI and implementation difficulty
- Honest evaluation — we'll tell you if you're not ready and what to fix first
- Vendor-neutral recommendations that fit your existing tech stack
- Clear business cases with projected ROI for leadership buy-in
Know where you stand before you invest
Every organization considering AI or advanced analytics should start with a clear picture of their readiness. An AI readiness assessment isn't a sales exercise — it's a structured evaluation that tells you honestly where you stand and what needs to happen before you invest.
The assessment answers three questions:
- ✓Can we do it? Do you have the data quality, infrastructure, and accessibility to support advanced analytics or AI? If not, what needs to change?
- ✓Should we do it? Is the proposed use case viable given your data and your business context? Will the model deliver enough value to justify the investment?
- ✓What comes first? If there are prerequisites — data quality improvements, infrastructure upgrades, governance gaps — what's the right sequence?
The result is a prioritized roadmap: quick wins you can pursue now, foundational investments that unlock future capabilities, and a clear sequence for getting there.
What we assess
Data Maturity
Quality, completeness, accessibility, and documentation of your current data assets. We evaluate whether your data can support the analytical workloads you're planning.
Technology Infrastructure
Cloud readiness, compute capacity, pipeline automation, and integration capabilities. Can your current stack handle advanced analytics workloads?
Organizational Readiness
Executive sponsorship, change management capacity, data literacy, and team structure. Technology alone doesn't drive adoption — people and process matter equally.
Use Case Viability
Is the proposed AI or analytics application feasible given your data? Will it deliver measurable business value? We evaluate feasibility before you spend on development.
Statistical & Predictive Analysis
Beyond readiness assessment, we build predictive models, statistical analyses, and advanced analytics solutions — turning validated use cases into production capabilities.
What you get
A typical AI readiness assessment runs 2-4 weeks and delivers:
- ✓Current-state scorecard. A structured rating of your data maturity, infrastructure, organizational readiness, and use case viability — with specific evidence behind each score.
- ✓Gap analysis. A clear list of what needs to change before you can pursue advanced analytics or AI — data quality issues, infrastructure gaps, governance needs, or skill gaps.
- ✓Use case evaluation. An honest assessment of your proposed AI use cases: which ones are feasible now, which ones need prerequisites, and which ones aren't viable given your current state.
- ✓Prioritized roadmap. A phased plan showing what to tackle first, what to defer, and what each phase delivers. Quick wins alongside longer-term investments.
The roadmap isn't a slide deck — it's an actionable plan with specific next steps, timelines, and resource requirements.
Frequently Asked Questions
How long does an AI readiness assessment take?
Typically 2-4 weeks. Week 1 focuses on stakeholder interviews and data landscape discovery. Weeks 2-3 involve technical assessment and use case evaluation. The final week delivers the scorecard, gap analysis, and prioritized roadmap.
What if we're not ready for AI?
That's a perfectly valid outcome — and a valuable one. The assessment will tell you exactly what needs to happen first: data quality improvements, infrastructure upgrades, governance foundations. Most organizations that aren't ready for AI are 3-6 months of foundational work away, not years.
Do you help implement the recommendations?
Yes. Many clients move from the assessment into a data engineering or data strategy engagement to address the gaps we identified. Others transition into a Data Team as a Service model for ongoing implementation. The assessment gives you the roadmap; we can help execute it.
What data do you need from us?
We'll need access to your key data systems (or representative samples), documentation of existing data processes, and time with the stakeholders who manage and consume data. We handle the analysis — we just need access and context.
Is this the same as a data audit?
Related but different. A data audit is broader — it evaluates your entire data stack, tools, processes, and costs. An AI readiness assessment is focused specifically on whether your organization can successfully deploy advanced analytics and AI. If you need the broader view, our Data & Automation Audit service covers that.
Learn more about AI readiness
How to Know If Your Organization Is Ready for AI — five signs you're ready and five signs you're not.
3 Things to Consider Before Investing in AI — practical guidance for evaluating AI investments.
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Assess Your AI Readiness
Start with a clear picture of where you stand — and what it takes to get where you want to go.