AI Readiness & Data Assessment Services

Prepare Your Organization for AI Integration

We help assess your organization’s readiness for AI and advanced analytics through our comprehensive AI Readiness Assessments (AIRA) and Data Assessment Services.

We evaluate your organization’s data capacity to meet new and emerging requirements for advanced data analytics. Our assessments include identifying incomplete data coverage, conducting comprehensive reviews of datasets and metadata, assessing existing data-sharing portals, and addressing other critical factors essential for implementing innovative AI applications.

Assessment Framework

A Comprehensive Approach to Evaluating AI Readiness Across Six Key Dimensions

The assessment includes stakeholder interviews and delivers a detailed report featuring a scorecard and actionable recommendations. The AIRA evaluates readiness across six key dimensions:

The Assessment Process

A Step-by-Step Collaboration to Ensure a Successful Assessment

We work with your team to evaluate the readiness and capacity of your data for advanced analytics and AI. Through a structured and collaborative process, we ensure your data can meet current and future needs while addressing gaps and opportunities.

Our Process

1. Project Kickoff: We meet with your team to align on objectives, define the scope, and finalize the plan.
2. Interdisciplinary Collaboration: We work with subject matter experts (SMEs) from your team to understand the relevance of key data points to your business outcomes. This collaboration fosters mutual understanding between disciplines, enabling the development of effective, actionable solutions.
3. Data and Metadata Review: We review datasets through a data science lens, evaluating their completeness, quality, and relevance to AI workflows. Metadata is assessed for human- and machine-readability, focusing on fields, completeness, schema, and overall quality.
4. Data Readiness Assessment: We evaluate the usefulness and readiness of each dataset including data quality factors including dataset formats, file types, field types, and field formats, and identify gaps and opportunities for improving the available data to ensure suitability for your AI initiatives.
5. Delivery and Portals Assessment: We review data-sharing portals using FAIR principles (Findable, Accessible, Interoperable, Reusable).
6. Recommendations: We deliver prioritized, actionable recommendations to improve data quality, coverage, and metadata structure. The recommendations include methods for improving data fields and formats, data coverage, metadata schema adoption, and methods to acquire new datasets to augment your data catalogue.
7. Final Report: A comprehensive report summarizes findings and provides a prioritized roadmap for next steps. We present the results and ensure alignment on future actions.

This collaborative approach ensures that both technical experts and business leaders are aligned, enabling solutions that address real-world challenges and support innovation.