Data Science
Artificial Intelligence (AI) for results. From Machine Learning to Generative AI to Agentic AI.

Around 46% of AI projects are scrapped before they reach production. The most common reasons are cost, security and lack of results. A thorough conception phase of an AI project is critical in mitigating these risks before they eat up resources.
What are your objectives and key results (OKRs)?
Do you know when to use require agentic AI (AAI), generative AI (GenAI) or machine learning (ML) – and how they fit together?
Do you have a plan for data and AI governance and cost containment? Does your project require agentic AI (AAI), generative AI (GenAI) or machine learning (ML)?
How do you know your data is “ready” for AI?
Some examples of our clients’ successes:
Transportation: Identify which fleet assets you should replace before maintenance and operational costs eat into service levels – and profits.
Construction: Forecast invoice payments to project your cashflow with accuracy and confidence. Using this information you can optimize your working capital.
Engineering & Design: Estimate labor and materials cost for commercial general contracting projects more accurately. Win more bids, reduce waste, and increase profitability.
Banking & Credit Unions: Create next best offer models for your customers that increase customer satisfaction, loyalty, and retention.
Not-for-Profit: Automate research and data entry to keep your limited resources focused on more valuable activities.
AI Readiness Workshop Discussion Example Topics:
Changing from KPI mindset to predictive targeting.
AAI, GenAI and ML – which is right for your vision?
The risks of the AI goldrush.
Do you have the right data environment to support machine learning?
Data issues to avoid and how to mitigate the risks.
What skill sets do you need?
How do you plan and budget for a machine learning project?
Additional Resources
More than trusted data analysts: the ultimate ally in your business armory.