Evangelos Bolofis | The AI Intersection

The Business Strategy Guide

The Business Leader’s Guide to Implementing an AI Strategy (That Actually Works)

By Evangelos Bolofis AI Expert at Cognizant

Business AI Strategy
An AI Strategy is a Data Strategy

Artificial Intelligence is the most powerful business transformation tool of our generation. But for most companies, it remains a nebulous buzzword. They buy expensive software, hire a data scientist they don’t know how to manage, and see zero tangible results.

Why?

Because they treat AI as a technology problem.

It’s not. It’s a business and data strategy problem.

This is the ebolofis.ai framework for designing and implementing an AI strategy that delivers real, measurable value.

Phase 1: Stop Asking “What Can We Do With AI?”

This is the single biggest mistake leaders make. It leads to aimless, expensive “AI for AI’s sake” projects.

The right question is: “What are our three biggest business challenges or opportunities?”

Are you struggling with customer churn? Are your marketing acquisition costs too high? Is your supply chain inefficient? Start with the business pain point, not the shiny tech solution.

Phase 2: Identify the Right AI Use Case (Find the “Low-Hanging Fruit”)

Once you have your business problems, find the one that is best suited for an AI solution. A great first project has three key characteristics:

  1. High-Quality Data Exists: AI is fueled by data. To predict customer churn, you need clean, historical data on customer behavior. If you don’t have the right data, your AI project is dead on arrival.
  2. A Clear Metric for Success: How will you know if the project works? Define the Key Performance Indicator (KPI) upfront. Examples: “Reduce customer service response times by 30%” or “Increase marketing lead conversion rate by 10%.”
  3. It’s an Augmentation, Not a Full Replacement: Choose a project that helps your employees, not one that aims to replace them. An AI tool that helps customer service reps find answers faster is a much better first project than one that tries to automate their entire job. This ensures buy-in and reduces internal friction.

Phase 3: The Pilot Project – Start Small, Learn Fast

Do not try to build a massive, company-wide AI system from day one. Start with a small, focused pilot project that lasts 6-12 weeks.

  • Assemble a “Tiger Team”: Get one person from the business side (who owns the problem), one from tech (who can build or integrate the solution), and one from data (who can prepare the data).
  • Use Off-the-Shelf Tools First: Before you build a custom model, ask: Can you solve 80% of the problem with a tool like Julius AI for analysis or by using an API from OpenAI?
  • Focus on the Workflow: The goal isn’t just to build a model; it’s to integrate it into how people actually work.

Phase 4: Measure, Iterate, and Scale

After the pilot, measure your results against the KPI you defined in Phase 2. Did it work? If yes, you now have a data-backed internal case study to get a bigger budget and scale the solution. If it failed, you learned a valuable lesson with minimal time and investment.

Bottom Line: An AI strategy isn’t a 50-page document that sits on a shelf. It’s a disciplined, iterative process of identifying business problems, running small experiments, and scaling what works. Stop chasing the hype and start solving real problems.

That is how you win with AI.