AI for Executives
- Majid Rizvi
- Feb 4, 2025
- 4 min read
Updated: Feb 11, 2025
February 4, 2025
In my recent discussions with a senior leader at a Pharma company, our conversation pivoted to AI, and I was asked to provide my thoughts on what is AI and its role and strategic potential. Initial thought that went through my mind was, “please narrow your ask” and “how much time do you have”. In all seriousness, I shared my understanding at a high-level and AI's potential and referenced some Use Cases.
This would be a good opportunity for me to share a high level view of what is AI (Understanding) and next steps to consider for executives who would and should embark on this journey.
Artificial Intelligence (AI) is transforming industries and providing tremendous opportunities in reshaping the business landscape. However, for many executives, AI remains a buzzword surrounded by hype and uncertainty as its origins are very technical in nature and few know how it can be leveraged in their space successfully. Typically it is. a bottoms up approach and executives are educated from their technical developers or by monitoring competition (keep in mind, it is more than technology implementation). Understanding what AI truly is, how it can be leveraged to further the strategic goals, and the right way to approach its implementation is critical for business leaders looking to stay competitive in an increasingly digital world.
Understanding AI: Beyond the Buzzword: At its core, AI refers to the ability of machines to perform tasks that typically require human intelligence. This includes learning from (historical) )data, recognizing patterns (trends), making decisions, and even understanding language. AI can be categorized into several key areas:
Machine Learning (ML): Algorithms that learn from data to make predictions or decisions. e.g. next best action.
Natural Language Processing (NLP): The ability of machines to understand and process human language. e.g. automated call centers.
Computer Vision: AI that can analyze and interpret visual information. e.g. Tesla self drive capability.
Automation & Robotics: AI-powered systems that can perform repetitive tasks efficiently. e.g. Amazon's warehouse robots.
How Executives Should Think About AI: AI should be seen as an enabler rather than a replacement. Instead of focusing on AI as a standalone technology, executives should consider it as a strategic tool to enhance business processes, improve efficiency, and unlock new opportunities. Key considerations include:
AI as a Competitive Advantage: Companies leveraging AI can make faster data-driven decisions, personalize customer experiences, and optimize operations.
AI for Efficiency & Cost Reduction: Automating repetitive tasks can free up employees to focus on higher-value work, reducing operational costs.
AI for Innovation & Growth: AI-driven insights can help identify new market opportunities, predict trends, and enhance product development.
Practical AI Use Cases for Businesses
Executives should focus on AI applications that align with their business goals. Some common use cases include:
Customer Service & Chatbots: AI-powered virtual assistants improve response times and customer satisfaction.
Predictive Analytics: AI helps forecast market trends, customer behavior, and business risks.
Fraud Detection & Security: AI enhances cybersecurity by identifying anomalies and preventing fraudulent activities.
Supply Chain Optimization: AI can predict demand, streamline logistics, and reduce waste.
Getting Started with AI: A Strategic Approach
For executives looking to integrate AI, a structured approach is key:
Define Clear Objectives: Look at company's strategic mandates and goals for 2025, then identify where AI can be leveraged to reach your goals (if applicable). As you develop the roadmap, you should have two options laid out, without AI and with the help of AI. These steps make the decision making process a lot easier as you get to see cost, timelines and risks clearly articulated.
Assess Data Readiness: AI thrives on quality data. Ensure your organization has a robust data strategy.
Start Small, Scale Gradually: Begin with pilot projects to test AI’s impact before full-scale implementation.
Invest in Talent & Partnerships: Hiring AI talent or collaborating with AI vendors can accelerate adoption.
Monitor & Adapt: AI is an evolving technology. Continuous evaluation and adjustment ensure long-term success.

