AI for the Busy Executive
What Every Business Leader Needs to Know

Since the release of OpenAI’s ChatGPT in November 2022, Artificial Intelligence (AI) has captivated global audiences. Terms like “Generative AI (GenAI)” and “Large Language Models (LLMs)” have become prevalent in business circles, with promises of improving productivity, optimizing operations, fostering creativity, and reducing costs.

However, as an AI consultant and educator, I’ve noticed a significant gap in understanding these tools among technical and non-technical leaders. Even seasoned professionals such as CTOs, medical experts, senior legal partners, and educators often mischaracterize GenAI as a sophisticated search engine or a magical fountain of knowledge.

This misunderstanding often leads to frustration. Leaders experimenting with tools like OpenAI’s ChatGPT, Anthropic’s Claude, or Google’s Gemini enter search-style queries, receive unexpected responses, and dismiss the technology as flawed or irrelevant. Yet, this view underestimates AI’s transformative potential. AI is reshaping industries, but for many executives, its complexity can seem intimidating, especially when contrasted with traditional deterministic IT systems.

The Transition to Probabilistic IT Systems

For decades, businesses relied on deterministic IT systems, where the same input always produces the same output. AI, especially GenAI models, operates probabilistically, generating a range of potential outputs for a given input. While probabilistic systems are familiar in areas like financial modeling, risk assessment, and weather forecasting, they can feel foreign to industries accustomed to predictability.

Adopting AI demands more than technical integration. It requires bridging probabilistic AI with deterministic systems and fostering a cultural shift to embrace this dynamic technology. To unlock AI’s potential, executives must understand this new paradigm.

Patterns: The Core of Machine Learning (ML)

The origins of AI date back to the 1940s, when researchers sought to understand the brain’s functions using mathematical models. This exploration gave rise to Machine Learning (ML), a branch of AI focused on identifying patterns in data and making predictions.

As Norbert Wiener, a pioneer in mathematics and computer science, noted, “One of the most interesting aspects of the world is that it can be considered to be made of patterns.” Patterns underpin business processes, natural phenomena, sports, music, and more. ML leverages pattern recognition to analyze data, predict outcomes, and generate new data, enabling advancements in fraud detection, personalized recommendations, and predictive analytics.

Understanding patterns is essential for deploying ML effectively. If a business problem lacks patterns, AI is likely not the solution.

Predictive AI (PredAI) vs. Generative AI (GenAI)

Most companies are familiar with business intelligence tools, which analyze patterns in data to identify trends. Predictive AI (PredAI) extends this capability by forecasting future events and optimizing operations. For example, major e-commerce platforms like Amazon, Alibaba, and Spotify rely on PredAI to drive sales and personalization, making it a cornerstone of global AI-driven revenue.

Generative AI (GenAI), on the other hand, creates entirely new content based on patterns in data. It can generate text, images, sounds, and videos with remarkable realism. This breakthrough, enabled by the Transformer model introduced by Google in 2017, has opened new opportunities for creative and strategic innovation.

GenAI streamlines content creation, automates customer support, enhances product design, and facilitates training simulations. Companies adopting GenAI report transformative improvements in efficiency, creativity, and engagement. Leaders must understand the distinct roles of PredAI and GenAI. While PredAI focuses on optimization, GenAI drives innovation. Combining both technologies—for example, using PredAI to forecast trends and GenAI to develop strategies—can yield powerful synergies.

Large Language Models: The Foundation of GenAI

Large Language Models (LLMs) are the engines behind GenAI. Trained on vast amounts of internet data, LLMs generate text based on probabilistic predictions. They excel in brainstorming, prototyping, translation, and summarization.

However, LLMs are not search engines. Unlike search tools that retrieve factual data, LLMs produce content based on patterns learned during training, reflecting probabilities rather than guaranteed accuracy. While they can generate creative and contextually relevant content, they may sometimes lack precision or reliability.

Organizations can pair LLMs with search engines or databases to combine creativity with accuracy, ensuring both innovation and factual correctness.

LLMs as Meta-Tools

A good way to decide LLM use cases in a company is to consider an LLM as a meta-tool, that is, a bridge that optimizes the use of other tools and maximizes their utility.

For example, by integrating with your CRM, an LLM can draft personalized email responses, summarize customer histories, or generate actionable insights from vast amounts of customer data.  GenAI can interpret raw data outputs from a traditional analytics platform and translate them into executive-ready insights.  You could even use LLMs to suggest creative next steps, making complex analytics tools more accessible and actionable.

For a well-run organization, your team’s expertise is the core engine of your business. The LLM can serve as a turbocharger by enabling faster research reducing hours of manual searching to mere minutes.  It can provide context-rich answers from your knowledge repository, allowing your team to act decisively even outside their core expertise.

An LLM can ingest information from multiple company sources and synthesize that information, providing clarity on complex issues or offering recommendations. This is especially critical for executives juggling vast amounts of data and needing actionable insights quickly.

LLMs can significantly improve productivity when used effectively. They build on the tools you already use and help your team work more efficiently and creatively.

Prompts and Context

The effectiveness of an LLM depends on the quality of its prompt and the context provided. Clear, detailed prompts yield better results. Adding context, whether a few words or extensive text, enhances response accuracy.  Context shapes the responses from an LLM and is key to getting an LLM to give you a useful completion.

Web-based chatbot tools like ChatGPT, Claude, or Gemini maintain ongoing conversation context, enabling more coherent interactions. Note chat web applications (e.g., chatgpt.com, claude.ai, or gemini.google.com) are designed and implemented to maintain the ongoing context of your conversation.  If your development team needs to create a similar chatbot using commercial LLM APIs, your developers must reproduce this ongoing conversation behavior.

For enterprise-level use, techniques like Retrieval Augmented Generation (RAG) or fine-tuning can improve model outputs. RAG enriches prompts with real-time context from external data while fine-tuning customizes models for specific domains.

Unlike RAG, fine-tuning reduces the need to exchange large volumes of context data with the language model, significantly lowering network data costs. However, fine-tuning modifies the model’s behavior at a foundational level, requiring extensive data science expertise and substantial data engineering effort.

Organizations need to weigh the advantages and disadvantages of both techniques.

Balancing Creativity with Precision

While LLMs excel in creative tasks, they are not ideal for problems requiring deterministic precision. For critical applications, such as retrieving factual data, traditional database queries may be more efficient. Combining LLMs with traditional IT systems ensures balance and leverages creativity without compromising reliability.

Deployment Risks and Governance

Deploying AI requires careful consideration of security, privacy, and governance. GenAI often processes sensitive data, necessitating encryption, compliance with regulations like GDPR, and robust governance frameworks to prevent bias or inappropriate outputs.

Governance ensures responsible AI use, with humans overseeing critical tasks requiring empathy and ethical judgment. Setting realistic expectations about AI’s capabilities prevents overreliance and unintended consequences.

GenAI’s limitations, including vulnerability to biases, highlight the need for continuous monitoring and human oversight. Companies should mitigate risks by reskilling employees, integrating ethical guidelines, and ensuring accountability in AI outcomes.

Next Steps for Executives

Generative AI (GenAI) is a powerful and rapidly advancing tool with applications across businesses, non-profits, and education. For most companies, it marks a shift from traditional deterministic IT systems to a probabilistic-based infrastructure, requiring organizations to adapt and learn new approaches.

This guide is designed to help executives understand the basics of AI, from pattern detection to practical deployment. Start with small, manageable projects that aren’t mission-critical but can deliver quick wins. Encourage a culture that values curiosity and innovation, and approach challenges with a “fail fast” mindset to learn and improve. With this strategy and a cautious approach to production deployments, your organization can effectively leverage AI and build a foundation for long-term success.