Demystifying AI: Practical Thoughts to Transform Strategy into Action

We can't hide anymore. AI has invaded our consciousness and is increasingly present in every part of our lives. It would be hard to imagine a conversation in which the AI topic is brought up and one or several participants don't have an opinion about it, whether good, bad, or indifferent.

It started not too long ago, and in its earlier form, it entered my life as a valuable spell and grammar checker. It is now unclear where it will stop, what will remain untouched by it, and at what pace we must embrace it in how we live and work. I have decided to embrace it, make the best of it, and contribute to its broad and safe adoption.

So, to get started, I wanted to address this first fundamental issue: When executives declare, “We need AI,” the intent is clear—but the path often isn’t. Just like me, Artificial Intelligence, particularly in the form of large language models (LLMs) like ChatGPT, has captured the imagination of corporate leaders across industries. Yet clarity is lacking, or confusion persists about what AI can truly deliver, when it’s the right tool for the job, and how to implement it meaningfully and practically.

With that as background, the remainder of this article is my attempt to be pragmatic, consider the organization’s readiness, identify where AI adds real value, and perhaps lay out some elements of a practical roadmap toward a sustainable, enterprise-wide AI strategy.

Understanding the Landscape: What AI Is (and Isn’t)

I must start this section with a significant caveat: I don't pretend to be an AI expert. This said, I found it helpful to start the debate with somewhat of a simplification of what the digital solutions that are often wrapped up in an AI debate are and how they differ from each other:

Traditional Data Processing / Workflow Automation: These tools execute structured, rule-based operations. They are often part of the newer AI developments as they are essential to string together information and actions that transcend individual corporate systems, which is also critical for creating value with AI. They have been around for a while and excel when the task at hand has clearly defined inputs and outputs with little to no variation. Think of actions like sending alerts when mandatory fields are missing, updating order statuses based on shipment scans, or compiling routine reports from databases. These highly repeatable tasks don’t benefit from AI’s interpretative power but thrive under robust automation frameworks, the foundation of robotic process automation.

Machine Learning or Narrow AI: When the problem involves finding patterns in historical data, like forecasting demand, predicting customer churn, or spotting fraud, machine learning models are effective and essential. These systems “learn” from past data to provide statistical insights or predictions, but they don’t inherently understand context or language like generative AI models do.

Generative AI / Large Language Models (e.g., ChatGPT): These models are designed to work with natural language. They shine when you need to generate or interpret unstructured text, summarize vast amounts of information, support conversational interfaces, and engage in broad-based reasoning. ChatGPT and similar tools are ideal for situations where the business deals with ambiguity, human interaction, or documentation at scale. This is where the most fascinating developments are happening almost daily. The power of those large models and the domain/knowledge they can process in practically real time allows them to find patterns and accompany humans in extremely sophisticated research, analysis, and reasoning. It truly augments human intelligence and gives people unprecedented access to knowledge and advanced problem-solving help at their fingertips.

Clarifying the Business Need: The 3 W’s

Before jumping to a technological solution, let's take a step back and articulate simply what we would want to see at the core of the AI business case:

1. What specific problem are we trying to address? Is it inefficiency, delay, error-prone manual entry, or something else?

2. Who is impacted? Identify the end users—internal teams, customers, suppliers—and define how their experience or outcomes will improve.

3. Why does solving this matter? Connect the initiative to clear outcomes like reducing costs, increasing productivity, enhancing customer satisfaction, or mitigating risk.

Too often, in such overhyped times, we tend to pursue AI for innovation's sake rather than its tangible value. Defining the problem clearly helps avoid over-engineering and ensures we use the right tool for the right job.

Evaluating Use Cases: Real-World Examples

By using the 3 W framework, companies can start to assess real opportunities. Below are three common use cases seen in logistics and operations, and how different technologies, including AI, fit into each.

1. Qualifying an Order at Input: Business leaders often seek to ensure that incoming orders are accurate, complete, and consistent with past behaviors. If this qualification process involves checking structured fields (e.g., zip code and city alignment, delivery dates), then traditional validation rules and workflow automation are ideal. These methods are fast, cheap, and highly reliable.

However, if the qualification process requires comparing free-text entries or detecting subtle changes in ordering patterns, machine learning may help. Anomaly detection models can highlight data that doesn’t align with historical norms.

If incoming orders are in the form of unstructured emails or scanned documents with handwritten notes, generative AI becomes useful. ChatGPT could extract structured information from these messages, summarize intent, and even flag ambiguous requests.

2. Exception Detection in the Order Lifecycle: The supply chain is full of predictable steps and checkpoints—order creation, dispatch, transit, delivery. Exceptions often arise when one of these steps fails or exceeds expected timeframes.

For example, a simple rules-based system can flag these issues if a shipment hasn’t moved in 24 hours or if transit time is unusually long. These types of time- or status-based alerts are easily automated without AI.

However, predicting that a shipment will become delayed—even before it happens—requires machine learning. An ML model could surface emerging risks by analyzing historical data such as route, time of year, carrier performance, and weather patterns.

Generative AI adds value when the goal is interpretation and communication. Imagine a user asking, “Which orders are at highest risk of delay, and why?” A ChatGPT-like assistant could analyze the data, summarize the reasoning, and present findings in everyday language—making insights accessible to non-technical users.

3. Quick Search & Inquiry Resolution: If customers or internal users need to look up information—like job numbers, purchase orders, or shipping statuses—structured search tools and BI dashboards may be sufficient.

But if the questions are vague (“Where’s the truck that was supposed to deliver the medical supplies yesterday?”) or span multiple data systems, then AI can offer a unified, conversational experience. It can interpret natural language, connect to various data sources, and respond with synthesized insights.

Beyond Use Cases: Building a Sustainable AI Capability

Having identified where AI can fit, the next step requires evolving from isolated pilots into a scalable and repeatable framework. This means moving from identifying a few tactical projects to embedding AI into the strategic fabric of your organization. This progression includes five foundational pillars:

1. Data and Process Maturity: Start by strengthening the foundation. Without clean, accessible, and governed data, even the best AI model will underperform. Similarly, if your processes are undocumented or vary wildly between teams, automation becomes difficult. Standardizing workflows and ensuring robust data pipelines will pay dividends across all AI efforts.

2. Use Case Prioritization: Not every AI project is worth pursuing. You must balance strategic impact with implementation feasibility. Focus first on high-value areas where outcomes are measurable—like time saved, error reduction, or customer satisfaction improvements. Quick wins build internal credibility and momentum.

3. Phased Execution - From PoC to Scale: AI success doesn’t happen overnight. Begin with limited Proofs of Concept (PoCs) to validate assumptions and test feasibility. From there, pilot in live environments with real users, making necessary process changes and collecting feedback. Once proven, use shared infrastructure and templates to scale across teams, geographies, or functions.

4. Governance, Risk, and Compliance: AI introduces unique ethical and regulatory challenges. Governance isn’t optional—it’s foundational. Establish frameworks to monitor for bias, maintain explainability, and ensure responsible use of data. Establish clear model ownership and versioning processes. Stay aligned with regulatory bodies, especially in highly regulated industries.

5. Talent and Cultural Readiness: Finally, invest in your people. Cross-functional teams that combine technical, operational, and business knowledge are critical. Promote a culture of curiosity, experimentation, and data-driven thinking. Offer training not only to data scientists but also to domain experts who will interact with or oversee AI tools. Change management should support employees whose roles evolve as AI takes on repetitive tasks.

Conclusion: AI as a Strategic Enabler, Not a Silver Bullet

Artificial intelligence is a means, not an end. Success lies in how thoughtfully it’s integrated into your business strategy. Like everything when facing such dramatic change, companies that thrive with AI focus on tangible business outcomes, align tools to the problem's nature, and invest in organizational readiness.

By taking a measured, transparent approach—grounded in clear goals, strong data, and realistic expectations—organizations can use AI to achieve lasting improvements in efficiency, customer experience, and innovation.

All these ring a bell for you and your team. Please feel free to reach out to talk more about it.