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The AI Strategist: Expert Q&A

Curated by Stefano Farisè

Practical answers to real questions on Artificial Intelligence, Machine Learning, Predictive Maintenance, Computer Vision, and Generative AI, developed in collaboration with Stefano Farisè, AI Business Developer & Subject Matter Expert at Antares Vision Group. Read More

About

Stefano Farisè

AI Business Developer & Subject Matter Expert, Antares Vision Group

I have spent my career at the intersection of industrial engineering, data, and applied Artificial Intelligence. Over the past years I have worked on predictive maintenance with smart sensors - turning raw vibration, temperature, and process signals into early warnings that prevent downtime - and on managing innovation projects with a strong AI component, bridging the gap between research-grade algorithms and production-grade industrial systems. Read More

Expert Questions & Answers

Q&A 1

What Is Artificial Intelligence, Really – and What Is It Not?

Every conversation I have in a pharmaceutical plant starts the same way: someone uses the word “AI” to describe five completely different things – a rules-based alarm, a…
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Q&A 2

Machine Learning vs. Traditional Software: Why Does “Training” Change Everything?

When I explain that a machine learning model is not programmed but trained, I often see skepticism – until I explain what this means for testing, validation, and…
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Q&A 3

Machine Learning, Deep Learning, Generative AI: What’s the Difference – and Which One Do I Need?

These three terms are used interchangeably in vendor presentations, and that is a problem: they refer to nested but very different technologies, with very different costs, data requirements,…
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Q&A 4

Why Does AI Need So Much Data – and Is My Serialization Data Enough?

Companies in pharma traceability sit on enormous datasets – billions of serialized events, years of aggregation hierarchies, complete audit trails. So the question comes naturally: “We have all…
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Q&A 5

How Does a Machine “See” a Defect? Computer Vision Explained Without the Magic

Visual inspection is where AI meets pharma most concretely: cameras, vials, blisters, labels moving at hundreds of pieces per minute. But when I ask people how they think…
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Q&A 6

What Exactly Is a “Model” – and Why Does It Degrade Over Time?

A model that performed brilliantly at validation can quietly become unreliable a year later without anyone changing a single line of code. This phenomenon has a name –…
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Q&A 7

Can We Trust AI in a GxP-Regulated Environment? Validation, Explainability, and Human Oversight

“It’s a black box – Quality will never accept it”. I hear this in almost every pharma conversation about AI. It’s a legitimate concern, but the answer is…
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Q&A 8

Predictive Maintenance: What Can AI Actually Predict – and What Can’t It?

Having spent years working on predictive maintenance with smart sensors, I can tell you where the real value is – and where the marketing promises quietly exceed what…
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Q&A 9

Generative AI and LLMs: Where Do They Genuinely Help in Pharma and Supply Chain – and What Is Hallucination?

Large Language Models are the most visible face of AI today, and the pressure to “do something with ChatGPT” reaches every boardroom. In our industry they have real,…
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Q&A 10

Where Should a Pharma or Traceability Company Start with AI? A Realistic Roadmap

This is the question that matters most, and the one where I’ve seen the most money wasted. The good news: the failure patterns are well known and avoidable….
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What You’ll Find Here

This section brings together expert answers to the most relevant questions around Artificial Intelligence and its practical application in pharmaceutical, traceability, manufacturing, and supply chain environments.

Expect to find:

  • Fundamental AI concepts explained in accessible language
  • Practical industrial and pharmaceutical use cases
  • Guidance on evaluating AI opportunities and investments
  • Insights into data, models, validation, and deployment challenges

The Q&A reflects real questions raised by companies exploring AI adoption and digital transformation. Key themes include:

  • Artificial Intelligence, Machine Learning, and Deep Learning
  • Computer Vision and automated inspection systems
  • Predictive Maintenance and industrial analytics
  • Generative AI, LLMs, and business applications
  • AI validation, explainability, and GxP compliance
  • Data quality, model performance, and lifecycle management
  • AI adoption strategies and implementation roadmaps

Whether you’re taking your first steps into AI or evaluating advanced industrial applications, this section provides the knowledge needed to make informed decisions and turn emerging technologies into real business outcomes.