AI Terms Every Business Leader Should Know

AI is posed to transform the business landscape.  You’ve already heard such statements. And perhaps you’re wondering How? And what does this mean for me and my business?

Our future is largely unknown but the torrent of hype surrounding AI may have you believing that we’re shortly facing the end of white collar jobs and human-operated cars. An overhyped narrative is effective at generating fear and influencing our buying and investment habits. Seemingly every product and app is having “AI” awkwardly crammed into its branding without a meaningful use case or value proposition. 

There’s excess hype, but, conversely, it’s unwise to dismiss AI’s potential impact on your business. By gradually educating yourself, you can cut through the hype and start to discern real AI opportunities and threats to your business.

This insight article is written for business leaders who are just starting their learning journey into AI.  “AI” is a broad term with several interpretations.  As captured in our insight Beyond the Hype: A Practical Look at AI Adoption in the Real World, we embrace John McCarthy’s definition, stating AI encompasses "the science and engineering of making intelligent machines, especially intelligent computer programs."  

AI is a suite of evolving, interrelated technologies which achieve this machine intelligence.  We’ll break this topic into relevant terms, build foundational knowledge, and offer insights on how you can continue your learning journey.

1. Generative AI: A Creative Partner

AI is far from new.  Particularly in the internet age, AI-powered algorithms have been watching our surfing habits, collecting our data, and making alarmingly relevant recommendations: think Amazon, Netflix, and YouTube recommendations.

With the late 2022 launch of ChatGPT 3.5, the “AI” hype train left the station like a bullet.  And of all the hype surrounding AI right now, much of that hype centers around Generative AI (often shortened to GenAI).

Heard of Gemini or Midjourney? These are examples of Generative AI, a type of AI that can create original content like images, music, text, and even videos. 

How does it work? Generative AI models are trained on massive amounts of data - typically supplied by humans. The AI learns to recognize patterns in this data and then use those patterns to generate new, unique content.  Example: supply hundreds of thousands of pictures of cats to a Generative AI model, and it learns to recognize what a cat looks like based on the common patterns.  From there, it can create its own unique image of a cat.

The Power and the Peril Back in 2017, Facebook researchers created two AI systems and encouraged them to negotiate and trade with each other. The AIs began communicating in English.  But over time, these AIs started modifying the English language into something unrecognizable to humans, presumably to make communication more efficient. The experiment was eventually shut down, highlighting both the power and potential risks of AI.

2. Machine Learning: A Self-Learner 

Traditional software was “rules-based,” meaning it follows a fixed set of rules. "If X happens, then perform Y." Machine learning is not fixed.  It's self-learning and adaptable, mimicking human intelligence. It analyzes data, identifies patterns, and makes decisions or predictions without being explicitly programmed to do so.

A little too human? A Personal Anecdote: Months ago, I asked Google’s AI assistant, Gemini, to compile research into a spreadsheet. Gemini excelled at small volume requests, and it felt like a productive, new co-worker at my side. However, as I increased my request volume, Gemini struggled, even resorting to human-like excuses - “this is proving harder than I expected” - and unfulfilled promises - “I’ll give you a status update in 15 minutes.”  (I’m still waiting on that status update).  Sound like any human coworkers you’ve had?  This experience highlights AI’s ability to mimic human behavior - the good and the… not so good.

3. AI Hallucinations: A Confident Fibber

AI is powerful, but it's not infallible. Earlier, we mentioned AIs ability to create new, unrecognizable languages.  The inner workings of AI’s neural networks are often referred to as “black boxes,” unreadable and unexplainable by a human. 

And, like human learning, AI learning isn’t immune to bias or gaps in training and experience. This can give rise to a hallucination, a phenomenon where AI generates completely made-up information and presents it with absolute confidence.

Walk on Water? Here’s an example of AI hallucination:

A thorough, confident but utterly absurd response

For now, we can expect to see disclaimers like this one appended to AI-generated responses.

Key takeaways for business leaders:

• Hyped but relevant. AI comes with many inflated promises, but one thing is certain - this powerful technology will continue to evolve and impact the business landscape.  Keep learning how the technology can benefit (and threaten) your business.

• Experiment. Start with an outcome in mind - one that is both valuable and low risk.  Experiment with AI tools that can help automate or augment your tasks.

• Build foundational knowledge. AI technologies and business applications are constantly evolving stay informed.

• ….but follow the incentives.  Learn to discern information based on its source.

About the Author

As a Success Architect at Liberated Leaders, Alan leverages 20 years of experience in technology leadership and consulting to help businesses optimize their technology strategies, gain an edge, and scale their operations. He is a twice certified executive and leadership coach who firmly believes that true business transformation can only occur with mindful investment in people and technology. Find out more about Alan on our About page.

Note: This blog was 90% human generated and 10% machine (AI) generated.