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AI: Hype vs. Reality

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Understanding AI as an Aggregate Term

Businesses hear bold claims about AI revolutionizing industries, yet many leaders struggle to separate hype from reality. What can AI actually do? Where does it deliver value, and what misconceptions should be avoided?

 

The challenge starts with how AI is discussed. It’s often portrayed as a single, all-powerful technology, when in reality, AI is an umbrella term that encompasses multiple specialized fields. To understand AI’s true capabilities, it’s important to break it down into its key categories, each with distinct functions and applications.

The Primary Categories of AI

Machine Learning (ML) – The Foundation of AI

Machine Learning is a subset of AI that enables systems to learn from data and make predictions or decisions without being explicitly programmed. Before the rise of generative AI, the term AI was often synonymous with machine learning.

  • How It Works: ML models analyze patterns in historical data to predict future outcomes.
  • Where It’s Used:
    • Demand forecasting in supply chains
    • Fraud detection in banking
    • Predictive maintenance in manufacturing

Generative AI – Creating, Not Just Predicting

Generative AI is a type of machine learning that produces new content rather than analyzing data for predictions. These models generate text, images, audio, and code based on the patterns they’ve learned from vast datasets.

  • How It Works: Instead of recognizing trends, generative models produce new outputs that mimic human-created content.
  • Where It’s Used:
    • AI-generated marketing content
    • Automated code writing
    • Image and video generation

Natural Language Processing (NLP) – Making AI Conversational

Natural Language Processing (NLP) enables computers to understand, interpret, and generate human language. While NLP powers AI chatbots and virtual assistants, it relies on machine learning and other AI techniques to function.

  • How It Works: NLP algorithms process text or speech to extract meaning, detect sentiment, or generate responses.
  • Where It’s Used:
    • AI-powered customer support
    • Language translation tools
    • Sentiment analysis for brand monitoring

Robotic Process Automation (RPA) – Often Mistaken for AI

Unlike the other AI subsets, RPA is not artificial intelligence. Instead, it is a form of automation that mimics human actions in software environments, following predefined rules without learning or adapting.

  • How It Works: RPA bots interact with software interfaces to execute repetitive, rule-based tasks.
  • Where It’s Used:
    • Data entry and invoice processing
    • Extracting data from emails and logging into systems
    • Automating HR onboarding tasks

Laying the Foundation Before Broad AI Adoption

While AI has the potential to drive significant business impact, most organizations need to lay the groundwork before it can be applied at scale.

  • Establishing a strong, scalable technical foundation – Clean, well-structured data and modern analytics platforms must be in place before attempting more advanced AI projects.
  • Building workforce skills – Teams must develop expertise in data literacy, modern analytics, and process automation, which provide immediate value and prepare organizations for more complex AI-driven initiatives.
  • Taking a strategic approach – AI adoption should focus on well-defined, high-impact use cases rather than broad, unfocused initiatives.

Crawl, Walk, Run—The Key to Success with AI

AI is powerful, but it is not a magic wand. Most organizations need to slow down and focus on the fundamentals—building a strong foundation of integrated processes and data while enabling teams to apply modern analytics that shift the business from reactive to proactive decision-making.

 

Whether you are just starting your modernization journey or have reached a plateau, Ascent Innovations can help you develop and implement a strategic plan that unlocks immediate value and drives game-changing business impact—building executive confidence and support for continued investment.

About the Author

John Bruhnke is Managing Director at Ascent. He has 25 years of management consulting experience focused on system implementation and, for the last 7 years, modern analytics in the manufacturing industry. He collaborates with executive and management teams to drive alignment on strategic goals and develop a collective vision for modernization that balances both immediate business needs and long-term strategy.  

Author: John Bruhnke

Managing Director

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