AI Company: We trained this dog to talk. It doesn’t actually understand language, but it kinda sounds like it’s having a conversation by mimicking the sound of human speech.
CEO: Awesome, I’ve fired my entire staff, how quickly can it start diagnosing medical disorders?
As exaggerated and humorous as the above scenario may be, it reflects the current landscape of the industry; there’s a lot of noise and hype around AI. Understanding how to effectively apply it without disrupting but instead improving operations is key for executives. This article is the first in a series intended to help draw some lines around the technology and guide executives and decision-makers in making value-added moves by utilizing it wisely.
First off, AI technologies have been around for quite some time. The recent uptick in content and solutions is due to recent innovations in generative and agentic AI.
The key thing is to not fall into the trap of thinking about or using AI just for AI’s sake. Rather, think of it for what it is: an automation tool designed to make processes more efficient and productive. It IS an automation capability. And it should be leveraged purposefully toward that end.
In addition, it’s important to understand there are pros and cons to using it, and there is a way to maximize its effectiveness in your organizations. At Flight Crew Consulting, we can help formulate a strategy to best implement these technologies to support your objectives and business strategies while minimizing disruption. The foundation of this series on AI is to understand what AI is and how the technology has evolved.
As of now, a lot of companies are popping up in the industry trying to monetize solutions and labeling them as “AI.” Again, this isn’t new. Automation has been an inherent part of the technology industry since its inception.
As executives, leaders, and frontline workers try to incorporate this technology into their work, it’s imperative to understand what AI actually is and how to best leverage it. Let’s start by reminding ourselves how this technology came to be over the last half century.
First Things First: AI Isn’t New
AI has existed in various forms for decades. The surge in tools, startups and media coverage is largely driven by advances in generative AI and agentic AI.
However, it’s important to avoid the trap of using AI just because it’s trendy. Think of AI as what it truly is: a form of automation, a tool designed to make processes more efficient, consistent, and productive. Like any tool, it has its strengths and weaknesses. Used well, it can be transformative. Used poorly, it can create chaos along with undo costs.
A Quick History of AI: How Did We Get Here?
Bear with me as I cleverly name and describe these eras of artificial intelligence.
Early stages (1950s–1980s)
The conceptual groundwork was laid in the 1950s by Alan Turing (a familiar name to my sci-fi friends) and John McCarthy, who developed LISP; a programming language enabling symbolic reasoning. The 70s and 80s saw the rise of expert systems; rule-based frameworks that enhanced decision-making and provided operational consistency, setting the foundation for today’s predictive analytics.
Middle stages (1990s–2010s)
This period introduced statistical learning, big data analytics and breakthroughs in speech and language recognition allowing machines to interpret human language more effectively. Advances in image and video recognition marked the era when systems could reliably identify objects. These were the days when you could have a system tell you “that’s a picture of a dog.” Generative AI also emerged during this time, with the development of Generative Adversarial Networks (GANs), empowering digital content creation.
Current stages (late 2010s–2020s)
Recent breakthroughs involved GPT models, which transitioned from general NLP (natural language processing) to sophisticated Large Language Models (LLMs). To reframe it: this was the breakthrough where systems could interact with us using natural language. These large language models were trained on the entirety of human language. We just dumped everything we had access to on the internet that was ever written digitally. Imagine making sense of that mass of information?
It worked and dramatically enhanced human-machine interaction allowing us to leverage capabilities like Text-to-Image, Text-to-Video, and Text-to-[Almost Anything] with Generative AI and the advent of Agentic AI; autonomous AI agents that can make decisions, plan, and execute tasks.
Future stages (next 5–25 years)
The near future will revolve around increased autonomy, multimodal integration, and deeper alignment with practical business objectives. Expect advancements in Adaptive AI, Context-Aware AI for personalized experiences and proactive actions. Longer-term developments will introduce Cognitive AI, Artificial General Intelligence (AGI) capable of general reasoning comparable to humans and possibly Superintelligence (ASI) and even Digital Consciousness or other wild evolutions down the road.
Wow a lot has happened recently! Let’s focus on the now. This technology, this automation, can be used today to improve business operations. Let’s work on that, until none of us need to work ever again.
So Today, What Is AI? What Isn’t It?
AI is an automation tool.
AI is not all-knowing.
AI is not the solution for every problem.
However, AI is fantastic at improving current service management practices. AI models are like very attentive students. They learn from data and the more high-quality data they receive the better they perform. AI should be leveraged to improve existing processes and then continually improve them over time.
Now that we’ve got a high-level understanding of the history of AI, the next post will cover the pros and cons of AI today and highlight the most appropriate use cases for service management.
Stay tuned for:
Part 2
- Highlight and dig into the Pros and Cons of AI
- Things to consider when making decisions around AI
- Use Cases for Service Management
Part 3
- Business Blockers
- Differentiation & Risks
- Strategy to Deploy Successfully
