Why You Need to Rethink AI and Automation

The world of technology, especially around artificial intelligence and automation, is changing at warp speed. Literally, things are shifting by the week. We’re in the middle of a rate of technological change faster than anything we’ve ever seen in history, and there’s no slowing down in sight. It feels like we’re just getting started.

For me, this isn't just a professional interest; it's a lifelong passion. For decades, a lot of what we discussed was purely theoretical. And now? To be at a point where everything we’ve been talking about is actually coming to life… It’s like a wish coming true. Science fiction is becoming science reality, and that’s just plain cool.

But here’s the thing that we should be consider. This rapid evolution, while incredibly exciting for someone like me, can also be a little terrifying for business leaders. There’s a lot of talk, a lot of hype, and a fair bit of confusion out there. That’s why I want to cut through the noise today and talk about some fundamental truths regarding AI and automation that every organization needs to grasp.

Demystifying the Duo: AI vs. Automation

One of the biggest misconceptions I hear people talk about is that AI and automation are the same thing. They’re not. They’re completely different fish in the sea, and understanding that difference is critical to leveraging their true power.

Let's start with automation. Automation has been around for a long time. You might have heard terms like Robotic Process Automation (RPA), which is a common form of it. Basically, automation is about taking repetitive steps that you do over and over again and programming your computer to follow a specific decision tree or workflow. It's built on "if-then" logic. It can appear like it has some intelligence because it follows predefined rules to handle tasks efficiently, but it's fundamentally about programming a machine to execute predictable, rule-based processes. Think of it as a highly efficient, tireless robot following a detailed set of instructions.

Now, Artificial Intelligence (AI) is where things get truly intelligent. The key difference here is that with AI, you’re not programming in all the decision points. Instead, you're allowing advanced models, particularly large language models, to make those decisions on their own. AI recognizes patterns in vast amounts of data and provides answers and options based on that understanding. It learns. It adapts. A programmer isn't building a rigid workflow; the AI is making decisions based on the information and knowledge it’s been given. That’s where "intelligence" truly comes into play.

The real magic, though, the real power, is when you combine automation and AI to work together. Imagine an automated workflow that, at certain junctures, isn't just following programmed "if-then" logic, but is instead going out and using artificial intelligence to make nuanced decisions, to interpret complex information, or to generate creative solutions. That’s when you can truly begin to leverage a new level of efficiency and optimization in your organization. It’s like giving your highly efficient robot the ability to think critically and learn on its own.

Confronting the Fear and Overcoming the Unknown

It’s completely natural to feel a bit apprehensive about all this. The real core of the apprehension is that people have always been afraid of what they don’t know. And right now, things are happening so fast, pushing outside the boundaries of how we’ve been used to operating for so long. It can be overwhelming. A lack of knowledge often leads to fear, and that’s okay. I believe there will come a time when all this is "old hat," and everyone gets it, but we’re not there yet.

What we're doing is helping people overcome their fear by helping them understand what this rapid change can mean for their organization. We show them how to implement these technologies in a way that gets them to the other side, efficiently and effectively.

Now, let's be honest about something people don't always like to hear. Humans make mistakes all the time. We get specifications wrong, we miss details. And sometimes, we even make the same stupid mistake more than once. Computers, on the other hand, don't do that. An AI might make a mistake, but once you fix it, that specific mistake isn't made again. It learns.

This brings up the broader point about regulation and oversight. AI is just a tool. Companies will either have to self-regulate or be regulated by a third-party entity. In fields like engineering or construction, the honest truth is that serious regulation often doesn't happen until something negative occurs. We can say we’ll be good stewards, but as humans, we often need guidance. 

This isn't unique to AI; there have always been good actors and bad actors in business. What doesn't exist today, for example, is a "Yelp for AI" – a clear way to assess which tools perform well and which don't. These supporting infrastructures will catch up, but technology is moving so fast they're playing catch-up.

Misconceptions about AI Adoption

If you follow the news, you’ve probably seen conflicting surveys about AI adoption rates. On one hand, you read that lots of companies are implementing AI and having mixed results. On the other, relatively few are adopting it with clear success. The truth is, both are somewhat accurate, and it's largely a matter of semantics.

My advice: don't believe all the hype. All the buzz is often shifting focus away from what businesses should be doing right now with what’s available.

What’s causing the confusion? First, there’s a fundamental misunderstanding of what "AI" means. When people talk about "generative AI" – the type that powers tools like ChatGPT – that’s a different beast from what many companies have been doing in the broader "AI world" for a long time.

Adding to the complexity is the fact that nearly every software company is now claiming to have "AI in their product." If you're using Salesforce, HubSpot, Google, or various project management platforms, you are, by default, interacting with some form of AI, often generative AI. It's not wrong to say that, but that's really not what using AI or implementing a strategic AI solution is all about for a business. And to make it even more confusing, everyone and their nephew is now calling themselves a "GenAI expert."

So, what are the real use cases for corporations, especially mid-sized to larger companies (say, over 50 employees)? We're seeing two main approaches:

1. Leveraging AI within existing internal systems: Companies are being selective, looking at the software they already use and exploring how generative AI features built into those platforms can optimize their operations. This is a smart, pragmatic starting point.
2. Developing comprehensive AI strategies: On the far end of the spectrum, Fortune 500 companies are looking at how all their processes across the board can be optimized and augmented using generative AI. They are looking at sweeping transformations.

Here’s a crucial point that I can’t emphasize enough, and it’s something I don’t see happening enough yet: AI is a tool, not a strategy.

It’s like saying, "I’m a builder, and I have a hammer strategy." You don't have a hammer strategy; you have a hammer that you use to build a building. Similarly, businesses need to look at their business strategy first. Then, and only then, should they ask: "How can we use these various pieces of technology, including those with AI and generative AI built into them, to make our business processes better?"

This leads directly to the core "hard work" that businesses need to do. You can start automating and streamlining processes in sales, operations, marketing, and customer service right now. But it requires you to literally sit down, take a hard look at your current business processes, and map out where those processes need to be changed, modified, or improved. If you think, "Oh, I’m just going to let AI do it for me," you're going to end up in a hot mess. You have to do the work of deciding how you want to implement it. You need the blueprints before you start hammering.

Navigating the Path Forward: Practical Steps and Future Outlook

Don't try to wait for some magical AI solution five years from now. There are great tools out there today, some that have been around longer than ChatGPT, that you can use for automating your business processes. Take those steps now, and you'll be in a much better position to leverage future technology.

One common concern I hear is about feeding AI systems with unstructured data or worrying about data structure. This comes down to an old mindset that people are going to have to get rid of: the rigid adherence to structured data. Modern language models don't need that linear, perfectly structured input anymore. If you give them the right context, they can look for patterns and make surprisingly accurate assumptions.

Of course, we're not going to just build something and roll it out into production without oversight. We'll always have a human in the loop, looking at outputs, testing it in multiple scenarios, and ensuring it delivers the right results. It really comes back to this: you have to have the knowledge and understand that the way things used to work is not the way they work now. If you can get past the old-mindset thought, and realize that you need someone to guide you along this new process, your organization will be in a much better position.

The tools we have today are powerful, but they’re just the tip of the iceberg. When leveraged in a systematic, holistic way, they can have a significant impact. We're at this interesting point where technology is advancing incredibly fast, but we also have these macroeconomic situations adding to the challenges business leaders face. They’re often afraid to make a move because of economic uncertainty, compounded by this unfamiliar technology. But leveraging this technology is precisely what will help them navigate through these turbulent waters. More business leaders just need to take that leap of faith, ideally with a guide to help them through it.

My concern is for the business leaders of today. It’s not that AI is going to take jobs per se. It’s that business leaders are going to make bad decisions that cause a loss of jobs. This has happened throughout history. It's not the technology itself, but the failure to adapt to societal shifts that leads to negative outcomes.

Taking the Leap of Faith (with a Guide)

The ocean of AI and automation is vast, and it’s moving fast. Understanding the nuances, embracing the change, and most importantly, taking deliberate action is what will define success. It’s about being proactive, not reactive, and making informed decisions that truly benefit your organization.

If you're curious to explore how these insights can translate into actionable strategies for your organization, I regularly host discussions and events where we dive deeper into practical applications and tailored solutions. Join me at one of my upcoming events or webinars to learn more and connect with other leaders navigating this exciting landscape.