The CEO now has a new and critically important job to add to their list: guide their enterprise into the AI era. You might say that this is a job for the CTO or CIO, and yes those folks will also be deeply involved. But ultimately it is up to the CEO to navigate their company through the biggest strategic and long-term shifts, and make no mistake, AI is absolute that and more.
The good news is this: AI will actually make the CEO’s job much easier. So while it is another thing on the list for now, ride through the initial hump and AI-driven CEOs will be the ones with fewer crises, greater accuracy in foresight, and finally room to breathe and think.
So how does a CEO take on this new role? By becoming AI-literate first and foremost. This doesn’t mean they must understand code or deep learning algorithms, but rather that they understand what AI is for, start to use relevant AI tools themselves, and lead others in priming the enterprise to become AI-ready.
As an AI-driven C-Suite tool for decisions-making, Cooper has conversations with business leaders and board members on a daily basis. Here is a curated discussion of the things that this group told us they would like to understand better on their journey to becoming AI-literate:
Topic 1 Understanding the terms: Generative AI vs. traditional AI
The blanket term of AI still applies to both sides, which simply means enabling a machine to adapt and grow on its own.
And from there, think of today's Gen-AI as taking existing content and building something different with those pieces. That's why it can talk to you in a natural-sounding text/voice, it can put together beautiful pictures, and it can produce new scripts. When talking casually about Gen-AI in an enterprise conversation, this will be most relevant to discussions around semi-creative work (e.g. marketing), or work that requires turning lots of information into a structured conversational narrative (e.g. call centers, meeting short-hand).
Traditional AI, or more specifically the machine-learning part, is no slouch either though. ML is exactly what it sounds like: getting a machine to learn from the information it extracts out of data. Most of the predictive and prescriptive things that you want to squeeze out of your data lives here. So in an enterprise, ML will be most relevant when talking about predictive analytics (sales, finance), automated workflow enhancements (supply chain, manufacturing), and generally getting faster at getting to insights.
Yes of course there are overlaps between the two. But this is a great place to start.
Topic 2 How AI logic works: AI needs the same logical steps for good decision-making that humans do
Where do you or the best human thinkers on your team start when solving a problem? You start by understanding the situation clearly. Then you look for the root causes. If you can do that well, then the solutions come naturally. You can proactively determine outcomes, and eventually look to change those outcomes by changing the underlying factors.
The golden 4-steps of analytics – descriptive, diagnostic, predictive, and prescriptive – apply to good thinking, no matter who (or what) is doing the thinking. AI-readiness doesn't have to be a complicated thing when you look at it this way. It just means that your data must be able to demonstrate what is going on accurately and it must be equipped to tell you why something is happening. If there are why’s that exist outside of your data and in human brains, then you are withholding information from your AI, essentially.
Topic 3 Why is AI different from BI (business intelligence)
Business Intelligence (BI) is about humans wrangling enterprise data to extract insights. Typically comprised of an individual or team of smart analysts, these folks extract, clean, and manipulate data from systems to create charts and dashboards. Data visualization is the key here: taking numbers and rows and turning them into a more discernible form. Typical applications for this are Tableau and PowerBI.
Going from BI to AI requires making your data capable (refer to our Topic 2 above) so that automated insights and learning can take place. Once set up properly, this allows for incomparable benefits vs. human-driven BI:
🌟 AI can absorb, cleanse, and monitor far more data than humans
✨ AI does not have the same cognitive biases as a human brain. It operates on mathematical logic
⭐ AI learns and evolves to minimize errors and grow in analytical capabilities far faster than a human (GPT-4 passed the bar exam with a 90th percentile score when it was under a year old)
The chasm between human-driven BI and AI will only continue to widen over time and present a real competitive advantage for those willing to invest in the latter. And by the way, those smart business analysts? They can now move onto training the AI to produce ever-better results and apply their uniquely human intelligence to the strategic and innovative echelons of the business that AI can’t yet tread.
Topic 4: How to get started with AI for your enterprise
The most important word you should be thinking about as you start your AI journey is EXPLAINABILITY. If an employee came to you and offered an important strategy, would you ever accept it if they told you they couldn’t explain their logic? The same should be the case for AI.
So think about experimenting in AI as simply an experimentation in logic. You are automating logic.
Mark our words, Explainability will be everything in the future of AI. How can you trust the output if you can’t understand the logic? So even in getting started on the AI journey, and start you must, build the discipline of explainability into your enterprise.
Topic 5: Set goals!
So what should be the goal when we consider AI for our business? Is the primary goal with AI to simply reduce human labor costs by replacing some of it with machines? Or is there more to this story?
Let’s focus on what machines do better than humans in this context:
🤖 Machines are built to process vast amounts of data, much faster than humans
🖐 They don't have input errors (fat-fingering)
🧠 They don't have qualitative biases. They run on math and logic
🤓 Because of the above, they are great learners
Put all that together, and what transformative goals can we aspire to for AI in our enterprise?
Topic 6: What does the future look like for the AI-enabled CEO?
In short, the future looks bright. As the sheer volume of data continues to grow, it will be up to machines to continuously process and monitor those terabytes. This will both ensure better governance and proactive management but also free up human minds to focus on bigger (funner) things.
AI will find winning patterns in the data objectively (mathematically) rather than how humans often do, based on our many cognitive biases. Enabled by AI, our decisions will become better because they will be predicated on true merit.
And let's face it: the most important job of the CEO is to make the big decisions for the enterprise (yes, even culture, strategy, crisis management are all decisions). So if you are a CEO, ultimately AI will bring further precision, speed, and objectivity to your decision-making and enable you and your enterprise to fly to greater heights.
We encourage all business leaders to see AI, not as something for someone else, but as something that will be directly beneficial to your day-to-day work. Our bright future has arrived.
