A CEOs Guide to Artificial Intelligence
Today’s CEOs are increasingly being asked to lead their business into a data-driven world, but does that mean that immersive courses in understanding the technology are crucial? Not necessarily — in fact, it’s much more important that CEOs understand the potential of the technology and how to drive culture change throughout their organization. While Artificial Intelligence (AI) is truly changing the landscape of business, it’s doing so because fearless leaders are dreaming about the changes and improvements that are now possible through the genius of technology. See how this cultural revolution in the way we work is sweeping through business and leading a fast and furious discourse on how organizations will interact with data and individuals in the future.
The Promise of AI
CEOs have likely seen several generations of “transformative business models”: cloud-based computing being the most recent. As businesses are still reeling from this rapid-fire shift to Software as a Service (SaaS) models, the promise of artificial intelligence has the C-suite scrambling to understand the implications for their business. Scrappy start-ups do as they have always done, harnessing a new technology direction to quickly make changes to their business model as tech is introduced into the marketplace. Larger businesses and enterprises may be slower to act, as they can be weighed down with limited budgets, heavy infrastructure and disparate legacy systems. It takes time to move in a new direction, but the promise of AI is significant enough that business leaders throughout the world are exploring how to deploy data-driven decisioning in their operations, marketing and accounting solutions. From computers that recognize an individual human’s face to predictions of sales based on the weather, AI can be found in any number of practical applications throughout the business world — as evidenced by the 270% growth rate that AI has enjoyed in the past several years according to Gartner research.
Understanding How AI Works
In this season, the hype around AI is beginning to manifest itself in workable business models such as chatbots, next-best actions for customer service and predictive analytics. These systems can sense, analyze and respond to their environments in a way that is both interactive and intelligent. Creating a machine that is able to make better decisions over time based on the validation of its hypotheses requires a great deal of programming and math. However, the beauty of AI is that once the background work is done, humans are able to interact with the systems to continue the cycle of learning. AI systems “see” and “hear” sensory inputs and are able to translate that information, extract value and provide intelligent feedback to the user. Sensors and IoT (Internet of Things) connected devices also serve as input mechanisms, allowing machines to “feel” when something is cold or hot, positive or negative. This level of intuition is what is new to the business horizon, and it provides organizations with an ever-expanding range of possibilities to solve business problems.
3 Levels of AI Comprehension
While AI may have initially brought to mind futuristic robots that have taken over the world, true intuitive thought and “leaps of logic” are still beyond the limits of current AI technology. For example, an AI program can identify the difference between dogs and humans. The same program may be able to recognize how the two relate to each other as owner and pet and make the leap that they were going for a walk because the owner was holding the dog’s leash. However, it would not be able to intuit — or make an educated, quantifiable guess — anything about their relationship to each other in the future. This type of abstraction is still beyond the limits of current AI computing. The three primary levels of AI comprehension can be defined as:
Recognition: Identify items in a picture or video
Comprehension: Determine how the items relate to each other
Abstraction: Evaluate the information and make a prediction about future performance
Each stage in the evolution of AI has taken years, but the advances are coming more quickly all the time as business leaders and technology teams come together to dream and create the interactions of the future.
Bridging the Knowledge Gap
Business people are struggling with an unfathomable knowledge gap between their understanding of business intelligence and AI and the possibilities for the future. Data scientists are swiftly becoming the bridge that helps cross this gap between technology teams and business leaders, providing the insight that can translate business needs into practical applications of AI and machine learning. As CEOs deepen their understanding of data and possibilities for their business, a data scientist or business analyst may provide the necessary cohesion to maintain forward momentum on these highly technical projects.
Creating a Culture of Innovation
Perhaps the most important challenge faced by CEOs when it comes to AI isn’t technical at all — it’s cultural. If the organization is not willing to embrace the future potential of this emerging technology, it’s unlikely that AI-based projects will be successful. There is a fundamental fear within many organizations that AI or machine learning tech will replace individual knowledge workers as the AI can produce similar results in some instances as long as the correct inputs are being provided. A great example is in healthcare, where nurses or intake professionals traditionally gather basic information while assessing patients in an emergency room. AI chatbots can be programmed to not only gather and log this information quickly but also use micro-data to determine the level of distress of the individual — potentially classifying their level of pain for more immediate action by doctors. Minute facial changes, heightened breathing and sweating are all inputs that an AI can process in milliseconds that might be overlooked by a harried charge nurse.
While that scenario sounds as though it could potentially replace a position, what it actually means is that the human nurses are freed of repetitive tasks so they are able to add more value to other interactions. These lower-level engagements with patients are simply a distraction for nurses, taking time away from patient care and their ability to connect on a deeper and more proactive level instead of being stuck in a place of reaction to outside stimulus. CEOs who are able to clearly communicate the value of AI to their organizations in a way that is both non-threatening and that drives excitement within staff are more likely to be able to successfully sustain change initiatives for the future.
Rethinking Traditional Business Models
In a traditional business model, managers, directors and even chief executives are accustomed to making decisions based on incomplete data or inaccurate assumptions. While this often works out, the deluge of data that is now available allows for more informed decisions to be made — as long as business leaders are willing to take the time to ask questions and refine their understanding of business problems. Machines are exceptional at uncovering patterns, and many of these designs can fool people into making certain decisions based on their intuition. With the introduction of AI-driven decisioning, it shouldn’t be surprising that there are unexpected variances in the data that point to inefficiencies, inaccuracies and outright errors. Understanding how to interpret this information can often fall on the shoulders of a data scientist, but helping work through those questions and drill into root causes of issues will be a crucial skill for all business leaders in this brave new world of data.
Getting Started with AI
Whether you have a million ideas you want to vet with your team or are just starting to consider how AI can impact your organization, the time to get started is now. Organizations of all sizes are embracing basic AI — everything from social media chatbots that can help customers place a simple order or learn more about a product to connected systems that predict which products consumers may purchase next based on the buying patterns of others throughout the world. Determining where to begin is challenging, but here are a few basic considerations as you’re prepping for action:
Determine the reporting structure for AI, and this could change for every organization depending on the needs of the business. Will AI be mostly used in marketing or communications, operations or as a predictive analytics engine to determine when a potential breach has occurred? Understanding the application of AI technology can help ensure that the project gets the support that it needs to be successful.
Will you hire or rent the technical know-how for implementation and ongoing support? Here again, there is no “right” answer, but it requires contemplating the breadth of the engagement and how quickly you want to ramp up for your AI-based project. A similar question is needed to determine whether you will buy a codebase that contains the majority of what you need and customize it, or build your AI applications from scratch.
What’s the business case for AI? The most successful organizations are the ones that are able to quantify the value that they expect to gain from AI in terms of time savings, productivity boosts or improved customer engagement rates.
Understand (and be able to articulate!) the “Why” of your project. Are you solving a problem, beating a competitor to a goal or simply exploring the potential for the new technology within your business? Being realistic about expectations helps reduce the potential for pushback from non-believers within your organization.
Artificial intelligence has far surpassed the time when it was simply a buzzword that people loved to throw around and is now a thriving part of the business landscape with over 60% of businesses adopting some form of AI in the past year alone. Understanding the potential for disruption in your industry — both positive and negative — and how AI can be leveraged will be crucial skills for successful CEOs both now and in the future. There’s one thing for sure: AI is here to stay. Business leaders can make a decision to avoid moving forward with any AI-driven initiatives, but the cost to the organization may be higher than stakeholders are willing to pay.