Zero to AI: A Guide to Seizing the AI Opportunity for your Business
From Siri to self-driving cars, Artificial Intelligence (AI) is increasingly playing a role in our everyday lives. For businesses, AI is advancing to create sustainable competitive advantages by increasing operational efficiencies, improving products, and enhancing the customer experience. In this digital era where every interaction is collected, data is an exponentially growing ocean (not lake) of information that holds enormous strategic potential. However, data in and of itself is not a strategic asset. It is only when insights are extracted from it and interpreted into meaningful opportunities leading to profitable growth that competitive advantage from the data is achieved. This is where AI comes in.
Did you Know?
The technology, media and telecommunications industry has the highest expectations for AI to accelerate new product and service offerings of all industries. 72% of companies in the technology, media, and telecommunications industry expect AI to have a significant impact on product offerings in the next five years.
AI is the application of powerful learning algorithms that have the ability to identify and unleash critical insights from your data. Businesses are therefore investing heavily to build AI capabilities to make sense of their data to become more data-driven. According to a recent BCG/MIT study, 84% of businesses say AI will enable them to obtain or sustain a competitive advantage and 83% believe AI is a strategic priority for their business today. However, only about 20% of organizations today have actually incorporated AI in some capacity. Even for those that have, many are struggling to adopt and scale AI in their organizations as most are not sure what to expect from AI or how it fits into their business model. If you are among the 80% of organizations that have not yet incorporated AI, you may be feeling anxious to get started and asking yourself how you can catch-up or where to start. In essence, how do you go from Zero to AI. The good news is that AI is still very much in its infancy and getting started is actually easier than you think. You just need to have sharp focus, commitment to the cause, and a genuine appetite to be data-driven. Sounds like you? Then let’s get started! Here’s a quick framework to help you go from Zero to AI:
Step 1: Define Your Strategic Focus
This is the most critical step that will not only set the tone on how the organization as a whole should rally, but specifically align the focus of your AI initiatives. Too often executives feel they are making big strides in the area of AI by hiring analytics talent or investing in advanced analytical tools and technologies but find themselves frustrated when they don’t see corresponding results. A recent McKinsey study observed that as little as 10 percent of the value that AI can bring to organizations has actually been unlocked. This is often because executive teams often do not have a clear vision or understanding of how AI can be applied to their business and achieve their objectives. In order to do that, the first step is to set objectives that are not only clear and transparent, but also are measurable with data so that AI can be applied. This is where the concept of OKR (Objectives and Key Results) becomes a very effective tool to align your AI initiatives.
OKR is a simple but powerful leadership process for setting, communicating and monitoring organizational quarterly goals and results used famously by Intel and Google and popularized by John Doerr.
OKRs are comprised of an objective — a clearly-defined goal — and one or more key results — specific measures used to track the achievement of that goal. OKRs are typically shared across the organization to provide teams visibility into the company’s “north star” goals to ensure alignment and focus of initiatives, including AI initiatives.
“OKRs have helped lead us to 10x growth, many times over. They’ve helped make our crazily bold mission of ‘organizing the world’s information’ perhaps even achievable. They’ve kept me and the rest of the company on time and on track when it mattered the most.” — Larry Page, Google
Step 2: Align AI capabilities around your strategy
Once OKRs are set and clearly cascaded throughout the organization, it is important to identify one or two use cases on how AI can be applied to achieve a specific key result underlying a specific objective. For example, if a telecom company has a “north star” goal of having the best customer service in the industry and one of the supporting objectives is to increase customer satisfaction — and thus one of the underlying key result is to improve NPS by 20% (Net Promoter Score, a commonly used customer loyalty/satisfaction measure) — then a use case may be to use AI to identify specific attributes and behaviors that drive NPS scores. Based on those findings, the telecom should use AI to build customer simulations (predicted customer interactions and behaviors) that leverage Machine Learning (ML) to optimize those behaviors.
This step is critical because it will 1) target the AI to a specific desired result that will help achieve a company objective; and 2) make the AI accountable as it will prove one way or the other if it is viable or if it should be redirected to another area, thereby minimizing investment exposure and ensuring focus. Most importantly, if the use case is successful, further effort to operationalize/scale the AI will create a sustainable competitive advantage that will continuously optimize your key objectives!
Step 3: Leverage AI to identify new opportunities
A huge collateral benefit of using AI, even on selective use cases or pilot projects as in the example above, is that the underlying data typically contains the critical data elements that can be leveraged to fuel other AI initiatives. Moreover, the same AI capabilities and resources that have been built from the previous use cases can be reapplied to new investigative initiatives. Based on the same data, AI can be used to identify new patterns of behaviors that can spawn new areas of opportunity. For example, for a specific use case we conducted for one of our clients where we used AI to create customer simulations that led to optimal NPS scores, we used AI with the same resources and the same underlying data to identify simulations that reduced customer churn rate — another key objective of the company but not initially intended for this use case. What resulted was a predictive churn algorithm that identified valuable at-risk customers which informed key retention initiatives. The moral of the story is that starting small and getting quick-wins will build momentum and a sound foundation for AI to proliferate and thrive!
Putting it all together
This new era of AI is transforming the way businesses are evolving and competing, exposing the widening chasm between Big Data and what to do with it. The good news is that the gap is not yet so large that you can’t make the leap, but the gap is widening quickly. The key is focus and to have realistic expectations of how AI can help unlock the value from your data, make smarter decisions and create sustainable competitive advantages. With rapid advancements in technology, it is becoming easier than ever for businesses of all sizes to apply AI to their business. The key is to take that all important first step!
