AI is a game changer. Period. And being a data and analytics guy, I could not be more excited about it.
The McKinsey research study “Notes from the AI frontier: Applications and value of deep learning” provided some valuable insights into where and how Artificial Intelligence (i.e., Deep Learning / Neural Networks (CNNs, RNNs, GANs), Reinforcement Learning and Deep Reinforcement Learning) will derive and drive new sources of customer, product and operational value, especially when compared to traditional analytic approaches. AI will add billions of dollars of financial and economic value to ALL industries. A no-brainer if ever one existed.
Figure 1: AI Potential Impact Across Industries
However, a recent Harvard Business Review article “Building the AI-Powered Organization” states that despite the promise of AI, many organizations’ efforts with AI are falling short. The article states that the problem starts at the highest levels of the organization; that it’s a failure of senior management to rewire the organization.
So, how does one rewire the organization in order to “Cross the AI Chasm”? Let’s start with a little analytics history lesson.
I originally postulated that “Crossing the Traditional / BI Analytics Chasm” required an economics mindset to leverage granular data to predict behaviors and performance at the level of the individual human or device. Crossing the Analytics Chasm requires organizations to understand how to leverage the economics of detailed, granular data (Big Data) to identify and capture the new sources of customer and market value creation. Specifically, crossing the traditional BI / Analytics Chasm requires:
While “Crossing the Traditional BI / Analytics Chasm” was primarily a data and economics challenge, “Crossing the AI Chasm” will primarily be an organizational and cultural challenge. Crossing the AI Chasm not only requires gaining organizational buy-in, but more importantly, Crossing the AI Chasm necessitates creating a culture of continuous learning at the front-lines of customer and/or operational engagement.Yea, Crossing the AI Chasm doesn’t happen in the ivory towers, it happens at the front-lines of customer and operational engagement. Surprise!
Let’s talk more about Crossing the AI Chasm through the story-telling power of infographics!!
“Crossing the AI Chasm” requires creating an operational environment which seeks to continuously learn from each and every customer engagement and/or product usage. Organizations need to nurture innovative thinking at the frontlines to fuel that continuously learning AI process.
Design thinking will play a critical role in unleashing creative thinking at the frontlines that might yield better predictors of performance and customer delight. A McKinsey article titled “Fusing data and design to supercharge innovation” highlighted the powerful potential of integrating AI and Design Thinking:
“While many organizations are investing in data and design capabilities, only those that tightly weave these disciplines together will unlock their full benefits.”
Digital transformation is teaching us that in knowledge-based industries, economies of learning is more powerful than economies of scale, and soon every industry will be a knowledge-based industry. All companies regardless of today’s artificially defined industry borders, will need to learn to exploit organizational diversity to uncover those variables and metrics that might be better predictors of performance and customer satisfaction (see Figure #3).
“How effective if your organization at leveraging data and analytics to power your business and operational models?” – Bill Schmarzo
Another key organizational challenge to crossing the AI Chasm will be leveraging the subject matter experts into the value creation process. This requires embracing a culture where the subject matter experts (technician, engineers, operators, doctors, nurses, teachers) closest to the customers or operations are tightly integrated in the value engineering process in order to capture, validate, codify and augment (empower) front-line intelligence (see Figure 4).
Figure 4: Value Engineering Framework
The Value Engineering Framework starts with the identification of a key business initiative that not only determines the sources of value, but also provides the framework for a laser-focus on delivering business value and relevance.
A diverse set of subject matter experts is necessary because it provides more perspectives on the key decisions upon which the data science effort needs to focus.
The heart of the Value Engineering Framework is the collaboration and ideation across and amongst the different subject matter experts to identify, validate, value and prioritize the key decisions (use cases) that they need to make in support of the targeted business initiative.
Warning: the majority of your operational intelligence probably does not reside in the ivory towers of headquarters. Better ease into that message with your senior executives (their feelings get hurt so easily).
Traditional management hears about the massive growth in data volumes and diversity and asks, “How are we going to manage all of that data?” To these folks, data is a cost to be minimized. But it is the new AI management that hears about the massive data volumes and diversity and asks, “How are we going to monetize all that data?” To these folks, data is an asset to be exploited for financial and operational gain.
Crossing the AI Chasm requires embracing the unique economic characteristics of data; an asset that never depletes, never wears out, and can be used across an unlimited number of use cases at a near zero marginal cost. In fact, not only do these assets never deplete or wear out, but these assets actually get more accurate, more predictive and more complete the more they are re-used and refined. This leads us to the Schmarzo Digital Asset Valuation Theorem in Figure 5.
The Economic Digital Asset Valuation Theorem highlights how the unique characteristics of digital assets manifest themselves at the macro-economic level:
“If you buy a Tesla today, I believe you're buying an appreciating asset, not a depreciating asset.” – Elon Musk
I believe that this statement from Elon Musk reveals one of the most provocative and game-changing concepts of our generation. The statement highlights the ultimate goal of AI-enhanced assets; that they appreciate in value as a result of the experience and intelligence gleaned from the operations of that asset. And this learning magnifies itself when there is collaboration across a collection of similar physical assets – vehicles, trains, airplanes, compressors, turbines, motors, elevators, cranes, etc. – so that the experience and intelligence can be amassed centrally – models refined and the learnings and intelligence back propagated to the individual assets. That is, what is experienced and learned by one asset, is validated, codified and propagated back to every asset making the collective assets more intelligent, and therefore more valuable. And in many cases, these AI models are learning without human intervention (see Figure 6).
Note: the creation of assets that leverage AI to appreciate, not depreciate, through the learning gained via usage is the ultimate realization of the potential of the Internet of Things (IoT).
What does it mean to “Think like a data scientist” and why is that important to crossing the AI Chasm? Here is why I believe that it is fundamental to crossing the AI Chasm to get everyone in the organization to “think like a data scientist”:
Dang, I could write an entire book about the importance of thinking like a data scientist. Oops, I already did…
“To change the game, change your frame.”
“Crossing the AI Chasm” will require that organizational leadership adopt a new mindset, but not one that is isolated to just those big wigs in the ivory tower. No, the mindset shift must focus on capturing the learning and consequently augmenting the intelligence of the front-line interactions.
Crossing the AI Chasm requires:
Crossing the AI Chasm doesn’t require the proverbial leap of faith. It just requires senior management to loosen the reins a bit and let the learnings fueled by AI flourish at the front-lines of customer and operational engagement.
What steps will you take today to create and foster the culture and practices to effectively cross the AI Chasm? These infographics will help you begin to assimilate the change in mentality necessary to ensure a successful crossing.