In a recent blog I stated that "Crossing the AI Chasm" is primarily an organizational and cultural challenge, not a technology challenge. That "Crossing the AI Chasm" not only requires organizational buy-in, but more importantly, necessitates creating a culture of adoption and continuous learning fueled at the front-lines of customer and/or operational engagement (see Figure 1). A recent Harvard Business Review (HBR) article "Building the AI-Powered Organization" agrees that despite the promise of AI, many organizations' efforts with it are falling short because of a failure by senior management to rewire the organization from the bottom up. The above points – interdisciplinary collaboration, data-driven at the front-line, and experimental and adaptive – are the hallmarks of an organization where everyone has been trained to "Think Like a Data Scientist." So, how can your organization embrace the liberating and innovative process of getting everyone to "Think Like a Data Scientist"?
AI is a game changer. 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.
There are several technology and business forces in-play that are going to derive and drive new sources of customer, product and operational value. As a set up for this blog on the Economic Value of Data Science, let's review some of those driving forces. "Due to its ability to substantially improve productivity and boost economic output, Artificial Intelligence (AI) has the potential to increase economic growth rates by a weighted average of 1.7% and profitability rates by 38% across a variety of industries by 2035. Source: NorthBridge Consultants "The Artificial Intelligence Revolution: New Challenges & Opport..." Figure 1: Source: "The Artificial Intelligence Revolution: New Challenges & Opportunities" Data Science (Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning) holds the potential to exploit Big Data and IoT to create new sources of economic value (wealth). But what is the source of this economic value when the AI tools that are driving this economic growth (TensorFlow, Spark ML, Caffee2, Keras) are open source and equally available to all players?
I love watching the NBA's Golden State Warriors play basketball. Their offensive "improvisation" is a thing of beauty in their constant ball movement in order to find the "best" shot. The coordinated decision-making is truly a thing of beauty, but here's the challenge: how would you "scale" the Warriors? You can't just add another player to the mix – even a perennial all-star like Boogie Cousins – and have the same level of success. One of the biggest challenges in this age of Digital Transformation is how are organizations going to exploit new technologies such as IoT and AI to "scale innovation?"
The COVID-19 crisis has hammered home the importance for organizations to become more digital. And I suspect that most organizations are thinking that just means being able to support remote customer engagements and business operations. Organizations that are thriving during COVID-19 are those that have gone beyond just "digitalizing" their engagements and operations, but are actively leveraging granular customer, product and operational data to build analytic profiles (digital twins) around which they can optimize key business processes and uncover new revenue opportunities. For example, OpenTable is helping their customers avoid long lines at grocery stores. OpenTable provides a free restaurant reservations app for diners.