Wait, the AI advantage is already here and gone?
That’s what Deloitte warns in their report “Future in the balance? How countries are pursuing an AI advantage”.A noteworthy quote:
“There are indications that the window for competitive differentiation with AI is rapidly closing. As AI technologies become easier to consume and get embedded in an increasing number of products and services, the early-mover advantage will rapidly diminish" (see Figure 1).
Figure 1: Source “Future in the balance? How Countries are Pursuing an AI Advantage”.
But of course, it’s not too late to benefit from the digital transformation potential of AI!
Because having AI capabilities is not the same thing as exploiting AI capabilities.
The report, however, does shed some guidance for organizations on the subject:
“AI success depends on getting the execution right. Organizations often must excel at a wide range of practices to ensure AI success, including developing a strategy, pursuing the right use cases, building a data foundation, and cultivating a strong ability to experiment.”
Great advice to which I want to apply an economics perspective– where economics is the branch of knowledge concerned with the production, consumption, and transfer of wealth (value)– to understand where and how organizations can focus their AI initiatives to derive and drive new sources of customer, product and operational insights.
Here’s my simple 3-step recipe for organizations seeking to apply AI to exploit the economics of data to digitally transform their business and operational models and master the art of identifying, capturing and operationalizing new sources of economic value creation:
Let’s jump into it and prove that it’s not too late for organizations that are seeking to exploit the economic value of their data with AI.
AI, it’s still cool, right?
The first step in exploiting the digital transformation potential of AI is to identify the right use cases against which to apply your AI capabilities. Organizations need to invest the time to identify the sources of value creation against which to prioritize and focus the organization’s AI efforts. And fortunately, we have the perfect Design Thinking tool for doing that – the Customer Journey Map (see Step 1).
Step 1: Identify Sources of Value Creation
The Customer Journey Map captures the decisionsthat a consumer or corporate customer needs to make in support of a specific journey. For a Business-to-Consumer (B2C) consumers, that could include buying insurance, buying a house, going on vacation, or going out to eat. For a Business-to-Business (B2B) corporate customer, that could include maintaining 100% operational uptime, optimizing product delivery, or reducing obsolete and excessive inventory.
It is against the high-value decisions uncovered by the Customer Journey Map that we will prioritize and focus our AI efforts.
The second step is to build out your data and AI capabilities that support the high-value decisions (use cases). That is, identifying the data and AI assets that your organization needs to build in order to optimize the customer journey mapped out in Step 1.
These data and analytic assets should focus on capturing the customer, product and operational insights necessary to 1) enhance the sources of customer value as well as 2) mitigate areas of customer pain or impediments along the customer journey (see Step 2).
Step 2: Capture Sources of Value Creation
As part of Step 2, we need to address the following questions:
These questions map to the Data Science Value Engineering Framework (Figure 2) that I will cover in a future blog (I’m planting reasons to keep you coming back for more).
Figure 2: Data Science Value Engineering Framework
The third step is to operationalize the customer, product and operational insights derived by AI in steps 1 and 2. An organization’s AI capabilities create the predictive outputs and prescriptive recommendations that must be operationalized within the organization’s operational systems including management dashboards and reports, mobile apps, websites, and enterprise systems (e.g., ERP, MRP, SCM, CRM, SFA, LMNOP). Step 3 involves embedding the AI analytics and operationalizing the customer, product and operational insights into the organization’s value chain creation process (see slide below).
Step 3: Operationalize Sources of Value Creation
A value chain is a set of activities that an organization performs to deliver a product or service of value to the market, whereby each step along the value chain adds more value to the product or service so that in the end, the value of the product or service is higher than the aggregated costs to create the product or service. A good example of a value chain is the value chain in the Oil & Gas industry (see Figure 3).
Figure 3: Oil & Gas Value Chain
So, what are the key characteristics of Digital Transformation?
AI plays a driving role in each of those characteristics and manifests itself across the entire Digital Transformation Value Creation Framework (Figure 4).
Figure 4: Digital Transformation Value Creation Framework
Economics is at the heart of Digital Transformation, and its ability to leverage data and analytics to create new sources of customer, product and operational value (wealth). And AI will continue to play a key role in deriving and driving new sources of customer, product and operational value, especially for organizations that follow this simple but effective 3-step recipe:
Yea, AI is still cool.