Digital Transformation And The AI Advantage

Bill Schmarzo

Wait, the artificial intelligence (AI) advantage is already here and gone? That’s what Deloitte warns in the 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.”

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). This enables us 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 three-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:

  • Step 1:  Pursue the right use cases
  • Step 2:  Build analytics capability
  • Step 3:  Embed the analytics

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?

Step 1: Pursue the right use cases (identify sources of value creation)

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 (Figure 1).

Figure 1: Identify sources of value creation 

The Customer Journey Map captures the decisions that a consumer or corporate customer needs to make in support of a specific journey. For a business-to-consumer (B2C) customer, 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.

Step 2: Build analytics capability (capture sources of value creation)

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 (Figure 2).

Figure 2: Capture sources of value creation

As part of Step 2, we need to address the following questions:

  • What are the key decisions that the customer needs to make along their journey?
  • What are KPIs or metrics against which progress and success will be measured?
  • What are the predictions that the customer needs to support their decisions?
  • What data sources might be useful in fueling those predictions?
  • Where and how will those predictions be operationalized (as part of Step 3)?

These questions map to the Data Science Value Engineering Framework (Figure 3) that I will cover in a future blog. (I’m planting reasons to keep you coming back for more.)

Figure 3: Data science value engineering framework

Step 3: Embed analytics (operationalize sources of value creation)

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, and so on). Step 3 involves embedding the AI analytics and operationalizing the customer, product, and operational insights into the organization’s value-chain creation process (Figure 4).

Figure 4: Operationalize analytics

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. In the end, the value of the product or service is higher than the aggregated costs to create it. A good example is in the oil & gas industry (Figure 5).

Figure 5: Oil & gas value chain

Digital transformation value creation framework

So, what are the key characteristics of digital transformation?

  • Sweeps aside traditional industry borders to create and capture new sources of customer, product, and operational value.
  • Identifies, captures, and operationalizes these new sources of customer, product, and operational value creation.
  • Exploits the economic value of data and analytics that get more accurate and more predictive through asset sharing, reuse, and refinement.

AI plays a driving role in each of those characteristics and manifests itself across the entire Digital Transformation Value Creation Framework (Figure 6).

Figure 6: Digital transformation value creation framework

Summary

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 three-step recipe.

Yea, AI is still cool.

The intelligent era is now possible with a hybrid strategy. Learn about different hybrid scenarios and other updates in the context of SAP Analytics Cloud by joining our webinar on July 25th.

This article originally appeared on LinkedIn and is republished by permission.


Bill Schmarzo

About Bill Schmarzo

Bill Schmarzo is CTO, IoT and Analytics at Hitachi Vantara. Bill drives Hitachi Vantara’s “co-creation” efforts with select customers to leverage IoT and analytics to power digital business transformations. Bill is an avid blogger and frequent speaker on the application of big data and advanced analytics to drive an organization’s key business initiatives. Bill authored a series of articles on analytic applications, and is on the faculty of TDWI teaching a course on "Thinking Like A Data Scientist." Bill is the author of “Big Data: Understanding How Data Powers Big Business” and "Big Data MBA: Driving Business Strategies with Data Science." Bill is also an Executive Fellow at the University of San Francisco School of Management, and Honorary Professor at NUI Galway at NUI Galway J.E. Cairnes School of Business & Economics.