Figure 1: How Data and Analytics Mastery is Transforming The S&P 500
Artificial Intelligence (AI) has proven its ability to re-invent key business processes, dis-intermediate customer relationships, and transform industry value chains. We only need to check out the market capitalization of the world’s leading data monetization companies in Figure 1 and their accelerating growth of intangible intelligence assets to understand that this AI Revolution is truly a game-changer!
Unfortunately, this AI revolution has only occurred for the high priesthood of Innovator and Early Adopter organizations that can afford to invest in expensive AI and Big Data Engineers who can “roll their own” AI-infused business solutions.
Technology vendors have a unique opportunity to transform how they serve their customers. They can leverage AI / ML to transition from product-centric vendor relationships, to value-based relationships where they own more and more of their customers’ business and operational success, and can participate in (and profit from) those successes.
Now this transition isn’t something unique. History has shown that there is a natural maturation whenever a new technology capability is introduced. This natural maturation is from hand-built solutions that can only be afforded by the largest companies, to packaged solutions that democratizes that technology for the masses.
A History Lesson on Economic-driven Business Transformation
Contrary to popular opinion, new technologies don’t disrupt business models and industry value creation processes. It is what organizations do with the technology that disrupt business models and industry value creation processes. Figure 2 shows a few history lessons where technology innovation changed the economics and created new business opportunities.
Figure 2: History Lesson on Economic-driven Business Transformation
Note: see the blog “A History Lesson on Economic-driven Business Transformation” for a more detailed analysis of the technology-driven business transformation.
And the major lesson from the history lessons in Figure 2?
It’s not the technology that causes the business disruption; it’s how organizations use the technology to attack current business models and formulate (re-invent) new ones that causes the disruptions and creates new economic value creation opportunities.
Welcome to the potential of Data Apps!
What are Data Apps?
The largest organizations can afford the data science and ML engineering skills to build their data and analytic assets. Unfortunately, the majority market lacks these resources. This is creating a market opportunity for Data Apps.
Data apps are a category of domain-infused, AI-powered apps designed to help non-technical users manage data-intensive operations to achieve specific business outcomes. Data apps use AI to mine a diverse set of customer and operational data, identify patterns, trends, and relationships buried in the data, and make timely predictions and recommendations. Data apps track the effectiveness of those recommendations to continuously refine AI model effectiveness.
Increasing revenues, reducing costs, optimizing asset utilization, and mitigating compliance and regulatory risks are all domains for which we should expect to see Data Apps. However, the Data Apps won’t do anyone any good if they are not easy to use and the analytic insights easy to consume.
Data Apps vendors must master “as-a-service” business models and adopt a more holistic customer-centric product development and engineering mindset:
When you engineer and sell a capability as a product, then it’s the user’s responsibility to figure out how best to use that product. But when your design a capability as a service or solution, then it’s up to the technology vendor to ensure that the service is capable of being used effectively by the user.
Vendors must invest time to understand their customers, their jobs-to-be-done, gains, and pains. Vendors need to invest to understand the totality of their customers’ journeys so that they can provide a holistic solution that is easy to use and consume, and delivers meaningful, relevant, and quantifiable business and operational outcomes (see Figure 3).
Figure 3: Learning the Language of Your Customer
CIPIO
We will start to see a movement to Data Apps to address high-value business and operational use cases such as customer retention, customer cross-sell/up-sell, customer acquisition, campaign effectiveness, operational excellence, inventory optimization, predictive maintenance, shrinkage, and fraud reduction.
I am excited to note that I have recently become an early investor in CIPIO, a data apps company that is focused on the Fitness Industry by addressing their critical business use cases including customer retention, campaign effectiveness, and customer acquisition. I will also serve on their Board of Advisors.
Figure 4 is a screenshot of their Retention analytics. This is a great example of the “Human in charge” approach of data apps; they are creating prescriptive recommendations based upon the individual’s predicted propensities, but it is still up to the business user to select the most appropriate action given the situation.
Figure 4: CIPIO Retention Screenshot
“Human in control” is a critical concept if we want our business stakeholders to feel comfortable working with these data apps. Data Apps aren’t removing humans from the process; they augment the human intelligence and human instincts based upon the predictive propensities found in the data.
I was drawn to the CIPIO opportunity because I believe that CIPIO and data apps represents the natural maturation of the AI technology. And if we remember our history lessons, when it comes to new technologies like AI¦
It’s not the technology that causes the business disruption; it’s how organizations use the technology to attack current business models and formulate (re-invent) new ones that causes the disruptions and creates new economic value creation opportunities.
Watch this space as I share more about my journey with CIPIO. Lots to learn!
