It’s Not Digital Transformation
It’s Experience Transformation.
Market advantage and success results from superior experiences (for customers, employees, partners, suppliers) compared to competitors. The transformation of experience is enabled by innovative processes and the platforms and technologies that support those processes.
Customer experience is widely discussed, but too many of those experiences still are needlessly unsatisfying. All experiences should be as satisfying as possible to maximize an enterprises performance.
Organizations that offer great experiences ultimately are most successful. Amazon is the example of a good customer experience. The experiences of Amazon employees, partners and suppliers, however, may be less optimal (if media reports are accurate … which they may not be).
The experiences of all these enterprise stakeholders is a compilation of the how well each business process used to deliver products and customer support is executed. These processes are comprised of the integrated actions of people and platforms.
The effectiveness of people is a function of their attitude, personality, intelligence, expertise and experience. The effectiveness of platforms is a function of their capabilities and the technology that delivers those capabilities.
General processes include:
The platforms that support these processes include these general elements
Digital and analog technologies are used to build these elements.
The world inherently is analog, not digital. While digital technologies certainly have valuable application, they are inefficient in that analog inputs and outputs of digital systems must be converted from analog to digital to analog representations.
As we move toward more deep learning and AI systems which are key to experience-enhancing platforms and the processes they support, the existing Von Neumann digital computing architectures cannot keep up (too slow, large, power hungry, etc.). Future architectures will be even more hybrid analog and digital with neural network and neuromorphic chip designs utilizing analog electronic components for optimizing performance. Platforms include semi-autonomous edge devices that communicate and interoperate with centralized devices which use and are managed by advanced software algorithms.
Semiconductor electronics affect most areas of Technology, but it faces challenges since:
· semiconductor improvements are slowing (reflecting the end of Moore's Law),
· the peak power per unit of chip area is decreasing (due to the end of Dennard scaling),
· the power budget per chip is not increasing (due to electro-migration and mechanical and thermal limits)
· chip designers have already used multi-core (which is limited by Amdahl's Law)
A path left for major improvements in performance-cost-energy is domain-specific architectures. These designs perform only a few specialized tasks but perform them extremely well.
New techniques such as innovative analog, quantum and bio-mechanical/chemical/electric processing may help continue the trend of more processing per unit of space and energy. Designs that incorporate more analog components will improve speed and power requirements for specific applications such as in medicine, biotech, drug design, cybersecurity, and search.
Neuromorphic technology is an essential enabler for cognitive computing. It is an architecture of small interconnected processors and memories which react to input patterns and can be compared to the brain in terms of its low power requirements, scalability, and instantaneous internal communications.
“Big data” includes data sets from many sources such as internal proprietary, social media, search engines, electronic device applications, ecommerce and others. Data from these sources will more and more be integrated, interpreted and applied through intermediate and end point platforms including in real time.
Ongoing development of related technology will lead increasingly to tools that are more integrated with human neuron-level function.
In addition, machines will incorporate capabilities of human emotions, for various applications, using the electrochemical and other physical characteristics of neurotransmitters. This so-called “affective processing”, among other things, will provide a more natural human interaction between machines and humans.
As an example of Experience Transformation, consider the following hypothetical application of emerging tech to retail commerce processes.
1. Men’s Apparel (MA) stores are starting up in the U.S.
2. MA analyzes local weather and other data to specify products for each store location
3. Specifications direct the “lights out” robotic manufacturing of products
4. Computer optimized sets of products are shipped to optimized locations of automated warehouses
5. At warehouses, robots handle materials and shipping preparation (on-line orders and to stores)
6. Machine learning systems predict demand characteristics and manage inventory
7. Fitting profile of customer is scanned, stored and used for article selection on-line and in-store
8. Machine learning analyzes and integrates customer feedback
9. Combined in-store and on-line predictive behavior analytics enhance business decisions.
Brick & Mortar
1. Customers order ahead for in-store pickup
2. In-store robots provide customer support
3. Robots monitor and restock inventory
4. Biometric monitoring and identification automates payment and checkout
1. Personalized product recommendations by AI
2. Chatbots handle questions
3. Packing of goods for shipment optimized by AI
4. Air and ground autonomous vehicles with optimized routing deliver goods to buyer
In summary, first determine how experiences can be transformed. Then apply the technology and human transformations needed to effect the experience transformation.
Copyright © 2019 TechWise Media LLC All Rights Reserved