Approaching digital transformation in the era of ubiquitous ambient intelligence expectation by customers
Ambient intelligence is the new buzz word in the customer driven economies. Similar to how people started perceiving internet as being ubiquitous but invisible in the customer driven economies; intelligence, connectedness and automated correlation mapping in real-time are also being expected as to be invisible and omni present.
An ideal digital transformation approach should converge towards the customer to deliver ambient intelligence while diverge at the backstage to deliver and cover more and more demand fulfilment journeys for the customer. System nodes within the digital transformation value chain should also have the ambient intelligence built in to understand the surrounding nodes in the value chain and deliver adaptive actions and insights keeping the customer journeys at the epicenter.
Based on my experience in driving various digital transformation journeys for our customers, I am outlining eight essential guidelines while approaching the transformation of the systems that forms the backbone for a futuristic digital business model.
1. Understanding the pedigree of the system and the purpose it is serving within the business value chain
Pedigree of the system has to be analyzed to evaluate the number of generations it has transformed for its fitment to reflect its ambient intelligence capabilities within the customer centric business value chain environment it belongs to. Pedigree analytics should also focus on areas like information architecture, system architecture, technology architecture and stack relevance, I/O relevance and their integrity, scalability and performance to deliver real-time outcomes with little latency between I/Os etc.
2. Visualness of the systems to portray the customer and customer segments
As part of the digital transformation initiatives to deliver ambient intelligence as part of our offerings, each of the systems handling the business value chain data should re-evaluate its as-is information model to analyze whether the same is having the visualness to portray the customer and their segments with respect to the role that the system performs. If not, the same has to be redefined by moving customer as the root node in the information model to ensure that the system at any point of the process/ workflow management is not losing the context of the customer.
3. Extensibility is key to adapt to the customer journeys
The customer segment that we deal with evolve continuously as the world around them are transforming digitally at a faster pace. Expectations are evolving to source to consumption models like “farm to fork”, “beans to coffee” etc. In order to deliver those macro level of supply chain details from the inventory sourcing standpoint, it is quite important that systems are architected for future extensibility. System readiness for such customer journey centric extensibilities will ensure quality and thereby contributing significantly to achieve tipping points of efficiency.
4. Reflection of the customers’ perspective on the I/Os
Irrespective of whether the system has direct proximity and channel of interaction with end customer or not, each of the systems in the customer centric value chain should reflect the customer perspective in its I/Os. As backstage systems are meant to be working in synchronization with front stage customer facing systems for the lean demand-supply model, the backstage systems should have meta inputs from the demand parameters, service parameters and market parameters for accurately ensuring supply through perpetual resource availabilities. Hence reflecting the customer perspective on the I/Os is a mandatory assurance to be ensured as part of the transformation.
5. Proximity to the digital nature for the I/Os
Digital nature of the I/Os that decides and influence the final outcomes is also quite important to ensure that decisions and outcomes are not diluted with too many samples. Many of the sample I/Os can be outliers. Decision impacting and influencing variables should be digitally transformed by eliminating outliers to ensure that the system undergoing transformation for ambient intelligence delivers the fine-tuned intelligence to aid the business processes in more constructive ways.
6. Real-time nature of the system
Real-time data lifecycle management is key to driving adaptable and intelligent business strategies that needs to emerge as the true outcomes of right digital transformation. Ambient intelligence delivery that happens in real time to the customer has to have the right backing up of process management systems that can reflect the same characteristics as well. Spontaneity and the elimination of time lag between inputs and outputs has to be defined as the true expectations of the systems undergoing transformation.
7. Weightage applied across artificial and human Intelligence for the business process managed by the system
Evaluating and analyzing the tasks managed by the system to figure out if any tasks would really involve human attributes such as human experience or intuition is also key. Right kind of weightage has to be applied across AI (wherever AI can augment) and human intelligence for the right digital future build up based on the macro level of task segregation to replace human intelligence with AI (wherever AI can augment).
8. Motivation factors applied for the human actors within the system for the journey towards near real time
If the workflow management handled by the system undergoing transformation can’t eliminate human attributes completely, system should have a strategy and model in place to ensure that the human actors involved in the process value chain are motivated enough to ensure the sanctity of the system to deliver predictable outcomes. While the actors can be equipped with right information, insights and tools to take the right actions, they are not ensuring the motivation attribute of the actors involved. Motivation factor as part of digital transformation can be addressed to an extent by incorporating loyalty models within the workflows and tagging them against the action precision and its timeliness.