Build A Connected Enterprise

Due to the rapid acceleration of digitalization, enterprise analytics engines are receiving an increasing amount of data from the buffets of our digital lives. Despite dramatic growth in structured business data over the past 20 years, an overwhelming majority of digital data still seems unorganized and fragmented due to extensive digital transformation projects.

But being a data-driven, connected organization today entails more than just relying on an analytics suite to fuel your insights. It involves leveraging every available piece of data to produce larger, richer, more in-depth perspectives of your clients, operations, personnel, and successful frameworks. A considerabl

This method of organizational leadership has value because it allows for quicker adaptation to shifting market conditions, more rapid innovation, and the creation of experiences that benefit all stakeholders equally.

Immediately deploying a workforce that has been educated to use AI and data technologies to tackle problems that would take months or even years to solve using conventional approaches; by connecting partner, vendor, and consumer data into value networks, all ecosystem stakeholders¹ visibility and data sharing would be smooth.

A wise man once said, “All change is hard at first, messy in the middle, and so gorgeous at the end.”

British Telecom, Openreach sought to update its internal procedures to guarantee always-on connectivity for each customer during the pandemic. Being one of the largest telecom entities in the UK, maintaining nearly all the public digital infrastructure in the country, they faced a significant roadblock, the legacy IT infrastructure. Openreach staff members utilized over 150 programs for every customer, frequently interleaved with laborious manual procedures. To solve the problem, they produced specific task and process maps and automated more than 200 processes utilizing tens of thousands of bots. To hasten the identification of fresh automation opportunities, Openreach additionally included an ML engine in its RPA.

Their method allowed Openreach to do away with 90% of their process documentation while reducing the average time spent on customer service by over 65%. As a result, they were able to switch the jobs of their customer service representatives from troubleshooting to up- and cross-selling.

Royal Philips implemented a similar transformation roadmap to automate its entire finance & accounting function, saving over $16 million and 770,000 person-hours. Mars deployed a value network platform to break down distribution data silos and reduce inventory traceability time from 4 days to a mere 2 hours, vastly reducing the impact of product recalls. A global American multinational corporation dealing in shoes, clothes, and accessories uses AI/ML demand sensing to provide its sales network with 90% more visibility into demand and inventory data across ten nations.

The battle for businesses to capitalize on data-driven differentiation is already well underway. They first acknowledge that the data derived from cognitive operations powered by AI will form the basis for future processes and productivity enhancement. Additionally, they maximize human potential by using AI and ML solutions that take on the responsibility of menial jobs and provide pertinent real-time information to each employee. They invest in value networks that are enabled by technology, enhancing supply resilience and optimising value for customers and ecosystem players.

In marketplaces where disruption is the norm, and even decades-old businesses need help to stay up with customer expectations, it is well known that data-driven organizations grow on average 30% faster than their traditional counterparts.

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies