We live in an age when the ability of businesses to adapt to evolving customer needs sets apart the success stories from the also-rans. The future belongs to businesses with hard-coded intelligence that can anticipate requirements and empathize with customers. One of the ways this is happening around the world is with the adoption of Intelligent Enterprise as an approach to doing business.
While Intelligent Enterprise is generally understood as an approach to management that applies technology and new service paradigms to improve business performance, in this paper, we focus on ‘how intelligent enterprises effectively use their data assets to achieve their desired outcomes faster with less risk’. But, like most path-breaking approaches to doing business, the journey to adopt this approach is not an easy one.
One of the key concerns for any business is the possibility of disruption while deploying innovation. Enterprises need to ‘keep the lights on’, with core processes remaining unaffected as innovations are explored. Bimodal IT, the practice of managing diverse but synchronous modes of work, is an enabler of innovation. While one mode focuses on predictability; the other allows for exploration and innovation. Mode 1 is focused on areas of business that are more predictable and mature. While such legacy environments do need to evolve for the digital world, this mode ensures that they do not come at the cost of business being thrown out of gear. Mode 2 is more exploratory, an approach that allows for an experimental path to solve new problems while focusing on areas of uncertainty
We bring this understanding of Bimodal IT into the way Intelligent Enterprise is helping organizations to move forward on the path to digital transformation. To do this, we look at use cases: While in some cases, we showcase how innovation coexists with the core processes, in others we look at how core processes themselves are becoming more future-ready.
John and the story of proactive asset maintenance
John had a problem on his hands: As the production supervisor of a utility major, he was responsible for managing gas turbine operations, ensuring continuous generation of power for a key region. But mechanical disruptions came unannounced, often having a massive impact on customers and revenue, and the cost of operations. The trouble was that first time fixes were largely impossible, as at least one inspection was needed in most cases. Getting spare parts in time was another challenge.
What John needed was a future-focused analytics engine that would have foresight on what might happen when a system deviated from its expected normal operating conditions. It would form the core of a state-of-the-art Unsupervised Continuous Deep Learning model that would dynamically discover fault patterns. The model would instantly learn and apply these patterns for predicting potential failures to assets. The mechanism would not only increase the prognostic accuracy but would also create a virtual twin of the physical asset. This would allow John to test the virtual twin conveniently in place of the real physical asset.
The interesting part of the solution is its Bimodal approach – it is an exploratory solution that sits outside the core business system, like SAP Cloud Platform. Though it is tightly integrated with the back office SAP system that runs business-critical processes to get the asset master and maintenance details, it does not disrupt the core processes. John, by virtue of this superior solution, will be able to proactively respond to asset maintenance challenges without risk of downtime.
Prognostic analytics is helping companies like John’s extend the lifecycle of industrial assets, especially given their high costs. Not only does it allow John's teams to leverage industrial data to lower maintenance costs, it results in increased safety, higher productivity, and better profits. Also, since this innovation sits outside his core business system, he is not worried about his daily business getting impacted. John also has a choice of scaling this innovation and making it a part of the mainstream process, at his own pace.
Jane and the story of magical in-store credit
Jane used to dread the hassle involved in store returns. The quizzical looks from the store staff, the confusion on policy, and the inconvenience of it all just discouraged her from the task. But her recent experience in returning a pair of jeans she had picked up at a sale at her favourite clothing store, took her by surprise. The new store app that she had downloaded only asked her to click a picture of her invoice. The system picked up details of her purchases and helped her choose the items she wanted to return. To her delight, Jane could choose between exchanging it, full refund or in-store credit, all from her app! To decode Jane's delight, let's go behind the scenes.
This is another example of Bimodal IT in action: At the backend sits SAP Leonardo Foundation Services atop the SAP Cloud platform that provides a slew of useful services that do not interfere with the core business processes. The new store application makes use of the image processing APIs that offer Optical Character Recognition service. They pass the invoice image to this service and get the details in text format. Once the item is read, the invoice price is known, the store policy logic kicks in and takes a decision on the return offer to Jane. The best part is that, to her, it appears as a seamless process.
Ron and the story of his productivity enhancing bot
Ron would spend a little longer on his coffee break before it was time to make financial postings to the SAP system. The fact was that while it was a part of Ron’s job as finance executive, it was a time consuming and tedious task. For every batch he picked up, he needed to cross the hurdles of validation for consistency before he could post the entries for future processing. But all was not well in this role – he would have a tough time keeping up with work, especially for postings such as journal entries wherein the information required him to crawl through his mails, and look for the relevant report or approval. Only when this manual search was complete, could he classify the entry and decide how it was to be posted in SAP. These ad-hoc manual activities took too much of his time before the final posting was done. But his life was about to change dramatically with the entry of Intelligent Enterprise.
Ron had at his disposal an intelligent bot that could automate the process of scanning his emails. Applying principles of Neuro Linguistic Programming, the bot could retrieve the relevant piece of communication as well as get them from system reports. Finally, depending on the historical records, it would recommend the entry to Ron. All that Ron had to do was to validate and submit. This level of automation made Ron a happy man – he could spend his time doing more of the thinking work than the manual task of retrieving records that the intelligent bot took over. This is an example of Bimodal IT strengthening core processes and making them more future-ready.
The intelligent now and future
Digital transformation is changing what an organization expects from technology. Departments across the board are seeking points of confluence with IT leaders to get a grip on data, and learn to use it to glean insights that improve the bottom line. Intelligent enterprises, through innovations like hyper-automation, and adaptive and intelligent business process management, reduce painstakingly long manual work, improve decision making and enrich user experience, leading to business agility.