Redefining Enterprise Applications with Machine Learning


Posted on: November 16, 2018 by Akshay Mathur

Businesses have started to experiment with Machine Learning and develop solutions with promising business cases. Converging with technologies of Cloud, Mobile, IoT and Analytics together, it is setting the stage for future enterprise applications.

Clear benefits of integrating Machine Learning into enterprise applications are smart business process, operation efficiency with preventive and prediction capability and improved decision making with deeper insights.

As more and more Machine Learning models are developed and trained, we will have mature, tangible and robust network of models that can be reused in various scenarios.

As per Gartner, 46% of CIOs are planning and actively experimenting with Artificial Intelligence and Machine Learning for their Digital Transformation journey.

All Cloud vendors including AWS, Azure, GCP, SAP and others are offering Artificial Intelligence and Machine Learning services.

For instance, SAP positions Machine Learning at the heart of its Intelligent Platform as key Intelligent Technology delivered from SAP Cloud Platform. It includes functional services for image, speech and text processing besides core capabilities to train and consume new models.

SAP has also enhanced its existing suite of SAP application with Machine Learning including SAP Cash Management, SAP Service Ticket Intelligence, SAP brand Impact and so on…

Some use-case of Machine Learning in Enterprise Applications

As said, Machine Learning enable new business process transformation and assist users with improved decision making. Few Atos application that leverage Machine Learning are -

  1. Connected Trucks – that enables automated check-up of truck health, identified components to be replaced, places order automatically, schedules and assign task to maintenance engineer using cameras, sensors and data analytics with machine learning.
  2. SEVA – SAP Ecosystem Virtual Assistant is a conversational bot for enterprise users to interact and perform transactional activities. This conversational application interacts with SAP systems via voice, and leverages SAP Cloud Platform and Google Dialogflow services to provide interactive experience to the users.
  3. Besides applications, Atos has launched BullSequana S ultra-scalable server enabling businesses to take full advantage of Artificial Intelligence. BullSequana S enterprise servers are optimized for Machine Learning, business‐critical computing applications and in-memory environments.

Preparing to deliver Machine Learning based Enterprise Apps

  1. Innovation approach – Developing Machine Learning based enterprise applications require new approach – innovation driven and user-centric approach. Design Thinking is the best suited approach for incubating such technologies as it enables business to build, experiment, fail fast and learn to develop future ready application leveraging Machine Learning.
  2. Reskilling resources – Machine Learning brings new and powerful tools for employees. It creates new roles in response to shift in productivity and efficiency. To justice with the roles, employees need to reskill and must be supported by enterprise to adopt new business process and workforce engagement.
  3. Finding the right infrastructure – Some companies may be hesitant to develop Machine Learning based enterprise applications due to privacy and latency concerns as these scenarios may handle very critical data and be bandwidth-demanding. They need an infrastructure that fit their specific needs to enable transformation. To that extent, Atos now proposes both public and private cloud options for SAP Cloud Platform, a platform-as-a-service solution to rapidly build, deploy and run next-generation applications.

Conclusion

The Machine Learning technology is evolving at a rapid rate. As more frameworks and models are evolved while businesses experiment and learn to leverage it, we will see more application developed using this technology and benefitting to all entities in the ecosystem - business, workforce, partners and customers.

Machine Learning is the focus of this year’s Atos IT challenge. Do you have an application idea with Machine Learning? Share it now and participate at Atos IT Challenge 2019.

Also, Atos will be present at TechEd Bangalore, November 28-30. I would be happy to continue the conversation with you and imagine new AI/ML scenarios to build differentiating enterprise applications to transform your business.

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About Akshay Mathur

Global Product Manager - SAP Cloud Platform
Akshay Mathur is working as Global Product Manager for SAP Cloud Platform and SAP Leonardo with SAP Practice of Atos Business & Platform Solutions. He has led incubation of these technologies into Atos portfolio by setting-up necessary skill-set, crafting Atos value proposition and consulting services, developing application catalog and demos as part of new innovations and offerings with SAP HANA Center of Excellence. He is Member of Atos Expert Community contributing to the domain of Mobile and IoT. Akshay is very passionate about Digital Transformation with customer-centric approach based on technologies of Mobile, Cloud, IoT, Artificial Intelligence and Machine Learning. An alumnus of IIM Lucknow and NIT Jalandhar; he is also pursuing his Ph.D. in Marketing Strategy from IIT ISM Dhanbad with a vision to develop framework for adoption of Marketing Automation. He believes in living agile - learn, unlearn and relearn – to stay current and get ahead.

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