Tech Mahindra plans to cut service delivery time for clients by reusing some standard components it has built up over the years. The move is expected to improve its productivity and margins, a senior executive said.

Tech Mahindra, among the top five Indian IT services exporters, has been building a library of project-specific intellectual property on a common platform over the past few years.

“You have to build IP to gain value and bring in knowledge in a reusable fashion. It has been happening with libraries, but not with end-to-end outcomes. So, out of 20 CRM projects, if the top five are repeated, can I cull out that knowledge so that the next project can be rolled out faster?” Abhijit Lahiri, head of new age delivery at Tech Mahindra, said.

At the base of this system is a central microservices portfolio enabled service, which is built upon microservices and open architecture. It connects six other marketplaces, which together forms the basis of the new system. These are upskilling as a service, capability as a service, digital inside continuous delivery, design thinking as a service and active workplace programme.

The company hopes to reduce time

to market and improve margins, and do more with the same number of resources or people.

Building IP is a key step towards that, increasingly important as contract values across the industry start moving down.

Once a particular software has been delivered, it becomes easier to replicate it on another project, with only the project specific customisation needing to be done separately.

“The biggest advantage is that you’ve already delivered the software and know what works. And then can build on that,” Lahiri said.

The company has already started shifting programs to this platform, which then learns from the available data and throws up trends on the overall marketplace.

“Everything we do to win a bid or project is captured in some form continuously by the AI engine, which then also forecasts what we need and what portfolio is running faster in the market,” he said.

This information can then be communicated to the sales team and help them pitch better for projects.

The company will face some challenges, including convincing clients to share IP, but says customers from non-competing industries could even stand to benefit through knowledge sharing.

The post TechM moves to new-age delivery platform appeared first on Artificial Intelligence.

Machine learning is gaining popularity to deal with increasingly complex data and analysis problems. Many projections also point that the highest growth is in India IT spending in software and IT services for 2017. This includes building new digital platforms with Machine Learning at the center. IBM continues to be at the forefront of it all.

‘Machine Learning’ termed by an IBM decades ago has evolved significantly. Today, it enables enterprises to drive critical insights. Businesses are increasingly using machine learning to support advanced analytics across a growing range of industries and initiatives. With India’s focus on digitization, it’s an apt time for organizations to make this transition.

Prasanna Keny, Senior Technical Manager, IBM India says that going forward, machine learning is going to be an imperative and not a choice.“According to a Harvard study 72 percent of organisations are vulnerable to disruption due to digitization and data intelligence. For the respondents, 29 percent of organisations said they are extremely susceptible to market disruption while about 43 percent responded they are significantly susceptible due to the digital intelligence and machine learning used by their competitors. That is how important machine learning, analytics, deep learning or artificial intelligence has become in terms of competition and the ability for an organisation to maintain the leadership position,” said, Keny.

Citing various examples Keny explained the use cases of machine learning. He said it can be used for customer analytics and fraud combating.“A use case of analytics and machine learning is for countering fraud. When we think of fraud, the image that crops up in our mind is online payment fraud. But this can be used in a variety of cases. For instance, we have customers using it for expense fraud, procurement fraud or in case of insurance fraud detection for claims,” added Keny.

However, the whole process or the journey of machine learning involves challenges as well. Some of the challenges are data management where there is unavailability of prior data, the evolving environment and toll sets users, difficulties in collaboration where multiple people are working together and operationalizing.

The post Machine learning is not a choice; it is an imperative: IBM appeared first on Artificial Intelligence.