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Major disruptive technologies such as artificial intelligence, big data, data science, and many more are determined to enhance customer engagement and drive in-depth and useful business outcomes for yielding higher revenue in this competitive market. There are professional companies and experts who are helping other companies in need to solve complicated business problems efficiently and effectively. The global AI market is thriving and is set to dominate all kinds of industries in the nearby future.
Here is an exclusive interview with Ram Narasimhan, Global Head of AI and Cognitive Services at Xebia who enlightens the readers on how Xebia is addressing the critical problems of other companies and trying to solve these by leveraging cutting-edge technologies like AI and machine learning.
Xebia is working with the customers right at the heart of their businesses and using technology as an enabler to solve their business problems. Most of the customers with whom the team has had a conversation are trying to address one or more of the pain areas which is affecting their business. The business drivers can vary based on their priority and where they are in their journey. It could be internal (cost optimization, operational efficiency, or regulatory and compliance) or external (customer experience, new market penetration, or product enhancement).
The solution to all these problems inherently boils down to how data is organized within their organization and how the business is using this data for their decision-making. Based on experiences, most of the customers fall into one of the four quadrants which in a way also define their current maturity level for using data for insights as well. Most of the clients want to use AI and ML to improve their businesses and operations, however, either they do not have the right infrastructure in place, their data are in silos, the data quality is very poor or they are not sure which AI /ML use cases they should take up first.
The company consults with customers across all these areas and ensures that they have the information needed to make the decision on their next steps. Being technology agnostic, they can provide an unbiased opinion on the right choice of technology, conduct workshops for identifying analytics use cases and quick wins, provide technology implementation services and conduct technology training through Xebia Academy arm to get the clients on their data journey.
In addition to this, the team also provides technology implementation services and has a whole suite of accelerators that are built in-house which can be integrated with the clients in a plug and play model considerably reducing the time for go-to-market.
AI is a core part of the DNA of all solution offerings. Intelligence is in-built into all kinds of offerings, whether platform design, solution architecture, intelligent process automation, or in-house products and accelerators.
Technology is rapidly evolving and intelligence is now built into almost all the aspects of the service offerings by the major infrastructure and cloud service providers. While the company is collaborating with customers, making the customers aware of those intelligence aspects and incorporating those elements into the solution design so that the customers start seeing the benefits to set the benchmark for the next phase of analytics. The same goes for all inbuilt accelerators and in-house solutions as well.
E.g. ML features like anomaly detection, fraud detection is now inbuilt into most of the cloud alerting and monitoring solutions. It is just a matter of configuration settings to set up the basic capabilities. Similarly, data quality solution, Gobbled, can integrate from all structured and unstructured data sources, perform all data quality steps, write your own scripts or use out-of-the-box rules and then run NLP algorithms for sentiment analysis, summarization, etc.
Ram Narasimhan is a visionary, analytical and people-oriented C-level executive. He is a seasoned entrepreneur who can drive business growth from 5-figure to 8-figure efficiently and effectively. Ram has more than 17 years of practical experience in CIOO and MD positions— a trusted strategic advisor to CEO, CTO, and Board of Directors, proficient in coordinating and delivering complicated cross-functional cross projects and can successfully build software for large-scale distributed systems. He has more than 15 international media publications along with over 45 LinkedIn articles with multiple feathers on a cap.
Ram is the Head of COE in a senior executive capacity who is equipped with exceptional leadership and P&L management capabilities. He directs and coordinates the overall management of the business unit to meet operational targets such as EBITA, GM, and revenue. He takes care of different aspects of data science consulting, big data consulting, deep learning, RPA, chatbots, and many more.
Ram is also a member of the Board of Studies for Universities (Xebia Education) and formulates courses for B. Tech and M. Tech with his superior technological leadership abilities.
The global artificial intelligence (AI) in the IoT market is expected to grow at a CAGR of 27.3% during the forecast period (2021-2026). In a survey conducted by Microsoft in 2020, 90% of the organizations consider IoT as critical to the overall success of their business.
All of the above-mentioned technologies can be associated with Industry 4.0 that involves the heavy use of automation and data exchange in manufacturing environments, encompassing areas such as cyber-physical systems, the internet of things (IoT), and cloud computing, among others. This trend is seeing large use of IoT and robotics (of course with inbuilt AI capabilities) across the globe for multiple areas of the connected factory, smart warehouses, enhanced supply chains, advanced robotics, and 3D printings to name a few. Use cases like predictive maintenance, connected devices, remote monitoring and control to improve uptime, helplines run faster, increase production, providing real-time alert and monitoring, and improving operational efficiency are some of the outcomes. However, collecting and processing such a large volume of data to extract quality information will continue to pose a challenge in industry-wide adoption.
The pandemic has redefined the way businesses function. The status quo is no longer applicable as industries are forced to rethink and reconfigure their digital strategies. For businesses primarily based on personal interaction and transport (retail, travel, hospitality), the challenges faced due to the pandemic were multidimensional and required a complete reimagining of their business and service delivery models. Only companies with a strong preparedness plan, hardened endpoint security, and an established cloud data strategy pivoted quickly.
According to the MicroStrategy Survey, 94% of organizations believe data and analytics solutions are critical for growth. There is a huge impetus to adopt big data and analytics across all the industries for both internal and external business functions. With employers seeing a future with a reduced or minimal workforce returning to work, automation is another area that is seeing a huge focus. Cloud is a big enabler in this digital transformation and analytics allowing organizations to rapidly transform and scale while saving cost and minimal operational overheads. However, a 2019 report from IBM found that the average company has only migrated about 20% of their data workload to the cloud, despite being aware of the benefits.
These numbers will change as we move towards the future— a shift towards augmented analytics combining AI, NLP, and ML to enhance the value of data analytics, moving to SaaS-based operating model on the cloud, data democratization or creating citizen data scientists, addressing challenges and concerns around data privacy and what is shared versus what is not shared, availability of high-quality data to be able to extract information from the huge volume of data as well as moving towards hyper-automation.
Artificial intelligence, automation, and robotics will change the future of work. While skill shift is nothing new, these technologies will accelerate the digital transformation much faster as compared to the previous ones. As per the Mckinsey survey, almost 70% of the industry executives plan to use more automation and AI in their workforce, especially in this post-pandemic new normal. This human-machine interaction will bring numerous benefits in the form of higher productivity, improved performance, and new prosperity, but these will also change the skills required of human workers, as they will need to upskill themselves technologically, along with social and emotional skills.
The distribution of the technology skills will depend on where you are as part of the bottom line or the top line. While the bottom line will need to know enough to be able to interact with machines and applications and support and maintain the daily activities, the top line will need to understand it better to be able to innovate, derive more value out of technology by recognizing additional areas of applications and define organization strategies for continuous improvement and growth.
The future of the workforce will be a significant shift (across people, process, and technology) and will require— digitization of the workforce in four categories such as cognitive, digital, interpersonal, and self-leadership, finding the set of tools that enable organizations to efficiently generate value through the process and making the evolution from a knowing culture to a learning culture, rely on data than on heuristics.
In the case of awards and recognition, the team has won multiple awards in 2018, 2019, and 2020 for innovation in emerging technologies and artificial intelligence while winning more than five hackathons in the last three years.
In the case of business strategy and growth, the company has established Analytics Centre of Excellence practice for global business serving seven regions and is focused on expanding market presence with alliances and partnerships with ten major providers.
For innovation and product engineering, the company is driving innovation with solutions such as big data as a service, machine learning as a service, and more in cloud space for customers.
Meanwhile, in the thought leadership and execution, the team is launching business-friendly solutions in the space of automated data quality frameworks, data lake, and machine learning models
Last, but not least, in the case of organizational development, AI and Cognitive Services have been growing practice leaders in multiple geographies with continuous learning, rewards, and certifications for engineers.
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