Recently, TCS announced that its artificial intelligence platform Ignio has doubled its year-on-year revenue, and crossed the $60 million. In the previous financial year, the revenue stood at $31 million. With this kind of growth rate, TCS management says that it has set its eyes on making Ignio the fastest growing software product to cross the $100 million mark by the end of the year.
Launched in 2014, Ignio was rolled put among a bunch of other software products by TCS, but once the software gained traction among enterprises, it started to be marketed as a standalone product, to differentiate it from TCS’ traditional services model. Combining cognitive capability that mixes AI, machine learning, and advanced automation, Ignio is a unique software product that covers AIOps, workload management, ERP and business operations.
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As a part of the unit called Digitate, the Ignio platform leverages AI and automates business processes, thus allowing businesses to forecast and prevent issues with agility, assurance and high customer experience. Over 100 large enterprises use Digitate’s cognitive automation software to achieve efficiency at the moment. In the last 12 months, Ignio clinched 52 new deals, bringing the total number of customers to 105. These are mostly global companies, distributed across the retail, manufacturing, telecom and banking and financial services sectors.
Ignio, Nia and HOLMES Competing To Capture The Market
There has been a heightened focus on AI enabled platforms by large Indian IT companies. The largest among such platforms — TCS (Ignio), Infosys (Nia) and Wipro (HOLMES) are competing in the same space of AI and automation enabled digital transformation. These companies in the past have been famous for their digital services, but now are emphasising on developing software platforms to clients enhance customer processes. To achieve this, companies are marketing their AI platforms separately from their other stack of services.
Infosys Nia which has been deployed across the company’s vast service lines, aims to help create operational efficiency through AI. Nia consists of Advanced Machine Learning, Contract Analysis and Chatbot capabilities to help companies get rid of the various problems they face, be it any sector. Nia has been built on the Infosys’ first-generation AI platform, Mana, and its Robotic Process Automation (RPA) solution, AssistEdge. Together, both these products have created more than 50 clients and 150+ deployments across different sectors, the company reported.
On the other hand, Wipro’s HOLMES has been implemented in enterprise operations across 350 of the company’s clients. Holmes integrates AI with automation to reduce operational pain points and accelerate efficiency. Wipro HOLMES has been successfully deployed in data and information-driven verticals, including Banking and Financial Services Institutions (BFSI), Retail, Manufacturing and Telecommunications. Wipro’s management has set a target of achieving $50 million in automation-only deals, according to a media report.
Even mid-sized companies have been focusing on developing AI-based platforms extensively. Just recently, Zensar rolled out its set of platforms for its customers that are based on AI to help drive value creation for the customer across various departments including sales, marketing, IT, talent supply chain, HR, collaboration, etc.
What Value Are These Platforms Creating?
IT infrastructure automation can be challenging and requires huge amounts of servicing in order to manage the processes. This has led to IT companies now offering automation services which also self-learn to make their maintenance easier. Such platforms are driven by pure business demand, wherein many are looking for capabilities to transform processes and make them both agile and intelligent.
Platforms like Ignio, Nia and HOLMES link together prediction, recommendation and autonomous execution on one platform, which can be extremely valuable for a customer trying to gain competitive advantage. Such systems are looking to provide rule-based reasoning wrapped with contextual and pre-built intelligence to give more accurate predictions and drastically enhance autonomous decision-making.
Over the years, companies have learned to create data strategies and while analytics helped them derive value, yet processes remained complex, and rampant with inefficiencies. What the above platforms are looking to achieve is finding and removing pain points in the processes, or anomalies that exist using AI. Then, they are expedited using automation tools.