<div align="justify">The year 2016 was one dominated by disruption — from society to politics, the economy to technology — in India, and around the world. The year 2017 is shaping up to be one where some of the dust of disruption begins to settle, and we find a new way forward. As the consequences of a customer-led and digital-centric market start to take shape, businesses in particular have had to come to grips with the stark reality that slow, calculated change won’t cut it in this new environment. In the race to disrupt or be disrupted, and as organisations face fierce competition both at home and abroad, emerging technologies like artificial intelligence (AI) are seeping into the mainstream enterprise.<br /><br />While self-driving cars and virtual assistants continue to dominate headlines and capture the imagination of consumers, enterprises in India are quietly grappling with how they too can effectively leverage AI as they seek the holy grail of deeper contextual insights. Think of it along these lines — touch-point humans have with technology can be used to gather data. Connect all of those dots and tremendous insights will emerge. It’s referred to as intelligent digital mesh, and analysts believe it will reorganise 21st century life in a major way.<br /><br />Today, AI in India is limited to a handful of use cases — primarily in ecommerce — but with the increased emphasis on digitising the economy, AI is expected to move into new sectors such as FinTech, manufacturing, healthcare, and education. According to ‘Tata Consultancy Services Global Trend Study’, 68% of companies use AI for IT functions, but 70% believe AI’s greatest impact by 2020 will be in functions outside of IT such as marketing, customer service, finance, and HR.<br /><br />India is making tremendous advancements in technology, but is lagging behind in utilising the full potential of AI — in part, because of a lack of an AI-ready infrastructure to support it (setting aside for a moment the additional clear need to address policy, security and skills development).<br /><br />According to another report by Assocham and PwC titled ‘Artificial Intelligence and Robotics–2017’, there are ample opportunities for national initiatives like Make in India, Skill India and Digital India from AI technologies. As data science gets set to drive the artificial intelligence (AI) market in 2017, a few Indian startups are initiating development of conversational bots, speech recognition tools, intelligent digital assistants and conversational services to be built over social media channels. <br /><br />While I’d like to imagine my grandkids will one day read about AI in their digital history books — and future generations looking back at early AI efforts with a wiser, aged perspective — the reality is, making AI commonplace will take a lot of work. And nobody will feel this pressure more than the engineers and IT managers who make it all work. How will networks handle such massive quantities of data?<br /><br />The AI data quagmire<br />With the increasing quantity of data, to be uploaded to the cloud, to data centres, to analytics platforms and more, IT faces an overcrowding problem. True, providers are enhancing their infrastructure for AI. Google’s cloud has AI capabilities built-in, IBM has released APIs for AI, and Amazon is committed to AI as well. Their new features are valuable, forward-looking and critical, speeding up AI for many different business cases. But overcrowding may take an entirely new architecture to be successful. Otherwise, how will any network admin or DevOps group cope with the sheer scale of data for AI? <br /><br />Luckily, a new wave of solutions address precisely this problem. It starts from the belief that enterprises should have fast, agile, and cloud-connected networks. Let’s face it, without optimising flows of traffic, AI will only clog up the digital veins and arteries of enterprises. Unless data can be pulled in from anywhere, and quickly integrated, companies won’t be able to take advantage of the ‘digital mesh’. <br /><br />Reimagining network for an AI world<br />The good news is that organisations don’t have to burn their hardware. Software can virtualise equipment for greater control and order. For example, imagine a reality where network flows can be managed centrally and new sites can be provisioned — all without typing a line of code. SD-WAN (software-defined wide-area networking) was built for this holistic yet agile approach. <br /><br />What if you need to make sure remote branches communicate with the cloud that’s processing artificial intelligence algorithms? Use a software-defined edge. And make sure your AI insights are easily available and quickly analyzed, with all the necessary data, by taking advantage of end-to-end network monitoring. Even if you must rely on external providers, monitoring cloud and SaaS products can ensure AI functions work the way you want, fast and effectively.<br /><br />We’re ultimately talking about a new networking approach — software-definition is the key to unlocking infrastructure to support AI. That is what will be written in (or uploaded into) the history books. In the future, when our grandkids learn about the rise of AI, surely, there will be breathtaking passages about human advancements we can’t even imagine now. But the IT infrastructure that makes it a reality? That will also be featured — and for me, that’s the most exciting part.<br /><br />(The author is Vice President, India and SAARC, Riverbed Technology)</div>
<div align="justify">The year 2016 was one dominated by disruption — from society to politics, the economy to technology — in India, and around the world. The year 2017 is shaping up to be one where some of the dust of disruption begins to settle, and we find a new way forward. As the consequences of a customer-led and digital-centric market start to take shape, businesses in particular have had to come to grips with the stark reality that slow, calculated change won’t cut it in this new environment. In the race to disrupt or be disrupted, and as organisations face fierce competition both at home and abroad, emerging technologies like artificial intelligence (AI) are seeping into the mainstream enterprise.<br /><br />While self-driving cars and virtual assistants continue to dominate headlines and capture the imagination of consumers, enterprises in India are quietly grappling with how they too can effectively leverage AI as they seek the holy grail of deeper contextual insights. Think of it along these lines — touch-point humans have with technology can be used to gather data. Connect all of those dots and tremendous insights will emerge. It’s referred to as intelligent digital mesh, and analysts believe it will reorganise 21st century life in a major way.<br /><br />Today, AI in India is limited to a handful of use cases — primarily in ecommerce — but with the increased emphasis on digitising the economy, AI is expected to move into new sectors such as FinTech, manufacturing, healthcare, and education. According to ‘Tata Consultancy Services Global Trend Study’, 68% of companies use AI for IT functions, but 70% believe AI’s greatest impact by 2020 will be in functions outside of IT such as marketing, customer service, finance, and HR.<br /><br />India is making tremendous advancements in technology, but is lagging behind in utilising the full potential of AI — in part, because of a lack of an AI-ready infrastructure to support it (setting aside for a moment the additional clear need to address policy, security and skills development).<br /><br />According to another report by Assocham and PwC titled ‘Artificial Intelligence and Robotics–2017’, there are ample opportunities for national initiatives like Make in India, Skill India and Digital India from AI technologies. As data science gets set to drive the artificial intelligence (AI) market in 2017, a few Indian startups are initiating development of conversational bots, speech recognition tools, intelligent digital assistants and conversational services to be built over social media channels. <br /><br />While I’d like to imagine my grandkids will one day read about AI in their digital history books — and future generations looking back at early AI efforts with a wiser, aged perspective — the reality is, making AI commonplace will take a lot of work. And nobody will feel this pressure more than the engineers and IT managers who make it all work. How will networks handle such massive quantities of data?<br /><br />The AI data quagmire<br />With the increasing quantity of data, to be uploaded to the cloud, to data centres, to analytics platforms and more, IT faces an overcrowding problem. True, providers are enhancing their infrastructure for AI. Google’s cloud has AI capabilities built-in, IBM has released APIs for AI, and Amazon is committed to AI as well. Their new features are valuable, forward-looking and critical, speeding up AI for many different business cases. But overcrowding may take an entirely new architecture to be successful. Otherwise, how will any network admin or DevOps group cope with the sheer scale of data for AI? <br /><br />Luckily, a new wave of solutions address precisely this problem. It starts from the belief that enterprises should have fast, agile, and cloud-connected networks. Let’s face it, without optimising flows of traffic, AI will only clog up the digital veins and arteries of enterprises. Unless data can be pulled in from anywhere, and quickly integrated, companies won’t be able to take advantage of the ‘digital mesh’. <br /><br />Reimagining network for an AI world<br />The good news is that organisations don’t have to burn their hardware. Software can virtualise equipment for greater control and order. For example, imagine a reality where network flows can be managed centrally and new sites can be provisioned — all without typing a line of code. SD-WAN (software-defined wide-area networking) was built for this holistic yet agile approach. <br /><br />What if you need to make sure remote branches communicate with the cloud that’s processing artificial intelligence algorithms? Use a software-defined edge. And make sure your AI insights are easily available and quickly analyzed, with all the necessary data, by taking advantage of end-to-end network monitoring. Even if you must rely on external providers, monitoring cloud and SaaS products can ensure AI functions work the way you want, fast and effectively.<br /><br />We’re ultimately talking about a new networking approach — software-definition is the key to unlocking infrastructure to support AI. That is what will be written in (or uploaded into) the history books. In the future, when our grandkids learn about the rise of AI, surely, there will be breathtaking passages about human advancements we can’t even imagine now. But the IT infrastructure that makes it a reality? That will also be featured — and for me, that’s the most exciting part.<br /><br />(The author is Vice President, India and SAARC, Riverbed Technology)</div>
