What did the buffalo say to his son when he left for college?

“Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo.” Prof. Pushpak Bhattachararya kick-started our NLP course at IIT Bombay with the ‘buffalo’ sentence. It worked perfectly for him; he could witness our amusement when he declared that it is a grammatically correct statement! Our ways to communicate continue to amuse me at times. As a matter of fact, it is often debated that language played the most important role in human evolution. In this article, I argue that language will also play the pivotal role in evolution of artificial intelligence.
A criteria to determine intelligence? Understanding a language. I am not talking about understanding grammar; that is a simpler task. Grammar is what we collectively, as a society, decide. Language is characteristic not of a society, but of a person. Each individual has a unique way to use and misuse language. That doesn’t form a communication barrier for us, though. Our ability to understand each other is remarkable. Can a machine be trained to have a similar ability?
As I mentioned in my last article, Machine learning has become a synecdoche for Data science; our so-called intelligent machines are limited to making direct inferences from data. Recently, iOS was blamed to be sexist, because when you type-in leadership roles like ‘CEO’, ‘CFO’ or ‘CTO’ using default keyboard, the emoji it suggests will be the default male in a suit. Did it choose to be sexist? It probably just reflects the data from conversations of millions of humans; it doesn’t choose to be good or bad, it only chooses to be like humans. Intelligence is more than this.
Arnold, in HBO’s Westworld, has the notion of a ‘consciousness pyramid’:

These layers of the pyramid portray the depth of consciousness. It made me wonder, does intelligence have similar depth to it?
Currently, we are working on harmonizing Point-Of-Sales (POS) data. Let me introduce you to my favorite member of our team, we call him Frank. Frank is 2 months old now. He is extremely good at understanding semi-structured sequences of words (To call our inputs as sentences would be incorrect). Why do we call him Frank?
“It’s alive. It’s alive… It’s alive,” – Frankenstein
What makes Frank lively? It is not the tendency to make the same decisions which humans make. We couldn’t settle for that, because we didn’t have sufficient data for supervised learning, and the data which we had has quite a few errors. Harmonization of POS data is simply too difficult and time consuming of a process to be done manually. Frank was initially designed as an unsupervised learner. It started by learning a vocabulary which we created using corpuses like OpenFoodFacts, DBPedia, WordNet, etc. Frank structures its vocabulary as word clusters by guessing the semantic distance of words using word embedding. The second level of learning comes into the picture when a new client is on-boarded; Frank learns the domain of the client depending upon how the client wants its data to be harmonized. Essentially, Frank biases its vocabulary to assign weightages to words as per their importance to the client.
At this stage, we started getting decent results, so we wondered, can we add supervision to it? After few failed attempts using mainstream techniques, we ended up building a layer which mimics survival instinct.
Philosophy of this third layer: “If it saves your life, 2+2=5”. We spawn multiple supervised learners at this level, each one of them aims at surviving consistently in at least one domain. Each of these classifiers behaves like the human notion of ‘stereotypes’ for Frank; each classifier knows how to behave in a particular context. To be precise, each of these classifiers is an over-fit. These classifier have a role similar to weak classifiers in boosting algorithms; difference being that weak classifiers are functionally weak, Frank’s classifiers are partially-trained directional fits. The benefit of using a functionally complex classifier as a weak classifier is that over time and data, it will stabilize to a less weak state, which is not the case with a conventional weak classifier.
How does Frank know which stereotype should be used when? The third level represents the confusion in Frank’s head, and the resolution comes at the fourth level; Frank has to make the ‘choice’. Frank adapts over time to understand which stereotype works better in which scenario. This latter half of Frank is a greedier and cleverer extension of “Random Forests” classifier. Depending on the domain, client, and the quality of feedback given by people who manually verify Frank’s results, classifiers might either be chosen over others, decommissioned, or put in cryosleep until they get better results. Since individual classifiers capture local patterns perfectly, we have simulated Long Short-Term Memory (LSTMs) without explicitly using the construct; Frank remembers local patterns, but the over-fit involved in finding those patterns is balanced with the choice Frank faces in selecting those.

Frank is ready to do his job of harmonization. Is Frank ready to face the world? Not yet. He can do his specific job very well, but the human world and our language has too much creativity for Frank to understand. Frank can understand structure and context, but it is not trained to understand metaphors, similes, sarcasm, etc. Yet, I feel Frank has made an important step in the direction which future generation of intelligent machines will be following.
Let’s end the article hoping that someday Frank will laugh at my geeky buffalo joke:
What did the buffalo say to his son when he left for college? Bison.

Automation, Predictive Analysis and AI will Transform Life Insurance in The Digital-Age

Do you remember the movie Elysium? Yes, the gigantic space habitat located in Earth’s orbit, which had the medical machine—Med-Bays that cured all diseases, even reversed the aging process by collecting the right data from blood samples. Future has always been a fountain of creative possibilities, for filmmakers and storytellers. Today, their visions have inspired many businesses, like wearable tech; connected devices are a reality. Wearable biometric sensors, such as FitBit can track information related to health and fitness.
As new technologies emerge, consumer trends and preferences change. Similarly, for the traditionally cautious and heavily regulated insurance industry, data technology is transforming their core business model. With the rise of Artificial Intelligence and Machine learning, insurance activities are becoming more automatable. Hence, the biggest opportunity for traditional insures will be to effectively capture and analyze data to better manage financial risks, develop new pricing models and mitigate personal health dangers.
Automation and Predictive Analysis will motivate people to improve their lifestyles, by gathering data from sensors to analyze people’s exercise habits, and vital signs, including their heart rate blood pressure, body temperature and an array of other health metrics. Through rule based algorithms, the accumulated data helps generate information that can recommend behavioral improvements (diet needs, or will suggest for increased activities in daily life)—predictive and prescriptive data and evaluate the results of these recommendations
Artificial Intelligence in Insurance:
The role of AI in logical intelligence is well documented and already being implemented in many industries. What has not been discussed in depth yet is emotional intelligence in relation to AI. This will be an even stronger future driver in the success of an automation/analytics solution for the insurance industry. How? Emotional intelligence can be initially derived from non-verbal gestures, including facial expressions or different tones of voice. Rather than relying on what an individual inputs as their activity (if the activity cannot be recorded automatically through hardware tracking), AI can possibly leverage EQ to ‘see through’ manual inputs of activity to accurately predict the likelihood of the individual successfully accomplishing their tasks. Though research in this area is still quite early, it is promising that insurance organizations are looking towards this direction. It will not only lead to a more efficient insurance industry as a whole but also a much more accurate view into an individual’s health status.
The challenges remain, but we are moving swiftly into a future in the insurance industry and other industries as well where quantifying human health will change the way many organizations operate, especially their interaction with consumers.
 
 
 

Purposeful AI™ for a Smarter Enterprise

Not long ago, when people heard about “Artificial Intelligence (AI)”, most imagined a world overrun by machines and robots. In contrast, mentions of AI today lead to engaging conversations about how it’s helping businesses innovate and transform. What has changed? While AI has been around for over 60 years, it’s only now that the technology – massive amounts of commodity compute and storage- and techniques – like deep learning, machine learning, and natural language processing – AI is contingent upon are becoming more broadly available, and also have matured enough to support a very broad variety of use cases across every industry. In addition, a big impetus has come from the increased availability of data, which is key to AI development. Just as humans learn and get better over time by processing information, AI techniques like machine learning and deep learning need sufficient data to “learn” and produce results that can simulate human decisions.

Impacting Businesses Across Functions

AI-based engines are already being used to do repetitive and mundane tasks, thus freeing up employees’ time to focus on new opportunities. In addition, AI can give organizations an edge in today’s competitive landscape, where staying customer-centric is a necessity. The technology is uniquely positioned to help businesses capitalize the opportunities these new-age customers bring. For instance, many companies that have been selling products for years, will soon have to rethink their processes and start selling products-on-demand services and to align with this, it will be necessary to reimagine their business processes. For instance, car manufacturers will have to replace reactive maintenance processes with AI-enabled preventive and even proactive maintenance facilitated by virtual agents that are accessible 24×7.

And it’s not just in the area of sales and marketing that AI can have an impact. It can transform product design and testing, by accelerating and automating processes to get products to market quickly. AI can also help financial institutions mitigate issues in navigating the ever-evolving regulatory environment and staying compliant. In particular, the impact of AI, which has the power to fundamentally reimagine processes, will be felt industry-wide and organization-wide.

AI Technology Trends

While there is tremendous emphasis on AI capabilities like deep learning and machine learning, attention should also be paid to smartly incorporate organizational knowledge to enable AI systems to function at higher cognition levels. This requires us to leverage Symbolic AI techniques, an AI technique that has been around for many years, and with its capability to understand and represent knowledge, it can play a significant role in today’s enterprise ecosystem which demands a broader view of AI.

Explainable AI, is also critical as it is no longer sufficient to merely compute a response that directs the next step in the process or provides necessary input for a complex decision; it is also increasingly important that we know why that response was generated, particularly when there is a need for compliance and regulation.

Finally, current AI approaches work well with training data that is labelled – an approach called Supervised Learning. However, enterprises lack sufficient training data that is labelled, and AI techniques need to evolve towards Unsupervised Learning models that do not require labelled data to train the AI models.

An Integrated AI Platform

As described above, businesses will need a range of AI capabilities to address the variety of use cases – cognitive automation, virtual agents, robotic process automation (RPA), high-performance and scalable machine learning and natural language processing rank among the top desired ones. But one is hard pressed to find one platform that can deliver all these together, or in any flexible combination desired by businesses. This is where I believe Infosys Nia™ can make a significant difference. It is an integrated AI platform that unifies many capabilities (including the ones I have enumerated in this paragraph) to deliver a more powerful and comprehensive solution which can be integrated into any enterprise landscape.

We have also made Infosys Nia agile and open so that it benefits from proven open source technologies. In the past 12 months, our AI platform has gone through rapid releases to ensure that it is relevant, significant, and true to the mission of delivering Purposeful AIÔäó to the enterprises. We believe that we can only do this by incorporating feedback, inputs and insights gathered from close engagements with customers who have embraced this platform.

Looking ahead

With AI, we are all on a journey, which will be more effective by staying committed to building open and agile systems. This can help to rapidly bring new capabilities that will enable AI systems to solve increasingly complex enterprise use cases. For now, I believe we are off to a great start with Infosys Nia by delivering purposeful AI to enterprises.

Connect with me on Twitter @navinb to converse about the latest on AI and related topics. Follow @PurposefulAI to learn more about the latest on the Infosys Nia front.

Infosys Nia™ – Making deliberate artificial intelligence a reality

I recently joined Infosys. Coming in from the outside I thought I had a fairly decent idea about what the Infosys AI platform might be about. I knew that in 2016 Infosys had announced an AI platform that was going to employ machine learning to amplify the deep knowledge of an organization to drive automation and innovation. With this platform, automation was being combined with knowledge and understanding of the business, and the IT landscape to solve customer challenges.

My first few days have revealed to me that what this platform, Infosys Nia™, is about is far more than what I had imagined it to be. If you are someone who is also wondering about how far the reach of this platform is, then hopefully you’ll find this post useful as I attempt to uncover what might not be completely obvious to all.

First, let’s consider what Infosys Nia is meant for and what it can do.

An imperative

According to Gartner, by 2018 six billion connected things will be requesting support, smart machines will distribute 10% of human work, and 50% of the fastest-growing companies will have fewer employees than instances of smart machines and algorithms. All of this activity is a sure recipe for tremendous volumes of data. In a world impacted by a deluge of data, there is an urgent need to be able to make sense of the most granular piece of data as well as from broad trends in ways that, when understood together the resultant meaning can lead to a purposeful change. And, as the ferocity of data generation and voracity of our data consumption grows unabated, we will need to move through this cycle of understanding, amplifying and then quickly impacting the appropriate areas without missing a beat or a moment. Sounds overwhelming? It is.

Except, if you have a truly intelligent platform at your disposal that discovers, learns, senses and acts to address problems, often before they arise. So whether it is a matter of improving DSO (Days Sales Outstanding), employing an automated resolution of payment exceptions, understanding voluminous and complex contracts, finding a better way to treat cancer, enabling a smarter harnessing of nature for better crops, or simply personalized customer service – all these can be continuously improved upon and renewed with the aid of an AI platform, that can tap into that deep seated knowledge in a manner that makes an optimal use of resources.

The cognitive underpinning

Machine Learning and knowledge management form essential building blocks of how Infosys Nia has been conceived. This enables discovery and capture of knowledge from sources of truth from both inside and outside the organization. Infosys Nia employs machine learning capabilities that extract patterns from datasets, or identifies one that was previously unknown. Knowledge management connects a set of disparate, unrelated things and captures the relationship between them, giving organizations much greater visibility and richer context. This makes for better decisions, improved productivity and higher levels of customer satisfaction. For example, while a software system can ingest system logs and records and use machine learning to identify that order validation is a step in the order-to-cash business process, a knowledge management system will not only identify it as a step in the order-to-cash process but also infer that order validation occurs after order entry and has a dependency on customer credit verifi¬cation. This knowledge base is at the core of Infosys Nia and enables a wide variety of powerful use cases.

What are the key features and aspects of Infosys Nia that enable such outcomes?

Key features and aspects

Open Source & Innovations – Infosys Nia benefits from a relentless focus on leveraging innovations in the open source ecosystem and innovating beyond, and evolving rapidly. Partnerships with academia, industry and relevant investments made by Infosys ensure that Infosys Nia remains accessible to anyone who is committed to working with it.

Integrated Knowledge based AI Platform – It is a unified platform with machine learning, natural language processing, knowledge modeling and automation capabilities including virtual agent technologies. This enables it to deliver a unified view of organizational knowledge and its seamless flow which in turn enables timely use.

Platform & Solutions approach – It co-exists and integrates seamlessly with an existing landscape. This is significant because while Infosys Nia is all about helping organizations disrupt their market, it is important that this be done with minimal or no disruption to their own business. An Infosys Nia customer also benefits from pre-packaged solutions (adapters, automatic co-relations and insights, ontologies, scripts, workflows). Out-of-box capabilities provide the tools necessary to build custom experiences.

Flexible & Modular – It is a modular platform, with options to add components over time. It comes with the ease of flexible deployment options including on-premise and public/private/hybrid cloud.

What sets Infosys Nia apart

Unlike some other AI offerings (platform-like or toolsets), Infosys Nia is a distinct offering.

First, it provides a unified view into knowledge assets and fosters knowledge sharing across all layers of the organization. It also opens up opportunities of simplification, optimization, and renovation of aging processes and landscapes. Second, it provides incomparable depth and breadth of support. What that means is that automation capabilities also span across all layers of support (L1, L2, & L3) for business processes and landscapes, and this means superior support, maintenance, automation, and amplification for digital & physical assets. Third, it enjoys the benefit of domain expertise like no other market offering that might claim similar capabilities. 35 years’ experience managing and operating systems and business process landscapes, globally, across industry segments has gone into building a platform that deeply understands varied business processes and how best their renewal can be effected.

So how does this net out?

Infosys Nia: Your platform for Purposeful AI™

Data analytics. Automation. Knowledge Management. Machine Learning.

As far as these features go, none of these are unique to Infosys Nia; many will claim to have one or more of these capabilities. What makes Infosys Nia unique is that it is an integrated platform with all of these capabilities designed to function harmoniously with each other. This means that one or more of these capabilities can be called upon individually or in any reasonable combination to address the issues at hand. In turn, this means that the possibilities for detecting opportunities for renewal and actually effecting the necessary changes are potentially unlimited. If we just look at Infosys Nia’s automation capabilities, you will find you have at your disposal the ability to employ automation in different ways ranging from the old-fashioned approach of automating fully defined routine tasks to cognitive automation (employing deep learning), and many flavors in between!

Get ready for an exciting ride, as the fun has only just begun.

If you are interested in checking out the blog posts on AI that have appeared on Infosysblogs.com in recent months, then here is the link to those posts:
http://www.infosysblogs.com/apps/mt-search.cgi IncludeBlogs=50&tag=artificial%20intelligence&limit=20.

If you are intending to attend Confluence 2017, then that will be an excellent opportunity to learn and experience more. In the meantime, join the conversation at: @PurposefulAI. Of course, I am happy to engage as well – connect with me at @puneetsuppal.

Regtechs – The Bank Friendly Fintechs

“Regulation needs to catch up with innovation” – Henry Paulson, Banker,
74th Secretary of Treasury – US.

Traditionally, banks have relied on policy, procedure and people to comply with regulations, rather than on technology – most repeatable compliance processes are mostly handled manually.

It is a known fact that regulatory pressure on banks has been increasing since 2008. BCG reports that the number of regulations that a bank has to track on a daily basis has increased from approximately 60 in 2011 to a whopping
200
in 2015. Strategic response of banks has been to handle the increasing regulatory pressure via process and people, hiring more staff for compliance. At one point, Citibank was reported to have 30,000 employees working on regulatory compliance.

Despite this, European and North American Banks have collectively paid USD 321 billion in compliance fees in the period from 2008 to 2016. With increasing regulatory pressures, high costs of litigation and compliance fees, shrinking margins and shortage of compliance experts, banks are now looking at fine tuning this process in order to ensure compliance and avoid costly fees, as well as, keep running costs low.

Technology has played a secondary role in banks’ efforts to meet regulatory requirements in the past, playing second fiddle to process and people led compliance. In the recent past, the term Regtech was coined to denote companies that aim to enable institutions in moving away from manual compliance to compliance supported by technology. Technology is going to be increasingly important, and will perhaps look to augment human capabilities in the future.

Regtech – What’s New?

Institute of International Finance defines Regtech as “the use of new technologies to solve regulatory and compliance requirements more effectively and efficiently”. Potential in this area is being widely recognized, with Regtech startups receiving a total funding of USD 2.99 billion between 2012 to 2016.

Regtechs differentiate themselves in several ways, most of them being technology based. Key ones being that they are:

  • Predominantly Saas based.
  •  Extensively use machine learning for automating analysis of both structured and unstructured data, making real time a reality for fraud identification, reporting.
  • Use new technologies like cryptography for better security, Blockchain to create better information sharing mechanism, biometrics for better identification, KYC processes.
  • Extremely agile which help in addressing changing regulatory requirements quickly and efficiently.

The short term focus of Regtechs is to make repeatable processes automated via technology, while creating self-learning systems to handle more complex requirements in the longer term.

How can Regtechs Help Banks

Regtechs, bucketed under the overall Fintech umbrella, have the potential to be a bank’s best friend. Some of the areas where Regtechs can assist banks include

  •     KYC, Identity management (Regtechs like Trunomi, Trulioo, KYC Exchange).
  •     Enterprise risk management (ArgosRisk, Finomial).
  •     Fraud prevention (Trustev).
  •     Compliance risk analytics (Corlytics).
  •     Enabling Supervision of banks by regulatory authorities (Vizor).
  •     Stress testing and capital planning (AlgoSave, Suade).

In addition to its usefulness to banks, regulators are also taking an active interest in this space. While private funding for Regtechs is concentrated in US and UK (US leads with 78%, UK coming in a far second with 9%), Regtechs are
receiving interest from regulators from other parts of the world as well. UK regulator FCA along with PRA have announced a fund to support adoption of new technologies for regulatory compliance or Regtechs. Singapore’s banking regulator MAS recently had a forum on Regtech as a part of its fintech program; and ASIC in Australia will feature Regtechs in its annual feature in April 2017. In India, a broader revolution is taking place, with e-KYC and
centralized KYC at the heart of it.  The aim is to enable greater compliance with focus on being real time, analytics,
and AI driven rather than post facto and rule based.

Regtechs have only begun to scratch the surface of usage of technology for compliance. We anticipate that there will be an increasing push towards Regtech adoption driven by increasingly complex regulations. Regtechs can help banks move from manual process driven compliance towards automated intelligent technology led compliance, helping them cut costs associated with compliance and hence innovate in an agile manner.

Bloggers

Bloggers Profiles


Abdul Razack

Dr. Abdul Razack

I head the Platform Divison at Infosys primarily focused on the Data, Advanced Analytics, Visualization and Automation solutions.

Before joining Infosys from SAP, l was most recently Senior Vice President for Custom Development & Co-Innovation. For almost three years in that role, I was missioned with delivering unique and differentiating customer-specific solutions. In this capacity I delivered over 40 new innovations based on SAP HANA and Cloud to customers worldwide across 12 different industry verticals.

My career spans more than 20 years, includes several engineering and consulting roles at Commerce One, Sybase, and KPMG Peat Marwick and SAP.

I hold a Masters in Electrical Engineering from Southern Illinois University and a Bachelors in Electronics & Communication Engineering from the University of Mysore, India.

 

Ganapathy Subramanian

Vice President – Solutions Head Platforms, Infosys

Dr. Ganapathy Subramanian

An IT industry veteran of 18+ years, in his current role Ganapathy Subramanian is responsible for the solutions and customer implementations built on top of the Infosys Nia platform. Prior to this Ganapathy was responsible for the Go to Market and Product Strategy of Infosys Information Platform. Well versed in working with open source cloud based technologies, Ganapathy has helped customers obtain not only real time insights, but also real time foresights and predictive capabilities within their transactional data.

Prior to joining Infosys Ganapathy was the Global Vice President of the Customer Engagement & Strategic Projects (CE & SP) team in SAP Labs India Bangalore. He was a member of SAP’s extended Global Leadership Team and part of the Global team of Executives at SAP responsible for the development of applications for mobile and cloud on SAP HANA. Ganapathy was recognized as “Leader of the Year” as a part of SAP Labs India Annual awards 2013. Prior to SAP, Ganapathy worked as a software Developer at IMR Global.

 

Nakul Arora

AVP, Product Management & Strategy, Infosys Ltd.

 Nakul Arora

Nakul heads Product Management & Strategy for Infosys Nia and Assist Edge product lines. Nakul brings 20+ years practical experience across all phases of SW/HW platform and product design lifecycles. Nakul holds a Bachelor’s in Biomedical Engineering and a and Master’s in Electrical Engineering from Wright State University and an MBA from Babson College.

 

 

 

Navin Budhiraja

SVP, Head – Architecture and Technology

Dr. Navin Budhiraja

In his current role, Navin leads the AI and Automation platforms for Infosys, while overall being responsible for key technology capabilities across the company. Navin is also head of all education at Infosys where his focus is on providing technology training at scale – both online and offline – for Infosys employees across the world using proprietary content and blended learning platforms. With a Ph.D. in Computer Science from Cornell University and a B.Tech in Computer Science from the Indian Institute of Technology Kanpur, Navin started his career at the IBM Watson Research Center. His background includes being the Chief Architect at SuccessFactors (acquired by SAP), CTO at CubeTree (acquired by SucessFactors), Co-founder and CTO at CoralTree, Vice President of Engineering at Center’d (acquired by Google), Chief Architect of Amazon AWS Flexible Payments Services, and Chief Architect at Vitria Technology.

 

Nicholas Martin Ball

Principal Data Scientist

Nicholas Martin Ball

Nick has been a data scientist for 17 years. After obtaining an undergraduate degree in geology at Cambridge University in England (2000), he completed Masters (2001) and PhD (2004) courses in Astronomy at the University of Sussex, then moved to North America, completing postdoctoral positions in Astronomy at the University of Illinois at Urbana-Champaign (2004-9, joint with the National Center for Supercomputing Applications), and the Herzberg Institute of Astrophysics in Victoria, BC, Canada (2009-2013). He joined Skytree in 2012, and Infosys in 2017 following its acquisition of Skytree. He has been using machine learning since 2000, applying it to large astronomical datasets, beginning with the Sloan Digital Sky Survey, and now to a wide range of problems at Infosys. In his spare time, he likes to play Scrabble, travel (road trips, hiking), and spend time with his wife.

 

Nitin Mahajan

Director – Product Strategy with EdgeVerve

Nitin Mahajan

Nitin has 29+ years of experience in the IT Industry in Process Automation, Delivery Management, Product and Platform development and implementations. He has led large Process Automation and Optimization Transformation/Cost Improvement Programs across multiple geographies – Japan, US, Europe and New Zealand. He has written white papers and POVs in the automation space along with analyst interactions for disseminating the advantages of process automation.

 

 

Puneet Suppal

AVP Product Strategy, Infosys Ltd.

Puneet Suppal

Puneet is a seasoned IT strategist and thought leader specializing in digital solutions. Experienced at leading global initiatives across multiple IT platforms, serving numerous verticals, Puneet specializes in driving business value by aligning people, technology, and business processes. He is currently focused on an integrated Artificial Intelligence platform with machine learning, natural language processing, knowledge modeling and automation capabilities to drive innovation. During his career he has focused on technology-driven innovation with customers and partners that drives business and social advancement. In particular, he has been passionate about crafting solutions that leverage the Internet of Things, often to deliver actionable analytics to address data problems. Recognized as an authority within the SAP ecosystem and beyond, he frequently writes and speaks on the importance of focusing on business processes, as well as digital solutions.

He can be followed on Twitter at: @puneetsuppal

 

Sid Bhattacharya

Associate Vice President – Platforms, Infosys

Sid Bhattacharya

In his current role, Sid is responsible for customer implementations on the Infosys Nia Platform. Prior to this role, Sid was responsible for product management, pre-sales and services for IIP (Infosys Information Platform) globally. Sid’s career spans over 17 years including founder, management and consulting roles at various organizations and startups. He holds a bachelor’s degree in computer science from BITS Pilani. He is passionate about open source software, new product ideas and is a tinkerer with technology.

https://www.linkedin.com/in/sidbhat

@sidbhat1976

 

Sudipto Shankar Dasgupta

Vice President and Head of Engineering for Platforms at Infosys

Sudipto Shankar Dasgupta

Sudipto is responsible for product development of the Infosys Nia Platform. Prior to the current role, Sudipto was heading the development of Infosys Information Platform and Infosys Knowledge Platform. Prior to joining Infosys in 2014, he was working at SAP for 10 years, with the last role there being that of the Chief Architect in Strategic Projects Group.

 

 

 

Sudhir Jha

SVP & Global Head of Product Management & Strategy

Sudhir Jha

Prior to joining Infosys in June of 2016 as SVP & Global Head of Product Management & Strategy, Sudhir spent more than 9 years at Google where his last role was as the product head of policy enforcement for Google’s multi-billion dollar Personalized Ads products. In a previous position, he led financial fraud management across all Google products, establishing company’s leadership in the industry. Before that he helped Google develop its first proprietary CRM system, now used by its worldwide sales team. Previously, Sudhir held leadership roles with three startups, two of which were successfully acquired. Sudhir started his career at Intel, where he held various roles in product marketing and software engineering during his eight-year tenure. Sudhir holds a bachelor’s degree in computer science from IIT Kanpur, a master’s degree in computer science from the University of Rochester and an MBA from Santa Clara University.

 

Vishwa Ranjan

Head – Augmented Reality / Virtual Reality, Infosys

Vishwa Ranjan

Vishwa Ranjan sees things others don’t, thanks to his 20-someodd years working in the computer graphics field. At Infosys, Vishwa helps to paint a better picture of augmented and virtual reality capabilities by showing how the technologies will impact consumers and professionals, how they’ll buttress new and old industries, and what paths are necessary to get there. When he’s not showcasing VR demos at the World Economic Forum in Davos, Switzerland, Vishwa can be found in the classroom using his doctorate in computer graphics to teach. Prior to joining Infosys, Vishwa pushed the limits of animation and visual effects for Industrial Light & Magic, Electronic Arts, and DreamWorks Animation, including work on films and video games in the Star Wars and Lord of the Rings franchises.

5 Practical Ways to Embrace Automation in your organization

Almost all coffee making recipes require the beans be ground, and then mixed with hot water long enough to allow the flavor to emerge, but not so long as to draw out bitter compounds.
And then came coffee pots, vacuum pots, moka pots, electric coffee maker, and what not! Its purpose? To infuse until the desired strength brew was achieved. Similarly, any organization that wishes to achieve the perfect blend of growth and sustainability must embrace automation.
While the benefits of enterprise automation initiatives have been well documented, in order to successfully reap value, organizations must understand that it will take more than just implementing tools. This is because the secret recipe is more strategic, and not tactical. Here are five strategic initiatives organizations must undertake to successfully introduce automation:

Educate

Organizations must understand the “can do & cannot do’s” of automation, before embracing it. Quite often, unrealistic expectations on technology kills future applications. It is therefore important to setup internal structures to educate, and evangelize the idea of automation. Also, automation is here to stay. It’s the key to our utopian, neon light future. So, the misconceptions about automation killing jobs must be addressed to ensure success.

Structure

With unlimited potential on offer, organizations would be well served to setup a Centre of Excellence to oversee the successful application of automation technologies. With enterprise software vendors racing to enrich their software suite’s capabilities with embedded AI, license costs in large disparate landscapes are set to be impacted. Instead of bearing the expensive license costs YoY, it would be better to implement fit-for-use automation use-cases over existing investments.

Functional Focus

While centralizing investments and developments, it is also important to prioritize investments into specific functions. Some functions like Procurement, Finance and IT are excellent starting points. While automation platform vendors are bundling use-cases in these functions, specific focus must be laid on leveraging automation for continuous improvement. This is the key to leverage its full potential.

User Experience

The need for positioning the user in the center of all automation initiatives is key to success. AI based systems require significant training to deliver value to users. It is therefore important to provide a superior user experience to drive engagement.

Choosing the right Partner

Given what’s at stake, it is recommended not to go with an unproven firm. In this journey, your preferred partner should display an eagerness to meet your organization’s challenges, and bring in rich domain, technology, industry and expertise. Some vendors deliver platform expertise without an industry focus, while some niche industry focused solutions. The key to success is to bring in cross functional experts to build fit-for-use solutions.
Final thoughts: Humans and machines can be partners and collaborate for shaping the future. As Bill Gates pointed out, “The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.”

3 Data Driven Challenges in Consumer Packaged Goods Companies

As one of the biggest drivers of change and innovation, the Consumer Packaged Goods (CPG) companies have always launched innovative products to meet a bolstering array of human wants. This rapid growth was made achievable and profitable only because of continuous product innovation. However, the past is no guide to the future.
According to Nielsen (NLSN) report, 2016, “only 15 percent of newly introduced consumer packaged goods succeed in the market.” Because the consumers are increasing finding the digital medium— e-commerce, Omni-channel retailing, and mobile platforms more compelling than the traditional stores. And, with this eruption there is a need for intelligent innovation in CPG industry.
Also, with this transition comes the enormous amount of ‘Big Data’, which can be used to drive business decisions, and can also help create a compelling and integrated view of the consumer experience. But doing so is not as easy it seems. Before turning any type of data into valuable insights, the CPG industry needs to address the following barriers:

Getting the right data

CPG companies today have abundant data, sourced internally and externally. But this doesn’t guarantee a great value to enterprises’ sales information. Since data obtained for entities may be incomplete or can be in an incorrect format, the quality of data is questionable.
So, when enterprises crawl in to gain a timely visibility into their sales information, it leads them to faulty conclusion. Data-driven decisions are only as good as the quality of data you collect. Using data to drive business decisions requires the right data collection system that can transform these data into actionable insights.

Gaining the proficiency to use data

Having more data doesn’t necessarily lead to actionable insights, and the best analytics tools can often fail to identify business objective.
CPG enterprises often assimilate data from different external sources (subsidiaries, channel partners or data aggregators), which inherently brings the issue around data contextualization.
For instance:

  • Altered product identification from original manufacturer designated IDs
  • Products attributed to different classifications across different value chain partners
  • Product information in a different language

Without data contextualization CPG companies won’t be able to derive meaningful insights in real time.
According to Nielsen, “Harmonization is an ongoing process that requires strict governance and constant monitoring.” But when the process isn’t in its place and is unable to draw from both local knowledge and global expertise, it delivers inaccurate information.

Deriving actionable insights from the harmonized data

The spiral of innovation in the digital era hasn’t been kind to the CPG companies. To top the already existing sales, promotional, supply-chain, and finance data; social media is a new entrant to add to the woes. And what becomes critical for CPG organizations now is how to manage, streamline sort, filter and make sense of these data mountains; making them easily graspable and insightful in real time.
As most CPG companies expand their foothold in developing markets they unlock new opportunities, and the challenge to capture critical downstream data in the distribution channel.