AIs Have Patterns; Humans Have Memories

 

Image by Claire on Unsplash

“Nostalgia is a sweet, incurable disease,” I remember posting this on one of my social feeds sometime back.

Now, why did this particular thought—and not anything else—surface as I began writing this post?

My best guess is that this post is about memories and AI, and because I’m generally a nostalgic creature, that’s what my brain came up with. But we would never know for sure.

These days, the increasingly capable AIs remember a lot of things, including from past conversations with you. And they are getting better at providing more relevant or contextual answers to your prompts.

But…but…

For all their monstrous computational prowess and the supposed ‘smarts’ of remembering, the AIs do not have memories—certainly not in the way humans have.

What the AIs have is a vast pool of data and the blazing-fast ability to pick out a matching pattern. It’s all statistics, mathematics, algorithms…and yes, the brute force of hundreds or thousands of CPUs and GPUs.

They can do all of that pattern-matching ad infinitum. But they have zero memories. None whatsoever.

It’s humans who have memories.

It’s humans who are transported back to a joyous moment in childhood at the touch of a scent from a favorite savory. 

It’s humans who zip across time to relive their crazy youth when a song from their college days turns up on the playlist.

And it’s humans again when a blurry video—stored somewhere in the AI cloud—of their wedding makes it vivid like it was yesterday, even when it’s played thirty or forty or fifty years later. 

The sounds, sights, and smells associated with each memory come calling to the doorstep of our mind as well.

AI can mimic (read ‘steal’) our art, our stories, our music. But it can never stop us from creativity and imagination (unless all you choose to do is watch reels and give bad prompts). 

And, of course, no AI can make and cherish memories like we humans do. Thankfully.

How CXOs Can Navigate the Heady Mix of AI, Crypto, Cloud...

Photo Imaging by Sanjay Gupta

If the recent hype around ChatGPT is anything to go by, the world seems to be reaching an inflection point in artificial intelligence (AI) and associated tools. (GPT stands for Generative Pre-trained Transformer, a large language model for generating text using deep learning.)

But AI is just one of several pathbreaking tech tools that CX and IT decision makers have at their disposal today to take their businesses to even higher levels of efficiency and agility. What will the future hold for contactless commerce and how is the customer experience being shaped and reshaped in retail? Should they experiment with the metaverse and non-fungible tokens (NFTs) and if so, how? What caveats lie ahead in a world pummeled by privacy challenges and user-trust issues?

Thankfully, insights from Harvard Business Review are at hand for CXOs to navigate the present with an eye on the future—in the form of a slim yet powerful guide of a book titled The Year in Tech 2023.

The book is neatly arranged into four sections with a view to providing some holistic crystal-gazing across a chosen set of emerging and mature technologies. The sections are named perceptively: The New Fundamentals (covering the metaverse, NFTs, stablecoins, contactless commerce, and the talent question); Fresh Takes on Mature Tech (the cloud, cookies, and ransomware); AI for the Rest of Us (data quality, no-code platforms, warehouse automation); and Trust Me (digital design choices and variation of digital trust around the world).

One of the best things about HBR’s content is its clarity and simplicity, and the same is reflected in this book—something that should be appreciated by the ever-pressed-for-time decision makers. And if they want to go in-depth into any topic that particularly interests them, there’s a ton of information on the web already.

Another highlight of the book is that it often seeks to present scenarios with an ethical lens. Socially responsible and forward-thinking enterprises will be able to benefit from such treatment.

Let me now give you a sampling of the insights gleaned from it.

One of my favorite passages is how the nature and function of the retail store will change dramatically in a contactless world [not fully contactless, I believe, but a mix determined by caution and convenience]. “It will become a space festooned with interactive displays and kiosks, virtual reality zones, and an array of robotic helpers, with fulfillment done from off-site warehouses or direct to the customer.”

A key factor in capitalizing on the opportunities and mitigating the risks, according to the book, will be the extent to which retailers can create “immersive, content-rich experiences that are highly personalized” for individual consumers.

Creating such personalized customer experiences will, of course, rely on the growing capabilities of AI tools. And while we are nowhere near generating $13 trillion of value each year (by 2030) predicted by the McKinsey Global Institute, the renewed interest in AI ever since ChatGPT broke onto the scene will only accelerate the competition among providers and the adoption among users.

Businesses of all sizes will play a role in such an accelerated adoption—and not just the Googles, Amazons, Facebooks, and Microsofts of the world who wield enormous compute and data power in their sprawling server farms.

The question is, How?

An interesting answer is given by Andrew Ng (of Baidu, Coursera, and Google Brain fame) in the chapter AI Doesn’t Have to be Too Expensive or Complicated. He posits that for far too long, much of the AI research was driven by software-centric development (also called model-centric development). In this model, the data is fixed and teams aim to optimize or invent new programs to learn well from the available data. Companies, especially tech giants, with large data sets used it to drive innovation. At AI’s current sophistication levels, however, argues Andrew, the bottleneck for many applications is getting the right data to feed to the software. In this context, it may be more fruitful to make sure companies have “good data” and not just “big data.”

This shift in approach implies that the data should be reasonably comprehensive in its coverage of important cases and labeled consistently. “Data is food for AI, and modern AI systems need not only calories, but also high-quality nutrition,” he writes. He calls the new model “data-centric AI development.”

To extend the benefit of AI to small and midsize businesses, no-code platforms that have been gaining traction of late will become increasingly important, the book notes in another chapter in the same section: “Where a team of engineers was once required to build a piece of software, now users with a web browser and an idea have the power to bring that idea to life themselves.” Most importantly, low-code platforms are making it possible to deploy AI without hiring “an army of expensive developers and data scientists.” (So ‘data scientist’ may not continue to be the sexiest job of the century after all!)

Among the mature technologies, the cloud will become even more compelling to business leaders in terms of embracing it for more workloads and use cases. The book cites how the cloud enabled the rapid development of the Covid-19 vaccine for Moderna, a relatively small firm compared to the pharma giants. Thanks to the flexibility and power of the cloud, Moderna was able to build and scale its operations on the cloud, and was able to “deliver its first clinical batch to the National Institutes of Health for phase one trial only 42 days after initial sequencing” of the virus.

Let’s switch back to an emerging star that continues to bewilder and bemuse CXOs across industries: the metaverse. For one, the book offers a relatively clearer definition of the metaverse: any digital experience on the internet that is persistent, immersive, three-dimensional, and virtual. Metaverse experiences enable people to play, work, connect, or buy (while the experiences are virtual, the things bought can be virtual or real).

Beyond the obvious use cases of gaming, virtual showrooms, and fashion shows, the book urges leaders to “look for applications” in less explored areas. “Almost every chief marketing officer already has made, or will soon make, a public commitment to sustainability-related environmental, social, and governance goals, and they will soon be measurable. What can you pilot in the metaverse that allows you to test more sustainable approaches to serving your customers?”

Such questioning by various stakeholders can open up the floodgates to innovative use cases of the metaverse and NFTs. The latter, driven by blockchain technology, have enabled a whole new range of ownership and trading activities in the digital realm.

Last but not the least, the book’s section on building and promoting digital trust, Trust Me, not only looks at interesting data on consumer attitudes and behaviors on digital trust around the globe, it stresses on the need for brands to make their design choices more carefully.

“When making design choices on a platform, managers should step back from short-term and narrow metrics like conversions and think through the broader questions about the value they create for their stakeholders,” it says. To get going, there are five questions brands must consider:

1. Are you transparent about prices and fees?
2. Do you make it easy to cancel your service?
3. Do you use default settings in a way that is genuinely helpful for customers?
4. Do you frame choices in a misleading way?
5. Do you create content that is addictive? [especially social media and video]

Most of the big tech platforms are routinely scrutinized and censured these days by regulators around the world for engaging in practices for short-term commercial gains that are harmful to consumers in the long term. We constantly hear of lawsuits, fines, and penalties.

However, businesses and brands that care for the long-term value they give to customers don’t have to wait for regulation to catch up—and make a fresh start themselves by following the best practices in developing digital trust and wellbeing. They can answer the above five questions honestly and take more proactive steps to protect consumers as well as their own reputation, and build lasting value for multiple stakeholders.

Thank you for reading and wish you all the best in treading the tech path in 2023 and beyond with caution, care, and accomplishment!

(Note: This post was first published on www.freshworks.com under a different headline and cover image.)

Will AI help companies deliver better CX in a multi-experience future?

Image: Freshworks

Customer experience (CX) may mean different things to different companies but it means only one thing to customers: whether they liked what a company offered or not in a given context or setting. And whether they are going to have that offering again—and, yes, whether they would grab anything else the company wants to sell them. Perhaps they would also spread the good word on the product or service used.

Alas, in most cases, it is the bad word that gets thrown around—often wildly and out of the company’s control into the ruthless arenas of social media.

For the past several years, most organizations have responded by throwing back more and more technology to fix their CX efforts. According to research firm Gartner, global spending on customer experience and relationship management (CRM) software reached $48.2 billion in 2018, a growth of 15.6% over the previous year.

But, despite the rising expenditure on tech and the best intentions of companies, the struggle to get a handle on customers and delight them with exceptional support and service continues. So, what is going on here? What challenges are companies facing in putting together a complete picture of their customers and serving them better? Whatever happened to the promise that up and coming technologies such as chatbots and artificial intelligence (AI) were supposed to hold in equipping organizations with the wherewithals to delight their customers (and do so at lower costs)?

We spoke to a few industry analysts and experts to dig deeper and see what gives.

One of the fundamental problems, they say, has to do with the ability to use the right data to get a comprehensive view of the customer. “After all these years, having a 360-degree view of the customer is still on the agenda for companies. One of the issues here is, what do you understand by a 360-degree view? Is it an electronic Rolodex? Is it an extended set of data about the customer, something that different vendor tools are now increasingly exchanging? Or is it something else?” says Brian Manusama, a senior director analyst at Gartner.

On his part, he offers a simple, functional definition. “If you ask me, it is the right data in order to serve your customers well. It can have just two or three components or even hundreds of components, including different metrics such as customer sentiment or behavior,” he avers.

There are other aspects to this challenge. According to Ray Wang, principal analyst, founder, and chairman of Constellation Research, “On the one hand, most companies don’t have access to all their internal data. This lives in siloed departmental systems that rarely talk to each other. On the other hand, most companies now rely on more external data which is often seen as not secure, not as safe, and in different data formats. The last part is that data often does not tie back to business processes or journeys—which means it’s hard to determine a recommendation or next best action.”

The ‘recommendation or next best action’ typically refers to suggestive responses provided by the AI engine that is increasingly getting embedded in chatbots, CRM, and other business software. Such recommendations are based on a knowledge repository comprising standard answers mapped to frequently asked questions, previous customer interactions, etc. It is now common industry wisdom that for better recommendations, it is necessary to have a rich data repository and a finely tuned machine learning model.

Wang points to a basic flaw in how most organizations have traditionally dealt with customer experience. “Most [customer] journeys have been designed for internal efficiency, not external efficiency. Customers don’t care what department you are in and this means the design point must revolve around the customer,” he says. To correct this anomaly, a lot of organizations are now “retooling” to support this from an internal process and technology point of view.

Another big headache for companies is to make their disparate CX systems talk to each other and work as an integrated solution. Today, there is a dearth of holistic solutions that can manage the entire customer lifecycle—from acquisition to retention to life-time value (LTV) management. “There are different piecemeal offerings from different solution providers. For example, there are a lot of sales analytics companies out there who help sales teams optimize their processes; likewise, there are a lot of marketing attribution and automation software that have AI capabilities to help marketers spend their budgets more optimally and so on. Similarly, on the customer success side, there are tools for churn prediction and other areas, but the overall customer journey stack is broken,” says Swaminathan Padmanabhan, director of data science at Freshworks.

According to him, it will be of fundamental value to customers “if we can tie all these capabilities together.”

A multi-experience world
Customers are now interacting with brands through a complex mesh of interfaces and touchpoints—physical as well as digital. “Do you know how many different ways one can order pizza from Domino’s? Twenty four!” says Manusama by way of an example. Such ordering ease includes the use of phone, text, social media, and voice assistants, besides showing up at physical stores and giving the order over the counter.

“We are moving toward a multi-experience world with three different modalities of customer experience across multiple digital touchpoints—gesture, text, and voice,” he says. At Gartner, analysts now call upon tech leaders to get ready to serve ‘the everything customer’—one who requires conflicting things at the same time: to be treated like everybody else but served on their own unique terms, to be connected yet sometimes left alone.

When it comes to customer experience, companies are compelled to move from a reactive way of working to a more proactive way. And while this complexity is generally good for customers, as it gives them more choice and hands them greater control over how they want to be served, it leaves companies in a constant state of flux.

The growing role of AI
Analytics and AI are playing a more important role than ever in improving customer experience, according to Wang. “We are moving from gut-driven to data-driven decisions and this requires a ton of analytics to quantify and anticipate customer needs and requirements,” he says. The rising capabilities of AI offer hope to organizations. “Over time, machine learning will support precision decisions, which means better personalization, fraud detection, and customer  experience,” says Wang. He doesn’t hesitate to call AI “the biggest shift” in CX.

Padmanabhan refers to a six-layer maturity model of AI to lay out the path ahead for customer engagement. In increasing order of sophistication and capabilities, these layers are Data Representation Layer, Knowledge Layer, Ranking and Relevance Layer, Forecasting Layer, Recommendation Layer, and Autopilot Layer. In his opinion, most companies and systems today are operating at the Ranking and Relevance Layer.

“For example, when a customer query comes up, the bot ranks the different solution artefacts and suggests the best solution artefact. Similarly, when you have a bunch of sales leads, the lead scoring system ranks them according to their probability of conversion,” he explains.

As the AI system matures, one can expect AI-based recommendations such as “increase the ad budget by 15% to 20% for a 10% increase in customer acquisitions” or “use this workflow to optimize customer experience” and other actionable insights like these.

The pinnacle of AI capability, according to Padmanabhan, would be realized in the Autopilot Layer. As the name suggests, at this level, AI can replace some common functions performed by service agents or other team members. Rather than recommend something to be done, an AI can execute it as well.

 Not that AI will possibly replace humans fully—nor is that the direction taken by companies or recommended by experts. “Today, we don’t say that we are going to completely replace human labour but say there are a lot of repetitive tasks that are involved in the support workflow or the sales workflow or the customer success workflow which can be automated. So the agents’ time can be better spent by using AI,” says Padmanabhan.

Keeping the human element in customer engagement while still using AI is “actually a question of service design,” says Manusama. What customers want are four things in how they are served: effortless, quick, convenient, and seamless across different channels. “Many companies are discovering that they can do this through self-service. However, for more complex situations, having the human touch will often be more relevant or appropriate. Basically, companies need to answer this question: Where is the business value getting generated for my customers?” he adds.

Another trend he sees is customer service vendors consolidating their solutions into engagement clouds. “Silos that existed previously are getting broken down,” he observes.

Wang’s bet is on a future built around “ambient experiences”. What we have to ask ourselves, he says, is this: When do we automate, when do we augment with humans, and when is something a pure human interaction.

The role of engagement clouds or customer engagement platforms assumes greater significance in this context.  “We need common data models, great integration, and very good journey orchestration. You can do it in platforms or you can do it with really good tooling. I’m betting that the platforms will do 80% of the work and the tooling will carry the other 20%,” says Wang.

Whichever way organizations tilt, AI is likely to play a greater role in a multi-touch, multi-experience world. Now, depending on how they are able to lend a helping hand—through automation with a smile or by being pesky or ‘unintelligent’—customers will choose to give them a thumbs up or thumbs down.

(This blog post, which I wrote as a lead editor in the corporate marketing team at Freshworks, first appeared on www.freshworks.com.)

How Paytm Uses Tech to Manage 200 Million Users

Key points:

- Paytm processed 1 billion transactions in the quarter ended March 2018
- The firm employs 200 product managers and over 700 engineers
- Its data science lab in Toronto, Canada, develops key tech tools
- App analytics and machine learning are used to retain users and for up-selling

Mobile wallets--mobile apps used to pay for recharges, groceries and other daily items--may have come of age in an increasingly digital India, but much goes behind-the-scenes to keep them working well and users hooked.

Paytm, which has 200 million monthly active users and processed close to 1 billion transactions in the quarter ended March 2018, is a case in point. It competes with MobiKwik, FreeCharge, PhonePe and several others in this space.

Discussing the tech strategy at the company in a recent interview, Deepak Abbot (pictured), senior vice president of One97 Communications Ltd, which owns and operates Paytm, said, “Though a payments firm, we are a technology company at the core and everyone here, including Vijay, is a hardcore techie--he even calls mid-level engineers sometimes to discuss architecture design.” (Vijay Shekhar Sharma is the chief executive of Paytm.)

Abbot said that most in top positions at the company either have technology background or are “quite comfortable” with tech. “Culturally, we have a tech mindset. That is another reason we have been able to build a very complex product in a flexible way.”

Sharing insights into what goes on ‘under the hood’ as they say in tech, Abbot said that quick decision-making and a product-centric approach drive software development. “In our meetings, once an idea is crystallized, Vijay is very clear about what product to build. As a result, the product managers are also clear how to get it done. And when the engineers are given very specific details, they are able to quickly build it,” he revealed.

The simplicity of the Paytm app belies its complex architecture and the number of people that work on it. For instance, Abbot said that there are as many as 200 product managers and 700-800 engineers working on different aspects of the app.

But how does Paytm define a product? “At Paytm, a product is defined as anything a consumer—be it is an end consumer, a merchant or a marketplace seller--interacts with,” said Abbot. For example, recharge is a product in itself. Paytm’s implementation of Unified Payments Interface (UPI), again, is a product (UPI is an easy, instantaneous payment system built by the National Payments Corporation of India or the NPCI). “And then you build use-cases on top of UPI such as P2P, P2M and B2B payments. Wallet--the most used product of Paytm--is another,” said Abbot. (P2P, P2M and B2B stand for person-to-person, person-to-merchant and business-to-business respectively.)

The idea of keeping all these products within the same Paytm app, according to him, is that users should move from one product to another seamlessly—something that requires “a highly scalable product” to be built.

Integration of multiple products within the same app also helps Paytm cross-sell more easily to customers, who may first use one product before being “nudged towards” others, said Abbot.

Talking about stickiness of the app and up-selling to users, he said, “We have observed that if a customer has only used Paytm for recharge, then the retention rate for such a user is 40% after three months. But if we can upgrade him to send money to others, they become power users of Paytm and the retention improves dramatically to 70%.”

Industry experts forecast bright days ahead for mobile wallets. The number of mobile wallet users is expected to grow from the current 200-250 million to around 500 million in the next couple of years, according to Probir Roy, co-founder of Paymate and an independent director at Nazara Technologies. While he believes that “the next big thing” will be “interoperability” among different wallets, he noted that it is a tough space to operate in and some consolidation is “bound to happen” in the coming years. “My guess is that the top two or three companies will have 80% of the market,” he concluded.

------Paytm Labs: Managing customer lifecycles-----

To make the most of app analytics that capture user behaviour, Paytm’s data science lab, Paytm Labs, in Toronto, Canada, works on developing multiple software tools. One such key tool is CLM or Customer Lifecycle Management.

According to Deepak Abbot, senior vice president of the company who is based at Paytm’s headquarters at Noida near Delhi, what CLM does is “catch every ‘signal’ from the app”. Explaining how it works, he said, “If you use the app for UPI, it segments you as a UPI user; if you do a recharge, it marks you as a recharge user. It also upgrades you automatically based on your behaviour or purchase history. So, for instance, if you make an electricity bill payment or a post-paid bill payment, it upgrades you to a post-paid user.” There is a lot of granularity built into the CLM tool to classify and reward different levels of users at different times.

The tool puts users in different segments and generates actionable triggers accordingly. “For example, if a premium user who earlier made a money transfer of Rs 5,000 has not used the app for a month, he will be shown a cashback offer or an ad on Facebook,” said Abbot. Similarly, alerts are shown for soon-to-expire mobile recharges and other bills. “The CLM tool uses such alerts and offers to get those customers back into the app. And if they are already in the app, it will customise the view for them by showing up frequently used icons upfront and hiding others,” he explained.

The entire user data in the Paytm app goes into a “data lake”, and the team in Canada uses it to formulate the rules of the risk engine and other software. The data lake, Abbot explained further, is a repository of multiple sources of data, including phone usage data, hardware data and address book; then there is transactional data plus the behavioural data (where the users navigate inside the app, how much time they spend shopping, etc). All this data is used through machine learning (ML) algorithms so that the alerts and promotions can be automated and personalized.

The Toronto team comprises 70 data scientists and engineers and, besides the CLM tool, has developed the company’s risk and customer score engines. “We just plug those products here (in India) and start using them,” said Abbot.

---##----

(Note: An edited version of the above post first appeared on www.livemint.com - where I used to work until recently. The interaction with Deepak Abbot took place during my Mint tenure.)

Artificial Intelligence has long way to go but it's already creating much value: Neil Jacobstein

Neil Jacobstein chairs the artificial intelligence (AI) and robotics track at Singularity University on the National Aeronautics and Space Administration (Nasa) Research Park campus in Mountain View, California. A former CEO of Teknowledge Corp., an early AI company, Jacobstein was in India recently to speak at the two-day SingularityU India Summit (held recently in association with INK, which hosts events such as INKtalks for the exchange of cutting-edge ideas). In an interview, Jacobstein talks about the confusion around AI, how job losses from AI should be tackled and the possibilities of a brighter future for humanity. Edited excerpts:

There are several definitions of AI. Which one is your favourite?

Artificial intelligence allows us to create pattern-recognition and problem-solving capability in a computer, using software algorithms. AI allows us to tackle practical business and technical problems, and it presents an opportunity for us to allow computers to do things that previously only humans did.

There seems to be a lot of confusion about what AI can or cannot do. What is your reading of the prevailing situation?

I think part of the confusion in the market might be that science-fiction movies have given people very vivid and sometimes incorrect view of what AI is capable of doing. Today, we have AI that is already at human levels of problem-solving in very narrow domains such as chess or go (a Japanese board game) or certain kinds of medical diagnostics. But we don’t have human-level AI that is general across the board. So we don’t have AI with natural language understanding at human level, and we don’t have AI that has humour or empathy at human levels. So it’s a kind of mixed landscape.

When do you think will we achieve “true AI”, so to say? What are the challenges to be overcome?

We have already achieved true AI in the sense of creating problem-solvers that add billions of dollars of value every year to various industries. That’s happening now. But if you are referring to artificial general intelligence that is at human levels, I think that probably won’t happen for several years: it could be as early as mid-2020s or as late as 2030s. The critical thing is not the time frame but the consequences of having AI at a human level and what that means for jobs, for global security, and for opportunity to solve problems.

While there are those who believe in the potential of AI and its applications, a sizeable number— including Stephen Hawking, Bill Gates and Elon Musk—have expressed fears that AI-powered machines could rule over humans. What’s your take on this?

To his credit, Elon has changed his views on this over time. He has invested over $1 billion in an entity called OpenAI to democratize access to AI and to create new AI test beds and capabilities that will allow us to build layers of control into AI software. He has also participated in creating conferences on the future of AI and sponsored Future of Life Institute’s conferences around developing new principles of AI safety, the so-called Asilomar 23 principles (futureoflife.org/ai-principles). So he’s interested in capturing the benefits of AI and wants to help us work systematically to reduce the downside risk.

There may be an alarmist element to job losses resulting from AI, but robots are indeed replacing humans. How do you think should the situation be handled?

I think there is a need to anticipate things and to have some empathy and foresightedness for people who will be affected by job losses. For one, the quality of life for the rich people goes down when there are a lot of angry and alienated and armed people around. So it makes sense to think ahead as to how we can educate people doing routine jobs now and, in anticipation of problems downstream, provide access to free, high-quality education. Not everyone will take advantage of that and not everyone will achieve high levels of skill in some new job. So it makes sense to have some kind of basic minimum income and there are different potential schemes for doing that—but nobody knows the exact answer to this.

While Peter Diamandis talks optimistically about the future in his book Abundance, there’s a widening gap between the rich and the poor? Do you think a technology like AI can bridge this gap?

I think rather than focus on the gap, it would be better to focus on the quality of life metrics: do people have access to high-quality, nutritious food? Do they have access to first-rate education or clean water? If you look at the evidence for abundance on Peter’s website or read Steven Pinker’s book, The Better Nature of Our Angels, what’s clear is that in some respects, we are living in the best times for humanity. The challenge is to create a world where, instead of having a world of haves and have-nots, we have a world of haves and super-haves. Now, the gap between haves and super-haves might still be very big, but the haves will at least have things they never had before.

You have spoken about the huge impact of atomically precise manufacturing in nanotechnology. When will it be achieved?

The kind of nanotech we have today is mostly materials science; it’s not molecular machines or atomically precise manufacturing. But I do think we will eventually have atomically precise manufacturing, as we know it’s possible and researchers have demonstrated in the lab the ability to manipulate atoms and molecules with precision. What’s missing is to do it at industrial scale; that may take years.

(Note: This interview first appeared on www.livemint.com.)

Telcos Undergoing Transformative Changes Due to Surging Data Demand

On 2 September 2009, The New York Times published an article headlined ‘Customers Angered as iPhones Overload AT&T’.

Calling the new iPhone 3GS a “data guzzler”, it went on to describe how the device choked up bandwidth on the telecom operator’s network, resulting in “dropped calls, spotty service, delayed text and voice messages and glacial download speeds”.

This is just one among several such instances of how telcos worldwide have been struggling to keep up with the burgeoning demand for data services. And much as they are trying, the demand surges keep happening in one or other part of the world (India being an apt case in point at the moment).

Telcos are fighting this battle on two counts. On the one hand, they have been upgrading their mobile networks from 2G to 2.5G to 3G to 4G. And, on the other, they have been deploying various information technology (IT) tools to operate more efficiently, reduce customer churn (customers migrating to other telcos) and to serve customers better.

The woes of telcos are not difficult to discern. From providing plain old voice telephone services up until the 1980s, operators now have to also provide text messaging, multimedia messaging, video on demand, gaming, music and several other value-added services on a mind-boggling variety of handsets.

In fact, the demand for data services is far outstripping that for voice services and causing major structural changes to the business models of telcos.

According to a report by Cisco Systems Inc., mobile data traffic will grow at a compound annual growth rate (CAGR) of 53% between 2015 and 2020, crossing 30 exabytes per month by 2020 (1 exabyte = 1 billion gigabytes or GB as it is popularly known. It is said that 5 exabytes of storage space will be taken up by all the words ever spoken by mankind).

One of the key factors in that data growth is the global popularity of smartphones to access the Internet, watch videos, consume news and other content, connect on social media or even plug into work-related applications such as email, analytics tools and customer relations software.

While much of the investments telcos are making goes into acquiring spectrum and upgrading their existing 2/2.5G networks to 3G and 4G, they are also investing significant amounts in their back-end systems that help them run those networks, including network-monitoring tools, billing software, customer experience management (CEM) solutions, etc. According to estimates by Analysys Mason, a research firm, CSPs will spend over $100 billion per year on software and related services by 2020.

In this context, India is one of the emerging market hotbeds where intense competition is playing out in the telecom market, especially for the relatively more lucrative and faster growing data segment. The latest salvo was fired in September by Reliance Jio Infocomm Ltd, the latest entrant in the country’s crowded mobile communication space. The company claims to have signed up 16 million subscribers in the very first month of the launch, touted to be the fastest such milestone anywhere in the world.

Among other things, one of the biggest competitive edges Jio has, as far as technology is concerned, is that its network is fully based on Internet protocol (IP), the same one using which all computing devices—from tiny smartphones to large web servers in data centres—connect to the Internet. Having an all-IP network allows a telco to use the same underlying infrastructure for voice as well as data and be more agile in terms of market offerings—which is why even voice can be considered just another app on Jio’s network.

Other telcos, in contrast, have a mix of IP networks and the traditional circuit-switched networks in the circles they operate in (India is divided into 22 telecom circles or geographically segregated service areas). From the vantage point of an all-IP telco, their operations would be more complicated and clunky.

That is not to say that telcos with a mixed network set-up are going to scrap their past investments in 2G and 3G technologies: instead, they will compete by optimising their multiple networks and invest in IT tools that allow them to be more efficient and agile.

According to Ekow Nelson, region head, IT and cloud, Ericsson India Pvt. Ltd, “Some of the telcos are looking for a radical transformation of their business in order to look like a digital enterprise. This is a complete transformation of their relationship with their suppliers and customers. Others are looking towards more incremental changes. There is a whole range of different approaches that the operators have and, of course, some of it is driven by where they see themselves (in the foreseeable future).”

“Part of the transformation comes from understanding that this is really about changing the way you approach and interact with your customers and changing the way you organize yourself,” says Nelson, referring to the digital transformation challenges for IT decision-makers at telecom operators.

For example, according to him, if a telco’s distribution channel is through shops and retailers, that is not digital. “A lot of young people buy services online and they want help online. So if you want to become a digital player, then most of your own operating model will have to shift: you need to build online capabilities that allow your customers to interact and operate with you in a way that is very different from walking into a shop.”

He believes that just as the music industry moved from buying and renting CDs to online audio streaming, so is the telecom sector shifting from buying recharge coupons to self-service portals and apps—that is, a digital distribution model. In the case of India, however, a hybrid model that optimises both physical and digital sales for different geographies and customer profiles looks more likely.

Given that roughly one-third (31.3%) of India’s population, according to the Census of India 2011, is in the age group of 18 to 35—a generation cohort more digital-savvy than the rest—telcos that build a greater connect with them can reap significant business benefits. And one tried-and-tested way to do that is to app-ify most of their offerings and throw as many customised pricing plans at them as IT agility allows them to.

An indication of the importance of an app-driven approach is the recent marketing campaign of Bharti Airtel Ltd, India’s largest telco with an India subscriber base of over 250 million. The ad shows how quickly the new and integrated MyAirtel app can be downloaded onto a smartphone.

Earlier, there were several apps for music, movies, money, news, etc. but the new app comes as an integrated bundle (Reliance Jio’s MyJio app, which launched before Airtel’s new app, works in a similar fashion).

According to Animesh Sahay, senior country director of sales (enterprise and telecom business), CA Technologies India Pvt. Ltd, a provider of enterprise software and services, “For telcos, it is becoming increasingly important as to how they can wrap the entire app in a fashion that they are able to record the customer experience. Today, if a customer has a bad experience with an app, they might give it a try twice or thrice, but after that they are just going to junk it.”

So it becomes very important to know what the customers are experiencing on the app and to get their feedback and tie it back to app development, he says.

A telco can install an app tool to have a view of exactly what the customer is doing, exactly where he had an issue, what the screen looked like when a particular transaction was happening on the app, etc.

In short, the tool allows the telco to replay the same series of steps the customer took and find out what went wrong and where.

Another thing operators need to do, according to Sahay, is to move away from the old, waterfall model of application development to agile development methods by embracing what is called DevOps. DevOps is the combination of development (Dev) and operations (Ops), referring to how the IT teams at most enterprises are divided.

Traditionally, there has been some friction or lack of coordination between the two teams that typically work in isolation. The DevOps movement calls for a greater cohesion between the two and the use of agile software methodology and tools that enable it.

The whole idea of DevOps and agile method is to release newer versions of software or apps as quickly as possible so that new features and benefits could be marketed to existing and potential users.

In addition, given the speed at which mobile technology is moving today, more and faster releases help fix multiple bugs and issues with the software.

The dynamism in the telco universe is causing many to move towards what is known as a catalogue-driven architecture, which allows a telco to dynamically serve up data plans and other service offerings (movie/music downloads, for instance) to customers even if third-party mobile valued-added services providers are involved.

Going forward, most telcos in India, including Airtel, Vodafone, Idea and others, will ramp up their digital transformation efforts to increase data revenue and stay relevant in a fiercely competitive market.

(This post first appeared in Mint: http://bit.ly/2fv9srl)

Big Data Analytics and the Global Hunger Challenge

(Image credit: Pixabay.com)

In a world where as many as one-ninth (around 800 million) of the global population of over 7 billion go hungry each day, 33% of the food produced for human consumption is wasted every year.

As regards India, it is home to the largest undernourished and hungry population in the world: 15.2% of India’s population is undernourished and 194.6 million people go hungry every day, according to India FoodBanking Network.

Certainly not a healthy picture—but possibly not one that technology cannot help redress.

According to a new report on McKinsey.com, global food waste and loss cost a staggering $940 billion A YEAR, with a carbon footprint of more than 8% of global greenhouse-gas emissions and a blue-water footprint that is 3.6 times the annual consumption of the US.

Such a sorry state of global food chain can be set right with appropriate use of digital innovation, including big data analytics, among others.

In my view, there is opportunity not just for governments but also for large businesses that plug into the huge global food supply chain in one way or another: the opportunity to apply creative thinking led by digital tools to bring down wastage, optimize costs and put more food on the table of poor people.

The McKinsey report suggests that cutting postharvest losses in half would produce enough food to feed a billion more people.

This and other social and economic benefits can be achieved by using technology to improve areas such as climate forecasting, demand planning, and the management of end-of-life products, argues McKinsey. The report quotes examples of work being done by startups and others in this area. For instance, a French startup, Phenix, runs a web-based marketplace to connect supermarkets with end-of-life food stocks to NGOs and consumers who could use them. “The platform enables the supermarkets to save the costs of disposal, gives consumable products a second life, and alleviates some of the social and environmental burden of waste,” it says.

For emerging economies such as India, the report suggests that innovations like precision agriculture, supply-chain efficiencies and agriculture-focused payment systems can make a huge difference.

For one, precision agriculture—which uses big data analytics, aerial imagery, sensors, etc.—is used to observe, measure and analyze the needs of individual fields and crops rather than take a one-size-fits-all approach to farming in a region or cluster of fields.

Startups as well as big behemoths are participating in this huge opportunity (the market for agricultural robotics alone is forecast to rise from $1 billion in 2014 to up to $18 billion by 2020).

So, while the startup Blue River uses computer vision and robotics to determine the needs of individual plants, Big Blue (also known as IBM) has developed a highly precise weather-forecast technology, Deep Thunder, and an agriculture-specific cloud technology.

Needless to say, we will need a basket of technologies from multiple vendors to keep large amounts of food from being thrown away or going waste, to optimize the yield from agriculture, to eliminate or reduce transportation inefficiencies—and do anything and everything to bring down the number of the daily hungry.

The CIO Role: Very Much Alive and Rising in Influence

Doubters of the CIO role and function, here’s some bad news for you: a new global survey reveals “an undeniable increase in the influence of the CIO.”

The survey, conducted by Harvey Nash, a leading professional recruitment firm, and consulting major KPMG, notes that more CIOs now report directly to the CEO (34%) than at any time in the past decade. And before I share other insights from the 18th edition of this global survey that received almost 3,400 responses from CIOs and technology leaders across 82 countries, let me pull out a happy tidbit: CIOs with a direct report to the CEO are also the happiest (87% report job fulfillment).

While there could be multiple reasons for this “happiness,” my surmise is that such a direct reporting structure would certainly take away many irritants for technology leaders and untangle them from other CXO/peer “issues.”

Another indicator of the increasing CIO influence is that 57% of CIOs now sit on the executive board or other senior leadership committees (up by 50% over 11 years), as per the survey. (I know it’s a long, relatively slow march, but hey, it’s happening :)

Among other highlights is the fact that one in four CIOs now spend at least one day each week outside their core function of IT. The CEO focus, too, is shifting from projects that save money to those that make money (see graphic).

The survey (titled Harvey Nash/KPMG 2016 CIO Survey) reveals that traditional IT priorities are seeing major shifts over the past four years: increasing operational efficiencies has dropped 16%, and delivering stable IT performance has dropped 27%.

These statistics not only point to the growing influence of the CIO in a company but also that they have more time for top/bottom-line improvement and innovation.

What continues to hold them back, however, is what this media release on the survey terms as “the greatest technology skills shortage since the Great Recession almost a decade ago.”

As many as 65% of CIO respondents say they believe a lack of talent will hinder their organizations’ ability to keep up with the pace of change—which is a 10% rise in the past year alone.

For the second consecutive year, data analytics is the most in-demand skill (39%). A high 89% of CIOs are worried about talent retention. (It’s another matter, perhaps, that there have been ample job movements and career shifts within the CIO community itself: 31% of the CIOs have been in their current role for less than two years and 15% moved job last year.)

The changing talent and CIO career dynamics could also be the result of the growing acceptance and impact of Digital.

One in five organizations now deploys a CDO (no, the CDO is not off the radar, though the initial euphoria on a separate digital officer seems to have come down a bit). More important, 58% of respondents reported that their organization has a clear digital vision and strategy.

In his executive summary for the report, Dr. Jonathan Mitchell, Non-Executive Chair, Global CIO Practice, Harvey Nash (a former CIO of Rolls-Royce and a tech veteran), writes: “There is little doubt that our industry is changing rapidly. In the last two years, IT leaders have become newly invigorated. The days of budget cuts and staff losses are well behind us. New challenges such as ‘digital’ have emerged. Was this yet another technology fad? No, was the resounding response from last year’s respondents. Digital is most definitely real and it is changing the way in which everyone thinks about IT.”

While there are many challenges for CIOs in the disruptive days ahead, let’s hope that the CIO role stays invigorated for many, many years to come.

(Lead image credit: Pixabay.com. This blog post first appeared on dynamicCIO.com.)

Disaster Management: Can Internet of Things Make a Difference?

What happens when disaster strikes? The answer depends, among other things, on where you are located. And if you live in a third-world, hot, crowded and messy country like India—all hell breaks loose.

Millions among India’s billion-plus citizens have seen that hell from up, close and personal: in the ruthless form of floods, earthquakes, cloudbursts, landslides and other disasters that destroy lives, livestock and the lock, stock and barrels that help people sustain their existence.

In fact, as I write these lines, the country is in the midst of disastrous rains and flooding in several states across its length and breadth.

On such occasions, the administration goes into an overdrive, the army and paramilitary forces are called in and the voluntary organizations are roped in for relief work. But Nature’s fury often proves too much and, despite all their efforts and hard work, the scale at which misery unfolds in the aftermath is astounding.

Can technology play a role in anticipating, mitigating, controlling and managing this misery? And if so, to what extent and in what ways?

Those were the questions that came flooding to my mind as I attended the launch recently of a white paper titled “Internet of Things (IoT) for Effective Disaster Management.” The paper was brought out by Digital India Action Group (DIAG), a think tank set up by IT vendor lobby group MAIT for “ideating and monitoring policy initiatives to support the Indian Government’s mission of Digital India.”

The objective of the paper is “to create awareness and appreciation about the potential use and applications of IoT for different aspects of disaster management.”

Alongside, DIAG also released another white paper, “Aadhaar-Enablement: A Framework for Citizen-Centric Services”.

For the uninitiated, Aadhaar is a 12-digit unique identity issued by the Government’s Unique ID Authority of India. Over 1 billion of these IDs have been given thus far in what is billed as the largest such exercise in the world.

While discussing Aadhaar and the potential of Aadhaar-based services is a Pandora’s box in itself, let me confine myself to IoT in disaster management for this post.

The role of IoT in disaster management, in keeping with the huge potential of this mother-of-all-technological-paradigm, is critical and wide-ranging. A multiplicity of agencies, infrastructure, devices, policies, and applications, among others, must come together to make the whole exercise “effective”, as the DIAG paper rightly highlights in its title.

The presence of a cross-section of officials and executives—from government, industry and consulting organizations (see pic)—is, one hopes, symbolic of the coordinated, on-ground effort that will be required in the days to come to give actual shape to the vision laid out in the document.

The IoT white paper recommends a “Seven-Point Action Plan” to shift from a “relief and recovery” model to “risk and vulnerability assessment” and address key issues and challenges related to management of natural and man-made disasters in India.

According to data from the IoT white paper, as much as 57% land area of India is vulnerable to earthquakes; 12% of this area is vulnerable to severe earthquakes. Besides, 68% land is vulnerable to drought, 12% land vulnerable to floods and 8%, to cyclones. The paper notes that many cities in India are also vulnerable to chemical, industrial and other man-made disasters.

The benefits of IoT in disaster management are easy to visualize (though difficult to implement, given the current realities of India): agencies can gain a clear picture of operations with real-time visibility of data as well as model data from multiple sources. This can further be transformed into accessible, actionable intelligence for faster, better-informed decisions. It is important, therefore, to create “a single, federated information hub.”

The paper calls for building an information backbone which all parties—government agencies, NGOs, infrastructure operators and community—can contribute to and work from.

One term in the paper that specifically caught my eye was “intuitive analytics” which seems to take the capabilities of the current big data analytics technologies to their optimal level.

In this context, SAP’s Lovneesh Chanana presented an insightful video of the city of Buenos Aires in Argentina. After the disastrous floods in the year 2013, which resulted in loss of close to a hundred lives and millions of dollars, the Argentine capital decided to put sensors in over 30,000 storm drains that measure, as per this report on the SAP site, “the direction, level and speed of water.” One of the key technologies to gather and analyze this huge amount of data in real-time is SAP HANA.

Technologies lie SAP HANA (or IBM Watson, for that matter) are not cheap to deploy for funds-starved governments. But consider the impact of not using the most advanced technologies: A World Bank forecast puts the annual losses from floods alone to reach as high as $1 trillion worldwide if cities don’t take preventive measures.

Each city, in my opinion, will need to take a deep view of what’s the best fit for it in terms of technologies, including IoT and the use of social platforms such as Twitter and Facebook. (If you think lightly of the idea, pause for a moment to consider that the US Geological Survey, a government entity, runs a service called the Tweet Earthquake Dispatch (TED). Under this, there are two Twitter accounts that send out earthquake alerts: @USGSted and @USGSBigQuakes.)

I remember reading a report a few years ago that was in a way precursor to the TED service. When, in the US, a 5.9-magnitude earthquake shook the Northeast in 2011, many New Yorkers learned about it on Twitter—seconds before the shaking actually started. Tweets from people at the epicenter near Washington, D.C., outpaced the quake itself, providing a unique early warning system. (Conventional alerts, by contrast, were said to take two to 20 minutes to be issued.)

Technology is advancing at a much faster pace now, especially with machine learning, robotics and drones appearing more frequently in headlines than ever before.

What should the Indian government and industry players be doing in tackling disasters with IoT and other tools?

The DIAG white paper gives some recommendations, the MAIT DIAG Seven-Point Action Plan, which includes:

- Release of cloud security and related guidelines as part of the Digital India policy framework.

- Inclusion of ICT in Disaster Management in the National Skills Development Framework and Plan.

- Release of IoT Policy for India.

- Development of framework for continuous industry participation in planning for disaster management.

- Back-end applications for asset management with disaster management authorities.

- Knowledge portal for sharing experiences and best practices.

- A comprehensive plan for prevention of cyber disasters.

Even if some of the above points are put into practice by a government-industry “action tank” (taking the think part to its logical conclusion), the disasters that certainly, unavoidably await the Indian multitudes can perhaps be mitigated and managed much better than before.

For CIOs, tech leaders and others who would like to dig deeper or get involved, here are some reference links:

Revised Draft IoT Policy of the Government

National Policy on Disaster Management

India Disaster Resource Network

UN ESCAP: ICT and Disaster Risk Reduction

 (The above blog post first appeared on dynamicCIO.com. Lead visual credit: Pixabay.com)

The Four Stages of Digital Disruption CXOs Should Know

It is easy for people within an industry to see something repeated quite often as clichéd, boring, hyped or done-to-death. But when it comes to the double dose of “Digital Disruption” (with two heavyweight words wrestling alongside), there is usually a lot of discomfort as well.

And while one often gets to hear the names of the usual “culprits”—the Ubers and Olas, the Airbnbs, the Facebooks of the world—who are causing or have caused a lot of disruption in the market, it is good to come across something that helps the existing enterprises or the incumbents chart digital territory with greater confidence.

McKinsey’s aptly titled “An incumbent’s guide to digital disruption” offers a few silver linings and plenty of hope. The introduction lures you in with these powerful words: “Incumbents needn’t be victims of disruption if they recognize the crucial thresholds in their life cycle, and act in time.”

It goes on to describe in interesting detail the four stages of disruption from an incumbent’s perspective, the barriers to overcome, and the choices and responses needed at each stage.

The four stages are identified in self-explanatory terms—Stage one: Signals amidst the noise; Stage two: Change takes hold; Stage three: The inevitable transformation; and Stage four: Adapting to the new normal.

The authors of the McKinsey article, Chris Bradley and Clayton O’Toole, also help the incumbent organizations in visualization of their current stage on an S-curve and mapping their moves and barriers along various inflection points on the graph.

The authors pepper these stages with real-life examples and insights, which makes for useful reading for companies that are in the midst of their own digital journeys and can take cues from those who have been there or done that (or not done that, for that matter).

Sample a few: as long as 10 years back, Norwegian media group Schibsted made the bold move to offer classifieds online—for free; Netflix “disrupted itself” in 2011 by shifting its focus from DVD rentals to online streaming; and Grocery retailer Aldi is said to have disrupted numerous incumbents globally with its low-price model.

You can read the full McKinsey article here or download an assessment guide that helps an organization in ascertaining its position in the digital journey by clicking this link.

It is always better to disrupt yourself than let someone else do it!

10 Interesting Business Use Cases of Internet of Things

The term “Internet of Things” often throws people, even in the technology industry, off balance. They begin struggling for definitions, explanations, market statistics and what not. There are those who throw multiple spanners in the works by citing security concerns (like they did with cloud). And then, those who generally do a lot of huffing and puffing.

Nothing wrong with that, actually. Any new or not-yet-mature technology segment goes through its own cycle of hype, hazards and hurrahs. So there’s no reason to treat IoT any different. Except perhaps that IoT is much bigger than a typical flavor-of-the-season type technology. (Without giving conflicting numbers but to keep things in some perspective, by 2020, billions of things/devices are to be connected and trillions of dollars in additional value will be generated.)

However, as the stats, standards and stumbling-blocks keep rolling in, the IoT pioneers and large ecosystem players continue to chip away at making it work. (Talking of chips, Intel has just bought Itseez Inc., an expert in computer vision algorithms and implementations for embedded and specialized hardware, an area of great interest to the chip giant for the automotive and video opportunities in IoT.)

I scoured the web for real-life business use cases of IoT solutions from across different sectors and scenarios. Here are some interesting ones (including a few from India as well):

- Miami International Airport, one of the busiest US airports (over 21 million passengers in 2015) has deployed Internet-connected sensors and IoT apps to provide detailed information to passengers based on their location and needs (the MIA mobile app for Android and iOS relies on a network of 400 beacons that transmit location information throughout the airport). For passengers, the app provides personalized directions through to airport and helps them find restaurants, services and baggage carousels based on their location.

- ATI Specialty Materials, a world leader in the production of special alloys and steels for the aerospace, oil & gas, and medical industries, uses the ThingWorx IoT platform from PTC—which provides a real-time layer that connects with their manufacturing, quality, maintenance, and ERP systems and allows them to rapidly create role based decision support “dashboards” and interactive applications.

- Using AMC Health’s mobile patient monitoring solution, an active pregnant woman who needs to track her blood sugar can use a mobile device to communicate readings from her glucometer at any time and any place she chooses, and that information is stored securely in the cloud. Her care provider has 24/7 access to her information and can determine whether she, her baby or both are at risk. Using this information, the woman’s health care provider can provide more timely and appropriate care for the benefit of both mother and baby.

- Ward Aquafarms, a 1000-cage aquaculture farm in Massachusetts, USA, uses thermal radiometry sensor enabled cameras from Mobotix running on the Verizon IoT platform to collect and analyze data such as environmental and sub-tidal water temperature, chlorophyll values, etc. Combined with satellite imaging data and analyzed properly, it helps Ward in its commitment to efficient and sustainable seafood production.

- The cities of San Diego (California) and Jacksonville (Florida) are running trials that use LED streetlight technology to collect real-time data not only to manage lighting, but also to manage parking, locate and identify potholes and keep track of repairs to municipal streets.

- John Deere has fitted its tractors sold globally with sensors. This helps the company update the farmer if a moving part of the tractor or the harvester is likely to fail, around one month before the event. (The analytics behind the predictive framework is said to have been done by a Bengaluru-based analytics firm, TEG Analytics.)

- Technologies like IoT often find usage in the unlikeliest of places. Take the case of successfully impregnating cows, for instance. A system called Gyujo, which was developed by Fujitsu, uses a pedometer strapped to the leg of the cow to help figure out exactly when is the best time to inseminate a cow. For farmers, the importance of getting this right is huge. Artificial insemination success rates today are around 70% with a pregnancy rate of around 40% when the detection rate of when the cow is in heat is 55%. Pushing that detection rate up to 95% (the level of accuracy claimed by Gyujo) causes the pregnancy rate to shoot up to 67%. (A cow in estrus “walks around furiously” typically at night, which is what Gyujo helps determine.)

- Tata Power Delhi Distribution Ltd (TPDDL) has taken a few steps in the IoT realm with a smart metering project. To manage peak demand and manage grid stress situations better, the company is working on an Automated Demand Response (ADR) mechanism for commercial and industrial high-end consumers (typically, users of 10 KW and above). The IoT project was undertaken to demonstrate technological capability, understand customer behavior, provider for a case study for the regulator to work on differential tariffs and financial incentives, and also to understand the processes required for scaling up as and when the need arises. Having successfully connected a total of 11 MVA non-critical load of Commercial and Industrial HT-consumers (it achieved a Demand Response of 7.2 MVA load during a DR Event in the year 2014), TPDDL is now confident of having the process capability to extend the IoT initiative to a larger base of consumers.

- Sheela Foam, the manufacturer of Sleepwell brand of mattresses in India, has introduced the IoT technology to help identify and offer the right kind of mattress to its customers as per their body shape. Every human body is different and needs a mattress that matches the body posture and the pressure distribution while sleeping. The company has devised an IoT based solution that is fitted to the mattresses on display at Sleepwell’s various retail outlets. There are sensors attached to this special mattress, called Sleepwell Sensobed, which scan and capture the various body shape related parameters when a person lies down on the mattress. The data is then analyzed and used to suggest perfectly matched mattresses to individual customers.

- IBM is using a slew of technologies, including IoT-based solutions, to digitally transform the Rashtrapati Bhavan in India. The company has created the business architecture and operating procedures, implemented the technology platform and solutions, and is managing the entire technology deployment. (The scope includes smart, eco-friendly solutions such as energy management, water management, waste and horticultural management, and security systems.)

The above is but a tiny representation of the humongous IoT ecosystem that is getting built even as I write these words. In all probability, the “thing/everything” part would be subsumed one day and we might refer to the Internet of Things simply as, well, the Internet.

(Image courtesy: IoTDisruptions.com. This blog post first appeared on dynamicCIO.com)