Category: Digital Economy

18 May 2019

The First Steps To Digital Transformation? Get Your Data In Order

Recently, Gartner announced its top 10 strategic technology trends for 2019. It is a nice list, touching on digital transformation trends that range from empowered edge computing to artificial intelligence-driven autonomous things. But while Gartner’s trends sound great in annual reports and Forbes articles, operationally, most enterprises aren’t properly (or digitally) prepared to adopt these trends. The reason why? Today’s pace of business and the disorderly data that’s needed to make sense of it all.

In the past, IT environments were simpler and more accessible for humans. But with the advent of cloud, containers, multi-modal delivery and other new technologies resulting in inordinately massive and complex environments, IT is being forced to move at machine speed, rendering manual processes too slow and inefficient.

To keep up with the rapid pace and scale of today’s digital environments, enterprises are turning to AIOps, which is powered by machine learning (ML) and artificial intelligence (AI). Unfortunately, ML-based algorithms and AI-based automation, key elements of unlocking digital transformation, are easier said than done. The underlying reason is that ML-based algorithms, by themselves, aren’t sophisticated enough to deal with today’s ephemeral, containerized, cloud-based world. ML needs to evolve into AI, and to do that, it needs cleaner actionable data to automate processes.

But attaining high-quality data presents its own unique challenges, and enterprises that do not have the right strategy in place will encounter cascading problems when trying to implement digital transformation initiatives in the future.

How To Build A High-Quality Data Strategy — Two Types Of Data

Imagine cooking a meal from scratch only to realize you forgot to chop an onion. You might be able to add it in later, but it won’t add the same texture and flavor. Too often, enterprises embark on an AI/ML transformation only to realize mid-development that they are missing key performance indicator (KPI) data that they did not foresee needing. Such mid-process realizations can have deleterious effects on a digital transformation initiative, stalling or even crippling its progress. Simply put, AI/ML doesn’t function without the right data.

The first step to building a high-quality data strategy is realizing that you need two separate data strategies: one for historical data and the other for real-time data or continuous learning.

Historical data is crucial for AI/ML strategies and serves as the fundamental building block for any effective anomaly detection, predictor or pattern analysis implementation. However, getting the right historic training data is much more difficult and challenging than many might assume.

There are several key questions to consider:

• What do your end goals and use cases for automation look like?

• What data do those use cases demand?

• How much of that data do you need?

• At what fidelity do you need that data?

Next, realize that training AI/ML on historical data is not enough. It needs to ingest real-time data to respond to and automate processes. Real-time data is the fuel that allows the ML algorithms to learn and adapt to new situations and environments. Unfortunately, real-time data presents its own set of challenges, too. The volume, velocity, variety and veracity of data can be overwhelming and expensive to manage.

Finally, enterprises must ensure the ML algorithms don’t acquire bad habits as a consequence of using poor data. And like bad human habits, it is hard to get an AI to unlearn a bad habit once formed. Specifically, these could be outliers that are erroneously deemed normal when they aren’t. Or they could present data gaps, which may skew newly learned behavior. Fundamentally, an AI/ML platform that does learn from bad data can ultimately result in extraneous false alerts and have negative impacts on IT operations. There are multiple ways to avoid going down this path, but they all boil down to one important thing: data quality.

The Two Most Important Ingredients For Data Quality

Historic and real-time training data are foundational to AI, ML and automation. However, data quality remains a major sore point for enterprises that underestimate the complexity of that challenge. Fortunately, data quality issues don’t have to be a terminal problem if approached strategically.

The most important step is to have full visibility both horizontally across operational silos and vertically, deep into infrastructure layers. You won’t know what KPIs are going to be important, so an ideal solution is one that allows you to ingest as much data as possible from as many places as possible right from the start.

It is also crucial that data be stored and normalized in a way that connects it to other data. Data that rests in silos will never be able to power automation; it has to have context. An ideal solution is one that can ingest data and contextualize it simultaneously. Spending time stitching data together, normalizing and correlating it after it is ingested is time-consuming and difficult.

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16 May 2019
How a 99-Year-Old Company Pivoted with a Digital Transformation

How a 99-Year-Old Company Pivoted with a Digital Transformation

When you hear Pitney Bowes, the first thing that probably comes to mind is postage scales. But over the past five years, this near-centenarian company has undergone a dramatic transformation. In a pivot toward shipping and e-commerce technology, Pitney Bowes has implemented a digital transformation strategy that’s arguably setting the new industry standard. (Just ask PwC, who’s said: “Doing digital right doesn’t mean you need to become the next Amazon, Netflix, or Google-or even the next Pitney Bowes.”)

Of course, no digital transformation can be called truly successful if it’s not driving business results. After years of stalled growth, in 2018 Pitney Bowes saw its second consecutive year of revenue growth, marking its best revenue growth in a decade.

So how does a 99-year-old mailing solutions company become a leading technology company? I recently sat down with its chief marketing officer, Bill Borrelle, to find out how Pitney Bowes has successfully reinvented itself for the modern world.

1. Change people’s perceptions.

“As a marketer,” says Bill, “we needed to recraft the narrative of the company, leaning on the proof points that already existed, and laying the runway for where we would go.”

In order to start changing perceptions and help both employees and consumers think differently about the company, Bill’s team launched a branding effort that reframed Pitney Bowes as “the craftsmen of commerce.” With this new lens, they’re placing the company at the crossroads of two seemingly counterintuitive ideas–the legacy of its history and the modernity of technology–and linking their past and their future together.

As just one proof point of their digital transformation, these “craftsmen of commerce” created the SendPro, a first-of-its-kind sending device for the modern mailer that combines hardware, software, and Internet-of-Things capabilities. The product is the embodiment of the tension between heritage and modernity–leveraging the precision the company is known for with the technology that’s shaping the future of commerce.

Already, the industry has started to see Pitney Bowes in a new light, with the company winning design awards typically won by cutting-edge technology companies. Last year, for example, its SendPro C-Series was recognized by the International Design Awards.

2. Transform the customer experience.

At the heart of its digital transformation strategy, Pitney Bowes aims to reimagine the customer experience. Says Bill, “Our goal was to be completely relevant to our clients in today’s changing world of commerce.” In order to deliver on that, the company needed to create products that would seamlessly merge the physical and the digital worlds.

This meant that all mailing devices became smart–suddenly, those iconic postage meters were connected to the internet, delivering real-time information. With a product called Relay, customers can now easily choose whether to deliver a message through physical mail or through email. All of these solutions are underpinned by the Pitney Bowes Commerce Cloud, a SaaS common data platform built on AWS.

As another milestone in its digital transformation journey, Pitney Bowes created a digital ecosystem that would enhance the customer experience regardless of how they interact with the company. Today, 600,000 of the roughly one million Pitney Bowes customers engage with its streamlined online experience–where they can do everything from buy supplies and view postage usage to learn USPS rates and seek technical support.

The result? Customer satisfaction ratings have increased significantly, and Pitney Bowes has more than doubled online sales thanks to its e-commerce experience.

3. Rethink the fundamentals of marketing.

Today’s technology can’t be marketed with outdated thinking. So while Pitney Bowes employs many of the hallmarks of modern marketing–data that enables unprecedented personalization and a tech stack with 70 marketing technologies–Bill also made a point of modernizing the foundational principles. Revitalizing the “4 P’s” from the 1960s, he introduced the mantra of the modern marketer: precision, pace, profit, and people.

In doing so, Bill was delivering a larger message about staying on the cutting edge of marketing just as they push the limits of technology. As he says, “I wanted the 250 marketers at Pitney Bowes to be modern marketers and raise the bar for ourselves.”

Toward the end of our conversation, Bill revealed something I thought was really fascinating: He told me that 80 percent of consumers still prefer to receive physical statements and invoices. It was a reminder that for all the strides companies like Pitney Bowes are making, there’s still a broader digital transformation happening today.

Or, as Bill says: “We’re a different company, but we’re still going. We have more to do.”

What does this mean for YOU? No matter what your size or industry, how can you take the lessons learned from Pitney Bowes’ digital transformations and apply them to your own business? With clever marketing and a focus on customer experience, you too can transform your business and prepare it for 2020 and beyond.


15 May 2019

How to Assess Digital Transformation Efforts

Not all organizations are succeeding with their digital transformation efforts. For one thing, the focus of their success metrics may be too narrow.

The operative word in digital transformation is “transformation,” not digital, which at least partially explains the concept’s successes and failures. If your company emphasizes digital at the expense of transformation, it may be overly focused on technology. If your company emphasizes transformation, it is more likely to address the cultural and technological aspects of digital transformation.

The different approaches use different sets of success metrics. Specifically, while one company may narrowly focus on metrics related to cloud migration or DevOps, the other measures success based on business objectives.

“Digital transformation is almost a bit of a bad name because it encourages people to buy digital products and tools as opposed to reconstructing their businesses,” said Mimi Brooks, CEO of Logical Design Solutions (LDS), a consulting firm that designs digital solutions for global enterprises. “There are still a lot of [organizations] that think becoming a digital business is about buying the digital platforms and tools that everybody else has, so I think we’ve got a bit of an idea problem there.”

Assessment should be continuous

One thing that differentiates today’s digital businesses from traditional companies is time. In the digital world, everything happens at an accelerated rate and to keep pace, businesses must evolve from periodic processes and mindsets to continuous processes and mindsets. For the past couple of decades, software development teams have been moving along a continuum of Agile, DevOps, and continuous integration and continuous delivery in a constant quest to deliver value to customers at an ever-accelerating rate. The problem with digital transformation efforts is that the continuous process mindset has not yet bubbled up and across non-digital native companies in many cases.

The business side of the house has to be as aligned around [Agile and DevOps] because they’re going to have to create roadmaps and requirements that can be turned into user stories that keep an engineering team delivering high velocity,” said Mike Cowden, president of digital transformation and software development enablement consulting company Slalom Build. “It’s a complete organizational mindset that has to take place in order for digital transformation to even happen. “

While digital natives have had the luxury of starting with a clean slate, traditional companies have to overcome the mentality of assessment, planning, execution, and evaluation as events versus continuous processes.

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14 May 2019

Five Things To Know About AI

Artificial intelligence (AI) holds a lot of promise when it comes to almost every facet of how businesses are run. Global spending on AI is rising with no signs of slowing down — IDC estimates that organizations will invest $35.8 billion in AI systems this year. That’s an increase of 44% from 2018. With all the fanfare, it’s easy to get lost in the noise and excitement — and with all of the vendors out there touting their various AI-based solutions, it’s also easy to get confused about which is which and what does what.

So, how do you muddle through the noise and make sure you really understand AI? Here are five things I believe you should be aware of when it comes to providing an AI solution or evaluating one for your business.

1. AI-Washing

Because AI is a trending technology that many believe holds great potential, vendors will sometimes claim they have AI-enabled capabilities when they really don’t. There’s no ruling body that defines what “AI” means — vendors are free to use it however they want. The same thing happened when the cloud entered the market, which caused the term “cloud washing” to emerge for products and services that were hyped as cloud-based but weren’t actually in the cloud. The same goes for “greenwashing” where companies lead consumers to believe they follow environmental best practices but really don’t.

Today’s “AI washing” makes it harder to tell truth from fiction. A Gartner press release from 2017 warned that AI washing is creating confusion and obscuring the technology’s real benefits. Many vendors are focused on the goal of “simply marketing their products as AI-based rather than focusing first on identifying the needs, potential uses, and the business value to customers,” according to Gartner research vice president Jim Hare.

It’s important to be clear about what AI is and about how a vendor is using the term. For instance, AI isn’t the same thing as automation. Automation allows process scripts to take care of previously manual, repetitive tasks, but the system isn’t learning and evolving. It’s just doing what it’s told to do. AI’s goal is generally to mimic human behavior and learn as it goes to become better at the tasks assigned to it over time.

2. Potential For Misuse

As with anything, AI can be used for nefarious purposes. A tool is only as “good” or “bad” as the hands that hold it. There are those who seek to use AI to control their citizenry via a nationwide network of facial recognition cameras (paywall) or build autonomous weapons, which I would consider bad applications. Fortunately, many hands have already found beneficial uses for AI, including accurate medical diagnoses, new cancer treatment approaches and language translation.

Another positive sign is that governments are working toward regulation and accountability. France and Canada announced plans to start the International Panel on AI to explore “AI issues and best practices,” and the U.S. Pentagon asked the Defense Innovation Board to create an ethical framework for using AI in warfare.

Ultimately, I believe AI is the best hope for overcoming the potential misuse of AI. For instance, much has been made of the inherent bias that keeps showing up in AI systems. IBM, for example, recently announced its automated bias-detection solutions. Since humanity can’t put the AI genie back in the bottle, we can devise good AI systems to help countermand its potential negative applications.

3. The Idea That AI Will Take People’s Jobs

Yes, it will eliminate some jobs — typically low-level and repetitive work — but it will likely create jobs, too. Gartner forecasted that AI will create more jobs than it eliminates by 2020, with a net increase of over two million jobs in 2025.

I believe AI also will take on tasks which the human brain is simply incapable of handling. AI can be trained to analyze vast data sets to gain insights that could elude the human mind. This could be particularly helpful in the creation of new drugs, saving time, effort and millions of dollars on development and clinical trials. I also believe AI could be useful for finding unique biological markers that enable individual-specific treatment. That said, this doesn’t mean that human oversight and involvement isn’t required.

4. The Idea That AI Will Change The Way People Think

AI probably won’t cause humans to rely on machines to do their jobs and make their decisions. AI, however well-developed it gets, can never replace the complexities of the human brain. That makes it even less reliable than our brains — meaning that AI compliments, rather than replaces, humans.

It’s unlikely that AI will yield flawless results. For instance, AI-powered speech transcriptions still serve up hilarious errors. Facial recognition programs still misidentify people. We can think of AI as an assistant to final human judgment, but a human must still be in the loop.

5. Lack Of Education

Here’s what I think is the biggest problem with AI in today’s world: We just don’t have enough people who are educated on how it works and how to leverage it. I think we’re staring right into the face of a looming skills gap.

For instance, an O’Reilly report on AI (via Information Age) found that over half of respondents felt their organizations needed machine learning experts and data scientists (although O’Reilly is an e-learning provider). And according to Deloitte, “Since nearly every major company is actively looking for data science talent, the demand has rapidly outpaced the supply of people with required skills.” In the United States alone, McKinsey projected (via Deloitte) that there will be a shortfall of 250,000 data scientists by 2024.

Students need to be learning about AI starting as early as middle school. Our children need to be equipped to handle the inevitable future that AI will bring. Otherwise, the shortage of workers who can actually leverage these technologies will expand. And that’s not good for anyone.

Act With Intelligence

Between the extremes of marketing hype and visions of Armageddon lies the truth of AI. Yes, there’s potential for misuse, but the majority of applications are and will be beneficial. You can’t ignore AI; organizations that find appropriate use cases for AI may get started sooner and find success sooner than their laggard competitors.


12 May 2019
How Digitalization – through automation and AI – is transforming demand planning

How Digitalization – through automation and AI – is transforming demand planning

The process of demand planning is undergoing enormous transformation. While it has historically been a reactive process involving responding to changing market conditions, the advent of technology is allowing – and at the same time forcing – demand planning to become much more strategic. Digitalising demand planning is becoming imperative for organizations that want to stay ahead of competitors, impress customers and drive company profits. Demand planning is no longer a case of simply reacting – instead, it requires continuous proactivity to successfully predict demand. In line with this, artificial intelligence (AI) is becoming an intrinsic part of the demand planning function, further boosting planning accuracy through sensing the markets’ desires.

A recent Capgemini report found that, when it comes to supply chain digitalization, organizations work on too many projects simultaneously, with close to 30 projects at pre-deployment stages. This high volume inevitably leads to some initiatives failing to take off, and places the most critical projects at risk. The digitalization of demand planning – and subsequent implementation of AI – is one example of a critical initiative which businesses must prioritize, and that has tangible and quick benefits, including:

Strategic decision-making

AI drives automation of the more traditional and labour-intensive tasks within demand planning to the next level – most notably, analyzing and interpreting batches of data. Not only is AI able to do this more accurately and quickly, but – by automating these critical but complex tasks – the team’s time is freed up so that they can focus on more strategic business endeavours.

Additionally, demand planners no longer need to dedicate large amounts of time to creating short-term demand plans or triggering stock replenishment – AI can do this for them. The team can then concentrate on progressing higher-value business objectives that will have a greater impact on the organization. Demand planners will need to interpret their role more strategically, e.g. dedicate more time to investigate how to improve operational efficiency, identify new ways to increase profits and become more involved in the business as a whole.

Improved forecasting

With so much data readily available, it has become more difficult to detect customer purchasing patterns. Artificial intelligence can work to cut through this noise, processing the data to uncover subtle patterns that humans would have missed. By aggregating datasets from Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) and Internet of Things (IoT) systems – and combining this with external variables and contextual data such as a calendar of events, seasonality and the weather – AI works to provide more accurate demand planning forecasts.

If this holistic approach is taken, AI forecasts can then be linked through supply and inventory planning to automate replenishment triggers, so that organizations consistently have the correct amount of products in stock. This results in increased sales by improving order fill rates and shelf availability.

For example, a global organization for personal care products built a demand-driven supply chain using data analytics to increase visibility into real-time demand trends. This enabled the company to produce and store the exact amount of inventory required to replace what consumers actually purchased, instead of manufacturing based on forecasts from historical data. The company also utilized point-of-sales (POS) data from retailers such as Walmart to generate forecasts that triggered shipments to stores and informed internal deployment decisions and tactical planning.

This approach helped the company to effectively track stock keeping units and shipping locations. As a result, it saw up to a 35% reduction in forecast errors for a one-week planning horizon and 20% for a two-week horizon.

More responsive   

Supply chain channels are undoubtedly vulnerable to a variety of external factors – for example natural disasters or availability of raw materials– that can impact demand forecasting. Rather than relying on historical data, AI and machine learning tools use real-time calculations to respond to and find resolutions for supply chain disruptions. As well as this, automation allows for rapid responses to changing consumer demand, improving sales and profits, and boosting consumer loyalty. This added responsivity boosts the accuracy of demand planning and limits monetary losses.

An office products retailer, for example, had disparate systems working autonomously with different SKUs, forecasting and planning processes. Management recognized that, without a “synchronized view of demand” of its supply chain, the company could not respond rapidly enough to market changes. Capgemini and a software solutions provider were brought in to implement an innovative solution designed to empower the retailer with synchronized decision-making and, ultimately, a unique competitive advantage. The solution is allowing the company to proactively meet fluctuations by tightly integrating a range of core business processes, starting at merchandise planning through to the replenishment process. The company expects this to increase top-line revenue by delivering real strategic value and strong demand chain results.

As with any significant organizational change, an agile approach – involving small steps, small failures, and fast recovery – can deliver the quick results that clearly demonstrate the value of cutting-edge demand planning approaches, such as the implementation of AI.

With this in mind, A proof of concept approach (POC) is highly recommended. This allows enterprises to gain a better understanding of the costs and returns of automation, as well as understand the skills and alterations that will be needed to accommodate it. Ultimately, the sooner an organization begins to adjust the way it goes about demand planning, the sooner the benefits will become apparent.


08 May 2019
Digital economy brings cybersecurity challenges

Digital economy brings cybersecurity challenges

With the boom in digital economy, government and companies need to enhance their cybersecurity in the wake of mounting threats to online data, said Wu Yunkun, president of a leading Chinese security company Qi An Xin Group.

“As the next generation of information technology is driving the digital transformation, data has become a key asset in the digital economy as well as a new target for hackers,”Wu said during the second Digital China Summit in Fuzhou, Fujian province.

The event opened on Monday and ended on Wednesday.

“The digital transformation, especially the application of artificial intelligence and other new technologies, also brings opportunities in the field of cybersecurity,”Wu said. ” More efforts are needed to construct a comprehensive defense system at the early age of informatization, build the situational awareness systems, threat intelligence analysis and security operation platforms.”

Wu said there will be a need for more professionals in the industry, and China will continue to increase its spending to combat internet crimes and attacks.

China’s digital economy accounted for 34.8 percent of its gross domestic product in 2018, remaining a key pivot for the country’s economic growth, a new report said.

Released by the China Academy of Information and Communications Technology, the report shows that the internet-based digital economy rose to 31.3 trillion yuan ($4.6 trillion) last year, up by 20.9 percent year-on-year.

Wu Hequan, an academician at the Chinese Academy of Engineering, said big data will bring both opportunities and challenges.

Wu noted such huge amounts of data can also bring forth new security issues, making big data security a new concern for users.

“However, big data also can help us prevent risks and solve security problems. Driven by big data analysis, we will be able to determine the cyberattack behaviors and alert the suspicious behavior beyond the baseline.”

“We need to combine big data, artificial intelligence, internet of things and other technologies to enhance the safety.” Wu added.

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06 May 2019
6 Solutions to Digitalization Headaches

6 Solutions to Digitalization Headaches

Don’t jump into a digitalization initiative without a solid plan. Make sure it has management buy-in and defined responsibilities.

Digitally transforming a company or certain processes equips many entities to make gains that improve competitiveness and their ability to meet company goals. However, the road to digitalization can get bumpy.

Thinking ahead and considering these six solutions could help companies overcome common obstacles.

Align the digitalization effort with the corporate strategy

At many companies, some team members, such as those working in the IT department, realize that it’s past time for those organizations to become more digitized. However, if they encounter friction from company leadership when bringing those concerns to light, it’ll be much harder to make progress. One common pitfall is that company leaders don’t see how digitalization fits in with the overall corporate strategy. However, research published in 2018 by McKinsey & Company found that 55% of companies that are leading in their digital strategies made sure the actions taken supported the corporate strategy.

So, people aiming to help their companies digitally transform should always assert how and why it’s in line with the corporate strategy. Moreover, they should specifically mention how the benefits of digitalization connect to organizational goals.

Create and maintain a digital-friendly corporate culture

Of course, it’s crucial to get members of company leadership in agreement with the need to go digital, but the transition cannot stop there. More specifically, a successful digital strategy requires looking carefully at the current company culture and determining whether attitudes within it could create barriers to digitalization.

Next, companies should strive to cultivate the characteristics of a digital-friendly culture. That may mean giving employees more tools that allow collaboration with team members from wherever they are, or offering courses or training workshops that help them move into the digital age. There is no single way to change a company’s culture so that it suits a digital transformation, but it’s worth taking the time to identify aspects of it that could stand in the way of digitalization.

Use a modern PDF solution

One crucial aspect of company digitalization involves improving how people within the company use files. It may seem like a relatively small detail, but some file formats make it easier than usual to move toward a paperless goal. PDF is an extremely popular format that people at most companies interact with daily.

However, as Reena Cruz, Brand Manager for PDF solutions company Inc., notes, “The very nature of the PDF file format presents a huge hurdle for businesses trying to digitize workflows. The format was primarily developed to preserve digital content and ensure that electronic documents can be universally shared and accessed on any platform.”

She continues “You’re limited to what you can do, which is good for the sender as it prevents unwanted tampering, but is not necessarily good for the intended recipient who needs to work with the content.” One of the main features of many PDF solutions on the market today is that they convert PDFs to other formats. For example, people could change a PDF pricelist to an Excel document, thereby making it substantially easier to edit.

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04 May 2019
Turbocharging India’s Digital Economy

Turbocharging India’s Digital Economy

New digital ecosystems are springing up across India’s economy, transforming business models and delivering huge productivity, efficiency, and growth benefits. And sectors that have not traditionally had technology at their core – such as agriculture, banking, health care, and logistics – are among those with the most potential.

MUMBAI – India is taking a great digital leap. Having reaped substantial rewards from building up its core digital sectors, such as information technology and business process management, the country is now seizing new digital opportunities in many more sectors, such as agriculture, education, energy, financial services, health care, and logistics. These opportunities could deliver up to $500 billion of economic value by 2025.

India’s digitization process has been the second-fastest among the 17 mature and emerging economies we studied. Admittedly, it started from a low base, but in the last five years alone, the number of Internet subscribers has almost doubled, reaching 560 million.

Last year, Indians downloaded 12.3 billion apps, second only to the Chinese, and they spent an average 17 hours per week on social media, more than Americans. As a result, Indians used more than 54 times as much data, on average, in 2018 than in mid-2016.

Both the public and private sectors have played an important role in driving digitization. Many public services are now accessible only when linked to the government’s Aadhaarbiometric digital-identification program, in which over 1.2 billion people are now enrolled. Aadhaar has thus helped to propel the development of many other digital services. About 80% of Indians now have digital bank accounts, with the vast majority of government benefits paid directly into Aadhaar-linked accounts. The Goods and Services Tax Network – a government platform for taxing wholesale and retail sales – has likewise created a powerful incentive for businesses to digitize their operations.

The private sector has facilitated this process, as competition has helped to reduce data costs by 95% from 2013 to 2017 and to make smartphones affordable. Falling costs have fueled rising data use: last year, Indian data subscribers used 8.3 GB of data per month, on average, compared to 5.5 GB used by Chinese subscribers. Together with rapid growth in telecom infrastructure, lower costs have also helped to reduce the digital divide: in the last four and a half years, India’s middle- and low-income states have accounted for 45% of the 293 million new Internet subscribers.

Digital business leaders are now spearheading even more innovative ways to reach and serve customers. New digital ecosystems are springing up across the economy, transforming business models and delivering huge productivity, efficiency, and growth benefits.

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02 May 2019
The 5 biggest mistakes companies make when implementing AI (VB Live)

The 5 biggest mistakes companies make when implementing AI (VB Live)

Bad AI isn’t a tech problem, its a human problem. Join this VB Live event to learn about the five biggest mistakes companies make when they bring cutting-edge customer service technology to their workflows, and how to leap over these pitfalls and into real results.

AI is quickly becoming a must-have when it comes to customer service technology, and companies have sky-high expectations when they add it to their CX mix. When their expectations aren’t met, however, it’s not necessarily the tech that’s to blame. More likely, it’s the humans who brought it on board. Here are some of the most common human errors when it comes to implementing AI.

Mistake #1: Confusing automation with AI

Using AI and automation interchangeably is a common and understandable mistake. Both can do “human-like,” work and improve both productivity and customer experience. But automation follows predetermined “rules,” while AI is designed to simulate human thinking. If your goal is to reproduce a simple, repetitive task normally performed by humans, for example, filling in forms, resetting passwords, or routing inquiries, then you’re probably in the market for automation. If you’re looking for a solution that’s able to do more complex things, including conducting actual conversations with customers, analyzing customer data, and offering up relevant answers and recommendations, you’ll need AI with analytical and natural language processing capabilities. Choose the wrong one for your situation, and you’ll either spend a lot more than you need to or get much less than you expect.

Mistake #2: Not determining success factors

If you don’t define up front what success will look like, what it will take to achieve it, and how you’ll measure it, you’ll never know if you’re getting a return on your investment. Attempting to do everything at once, or choosing a broad, undefined goal (“Improve customer service”), is a set-up for failure. Instead, target a few specific KPIs. Then think about which teams need to be involved and what processes need to be implemented or changed to ensure success.

More important, make sure there’s internal alignment on goals. Otherwise, while you’re using your AI solution to deflect routine inquiries so your agents are free to focus on high level inquiries, leadership might look at what’s happening and wonder why call handle time is staying the same or even going up. Get consensus up front, and the tech won’t get blamed for failing at something it was never intended to do.

Mistake #3: Not getting organizational buy-in

Even the best AI solution won’t make a dent unless everyone affected by it is informed and on board. Customer service employees may hear the word “AI” and assume they’re going to lose their jobs. Be transparent about the ramifications of the new technology: Will employees be shifted to new roles or learn new skill sets? Will processes and procedures change? Will the AI, in fact, free employees to do more interesting, high-level work?

Meanwhile, leadership needs to understand that there will be ramp-up time to realize the value of the new solution. There’s a learning curve with any new technology or change in duties, and teams will need time to get up to speed. You’ll also need to fine tune and adjust the tech as you start using it in the real world. Set expectations up front.

Mistake #4: Not considering the impact on the entire customer journey

When you alter one stage in the customer journey, there’s a ripple effect throughout the entire experience. You’ll need a holistic view, so you can anticipate and address issues that could arise when you plug AI into one or more touchpoint along the path. If you use AI in pre-sale to create a great experience for potential customers, what happens when they’re at the support stage of the journey? Will customer support agents have the training and/or tools to provide an equally good experience? Look at the big picture and do what it takes to keep the journey coherent and consistent.

Mistake #5: Not understanding the cause of the problems you’re trying to solve

If, in spite of your best efforts, your AI solution still isn’t moving the dial, it’s possible that you didn’t adequately investigate the root causes of the problems you were trying to solve. If, for example, your goal is to improve your NPS (Net Promoter Score), you’ll first need to dig in and understand what’s keeping your scores down. If it’s because your customers are frustrated with wait times or the time it takes to resolve issues, AI might help. But even the best AI solution in the world won’t work if what customers are actually unhappy with is your shipping and return policy.

The potential of AI for customer experience is undeniable. Get the human factor right, and you’re far more likely to get results.


25 Apr 2019
Elon Musk: Brain-Computer Interface Update “Coming Soon”

Elon Musk: Brain-Computer Interface Update “Coming Soon”


SpaceX and Tesla CEO Elon Musk hinted at what could be the announcement of a brain-machine interface that could one day hook human brains up to computers on Sunday. In response to a question asking for an update on Neuralink, a neurotechnology startup he founded in 2016, Musk replied that new information would be “coming soon”.

A “direct cortical interface,” according to Musk, could allow humans to reach higher levels of cognition —and give humans a better shot at competing with artificial intelligence, the Wall Street Journal reported in 2017. It’s unclear, though, whether Neuralink’s main objective is to do just that or to connect human brains to computers for consumer applications.

AI Overlords

Musk has repeatedly warned of evil AI overlords in the past, saying that AI could become “an immortal dictator from which we could never escape” in a 2018 documentary called “Do You Trust This Computer?”

Most of what Neuralink is working on, including any plans for a brain computer interface, are still tightly under wraps. In one tantalizing clue, Bloomberg recently reported on a still unpublished academic paper by five authors who have been employed by or associated with Neuralink — though it’s unclear whether Musk’s tweet referred to their work.

Sewing Machine For The Brain

The paper describes a “sewing machine” for the brain in the form of a needle-like device that is inserted into a rat’s skull to implant a bendable polymer electrode in the brain that would read the brain’s electrical signals.

Of course, human trials are still a long time out. Neuralink has yet to comment on any possible timelines or announcements.

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