Realizing the true magic of AI by delivering Transformative Experiences

It’s all about data, analytics and human-centric design

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Transformative user experience and intelligent action – these are the magical things that AI provides. Now that we can integrate mountains of data and process it with machine learning models, we’re starting to realize the true magic of AI. It’s most valuable when we deliver intelligence at the end point of interaction– with employees and customers – on a day-to-day basis.

And it doesn’t take in-your-face robots to make that happen. Effective AI is subtler; it’s a transparent extension of human intuition that serves to simplify interactions without being intrusive or annoying, which is accomplished with human-centric design.

AI is all around us

Let’s examine some of the ways AI is transforming the way we go about our daily lives – at work and at home.

  1. Computer vision experiences are some of the most dramatic being delivered by AI. Deep neural networks enable computers to understand images and video, and the content within them. These models are used on the edge to deliver experiences such as augmented reality.

Factory of the future. Microsoft HoloLens – a holographic computer – provides information, instructions and alerts based on the objects it recognizes so you don’t have to fumble around with a laptop or a mobile device and are free to use your hands to get your job done.

File insurance claims quickly and efficiently. Being in a car accident is stressful enough; filing an insurance claim doesn’t have to be. By reading data from photos taken with your mobile device (driver’s license, license plates and damaged cars), computer vision can recognize makes and models of vehicles and assess damage. Through intelligent automation, your claim gets filed, and tow trucks and rental cars get ordered – all automatically.

  1. Natural language processing is transforming the graphical user interface into the natural language interface through the use of chatbots, virtual agents, and intelligent search applications.

A hassle-free way to resolve computer problems. You no longer have to pick up the phone or write and respond to emails in order to open a ticket for helpdesk service. An enterprise virtual agent can understand issues, such as a broken printer, and then automatically generates a ticket. Need an update? Simply ask the virtual agent a question.

Easily find the information you’re looking for. Now you can search the contents of unstructured documents just by asking a question. Natural language understanding enables the enterprise virtual agent to understand the contents of unstructured documents, and provide you with the relevant passage of text.

  1. Speech recognition has changed the way we interact, and with whom we interact. Cortana, Siri, and Alexa have infiltrated and enhanced almost every aspect of our lives.

Cortana helps you be more efficient at work, understands your schedule and plays music for you.

Alexa orders your pizza, turns on your lights and adjusts your home’s temperature.

Siri finds a restaurant for you, guides you there and helps you find a parking spot.


Top 5 AI Trends for Business Leaders

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Artificial intelligence has been one of the technologies to make heads of people from diverse industries turn. Apart from laymen and consumers who were fascinated by the potential and functionality of artificial intelligence, it is the business leaders, corporate managers and industry influencers who have joined the bandwagon of the population to be stunned by the technology. Thanks to this, they are now increasingly looking to bring in the technology to their businesses for optimized performances and experiment on various levels. If you are business leaders, looking to incorporate the benefits of artificial intelligence in your business, you need to know the following.

Business-Centric Applications

Conducting experiments with artificial intelligence for your business is way different from conducting business-centric experiments. Instead of exploring the potential of artificial intelligence for your business, you need to find areas of shortcomings in your business and focus on implementing artificial intelligence to it. That is would fetch you better results in the longer run. If you are a publishing house, focusing on delivering books using smart drones makes sense. But you know what’s more sensible? Using artificial intelligence systems to fight attrition, turnaround times, quality optimization, project management and more!

Always Put Your End-user First

The very aspect of technology is to make the lives of customers and laymen better. That is why businesses and brands exist. So when you are working on implementing artificial intelligence for your business, put your customers and employees first and see how they would react, respond and adapt it. This begins by understanding the behaviour of each persona and reverse engineering the designing process. When you analyze the behaviour, you would also be surprised to see that a few areas would not actually need the application of artificial intelligence. This again ties back to the first supporting idea we mentioned.

ai blog 6Have a Uniform Understanding About AI In Your Organization

A good business organization is one where every employee has a holistic idea about the business his or her company is into. From the CEO who runs the show to the last level of employee who ensures it runs smoothly, everyone needs to have an understanding of artificial intelligence as much as you do, at least the basic jargons. You need to talk about in your boardroom meetings and ensure your people talk about it during their breaks and work so that you get the topic of artificial intelligence implementation going around at your workplace.


Machine Learning is Here to Stay

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Welcome to the fourth industrial revolution—a world where artificial intelligence (AI) drives today’s technological disruption, blurring the lines between tangible and digital realities. Companies continue to shift toward AI and machine learning (ML) processes, and business leaders are quickly realizing the potential gains from investing in them. These gains include faster, smarter automation, predictive analytics, and new-and-improved ways to establish customer connections—among countless other possibilities.

According to the International Data Corporation (IDC), spending on AI and ML will grow from $12B in 2017 to $57.6B by 2021. Deloitte Global predicts the number of machine learning pilots and implementations will double in 2018 compared to 2017, and double again by 2020.

These predictions are beginning to play out. In 2018, the Massachusetts Institute of Technology announced its plan to create a new college (backed by a planned investment of $1 billion) dedicated to “educating the bilinguals of the future: people in fields like biology, chemistry, politics, history, and linguistics who are also skilled in the techniques of modern computing that can be applied to them.”

AI and ML strategies are rapidly evolving. No longer just an intangible concept, AI solutions have already entered our daily lives in the form of smart speakers, customer service chatbots, and autonomous vehicle features.

So, what is machine learning?

By offering a specific set of guidelines, scientists enable machines to create their own logic, thereby developing the ability to explore and analyze data on their own. The term “machine learning” helps to define this process. Machine learning is one of the main ways artificial intelligence is created.

What makes machine learning tick?

Think of algorithms as the rules machines are instructed to follow. Initially, machines are introduced to a set of data and “asked” to begin exploring that information. This introductory set of data is called training data. Once the machine has worked through its training data, it can begin recognizing patterns and even make decisions according to specific algorithms.

Some computers can even aim for specific goals and receive rewards upon meeting them. As this “learning” process evolves, computers are able to alter new inputs into outputs. These outputs might include: new data, labeled data, decisions, and more.

While ultimately machines might arrive at an operations state where human intervention is no longer necessary, we’re not there yet. According to a recent report from the McKinsey Global Institute, AI techniques require models to be retrained to match potential changing conditions, so training data must be refreshed frequently. McKinsey notes that in one-third of the cases, the model needs to be refreshed at least monthly, and almost one in four cases requires a daily refresh.

How is AI at work today?

AI is everywhere. It backs the speech recognition system in your smartphone, and will power the robotic vision of your future self-driving car. It also drives the risk-management methods improving the security of your local bank. AI continually improves your government’s emergency response and national defense initiatives. Counting on your doctor to securely manage your health information? AI protects that data. Shopping online for that perfect product? Chances are, AI powered your chatbot customer service rep, as well as populated your search results with relevant items to purchase.

Just scan the latest headlines—organizations are rapidly realizing the benefits of AI-backed systems and incorporating them into their daily processes.

Taking inspiration from the NYPD, the London Assembly police and criminal committee recently announced that implementing AI technologies would enable the addition of over 500 officers to their current ranks, as well as save up to £30 million. MIT researchers are currently developing a fully automated molecule-design process, potentially leading to faster, more consistent pharmaceutical products.

AI is even getting artistic. An auction house recently planned to sell an AI-produced work of art. Crafted from an AI-powered algorithm, the canvas portrays an image based off of a dataset of 15,000 portraits painted between the 14th and 20th centuries.

Whether offering AI-powered purchasing suggestions or featured insights tailored to specific shoppers, the world of retail is constantly exploring the many benefits of AI. Sephora now utilizes a chatbot that offers beauty tips to customers within a one-on-one environment. Coca-Cola recently unveiled an AI-backed vending machine that communicated with users, encouraging them to recycle via tailored “facial” expressions.

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Getting started with AI

Even if you’ve yet to jump on the AI train—it’s not too late. AI has already proven to be a profitable investment, and will only continue to improve business solutions as technology advances. Since every organization is different, every AI investment strategy also varies. Ultimately, an AI investment strategy should strengthen and solve for the overall business strategy. Where could machine learning shorten product development feedback loops? How can data on customer behaviors and preferences be used to drive better, more personalized customer experiences, in real-time? How could predictive modeling refine your competitive focus?

Your organization’s ML and AI will be as strong as the data you use to train it. Appen can help. With over 20 years of data expertise behind us, we’re providing high-quality training data that is used by leading global technology companies, governments, and other organizations, across a variety of data types, to help them build better machine learning-based products. We collect and annotate speech, sound, image, video, and text data to power machine learning and AI solutions. We also review and annotate data from existing products and services to evaluate content relevance and improve user experience.



AI Is Driving Innovation In eCommerce And Retail

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Companies across a range of industries are adopting machine learning technologies — and as early adopters, eCommerce and retail companies have seen the biggest wins from investing in machine learning. By applying artificial intelligence to key business problems, eCommerce companies are using machine learning models to drive higher sales, predict demand, and personalize the shopping experience through more relevant search results and real-time customer service.

To train machine learning and AI algorithms to respond to high volumes of customers, retailers must collect large  amounts of training data. We recently blogged about a 2018 report from McKinsey that analyzed hundreds of use cases for AI. While marketing and sales departments often see the quickest wins with AI, customer service teams can provide better experiences in real-time through smarter analytics and chatbots. McKinsey also recommends that retailers with physical stores invest in AI to optimize supply chains and improve inventory management. With technology that takes the entirety of an individual’s behaviors and preferences into consideration when generating a search result, pricing quote, or next best action, retailers can use AI to optimize product recommendations and personalize the customer experience.

Read on to learn how leading companies are using ML and AI to deliver better eCommerce and retail experiences.

Boosting eCommerce sales using AI tools

Given their wealth of data around customer behavior and preferences, large online retailers are at the forefront of AI-driven personalization. Winning strategies include personalizing website content and product recommendations based on previous customer behavior, personalizing communications based on both behavior and learned preferences, using chatbots to help customers navigate the shopping experience, and connecting social media and programmatic ad buying to serve up the most relevant and high-converting ads to customers who are most likely to make a purchase.

Retailers using AI to personalize the customer experience have seen increased profits and business value. A recent report from Boston Consulting Group found that retailers that adopted personalization strategies saw sales gains of 6-10% — a rate two to three times faster than retailers who did not. Using AI to personalize shopping will also boost profitability by 59% for wholesale and retail companies by 2035, according to Accenture.

Italian fashion brand Cosabella, working with an AI start-up software firm called Sentient, is using AI to rapidly test multiple user experience designs for its website. Unlike traditional A/B testing, this multi-variant testing allows their product recommendation and communications engines to gain insight from and adapt to customer behavior. The brand claims the AI process immediately boosted sales by 35%.

An AI-powered humanoid robot called Pepper that has been programmed to “perceive human emotions” has boosted sales and in-store customer interactions in both retail shops and cafes in California. After using Pepper in its retail outlets, retailer The Ave reported a 98% increase in customer interactions, a 20% increase in foot traffic, and a 300% increase in revenue.

Using AI to personalize the customer experience

Online retailers are investing in AI-powered personalization engines to bring together the human interaction of an in-store experience with the convenience of an online sale. These AI tools help shoppers search for products online by speaking, or even using images uploaded from their smartphone, to emulate the experience of interacting with a person. This strategy helps retailers differentiate themselves, boost word-of-mouth sales, and increase customer loyalty.

Skechers uses AI for site search relevance for their lifestyle and sportswear brand. As online shoppers click on a product they’re interested in, AI-powered tools analyze the Skechers catalogue in real-time to serve up similar or related items. This provides a more seamless, intuitive shopping experience for customers, and helps surface the products they actually want — boosting both customer satisfaction and sales.

A unified communications cloud provider, Star2Star, uses Conversica’s customizable sales assistant software bot to cross-sell or re-engage existing leads — in one case resulting in a 30% email response rate within hours. Bosch Automotive also uses Conversica, attributing an azerage increase of 60 sales per month at one Toyota dealership to the software.

Predicting demand and preferences with AI tools

AI algorithms are able to handle deep learning, statistical programming, and predictive analysis of huge amounts of data in a way that humans cannot. The popular Netflix series, House of Cards, resulted from AI pattern learning techniques. The company analyzed datasets of their most-watched TV shows to predict what kind of drama customers would most enjoy.

enter the password in supermarketRetail giants like Amazon have used machine learning algorithms since 2014 to forecast demand, drive stocking efficiencies across warehouses, and set prices based on analysis of how much consumers are willing to spend on certain items.

As the Harvard Business Review states, “…this level of prediction requires detecting subtle patterns from massive data sets that are constantly in flux: consumers’ purchase histories, product preferences, and schedules; competitors’ pricing and inventory; and current and forecasted product demand.”

AI and machine learning can help analyze these massive data sets. At Appen, we’ve helped leading retailers improve their machine learning programs through high-quality, human-annotated data. Whether you are looking to improve onsite search, provide personalization or improve your customer service, we can provide the high-quality data needed to enhance your machine learning models.


AI drives critical need for a digital ethics framework

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With digital ethics, “I know it when I see it” isn’t good enough

“I shall not today attempt further to define the kinds of material … within that shorthand description … but I know it when I see it.” This rationale is used to categorize the struggle to define something that is hard to define, like taste, art, beauty or, in this case, obscenity, as famously stated by U.S. Supreme Court Justice Potter Stewart.

That perspective could easily be applied to digital ethics today. Gartner defines digital ethics as the systems of values and moral principles for the conduct of electronic interactions. But what does this really mean? Everyone agrees we need to decide what is ethical and what is not, yet most executives and organizations seem to operate on an “I know it when I see it” basis.

Taking the lead on defining digital ethics

Technological advances like cloud, big data and artificial intelligence (AI) are creating new market opportunities and will help solve big societal problems. But the pace and widespread adoption of innovation continues to accelerate, and will introduce challenges that will affect wide swaths of society. Waiting for local, national or international legislation to pass is not an option. It simply takes too long. At Avanade, we believe that responsibility for defining the ethics around building, using and applying technology lies with the organizations that are driving it.  Those who lead the charge must play an active role in developing both informal and formal regulations.

Microsoft is one of those leading companies putting a stake in the ground, and recently described the societal impact of artificial intelligence in a ground-breaking book called “The Future Computed: Artificial intelligence and its role in society.” The book examines the use cases and potential dangers of AI technology and gives guidance how avoid or mitigate them. Digital ethics was also front and center at the recent World Economic Forum in Davos, Switzerland, where several sessions were devoted to AI and how to use it responsibly.

AI brings new urgency to digital ethics

There’s good reason the topic of digital ethics is gaining visibility. As technologies like AI grow, so too does their potential impact. For example, imagine there is a medically unfounded correlation between two data groups, such as gender and fatality rate among individuals who have a certain symptom. An AI-based tool used by a hospital to determine the urgency for surgery based on this data might connect the two data groups and make a wrong recommendation to proceed with surgical intervention. Medical expertise and common sense will be required.

This may seem like a dramatic example, but it underscores the need for human involvement, especially in situations where ethical dilemmas could arise. We are starting to see the impact of digital ethics play out each day, from those that affect the individual (Does an autonomous vehicle save a pedestrian or a passenger?) to those that impact the broader community (How do we maintain control over machines that become increasingly intelligent and powerful?). We all agree that decisions affecting human lives require human involvement. We need to look beyond pure logic and bring in compassion, a value for life and dignity, and simple common sense.

But that’s easier said than done. Avanade research shows that 89 percent of executives have encountered an ethical dilemma at work, but 87 percent admit they are not prepared to address ethical concerns caused by the increased use of smart technologies and digital automation. People are hungry for guidance that is thoughtful, effective and ready to be put into practice.

Four pillars of a digital ethics framework

Digital ethics is one of the most important building blocks for the success of a range of technologies.  People will not trust systems or companies they do not believe are ethical, nor will they do business with them or work for them. Having a strong sense of ethics, especially in the digital space, will quickly become necessary for a digital organization’s short- and long-term sustainability.

At Avanade, we are already defining parameters around digital ethics and believe we can do much better than a “we know it when we see it” approach.  We established four pillars to help determine if a product or a service is digitally ethical:

  • Fairness and inclusiveness.

It must not apply or lead to unlawful or unethical discrimination. It must comply with data protection and security principles while also helping to create a better information society – one that is open, pluralistic, tolerant, equitable and just.

  • Human accountability.

Humans need to be ultimately accountable for the deployment and outcomes of a computer-generated diagnosis or decision. Only humans can apply compassion, empathy and common sense to ethical dilemmas.

  • Trustworthiness.

AI systems must be reliable, capable, safe, transparent and truthful in their digital practices. This includes making it clear to users whether they are dealing with a computer or a human, and ensuring design starts with reliable and trustworthy data.

  • Adaptability.

As our understanding of digital ethics evolves, so should the tools we create. Flexibility will allow for this evolution and enable us to incorporate feedback and learn from it.

Only by meeting these criteria can we be confident that an AI system is digitally ethical.

“Ethics is knowing the difference between what you have a right to do and what is right to do,” Justice Potter also once said. Our hope is that this framework can help move the discussion from the right to do something to agreeing upon the right thing to do.



Exploring the human benefits of artificial intelligence


Did you know the telephone was the first virtual reality technology? It took us five decades to adopt it as a commodity of life. It a took a little less time to adopt radios and televisions. But we swiftly embraced the PC and mobile phone in less than ten years.

We speed up the adoption of new technologies because we use new capabilities to create them. It reduces how long it takes to for us to accept the next “big thing”.

Today, we interact with machines in ways that were once the exclusive territory of humans. Chatbots, or virtual assistants, are the first evidence of machines interacting with us in human-like ways. Microsoft’s Cortana helps you with any search topic from your Windows 10 machine. Alexa, Amazon’s virtual assistant, receives verbal commands. The chat window at the bottom of your bank’s web site prompts you to speak to a specialist.

So, how will AI impact our workforce?

Instead of assuming we’re all automatically out of jobs, let’s consider the value of being human. Humans are creative, imaginative, and often unpredictable. We base our decisions on fact or gut instinct to a situation. We can adapt to change and are guided by morals and principles. A computer “thinks” in a pattern that is logical and structured. Humans program AI to make the smartest and most consistent decisions, but if AI meets an unexpected condition, it will not always do the right thing. So, human and machine must work together.

Imagine this: you’re a new customer service representative for a utility company and you accept calls directly from customers. Your company has a low rating for its customer service and is losing customers. Many of your colleagues are quitting due to boredom. The utility company implements a new system, and instead of prompting customers to “press 1” and wait for more automated prompts, your pleasant voice greets them on the first ring with “how can I help?”

One customer states she wants to pay her bill, and you oblige and conclude the call. Another customer calls at the same time for a different reason and you deal with that request with ease and accuracy. Yes, it may be your voice, but it is not you. Your voice and personality answers hundreds of simultaneous calls – and only when your alter-ego trips up and can’t complete the task will you get forwarded a call. The customer isn’t startled by the change in voice or tone, and you resolve the problem because you see the details of his call already on your device.

“Being human” becomes a premium asset

In our example, the customer feels like he is important to the utility company because they are providing a human to help him. Yes, the utility company could have spawned instances of the same voice and personality to answer all calls. But this approach contributes to lower customer satisfaction because it sounds recorded – especially if they call and hear that voice more than once.

The utility company reduces its customer service staff. Yet, displaced workers are not dismissed. Value is in their innovation and creativity, as well as knowledge of the business and systems. They are now focused on managing the utility network from a control centre, with new responsibilities to use analytics and machine learning to expand and maintain the network.

Therefore, AI can help to increase job satisfaction

You hired out your voice to the utility company to answer calls and provide the “human touch” to their brand. Now you can take your daughter to school or watch your son’s dance recital while “you” are working. You are paid through new usage terms and conditions that give you flexibility and increase work/life balance. You are no longer tied to eight-hour shifts, five days a week.

The customer service scenario may sound preposterous, but the technologies already exist.

Montreal-based start-up Lyrebird created a system to mimic a person’s voice.

  • Google’s DeepMind research company developed a deep neural network that synthesizes realistic human speech.
  • Microsoft is making it easy to integrate artificial understanding into nearly any software application with Cognitive Services.
  • Large consultancies, like Avanade, are stitching emerging technologies together and helping customers use them to increase revenue or drive down cost.


The customer service scenario may prompt concerns over legalities, security, privacy, employment, compensation, discrimination, and identity theft. But it portrays an organization’s responsibility to provide new opportunities for displaced workers.

Despite the challenges, organizations are digitally transforming to compete for customers and AI promises a way forward. With investment in AI expected to grow from $640m in 2016 to $37bn by 2025, the scenario I describe is coming – and humans will likely accept the change as business as usual.


How will artificial intelligence change the way you lead?

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There have been countless books written about the tenets and principles of effective leadership. The lives of everyone from Ghandi to a coyote have been mined for insights into how to manage people for success. But what of the new world where leaders will be required to manage both people and machines to thrive?

There’s no doubt that the breadth and depth of artificial intelligence, machine learning and robotic-process automation capabilities are growing fast. And, while there is some talk about the possibility of working for a robo-boss in the future, the reality is more likely to be a significant shift in the skills that leaders will require to succeed in this new digital workplace.

So, what does that mean?

A recent Avanade survey showed that 85 percent of executives agree that company leadership needs to be able to manage both humans and machines if they plan to successfully integrate artificial intelligence into their organizations. Indeed, more than half of the C-level executives surveyed believe that an understanding of new and emerging technologies will be more important for leaders than a deep specialization in strategy, sales and marketing. Accenture affirms the need for a balance of skills as it identifies three elements to an executive’s AIQ—technology, data and people.

Just like today, leaders will need to have a balance of intellectual (IQ) and emotional (EQ) intelligence to manage in the AI infused workplace. On the IQ side, leaders will need to have a vision for the AI-first world in their organizations and know where it can be used to free employees to spend more time on complex tasks and enhance productivity. But, even more important, EQ and people-centric skills will be critical to evangelize the positive impacts and keep people engaged, address anxiety around the changing workforce, and help them reskill to focus on new ways of working and thinking.

In fact, with advanced analytics producing insights far greater and faster than the human brain is capable of, the “softer” management skills will be more important than deep subject expertise or raw intelligence. Topics like digital ethics and trust will come to the forefront. According to Harvard Business Review, “Certain qualities, such as deep domain expertise, decisiveness, authority, and short-term task focus, are losing their cachet, while others, such as humility, adaptability, vision, and constant engagement, are likely to play a key role in more-agile types of leadership.”

Of course, this is not the first time that leaders have taken on new skills in response to technology advances. Executives of a certain age will recall when dictation machines and typing pools were replaced by personal computers and the gap between those who learned to type in school and those who didn’t became quickly apparent. We adjusted, and we adjusted again when typewritten memos gave way to emails, then blogs, then tweets.

What’s next?

Executives in all industries need to be open to expanding and pivoting their skillset along with the rest of their workforces. Understanding the capabilities of AI is important, but so is attending to the needs of people who are affected by changes to the workforce. Along with the wisdom of [insert-your-favorite-business-guru-here], learn about the technology that is changing the game within your organizations and on the larger competitive landscape.

With a solid understanding of the capabilities of humans and machines, leaders will be prepared to draw upon the strengths of each to grow and sustain a digital workplace.