Bottom Line: AI and machine learning are enabling omnichannel strategies to scale by providing insights into the changing needs and preferences of customers, creating customer journeys that scale, delivering consistent experiences.

For any omnichannel strategy to succeed, each customer touchpoint needs to be orchestrated as part of an overarching customer journey. That’s the only way to reduce and eventually eliminate customers’ perceptions of using one channel versus another. What makes omnichannel so challenging to excel at is the need to scale a variety of customer journeys in real-time as customers are also changing.

89% of customers used at least one digital channel to interact with their favorite brands and just 13% found the digital-physical experiences well aligned according to Accenture’s omnichannel study. AI and machine learning are being used to close these gaps with greater intelligence and knowledge. Omnichannel strategists are fine-tuning customer personas, measuring how customer journeys change over time, and more precisely define service strategies using AI and machine learning. Disney, Oasis, REI, Starbucks, Virgin Atlantic, and others excel at delivering omnichannel experiences using AI and machine learning for example.

Omnichannel leaders including Amazon use AI and machine learning to anticipate which customer personas prefer to speak with a live agent versus using self-service for example. McKinsey also found omnichannel customer care expectations fall into the three categories of speed and flexibility, reliability and transparency, and interaction and care. Omnichannel customer journeys designed deliver on each of these three categories excel and scale between automated systems and live agents as the following example from the McKinsey article, How to capture what the customer wants illustrate:


The foundation all great omnichannel strategies are based on precise customer personas, insight into how they are changing, and how supply chains and IT need to flex and change too. AI and machine learning are revolutionizing omnichannel on these three core dimensions with greater insight and contextual intelligence than ever before.

10 Ways AI & Machine Learning Are Revolutionizing Omnichannel

The following are 10 ways AI & machine learning are revolutionizing omnichannel strategies starting with customer personas, their expectations, and how customer care, IT infrastructure and supply chains need to stay responsive to grow.

  1. AI and machine learning are enabling brands, retailers and manufacturers to more precisely define customer personas, their buying preferences, and journeys. Leading omnichannel retailers are successfully using AI and machine learning today to personalize customer experiences to the persona level. They’re combining brand, event and product preferences, location data, content viewed, transaction histories and most of all, channel and communication preferences to create precise personas of each of their key customer segments.
  2. Achieving price optimization by persona is now possible using AI and machine learning, factoring in brand and channel preferences, previous purchase history, and price sensitivity. Brands, retailers, and manufacturers are saying that cloud-based price optimization and management apps are easier to use and more powerful based on rapid advances in AI and machine learning algorithms than ever before. The combination of easier to use, more powerful apps and the need to better manage and optimize omnichannel pricing is fueling rapid innovation in this area. The following example is from Microsoft Azure’s Interactive Pricing Analytics Pre-Configured Solution (PCS). Source: Azure Cortana Interactive Pricing Analytics Pre-Configured Solution.


  1. Capitalizing on insights gained from AI and machine learning, omnichannel leaders are redesigning IT infrastructure and integration so they can scale customer experiences. Succeeding with omnichannel takes an IT infrastructure capable of flexing quickly in response to change in customers’ preferences while providing scale to grow. Every area of a brand, retailer or manufacturer’s supply chain from their supplier onboarding, quality management and strategic sourcing to yard management, dock scheduling, manufacturing, and fulfillment need to be orchestrated around customers. Leaders include C3 Solutions who offers a web-based Yard Management System (YMS) and Dock Scheduling System that can integrate with ERP, Supply Chain Management (SCM), Warehouse Management Systems (WMS) and many others via APIs. The following graphic illustrates how omnichannel leaders orchestrate IT infrastructure to achieve greater growth. Source: Cognizant, The 2020 Customer Experience.


  1. Omnichannel leaders are relying on AI and machine learning to digitize their supply chains, enabling on-time performance, fueling faster revenue growth. For any omnichannel strategy to succeed, supply chains need to be designed to excel at time-to-market and time-to-customer performance at scale. 54% of retailers pursuing omnichannel strategies say that their main goal in digitizing their supply chains was to deliver greater customer experiences. 45% say faster speed to market is their primary goal in digitizing their supply chain by adding in AI and machine learning-driven intelligence. Source: Digitize Today To Future-Proof Tomorrow(PDF, 16 pp., opt-in).


  1. AI and machine learning algorithms are making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer. By definition propensity models rely on predictive analytics including machine learning to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. Every business excelling at omnichannel today rely on propensity models to better predict how customers’ preferences and past behavior will lead to future purchases. The following is a dashboard that shows how propensity models work. Source: customer propensities dashboard is from TIBCO.


  1. Combining machine learning-based pattern matching with a product-based recommendation engine is leading to the development of mobile-based apps where shoppers can virtually try on garments they’re interested in buying.Machine learning excels at pattern recognition, and AI is well-suited for creating recommendation engines, which are together leading to a new generation of shopping apps where customers can virtually try on any garment. The app learns what shoppers most prefer and also evaluates image quality in real-time, and then recommends either purchase online or in a store. Source: Capgemini, Building The Retail Superstar: How unleashing AI across functions offers a multi-billion dollar opportunity.


  1. 56% of brands and retailers say that order track-and-traceability strengthened with AI and machine learning is essential to delivering excellent customer experiences. Order tracking across each channel combined with predictions of allocation and out-of-stock conditions using AI and machine learning is reducing operating risks today. AI-driven track-and-trace is invaluable in finding where there are process inefficiencies that slow down time-to-market and time-to-customer. Source: Digitize Today To Future-Proof Tomorrow (PDF, 16 pp., opt-in).
  2. Gartner predicts that by 2025, customer service organizations who embed AI in their customer engagement center platforms will increase operational efficiencies by 25%, revolutionizing customer care in the process. Customer service is often where omnichannel strategies fail due to lack of real-time contextual data and insight. There’s an abundance of use cases in customer service where AI and machine learning can improve overall omnichannel performance. Amazon has taken the lead on using AI and machine learning to decide when a given customer persona needs to speak with a live agent. Comparable strategies can also be created for improving Intelligent Agents, Virtual Personal Assistants, Chatbot and Natural Language (NLP) performance.  There’s also the opportunity to improve knowledge management, content discovery and improve field service routing and support.
  3. AI and machine learning are improving marketing and selling effectiveness by being able to track purchase decisions back to campaigns by channel and understand why specific personas purchased while others didn’t. Marketing is already analytically driven, and with the rapid advances in AI and machine learning, markets will for the first time be able to isolate why and where their omnichannel strategies are succeeding or failing. By using machine learning to qualify the further customer and prospect lists using relevant data from the web, predictive models including machine learning can better predict ideal customer profiles. Each omnichannel sales lead’s predictive score becomes a better predictor of potential new sales, helping sales prioritize time, sales efforts and selling strategies.
  4. Predictive content analytics powered by AI and machine learning are improving sales close rates by predicting which content will lead a customer to buy. Analyzing previous prospect and buyer behavior by persona using machine learning provides insights into which content needs to be personalized and presented when to get a sale. Predictive content analytics is proving to be very effective in B2B selling scenarios, and are scaling into consumer products as well.

Louis Columbus is an enterprise software strategist with expertise in analytics, cloud computing, CPQ, Customer Relationship Management (CRM), e-commerce and Enterprise Resource Planning (ERP).

 Read Source Articles: Forbes

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1.Supplement not replacement

If the buzz around Artificial Intelligence (AI) has left you nervous that it would soon take away your job and the technology works better than your brain, you are probably mistaken.

First, there is nothing artificial about intelligence and unlike industrial automation that is actually taking away jobs globally, AI is only going to supplement human intelligence across the spectrum -- from banking to media.

2.Not equivalent to human intelligence

While some forms of AI might give the impression of being clever, it would be unrealistic to think that current AI is similar or equivalent to human intelligence.

"Some forms of Machine Learning (ML) -- a category of AI -- may have been inspired by the human brain, but they are not equivalent," says Alexander Linden, Research Vice President at Gartner.

The image-recognition technology, for example, is more accurate than most humans, but is of no use when it comes to solving a Math problem.

3.Continuous need to train and re-train

When it comes to bias, an ML model will always operate the way you've trained it, said Olivier Klein, Head of Emerging Technologies, Asia-Pacific at Amazon Web Services (AWS), which is retail giant Amazon's Cloud arm.

"If you train a model with a bias, you would end up with a biased model. You continuously need to train and re-train your ML model and the most important thing is that you need some form of feedback from the end-consumers," Klein told IANS.

"ML is absolutely not about replacing humans but enhancing the experiences," he added.

4.AI misconceptions

IT and business leaders are often confused about what AI can do for their organisations and are challenged by several AI misconceptions.

According to Gartner, they must separate reality from myths to devise their future strategies.

"Every organisation should consider the potential impact of AI on its strategy and investigate how this technology can be applied to its business problems," said Gartner.

5.Not everything can be automated

Klein said that humans are really good at learning quickly with very little information.

"ML models are the opposite. They require a lot of data inputs to be able to be trained.

"I would argue that you show someone a bicycle a few times and you show them how to ride a bicycle and the human being is able to ride that bicycle pretty easily. To just train a robot to ride a bicycle takes millions of hours of training," explained Klein.

Read Source Article: The Economic Times

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Humans look at AI in both awe and fear. When I tell folks that I work for a company that builds AI-powered virtual assistants, they frequently ask me if AI is close to becoming sentient. Is it close to becoming human? Are we fast approaching a future where Skynet from the Terminator is real? Will AI become too smart for its own good, like Ava in Ex Machina?

In short: no, at least not yet. Instead, we’re reaching an era where it’s becoming easier for machines to pass the Turing Test, a test designed to see if a machine can exhibit behavior indistinguishable from a human. What most people do not realize however, is that even the most successful AI (the type that might pass the Turing Test) actually has humans behind it. In fact, the human touch is critical. Without it, every word uttered or typed has a certain emptiness to it, seeming – for lack of a better word – robotic.

When it comes to using AI in business, it’s all the more important that it interacts with people in a way that’s authentic. And the only way to infuse this human element into AI, and in the process make AI more conversational, is – you guessed it – with humans! If we say goodbye to the human touch entirely, the technology won’t result in the sought-after outcomes that businesses are looking for, like enhanced customer experiences, efficiency and real ROI. In some sense, it’s no different than when a human takes on a task – it learns from experience and gets better at executing over time. So, in the same way humans learn from experience and other humans, so does AI.

AI needs a teacher

Though people have been talking and speculating about AI for years, in reality it’s still in its infancy. AI is being used today in ways that show tremendous promise, but like talented new employees, AI needs people to learn from. You wouldn’t expect someone new to fend for themselves to figure out how to get a job done right, and AI is no different.

Machine learning is the backbone of AI — the technique that gives AI the ability to learn from experience and the data that experience generates. AI needs a constant feedback loop that allows it to learn. It is this continuous learning throughout the life cycle of processes that provides the ever-increasing benefits that businesses expect.

Think of it this way: people go to school throughout their childhood, and in many cases college, as a foundation for success. While this education gives them the foundation, it is practical work experience and the guidance and training from others that ultimately helps people to develop specific skills and expertise. AI is the same, it needs human understanding and experience to complement its effectiveness at the same time it trains the AI applications to be more self-sufficient.

Conversational dialogue is complex 

Humans, by nature, are complex and unpredictable, and it often takes another human to understand the nuances of conversation. For example, when a human takes a dinner reservation and asks the patron how many people will be in the party, a human knows, “My wife and me” means two, but an AI algorithm may not know that.

Too often in the customer service realm, callers are greeted by a virtual assistant programmed to only understand a limited set of questions, options, and commands. In this all-too-common scenario, the person’s questions often fall outside of this narrow scope of options and lead to a poor customer experience, leaving people frustrated and unhappy.

Issues like this can have some serious consequences. While Statista reports the global chatbot market is expected to reach $1.25 billion by 2025, a recent Gartner study found that 40 percent of bot/virtual assistant applications launched in 2018 will have been abandoned.

This is, in part, because these systems require mountains of relevant data in order to truly understand complex human dialogue and a highly sophisticated AI engine in order to extract contextual meaning from data. Today’s chatbots are essentially digital IVRs, limited in scope to a specific set of tasks with no real time human interaction to make them better over time. As a result, chatbots simply can’t deal with the complexity of human dialogue.

With humans supporting AI, the number of questions an AI system can answer skyrockets to near human levels of understanding. The difference: humans are able to discern messages that are impossible to translate with existing natural language processing (NLP) and speech recognition techniques, like sarcasm, slang, colloquial expressions or thick accents. Additionally, human brains are able to listen and understand through background noise — AI technology isn’t so savvy, at least not yet.

Two brains are better than one

If you take a look at some of the most exciting applications of AI, it’s evident that when AI and humans work together, its impact is unmatched. In healthcare, AI can uncover diagnoses that doctors may have overlooked, but it’s the doctors themselves that make the final decisions. This process, in turn, trains the AI to become even stronger and more accurate the next time around.

In finance, AI can offer people advice on where to invest their money, but humans need to train these systems to consider factors like age and changing life conditions like marital status. In customer care, AI can sort through thousands of social posts to identify those worth a human’s attention and suggest responses, but then have to hand it off to a human to make adjustments and personalize based on the situation at hand.    

It’s true: AI will get smarter over time, but people aren’t going anywhere. In fact, humans will dictate whether AI is successful. As our needs change, jobs evolve and language gets muddled with slang, AI will need an inside source to stay up to speed. With humanity as AI’s counterpart, our awe over it’s power will outpace our fear.

Read Source Article: TNW

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Artificial Intelligence is way overhyped.

I remember well how over-hyped AI was back in the early 1980s when I worked with Applied Expert Systems, a startup founded by some MIT professors that aspired to use expert systems to transform the world of personal financial planning.

I helped bring the software to the company and participated in so-called knowledge engineering by interviewing a personal financial planning expert. The idea was to convert the expert's decision making rules into software and build a system that would replace personal financial planners.

Sadly for those who invested time and money in this company, its product never found much of a market and it folded.

And nearly 40 years later, it looks to me as though the promise of AI way ahead of what it will deliver.


Here are three reasons I've reached that conclusion.

1. Many CEOs Are Being Scared Into Caring Too Much About AI

Consulting firms have the power to scare companies into paying for projects designed to alleviate the fear they create. A case in point is pwc, which published a 2019 survey of CEOs which found that "80% of those surveyed believed that AI will significantly change the way they do business in the next five years."

Are all CEOs taking the lead in driving AI as their first strategic priority? Not really.

As Kartik Hosanagar, Prof. of Operations, Information and Decisions, at Wharton told me in a February 8 interview, "There are three kinds of CEOs when it comes to AI. The first category are the blind followers -- they don't understand AI but they've heard it's a 'thing' and they trust it. The second are the ignorant skeptics -- they don't understand AI and don't trust it. And the third are savvy managers who are integrating AI into their business. I have not done formal research but there are more skeptics than blind followers."

2. There Are Very Few Examples of High Payoff AI Applications

Experts I talked to cite Google as the source of at least one successful use of AI. While I do not know how much Google spent on this application, I am convinced that its payoff is economically significant.

Despite the paucity of compelling examples of high-payoff AI applications, the market forecasts for AI are pretty large. For example, IDC estimated that spending on cognitive and AI systems for 2018 totaled $24 billion and would grow at a 37,3% compound annual rate to $77.6 billion in 2022.

AI expected most of the 2018 spending to go to four application areas: largest automated customer service agents ($2.9 billion), automated threat intelligence and prevention systems ($1.9 billion), sales process recommendation and automation ($1.7 billion) and automated preventive maintenance ($1.7 billion).

Over the longer term. IDC expects the fastest growth in other areas. Specifically, the following will receive the fastest growth in five year average investment through 2022: pharmaceutical research and discovery (46.8%), expert shopping advisors & product recommendations (46.5%), digital assistants for enterprise knowledge workers (45.1%), and intelligent processing automation (43.6%).

There are some startups that are using AI. As Adam Pah, clinical assistant professor of management and organization at Northwestern's Kellogg School, told me in a February 12 interview,

There is a startup in China that uses AI to make consumer micro-loans with a higher chance of being paid back. Other examples include Amplero, which is getting 100% growth through more effective ad targeting. Hello Fresh has boosted revenues 4% just from pushing its AI-developed recommendations to households. And Lemonade sells renter's insurance using AI instead of insurance agents."

A fairly typical story about a company that's trying AI is a system called Philyra -- intended to help invent new types of perfume -- built by Symrise, a large perfume maker.

There is no compelling payoff from this system after two years of trying. According to MIT Technology Review, Philyra was developed in partnership with IBM, has taken two years to get working, and is just being used by a handful of Symrise 70 fragrance designers.

Let's take a deeper look at the Google example. As Kartik 

Hosanagar, Prof. of Operations, Information and Decisions, at Wharton told me in a February 8 interview, "Google decided that AI was going to be the next big thing so it moved from operating a centralized AI group to lending them out to the product teams for three to six months."

An initial result of this effort was to improve the quality of Google's search function. "Using machine learning, Google was able to track which search results users actually clicked on most frequently. Often Google's algorithm listed the most frequently clicked result third on the list. Using AI, Google improved its search algorithm so that 95% of the time, the most frequently clicked link made it to the top of the list," said Hosanagar.

I am guessing that Google has found a way to use this improvement in its algorithm to boost its revenues.

Meanwhile, Google has also used AI to reduce its data center costs. As he explained, "Google used machine learning to predict its electricity costs every hour. By making accurate predictions of how much electricity the company would need, Google was able to reduce it electricity costs in its data centers by 40%."

3. Very Few Companies Can Afford or Find Good Uses For AI

AI engineers are expensive -- their total compensation packages can go into the millions of dollars. It does not seem likely that large companies with limited AI capabilities will be willing or able to attract and retain such talent.

Moreover, even if they could, at this point it is unclear how companies could implement AI applications that would enable them to earn a high return on their investment.

Hosanagar thinks that the cost of building AI applications will drop as they did for iPhone apps. As he said, "When the iPhone first came out, it cost $500,000 to $1 million to build an app -- now the cost is $25,000 to $30,000. The costs of AI applications will drop -- in part with help from open source technology."

He also suggests that companies start small. "Companies should not try to build AI applications that will boost revenue or reduce costs right away. Instead they should set a goal in the first 12 to 24 months of increasing their organizational learning and expect to build high payoff AI applications over five years," said Hosanagar.

Having lived through a previous wave of enthusiasm for AI in business, I am conditioned to be skeptical about whether reality will live up to the hype.

Read Source Article Forbes 

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The AI icon Andrew Ng is quoted as saying, “Artificial intelligence is the new electricity.”  For anyone reading this, that’s a powerful analogy. Electricity empowers our surge in science and globalization over the past 100+ years, but the analogy falters in one overlooked regard.  

Few of us know how the electric revolution began, and like all revolutions, it was dirty. Many are unaware, but those of us who forget history are condemned to repeat it.

The sins of electricity are buried in the history books. You might know a fraction about the feud between Tesla and Edison. Fewer discuss the fires of houses with poor electrical conduit and insulation, and even fewer are told about Topsy the elephant, who was electrocuted for entertainment with 6,600 volts before a crowd.  

To us, those are sins of a revolution past, but the sins of our “new electricity” have yet to occur. Elon Musk has been quoted telling governors that AI poses an “existential risk” to humanity.  It’s our duty to be wiser, stronger, and more thorough, as the global risks of AI cause the stakes to be at an all-time high. Know what terrors we should we keep our eyes open for, and know those that are already here.

Risk 1: Militarized AI

Plan it, build it, blow it up — the stories of AI in the military have fueled classic movies like War Games, and even given rise to some of the biggest cautionary mistakes of our time. Neil Fraser wrote about an alleged attempt to use neural networks in the 1980s to identify enemy tanks, where the input data of enemy tanks versus trees were taken on two different days.  

The final result? The neural network would attack trees on overcast days, due to data bias. This story has been told in many outlets as a cautionary tale, but several decades later we find ourselves surrounded by highly funded killing machines and a foot on the AI accelerator.  

“Killer bots” isn’t a cautionary tale or a Hollywood feature, it’s world news.  China is assigning their brightest children for their AI weapons development program. The US, China, and many other nations are now racing to develop deadly AI applications.  It’s hard to think of something more dangerous than a global nuclear war, but the top governments of the world are recruiting, incentivizing, and developing ideas for applying just that.  The US is recruiting services from top companies like Microsoft, which is causing extreme unrest inside those companies.  

Risk 2: Cyber attack AI

Less frightening, but also something you may not have considered, as our world depends more on technology, the military and civil application of AI can spill into cyber attacks as well.  Lots of computer viruses are programmed by smart people who can teach the software how to hide on most systems. Part of how we detect new worms and viruses is specifically seeing if they attack or act in a specific distinguishable way.  

For instance, some trojan horses will go dormant during common “work hours” to avoid detection, and then activate later when they are unlikely to be observed. What if rather than being programmed, a cyber attack could learn and adapt?

Adaptable AI will be key in cyber defense to prevent the influx of weaponized cyber attacks.  Darktrace uncovered several styles of attack, and identified that hardcoded thresholds for detecting attacks is something of the past. We’ll need intelligent cybersecurity to stay ahead of blackhat in the world of AI, or we will see a new influx of advanced computer infections.

Risk 3: Manipulative AI

In the next 10 years, you will be able to call for help, in chat or on phone, have a conversation, and NEVER know if you spoke with a human or a bot. That may sound crazy, but current machine learning is capable of generating 100 percent AI news anchors with near-believable voice and visuals. The explosion of generative AI has only recently started to surface, so it’s fair to believe that in 10 years, or fewer, we will have AI managing human interactions.

Matt Chessen writes about the emergence of such technology and terms them MADCOMs (machine-driven communication tools). Imagine an influential political pundit and multiply it by 100. Using your profile, your online fingerprint, and advanced psychology a MADCOM could speak directly to your personal interests in a form of propaganda that’s never been seen.

Computational propaganda is already a growing term for social media manipulation via big data, but as the line blurs between people and machines online, the authenticity for making an opinion seem accepted and backed by many will become indistinguishable from a purchased MADCOM hype. “Pliable reality will become the norm,” writes Chessen.

US Congress has already reviewed the Countering Foreign Propaganda and Disinformation Act, but as the AI revolution evolves, we might see a stronger call to secure the clarity of information, which up until recently has been provided solely by humans.

So… what should we take away from this?

The unknown has always been a generator of fear for humanity. Despite the risks listed above, there’s always the simplest risk of all, that we don’t even see it coming. Throngs of developers are working in part, like a multicellular organism, and organizing their uploads to the cloud, a single host that doesn’t need us when we’re done.  

AI is the first initiative that, should we succeed, we will create something smarter than ourselves.  Initiatives like aren’t trying to create algorithms that identify obscenity, mood, or plans of action. It’s trying to solve the question of general intelligence. Most people’s inclination is to hit the brakes and try to mitigate the risks, but that ship has sailed.  

The smartest thing any of us can do is to educate ourselves. The old adage “keep your enemies closer” rings true, because if only a few large companies are heading up AI research, then they alone, wittingly or not, will control the fate of AI for us all.  Studying AI is the best risk mitigation we have as we hopefully “speak up” and steer this revolution of “new electricity.”

Read Source Article The Next Web

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