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Support for Remote Workers Providing Extra Boost for Conversational AI




Since the coronavirus hit in mid-March and the number of remote workers skyrocketed, conversational AI is being employed in a support role. (Credit: Getty Images) 

By AI Trends Staff 

Conversational AI refers to the use of chatbots, messaging apps, and voice-based assistants to automate customer communications with a brand.   

Software that combines these features to carry on a human-like conversation might be called a “bot.” The term “chatbot” might refer to text-only bots. Amazon Alexa or Google Home virtual assistants use conversational AI; they learn about the customer and the customer learns about them. With deep learning underlying the interaction, the conversation experience should improve over time.  

The advantages of conversational AI in marketing include an instant response, which leads to higher conversion rates of queries to sales.  

Shane Barker, digital marketing consultant, cofounder of Attrock

The adoption of conversational AI is being fueled by the rise in use of messaging apps and voice-based assistants, according to an account from the site of Shane Barker, a digital marketing consultant and cofounder of Attrock, a digital marketing agency.  

The most popular messaging app, according to Statista, is WhatsApp, from a US startup now owned by Facebook, with over 1.6 billion users. That is followed by: Facebook Messenger with 1.3 billion users; WeChat, developed by TenCent of China, with 1.1 billion users;  QQMobile, also from Tencent, with 800 million users; Snapchat from Snap, Inc. of the US, with 314 million users; and Telegram from Telegram Messenger, founded in Russia in 2013 on the macOS and released on Android in May of this year, with 200 million users.  

“If you are not using conversational AI platforms yet, you should start now,” advised Barker. 

The conversations could be text-based or audio-based, and can be done on any messaging or voice-based communication platform. While conversational AI is the technology behind chatbots and voice-based assistants, it is not synonymous with either. You can use a messaging service, a website chatbot or a voice-based assistant, and use conversational AI to automate conversations on it, Barker advises. 

How Conversational AI Can Help Your Business 

Some conversational AI technologies are advanced enough to understand the context and personalize the conversations. User-friendly chatbots can generate leads and help drive sales. The first and most common use of conversational AI is to provide around-the-clock customer service. The bot can answer commonly-asked customer questions, resolve problems and point to solutions. The user company can build a customized database of information that can feed the conversational AI platform to make it more accurate.   

A website chatbot can interact with users and direct them to the right pages, products, or services — basically leading them down the sales funnel. The bot can also drive conversions by cross-selling or up-selling products. The bot can be trained to suggest complementary or higher-value products. The platform can also deliver offers and promotions to customers.  

As far as lead generation is concerned, conversational AI-based chatbots can schedule appointments and collect email addresses during non-working hours. You can then pass that information on to your sales team, who can then nurture those leads.  

Among the conversational AI platforms recommended by Barker are:   

  • LivePerson from LivePerson of New York City, with an AI offering released in 2018 from the company founded in 1998; 
  • SAP Conversational AI from SAP, the German multinational software company; 
  • KAI from Kasisto of New York City, founded in 2013;  
  • MindMeld now from Cisco Systems, founded in 2011 and acquired in 2017; 
  • Mindsay from Mindsay, headquartered in Paris; founded in 2016.

iAdvize Taps Network of Freelance Experts for Customer Service  

Another player is iAdvize, founded in France in 2010, offering a chat tool focused on customer service. Today iAdvize is a leading conversational platform in Europe and is now expanding in the US. The company says the tool is currently being used by over 2,000 e-commerce websites worldwide including Samsung, Disney and Lowe’s. 

The platform uses AI to identify each customer’s needs and connects them to a mix of in-store associates, in-house agents, chatbots and on-demand product experts from ibbu. Founded by iAdvize in 2016, ibbu today uses over 20,000 knowledgeable product experts from around the world who chat with customers and are paid for the advice.   

The freelancers are vetted to be experts in electronics, home improvement, sporting goods, hobbies, and other product segments. They get paid a percentage of sales they generate. Ibbu experts the company says have conducted over 1 million conversations with iAdvize’s e-commerce customers. 

Customers using iAdvize have seen an increase in online sales of 5% to 15%, according to the company. iAdvize was co-founded by Julien Hervouet, now the CEO. He stated in a press release on the announcement of ibbu in the UK in 2016, “We believe the future of marketing is conversational commerce, where brands use genuine fans to improve the customer’s experience of the brand.” 

How Adobe Used an AI Chatbot to Support 22,000 Remote Workers  

Cynthia Stoddard, Senior VP and CIO at Adobe

When the COVID-19 virus hit in March throughout the US, Adobe like many companies sent their workers home and shifted into remote work over a single weekend. “Not surprisingly, our existing processes and workflows weren’t equipped for this abrupt change,” stated Cynthia Stoddard, Senior VP and CIO at Adobe, in a written account published in VentureBeat. “Customers, employees, and partners — many also working at home — couldn’t wait days to receive answers to urgent questions.” 

The first step was to launch an organization-wide channel using Slack, a business communications platform from Slack Technologies, launched in 2013 in San Francisco. The 24×7 global IT help desk would support the channel, with the rest of IT available for rapid event escalation. 

The same questions and issues came up frequently. “We decided to optimize our support for frequently asked questions and issues,” Stoddard stated. They combined AI, machine learning and natural language processing to build a chatbot. Its answers could be as simple as directing employees to an existing knowledge base or FAQ, or walking them through steps to solve a problem. The team focused on the eight most frequently-reported topics, then continued to add capabilities based on what delivers the biggest benefits.  

“The results have been remarkable,” she wrote. Since going live on April 14, the system has responded to more than 3,000 queries and has noticed improvement in some critical issues. For example, more employees are seeking IT support through email. It was important to speed the turnaround time on these queries.  

With the help of a deep learning and NLP based routing mechanism, 38% of email tickets are now automatically routed to the correct support queue within six minutes,” she stated. “The AI routing bot uses a neural network-based classification technique to sort email tickets into classes, or support queues. Based on the predicted classification, the ticket is automatically assigned to the correct support queue.” 

The average time required to dispatch and route email tickets has been reduced by the AI chatbot from about 10 hours to less than 20 minutes. Continuous supervised training on the bot has helped Adobe achieve 97% accuracy, nearly on a par with a human expert. Call volumes for internal support have dropped by 35% as a result.  

The neural network model is retrained every two weeks by adding new data from resolved tickets to the training set. They leveraged the work done for a company chatbot for finance. Adobe continues to look at robotic process automation, to explore business improvements through the combination of autonomous software robots and AI.   

Keeping employees in the loop about the AI and chatbot technology being employed is critical. “When introducing a new/unknown technology tool, it’s critical to keep employee experience at the core of the training and integration process – to ensure they feel comfortable and confident with the change,” Stoddard wrote. 

Read the source articles from the sites of Shane Barker and Statista, from the website of  iAdvize and in VentureBeat 



How 5G Will Impact Customer Experience?




5G is the breakthrough technology promised to bring new innovations, change the way people are traversing through the Internet with its faster connection speeds, lower latency, high bandwidth, and ability to connect one million devices per square kilometre. Telcos are deploying 5G to enhance our day-to-day lives.

“When clubbed with other technologies like Artificial Intelligence, Internet of Things (IoT), it could mean a lot to a proliferation of other technologies like AR/VR, data analytics.” 

5G can be a boon for businesses with the delivery of increased reliability, efficiency and performance if it can be used to drive more value to the customers as well as the business stakeholders and meet their expectations with the help of digital technologies as mentioned below:

Consumer Expectations are on the Rise

In modern days, customer service teams provide and manage customer support via call centres and digital platforms. The rollout of 5G is expected to unleash more benefits with a positive impact on customer service as they improve their present personalized service offerings to customers and allow it to further create new solutions that could develop their customer engagement to win great deals.

For instance, salespeople in a retail store are being imbibed with layers of information about customers’ behaviour and choices that will help them build a rich and tailored experience for the customers walking down the store.

Video Conferencing/streaming is Just a Few Clicks Away

Video support is considered to be a critical part of Consumer Experience (CX) and will open new avenues for consumer-led enterprises.

“As per a survey conducted by Oracle with 5k people, 75% of people understand the efficiency and value of video chat and voice calls.” 

CX representatives used the video support feature to troubleshoot highly technical situations through video chat and screen sharing options with few clicks, potentially reducing the number of in-house technician visits during critical situations like coronavirus pandemic.

Also, nowadays video conferencing is facilitated with an option to record a quick instant video describing the process/solution and discarding the long process of sending step-by-step emails. Enterprises can develop advanced user guide for troubleshooting issues featuring video teasers for resolving common problems.

However, high-definition video quality is preferable for video conferencing, chat and demands for an uninterrupted network with smooth video streaming. This means operators need to carry out network maintenance activities on regular intervals to check whether there is any kind of 5G PIM formation on these network cell towers that could reduce receive sensitivity and performance, thereby deteriorating network speed, video resolution etc.

Thus, PIM testing becomes critical for delivering enhanced network services without interference, necessary for high-resolution online video conferencing, chats, and many more.

Increased Smart Devices and the Ability to Troubleshoot via Self-Service

The inception of 5G will give a boost to the IoT and smart device market which is already growing.

These smart devices IoT connections are expected to become twice in number between 2019 and 2025 i.e. more than 25Bn as per the GSM association which is an industry organization representing telecom operators across the globe.

With lower latency and improvisation in reliability, 5G has a lot more to offer as it connects a large number of devices. This will ultimately curb the manpower needed for customer support thereby reducing labour costs for the enterprise. Moreover, these IoT connected devices and high-speed network of 5G permit consumers to self-troubleshoot these devices at their own homes.

In order to facilitate these high-resolution networks, telecom operators need to perform 5G network testing and identify issues, take corrective actions that could improve their network and integrate with advanced capabilities, making it more efficient than previous connections with the wider network coverage.

Enhanced Augmented Reality (AR) / Virtual Reality (VR) Capabilities

As these tools are being widely used, customers are provided with virtual stores or immersive experiences using AR to view a sneak peek of the products in their house in real-time.

“‘Augmented Retail: The New Consumer Reality’ study by Nielsen in 2019 suggested that AR/VR has created a lot of interest in people and they are willing to use these technologies to check out products.” 

Analysis of Bulk Data With Big Data Analytics

Enterprises have to deal with a huge volume of data daily. 5G has the ability to collect these data and with its advanced network connectivity across a large number of devices, it delivers faster data analytics too.

Companies will be able to process this vast amount of unstructured data sets combined with Artificial Intelligence (AI) to extract meaningful insights and use them for drafting business strategies like using customer behaviour data sets to study their buying behaviour and targeting such segment with customized service offerings as per their requirement.

As per Ericsson’s AI in networks report, 68% of Communications Service Providers (CSPs) believe improving CX is a business objective while more than half of them already believe AI will be a key technology that will assist in improving the overall CX. Thus, big data analytics will be crucial for harnessing all new data and enhance the customer experience.


Looking from a CX point of view, 5G benefits will far extend beyond the experience of a citizen. Real-time decisions will accelerate with the prevalence of 5G and application of other new-age technologies like AI, ML, IoT, etc. As 5G deployment will continue to grow, so is the transition of each trending processes mentioned above that will ultimately improve your business in terms of productivity, gain a large customer base and bring more revenues.


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Resiliency And Security: Future-Proofing Our AI Future




Deploying AI in the enterprise means thinking forward for resiliency and security (GETTY IMAGES)

By Allison Proffitt, AI Trends

On the first day of the Second Annual AI World Government conference and expo held virtually October 28-30, a panel moderated by Robert Gourley, cofounder & CTO of OODA, raised the issue of AI resiliency. Future-proofing AI solutions requires keeping your eyes open to upcoming likely legal and regulatory roadblocks, said Antigone Peyton, General Counsel & Innovation Strategist at Cloudigy Law. She takes a “use as little as possible” approach to data, raising questions such as: How long do you really need to keep training data? Can you abstract training data to the population level, removing some risk while still keeping enough data to find dangerous biases?

Stephen Dennis, Director of Advanced Computing Technology Centers at the U.S. Department of Homeland Security, also recommended a forward-looking posture, but in terms of the AI workforce. In particular, Dennis challenged the audience to consider the maturity level of the users of new AI technology. Full automation is not likely a first AI step, he said. Instead, he recommends automating slowly, bringing the team along. Take them a technology that works in the context they are used to, he said. They shouldn’t need a lot of training. Mature your team with the technology. Remove the human from the loop slowly.

Of course, some things will never be fully automated. Brian Drake, U.S. Department of Defense, pointed out that some tasks are inherently human-to-human interactions—such as gathering human intelligence. But AI can help humans do even those tasks better, he said.

He also cautioned enterprises to consider their contingency plan as they automate certain tasks. For example, we rarely remember phone numbers anymore. We’ve outsourced that data to our phones while accepting a certain level of risk. If you deploy a tool that replaces a human analytic activity, that’s fine, Drake said. But be prepared with a contingency plan, a solution for failure.   

Organizing for Resiliency

All of these changes will certainly require some organizational rethinking, the panel agreed. While government is organized in a top down fashion, Dennis said, the most AI-forward companies—Uber, Netflix—organize around the data. That makes more sense, he proposed, if we are carefully using the data.

Data models—like the new car trope—begin degrading the first day they are used. Perhaps the source data becomes outdated. Maybe an edge use case was not fully considered. The deployment of the model itself may prompt a completely unanticipated behavior. We must capture and institutionalize those assessments, Dennis said. He proposed an AI quality control team—different from the team building and deploying algorithms—to understand degradation and evaluate the health of models in an ongoing way. His group is working on this with sister organizations in cyber security, and he hopes the best practices they develop can be shared to the rest of the department and across the government.

Peyton called for education—and reeducation—across organizations. She called the AI systems we use today a “living and breathing animal”. This is not, she emphasized, an enterprise-level system that you buy once and drop into the organization. AI systems require maintenance, and someone must be assigned to that caretaking.

But at least at the Department of Defense, Drake pointed out, all employees are not expected to become data scientists. We’re a knowledge organization, he said, but even if reskilling and retraining are offered, a federal workforce does not have to universally accept those opportunities. However, surveys across DoD have revealed an “appetite to learn and change”, Drake said. The Department is hoping to feed that curiosity with a three-tiered training program offering executive-level overviews, practitioner-level training on the tools currently in place, and formal data science training. He encouraged a similar structure to AI and data science training across other organizations.

Bad AI Actors

Gourley turned the conversation to bad actors. The very first telegraph message between Washington DC and Baltimore in 1844 was an historic achievement. The second and third messages—Gourley said—were spam and fraud. Cybercrime is not new and it is absolutely guaranteed in AI. What is the way forward, Gourley asked the panel.

“Our adversaries have been quite clear about their ambitions in this space,” Drake said. “The Chinese have published a national artificial intelligence strategy; the Russians have done the same thing. They are resourcing those plans and executing them.”

In response, Drake argued for the vital importance of ethics frameworks and for the United States to embrace and use these technologies in an “ethically up front and moral way.” He predicted a formal codification around AI ethics standards in the next couple of years similar to international nuclear weapons agreements now.


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AI Projects Progressing Across Federal Government Agencies




The AI World Government Conference kicked off virtually on Oct. 28 and continues on Oct. 29 and 30. Tune in to learn about AI strategies and plans of federal agencies. (Credit: Getty Images)

By AI Trends Staff

Government agencies are gaining experience with AI on projects, with practitioners focusing on defining the project benefit and the data quality is good enough to ensure success. That was a takeaway from talks on the opening day of the Second Annual AI World Government conference and expo held virtually on October 28.

Wendy Martinez, PhD, director of the Mathematical Statistics Research Center, US Bureau of Labor Statistics

Wendy Martinez, PhD, director of the Mathematical Statistics Research Center, with the Office of Survey Methods Research in the US Bureau of Labor Statistics, described a project to use natural language understanding AI to parse text fields of databases, and automatically correlate them to job occupations in the federal system. One lesson learned was despite interest in sharing experience with other agencies, “You can’t build a model based on a certain dataset and use the model somewhere else,”  she stated. Instead, each project needs its own source of data and model tuned to it.

Renata Miskell, Chief Data Officer in the Office of the Inspector General for the US Department of Health and Human Services, fights fraud and abuse for an agency that oversees over $1 trillion in annual spending, including on Medicare and Medicaid. She emphasized the importance of ensuring that data is not biased and that models generate ethical recommendations. For example, to track fraud in its grant programs awarding over $700 billion annually, “It’s important to understand the data source and context,” she stated. The unit studied five years of data from “single audits” of individual grant recipients, which included a lot of unstructured text data. The goal was to pass relevant info to the audit team. “It took a lot of training, she stated. “Initially we had many false positives.” The team tuned for data quality and ethical use, steering away from blind assumptions. “If we took for granted that the grant recipients were high risk, we would be unfairly targeting certain populations,” Miskell stated.

Dave Cook, senior director of AI/ML Engineering Services, Figure Eight Federal

In the big picture, many government agencies are engaged in AI projects and a lot of collaboration is going on. Dave Cook is senior director of AI/ML Engineering Services for Figure Eight Federal, which works on AI projects for federal clients. He has years of experience working in private industry and government agencies, mostly now the Department of Defense and intelligence agencies. “In AI in the government right now, groups are talking to one another and trying to identify best practices around whether to pilot, prototype, or scale up,” he said. “The government has made some leaps over the past few years, and a lot of sorting out is still going on.”

Ritu Jyoti, Program VP, AI Research and Global AI Research lead for IDC consultants, program contributor to the event, has over 20 years of experience working with companies including EMC, IBM Global Services, and PwC Consulting. “AI has progressed rapidly,” she said. From a global survey IDC conducted in March, business drivers for AI adoption were found to be better customer experience, improved employee productivity, accelerated innovation and improved risk management. A fair number of AI projects failed. The main reasons were unrealistic expectations, the AI did not perform as expected, the project did not have access to the needed data, and the team lacked the necessary skills. “The results indicate a lack of strategy,” Joti stated.

David Bray, PhD, Inaugural Director of the nonprofit Atlantic Council GeoTech Center, and a contributor to the event program, posted questions on how data governance challenges the future of AI. He asked what questions practitioners and policymakers around AI should be asking, and how the public can participate more in deciding what can be done with data. “You choose not to be a data nerd at your own peril,” he said.

Anthony Scriffignano, PhD, senior VP & Chief Data Scientist with Dun & Bradstreet, said in the pandemic era with many segments of the economy shut down, companies are thinking through and practicing different ways of doing things. “We sit at the point of inflection. We have enough data and computer power to use the AI techniques invented generations ago in some cases,” he said. This opportunity poses challenges related to what to try and what not to try, and “sometimes our actions in one area cause a disruption in another area.”

AI World Government continues tomorrow and Friday.

(Ed. Note: Dr. Eric Schmidt, former CEO of Google is now chair of the National Security Commission on AI, today was involved in a discussion, Transatlantic Cooperation Around the Future of AI, with Ambassador Mircea Geoana, Deputy Secretary General, North Atlantic Treaty Organization, and Secretary Robert O. Work, vice chair of the National Security Commission. Convened by the Atlantic Council, the event can be viewed here.)


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