Key Considerations in AI Vendor Selection, Deployment

The world of artificial intelligence is frightening. No, not the danger of an army of AI-powered robots taking over the world (though that is a bit concerning). The real fear is that the wrong vendor is chosen or the rollout handled poorly. After all, AI is complex, not fully mature, in some cases poorly understood, and […]

The world of artificial intelligence is frightening. No, not the danger of an army of AI-powered robots taking over the world (though that is a bit concerning). The real fear is that the wrong vendor is chosen or the rollout handled poorly. After all, AI is complex, not fully mature, in some cases poorly understood, and involves great changes to how an organization thinks and operates.

Much of the complexity stems from the fact that AI has no single meaning or definition. It is a combination of several elements (machine learning, natural language processing, computer vision and others). This means that use cases tend to be unique and complex. Companies not big enough to hire expertise rely deeply on consultants and vendors, likely more than in more familiar areas. AI is not for the corporate faint of heart.

So how should organizations approach AI?

The first step in any corporate initiative is to fully understand what is on the table. It seems almost needless to say that organizations must educate themselves about AI before taking the plunge. But, in this case, it’s so important that it is worth stating the obvious. They must assess what data they have to feed into the system and if remedial work is necessary to enable that data to be used.

Tractica Research Director Aditya Kaul suggests that organizations understand the difference between the AI platforms that process raw data to reach conclusions and perception-driven approaches that focus on the intricacies and nuances of language and vision. The next step is to experiment on a wide variety of use cases and settle on those that bring the greatest value to the organization. It is important to understand the metrics that will be used to gauge success, such as increased productivity or reduced costs.

Moving Ahead with AI

At that point, they are set to move ahead aggressively. “Once companies have a good understanding of the AI technologies and use cases, they can go [choose] a third-party enterprise-grade AI platform and build a robust framework around data and model warehousing that allows for efficient production-grade AI that can be swiftly deployed into client-facing products and services,” Kaul wrote to IT Business Edge in response to emailed questions.

This suggests deep changes, which makes choosing vendors an even more vital decision than better understood limited technology deployments. The stakes are high. It is a nascent field where some companies no doubt are selling vaporware and some perhaps haven’t figured out their own value proposition. It’s best to be very careful. “If your AI vendor won’t promise you real ROI, it’s because they can’t deliver,” wrote Ben Lamm, the co-founder and CEO of Hypergiant. “If a vendor is trying to skirt around a clear statement of value, you know they won’t serve you well in the long run.” Organizations should do the same block and tackling that is done for any other significant investment. Credentials should be checked, deep conversations conducted and a high comfort level achieved. “One of the most important things enterprises can look for in an AI vendor is understanding the success of their customer base,” wrote Peresh Kharya, the director of Accelerated Computing for NVIDIA. “Don’t be afraid to ask which of their customers are successful and how has their new AI solution benefited their business. Asking this question will help you gauge the tangible business value the vendor is promoting.”

Organizations can take steps to increase the odds that they will choose the right vendor. Dave Damer, the founder and CEO of Testfire Labs, offers three tips. The first two focus on precisely what the vendor will be providing. Companies should ask if the prospective vendor delivers packaged solutions, custom solutions or both, and if it has the necessary expertise in house or must outsource. Finally, the organization must understand what will happen after the deployment is done. “A lack of employee training or further customization of models can lead to unusable and/or ineffective technology,” Damer wrote.

Best of Breed or Single Vendor?

A longstanding debate in telecom and IT circles is whether platforms are better coming from a single vendor or “best in breed” arrangements in which the top elements are cherry picked and strung together. The single vendor platforms presumably are better integrated and have deeper and easier to use management functions, while the best in breed approach potentially offers better performance.

The pendulum is swinging toward multiple vendors, at least according to Tracy Malingo, the senior vice president of Product Strategy at Verint, which bought AI firm Next IT last December. “This is actually one of the biggest shifts that we’ve seen in AI,” Malingo wrote. “As major players have sought to lock in ecosystems and as companies have evolved in their understanding and needs for AI, we’ve seen the market begin to shift toward best of breed over single-source vendors. That trend will continue in the future.”

The bottom line is that AI is a slippery slope: That slope can arc toward more efficient operations and a healthier bottom line – or toward confusion, failed implementations and all the headaches that those results bring on. “Organizations should have a clear understanding of what business issues they’re trying to solve with AI,” wrote Guy Yehiav, the CEO of Profitect. “How will the technology they’re evaluating make an impact to both top and bottom line and what is the approach to roll it out across the business? If analytics and AI are done well, the impact should be quick and results tangible.”

Read the source article at IT Business Edge.

Here are 9 AI Use Cases Happening in Business Today

Artificial intelligence (AI) is increasingly getting attention from enterprise decision makers. Given that, it’s no surprise that AI use cases are growing. According research conducted by Gartner, smart machines will achieve mainstream adoption by 2021, with 30 percent of large companies using AI. These technologies, which can take the form of cognitive computing, machine learning and deep learning, are now […]

Artificial intelligence (AI) is increasingly getting attention from enterprise decision makers. Given that, it’s no surprise that AI use cases are growing. According research conducted by Gartner, smart machines will achieve mainstream adoption by 2021, with 30 percent of large companies using AI.

These technologies, which can take the form of cognitive computing, machine learning and deep learning, are now tapping advanced capabilities such as image recognition, speech recognition, the use of smart agents, and predictive analytics to reinvent the way organizations do business. Combined with other digital technologies, including the Internet of Things (IoT), a new era of AI promises to transform business.

Here’s a look at 10 leading AI use cases and how organizations can use them to gain a competitive advantage:

Marketing: AI for Real Time Data 

The use of real-time data, Web data, historical purchase data, app use data, unstructured data and geolocation information have introduced the ability to deliver information, product recommendations, coupons and incentives at the right time and place. AI allows companies to engage in personalized marketing and slide the dial closer to one-to-one relationships.

In addition, businesses gain competitive advantage by using machine learning and deep learning for sentiment analysis by analyzing e-mail and social media streams. More advanced systems can detect a person’s mood from photos and videos. This helps systems respond contextually and create more targeted marketing and interactions.

Retail Sales: AI for Voice and Image Search

Artificial intelligence in retail is transforming the way people shop and buy items ranging from clothes to cars. Voice search and image search are now widespread. Amazon and many other retailers now incorporate these tools in their apps. Next generation AI is also taking shape. For example, augmented reality (AR) lets shoppers view a sofa or paint color superimposed in their house or office. Virtual reality (VR) allows consumers to sit inside a vehicle and even test drive it without leaving home. Audi, BMW and others have developed VR systems for shoppers.

But the AI use cases don’t stop there. AI in retail extends to bots and virtual assistants that recommend products and provide information; algorithms that helps sales teams focus on high value customers and high probability transactions; and predictive analytics that factor in weather, the price of raw goods and components, or inventory levels to adjust pricing and promotions dynamically. Clothing retailer North Face, for instance, asks customers a series of questions related to a purchase at its website. Not only does this lead customers to the right product, it taps machine learning to gain insights that potentially lead to higher cart values and additional sales.

Customer Support: AI for Natural Language

AI in retail is emerging as a powerful force, but customer support is also harnessing the technology for competitive advantage. Bots and digital assistants are transforming the way support functions take place. These technologies increasingly rely on natural language processing to identify problems and engage in automated conversations. AI algorithms determine how to direct the conversation or route the call to the right human agent, who has the required information on hand. This helps shorten calls and it produces higher customer satisfaction rates. A Forrester study found that 73 percent of customers said that valuing their time is the most important thing a company can do to provide them with good online customer service.

Manufacturing: AI Powers Smart Robots

Robotics has already changed the face of manufacturing. However, robots are becoming far more intelligent and autonomous, thanks to AI. What is machine learning used for in factories? Many companies are building so-called “smart manufacturing” facilities that use AI to optimize labor, speed production and improve product quality. Companies are also turning to predictive analytics to understand when a piece of equipment is likely to require maintenance, repair or replacement.

For example, Siemens is now equipping gas turbine systems with more than 500 sensors that continuously monitor devices and machines. All this data is helping create the manufacturing facility of the future, sometimes referred to as Industry 4.0. Smart manufacturing–which merges the industrial IoT and AI–is projected to grow from $200 billion in 2018 to $320 billion by 2020, according to a study conducted by market research firm TrendForce.

Read the source article in Datamation.

HBR: 3 Types of Real World AI to Support Your Business Today

In 2013, the MD Anderson Cancer Center launched a “moon shot” project: diagnose and recommend treatment plans for certain forms of cancer using IBM’s Watson cognitive system. But in 2017, the project was put on hold after costs topped $62 million—and the system had yet to be used on patients. At the same time, the […]

In 2013, the MD Anderson Cancer Center launched a “moon shot” project: diagnose and recommend treatment plans for certain forms of cancer using IBM’s Watson cognitive system. But in 2017, the project was put on hold after costs topped $62 million—and the system had yet to be used on patients. At the same time, the cancer center’s IT group was experimenting with using cognitive technologies to do much less ambitious jobs, such as making hotel and restaurant recommendations for patients’ families, determining which patients needed help paying bills, and addressing staff IT problems.

The results of these projects have been much more promising: The new systems have contributed to increased patient satisfaction, improved financial performance, and a decline in time spent on tedious data entry by the hospital’s care managers. Despite the setback on the moon shot, MD Anderson remains committed to using cognitive technology—that is, next-generation artificial intelligence—to enhance cancer treatment, and is currently developing a variety of new projects at its center of competency for cognitive computing.

The contrast between the two approaches is relevant to anyone planning AI initiatives. Our survey of 250 executives who are familiar with their companies’ use of cognitive technology shows that three-quarters of them believe that AI will substantially transform their companies within three years. However, our study of 152 projects in almost as many companies also reveals that highly ambitious moon shots are less likely to be successful than “low-hanging fruit” projects that enhance business processes. This shouldn’t be surprising—such has been the case with the great majority of new technologies that companies have adopted in the past. But the hype surrounding artificial intelligence has been especially powerful, and some organizations have been seduced by it.

In this article, we’ll look at the various categories of AI being employed and provide a framework for how companies should begin to build up their cognitive capabilities in the next several years to achieve their business objectives.

Three Types of AI

It is useful for companies to look at AI through the lens of business capabilities rather than technologies. Broadly speaking, AI can support three important business needs: automating business processes, gaining insight through data analysis, and engaging with customers and employees.

Process automation.

Of the 152 projects we studied, the most common type was the automation of digital and physical tasks—typically back-office administrative and financial activities—using robotic process automation technologies. RPA is more advanced than earlier business-process automation tools, because the “robots” (that is, code on a server) act like a human inputting and consuming information from multiple IT systems. Tasks include:

  • transferring data from e-mail and call center systems into systems of record—for example, updating customer files with address changes or service additions;
  • replacing lost credit or ATM cards, reaching into multiple systems to update records and handle customer communications;
  • reconciling failures to charge for services across billing systems by extracting information from multiple document types; and
  • “reading” legal and contractual documents to extract provisions using natural language processing.RPA is the least expensive and easiest to implement of the cognitive technologies we’ll discuss here, and typically brings a quick and high return on investment. (It’s also the least “smart” in the sense that these applications aren’t programmed to learn and improve, though developers are slowly adding more intelligence and learning capability.) It is particularly well suited to working across multiple back-end systems.At NASA, cost pressures led the agency to launch four RPA pilots in accounts payable and receivable, IT spending, and human resources—all managed by a shared services center. The four projects worked well—in the HR application, for example, 86% of transactions were completed without human intervention—and are being rolled out across the organization. NASA is now implementing more RPA bots, some with higher levels of intelligence. As Jim Walker, project leader for the shared services organization notes, “So far it’s not rocket science.”

One might imagine that robotic process automation would quickly put people out of work. But across the 71 RPA projects we reviewed (47% of the total), replacing administrative employees was neither the primary objective nor a common outcome. Only a few projects led to reductions in head count, and in most cases, the tasks in question had already been shifted to outsourced workers. As technology improves, robotic automation projects are likely to lead to some job losses in the future, particularly in the offshore business-process outsourcing industry. If you can outsource a task, you can probably automate it.

Read the source article at Harvard Business Review.

AI Tiptoes Into the Workplace, in the Beginning of a Wave

There is no shortage of predictions about how artificial intelligence is going to reshape where, how and if people work in the future. But the grand work-changing projects of A.I., like self-driving cars and humanoid robots, are not yet commercial products. A more humble version of the technology, instead, is making its presence felt in […]

There is no shortage of predictions about how artificial intelligence is going to reshape where, how and if people work in the future.

But the grand work-changing projects of A.I., like self-driving cars and humanoid robots, are not yet commercial products.

A more humble version of the technology, instead, is making its presence felt in a less glamorous place: the back office.

New software is automating mundane office tasks in operations like accounting, billing, payments and customer service.

The programs can scan documents, enter numbers into spreadsheets, check the accuracy of customer records and make payments with a few automated computer keystrokes.

The technology is still in its infancy, but it will get better, learning as it goes. So far, often in pilot projects focused on menial tasks, artificial intelligence is freeing workers from drudgery far more often than it is eliminating jobs.

The bots are mainly observing, following simple rules and making yes-or-no decisions, not making higher-level choices that require judgment and experience. “This is the least intelligent form of A.I.,” said Thomas Davenport, a professor of information technology and management at Babson College.

But all the signs point to much more to come. Big tech companies like IBM, Oracle and Microsoft are starting to enter the business, often in partnership with robotic automation start-ups. Two of the leading start-ups, UiPath and Automation Anywhere, are already valued at more than $1 billion. The market for the robotlike software will nearly triple by 2021, by one forecast.

“This is the beginning of a wave of A.I. technologies that will proliferate across the economy in the next decade,” said Rich Wong, a general partner at Accel, a Silicon Valley venture capital firm, and an investor in UiPath.

The emerging field has a klutzy name, “robotic process automation.” The programs — often called bots — fit into the broad definition of artificial intelligence because they use ingredients of A.I. technology, like computer vision, to do simple chores.

For many businesses, that is plenty. Nearly 60 percent of the companies with more than $1 billion in revenue have at least pilot programs underway using robotic automation, according to research from McKinsey & Company, the consulting firm.

The companies and government agencies that have begun enlisting the automation software run the gamut. They include General Motors, BMW, General Electric, Unilever, Mastercard, Manpower, FedEx, Cisco, Google, the Defense Department and NASA.

State Auto Insurance in Ohio Ahead of the Curve

State Auto Insurance Companies in Columbus, Ohio, started its first automation pilot project two years ago. Today, it has 30 software programs handling back-office tasks, with an estimated savings of 25,000 hours of human work — or the equivalent of about a dozen full-time workers — on an annualized basis, assuming a standard 2,000-hour work year.

Holly Uhl, a technology manager who leads the automation program, estimated that within two years the company’s bot population would double to 60 and its hours saved would perhaps triple to 75,000, nearly all in year-after-year savings rather than one-time projects.

Cutting jobs, Ms. Uhl said, is not the plan. The goal for the company, whose insurance offerings include auto, commercial and workers’ compensation, is to increase productivity and State Auto’s revenue with limited additions to its head count, she said.

Ms. Uhl said her message to workers is: “We’re here to partner with you to find those tasks that drive you crazy.”

Rebekah Moore, a premium auditor at the company, had one in mind. Premium auditors scrutinize insurance policies and make recommendations for changing rates. They audit less than half of the policies, Ms. Moore said.

The policies that will not be audited then have to be set aside and documented. That step, she explained, is a routine data entry task that involves fiddling with two computer programs, plugging in codes and navigating drop-down menus. It takes a minute or two. But because auditors handle many thousands of policies, the time adds up, to about an hour a day, she estimated.

Starting in May, a bot took over that chore. “No one misses that work,” Ms. Moore said.

Is she worried about the bots climbing up the task ladder to someday replace her? Not at all, she said. “We’ll find things to do with our time, higher-value work,” said Ms. Moore, 37.

On State Auto’s current path, her confidence seems justified. If the company hits its target of 75,000 hours in savings by 2020, that would be the equivalent of fewer than 40 full-time workers, compared with State Auto’s work force of 1,900. The company plans to grow in the next two years. If so, State Auto would most likely be hiring a few dozen people fewer than it would otherwise.

UiPath Envisions One Bot Per Employee

Automation companies are eager to promote the bots as helpful assistants instead of job killers. The technology, they say, will get smarter and more useful, liberating workers rather than replacing them.

“The long-term vision is to have one bot for every employee,” said Bobby Patrick, chief market officer for UiPath. The company, which is based in New York, recently reported that its revenue more than tripled in the first half of 2018, to a yearly rate of more than $100 million.

Mihir Shukla, chief executive of Automation Anywhere, refers to his company’s bots as “digital colleagues.” In July, the company announced it had raised a $250 million round of venture funding, valuing the company at $1.8 billion.

The market for A.I.-enhanced software automation is poised for rapid growth, but that expansion, analysts say, will ultimately bring job losses.

Forrester Research estimated that revenue would nearly triple to $2.9 billion over the next three years. And by 2021, robotic automation technology will be doing the equivalent work of nearly 4.3 million humans worldwide, Forrester predicted.

In a dynamic global labor market, that is not a clear-cut forecast of 4.3 million layoffs. The bots may do work not previously done by humans, and people may move onto new jobs.

“But these initial bots will get better, and the task harvesting will accelerate,” said Craig Le Clair, an analyst for Forrester. “For workers, there will be a mix of automation dividends and pain.”

The recent research has examined jobs as bundles of tasks, some of which seem ripe for replacement and others not. So the technology’s immediate impact will resemble the experience to date with robotic software, changing work more than destroying jobs.

For Ms. Uhl of State Auto, the most persistent pushback has come not at the company but at home, from her two young sons, Christian, 9, and Elijah, 7, who are eager to glimpse the future.

Hearing their mother talk about robots at work, they keep asking her to bring one home. “It’s not the kind of robot you can see,” Ms. Uhl said she has told her disappointed sons.

Read the source article in The New York Times.

Fintech: Sector Can Benefit From Up To $512 Billion via Intelligent Automation

A new report from Capgemini’s Digital Transformation Institute reveals that, by 2020, the financial services industry could reap up to $512 billion in new global revenues through intelligent automation. The report, Growth in the Machine, demonstrates the advantages of applying the right combination of robotic process automation (RPA), artificial intelligence (AI), and business process optimisation in the sector […]

A new report from Capgemini’s Digital Transformation Institute reveals that, by 2020, the financial services industry could reap up to $512 billion in new global revenues through intelligent automation.

The report, Growth in the Machine, demonstrates the advantages of applying the right combination of robotic process automation (RPA), artificial intelligence (AI), and business process optimisation in the sector to achieve what Capgemini terms “intelligent automation”.

Capgemini surveyed 1,500 senior executives from 750 global organisations in retail and commercial banking, capital markets, and insurance. The survey covered organisations in the UK, US, France, Germany, Italy, the Netherlands, Spain, Sweden, and India, 42 percent of which had global revenues greater than $10 billion.

A business could already realise a 10-25 percent uptick in cost savings by applying RPA, according to Capgemini. However, that could potentially scale up to 30-50 percent with the application of AI-enhanced automation.

To date, automation technologies, such as RPA, have been implemented by the financial services industry to drive down costs and create new business efficiencies – mirroring how AI is being adopted by most industries, according to another Capgemini report this week.

Revenues or savings?

But leaders in the sector don’t see AI and automation in simple cost-savings terms, cautions the consultancy.

Leaders in the financial services industry have already begun taking automation directly to their customers, says Capgemini, adding that they are using it as a revenue generator rather than just a means to slash internal costs.

The report says that, on average, over one-third (35 percent) of financial services firms have seen a two to five percent increase in top-line growth from automation, with faster time to market and improved cross-selling being the key factors that influence gains.

Anirban Bose, head of Capgemini’s Financial Services Global Business Unit said, “The most visionary financial services firms have leaders with a sophisticated view of the potential impact that automation can have throughout their business. And they’re already reaping the rewards.

“Hundreds of billions of dollars in automation-generated revenue is up for grabs in the coming years. Only those companies that deploy this technology in a way that looks beyond cost-cutting and focuses on creating value for customers and shareholders will be able to win in the marketplace.”

Slow to adopt

With substantial gains within reach thanks to intelligent automation, it’s no surprise that an increasing number of financial services firms are considering deploying the technology on the front line.

However, despite the obvious opportunities, the adoption of intelligent automation has been slow to date. Only 10 percent of companies have implemented the technology at scale, says the report, with the majority struggling with business, technology, and staffing challenges.

The study finds that several factors are preventing organisations from moving beyond proof of concept to live deployment of intelligent automation systems.

For example, around four in 10 organisations (43 percent) are struggling to establish a clear business case. Many are also struggling to persuade leadership to commit to a cohesive intelligent automation strategy (41 percent.)

More, the successful deployment and scaling of automation programmes requires expert staff with a deep understanding of RPA and AI technologies. However, almost half of businesses (48 percent) say they struggle to find the right resources to implement intelligent automation effectively.

Meanwhile, 46 percent say that the lack of an adequate data management strategy is hampering progress, as AI-based automation algorithms require the right data to be available at sufficient volumes.

Capgemini reveals that only around one in four organisations has the technological maturity to implement cognitive automation technologies (comprising machine learning, computer vision, and biometrics). Most organisations still have traditional RPA, or – at best – natural language processing (NLP) in the backbone of their automation programmes.

Internet of Business says

Capgemini warns that exploring intelligent automation could be critical for the long-term health of the financial services sector, because of the growing threat from non-traditional players.

The study says that nearly half (45 percent) of organisations believe that so-called ‘BigTech’ players, such as Amazon and Alphabet/Google, will be their competitors in the next five years.

That much is certain. However, the report comes in the wake of two others this week: one from Capgemini on AI adoption in the enterprise, and another from Riot Research, suggesting that the AI bubble is set to burst.

Put the three reports together, and a granular picture emerges of AI adoption opportunities over the next five years, as a component of an overall trend towards automation.

Read the source article at Internet of Business.

Here is the Essential Landscape for Enterprise AI Companies

Enterprise companies comprise a $3.4 trillion market worldwide of which an increasingly larger share is being allocated to artificial intelligence technologies. By our definition, “enterprise” technology companies create tools for workplace roles and functions that a large number of businesses use. For example, Salesforce is the primary enterprise software used by sales professionals in a company. Also […]

Enterprise companies comprise a $3.4 trillion market worldwide of which an increasingly larger share is being allocated to artificial intelligence technologies.

By our definition, “enterprise” technology companies create tools for workplace roles and functions that a large number of businesses use. For example, Salesforce is the primary enterprise software used by sales professionals in a company. Also known as a type of customer relationship management software, or CRM, it is the system of record for sales professionals to enter in their contacts, progress of leads, and for sales metrics to be tracked. Any company directly selling their products and services would benefit from a CRM.

Plenty of enterprise companies use combinations of automated data science, machine learning, and modern deep learning approaches for tasks like data preparation, predictive analytics, and process automation. Many are well-established players with deep domain expertise and product functionality. Others are hot new startups applying artificial intelligence to new problems. We cover a mix of both.

To help you identify the best tools for your business, we’ve broken up our landscape of enterprise AI solutions into functional categories to match organizational workflows and use cases. Most of these enterprise companies can be classified in multiple categories, but we focused on the primary value add and differentiation for each company.

BUSINESS INTELLIGENCE (BI)

This function derives intelligence from company data, encompasses the business applications, tools, and workflows that bring together information from all parts of the company to enable smart analysis. From streamlining data preparation like Paxata and Trifacta, to connecting data more effectively from different silos like Tamr and Alation, and even automating reports and generating narratives like Narrative Science and Yseop, enterprise companies are improving BI workflows with artificial intelligence.

PRODUCTIVITY

Productivity at work is often stunted by a myriad of tiny tasks that consume your attention, i.e. “death by a thousand cuts.” Many productivity tools have emerged to eliminate such tasks, such as the endless back and forth required to schedule meetings. Luckily, many of these productivity tools are virtual scheduling assistants like X.ai, FreeBusy, and Clara Labs.

CUSTOMER MANAGEMENT

Taking care of your customers is no easy task. Enterprise companies have recognized this critical area as ripe for disruption with artificial intelligence. DigitalGenius utilizes AI to sift through your customer service data and automate customer service operations. Inbenta’s AI-powered natural language search enables delivery of self-service support in forums and virtual agents. Luminoso creates visual representations of customer feedback, allowing companies to better understand what consumers want.

HR & TALENT

With the average tenure of a hire getting shorter, hiring and talent management is arguably one of the most difficult areas for every company to tackle. Where can you find the right candidates and how do you keep hires engaged? Companies like Entelo and Scout work from the top of the funnel to get you the most qualified candidates while others like hiQ Labs utilize public data to warn you of staff attrition risks and enable you to create retention strategies.

B2B SALES & MARKETING

No one likes to waste time tediously doing data entry or spend hours sometimes googling and sifting through LinkedIn trying to get that marginal bit of information on a lead. Perhaps that’s why professionals in these functions are willing to embrace and experiment with new tools. Some automate data entry and improve forecasting accuracy like Fusemachines and the AI-powered sales assistant Tact, while others like Lattice Engines and Mintigo utilize thousands of data sources to surface the most qualified prospects and opportunities. You also have Salesforce’s Einstein who has the intention of bringing AI and automation throughout the entire sales ecosystem.

CONSUMER MARKETING

So much data and intelligence can be gathered about your consumers through social channels, distribution channels, media channels, etc. Smart tools can not only crawl through this data, but analyze and understand what’s being said or done. Lexalytics is a text analytics platform that translates billions of unstructured data pieces and online signals into actionable insights for the company. Affinio uses deep learning to surface social fingerprints for the brand by creating interest-based clusters. Brands now have a better understanding of their customer segments, behaviors, and sentiments.

FINANCE & OPERATIONS

Finance & operations includes the back office, forecasting, accounting, and operational roles required to run a company. Since nobody likes paperwork, this area is ripe for automation. HyperScience recently came out of stealth with their $18 million Series A in December of 2016 to completely automate back office operations like form processing and data extraction through AI. Another company called AppZen is an automated audit platform that can instantly detect fraud and compliance issues, freeing up T&E teams from tedious manual audits and checks. The tools in this space reap immediate returns for companies due to the volume and repetitive nature of some of the tasks.

Read the source article at TopBots.

Edmonton Startup using AI to Fix ‘Broken Meetings’

Meetings cost time and money to run, and many of them are unnecessary, says Testfire Labs CEO Dave Damer. His solution: the company’s AI assistant, Hendrix.ai. Currently in its beta test phase, it takes a meeting’s minutes, noting questions, answers and action items by listening via microphone. Its meeting summaries leave out “chit chat” for […]

Meetings cost time and money to run, and many of them are unnecessary, says Testfire Labs CEO Dave Damer. His solution: the company’s AI assistant, Hendrix.ai.

Currently in its beta test phase, it takes a meeting’s minutes, noting questions, answers and action items by listening via microphone. Its meeting summaries leave out “chit chat” for clarity. Exact transcripts aren’t kept for reasons of confidentiality, said Damer, who founded the company in 2017.

“The demands to do more with less in modern business keep increasing,” Damer said. “AI gives us an opportunity to legitimately take things off peoples’ hands that are generally mundane tasks so they can focus on higher-value work.”

Hendrix.ai also tracks attendance rates, numbers of last-minute meetings and meeting lengths.

On May 25, Testfire Labs won a Startup Canada regional innovation award for its work on Hendrix.ai. Startup Canada CEO Victoria Lennox said the adjudicators liked how Testfire Labs integrated AI into audio-to-text technology with Hendrix.ai.

“There’s a lot of audio-to-text tools and they’re growing more and more,” Lennox said. What made Hendrix.ai different was its focus on meetings.

Damer’s goal is for Hendrx.ai to reach companies with more than 1,000 staff. It’s being tested by 100 organizations in its beta phase, including the City of Victoria and the Northern Alberta Institute of Technology. Torsten Prues of NAIT’s information technology department said he’s been using the system with a team of six since January.

“What made us interested (in using Hendrix.ai) was that NAIT is very meeting-heavy, and people don’t like taking minutes,” he said.

Edmonton: ‘On the cusp’ of tech

Damer graduated from the University of Alberta in 1991 as a computer engineer and has 25 years of experience in the technology industry. He calls Edmonton a good home for a tech startup, with an industry that’s “on the cusp.”

Before Testfire Labs, Damer founded ThinkTel Communications Ltd. in 2003, and spent 14 years there. ThinkTel is now the business services division of Distributel, an independent communications company.

Damer started Testfire Labs as a more creative project. Currently valued at $5 million, with 10 employees, Damer hopes to grow the business to $20 million next year. He sees Hendrix.ai becoming an asset to workplaces as more tasks — like note taking — become automated.

Randy Goebel, a professor at the University of Alberta and expert in natural language processing, said applying the science of natural language understanding to everyday use is “extremely difficult in practice.” The science is there, he said, but businesses like Hendrix are challenged with translating that science into something people will pay for.

“They provide a line of sight to scientists to add value to their work,” said Goebel, who is also a researcher at the Alberta Machine Intelligence Institute.

While the system is being honed for summarizing meetings, Damer plans to include more features that track whether certain speakers dominate meetings, gauge what tones discussions take, and find possible areas where different teams can collaborate.

“You can do so much more than notes,” he said. “We can do tone analysis on whether it was a positive, negative or neutral conversation. Was there joy in the words, or was there fear. What are the emotions that are being expressed.”

Read the source article in the Edmonton Journal.

Giving Artificial Intelligence a Little Common Sense

By Mark Barry of Luminoso As we all go about our day using the internet, talking to brands, and interacting online, we don’t adhere strictly to the writing guidelines of the Associated Press. We use a lot of slang, jargon, technical corporate terms, figurative language, and other communication shorthands — unstructured digital data that other humans understand easily, […]

By Mark Barry of Luminoso

As we all go about our day using the internet, talking to brands, and interacting online, we don’t adhere strictly to the writing guidelines of the Associated Press. We use a lot of slang, jargon, technical corporate terms, figurative language, and other communication shorthands — unstructured digital data that other humans understand easily, but that confounds Artificial Intelligence (AI).

The problem is that we tend to use very colorful, interesting, and funny language because we know that our words could reach a larger audience that way. As linguistic philosopher Paul Grice suggested, “we are concise in our language, leaving out things we expect everyone to know because we don’t want to be considered boring.” This results in data that is very unpredictable, disorganized, and constantly evolving, ultimately leaving AI in the dark because it lacks a little something called common sense.

This glue (common sense) that holds language together makes understanding it  uniquely difficult for AI systems because it is often left out of the datasets. To get there, we need to provide AI with a very deep, intuitive understanding of the language we use, along with the nuances and expressions that come along with it.

As linguist George Lakoff suggests, we understand complicated concepts by making analogies to simpler ideas. For example, in English, if we’re talking about data, we often use words for water, like information flow, as a way of conveying a core concept.

For instance, if you and I were having a conversation, I would never feel the need to tell you that if I drop something, it falls down. I expect you to know that. I would also expect you to know that when you get your morning coffee, it comes in a mug. These unspoken details are what AI systems need to make the right connections and contextualize our conversations.

Understanding Customers

When businesses started trying to understand digital customer feedback, they began with sentiment analysis (the ability to tell whether or not a statement is negative or positive). But the ceiling was hit pretty quickly when they realized that sentiment analysis misses out on the important stuff. It doesn’t truly capture why something is positive or negative, how to address it, or which aspects are more positive than others, let alone whether you’re even asking the right questions in the first place.

Over time, AI became smarter thanks to various deep learning techniques, but a different problem emerged: lack of data. Case in point; when Google’s DeepMind AI began learning to beat human players in the game of Go , it was crunching data on three million Go games. But that wasn’t enough. Once they ran out of data, they had it start playing itself. This is problematic though, because to teach an AI how to understand what customers are saying, you can’t exactly have it talk to itself to generate new training data.

It became clear that in order to build an AI that could learn how to understand customer feedback and add value to the conversation, we need to provide more sources of knowledge about how the world works, beyond the limited data from customer communications.

Developing Common Sense AI

ConceptNet is a large, open source knowledge base created at the MIT media lab in 2006. The knowledge base represent concepts, not just words, so that relations can be made and understood as a graph.

At Luminoso, our approach to embedding common sense into our products is to take background knowledge from ConceptNet and pair it with machine learning. This allows the system to adapt to new datasets without the need for heavy resources or lots of training data.

Meaning, the system strives to offer the kind of machine learning and natural language understanding you hear everyone talking about without the need for human intervention.

Luminoso uses a very small amount of data — the kind of data you already have — so you can start applying it to all sorts of unstructured text and start extracting insights to disseminate throughout the organization.

To demonstrate the value of common sense AI out in the wild, here’s a quick use-case: Luminoso was working with a Japanese car manufacturer not long ago. We were looking at feedback from dealerships to help unearth issues they hadn’t yet addressed but knew customers were talking about.

These customers were coming into the dealerships talking about how their cars smelled bad, and they used a lot of different words to describe this smell. Some described it as a wet dog even though they didn’t have a dog, some thought it smelled like an attic, and others simply said it smelled funky. And all of this feedback was being categorized by customer service agents as other, which is that bucket in your call center where unanticipated things that are truly going wrong go to haunt you.

Luminoso investigated this other bucket and found that these different descriptors for a bad odor were related to reports of dew or condensation inside certain models. The manufacturer was able to look into these models and found that the air conditioning hose was coming undone, causing water to leak into the car, leading to condensation, mold, and lo and behold, a bad smell.

Being able to tell dealerships how to address customers who come in with this issue was a huge win for their overall customer satisfaction, and it allowed them to unearth similar insights that may have eluded them going forward.

Ultimately, with the right common sense foundation in natural language understanding, these “lost in translation” moments become easy to reveal along with other “unknown unknowns” that companies spend years trying to get to the bottom of. So whether your goal is to improve customer or employee satisfaction, you’ll need a little common sense to really hear what they’re saying.

For more information, go to Luminoso.

 

How Top Brands Use AI To Enhance Marketing -Infographic 2018

We all know how marketing can enhance sales. However, enhancing marketing requires real skills and creativity. Or does it? Can machines do a better job at promoting, advertising, and ultimately selling products than a creative human being? A complete switch to AI seems futuristic, but some major brands have already implemented the tactic of using […]

We all know how marketing can enhance sales. However, enhancing marketing requires real skills and creativity. Or does it? Can machines do a better job at promoting, advertising, and ultimately selling products than a creative human being? A complete switch to AI seems futuristic, but some major brands have already implemented the tactic of using chatbots, voice assistants, and other smart systems for marketing enhancement purposes. In fact, a vast majority of globally known brands have already benefited from using AI. Let’s see why and how.

The AI may be more efficient at certain tasks, but it’s still a long way from being able to replace people. For instance, only 7% of customers are open to buying a product through a chatbot. However, companies like Nordstrom have found the right job position for AI-powered devices. Namely, their digital tool called ‘Style Boards’ allows salespersons to create and send personalized recommendations to existing and potential customers. Combine that with one of the many Nordstrom promo offers, and you’ll get an outstanding value purchase.

Another good example comes from one of the most recognizable brands worldwide. Nike uses an Artificial Intelligence system called ‘Nike On Demand’ to encourage people to lead a healthier life and exercise more by sending motivational messages. Still, if AI fails to motivate you, Nike deals and promo codes certainly won’t. Whether AI is taking over our jobs or not is debatable, but it’s a well-known fact that more and more brands are exploiting it for marketing purposes. Take a look at the infographic below, and you’ll learn that the future is a lot closer than you think.

To view the source infographic, go to 16Best.net.

Alibaba Furthers Use of AI to Power the Future of Business

Alibaba Group is powering ahead with a range of AI research and initiatives in a bid to realise its vision: To make it easy to do business everywhere and anywhere. That’s according to an Alibaba Group chief scientist and associate dean of machine intelligence and technology, Xiaofeng Ren, who spoke at CeBIT about how to […]

Alibaba Group is powering ahead with a range of AI research and initiatives in a bid to realise its vision: To make it easy to do business everywhere and anywhere.

That’s according to an Alibaba Group chief scientist and associate dean of machine intelligence and technology, Xiaofeng Ren, who spoke at CeBIT about how to develop AI applications that power the future of business.

“Alibaba has changed the everyday life of the Chinese in China. Looking forward, our visionary leader, Jack Ma, wants us to be able to reach two billion consumers and to help 10 million businesses around the world. That’s a very big call, but we already have half of the platforms in place.”

He explained how the company’s enabling services including Ant Financial (mobile online payment platform); Alibaba Cloud (public cloud service); cainiao (logistics branch); and alimama.com (an online marketing and trading platform) are making the vision a reality.

With technology the one universal driver behind all of these things, Ren said AI is a big part of the strategic play. He noted the world is in the third or fourth wave of technological development, and AI fits right into the equation, promising to change both business and society on a massive scale.

“There’s a blending of the digital world and the physical world where we have IoT, robotics, 3D printing, nanotechnologies – there are a lot of things that are happening that could further power the growth of the fourth wave. And I certainly think that artificial intelligence will be a big part of this revolution,” he said.

Ren’s machine intelligence technology division spearheads Alibaba’s efforts into AI, and aims to bring sweeping and disruptive changes both to the online player and to China’s dynamic business landscape. The independent R&D division is working across the Alibaba group in areas including speech recognition, computer vision, natural language understanding and optimisation and learning.

Projects underway

Ren detailed how emerging technologies are being developed at Alibaba, and how they will impact its future and the global retail landscape and enhance customer experience. One example of business innovation comes in the form of AliMe, a chatbot Alibaba developed that understands what people say both in terms of the text and the speech.

“It can respond to you in a number of ways. It can be a shopping assistant. For example, if you want to buy a train ticket, it can help you with it. It can be a customer service. It can help you with some of the problems and provide information to you. It can also be a generic ‘chatty cathy,’” he said, explaining some people just want to chat if they are bored,” he explained.

“Last year, this system handled about 95 per cent of requests – 9 million requests – and people were really happy with how this system performed in the real-world as a stress test.”

Another example is the work the company has done over the past four years around ‘Image Search’, which is being used by Taobao, the Chinese online shopping website, headquartered in Hangzhou, China, and a subsidiary of Alibaba Group. Taobao is considered one of the world’s biggest e-commerce websites, as well as one of the world’s top 10 most visited websites according to Alexa.

“On the Taobao app, when you want to search for a product, you can actually take a picture and upload a picture and it will find a similar picture in the catalogue,” Ren said. “There are certain limits on how you can describe a product. But on the other hand, a picture is worth a thousand words. One you put in a picture, it becomes much more clear what you’re looking for.”

He said 14 billion people use this feature everyday, and the company has three billion images of over 10 million products. “Once we have a technology and we get it to the mature point, we actually make it available for everyone to use.”

Image Search – which is now mature enough and announced as part of the Alibaba Cloud offering in March  – was recently picked up by The Iconic, an Australia online fashion and footwear store.

“Today you can actually use the search functionality in Iconic. It works in a similar way, you can take pictures, upload the pictures, and find similar products,” Ren said. “I’m excited to see in terms of the work of Image Search, not just into one product, or one part of the world, but useful to a lot more people and a lot more businesses.”

Other projects come in the area of media and video in terms of copyright protection and content, as well as indexing and search. Alibaba launched Whale Watching last year, a unified platform for protecting and trading video content.

Read the source article at CMO.