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.

A Strong Digital Base is Critical for Success with AI

By Jacques Bughin and Nicolas van Zeebroeck of McKinsey The diffusion of a new technology, whether ATMs in banking or radio-frequency identification tags in retailing, typically traces an S-curve. Early on, a few power users bet heavily on the innovation. Then, over time, as more companies rush to embrace the technology and capture the potential gains, the market […]

By Jacques Bughin and Nicolas van Zeebroeck of McKinsey

The diffusion of a new technology, whether ATMs in banking or radio-frequency identification tags in retailing, typically traces an S-curve. Early on, a few power users bet heavily on the innovation. Then, over time, as more companies rush to embrace the technology and capture the potential gains, the market opportunities for non-adopters dwindle. The cycle draws to a close with slow movers suffering damage.

Our research suggests that a technology race has started along the S-curve for artificial intelligence (AI), a set of new technologies now in the early stages of deployment. It appears that AI adopters can’t flourish without a solid base of core and advanced digital technologies. Companies that can assemble this bundle of capabilities are starting to pull away from the pack and will probably be AI’s ultimate winners.

Executives are becoming aware of what is at stake: our survey research shows that 45 percent of executives who have yet to invest in AI fear falling behind competitively. Our statistical analysis suggests that faced with AI-fueled competitive threats, companies are twice as likely to embrace AI as they were to adopt new technologies in past technology cycles.

AI builds on other technologies

To date, though, only a fraction of companies—about 10 percent—have tried to diffuse AI across the enterprise, and less than half of those companies are power users, diffusing a majority of the ten fundamental AI technologies. An additional quarter of companies have tested AI to a limited extent, while a long tail of two-thirds of companies have yet to adopt any AI technologies at all.

The adoption of AI, we found, is part of a continuum, the latest stage of investment beyond core and advanced digital technologies. To understand the relationship between a company’s digital capabilities and its ability to deploy the new tools, we looked at the specific technologies at the heart of AI. Our model tested the extent to which underlying clusters of core digital technologies (cloud computing, mobile, and the web) and of more advanced technologies (big data and advanced analytics) affected the likelihood that a company would adopt AI. As Exhibit 1 shows, companies with a strong base in these core areas were statistically more likely to have adopted each of the AI tools—about 30 percent more likely when the two clusters of technologies are combined. These companies presumably were better able to integrate AI with existing digital technologies, and that gave them a head start. This result is in keeping with what we have learned from our survey work. Seventy-five percent of the companies that adopted AI depended on knowledge gained from applying and mastering existing digital capabilities to do so.

Companies with a strong base in core digital technologies and big data analytics are more likely to have adopted an array of AI tools.

This digital substructure is still lacking in many companies, and that may be slowing the diffusion of AI. We estimate that only one in three companies had fully diffused the underlying digital technologies and that the biggest gaps were in more recent tools, such as big data, analytics, and the cloud. This weak base, according to our estimates, has put AI out of reach for a fifth of the companies we studied.

Leaders and laggards

Beyond the capability gap, there’s another explanation for the slower adoption of AI among some companies: they may believe that the case for it remains unproved or that it is a moving target and that advances in the offing will give them the chance to leapfrog to leadership positions without a need for early investments.

Read the source study at McKinsey.com.

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.

Overcome the Inertia That Keeps Businesses From Deploying AI – Here is How

By Harry Kabadaian, CEO of Fancy Lab, a digital marketing agency Artificial intelligence (AI) isn’t merely “important” to innovation and basic processes at the organization of the future, it’s indispensable. To thrive in that future, businesses already are in early-stage explorations to transform into AI-driven workplaces. But despite the high interest level in leveraging AI in business, […]

By Harry Kabadaian, CEO of Fancy Lab, a digital marketing agency

Artificial intelligence (AI) isn’t merely “important” to innovation and basic processes at the organization of the future, it’s indispensable.

To thrive in that future, businesses already are in early-stage explorations to transform into AI-driven workplaces. But despite the high interest level in leveraging AI in business, implementation remains quite low. According to Gartner’s 2018 CIO Agenda Survey, only four percent of Chief Information Officers (CIOs) have implemented AI. The survey report is careful to note we’re about to see more growth in “meaningful” deployments: 46 percent more CIOs had made plans for AI implementation by February, when the report was published.

t won’t happen instantly. First, you must understand your business in terms of goals, technology needs and the impact its adoption will have on employees and customers. Plenty can go wrong as you address any of those points. Here are a few tips to help achieve minimum resistance.

1. Treat AI as a business initiative, not a technical specialty.

Many organizations view AI’s implementation as a task for the IT department. That mistake alone could give rise to most of your future challenges.

AI is a business initiative in the sense that successful adoption calls for active participation throughout the process — not simply when it’s deployed. The same people currently responsible for running daily business processes must have real roles to help build and maintain the AI-driven model.

Here’s how it looks in real life:

  • The organization requires collaboration and support from data scientists and the IT team.
  • IT is responsible for deploying machine-learning models that are trained on historical information, demanding a prediction-data pipeline. (Creating that pipeline is a process unto itself, with specific requirements for each of the multiple tasks.)

The odds of finding success with AI implementation increase when the whole team is on board to acquire data, analyze it and develop complex systems to work with the information.

2. Teach staff to identify problems that AI can solve.

AI-driven enterprises often search out data scientists with deep knowledge of their business. A better approach would be teaching employees to identify problems that AI can solve and then guiding workers to create their own models. Your team members already understand how your business operates. In fact, they even know the factors that trigger specific responses from partners, customers and prospects.

IT can help businesses analyze and understand the context of each model. It also can plan its deployment using supported systems. Specifically, IT should be able to obtain answers on topics such as:

  • The usage pattern required by a particular business process.
  • The optimal latency period between a prediction request and its service.
  • Models that need to be monitored for update, latency and accuracy.
  • The tolerance of a business process to predictions delayed or not made.

Employees who tackle problems with an AI mindset can monitor business processes and learn to ask the right questions when it matters.

3. Allow business professionals to build machine-learning models.

A company trying to transform its complete scope of operations with AI might view the timeline as a bit slow. The current approach hinges on manually building machine-learning models. When asked, businesses managers ranked time to value among the biggest challenges. Respondents in the Gartner survey revealed their teams took an average of 52 days to build a predictive model and even longer to deploy it into production. Management teams often have little means to determine the model’s quality, even after months of development by data scientists.

An automated platform could transform AI’s economics, producing machine-learning models in hours or even minutes — not months. Such a platform also should allow business leaders to compare multiple models for accuracy, latency and analysis so they can select the most suitable model for any given task.

Equipping your staff with the right tools and skills empowers them to contribute to a system that’s optimized for your business. What’s more, automated platforms can help them create the models they need to transform processes.

Considering the many challenges businesses face when deploying AI, it’s understandable so many still lag behind. Organizations that have overcome these barriers can attest to AI’s power to revolutionalize business through process improvement and increased employee productivity.

End-use technologies require human participation as an input. Without human creators, technology can’t successfully morph into human roles.

Read the source article in Entrepreneur.

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.

Accenture: Most Health Organizations Can’t Ensure Responsible AI Use

Despite a growing interest in artificial intelligence, most healthcare organizations still lack the tools necessary to ensure responsible use of such technologies, finds a report from Accenture Health. According to the report, Digital Health Technology Vision 2018, 81% of healthcare executives said they are not yet prepared to face the societal and liability issues needed to explain […]

Despite a growing interest in artificial intelligence, most healthcare organizations still lack the tools necessary to ensure responsible use of such technologies, finds a report from Accenture Health.

According to the report, Digital Health Technology Vision 2018, 81% of healthcare executives said they are not yet prepared to face the societal and liability issues needed to explain their AI systems’ decisions. Additionally, while 86% of respondents said that their organizations are using data to drive automated decision-making, the same proportion (86%) report they have not invested in the capabilities needed to verify data sources across their most critical systems.

Kaveh Safavi, head of Accenture’s health practice, observed that the current lack of AI data verification investment activity is exposing healthcare organizations to inaccurate, manipulated and biased data that can lead to corrupted insights and skewed results. “The 86% figure is critical,” he stated, “given that 24% of executives also said that they have been the target of adversarial AI behaviors, such as falsified location data or bot fraud on more than one occasion.” On a positive note, the study found that 73% of respondents plan to develop internal ethical standards for AI to ensure that their systems act responsibly.

Kaveh Safavi of Accenture

As a growing number of AI-powered healthcare tools enter the market, hospitals, clinics and other healthcare organizations are using intelligent technologies in various ways to become more agile, productive and collaborative. “Until recently, AI was mainly used as a back-end tool but is increasingly becoming part of the everyday consumer and clinician experience,” Safavi noted.

AI’s ability to sense, understand, act and learn enables it to augment human activity by supporting, or even taking over, tasks that bridge administrative and clinical healthcare functions — from risk analysis to medical imaging to supporting human judgment. “In terms of value and cost-savings, there are many ways in which AI can improve and change healthcare,” Safavi observed. Accenture estimates that key clinical health AI applications can create $150 billion in annual savings for the U.S. healthcare economy by 2026.

Moving toward greater compliance

Healthcare organizations recognize the value of AI not only for its potential cost savings, but also for its ability to tackle entrenched issues related to sustainability and access. Adopters are also hoping that IT will help them address the growing healthcare workforce shortage and the increasing dissatisfaction of healthcare consumers. “AI can help increase productivity and personalization in healthcare in ways that few other technologies can,” Safavi explained.

Ultimately, healthcare organizations will need to turn to AI-powered automation to improve a wide range of services. “Because of this, the market will help drive compliance over time as trust both from consumers and clinicians is the only way to truly foster adoption,” Safavi predicted.

Read the source article in Information Week.

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.

Opinion: Data is Holding Back AI

By Sultan Meghji, Founder & Managing Director at Virtova  I remember grumbling, “Good lord this is a waste of time,” in 1992 while I was working on an AI application for lip-reading. The grumble escaped my lips because I felt like I was spending half my time inputting data cleanly into the video processing neural […]

By Sultan Meghji, Founder & Managing Director at Virtova 

I remember grumbling, “Good lord this is a waste of time,” in 1992 while I was working on an AI application for lip-reading. The grumble escaped my lips because I felt like I was spending half my time inputting data cleanly into the video processing neural network. Bouncing from a video capture device to a DEC workstation to a Convex Supercomputer to a Cray, I felt like I had been thrown into a caldron of Chinese water torture.

Sultan Meghji, Founder & Managing Director at VirtovaSitting over my head was a joke happy birthday poster from Arthur C. Clarke’s Space Odyssey series featuring HAL 9000. I found it ironic that I was essentially acting like a highly-trained monkey, while a fictional AI stared down at me, laughing. Over the two years of that AI project, I easily spent 60% of my time just getting the data captured, cleaned, imported and in a place where it could be used by the training system. AI, as practitioners know, is the purest example of garbage in, garbage out. The worst part is that sometimes you don’t realize it until your AI answers “anvil” when you ask it what someone’s favorite food is.

Last month, I was having a conversation with the CEO of a well-respected AI startup when I was struck by deja-vu. He said, “I swear, we have spent at least half of our funding on data management.” I wondered if this could actually be the case, so I pushed him, probing him with questions on automation, data quality and scaling. His answers all sounded remarkably familiar. Over the next two weeks, I contacted a few other AI startup executives — my criteria was that they had raised at least $10 million in funding and had a product in the market — and their answers were all strikingly similar.

To be sure, there are significant improvements being made to decrease the amount of information needed to train AI systems and in building effective learning transference mechanisms. This week, in fact, Google revealed solid progress with the news that its AlphaGo is now learning automatically from itself. The advancement trends will continue, but such innovations are still very much still in their early stages. In the meantime, AI hype is very likely to outstrip real results.

So what are some things that can be done to raise the quality of AI development? Here are my suggestions for building a best-in-class AI system today:

Rely on peer-reviewed innovation. Companies using AI backed by thoughtful study, preferably peer reviewed by academics, are showing the most progress. However, that scrutiny should not stop with the algorithm. That same critical analysis should be true of the data. To that point, I recently suggested to a venture capital firm that if the due diligence process for a contemplated investment revealed a great disparity between the quality of the algorithms and the quality of the data utilized by the start-up, it should pass on the investment. Why? Because that disparity is a major red flag.

Organize data properly. There is an incredible amount of data being produced each day. But it should be kept in mind that learning vs. production data is different, and data must be stabilized as you move from a training environment to a production one. As such, utilizing a cohesive internal data model is critical, especially if the AI is built according to a recent ‘‘data-driven’ architecture vs. a ‘model-driven’ system. Without a cohesive system, you have a recipe for disaster. As one CEO recently told me, a year of development had to be discarded because his company hadn’t configured its training data properly.

Automate everything in the production environment. This goes hand in hand with being organized, but it needs to be called out separately. Transitioning from the research lab to the production environment, no matter what system you are building, requires a fully automated solution. One of the benefits of the maturation of Big Data and IOT systems is that building such a solution is a relatively straightforward part of developing an AI system. However, without full automation, errors in learning, production and a strain on human resources compound flaws and make their repair exceedingly difficult.

Choose quality over quantity. Today, data scientists find themselves in a situation where a large amount of the data they collect is of terrible quality. An example is clinical genetics, where the data sources used to analyze gene sequence variation are so inconsistent that ‘database of database’ systems have been built to make sense of the datasets. In the case of genetic analysis systems, for example, over 200 separate databases are often utilized. Banks too often must extract data from at least 15 external systems. Without a systemic basis for picking and choosing the data, any variances in data can work against the efficacies of an AI system.

Scale your data (and that’s hard to do). Given my previous comments about Big Data and IOT, you might think that scaled data management is easily available. But you would be wrong. That’s because once you clear the previous four steps, you may end up with very small relevant sample sets. In some applications, a small dataset may represent a good start; however, that doesn’t fly in AI systems. Indeed, would you want to release an AI program such as autonomous cars or individualized cancer drugs into the wild after being trained on a small database?

In aggregate, the considerations described above represent some fundamental starting points for ensuring that you are holding your data to the same standards to which you hold your AI. Ahead of coming technical advancements, especially around data management and optimization in algorithm construction, these tenets are a good starting point for those trying to avoid the common garbage in, garbage out issues that are (unfortunately) typifying many AI systems today.

The author is an experienced executive in high tech, life sciences and financial services. Starting his career as a technology researcher over 25 years ago, he has served in a number of senior management roles in financial services firms, as well as starting and exiting a number of startups.

Read the source article in The Financial Revolutionist.

Human Challenges Face Today’s AI Business Strategies

The hype surrounding artificial intelligence (AI) is intense despite that fact that as yet, artificial intelligence (AI) for most enterprises is still at an early, or planning, stage. While a lot has been done, there is a lot more to do before it becomes commonplace. However, that hasn’t stopped speculation about the impact on employment […]

The hype surrounding artificial intelligence (AI) is intense despite that fact that as yet, artificial intelligence (AI) for most enterprises is still at an early, or planning, stage. While a lot has been done, there is a lot more to do before it becomes commonplace. However, that hasn’t stopped speculation about the impact on employment and what it might mean for workers, especially those whose jobs are repetitive and considered low skilled.

In October last year,  a survey carried out by analytics giant Cary, N.C-based analytics giant SAS showed that the vast majority of organizations have begun to talk about artificial intelligence, and a few have even begun to implement suitable projects. There is much optimism about the potential of AI, although fewer were confident that their organization was ready to exploit that potential.

AI Human Challenges 

The reason for this is not because there is a lack of technologies on the market. What the research uncovered was that the challenges come from a shortage of data science skills to maximize value from emerging AI technology, and deeper organizational and societal obstacles to AI adoption. Some of the figures contained in the report show that:

  • 55 percent of survey respondents felt that the biggest challenge related to AI was the changing scope of human jobs in light of AI’s automation and autonomy.
  • 41 percent of respondents raised questions about whether robots and AI systems should have to work “for the good of humanity” rather than simply for a single company, and how to look after those who lost jobs to AI systems.

It also showed that several organizations had a senior-level sponsor for AI and advanced analytics. In some cases, this was a member of the C-suite, and in a few, the CEO. In others, it was a more junior director, usually one with an interest in the area. One respondent mentioned that the organization planned to appoint a Chief Data Officer within the next six months, who would take on responsibility for this area.

And it’s not the only research that has raised the issue of the impact AI will have on jobs. Recently, we were able to identify seven jobs that might be overtaken by the growth in the use of AI in the enterprise. That said there are ways that enterprises – and individuals – can meet the challenge.

Building a Talent Pipeline

AI is generating a demand for new skill sets in the workplace. However, there is a widespread shortage of talent that possess the knowledge and capabilities to properly build, fuel, and maintain these technologies within their organizations, according to Mohit Joshi president and head of banking, financial services and insurance, as well as, healthcare and life sciences at Bengalaru, India-based Infosys. The simple answer is up-skilling. “The lack of well-trained professionals who can build and direct a company’s AI and digital transformation journeys noticeably hinders progress and continues to be a major hurdle for businesses. But there is also opportunity here too and a way to redeploy workers who face redundancy because of AI,” he says.

To mitigate this, businesses should look inward and create on-the-job training and  to build these skills internally. With the proper staff powering AI, employees are able to focus on other critical activities and boost productivity creating a larger ROI. If an enterprise’s digital transformation goal is for AI to become a business accelerator, it needs to be an amplifier of its people. “It’s going to take work to give everyone access to the fundamental knowledge and skills in problem-finding and remove the elitism around advanced technology, but the boost to productivity and ROI will be worth it in the end,” says Joshi. Businesses that haven’t yet allocated budget for AI should start small by manually auditing the organization to streamline processes and free up employees’ bandwidth. This allows decision makers to clearly see which systems aren’t utilized effectively and which areas could benefit from technology down the road.

Shifting Roles

Anthony Macciola, Chief Innovation Officer and is responsible for AI initiatives at Moscow, Russia-based global giant ABBYY, a company that uses machine learning, robotic process automation and text analytics to improve business outcomes. He says that the introduction of AI into the general workplace will result in more tasks being addressed by system of record applications and shift knowledge workers’ roles from a control to an expertise standpoint.

He cites an example of how this will work in the mortgage lending market. The dependency on a loan origination officer to drive the loan process will diminish over time due to the loan origination system being able to make intelligent decisions based on past funding behavior. This will leave only rules-based exceptions to require a loan processor’s attention. As a result, this will lighten the overall workload for loan officers, allowing them to be more responsive when an exception rises and should allow mortgage lenders to increase the productivity of their operations.

“As software gets smarter, dependency on the workforce shrinks and knowledge workers who have typically conducted manual input tasks or controlled processes in fintech, healthcare, transportation and logistics, and government customer/constituent engagement scenarios will become more narrowly focused from a role and responsibility standpoint,” he says.

Read the source article at CMSWire.com.