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What You Need To Know About Location Intelligence In 2020

What You Need To Know About Location Intelligence In 2020

  • 53% of enterprises say that Location Intelligence is either critically important or very important to achieving their goals for 2020.
  • Leading analytics and platform vendors who offer Location Intelligence include Alteryx, Microsoft, Qlik, SAS, Tableau and TIBCO Software.
  • Location Intelligence vendors providing specialized apps and platforms include CARTO, ESRI, Galigeo, MapLarge, and Pitney Bowes.
  • Product Managers need to consider how adding Location Intelligence can improve the contextual accuracy of marketing, sales, and customer service apps and platforms.
  • Marketers need to look at how they can capitalize on smartphones’ prolific amounts of location data for improving advertising, buying, and service experiences for customers.
  • R&D, Operations, and Executive Management lead all other departments in their adoption and use of Location Intelligence this year.
  • Enterprises favor cloud-based Location Intelligence deployments in 2020, with on-premise deployments also seeing new sales this year.

These and many other fascinating insights are from Dresner Advisory Services’ 2020 Location Intelligence Market Study, their 7th annual report that examines enterprise end-users’ requirements and features including geocoding support, location intelligence visualization, analytics capabilities, and third-party GIS integration. The study is noteworthy for its depth of insights into industry adoption of Location Intelligence and how user requirements drive industry capabilities. Dresner Advisory Services defines location intelligence as a form of Business Intelligence (BI), where the dominant dimension used for analysis is location or geography. Most typically, though not exclusively, analyses are conducted by viewing data points overlaid onto an interactive map interface.

“When we began covering Location Intelligence in 2014, we saw the potential for the topic to gain mainstream interest,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. “With the growth in visualization and the emergence of the Internet of Things (IoT), incorporating maps and location into business analyses have become increasingly important to many organizations.” Please see page 11 for a description of the methodology and page 13 for an overview of study demographics. Wisdom of Crowds® research is based on data collected on usage and deployment trends, products, and vendors.

Key insights from the study that provides an excellent background on the current state of location intelligence in 2020 include the following:

  • R&D, Operations, and Executive Management lead all enterprise areas in adoption with Location Intelligence being considered critical to their ongoing operations. The majority of Marketing & Sales leaders see Location Intelligence as very important to their ongoing operations. The following graphic compares how important Location Intelligence is to each of the seven departments included in the survey:
  • 90% of Government organizations consider Location Intelligence to be critical or very important to their ongoing operations. Healthcare providers have the second-highest number of organizations who rate Location Intelligence as critical. The study found that mean importance levels are similar across Business Services, Financial Services, Manufacturing, and Consumer Services organizations and decline further among Technology, Retail/Wholesale, and Higher Education segments.
  • Data visualization/mapping dominates all other Location Intelligence use cases in 2020, with over 70% of organizations considering it critical or very important to accomplishing their goals. The study found that the majority of other use cases haven’t achieved the broad adoption data visualization & mapping has. Despite the lower levels of criticality assigned to the nine other use cases, they each show the potential to streamline essential marketing, sales, and operational areas of an enterprise. Site planning/site selection, geomarketing, territory management/optimization, and logistics optimization make up a tier of secondary interest that taken together streamlines supply chains while making an organization easier to buy from. The Dresner research team also defines the third tier of use cases led by fleet routing and citizen services, followed by IoT & smart cities, indoor mapping, and real estate investment/pricing analysis. Despite IoT being over-promoted by vendors, just over 50% of enterprises say the technology is not important to them at this time. The following graphic compares Location Intelligence use cases by the level of criticality as defined by responding organizations:
  • R&D leads all departments in data visualization/mapping adoption, reflecting the high level of importance this use case has across entire enterprises as well. Additional departments and functional areas relying on data visualization/mapping include Operations, Business Intelligence Competency Center (BICC), and Executive Management. Geomarketing is seeing the most significant adoption in Marketing & Sales. Operations lead all other functional areas in the adoption of logistics optimization and fleet routing use cases. Dresner’s research team found that R&D’s interest in Location Intelligence, which varies across use cases, may reflect the use of packaged applications as well as select custom development.
  • Map-based visualization, dashboard inclusion of maps, and drill-down navigation through map interfaces are the three highest priority features enterprises look for today. These three features are considered very important to between 64% to 67% of leaders interviewed. Layered visualizations, multi-layer support, and custom region definition are the next most important features. The following graphic provides an overview of prioritized Location intelligence visualization features.
  • Executive Management, BICC, and Operations have the highest level of interest in map-based visualizations that further accelerate the adoption of Location Intelligence across enterprises. Executive Management also leads all others in their interest in dashboard inclusion of maps and custom map support. Executive Management’s increasing adoption of multiple Location intelligence use cases is a catalyst driving greater enterprise-wide adoption. R&D’s prioritizing the layering of visualizations on top of maps, offline mapping and animation of data on maps are leading indicators of these use cases attaining greater enterprise adoption in future years.
  • Four of the top ten Location Intelligence features are considered very important/critical to enterprises, reflecting a maturing market. The most popular (counting, quantifying, or grouping) is critical or very important to 46% of organizations and at least important to nearly 70%. Another indicator of how quickly Location Intelligence is maturing in enterprises is the advanced nature of analytics features being relied on today. Predicting trends and volatility, detecting clusters and outliers, and measuring distances reflect how multiple departments in enterprises are collaborating using Location Intelligence to achieve their shared goals.
  • Government dominates the use of data visualization/mapping with a strong interest in site planning/site selection, citizen services, fleet routing, and territory management. Business Services are most interested in using Location Intelligence for Indoor Mapping and IoT & Smart Cities. Geomarketing is the most adopted feature in Higher Education, Financial Services, Healthcare, and Retail/Wholesale. Manufacturing and Retail/Wholesale lead all other industries in their adoption of Logistics Optimization. The following graphic provides insights into Location Intelligence use case by industry:
  • Executive Management and Business Intelligence Competency Centers (BICC) most prioritize Location Intelligence applications that have built-in or native geocoding. Enterprises are looking at how built-in or native geocoding can scale across their Location Intelligence use cases and broader BI strategy with Executive Management taking the lead on achieving this goal. Automated geocoding support and street-level geocoding support are also a high priority to Executive Management. Marketing/Sales lead all other departments in their interest in geofencing/reverse geofencing, indicating enterprises are beginning to use these geocoding features to achieve greater accuracy in their marketing and selling strategies. It’s interesting to note that geofencing/reverse geofencing has progressed from R&D in previous studies to Marketing/Sales putting the highest priority on it today. Dresner’s research team interprets the shift to customer-facing strategies being an indicator of broader enterprise adoption for geofencing/reverse geofencing.
  • 61% of organizations say Google integration is essential to their Location Intelligence strategies. Google continues to dominate organizations’ roadmaps as the integration of choice for adding more GIS data to Location Intelligence strategies. ESRI is the second choice with 45% of organizations naming it as an integration requirement. Database extensions (30%) are the next most cited, followed by OpenStreetMap (20%). All other choices are requirements at less than 20% of organizations.

How To Redefine The Future Of Fraud Prevention

How To Redefine The Future Of Fraud Prevention

Bottom Line: Redefining the future of fraud prevention starts by turning trust into an accelerator across every aspect of customer lifecycles, basing transactions on identity trust that leads to less friction and improved customer experiences.

Start By Turning Trust Into A Sales & Customer Experience Accelerator

AI and machine learning are proving to be very effective at finding anomalies in transactions and scoring, which are potentially the most fraudulent. Any suspicious transaction attempt leads to more work for buying customers to prove they are trustworthy. For banks, e-commerce sites, financial institutes, restaurants, retailers and many other online businesses, this regularly causes them to lose customers when a legitimate purchase is being made, and trusted customer is asked to verify their identity. Or worse, a false positive that turns away a good customer all together damages both that experience and brand reputation.

There’s a better way to solve the dilemma of deciding which transactions to accept or not. And it needs to start with finding a new way to establish identity trust so businesses can deliver better user experiences. Kount’s approach of using their Real-Time Identity Trust Network to calculate Identity Trust Levels in milliseconds reduces friction, blocks fraud, and delivers an improved user experience. Kount is capitalizing on their database that includes more than a decade of trust and fraud signals built across industries, geographies, and 32 billion annual interactions, combined with expertise in AI and machine learning to turn trust into a sales and customer experience multiplier.

How Real-Time AI Linking Leads To Real-Time Identity Trust Decisions

Design In Identity Trust So It’s The Foundation of Customer Experience

From an engineering and product design standpoint, the majority of fraud prevention providers are looking to make incremental gains in risk scoring to improve customer experiences. None, with the exception of Kount, are looking at the problem from a completely different perspective, which is how to quantify and scale identity trust. Kount’s engineering, product development, and product management teams are concentrating on how to use their AI and machine learning expertise to quantify real-time identity trust scores that drive better customer experiences across the spectrum of trust. The graphic below illustrates how Kount defines more personalized user experiences, which is indispensable in turning trust into an accelerator.

An Overview of Kount’s Technology Stack

How To Redefine The Future Of Fraud Prevention

Realize Trust Is the Most Powerful Revenue Multiplier There Is

Based on my conversations with several fraud prevention providers, they all agree that trust is the most powerful accelerator there is to reducing false positives, friction in transactions, and improving customer experiences. They all agree trust is the most powerful revenue multiplier they can deliver to their customers, helping them reduce fraud and increase sales. The challenge they all face is quantifying identity trust across the wide spectrum of transactions their customers need to fulfill every day.

Kount has taken a unique approach to identity trust that puts the customer at the center of the transactions, not just their transactions’ risk score. By capitalizing on the insights gained from their Identity Trust Global Network, Kount can use AI and machine learning algorithms to deliver personalized responses to transaction requests in milliseconds. Using both unsupervised and supervised machine learning algorithms and techniques, Kount can learn from every customer interaction, gaining new insights into how to fine-tune identity trust for every customer’s transaction.

In choosing to go in the direction of identity trust in its product strategy, Kount put user experiences at the core of their platform strategy. By combining adaptive fraud protection, personalized user experience, and advanced analytics, Kount can create a continuously learning system with the goal of fine-tuning identity trust for every transaction their customers receive. The following graphic explains their approach for bringing identity trust into the center of their platform:

Putting Customers & Their Experiences First Is Integral To Succeeding With Identity Trust

How To Redefine The Future Of Fraud Prevention

 

Improving customer experiences needs to be the cornerstone that drives all fraud prevention product and services road maps in 2020 and beyond. And while all fraud prevention providers are looking at how to reduce friction and improve customer experiences with fraud scoring AI-based techniques, their architectures and approaches aren’t going in the direction of identity trust. Kount’s approach is, and it’s noteworthy because it puts customer experiences at the center of their platform. How to redefine the future of fraud prevention needs to start by turning trust into a sales and customer experience accelerator, followed by designing in identity trust. Hence, it’s the foundation of all customer experiences. By combining the power of networked data and adaptive AI and machine learning, more digital businesses can turn trust into a revenue and customer experience multiplier.

10 Ways AI Is Going To Improve Fintech In 2020

Bottom Line: AI & machine learning will improve Fintech in 2020 by increasing the accuracy and personalization of payment, lending, and insurance services while also helping to discover new borrower pools.

Zest.ai’s 2020 Predictions For AI In Credit And Lending captures the gradual improvements I’ve also been seeing across Fintech, especially at the tech stack level. Fintech startups, enterprise software providers, and the investors backing them believe cloud-based payments, lending, and insurance apps are must-haves to drive future growth. Combined with Internet & public cloud infrastructure and mobile apps, Fintech is evolving into a fourth platform that provides embedded financial services to any business needing to subscribe to them, as Matt Harris of Bain Capital Ventures writes in Fintech: The Fourth Platform – Part Two. Embedded Fintech has the potential to deliver $3.6 trillion in market value, according to Bain’s estimates, surpassing the $3 trillion in value created by cloud and mobile platforms. Accenture’s recent survey of C-suite executives’ adoption and plans found that 84% of all executives believe they won’t achieve their growth objectives unless they scale AI, and 75% believe they risk going out of business in 5 years if they don’t. The need to improve payment, lending and insurance combined with customers’ mercurial preferences for how they use financial services are challenges that AI and machine learning (ML) are solving today.

How AI & Machine Learning Will Improve Fintech In 2020

Fintech’s traditional tech stacks weren’t designed to anticipate and act quickly on real-time market indicators and data; they are optimized for transaction speed and scale. What’s needed is a new tech stack that can flex and adapt to changing market and customer requirements in real-time. AI & machine learning are proving to be very effective at interpreting and recommending actions based on real-time data streams. They’re also improving customer experiences and reducing risk, two additional factors motivating lenders to upgrade their traditional tech stacks with proven new technologies.

The following are ten predictions of how AI will improve FinTech in 2020, thank you Zest.ai for your insights and sharing your team’s expertise on these:

  1. Zest predicts lenders will increase the use of ML as the way to grow into the no-file/thin-file segments, especially rising Gen Zers with little to no credit history. Traditional tech stacks make it difficult to find and grow new borrower pools. Utah-based auto lenderPrestige Financial Services chose to rely on an AI solution instead. The chose Zest AI to find and cultivate a borrower pool of people in the 19-35 age group. Using an AI-based loan approval workflow, Prestige was able to increase loan approval rates by 25%, and for people under 20 by threefold.
  2. Mortgage lenders’ adoption of AI for finding qualified first-time homeowners is going to increase as more realize Gen Z (23 – 36-year-olds) are the most motivated of all to purchase a home. In 2020, long-standing assumptions about first-time homebuyers and their motivations are going to change. A recent story in HousingWire, “This generation is the most willing to do whatever it takes to buy a home,” explains that Gen Z, or those people born between 1996 and 2010, are the most likely to relocate to purchase a new home. A recent TransUnion market analysis found 70% of Gen Z prospective home buyers are willing to relocate to buy their first home, leading all active generations. 65% of Gen Xers, or those born between 1965 to 1980, were the second most likely to move. AI and ML can help lenders more precisely target potential Gen Z first-time homebuyers, measuring the impact of their marketing campaigns on attracting new borrowers. The TransUnion market analysis finds that 58% of respondents are delaying a home purchase due to anticipated high down payments or monthly payments. 51% said the need to obtain a 10% to 20% down payment was stopping them. According to Joe Mellman, TransUnion senior vice president, and mortgage business leader, “Many of our potential first-time homebuyer respondents don’t seem to be aware of the wide variety of financing options available to them.” The TransUnion market analysis found that many of the potential first-time homeowner respondents have never heard of low down-payment options from Fannie Mae, Freddie Mac, or of the Federal Housing Administration.
  3. Zest predicts banks and other financial institutions will strengthen their business cases for AI pilots and production-level deployments by recognizing the operating expense (OPEX) savings of ML. Several recurring costs involved in developing, validating and deploying credit risk models can be reduced or cut by switching to machine learning, according to Zest. Lenders can get the most out of their data acquisition spending by using modern ML tools to assess which data sources yield the most predictive power for a model. Lenders will also switch to ML to simplify their IT and risk operations by consolidating into fewer models that can do the work of what used to be multiple individual linear models for every customer segment.
  4. Compliance cost growth will decline even faster due to ML. Financial institutions that have AI/ML algorithms in production log every change in a model and can produce all the required model risk governance documents in minutes instead of a compliance team manually taking weeks to do it. Automated tools also shrink the time it takes to do fair lending testing by building less discriminatory models on the fly rather than the time-intensive approach of drop-one-variable-and-test. Time is money, especially in lending.
  5. AI and ML will gain critical mass in collections, providing insights into which approach is the most effective for a given customer. Zest has built collections models for a few financial services firms and has found them to be very effective. Collections logic, predicting which customers to wait on when bills are past due, is a strong fit for machine learning. With one bank, Zest found that ML models can, for example, accurately target the borrowers most likely to make a certain minimum payment based on the value of their loan within 60 days of falling behind their due date. In three months, Zest built two models from traditional credit bureaus and the bank’s proprietary collections metrics to predict this repayment propensity of borrowers. One insight into the data was that borrower behavior accounted for just over half of the bank’s ability to collect missed payments, but operations played a significant role.
  6. If there’s a downturn, ML will get blamed (even though it can actually help in a downturn). Pankaj Kulshreshtha, CEO of Scienaptics, originally made this observation at the Money 20/20 Conference held earlier this year. Models built only in good times can see their correlations break when times go bad. Lenders who observe best practices in AI and ML adoption will make sure to stress-test their models, perhaps by including synthetic data to add heterogeneity. Better ML monitoring will be important, too. “ML models and algorithmic monitors can do a better job seeing around corners, spotting rising numbers of inbound outlier applicants that signal more volatile conditions ahead,” says Seth Silverstein, Executive Vice President of Credit Risk Analytics for Zest AI.  An effective ML monitoring tool should excel at spotting outlier applicants and feature drift, ensuring more accurate model outcomes.
  7. 2020 is going to be a break-out year for partnerships and co-opetition as payments, lending and insurance firms vie for a growth position in embedded financial services. Matt Harris of Bain Capital Ventures’ prediction of embedded fintech suggests a proliferation of cloud-based Fintech apps around the core: payments, lending, insurance. That creates an ideal situation for AI-related alliances and partnerships among the incumbent lenders, startups, data aggregators and the CRAs. To Harris, the layers of the stack are centered around connectivity, intelligence, and ubiquity. According to Crunchbase, there have been 51 Fintech acquisitions in 2019 alone. Plaid’s acquisition of Quovo in January for approximately $200 million and Fiserv’s acquisition of First Data reflect how Fintechs are creating their own unique tech stacks already.
  8.  Zest predicts Fintechs will seek out AI and ML modeling expertise more so than build expertise and teams on their own, which will be costlier and take longer. Embedded Fintech’s future adoption rate is predicated on how effective development efforts are today at minimizing incidental bias and providing customers with greater visibility into how and why models provide specific results “Some of these startups are bringing their own data science and ML models. We have to hope these firms own, build, or buy the tools to ensure their models are inclusive, free of incidental bias, and use transparent AI customers can trust. We see explainable AI as being an essential feature or service in that tech stack,” says Zest’s Silverstein.
  9.  Fintechs will rely on AI and ML to help close the talent gap each of them has today while also improving the effectiveness of their talent management strategies. Finding, recruiting, and hiring the best candidates for development, engineering, marketing, sales, and senior management roles is an area Fintechs will increasingly adopt AI and ML for in 2020. Fintech CEOs and CHROs will begin upskilling programs for themselves and their teams to increase AI fluency and skills mastery in 2020. According to a recent Harris Interactive survey completed in collaboration with Eightfold titled Talent Intelligence And Management Report 2019-2020, 73% of U.S. CEOs and CHROs plan to use more AI in the next three years to improve talent management.
  10. Credit unions will adopt ML in 2020 to automate routine tasks and free up human underwriters to focus on providing more personalized services, including improvements in inquiry resolution & dispute and fraud management. Credit unions are built on an annuity-based business model that delivers successively higher profitability the longer a member is retained. Credit unions will capitalize on ML by driving up loan approvals with no added risk and automating more of the loan approval process. By the end of 2020, according to a Fannie Mae survey of mortgage lenders, 71% of credit unions plan to investigate, test, or fully implement AI/ML solutions – up from just 40% in 2018. AI and ML will also be adopted across credit unions to improve inquiry resolution & dispute and fraud management while improving multichannel customer experiences. Providing real-time, relevant responses to customers to expedite inquiries and dispute resolutions using AI and ML is going to become commonplace in 2020. AI and ML are predicted to make a significant contribution to automating anomaly detection and borrower default risk assessment as the graphic below from Fannie Mae’s Mortgage Lender Sentiment Survey® How Will Artificial Intelligence Shape Mortgage Lending? Q3 2018 Topic Analysis illustrates:

 

 

10 Predictions How AI Will Improve Cybersecurity In 2020

10 Predictions How AI Will Improve Cybersecurity In 2020

Capgemini predicts 63% of organizations are planning to deploy AI in 2020 to improve cybersecurity, with the most popular application being network security.

Cybersecurity is at an inflection point entering 2020. Advances in AI and machine learning are accelerating its technological progress. Real-time data and analytics are making it possible to build stronger business cases, driving higher adoption. Cybersecurity spending has rarely been linked to increasing revenues or reducing costs, but that’s about to change in 2020.

What Leading Cybersecurity Experts Are Predicting For 2020

Interested in what the leading cybersecurity experts are thinking will happen in 2020, I contacted five of them. Experts I spoke with include Nicko van Someren, Ph.D. and Chief Technology Officer at Absolute Software; Dr. Torsten George, Cybersecurity Evangelist at Centrify; Craig Sanderson, Vice President of Security Products at Infoblox; Josh Johnston, Director of AI, Kount; and Brian Foster, Senior Vice President Product Management at MobileIron. Each of them brings a knowledgeable, insightful, and unique perspective to how AI and machine learning will improve cybersecurity in 2020. The following are their ten predictions:

  1. AI and machine learning will continue to enable asset management improvements that also deliver exponential gains in IT security by providing greater endpoint resiliency in 2020. Nicko van Someren, Ph.D. and Chief Technology Officer at Absolute Software, observes that “Keeping machines up to date is an IT management job, but it’s a security outcome. Knowing what devices should be on my network is an IT management problem, but it has a security outcome. And knowing what’s going on and what processes are running and what’s consuming network bandwidth is an IT management problem, but it’s a security outcome. I don’t see these as distinct activities so much as seeing them as multiple facets of the same problem space, accelerating in 2020 as more enterprises choose greater resiliency to secure endpoints.”
  2. AI tools will continue to improve at drawing on data sets of wildly different types, allowing the “bigger picture” to be put together from, say, static configuration data, historic local logs, global threat landscapes, and contemporaneous event streams.  Nicko van Someren, Ph.D., and CTO at Absolute Software also predict that“Enterprise executives will be concentrating their budgets and time on detecting cyber threats using AI above predicting and responding. As enterprises mature in their use and adoption of AI as part of their cybersecurity efforts, prediction and response will correspondingly increase.”
  3. Threat actors will increase the use of AI to analyze defense mechanisms and simulate behavioral patterns to bypass security controls, leveraging analytics to and machine learning to hack into organizations. Dr. Torsten George, Cybersecurity Evangelist at Centrify, predicts that “threat actors, many of them state-sponsored, will increase their use and sophistication of AI algorithms to analyze organizations’’ defense mechanisms and tailor attacks to specific weak areas. He also sees the threat of bad actors being able to plug into the data streams of organizations and use the data to further orchestrate sophisticated attacks.”
  4. Given the severe shortage of experienced security operations resources and the sheer volume of data that most organizations are trying to work through, we are likely to see organizations seeking out AI/ML capabilities to automate their security operations processes. Craig Sanderson, Vice President of Security Products at Infoblox also predicts that “while AI and machine learning will increasingly be used to detect new threats it still leaves organizations with the task of understanding the scope, severity, and veracity of that threat to inform an effective response. As security operations becomes a big data problem it necessitates big data solutions.”
  5. There’s going to be a greater need for adversarial machine learning to combat supply chain corruption in 2020. Sean Tierney, Director of Threat Intelligence at Infoblox, predicts that “the need for adversarial machine learning to combat supply chain corruption is going to increase in 2020. Sean predicts that the big problem with remote coworking spaces is determining who has access to what data. As a result, AI will become more prevalent in traditional business processes and be used to identify if a supply chain has been corrupted.”
  6. Artificial intelligence will become more prevalent in account takeover—both the proliferation and prevention of it. Josh Johnston, Director of AI at Kount, predicts that “the average consumer will realize that passwords are not providing enough account protection and that every account they have is vulnerable. Captcha won’t be reliable either, because while it can tell if someone is a bot, it can’t confirm that the person attempting to log in is the account holder. AI can recognize a returning user. AI will be key in protecting the entire customer journey, from account creation to account takeover, to a payment transaction. And, AI will allow businesses to establish a relationship with their account holders that are protected by more than just a password.”
  7. Consumers will take greater control of their data sharing and privacy in 2020. Brian Foster, Senior Vice President Product Management at MobileIron, observes that over the past few years, we’ve witnessed some of the biggest privacy and data breaches. As a result of the backlash, tech giants such as Apple, Google, Facebook and Amazon beefed up their privacy controls to gain back trust from customers. Now, the tables have turned in favor of consumers and companies will have to put privacy first to stay in business. Moving forward, consumers will own their data, which means they will be able to selectively share it with third parties, but most importantly, they will get their data back after sharing, unlike in years past.
  8. As cybersecurity threats evolve, we’ll fight AI with AI. Brian Foster, Senior Vice President Product Management at MobileIron, notes that the most successful cyberattacks are executed by highly professional criminal networks that leverage AI and ML to exploit vulnerabilities such as user behavior or security gaps to gain access to valuable business systems and data. All of this makes it extremely hard for IT security organizations to keep up — much less stay ahead of these threats. While an attacker only needs to find one open door in an enterprise’s security, the enterprise must race to lock all of the doors. AI conducts this at a pace and thoroughness human ability can no longer compete with, and businesses will finally take notice in 2020.
  9. AI and machine learning will thwart compromised hardware finding its way into organizations’ supply chains. Rising demand for electronic components will expand the market for counterfeit components and cloned products, increasing the threat of compromised hardware finding its way into organizations’ supply chains. The vectors for hardware supply-chain attacks are expanding as market demand for more and cheaper chips, and components drive a booming business for hardware counterfeiters and cloners. This expansion is likely to create greater opportunities for compromise by both nation-state and cybercriminal threat actors. Source: 2020 Cybersecurity Threats Trends Outlook; Booz, Allen, Hamilton, 2019.
  10. Capgemini predicts 63% of organizations are planning to deploy AI in 2020 to improve cybersecurity, with the most popular application being network security. Capgemini found that nearly one in five organizations were using AI to improve cybersecurity before 2019. In addition to network security, data security, endpoint security, and identity and access management are the highest priority use cases for improving cybersecurity with AI in enterprises today. Source: Capgemini, Reinventing Cybersecurity with Artificial Intelligence: The new frontier in digital security.
10 Predictions How AI Will Improve Cybersecurity In 2020

Source: Capgemini, Reinventing Cybersecurity with Artificial Intelligence: The new frontier in digital security.

What’s New In Gartner’s Hype Cycle For AI, 2019

What's New In Gartner's Hype Cycle For AI, 2019

  • Between 2018 and 2019, organizations that have deployed artificial intelligence (AI) grew from 4% to 14%, according to Gartner’s 2019 CIO Agenda survey.
  • Conversational AI remains at the top of corporate agendas spurred by the worldwide success of Amazon Alexa, Google Assistant, and others.
  • Enterprises are making progress with AI as it grows more widespread, and they’re also making more mistakes that contribute to their accelerating learning curve.

These and many other new insights are from Gartner Hype Cycle For AI, 2019 published earlier this year and summarized in the recent Gartner blog post, Top Trends on the Gartner Hype Cycle for Artificial Intelligence, 2019.  Gartner’s definition of Hype Cycles includes five phases of a technology’s lifecycle and is explained here. Gartner’s latest Hype Cycle for AI reflects the growing popularity of AutoML, intelligent applications, AI platform as a service or AI cloud services as enterprises ramp up their adoption of AI. The Gartner Hype Cycle for AI, 2019, is shown below:

Details Of What’s New In Gartner’s Hype Cycle For AI, 2019

  • Speech Recognition is less than two years to mainstream adoption and is predicted to deliver the most significant transformational benefits of all technologies on the Hype Cycle. Gartner advises its clients to consider including speech recognition on their short-term AI technology roadmaps. Gartner observes, unlike other technologies within the natural-language processing area, speech to text (and text to speech) is a stand-alone commodity where its modules can be plugged into a variety of natural-language workflows. Leading vendors in this technology area Amazon, Baidu, Cedat 85, Google, IBM, Intelligent Voice, Microsoft, NICE, Nuance, and Speechmatics.
  • Eight new AI-based technologies are included in this year’s Hype Cycle, reflecting Gartner enterprise clients’ plans to scale AI across DevOps and IT while supporting new business models. The latest technologies to be included in the Hype Cycle for AI reflect how enterprises are trying to demystify AI to improve adoption while at the same time, fuel new business models. The new technologies include the following:
  1. AI Cloud Services – AI cloud services are hosted services that allow development teams to incorporate the advantages inherent in AI and machine learning.
  2. AutoML – Automated machine learning (AutoML) is the capability of automating the process of building, deploying, and managing machine learning models.
  3. Augmented Intelligence – Augmented intelligence is a human-centered partnership model of people and artificial intelligence (AI) working together to enhance cognitive performance, including learning, decision making, and new experiences.
  4. Explainable AI – AI researchers define “explainable AI” as an ensemble of methods that make black-box AI algorithms’ outputs sufficiently understandable.
  5. Edge AI – Edge AI refers to the use of AI techniques embedded in IoT endpoints, gateways, and edge devices, in applications ranging from autonomous vehicles to streaming analytics.
  6. Reinforcement Learning – Reinforcement learning has the primary potential for gaming and automation industries and has the potential to lead to significant breakthroughs in robotics, vehicle routing, logistics, and other industrial control scenarios.
  7. Quantum Computing – Quantum computing has the potential to make significant contributions to the areas of systems optimization, machine learning, cryptography, drug discovery, and organic chemistry. Although outside the planning horizon of most enterprises, quantum computing could have strategic impacts in key businesses or operations.
  8. AI Marketplaces – Gartner defines an AI Marketplace as an easily accessible place supported by a technical infrastructure that facilitates the publication, consumption, and billing of reusable algorithms. Some marketplaces are used within an organization to support the internal sharing of prebuilt algorithms among data scientists.
  • Gartner considers the following AI technologies to be on the rise and part of the Innovation Trigger phase of the AI Hype Cycle. AI Marketplaces, Reinforcement Learning, Decision Intelligence, AI Cloud Services, Data Labeling, and Annotation Services, and Knowledge Graphs are now showing signs of potential technology breakthroughs as evidence by early proof-of-concept stories. Technologies in the Innovation Trigger phase of the Hype Cycle often lack usable, scalable products with commercial viability not yet proven.
  • Smart Robots and AutoML are at the peak of the Hype Cycle in 2019. In contrast to the rapid growth of industrial robotics systems that adopted by manufacturers due to the lack of workers, Smart Robots are defined by Gartner as having electromechanical form factors that work autonomously in the physical world, learning in short-term intervals from human-supervised training and demonstrations or by their supervised experiences including taking direction form human voices in a shop floor environment. Whiz robot from SoftBank Robotics is an example of a SmartRobot that will be sold under robot-as-a service (RaaS) model and originally be available only in Japan. AutoML is one of the most hyped technology in AI this year. Gartner defines automated machine learning (AutoML) as the capability of automating the process of building, deploying, or managing machine learning models. Leading vendors providing AutoML platforms and applications include Amazon SageMaker, Big Squid, dotData, DataRobot, Google Cloud Platform, H2O.ai, KNIME, RapidMiner, and Sky Tree.
  • Nine technologies were removed or reassigned from this years’ Hype Cycle of AI compared to 2018. Gartner has removed nine technologies, often reassigning them into broader categories. Augmented reality and Virtual Reality are now part of augmented intelligence, a more general category, and remains on many other Hype Cycles. Commercial UAVs (drones) is now part of edge AI, a more general category. Ensemble learning had already reached the Plateau in 2018 and has now graduated from the Hype Cycle. Human-in-the-loop crowdsourcing has been replaced by data labeling and annotation services, a broader category. Natural language generation is now included as part of NLP. Knowledge management tools have been replaced by insight engines, which are more relevant to AI. Predictive analytics and prescriptive analytics are now part of decision intelligence, a more general category.

Sources:

Hype Cycle for Artificial Intelligence, 2019, Published 25 July 2019, (Client access reqd.)

Top Trends on the Gartner Hype Cycle for Artificial Intelligence, 2019 published September 12, 2019

State Of AI And Machine Learning In 2019

  • Marketing and Sales prioritize AI and machine learning higher than any other department in enterprises today.
  • In-memory analytics and in-database analytics are the most important to Finance, Marketing, and Sales when it comes to scaling their AI and machine learning modeling and development efforts.
  • R&D’s adoption of AI and machine learning is the fastest of all enterprise departments in 2019.

These and many other fascinating insights are from Dresner Advisory Services’6th annual 2019 Data Science and Machine Learning Market Study (client access reqd) published last month. The study found that advanced initiatives related to data science and machine learning, including data mining, advanced algorithms, and predictive analytics are ranked the 8th priority among the 37 technologies and initiatives surveyed in the study. Please see page 12 of the survey for an overview of the methodology.

“The Data Science and Machine Learning Market Study is a progression of our analysis of this market which began in 2014 as an examination of advanced and predictive analytics,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. “Since that time, we have expanded our coverage to reflect changes in sentiment and adoption, and have added new criteria, including a section covering neural networks.”

Key insights from the study include the following:

  • Data mining, advanced algorithms, and predictive analytics are among the highest-priority projects for enterprises adopting AI and machine learning in 2019. Reporting, dashboards, data integration, and advanced visualization are the leading technologies and initiatives strategic to Business Intelligence (BI) today. Cognitive BI (artificial-intelligence-based BI) ranks comparatively lower at 27th among priorities. The following graphic prioritizes the 27 technologies and initiatives strategic to business intelligence:

  • 40% of Marketing and Sales teams say data science encompassing AI and machine learning is critical to their success as a department. Marketing and Sales lead all departments in how significant they see AI and machine learning to pursue and accomplish their growth goals. Business Intelligence Competency Centers (BICC), R&D, and executive management audiences are the next most interested, and all top four roles cited carry comparable high combined “critical” and “very important” scores above 60%. The following graphic compares the importance levels by department for data science, including AI and machine learning:

  • R&D, Marketing, and Sales’ high level of shared interest across multiple feature areas reflect combined efforts to define new revenue growth models using AI and machine learning. Marketing, Sales, R&D, and the Business Intelligence Competency Centers (BICC) respondents report the most significant interest in having a range of regression models to work with in AI and machine learning applications. Marketing and Sales are also most interested in the next three top features, including hierarchical clustering, textbook statistical functions, and having a recommendation engine included in the applications and platforms they purchase. Dresner’s research team believes that the high shared interest in multiple features areas by R&D, Marketing and Sales is leading indicator enterprises are preparing to pilot AI and machine learning-based strategies to improve customer experiences and drive revenue. The following graphic compares interest and probable adoption by functional area of the enterprises interviewed:

  • 70% of R&D departments and teams are most likely to adopt data science, AI, and machine learning, leading all functions in an enterprise. Dresner’s research team sees the high level of interest by R&D teams as a leading indicator of broader enterprise adoption in the future. The study found 33% of all enterprises interviewed have adopted AI and machine learning, with the majority of enterprises having up to 25 models. Marketing & Sales lead all departments in their current evaluation of data science and machine learning software.

  • Financial Services & Insurance, Healthcare, and Retail/Wholesale say data science, AI, and machine learning are critical to their succeeding in their respective industries. 27% of Financial Services & Insurance, 25% of Healthcare and 24% of Retail/Wholesale enterprises say data science, AI, and machine learning are critical to their success. Less than 10% of Educational institutions consider AI and machine learning vital to their success. The following graphic compares the importance of data science, AI, and machine learning by industry:

  • The Telecommunications industry leads all others in interest and adoption of recommendation engines and model management governance. The Telecommunications, Financial Services, and Technology industries have the highest level of interest in adopting a range of regression models and hierarchical clustering across all industry respondent groups interviewed. Healthcare respondents have much lower interest in these latter features but high interest in Bayesian methods and text analytics functions. Retail/Wholesale respondents are often least interested in analytical features. The following graphic compares industries by their level of interest and potential adoption of analytical features in data science, AI, and machine learning applications and platforms:

  • Support for a broad range of regression models, hierarchical clustering, and commonly used textbook statistical functions are the top features enterprises need in data science and machine learning platforms. Dresner’s research team found these three features are considered the most important or “must-have” when enterprises are evaluating data science, AI and machine learning applications and platforms. All enterprises surveyed also expect any data science application or platform they are evaluating to have a recommendation engine included and model management and governance. The following graphic prioritizes the most and least essential features enterprises expect to see in data science, AI, and machine learning software and platforms:

  • The top three usability features enterprises are prioritizing today include support for easy iteration of models, access to advanced analytics, and an initiative, simple process for continuous modification of models. Support and guidance in preparing analytical data models and fast cycle time for analysis with data preparation are among the highest- priority usability features enterprises expect to see in AI and machine learning applications and platforms. It’s interesting to see the usability attribute of a specialist not required to create analytical models, test and run them at the lower end of the usability rankings. Many AI and machine learning software vendors rely on not needing a specialist to use their applications as a differentiator when the majority of enterprises value  support for easy iteration of models at a higher level as the graphic below shows:

  • 2019 is a record year for enterprises’ interest in data science, AI, and machine learning features they perceive as the most needed to achieve their business strategies and goals. Enterprises most expect AI and machine learning applications and platforms to support a range of regression models, followed by hierarchical clustering and textbook statistical functions for descriptive statistics. Recommendation engines are growing in popularity as interest grew to at least a tie as the second most important feature to respondents in 2019. Geospatial analysis and Bayesian methods were flat or slightly less important compared to 2018. The following graphic compares six years of interest in data science, AI, and machine learning techniques:

How AI Is Protecting Against Payments Fraud

  • 80% of fraud specialists using AI-based platforms believe the technology helps reduce payments fraud.
  • 63.6% of financial institutions that use AI believe it is capable of preventing fraud before it happens, making it the most commonly cited tool for this purpose.
  • Fraud specialists unanimously agree that AI-based fraud prevention is very effective at reducing chargebacks.
  • The majority of fraud specialists (80%) have seen AI-based platforms reduce false positives, payments fraud, and prevent fraud attempts.

AI is proving to be very effective in battling fraud based on results achieved by financial institutions as reported by senior executives in a recent survey, AI Innovation Playbook published by PYMNTS in collaboration with Brighterion. The study is based on interviews with 200 financial executives from commercial banks, community banks, and credit unions across the United States. For additional details on the methodology, please see page 25 of the study. One of the more noteworthy findings is that financial institutions with over $100B in assets are the most likely to have adopted AI, as the study has found 72.7% of firms in this asset category are currently using AI for payment fraud detection.

Taken together, the findings from the survey reflect how AI thwarts payments fraud and deserves to be a high priority in any digital business today. Companies, including Kount and others, are making strides in providing AI-based platforms, further reducing the risk of the most advanced, complex forms of payments fraud.

Why AI Is Perfect For Fighting Payments Fraud

Of the advanced technologies available for reducing false positives, reducing and preventing fraud attempts, and reducing manual reviews of potential payment fraud events, AI is ideally suited to provide the scale and speed needed to take on these challenges. More specifically, AI’s ability to interpret trend-based insights from supervised machine learning, coupled with entirely new knowledge gained from unsupervised machine learning algorithms are reducing the incidence of payments fraud. By combining both machine learning approaches, AI can discern if a given transaction or series of financial activities are fraudulent or not, alerting fraud analysts immediately if they are and taking action through predefined workflows. The following are the main reasons why AI is perfect for fighting payments fraud:

  • Payments fraud-based attacks are growing in complexity and often have a completely different digital footprint or pattern, sequence, and structure, which make them undetectable using rules-based logic and predictive models alone. For years e-commerce sites, financial institutions, retailers, and every other type of online business relied on rules-based payment fraud prevention systems. In the earlier years of e-commerce, rules and simple predictive models could identify most types of fraud. Not so today, as payment fraud schemes have become more nuanced and sophisticated, which is why AI is needed to confront these challenges.
  • AI brings scale and speed to the fight against payments fraud, providing digital businesses with an immediate advantage in battling the many risks and forms of fraud. What’s fascinating about the AI companies offering payments fraud solutions is how they’re trying to out-innovate each other when it comes to real-time analysis of transaction data. Real-time transactions require real-time security. Fraud solutions providers are doubling down on this area of R&D today, delivering impressive results. The fastest I’ve seen is a 250-millisecond response rate for calculating risk scores using AI on the Kount platform, basing queries on a decades-worth of data in their universal data network. By combining supervised and unsupervised machine learning algorithms, Kount is delivering fraud scores that are twice as predictive as previous methods and faster than competitors.
  • AI’s many predictive analytics and machine learning techniques are ideal for finding anomalies in large-scale data sets in seconds. The more data a machine learning model has to train on, the more accurate its predictive value. The greater the breadth and depth of data, a given machine learning algorithm learns from means more than how advanced or complex a given algorithm is. That’s especially true when it comes to payments fraud detection where machine learning algorithms learn what legitimate versus fraudulent transactions look like from a contextual intelligence perspective. By analyzing historical account data from a universal data network, supervised machine learning algorithms can gain a greater level of accuracy and predictability. Kount’s universal data network is among the largest, including billions of transactions over 12 years, 6,500 customers, 180+ countries and territories, and multiple payment networks. The data network includes different transaction complexities, verticals, and geographies, so machine learning models can be properly trained to predict risk accurately. That analytical richness includes data on physical real-world and digital identities creating an integrated picture of customer behavior.

Bottom Line:  Payments fraud is insidious, difficult to stop, and can inflict financial harm on any business in minutes. Battling payment fraud needs to start with a pre-emptive strategy to thwart fraud attempts by training machine learning models to quickly spot and act on threats then building out the strategy across every selling and service channel a digital business relies on.

10 Charts That Will Change Your Perspective Of AI In Marketing

 

  • Top-performing companies are more than twice as likely to be using AI for marketing (28% vs. 12%) according to Adobe’s latest Digital Intelligence Briefing.
  • Retailers are investing $5.9B this year in AI-based marketing and customer service solutions to improve shoppers’ buying experiences according to IDC.
  • Financial Services marketers lead all other industries in AI application adoption, with 37% currently using them today.
  • Sales and Marketing teams most often collaborate using Configure-Price-Quote (CPQ) and Marketing Automation AI-based applications, with sales leaders predicting AI adoption will increase 155% across sales teams in two years.

Artificial Intelligence enables marketers to understand sales cycles better, correlating their strategies and spending to sales results. AI-driven insights are also helping to break down data silos so marketing and sales can collaborate more on deals. Marketing is more analytics and quant-driven than ever before with the best CMOs knowing which metrics and KPIs to track and why they fluctuate.

The bottom line is that machine learning and AI are the technologies CMOs and their teams need to excel today. The best CMOs balance the quant-intensive nature of running marketing with qualitative factors that make a company’s brand and customer experience unique. With greater insight into how prospects make decisions when, where, and how to buy, CMOs are bringing a new level of intensity into driving outcomes. An example of this can be seen from the recent Forbes Insights and Quantcast research, Lessons of 21st-Century Brands Modern Brands & AI Report (17 pp., PDF, free, opt-in). The study found that AI enables marketers to increase sales (52%), increase in customer retention (51%), and succeed at new product launches (49%). AI is making solid contributions to improving lead quality, persona development, segmentation, pricing, and service.

The following ten charts provide insights into how AI is transforming marketing:

  • 21% of sales leaders rely on AI-based applications today, with the majority collaborating with marketing teams sharing these applications. Sales leaders predict that their use of AI will increase 155% in the next two years. Sales leaders predict AI will reach critical mass by 2020 when 54% expect to be using these technologies. Marketing and sales are relying on AI-based marketing automation, configure-price-quote (CPQ), and intelligent selling systems to increase revenue and profit growth significantly in the next two years. Source: Salesforce Research, State of Sales, 3rd edition. (58 pp., PDF, free, opt-in).

  • AI sees the most significant adoption by marketers working in $500M to $1B companies, with conversational AI for customer service is the most dominant. Businesses with between $500M to $1B lead all other revenue categories in the number and depth of AI adoption use cases. Just over 52% of small businesses with sales of $25M or less are using AI for predictive analytics for customer insights. It’s interesting to note that small companies are the leaders in AI spending, at 38.1%, to improve marketing ROI by optimizing marketing content and timing. Source: The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte and American Marketing Association. (71 pp., PDF, free, no opt-in).

  • 22% of marketers currently are using AI-based applications with an additional 57% planning to use in the next two years. There are nine dominant use cases marketers are concentrating on today, ranging from personalized channel experiences to programmatic advertising and media buying to predictive customer journeys and real-time next best offers. Source: Salesforce’s State of Marketing Study, 5th edition

  • Content personalization and predictive analytics from customer insights are the two areas CMOs most prioritize AI spending today. The CMO study found that B2B service companies are the top user of AI for content personalization (62.2%) and B2B product companies use AI for augmented and virtual reality, facial recognition and visual search more than any other business types. Source: CMOs’ Top Uses For AI: Personalization and Predictive Analytics. Marketing Charts. March 14, 2019

  • Personalizing the overall customer journey and driving next-best offers in real-time are the two most common ways marketing leaders are using AI today, according to Salesforce. Improving customer segmentation, improving advertising and media buying, and personalizing channel experiences are the next fastest-growing areas of AI adoption in marketing today. Source: Salesforce’s State of Marketing Study, 5th edition

  • 81% of marketers are either planning to or are using AI in audience targeting this year. 80% are currently using or planning to use AI for audience segmentation. EConsultancy’s study found marketers are enthusiastic about AI’s potential to increase marketing effectiveness and track progress. 88% of marketers interviewed say AI will enable them t be more effective in getting to their goals. Source: Dream vs. Reality: The State of Consumer First and Omnichannel Marketing. EConsultancy (36 pp., PDF, free, no opt-in).

  • Over 41% of marketers say AI is enabling them to generate higher revenues from e-mail marketing. They also see an over 13% improvement in click-thru rates and 7.64% improvement in open rates. Source: 4 Positive Effects of AI Use in Email Marketing, Statista (infographic), March 1, 2019.

Additional data sources on AI’s use in Marketing:

15 examples of artificial intelligence in marketing, eConsultancy, February 28, 2019

4 Positive Effects of AI Use in Email Marketing, Statista, March 1, 2019

4 Ways Artificial Intelligence Can Improve Your Marketing (Plus 10 Provider Suggestions), Forbes, Kate Harrison, January 20, 2019

AI: The Next Generation Of Marketing Driving Competitive Advantage Throughout The Customer Life Cycle, Forrester Consulting. February 2017 (10 pp., PDF, free, no opt-in).

Artificial Intelligence for Marketing (complete book) (361 pp., PDF, free, no opt-in)

Artificial Intelligence Roundup, eMarketer, May 2018 (15 pp., PDF, free, no opt-in)

Digital Intelligence Briefing, Adobe, 2018 (43 pp., PDF, free, no opt-in).

How 28 Brands Are Using AI to Enhance Their Marketing [Infographic], Impact Blog

How AI Is Changing Sales, Harvard Business Review, July 30, 2018

How Top Marketers Use Artificial Intelligence On-Demand Webinar with Vala Afshar, Chief Digital Evangelist, Salesforce and Meghann York, Director, Product Marketing, Salesforce

How To Win Tomorrow’s Car Buyers – Artificial Intelligence in Marketing & Sales, McKinsey Center for Future Mobility, McKinsey & Company. February 2019. (44 pp., PDF, free, no opt-in)

IDC MarketScape: Worldwide Artificial Intelligence in Enterprise Marketing Clouds 2017 Vendor Assessment, (11 pp., PDF, free, no opt-in.)

In-depth: Artificial Intelligence 2019, Statista Digital Market Outlook, February 2019 (client access reqd).

Leading reasons to use artificial intelligence (AI) for marketing personalization according to industry professionals worldwide in 2018, Statista.

Lessons of 21st-Century Brands Modern Brands & AI Report, Forbes Insights and Quantcast Study (17 pp., PDF, free, opt-in),

Powerful pricing: The next frontier in apparel and fashion advanced analytics, McKinsey & Company, December 2018

Share of marketing and agency professionals who are comfortable with AI-enabled technology automated handling of their campaigns in the United States as of June 2018, Statista.  

The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte and American Marketing Association. (71 pp., PDF, free, no opt-in).

Visualizing the uses and potential impact of AI and other analytics, McKinsey Global Institute, April 2018.  Interactive page based on Tableau data set can be found here.

What really matters in B2B dynamic pricing, McKinsey & Company, October 2018

Winning tomorrow’s car buyers using artificial intelligence in marketing and sales, McKinsey & Company, February 2019

Worldwide Spending on Artificial Intelligence Systems Will Grow to Nearly $35.8 Billion in 2019, According to New IDC Spending Guide, IDC; March 11, 2019

What’s Next For You? How AI Is Transforming Talent Management

Bottom Line: Taking on the talent crisis with greater intelligence and insight, delivering a consistently excellent candidate experience, and making diversity and inclusion a part of their DNA differentiates growing businesses who are attracting and retaining employees. The book What’s Next For You? by Ashutosh Garg, CEO and Co-Founder and Kamal Ahluwalia, President of eightfold.ai provide valuable insights and a data-driven roadmap of how AI is helping to solve the talent crisis for any business.

The Talent Crisis Is Real

  • 78% of CEOs and Chief Human Resource Officers (CHROs) say talent programs are important, with 56% say their current programs are ineffective.
  • 83% of employees want a new job yet only 53% want to leave for a new company.
  • 57% of employees say diversity and inclusion initiatives aren’t working, and 40% say their companies lack qualified diverse talent.
  • Nearly 50% of an organizations’ top talent will leave their jobs in the first two years of being hired.
  • 28% of open positions today won’t be filled in the next 12 months.

The above findings are just a sample of the depth of data-driven content and roadmap the book What’s Next For You? delivers. Co-authors Ashutosh Garg’s and Kamal Ahluwalia’s expertise in applying AI and machine learning to talent management problems with a strong data-first mindset is evident throughout the book. What makes the book noteworthy is how the authors write from the heart first with empathy for applicants and hiring managers, supporting key points with data. The empathetic, data-driven tone of the book makes the talent crisis relatable while also illustrating how AI can help any business make better talent management decisions.

“Businesses are having to adapt to technology changes and changes in customer expectations roughly every 10 years – a timeframe that is continuing to shrink. As a result, business leaders need to really focus on rethinking their business strategy and the associated talent strategy, so they have the organizational capability to transform and capitalize on the inevitable technology shifts,” writes John Thompson, Venture Partner, Lightspeed Venture Partners and Chairman of the Board at Microsoft in the forward.

The book cites talent management researchers and experts who say “our current knowledge base has a half-life of about two years, and the speed of technology is outperforming us as humans because of what it can do quickly and effectively“ (p.64). John Thompson’s observations in the forward that the time available for adapting to change is shrinking is a unifying thread that ties this book together. One of the most convincing is the fact that using today’s Applicant Tracking Systems (ATS) and hiring processes prone to biases, there’s a 30% chance a new hire will not make it through their first year. If the new hire is a cloud computing professional, this equates to a median salary of $146,350 and taking best-case 46 days to find their replacement. The cost and time loss of losing just one recruited cloud computing professional can derail a project for months. It will cost at least $219,000 or more to replace just that one engineer. Any manager who has lost a new hire within a year can relate to how real the talent crisis is and how urgent it is to solve it.

The Half-Life Of Skills Is Shrinking Fast

The most compelling chapter of the book illustrates how today’s talent crisis can be solved by taking an AI-enabled approach to every aspect of talent management. Chapter 4, The Half-Life Of Skills Is Shrinking Fast, delves into how AI can find candidates who can unlearn old concepts, and quickly master new ones. The book calls out this attribute of any potential new hire as being essential for them to adapt.  Using higher quality data than is available in traditional ATS systems, the authors illustrate how AI-based systems can be used for evaluating both the potential and experiences of applicants to match them with positions they will excel in. The authors make a convincing argument that AI can increase the probability of new candidate success. They cite a well-known Leadership IQ statistic of 46% of all new employee hires failing to adapt within 18 months, and the Harvard Business Review study finding between 40% to 60% of new upper management hires fail within 18 months. The authors contend that even Leonardo Da Vinci, one of the primary architects of the Renaissance, would have trouble finding work using a traditional resume entered into an ATS system today because his exceptional capabilities and potential would have never been discovered. When our existing process of recruiting is based on practices over 500 years old, as this copy of Leonardo Da Vinci’s resume illustrates, it’s time to put AI to work matching peoples’ potential with unique position requirements.

When Employees Achieve Their Potential, Companies Do Too   

Attracting the highest potential employees possible and retaining them is the cornerstone of any digital business’ growth strategy today and in the future. The book addresses the roadblocks companies face in attaining that goal, with bias being one of the strongest. “For example, McKinsey & Co., a top consulting agency, studied over 1,000 companies across 12 countries and found that firms in the top quartile of gender diversity were a fifth more likely to have above-average profits than those in the bottom quartile,” (p. 105). Further, “diverse executive boards generate better financial returns, and gender-diverse teams are more creative, more productive and more confident.” (p. 105).

In conclusion, consider this book a roadmap of how hiring and talent management can change for the better based on AI. The authors successfully illustrate how combining talent, personalization at scale, and machine learning can help employees achieve their potential, enabling companies to achieve theirs in the process.

Indeed’s 10 Most Popular AI & Machine Learning Jobs This Year

Indeed's 10 Most Popular AI & Machine Learning Jobs This Year

  • AI and Machine Learning job postings on Indeed rose 29.10% over the last year between May 2018 and May 2019.
  • Machine Learning and Deep Learning Engineers are the most popular jobs posted on Indeed between 2018 and 2019.
  • Machine Learning Engineers are earning an average salary of $142,858.57 in 2019 based on an analysis of all open positions on Indeed.
  • Indeed is seeing a leveling off of candidate-initiated searches for AI & Machine Learning (ML) jobs, dropping 14.5% between May 2018 and May 2019

These and many other insights are from Indeed’s recent report of the top 10 AI Jobs, and Salaries. Indeed’s analytics team completed an analysis of AI and machine learning hiring trends in 2019 to discover the top positions, highest salaries, and where the best opportunities are. The following are key insights from their latest study of AI and machine learning recruiting and hiring trends:

  • Machine Learning Engineers earn an average salary of $142,858.57 in 2019 based on an analysis of all open positions on Indeed. The Indeed analytics team found that the average annual salary for Machine Learning Engineers has grown by $8,409 in just a year, increasing 5.8%. Algorithm engineer’s average annual salary rose to $109,313 this year, an increase of $5,201, or 5%. Both salary bumps are likely a result of organizations’ spending more to attract talent to these crucial roles in a competitive AI job market

Indeed's 10 Most Popular AI & Machine Learning Jobs This Year

  • Machine Learning and Deep Learning Engineers are the most sought-after, popular jobs posted on Indeed between 2018 and 2019.  The Indeed analytics team identified the top 10 positions with the highest percentage of job descriptions that include the keywords “artificial intelligence” or “machine learning.” New jobs appearing on the list for the first time include Senior Data Scientist, Junior Data Scientist, Developer Consultant, Director of Data Science, and Lead Data Scientist. The inclusion of five new titles and the mix of skills shown in the table below reflects organizations’ growing expertise using AI, deep learning, and machine learning to drive business outcomes.

Indeed's 10 Most Popular AI & Machine Learning Jobs This Year

  • AI and Machine Learning job postings on Indeed rose 29.10% over the last year between May 2018 and May 2019.  Indeed found the increase is significantly less than it was for the previous two years. During the same period, May 2017 to May 2018 AI job postings on Indeed rose 57.91%, and a whopping 136.29% between May 2016 and May 2017.
  • Indeed is seeing a leveling off of candidate-initiated searches for AI & Machine Learning (ML) jobs, dropping 14.5% between May 2018 and May 2019. In comparison, searches increased 32% between May 2017 and May 2018 and 49.1% between May 2016 and May 2017. There are demand-and supply-side explanations for the 14.5% drop. From the demand side, the effects of AI and machine learning reaching broader adoption and maturing in organizations is leading to a greater variety of skills being recruited for. The 14.5% reduction reflects the broadening base of skills enterprises need to get the most out of AI and machine learning. From a supply side, potential job candidates are seeing the broadening base of skills they need to get hired, which are quickly making job descriptions from two years ago or longer obsolete. Finding candidates who have capabilities and potential to excel in AI and machine learning positions needs to get beyond just relying on job descriptions. Eightfold is doing just that by relying on machine learning algorithms to match candidates who have the optimal set of capabilities and potential for every open position an organization has.
  • New York, San Francisco, and Washington D.C. are the top three cities for AI and machine learning jobs in 2019. Indeed’s 2018 study also found New York and San Francisco leading all other metropolitan areas in open positions. New York’s diverse industries that range from banking, financial services, institutional investing, insurance to a growing AI startup community all contribute to its ranking first in the U.S. for AI positions.

Indeed's 10 Most Popular AI & Machine Learning Jobs This Year

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