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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.

What Needs To Be On Your CPQ Channel Roadmap In 2019

Bottom Line:  Adding new features to your CPQ channel selling platform directly benefits your resellers and channel partners, driving greater revenue, channel loyalty, and expansion into new markets.

Personalization Is Key To CPQ Succeeding In Channels

Sustaining and strengthening relationships across all indirect selling channels succeeds when dealers, multi-tier distributors, resellers, intermediaries, and service providers each can personalize the CPQ applications and platforms they use. Larger dealers, distributors, and resellers are adept at personalizing CPQ selling portals by the various roles in their organization. Personalization combined with a highly intuitive, configurable interface improves CPQ applications’ ease of use, enabling channel partners to get more done. The more intuitive and easy a CPQ application is to use, the more channel partners rely on it to place orders. When distributors are representing, on average, 12 different manufacturers,  the one with the most intuitive, easily used CPQ system often gets the majority of sales.

Another aspect of personalization is defining levels of resellers. When many organizations first launch their CPQ channel selling strategies, one of the first requests they have is to organize all channel partners into performance categories. Differentiating channel partners on sales performance, customer satisfaction, and aftermarket revenue then gamifying how every one of them can move up a level is proving to be very effective at increasing channel sales. Competing with one another to be the top reseller for the manufacturing and service companies lifts an entire channel network to higher performance.

Every dealer, multi-tier distributor, reseller, intermediary, and service provider also has a unique way of selling that works best for their business. Another must-have feature on any CPQ channel roadmap is greater workflow flexibility to support increasingly complex, IoT- and AI-enabled configurable products. Smart, connected products are the future of manufacturing and channel sales. Capgemini estimates that the size of the connected products market will be $519B to $685B by 2020. Workflows like the one shown below of an internal sales rep using a multichannel CPQ system to order a customized product are due for a refresh to support even greater flexibility for more channels and greater product options.


Most Valuable Features For A CPQ Channel Roadmap In 2019

There’s a direct link between how effective a CPQ platform is across multi-tier distribution networks and the productivity of sales teams using them. 83% of sales teams are using CPQ apps today based on Accenture Interactive’s recent study, Empowering Your Sales Force: It’s Not Just Automation, It’s Personal (8 pp., PDF, no opt-in). There’s ample evidence that the more effective a CPQ platform is at equipping dealers, multi-tier distributors, resellers, intermediaries, and service providers, the greater the sales they achieve. The 2019 B2B Buyers Survey Report, by DemandGen in collaboration with DemandBase, found that B2B buyers are more likely to purchase from sales representatives who demonstrate a stronger knowledge of the solution area and the business landscape (65%) compared to competitors. B2B buyers also give high praise for sales teams who can provide quotes quickly and respond to their inquiries promptly (63%), in addition to providing higher-quality content (61%). Each of these benefits is derived from a CPQ platform that can scale across every phase of the selling lifecycle.

The following are the key features needed on CPQ channel roadmaps in 2019 to stay competitive and scale sales and revenue on pace with market growth:

  • Greater personalization for each type of partner portal supported by real-time integration to CRM and ERP systems, designed to scale for sales team turnover across multi-tier distribution networks. Channel partners’ sales teams tend to churn quickly, and it’s best to design in intuitive, easily configured portals by sales role to help new hires get up to speed fast. Channel sales associates are typically the fastest-churning area of any selling business. With greater personalization comes the need for greater integration to provide the data needed to enable partner portals to have a greater depth of functionality. The following graphic from Deloitte’s recent study, Configure, Price, and Quote (CPQ) Capabilities illustrates this point:

  • Support for multi-tier pricing, price management, price optimization, price enforcement, and special workflows, including Special Pricing Requests (SPR). Baseline CPQ platforms support price management and have successfully transitioned multi-tier distribution networks off of Microsoft Excel spreadsheets to a single pricing model that scales across all products and channels. Consider adopting advanced pricing logic to support SPRs so sales operations teams don’t have to do this process manually. In manufacturers who have transitioned from manual to automated SPR approvals, average deal sizes have increased over 60%, and productivity jumped over 76% according to a recent Gartner survey.
  • Augment advanced product configuration tools by making them more intuitive and easier to use to sell the more advanced products in your catalog. It’s time to push the boundaries of CPQ channel selling systems to sell more complex products and drive greater revenue and margins. Forward-thinking manufacturers are taking a virtual design and 3D-based design approach to accomplish this. Enabling channel partners to take larger orders for more complex products is paying off.
  • Upgrade guided selling strategies to be more than catalog-based selection systems, mining customer data using machine learning to see which products they have the greatest propensity to buy when. It’s time to migrate off of the guided selling systems that are selecting products from catalogs that may deliver the best gross margins or have a traditionally high attach rate with the product the customer is buying. Machine learning is making it possible to provide greater accuracy and precision to recommendations than ever before.
  • Improve the usability of sales promotions, rebates, and most importantly, Market Development Funds (MDF). It’s amazing how much time manufacturers are spending manually handling MDF claims today. It’s time to automate this area of the CPQ channel roadmap and save thousands of hours and dollars a year while enabling resellers to get reimbursed faster or get the funds they need to grow their businesses.
  • Contract management is a must-have for CPQ channel roadmaps today. Integrating a cloud-based contract management system into a CPQ platform is vital for taking one more step towards an end-to-end quote-to-cash workflow being in place. Real-time integration to contract management can save days of waiting for contract approvals, all leading to more closed deals and faster, more lucrative sales cycles.
  • Manufacturers can realize greater revenue potential through their channels by combining machine learning insights to find those aftermarket customers most ready to buy while accelerating sales closing cycles with CPQ. Manufacturers want to make sure they are getting their fair share of the aftermarket. Using a machine learning-based application, they can help their resellers increase average deal sizes by knowing which products and services to offer when. They’ll also know when to present upsell and cross-sell offers into an account at a specific point in time when they will be most likely to lead to additional sales, all based on machine learning-based insights. Combining machine learning-based insights to guide resellers to the most valuable and highest probability customer accounts ready to buy with an intuitive CPQ system increases sales efficiency leading to higher revenues.

Conclusion

Now that the solutions exist for resellers to simplify CPQ selling strategies, it’s up to each manufacturer to decide how competitive they want their channel partner roadmap to be. Any given manufacturer’s quoting and configuration tools today are competing with 11 others on average for a reseller’s time, it is clear that roadmaps need a refresh to stay competitive. Suggested options include offering greater personalization, multi-tier pricing and a more thorough approach to price management, advanced product configuration support, revamped guided selling strategies and improved usability of sales promotions, rebates, and Market Development Funds (MDF). Manufacturers need to prioritize each of these features relative to their product- and revenue-specific goals by channel. A fascinating company who has deep expertise in designing, implementing, and scaling analytics, service, sales, IoT, and CPQ solutions for manufacturers is eLogic. The company’s mission is to enable manufacturers to achieve the highest value customer engagement and product & service lifecycle performance. eLogic is regarded as the leading system integration partner in CPQ and product configuration and is considered a global leader in delivering business solutions for manufacturers across SAP configuration technologies and Microsoft Dynamics 365, Power Platform & Azure.

10 Charts That Will Change Your Perspective Of Amazon’s Patent Growth

10 Charts That Will Change Your Perspective Of Amazon's Patent Growth

  • Since 2010 Amazon has grown its patent portfolio from less than 1,000 active patents in 2010 to nearly 10,000 in 2019, a ten-fold increase in less than a decade.
  • Amazon heavily cites Microsoft, IBM, and Alphabet, with 39%, 32% and 28% of Amazon’s total Patent Asset Index
  • Amazon’s patent portfolio is dominated by Cloud Computing, with the majority of the patents contributing to AWS’ current and future services roadmap. AWS achieved 41% year-over-year revenue growth in the latest fiscal quarter, reaching $7.6B in revenue.

Patents are fascinating because they provide a glimpse into potential plans, and roadmaps tech companies are considering. Amazon has one of the most interesting patent portfolios today that encompass a wide spectrum of technologies, from aircraft technology, drones, cloud computing, to machine learning. Interested in learning more about Amazon’s unique patent portfolio, I contacted PatentSight, a LexisNexis company, one of the leading providers of patent analytics and provider of the PatentSight analytics platform used for creating the ten charts shown below.

  • Amazon patents grew at a Compound Annual Growth Rate (CAGR) of above 35% between 2010 and 2019. PatentSight’s analysis shows that Amazon’s patent portfolio has increased tenfold in the last decade, and is comprised entirely of organic patents with only a small percentage gained from acquisitions. PatentSight also finds that Amazon’s patents have a falling average quality as measured by their Competitive Impact score shown on the vertical axis of the chart below. As Amazon’s patent portfolio has grown, there has been a downward trend of quality. William Mansfield, Head of Consulting and Customer Success at LexisNexis PatentSight explains why. “To maintain a high quality when growing the portfolio is difficult, as each patent would need to be equally as good as or better than the previous,” he said. Mr. Mansfield’s analysis found that Amazon’s portfolio has an average Competitive Impact of 2 today, double the PatentSight database average of 1.

  • Amazon’s patent portfolio is unique in that 100% of it is protected in the U.S. “The protection strategy of Amazon is also uncommon. While it can be the case that US firms tend to be US-centric, Amazon is an extreme case,” said William Mansfield. It’s surprising how many Amazon patents are active only in the USA (86%) and invented in the USA and active only in the USA (81%). William explained that “one factor for this US-centricity could be the great acceptance of software patents in the USA, we do also see high US-only filing for other tech giants, but are a level of around 60% vs. Amazon’s 86%.”

  • PatentSight found that the majority of the Amazon portfolio falls in the 2nd decile of Competitive Impact (top 20% – 10%). Comparable technology-based organizations have a higher density of patents in the top 10% of Competitive Impact, which is another unusual aspect regarding Amazon’s patent growth. “This is unusual compared to other big tech companies which have more in the top 10%, it could be Amazon is holding onto more lower value assets than required,” William Mansfield remarked.

  • Amazon’s patent citations most often cite Microsoft, IBM, and Alphabet, with 39%, 32% and 28% of Amazon’s total Patent Asset Index. Interesting that PatentSight’s analysis finds the reciprocal is not the case. A much smaller percentage of companies cite Amazon in return. This can be attributed to a few other firms having the breadth and depth of patent development that Amazon does today.  PatentSight found that less than 10% of their respective portfolios even mention Amazon.  William Mansfield explains that “one factor here is the larger size of these companies, vs. Amazon. However, even in absolute terms, Microsoft and IBM cite Amazon much less than the other way round. However, citation value is close to equal in absolute terms between Amazon and Alphabet.”

  • Relying on patents to keep AWS’ rapid growth going appears to be Amazon’s high priority patent strategy today. As can be seen from the portfolio below, Cloud Computing patents dominate Amazon’s patent portfolio today. In the latest fiscal quarter ending March 31, 2019, AWS delivered $7.9B in revenue and$2.2B in operating income, growing 41% year-over-year. “Amazon’s ongoing developments in alternative delivery methods in Urban Logistics and Drones are noteworthy with Drones being one area of particular strength in the portfolio as seen from the high Competitive Impact, despite the smaller portfolio size,” notes William Mansfield.

  • Amazon’s prioritization of cloud computing, AI, and machine learning patents is evident when 18 years of patent history is compared. The proliferation of AI and machine learning-based services on the AWS platform is apparent in the trend line starting in 2014. The success of Amazon’s SageMaker machine learning platform is a case in point. Amazon SageMaker enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at scale.

  • Amazon is already one of the top 10 patent holders in Drone technology, just behind Alphabet and Toyota Motors. PatentSight defines Drone technology as encompassing aviation, autonomous robots, and autonomous driving. Amazon’s rapid ascent in this area is attributable to the logistics and supply chain efficiencies possible when Drones and their related technologies are applied to their supply chain’s more complex challenges.

  • PatentSight finds that FinTech is an area of long-standing strength in the Amazon patent portfolio, attribute to their payment systems being the backbone of their e-commerce business. Reflecting how diverse their business model has become, Amazon is now one of the top 15 patent holders in this area due to cloud computing, AI, and machine learning taking precedence. “FinTech is a highly competitive field with many established players, and while Amazon is not in the top 10, but top 15 players, it’s still an impressive achievement,” said William Mansfield.

  • Amazon’s patent portfolio in speech recognition encompasses Alexa, its related patents, and Amazon Lex, an AWS service used for creating conversational interfaces for applications. Alphabet, Apple, Microsoft, and Samsung are patent leaders, according to PatentSight’s analysis. The fact that Amazon is in the top 10 speaks to the level of activity and patent production going on in the Alexa research and development and product teams.

  • Amazon’s patent strategy is eclectic yet always anchored to cloud computing to make AWS the platform of choice. The following selected patens reflect how broad the Amazon patent portfolio is. What each share in common is a reliance on AWS as the platform to ensure service consistency, reliability, and scale. An example of this is their patents Video Game Streaming.

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.

Salesforce Now Has Over 19% Of The CRM Market

 

  • Salesforce dominated the worldwide CRM market with a 19.5% market share in 2018, over double its nearest rival, SAP, at 8.3% share.
  • Worldwide spending on customer experience and relationship management (CRM) software grew 15.6% to reach $48.2B in 2018.
  • 72.9% of CRM spending was on software as a service (SaaS) in 2018, which is expected to grow to 75% of total CRM software spending in 2019.
  • Worldwide enterprise application software revenue totaled more than $193.6B in 2018, a 12.5% increase from 2017 revenue of $172.1B. CRM made up nearly 25% of the entire enterprise software revenue market.

CRM remains the largest and fastest growing enterprise software category today according to the latest market sizing, and market share research Gartner published this weekGartner defines CRM as providing the functionality to companies across the four segments of customer service and support, digital commerce, marketing, and sales. All four subsegments of the CRM market grew by more than 13.7%, with marketing emerging as the fastest growing segment, increasing by 18.8% and representing more than 25% of the entire CRM market. Customer service and support retain its No. 1 position, contributing 35.7% of CRM market revenue, attaining $17.1B in revenues in 2018.

Key insights include the following:

  • With 19.5% market share, Salesforce has over 2X the CRM sales SAP has and over 3X of Oracle. Salesforce continues to dominate CRM globally, increasing its market share from 18.3% in 2017 to 19.5% in 2018. Adobe is the only other vendor to grow its market share in 2018. Microsoft and SAP successfully held onto to market share while Oracle lost share.

  • Adobe and Salesforce grew faster than the overall market, increasing CRM revenues 21.7% and 23.2% respectively. Adobe’s CRM sales jumped from $2B in 2017 to $2.4B in 2018. Salesforce CRM revenues increased from $7.6B in 2017 to $9.4B in 2018, growing the fastest of all competitors in this market. SAP grew 15.5% between 2017 and 2018, just below the overall market growth of 15.6%. Microsoft (15%) and Oracle (7.1%) grew slower than the market. The following graphic compares growth rates between 2017 and 2018.

  • Adobe dominates the marketing subsegment of CRM with 19% market share in 2018. Salesforce has 11.7% of the marketing subsegment, followed by IBM (5.7%), SAP (4%), Oracle (3.6%) and HubSpot (3.4%). Gartner estimates the marketing subsegment was a $12.2B market in 2018, increasing from $10.3B in 2017, achieving 18.8% growth in just a year.
  • Eastern and Western Europe were the fastest growing regions at 19.7% and 17.5% respectively. North America and Western Europe were the largest two regions with North America growing at 15.2% to reach $28.1B in revenue.

Sources:

Gartner Says Worldwide Customer Experience and Relationship Management Software Market Grew 15.6% in 2018

Market Share: Customer Experience and Relationship Management, Worldwide, 2018 (client access required)

How To Improve Supply Chains With Machine Learning: 10 Proven Ways

Bottom line: Enterprises are attaining double-digit improvements in forecast error rates, demand planning productivity, cost reductions and on-time shipments using machine learning today, revolutionizing supply chain management in the process.

Machine learning algorithms and the models they’re based on excel at finding anomalies, patterns and predictive insights in large data sets. Many supply chain challenges are time, cost and resource constraint-based, making machine learning an ideal technology to solve them. From Amazon’s Kiva robotics relying on machine learning to improve accuracy, speed and scale to DHL relying on AI and machine learning to power their Predictive Network Management system that analyzes 58 different parameters of internal data to identify the top factors influencing shipment delays, machine learning is defining the next generation of supply chain management. Gartner predicts that by 2020, 95% of Supply Chain Planning (SCP) vendors will be relying on supervised and unsupervised machine learning in their solutions. Gartner is also predicting by 2023 intelligent algorithms, and AI techniques will be an embedded or augmented component across 25% of all supply chain technology solutions.

The ten ways that machine learning is revolutionizing supply chain management include:

  • Machine learning-based algorithms are the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems. Machine learning and AI-based techniques are the foundation of a broad spectrum of next-generation logistics and supply chain technologies now under development. The most significant gains are being made where machine learning can contribute to solving complex constraint, cost and delivery problems companies face today. McKinsey predicts machine learning’s most significant contributions will be in providing supply chain operators with more significant insights into how supply chain performance can be improved, anticipating anomalies in logistics costs and performance before they occur. Machine learning is also providing insights into where automation can deliver the most significant scale advantages. Source: McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus

  • The wide variation in data sets generated from the Internet of Things (IoT) sensors, telematics, intelligent transport systems, and traffic data have the potential to deliver the most value to improving supply chains by using machine learning. Applying machine learning algorithms and techniques to improve supply chains starts with data sets that have the greatest variety and variability in them. The most challenging issues supply chains face are often found in optimizing logistics, so materials needed to complete a production run arrive on time. Source: KPMG, Supply Chain Big Data Series Part 1

  • Machine learning shows the potential to reduce logistics costs by finding patterns in track-and-trace data captured using IoT-enabled sensors, contributing to $6M in annual savings. BCG recently looked at how a decentralized supply chain using track-and-trace applications could improve performance and reduce costs. They found that in a 30-node configuration when blockchain is used to share data in real-time across a supplier network, combined with better analytics insight, cost savings of $6M a year is achievable. Source: Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, December 18, 2018, by Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra

  • Reducing forecast errors up to 50% is achievable using machine learning-based techniques. Lost sales due to products not being available are being reduced up to 65% through the use of machine learning-based planning and optimization techniques. Inventory reductions of 20 to 50% are also being achieved today when machine learning-based supply chain management systems are used. Source: Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in).

  • DHL Research is finding that machine learning enables logistics and supply chain operations to optimize capacity utilization, improve customer experience, reduce risk, and create new business models. DHL’s research team continually tracks and evaluates the impact of emerging technologies on logistics and supply chain performance. They’re also predicting that AI will enable back-office automation, predictive operations, intelligent logistics assets, and new customer experience models. Source: DHL Trend Research, Logistics Trend Radar, Version 2018/2019 (PDF, 55 pp., no opt-in)

  • Detecting and acting on inconsistent supplier quality levels and deliveries using machine learning-based applications is an area manufacturers are investing in today. Based on conversations with North American-based mid-tier manufacturers, the second most significant growth barrier they’re facing today is suppliers’ lack of consistent quality and delivery performance. The greatest growth barrier is the lack of skilled labor available. Using machine learning and advanced analytics manufacturers can discover quickly who their best and worst suppliers are, and which production centers are most accurate in catching errors. Manufacturers are using dashboards much like the one below for applying machine learning to supplier quality, delivery and consistency challenges. Source: Microsoft, Supplier Quality Analysis sample for Power BI: Take a tour, 2018

  • Reducing risk and the potential for fraud, while improving the product and process quality based on insights gained from machine learning is forcing inspection’s inflection point across supply chains today. When inspections are automated using mobile technologies and results are uploaded in real-time to a secure cloud-based platform, machine learning algorithms can deliver insights that immediately reduce risks and the potential for fraud. Inspectorio is a machine learning startup to watch in this area. They’re tackling the many problems that a lack of inspection and supply chain visibility creates, focusing on how they can solve them immediately for brands and retailers. The graphic below explains their platform. Source: Forbes, How Machine Learning Improves Manufacturing Inspections, Product Quality & Supply Chain Visibility, January 23, 2019

  • Machine learning is making rapid gains in end-to-end supply chain visibility possible, providing predictive and prescriptive insights that are helping companies react faster than before. Combining multi-enterprise commerce networks for global trade and supply chain management with AI and machine learning platforms are revolutionizing supply chain end-to-end visibility. One of the early leaders in this area is Infor’s Control Center. Control Center combines data from the Infor GT Nexus Commerce Network, acquired by the company in September 2015, with Infor’s Coleman Artificial Intelligence (AI) Infor chose to name their AI platform after the inspiring physicist and mathematician Katherine Coleman Johnson, whose trail-blazing work helped NASA land on the moon. Be sure to pick up a copy of the book and see the movie Hidden Figures if you haven’t already to appreciate her and many other brilliant women mathematicians’ many contributions to space exploration. ChainLink Research provides an overview of Control Center in their article, How Infor is Helping to Realize Human Potential, and two screens from Control Center are shown below.

  • Machine learning is proving to be foundational for thwarting privileged credential abuse which is the leading cause of security breaches across global supply chains. By taking a least privilege access approach, organizations can minimize attack surfaces, improve audit and compliance visibility, and reduce risk, complexity, and the costs of operating a modern, hybrid enterprise. CIOs are solving the paradox of privileged credential abuse in their supply chains by knowing that even if a privileged user has entered the right credentials but the request comes in with risky context, then stronger verification is needed to permit access.  Zero Trust Privilege is emerging as a proven framework for thwarting privileged credential abuse by verifying who is requesting access, the context of the request, and the risk of the access environment.  Centrify is a leader in this area, with globally-recognized suppliers including Cisco, Intel, Microsoft, and Salesforce being current customers.  Source: Forbes, High-Tech’s Greatest Challenge Will Be Securing Supply Chains In 2019, November 28, 2018.
  • Capitalizing on machine learning to predict preventative maintenance for freight and logistics machinery based on IoT data is improving asset utilization and reducing operating costs. McKinsey found that predictive maintenance enhanced by machine learning allows for better prediction and avoidance of machine failure by combining data from the advanced Internet of Things (IoT) sensors and maintenance logs as well as external sources. Asset productivity increases of up to 20% are possible and overall maintenance costs may be reduced by up to 10%. Source: Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in).

References

Accenture, Reinventing The Supply Chain With AI, 20 pp., PDF, no opt-in.

Bendoly, E. (2016). Fit, Bias, and Enacted Sensemaking in Data Visualization: Frameworks for Continuous Development in Operations and Supply Chain Management Analytics. Journal Of Business Logistics37(1), 6-17.

Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, December 18, 2018, by Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra

The Most Innovative Companies of 2019 According to BCG

Google Press

Alphabet/Google is now the most innovative company in the world according to BCG, unseating Apple’s 13-year dominance of their annual rankings.

  • Alphabet/Google is now the most innovative company in the world according to BCG, unseating Apple’s 13-year dominance of their annual rankings.
  • Strong AI innovators are over three times more likely to have deep expertise in Big Data Analytics.
  • The ten most innovative companies in the world extensively use AI and platforms today to grow faster than competitors and markets.
  • T-MobileDow DuPontValeStryker, and Rio Tonto join the list of the top 50 most innovative companies for the first time this year.
  • Fastest movers include Adidas, who jumped from 35th to 10thSAP who increased from 42nd to 28th and Phillips who improved from 49th to 29th.

These and many other insights are from the Boston Consulting Group’s 13th annual report defining the world’s most innovative companies in 2019. The Most Innovative Companies 2019: The Rise of AI, Platforms, and Ecosystems is a fascinating glimpse into the rising importance of Artificial Intelligence (AI) and of platforms that support innovation. What makes this survey noteworthy is how it captures how AI’s use is rapidly expanding and how enterprises are relying on platforms to scale their efforts in this area. BCG is providing an Interactive Guide that compares the 50 most innovative companies in the world, sortable by industry, company and year. There’s also interactive analysis of Steady Innovators or those companies who’ve appeared on the list every year since 2005. There are breakouts of New Entrants, Returnees, and Movers for easier analysis. The report is available for download here (28 pp., PDF, free). Forbes also has an annual list of the world’s most innovative companies you can find here. The methodology Forbes uses is explained in the post, How We Rank The Most Innovative Companies 2018. Key insights from BCGs’ most innovative companies of 2019 include the following:

  • What differentiates the world’s most innovative companies are their creation and use of AI and platforms with Alphabet/GoogleAmazonApple, and Microsoft leading all others. Each of them is actively creating and providing AI-based applications, platforms and ecosystems that enable enterprises to improve customer experiences, creating entirely new revenue streams, business models and competitive advantages. Alphabet/Google has defined its direction as an “AI first” company, intentionally creating a culture of AI-driven innovation. The following is BCG’s list of the most innovative companies of 2019:

  • Enterprises who rate themselves strongest at innovation and better than average at AI base their self-evaluations on successfully changing customer experiences. BCG found that the most advanced enterprises using AI today are succeeding at changing customer experiences, creating new business models and measuring AI’s contribution to streamlining internal processes. 19.2% of all enterprises interviewed perceive themselves as being better than average at AI and strong innovators. The following graphic compares how enterprises rate themselves at AI versus their strength at innovation:

  • Strong AI innovators are over three times more likely to have deep expertise in Big Data Analytics. Enterprises who perceive themselves as strong AI innovators based on their success using AI to improve customer experiences, create new business models and streamline operations are two times as likely to be faster at adopting new technologies. They’re also 65% more likely to be actively targeting technology platforms to scale their AI initiatives and strategies further. The following graphic compares strong and weak innovators’ relative levels of adoption across 15 different innovation and product development categories:

  • Big Data Analytics, the speed of adopting tech, digital design, and technology platforms are the four areas enterprises who consider themselves strong innovators have the widest perceived advantage over weak innovators. When enterprises were asked which of the following 15 areas of innovation and product development will be the most impactful over the next 3 to 5 years, Big Data Analytics was far and away the most valued by strong versus weak innovators. Digital Design and Speed of Adopting Tech are two additional areas of innovation and product development that most differentiate the most and least innovative companies.

 

74% Of Data Breaches Start With Privileged Credential Abuse

Centrify’s survey shows organizations are granting too much trust and privilege, opening themselves up to potential internal and externally-driven breaches initiated with compromised privileged access credentials. Photo credit: iStock

Enterprises who are prioritizing privileged credential security are creating a formidable competitive advantage over their peers, ensuring operations won’t be interrupted by a breach. However, there’s a widening gap between those businesses protected from a breach and the many who aren’t. In quantifying this gap consider the typical U.S.-based enterprise will lose on average $7.91M from a breach, nearly double the global average of $3.68M according to IBM’s 2018 Data Breach Study.

Further insights into how wide this gap is are revealed in Centrify’s Privileged Access Management in the Modern Threatscape survey results published today. The study is noteworthy as it illustrates how wide the gap is between enterprises’ ability to avert and thwart breaches versus their current levels of Privileged Access Management (PAM) and privileged credential security. 74% of IT decision makers surveyed whose organizations have been breached in the past, say it involved privileged access credential abuse, yet just 48% have a password vault, just 21% have multi-factor authentication (MFA) implemented for privileged administrative access, and 65% are sharing root or privileged access to systems and data at least somewhat often.

Addressing these three areas with a Zero Trust approach to PAM would make an immediate difference in security.

“What’s alarming is that the survey reveals many organizations, armed with the knowledge that they have been breached before, are doing too little to secure privileged access. IT teams need to be taking their Privileged Access Management much more seriously, and prioritizing basic PAM strategies like vaults and MFA while reducing shared passwords,” remarked Tim Steinkopf, Centrify CEO. FINN Partners, on behalf of Centrify, surveyed 1,000 IT decision makers (500 in the U.S. and 500 in the U.K.) online in October 2018. Please see the study here for more on the methodology.

How You Choose To Secure Privileged Credentials Determines Your Future 

Identities are the new security perimeter. Threats can emerge within and outside any organization, at any time. Bad actors, or those who want to breach a system for financial gain or to harm a business, aren’t just outside. 18% of healthcare employees are willing to sell confidential data to unauthorized parties for as little as $500 to $1,000, and 24% of employees know of someone who has sold privileged credentials to outsiders, according to a recent Accenture survey.

Attackers are increasingly logging in using weak, stolen, or otherwise compromised credentials. Centrify’s survey underscores how the majority of organizations’ IT departments have room for improvement when it comes to protecting privileged access credentials, which are the ‘keys to the kingdom.’ Reading the survey makes one realize that forward-thinking enterprises who are prioritizing privileged credential security gain major cost and time advantages over their competitors. They’re able to keep their momentum going across every area of their business by not having to recover from breaches or incur millions of dollars on losses or fines as the result of a breach.

One of the most promising approaches to securing every privileged identity and threat space within and outside an organization is Zero Trust Privilege (ZTP). ZTP enables an organizations’ IT team to grant least privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment.

Key Lessons Learned from the Centrify Survey

How wide the gap is between organizations who see identities as the new security perimeter and are adopting a Zero Trust approach to securing them and those that aren’t is reflected in the results of Centrify’s Privileged Access Management in the Modern Threatscape surveyThe following are the key lessons learned of where and how organizations can begin to close the security gaps they have that leave them vulnerable to privileged credential abuse and many other potential threats:

  • Organizations’ most technologically advanced areas that are essential for future growth and attainment of strategic goals are often the most unprotected. Big Data, cloud, containers and network devices are the most important areas of any IT infrastructure. According to Centrify’s survey, they are the most unprotected as well. 72% of organizations aren’t securing containers with privileged access controls. 68% are not securing network devices like hubs, switches, and routers with privileged access controls. 58% are not securing Big Data projects with privileged access controls. 45% are not securing public and private cloud workloads with privileged access controls. The study finds that UK-based businesses lag U.S.-based ones in each of these areas as the graphic below shows:

  • Only 36% of U.K. organizations are very confident in their company’s current IT security software strategies, compared to 65% in the U.S. The gap between organizations with hardened security strategies that have a higher probability of withstanding breach attempts is wide between U.K. and U.S.-based businesses. 44% of U.K. respondents weren’t positive about what Privileged Access Management is, versus 26% of U.S. respondents. 60% of U.K. respondents don’t have a password vault.

  • Just 35% of U.S. organizations and 30% of those in the UK are relying on Privileged Access Management to manage partners’ access to privileged credentials and infrastructure. Partners are indispensable for scaling any new business strategy and expanding an existing one across new markets and countries. Forward-thinking organizations look at every partner associates’ identity as a new security perimeter. The 35% of U.S.-based organizations doing this have an immediate competitive advantage over the 65% who aren’t. By enforcing PAM across their alliances and partnerships, organizations can achieve uninterrupted growth by eliminating expensive and time-consuming breaches that many businesses never fully recover from.
  • Organizations’ top five security projects for 2019 include protecting cloud data, preventing data leakage, analyzing security incidents, improving security education/awareness and encrypting data. These top five security projects could be achieved at scale by having IT teams implement a Zero Trust-based approach to Privileged Access Management (PAM). The time, cost and scale advantages of getting the top five security projects done using Zero Trust would free up IT teams to focus on projects that deliver direct revenue gains for example.

Conclusion

Centrify’s survey shows organizations are granting too much trust and privilege, opening themselves up to potential internal and externally-driven breaches initiated with compromised privileged access credentials. It also reveals that there is a strong desire to adhere to best practices when it comes to PAM (51% of respondents) and that the reason it is not being adequately implemented rarely has to do with prioritization or difficulty but rather budget constraints and executive buy-in.

The survey also shows U.K. – and U.S.-based organizations need to realize identity is the new security perimeter. For example, only 37% of respondents’ organizations are able to turn off privileged access for an employee who leaves the company within one day, leaving a wide-open exposure point that can continue to be exploited.

There are forward-thinking organizations who are relying on Zero Trust Privilege as a core part of their digital transformation efforts as well. The survey found that given a choice, respondents are most likely to say digital transformation (40%) is one of the top 3 projects they’d prefer to work on, followed by Endpoint Security (37%) and Privileged Access Management (28%). Many enterprises see digital transformation’s missing link being Zero Trust and the foundation for redefining their businesses by defining every identity as a new security perimeter, so they can securely scale and grow faster than before.

Vodafone’s 2019 IoT Barometer Reflects Robust Growth In The Enterprise

  • 85% of enterprises who develop deep expertise with IoT succeed at driving revenue faster than competitors.
  • 81% of enterprises say Artificial Intelligence streamlines interpreting and taking action on data insights gained from IoT systems and sensors.
  • 68% of enterprises are using IoT to track the security of physical assets, making this use case the most common across enterprises today.
  • Transport & Logistics and Manufacturing & Industrials saw the most significant increase in adoption between 2018 and 2019.

These and many other fascinating insights are from the 6th annual Vodafone IoT Barometer, 2019.  The entire report can be downloaded here (PDF, 32 pp., e-mail opt-in). The methodology is based on 1,758 interviews distributed across the Americas (22%), EMEA (49%) and Asia-Pacific (29%). Eight vertical markets were included with manufacturing (22%), healthcare and wellness (14%) and retail, leisure, and hospitality (14%) being the three most represented markets.  Vodaphone is making an interactive tool available here for exploring the results.

Key insights from Vodafone’s 2019 IoT Barometer include the following:

  • 34% of global businesses are now using IoT in daily operations, up from 29% in 2018, with 95% of IoT adopters are already seeing measurable benefits. 81% of IoT adopters say their reliance on IoT has grown, and 76% of adopters say IoT is mission-critical to them. 58% are using analytics platforms to get more insights from their IoT data to improve decision making. 71% of enterprises who have adopted IoT expect their company and others like them will start listing data resources on their balance sheets as assets within five years.

  • 95% of enterprises adopting IoT are achieving tangible benefits and positive ROI. 52% of enterprises report significant returns on their IoT investments. 79% say IoT is enabling positive outcomes that would have been impossible without it, further reflecting robust growth in the enterprise. Across all eight vertical markets reducing operating costs (53%) and gaining more accurate data and insights (48%) are the most common benefits. Transitioning an IoT pilot to production based on cost reduction and improved visibility creates a compelling ROI for many enterprises. The following graphic compares IoT’s benefits to enterprises. Please click on the graphic to expand for easier reading.

  • Transport & Logistics and Manufacturing & Industrials saw the greatest increase in adoption between 2018 and 2019. Transport and Logistics had the highest IoT adoption rate at 42% followed by Manufacturing and Industrials at 39%. Manufacturers are facing the challenges of improving production efficiency and product quality while accelerating time-to-market for next-generation smart, connected products. IoT contributes to productivity improvements and creates opportunities for services-based business models, two high priorities for manufacturers in 2019 and beyond.  The following graphic from the interactive tool compares IoT adoption by industry based on Vodaphone’s IoT barometer data over the last six years:

  • 89% of most sophisticated enterprises have multiple full-scale projects in production, orchestrating IoT with analytics, AI and cloud, creating a technology stack that delivers real-time insights. Enterprises who lead IoT adoption in their industries rely on integration to gain scale and speed advantages quickly over competitors. The greater the real-time integration, the greater the potential to digitally transform an enterprise and remove roadblocks that get in the way of growing. 95% of adopters where IoT is fully integrated say it’s enabling their digital transformation, compared with 55% that haven’t started integration. The following graphics reflect how integrated enterprises’ IoT projects are with existing business systems and processes and the extent to which enterprises agree that IoT is enabling digital transformation.

  • 68% of enterprises are using IoT to track the security of physical assets, making this use case the most common across enterprises today. 57% of all enterprises are using IoT to manage risk and compliance. 53% are using it to increase revenue and cut costs, with 82% of high performing enterprises rely on IoT to manage risk and compliance. The following graphic compares the types of variables enterprises are using IoT to track today and plan to in the future.

  • IoT adoption is soaring in Americas-based enterprises, jumping from 27% in 2018 to 40% in 2019. The Americas region leads the world in terms of IoT usage assessed by strategy, integration, and implementation of IoT deployments. 73% of Americas-based enterprises are the most likely to report significant returns from their IoT investments compared to 47% for Asia-Pacific (APAC) and 45% for Europe, Middle East and Africa (EMEA).
  • 52% of IoT-enabled enterprises plan to use 5G when it becomes available. Enterprises are looking forward to 5G’s many advantages including improved security via stronger encryption, more credentialing options, greater quality of service management, more specialized services and near-zero latency. Vodafone predicts 5G will be a strong catalyst of growth for emerging IoT applications including connected cars, smart cities, eHealth and industrial automation.

 

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