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Posts tagged ‘Artificial Intelligence’

How To Improve Channel Sales With AI-Based Knowledge Sharing Networks

How To Improve Channel Sales With AI-Based Knowledge Sharing Networks

Bottom Line: Knowledge-sharing networks have been improving supply chain collaboration for decades; it’s time to enhance them with AI and extend them to resellers to revolutionize channel selling with more insights.

The greater the accuracy and speed of supply chain-based data integration and knowledge, the greater the accuracy of custom product orders. Add to that the complexity of selling CPQ and product configurations through channels, and the value of using AI to improve knowledge sharing networks becomes a compelling business case.

Why Channels Need AI-Based Knowledge Sharing Networks Now

Automotive, consumer electronics, high tech, and industrial products manufacturers are combining IoT sensors, microcontrollers, and modular designs to sell channel-configurable smart vehicles and products. AI-based knowledge-sharing networks are crucial to the success of their next-generation products. Likewise, to sell to any of these manufacturers, suppliers need to be pursuing the same strategy. AI-based services, including Amazon Alexa, Microsoft Cortana, and Google Voice and others, rely on knowledge-sharing networks to collaborate with automotive supply chains and strengthen OEM partnerships. The following graphic reflects how successful Amazon’s Alexa Automotive OEM sales team is at using knowledge-sharing networks to gain design wins across their industry.

The following are a few of the many reasons why creating and continually fine-tuning an AI-based knowledge-sharing network is an evolving strategy worth paying attention to:

  • Supply chains are the primary source of knowledge that must permeate an organization’s structure and channels for the company to stay synchronized to broader market demands. For CPQ channel selling strategies to thrive, they need real-time pricing, availability, available-to-promise, and capable-to-promise data to create accurate, competitive quotes that win deals. The better the supplier collaboration across supply chains and with channel partners, the higher the probability of selling more. A landmark study of the Toyota Production System by Professors Jeffrey H Dyer & Kentaro Nobeoka found that Toyota suppliers value shared data more than cash, making knowledge sharing systems invaluable to them (Dyer, Nobeoka, 2000).
  • Smart manufacturing metrics also need to be contributing real-time data to knowledge sharing systems channel partners use, relying on AI to create quotes for products that can be built the fastest and are the most attractive to each customer. Combining manufacturing’s real-time monitoring data stream of ongoing order progress and production availability with supply chain pricing, availability, and quality data all integrated to a cloud-based CPQ platform gives channel partners what they need to close deals now. AI-based knowledge-sharing networks will link supply chains, manufacturing plants, and channel partners to create smart factories that drive more sales. According to a recent Capgemini study, manufacturers are planning to launch 40% more smart factories in the next five years, increasing their annual investments by 1.7 times compared to the previous three years, according to their recent Smart factories @ scale Capgemini survey. The following graphic illustrates the percentage growth of smart factories across key geographic regions, a key prerequisite for enabling AI-based knowledge-sharing networks with real-time production data:
  • By closing the data gaps between suppliers, manufacturing, and channels, AI-based knowledge-sharing networks give resellers the information they need to sell with greater insight. Amazon’s Alexa OEM marketing teams succeeded in getting the majority of design-in wins with automotive manufacturers designing their next-generation of vehicles with advanced electronics and AI features. The following graphic from Dr. Dyer’s and Nobeoka’s study defines the foundations of a knowledge-sharing network. Applying AI to a mature knowledge-sharing network creates a strong network effect where every new member of the network adds greater value.
  • Setting the foundation for an effective knowledge sharing network needs to start with platforms that have AI and machine learning designed in with structure that can flex for unique channel needs. There are several platforms capable of supporting AI-based knowledge-sharing networks available, each with its strengths and approach to adapting to supply chain, manufacturing, and channel needs. One of the more interesting frameworks not only uses AI and machine learning across its technology pillars but also takes into consideration that a company’s operating model needs to adjust to leverage a connected economy to adapt to changing customer needs. BMC’s Autonomous Digital Enterprise (ADE) is differentiated from many others in how it is designed to capitalize on AI and Machine Learning’s core strengths to create innovation ecosystems in a knowledge-sharing network. Knowledge-sharing networks thrive on continuous learning. It’s good to see major providers using adaptive and machine learning to strengthen their platforms, with BMC’s Automated Mainframe Intelligence (AMI) emerging as a leader. Their approach to using adaptive learning to maintain data quality during system state changes and link exceptions with machine learning to deliver root cause analysis is prescient of where continuous learning needs to go.  The following graphic explains the ADE’s structure.

Conclusion

Knowledge-sharing networks have proven very effective in improving supply chain collaboration, supplier quality, and removing barriers to better inventory management. The next step that’s needed is to extend knowledge-sharing networks to resellers and enable knowledge sharing applications that use AI to tailor product and service recommendations for every customer being quoted and sold to. Imagine resellers being able to create quotes based on the most buildable products that could be delivered in days to buying customers. That’s possible using a knowledge-sharing network. Amazon’s success with Alexa design wins shows how their use of knowledge-sharing systems helped to provide insights needed across automotive OEMs wanted to add voice-activated AI technology to their next-generation vehicles.

References

BMC, Maximizing the Value of Hybrid IT with Holistic Monitoring and AIOps (10 pp., PDF).

BMC Blogs, 2019 Gartner Market Guide for AIOps Platforms, December 2, 2019

Cai, S., Goh, M., De Souza, R., & Li, G. (2013). Knowledge sharing in collaborative supply chains: twin effects of trust and power. International journal of production Research51(7), 2060-2076.

Capgemini Research Institute, Smart factories @ scale: Seizing the trillion-dollar prize through efficiency by design and closed-loop operations, 2019.

Columbus, L, The 10 Most Valuable Metrics in Smart Manufacturing, Forbes, November 20, 2020

Jeffrey H Dyer, & Kentaro Nobeoka. (2000). Creating and managing a high-performance knowledge-sharing network: The Toyota case. Strategic Management Journal: Special Issue: Strategic Networks, 21(3), 345-367.

Myers, M. B., & Cheung, M. S. (2008). Sharing global supply chain knowledge. MIT Sloan Management Review49(4), 67.

Wang, C., & Hu, Q. (2020). Knowledge sharing in supply chain networks: Effects of collaborative innovation activities and capability on innovation performance. Technovation94, 102010.

 

How Barclays Is Preventing Fraud With AI

How Barclays Is Preventing Fraud With AI

Bottom Line: Barclays’ and Kount’s co-developed new product, Barclays Transact reflects the future of how companies will innovate together to apply AI-based fraud prevention to the many payment challenges merchants face today.

Merchant payment providers have seen the severity, scope, and speed of fraud attacks increase exponentially this year. Account takeovers, card-not-present fraud, SMS spoofing, and phishing are just a few of the many techniques cybercriminals are using to defraud merchants out of millions of dollars. One in three merchants, 32%, prioritize payment providers’ fraud and security strengths over customer support and trust according to a recent YouGov survey.  But it doesn’t have to be a choice between security and a frictionless transaction.

Frustrated by the limitations of existing fraud prevention systems, many payment providers are working as fast as they can to pilot AI- and machine-learning-based applications and platforms. Barclays Payment Solutions’ decision to work with AI-based solution Kount is what the future of AI-based fraud prevention for payment providers looks like.

How AI Helps Thwart Fraud And Increase Sales at Barclays   

Barclays Payment Services handles 40% of all merchant payments in the UK. They’ve been protecting merchants and their customers’ data for over 50 years, and their fraud and security teams have won industry awards. For Barclays, excelling at merchant and payment security is the only option.

In order to offer an AI-based suite of tools to help merchants make their online transactions both simpler and safer, Barclays chose to partner with Kount. Their model of innovating together enables Barclays to strengthen their merchant payment business with AI-based fraud prevention and gain access to Kount’s Identity Trust Global Network, the largest network of trust and fraud-related signals. Kount gains knowledge into how they can fine-tune their AI and machine learning technologies to excel at payment services. Best of all, Barclays’ merchant customers will be able to sell more by streamlining the payment experience for their customers. The following is an overview of the Barclays Transact suite for merchants.

Barclays and Kount defined objectives for Barclay Transact: protect against increasingly sophisticated eCommerce fraud attempts, improve their merchants’ customer experiences during purchases, prepare for UK-mandated Strong Customer Authentication (SCA) by allowing businesses to take advantage of Transaction Risk Analysis (TRA) exemptions, optimize payment acceptance workflows and capitalize on Kount’s Identity Trust Global Network.

Adding urgency to the co-creation of Barclays Transact are UK regulatory requirements. To help provide clarity and support to merchants and the market from the impact of Covid-19 the Financial Conduct Authority (FCA) have agreed to delay the enforcement of a Strong Customer Authentication (SCA) until 14 September 2021 in the UK. The European Economic Area (EEA) deadline remains 31 December, 2020. Kount’s AI- and machine learning algorithms designed into Barclay Transact, tested at beta sites and fine-tuned for the first release, are effective in meeting UK government mandates.

How AI Is Turning Trust Into A Sales Accelerator At Barclays

The Barclays Payment Solutions and Kount teams believe that the more ambitious the goals for Barclays Transact to deliver value to merchants, the stronger the suite will be. Here are examples of goals businesses can achieve with this partnership:

  1. Achieve as few false positives as possible by making real-time updates to machine learning algorithms and fine-tuning merchant responses.
  2. Reduce the number of manual reviews for fraud analysts consistently by applying AI and machine learning to provide early warning of anomalies.
  3. Minimize the number of chargebacks to merchant partners.
  4. Reduce the friction and challenges merchants experience with legacy fraud prevention systems by streamlining the purchasing experience.
  5. Enable compliance to UK-mandated regulatory requirements while streamlining merchants and their customers’ buying experiences.

Barclays Transact analyzes every transaction in real-time using Kount’s AI-based fraud analysis technology, scoring each on a spectrum of low to high risk. Each Barclays merchant’s gateway then uses this score to identify the transactions which qualify for TRA exemptions. This results in a more frictionless payment and checkout experience for customers, resulting in lower levels of shopping cart abandonment and increased sales. Higher-risk transactions requiring further inspection will still go through two-factor authentication, or be immediately declined, per the regulation and customer risk appetite. The following is an example of the workflow Barclays and Kount were able to accomplish by innovating together:

Conclusion 

Improving buying experiences and keeping them more secure on a trusted platform is an ambitious design goal for any suite of online tools. Barclays and Kount’s successful development and launch of a co-developed product is prescient and points the way forward for payment providers who need AI expertise to battle fraud now. A bonus is how the partnership is going to enrich the Kount Identity Trust Global Network, the largest network of trust and risk signals, which is comprised of 32 billion annual interactions from more than 6,500 customers across 75+ industries. “We are excited to be partnering with Kount, because they share our goal of collaborative innovation, and a drive to deliver best-in-class shopper experiences. Thanks to Kount’s award-winning fraud detection software, the new module will not only help customers to fight fraud and prevent unwanted chargebacks, but it will also help them to maximize sales, improve customer experience, and better prepare for the introduction of SCA,” David Jeffrey, Director of Product, Barclaycard Payments said.

10 Ways Enterprises Are Getting Results From AI Strategies

10 Ways Enterprises Are Getting Results From AI Strategies

  • One in 10 enterprises now use 10 or more AI applications; chatbots, process optimization, and fraud analysis lead a recent survey’s top use cases according to MMC Ventures.
  • 83% of IT leaders say AI & ML is transforming customer engagement, and 69% say it is transforming their business according to Salesforce Research.
  • IDC predicts spending on AI systems will reach $97.9B in 2023.

AI pilots are progressing into production based on their combined contributions to improving customer experience, stabilizing and increasing revenues, and reducing costs. The most successful AI use cases contribute to all three areas and deliver measurable results. Of the many use cases where AI is delivering proven value in enterprises today, the ten areas discussed below are notable for the measurable results they are providing.

What each of these ten use cases has in common is the accuracy and efficiency they can analyze and recommend actions based on real-time monitoring of customer interactions, production, and service processes. Enterprises who get AI right the first time build the underlying data structures and frameworks to support the advanced analytics, machine learning, and AI techniques that show the best potential to deliver value. There are various frameworks available, with BMC’s Autonomous Digital Enterprise (ADE) encapsulating what enterprises need to scale out their AI pilots into production. What’s unique about BMC’s approach is its focus on delivering transcendent customer experiences by creating an ecosystem that uses technology to cater to every touchpoint on a customer’s journey, across any channel a customer chooses to interact with an enterprise on.

10 Areas Where AI Is Delivering Proven Value Today

Having progressed from pilot to production across many of the world’s leading enterprises, they’re great examples of where AI is delivering value today. The following are 10 areas where AI is delivering proven value in enterprises today

  • Customer feedback systems lead all implementations of AI-based self-service platforms. That’s consistent with the discussions I’ve had with manufacturing CEOs who are committed to Voice of the Customer (VoC) programs that also fuel their new product development plans. The best-run manufacturers are using AI to gain customer feedback better also to improve their configure-to-order product customization strategies as well. Mining contact center data while improving customer response times are working on AI platforms today. Source: Forrester study, AI-Infused Contact Centers Optimize Customer Experience Develop A Road Map Now For A Cognitive Contact Center.
  • McKinsey finds that AI is improving demand forecasting by reducing forecasting errors by 50% and reduce lost sales by 65% with better product availability. Supply chains are the lifeblood of any manufacturing business. McKinsey’s initial use case analysis is finding that AI can reduce costs related to transport and warehousing and supply chain administration by 5% to 10% and 25% to 40%, respectively. With AI, overall inventory reductions of 20% to 50% are possible. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? McKinsey & Company.

10 Ways Enterprises Are Getting Results From AI Strategies

  • The majority of CEOs and Chief Human Resource Officers (CHROs) globally plan to use more AI within three years, with the U.S. leading all other nations at 73%. Over 63% of all CEOs and CHROs interviewed say that new technologies have a positive impact overall on their operations. CEOs and CHROs introducing AI into their enterprises are doing an effective job at change management, as the majority of employees, 54%, are less concerned about AI now that they see its benefits. C-level executives who are upskilling their employees by enabling them to have stronger digital dexterity skills stand a better chance of winning the war for talent. Source: Harris Interactive, in collaboration with Eightfold Talent Intelligence And Management Report 2019-2020 Report.

10 Ways Enterprises Are Getting Results From AI Strategies

  • AI is the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems. 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 AI can contribute to solving complex constraints, cost, and delivery problems manufacturers are facing today. For example, AI is 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.

10 Ways Enterprises Are Getting Results From AI Strategies

  • AI sees the most significant adoption by marketers working in $500M to $1B companies, with conversational AI for customer service as 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).
  • A semiconductor manufacturer is combining smart, connected machines with AI to improve yield rates by 30% or more, while also optimizing fab operations and streamlining the entire production process. They’ve also been able to reduce supply chain forecasting errors by 50% and lost sales by 65% by having more accurate product availability, both attributable to insights gained from AI. They’re also automating quality testing using machine learning, increasing defect detection rates up to 90%. These are the kind of measurable results manufacturers look for when deciding if a new technology is going to deliver results or not. These and many other findings from the semiconductor’s interviews with McKinsey are in the study, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? . The following graphic from the study illustrates the many ways AI and machine learning are improving semiconductor manufacturing.

10 Ways Enterprises Are Getting Results From AI Strategies

  • AI is making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer. By definition, propensity models rely on predictive analytics including machine learning to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. Every business excelling at omnichannel today rely on propensity models to better predict how customers’ preferences and past behavior will lead to future purchases. The following is a dashboard that shows how propensity models work. Source: customer propensities dashboard is from TIBCO.
  • AI is reducing 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.
  • Detecting and acting on inconsistent supplier quality levels and deliveries using AI-based applications is reducing the cost of bad quality across electronic, high-tech, and discrete manufacturing. 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. Using AI, 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.

10 Ways Enterprises Are Getting Results From AI Strategies

  • Optimizing Shop Floor Operations with Real-Time Monitoring and AI is in production at Hitachi today. Combining real-time monitoring and AI to optimize shop floor operations, providing insights into machine-level loads and production schedule performance, is now in production at Hitachi. Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimizing the best possible set of machines for a given production run is now possible using AI.  Source: Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics, and AI Are Making Factories Intelligent and Agile, Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D.

10 Ways Enterprises Are Getting Results From AI Strategies

Additional reading:

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

Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)

Artificial Intelligence: The Ultimate Technological Disruption Ascends, Woodside Capital Partners. (PDF,

DHL Trend Research, Logistics Trend Radar, Version 2018/2019 (PDF, 55 pp., no opt-in)

2018 (43 pp., PDF, free, no opt-in).

Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in)

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)

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

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

bes Insights and Quantcast Study (17 pp., PDF, free, opt-in),

Marketing & Sales Big Data, Analytics, and the Future of Marketing & Sales, (PDF, 60 pp., no opt-in), McKinsey & Company.

McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus

McKinsey & Company, Notes from the AI frontier: Modeling the impact of AI on the world economy, September 2018 By Jacques Bughin, Jeongmin Seong, James Manyika, Michael Chui, and Raoul Joshi

Papadopoulos, T., Gunasekaran, A., Dubey, R., & Fosso Wamba, S. (2017). Big data and analytics in operations and supply chain management: managerial aspects and practical challenges. Production Planning & Control28(11/12), 873-876.

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

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

World Economic Forum, Impact of the Fourth Industrial Revolution on Supply Chains (PDF, 22 pgs., no opt-in)

World Economic Forum, Supply Chain 4.0 Global Practices, and Lessons Learned for Latin America and the Caribbean (PDF, 44 pp., no opt-in)

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

 

Six Areas Where AI Is Improving Customer Experiences

Six Areas Where AI Is Improving Customer Experiences

Bottom Line: This year’s hard reset is amplifying how vital customer relationships are and how much potential AI has to find new ways to improve them.

  • 30% of customers will leave a brand and never come back because of a bad experience.
  • 27% of companies say improving their customer intelligence and data efforts are their highest priority when it comes to customer experience (CX).
  • By 2023, 30% of customer service organizations will deliver proactive customer services by using AI-enabled process orchestration and continuous intelligence, according to Gartner.
  • $13.9B was invested in CX-focused AI and $42.7B in CX-focused Big Data and analytics in 2019, with both expected to grow to $90B in 2022, according to IDC.

The hard reset every company is going through today is making senior management teams re-evaluate every line item and expense, especially in marketing. Spending on Customer Experience is getting re-evaluated as are supporting AI, analytics, business intelligence (BI), and machine learning projects and spending. Marketers able to quantify their contributions to revenue gains are succeeding the most at defending their budgets.

Fundamentals of CX Economics

Knowing if and by how much CX initiatives and strategies are paying off has been elusive. Fortunately, there are a variety of benchmarks and supporting methodologies being developed that contextualize the contribution of CX. KPMG’s recent study, How Much Is Customer Experience Worth? provides guidance in the areas of CX and its supporting economics. The following table provides an overview of key financial measures’ interrelationships with CX. The table below summarizes their findings:

The KPMG study also found that failing to meet customer expectations is two times more destructive than exceeding them. That’s a powerful argument for having AI and machine learning ingrained into CX company-wide. The following graphic quantifies the economic value of improving CX:

Six Areas Where AI Is Improving Customer Experiences

 

Where AI Is Improving CX

For AI projects to make it through the budgeting crucible that the COVID-19 pandemic has created, they’re going to have to show a contribution to revenue, cost reduction, and improved customer experiences in a contactless world. Add in the need for any CX strategy to be on a resilient, proven platform and the future of marketing comes into focus. Examples of platforms and customer-centric digital transformation networks that can help re-center an organization on data- and AI-driven customer insights include BMC’s Autonomous Digital Enterprise (ADE) and others. The framework is differentiated from many others in how it is designed to capitalize on AI and Machine Learning’s core strengths to improve every aspect of the customer (CX) and  employee experience (EX). BMC believes that providing employees with the digital resources they need to excel at their jobs also delivers excellent customer experiences.

Having worked my way through college in customer service roles, I can attest to how valuable having the right digital resources are for serving customers What I like about their framework is how they’re trying to go beyond just satisfying customers, they’re wanting to delight them. BMC calls this delivering a transcendent customer experience. From my collegiate career doing customer service, I recall the e-mails delighted customers sent to my bosses that would be posted along a wall in our offices. In customer service and customer experience, you get what you give. Having customer service reps like my younger self on the front line able to get resources and support they need to deliver more authentic and responsive support is key. I see BMC’s ADE doing the same by ensuring a scalable CX strategy that retains its authenticity even as response times shrink and customer volume increases.

The following are six ways AI can improve customer experiences:

  • Improving contactless personalized customer care is considered one of the most valuable areas where AI is improving customer experiences. These “need to do” marketing areas have the highest complexity and highest benefit. Marketers haven’t been putting as much emphasis on the “must do” areas of high benefit and low complexity, according to Capgemini’s analysis. These application areas include Chatbots and virtual assistants, reducing revenue churn, facial recognition and product and services recommendations. Source:  Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. (PDF, 28 pp).

Six Areas Where AI Is Improving Customer Experiences

  • Anticipating and predicting how each customers’ preferences of where, when, and what they will buy will change and removing roadblocks well ahead of time for them. Reducing the friction customers face when they’re attempting to buy within a channel they’ve never purchased through before can’t be left to chance. Using augmented, predictive analytics to generate insights in real-time to customize the marketing mix for every individual Customer improves sales funnels, preserves margins, and can increase sales velocity.
  • Knowing which customer touchpoints are the most and least effective in improving CX and driving repurchase rates. Successfully using AI to improve CX needs to be based on data from all trackable channels that prospects and customers interact with. Digital touchpoints, including mobile app usage, social media, and website visits, all need to be aggregated into data sets ML algorithms to use to learn more about every Customer continually and anticipate which touchpoint is the most valuable to them and why. Knowing how touchpoints stack up from a customer’s point of view immediately says which channels are doing well and which need improvement.
  • Recruiting new customer segments by using CX improvements to gain them as prospects and then convert them to customers. AI and ML have been used for customer segmentation for years. Online retailers are using AI to identify which CX enhancements on their mobile apps, websites, and customer care systems are the most likely to attract new customers.
  • Retailers are combining personalization, AI-based pattern matching, and product-based recommendation engines in their mobile apps enabling shoppers to try on garments they’re interested in buying virtually. Machine learning excels at pattern recognition, and AI is well-suited for fine-tuning recommendation engines, which are together leading to a new generation of shopping apps where customers can virtually try on any garment. The app learns what shoppers most prefer and also evaluates image quality in real-time, and then recommends either purchase online or in a store. Source: Capgemini, Building The Retail Superstar: How unleashing AI across functions offers a multi-billion dollar opportunity.

Six Areas Where AI Is Improving Customer Experiences

  • Relying on AI to best understand customers and redefine IT and Operations Management infrastructure to support them is a true test of how customer-centric a business is. Digital transformation networks need to support every touchpoint of the customer experience. They must have AI and ML designed to anticipate customer needs and deliver the goods and services required at the right time, via the Customer’s preferred channel. BMC’s Autonomous Digital Enterprise Framework is a case in point. Source: Cognizant, The 2020 Customer Experience.

Six Areas Where AI Is Improving Customer Experiences

Additional Resources

4 Ways to Use Machine Learning in Marketing Automation, Medium, March 30, 2017

84 percent of B2C marketing organizations are implementing or expanding AI in 2018. Infographic. Amplero.

AI, Machine Learning, and their Application for Growth, Adelyn Zhou. SlideShare/LinkedIn. Feb. 8, 2018.

AI: The Next Generation of Marketing Driving Competitive Advantage throughout the Customer Life Cycle (PDF, 10 pp., no opt-in), Forrester, February 2017.

Artificial Intelligence for Marketers 2018: Finding Value beyond the Hype, eMarketer. (PDF, 20 pp., no opt-in). October 2017

Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)

Artificial Intelligence: The Ultimate Technological Disruption Ascends, Woodside Capital Partners. (PDF, 111 pp., no opt-in). January 2017.

AWS Announces Amazon Machine Learning Solutions Lab, Marketing Technology Insights

B2B Predictive Marketing Analytics Platforms: A Marketer’s Guide, (PDF, 36 pp., no opt-in) Marketing Land Research Report.

Campbell, C., Sands, S., Ferraro, C., Tsao, H. Y. J., & Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business Horizons, 63(2), 227-243.

David Simchi-Levi

Earley, S. (2017). The Problem of Personalization: AI-Driven Analytics at Scale. IT Professional, 19(6), 74-80.

Four Use Cases of Machine Learning in Marketing, June 28, 2018, Martech Advisor,

Gacanin, H., & Wagner, M. (2019). Artificial intelligence paradigm for customer experience management in next-generation networks: Challenges and perspectives. IEEE Network, 33(2), 188-194.

Hildebrand, C., & Bergner, A. (2019). AI-Driven Sales Automation: Using Chatbots to Boost Sales. NIM Marketing Intelligence Review11(2), 36-41.

How Machine Learning Helps Sales Success (PDF, 12 pp., no opt-in) Cognizant

Inside Salesforce Einstein Artificial Intelligence A Look at Salesforce Einstein Capabilities, Use Cases and Challenges, Doug Henschen, Constellation Research, February 15, 2017

Kaczmarek, J., & Ryżko, D. (2009). Quantifying and optimising user experience: Adapting AI methodologies for Customer Experience Management.

KPMG, Customer first. Customer obsessed. Global Customer Experience Excellence report, 2019 (92 pp., PDF)

Machine Learning for Marketers (PDF, 91 pp., no opt-in) iPullRank

Machine Learning Marketing – Expert Consensus of 51 Executives and Startups, TechEmergence.

Marketing & Sales Big Data, Analytics, and the Future of Marketing & Sales, (PDF, 60 pp., no opt-in), McKinsey & Company.

OpenText, AI in customer experience improves loyalty and retention (11 pp., PDF)

Sizing the prize – What’s the real value of AI for your business and how can you capitalize? (PDF, 32 pp., no opt-in) Pw

The New Frontier of Price Optimization, MIT Technology Review. September 07, 2017.

The Power Of Customer Context, Forrester (PDF, 20 pp., no opt-in) Carlton A. Doty, April 14, 2014. Provided courtesy of Pegasystems.

Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting

Using machine learning for insurance pricing optimization, Google Cloud Big Data and Machine Learning Blog,

What Marketers Can Expect from AI in 2018, Jacob Shama. Mintigo. January 16, 2018.

Remote Recruiting In A Post COVID-19 World

Remote Recruiting In A Post COVID-19 World

Bottom Line: Virtual career fairs and events, fully-remote recruiting, more personalized career paths, and greater insights into candidate experiences are quickly becoming the new normal in a post-COVID-19 world.

The COVID-19 pandemic is quickly changing how every organization is attracting, recruiting, and retaining employees on their virtual teams, making remote work the new normal. Recruiting systems, Applicant Tracking Systems (ATS), and talent management systems were designed for one-on-one personal interactions, not virtual ones. Legacy Human Resource Management (HRM) systems are already showing signs of not being able to scale and meet the challenges of the brave, new post-COVID-19 world. The majority of legacy systems are built for transaction scale and can’t see candidate potential. Closing the gaps between legacy talent management systems and new virtual event recruiting and AI-based talent management platforms are changing that by putting candidate potential at the center of their architectures.

Closing The Virtual Event Recruiting Gap Needs To Happen First

Many organizations during this time of year prioritize recruiting the best and brightest college seniors they can attract during in-person interviews on campus. That’s no longer an option today. College recruiters are resorting to individual Skype or Zoom sessions with candidates while attempting to keep track of interviews the best they can with Excel, Google Sheets, and e-mail. Recruiters trying to recruit for mid-level and senior positions are under increasing pressure from hiring managers to arrange interviews with the highest quality candidates possible.

Seeing an opportunity to help organizations find, engage, and recruit using online events, Eightfold.ai has created and launched Virtual Event Recruiting. Eightfold.ai is best-known for its Talent Intelligence Platform™, the first AI-powered solution and most effective way for companies to identify promising candidates, reach diversity hiring goals, retain top performers, and engage talent. Eightfold.ai’s recent webinar How To Hold Virtual Recruiting Events is worth checking out if you’re interested in how Virtual Event Recruiting is evolving..

What Does Success Look Like In Virtual Event Recruiting?

The table stakes for any Virtual Event Recruiting solution need include support for students just starting their careers, veterans, return-to-work mothers, and experienced professionals. For a solution to be effective, it also needs to enable companies and job seekers to connect, giving companies greater scale than is possible for physical recruiting events. Ideally, any virtual event recruiting system needs to provide the following:

  • The ability to upload resume books and use AI to find the highest quality matches for open positions in real-time. Machine learning algorithms excel at pattern matching and can save recruiters thousands of hours of drudgery by immediately seeing the highest quality matches for open positions.
  • Provide a planning center that also serves a company’s specific talent community and provide tools to grow it by tailoring events to their specific interests while seeking the best-qualified candidates for open positions. Creating, launching, and tracking recruiting campaigns from the same dashboard that tracks invitations, registrations, and open positions being filled gives recruiters the end-to-end visibility they need to succeed with a virtual event. It’s important to have Assessments included in every virtual event to measure candidates’ experiences and see what’s going well and which areas need to improve. The following is an example of what Eightfold’s planning center looks like:

Remote Recruiting In a Post COVID-19 World

  • Rely on AI to match high-potential candidates with the best possible virtual events to increase opt-in and participation rates. For a virtual event recruiting solution to be effective, high-potential candidates need to be matched with positions they will most excel in. A first step to making this happen is using AI to understand every candidate’s strengths and inviting them to the virtual events that will help them the most in choosing the best position given their potential.

Remote Recruiting In a Post COVID-19 World

  • Guide candidates to the positions that best match their existing capabilities and future potential. Instead of relying on keyword matching from resumes alone, virtual event recruiting applications need to suggest those positions high-potential candidates have the greatest potential to excel at. Using AI to combine all available data on a candidate, so their existing capabilities and future potential are taken into account is key to making more successful hires. Integrating job recommendations with virtual event recruiting is a must-have for any organization looking to add staff in 2020 and beyond.

Remote Recruiting In a Post COVID-19 World

  • After the virtual event, all potential candidates for an open position need to be stacked-ranked so recruiters can prioritize who they contact. By providing personalization at scale to every candidate by providing them recommendations for the positions they are the strongest match for, recruiters will find following-up is easier to accomplish than cold-calling a candidate found on LinkedIn, for example. Stack ranking needs to include members of the existing talent community and organization is cultivating as well. An excellent example of how this could work is shown below:

Remote Recruiting In a Post COVID-19 World

Conclusion

University campuses need to consider partnering with Eightfold.ai to make it easier for their graduating students to find best available careers, perhaps across a much broader range of companies than ever visited any individual campus. And there is no reason this paradigm can’t be applied to other job fairs and recruiting events like Grace Hopper.

Improving event virtual recruiting needs to be the priority recruiters and HR professionals take action on first to stay competitive from a talent management standpoint. Organizations that will win the war for talent in this new remote, distributed workforce era are already looking at how to excel at virtual recruiting. Having a talent intelligence platform that can provide end-to-end visibility and personalization at scale is the future of talent management.

 

How To Reduce The Unemployment Gap With AI

How To Reduce The Unemployment Gap With AI

It’s time for AI startups to step up and use their formidable technology expertise in AI to help get more Americans back to work now.

Bottom Line: A.I.’s ability to predict and recommend job matches will help get more Americans back to work, helping to reduce the 22 million unemployed today.

One in ten Americans is out of work today based latest U.S. Department of Labor data. They’re primarily from the travel and hospitality, food services, and retail trade and manufacturing industries, with many other affected sectors. McKinsey & Company’s recent article, A new AI-powered network, is helping workers displaced by the coronavirus crisis provides context around the scope of challenges involved in closing the unemployment gap. McKinsey, Eightfold A.I., and the FMI – The Food Industry Association combined efforts to create the Talent Exchange, powered by Eightfold.ai in a matter of weeks. McKinsey insights across a broad base of industries to help Eightfold and FMI create the Talent Exchange in record time. “In talking with clients across the U.S., it became very clear that there is a huge labor mismatch, and individuals are being affected very differently—from retailers furloughing tens of thousands of workers to other organizations needing to hire more than 100,000 workers quickly. We’re excited to help bring a scalable offering to the market,” said McKinsey partner Andrew Davis. McKinsey and FMI collaborating with Eightfold speak volumes to how Americans are coming together to combat the COVID-19 fallout as a team.

And with the food & agriculture, transportation, and logistics industries considered essential, critical infrastructure by Cybersecurity and Infrastructure Security Agency (CISA), demand for workers is more urgent than ever. Eightfold’s Talent Exchange launched last weekend and already has more than 600,000 jobs uploaded that employers need to fill and is available in 15 languages. Eightfold is making the Talent Exchange available free of charge through the COVID-19 epidemic. The Talent Exchange is also being extended to other industries and eco-systems, illustrating how the Eightfold A.I. platform can provide transferability of skills across roles and industries.

Getting Americans Back To Work Using A.I.

Earlier this week Eightfold, FMI – The Food Industry Association and Josh Bersin, the noted global research analyst, public speaker, and writer on many aspects of human resources and talent management, hosted the webinar, COVID-19: Helping the food industry on the front lines with A.I. It’s available to watch here and includes a walk-through of the Eightfold Talent Exchange. The following graphic explains how the Talent Exchange addresses the needs of downsizing companies, impacted workers and hiring companies:

How To Reduce The Unemployment Gap With AI

Eightfold’s Talent Exchange Is A Model For How To Use A.I. For Good

Eightfold’s Talent Exchange uses A.I. algorithms to match candidates with available roles, based on each individual’s skills and previous experience.

Current employers who have to furlough or lay off employees can invite employees to participate in the program. Eightfold also designed in a useful feature that enables employers to add lists of impacted employees and send them a link to register for the Exchange. Employers can view their entire impacted workforce in a single dashboard and can filter by role, department, or location to see details about the talent needs from hiring companies and how their impacted employees are getting placed in new roles. The following is the Talent Exchange dashboard  for current employers showing progress in placing employees with furlough and outplacement partners, including the number of offers accepted by each:

How To Reduce The Unemployment Gap With AI

Employees impacted by a furlough or lay-off can create and update profiles free on the Talent Exchange, defining their job preferences, skills, and experience. That’s invaluable data for hiring companies relying on the platform to make offers and fill positions quickly.  How current employers handle furloughs and lay-offs today will be their identity for years to come, a point John Bersin made during the webinar saying “employers who thrive in the future are going to build long-term relationships with employees today.” Employees receive the following when their current employer adds their name to the Eightfold Talent Exchange. The fictional Company Travel Air is used for this example:

Hiring companies see candidate matches generated by the Exchange, so they can contact these prospects or immediately offer them new jobs. Eightfold’s A.I. engineering teams have automated and personalized this contact as well, expediting the process even further. Hiring companies can add onboarding instructions to allow new hires to start as soon as they are ready and have real-time views of their hiring dashboard shown below:

How To Reduce The Unemployment Gap With AI

Conclusion

Combining A.I.’s innate strengths with H.R. and talent management professionals’ expertise and insights is closing the unemployment gap today. Employers furloughing or laying off employees need to look out for them and get their profile data on the Talent Exchange, helping them find new jobs with hiring companies. As was so well-said by Josh Bersin during the webinar this week, “smart employers should think of their hourly workers as talent, not fungible, replaceable bodies.” For hiring companies in a war for proven employees with talent today, that mindset is more important than ever.

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:

 

 

Predicting How AI Will Improve Talent Management In 2020

Predicting How AI Will Improve Talent Management In 2020

47% of U.S.-based enterprises are using AI today for recruitment, leading all countries in the survey. U.S.-based enterprises’’ adoption of AI for recruitment soared in the last year, jumping from 22% in 2018 to 47% this year based on last years’ Harris Interactive Talent Intelligence and Management Report 2018.

  • 73% of U.S. CEOs and CHROs plan to use more AI in the next three years to improve talent management.
  • U.S.-based enterprises’’ adoption of AI for recruitment soared in the last year, jumping from 22% in 2018 to 47% this year.
  • U.S.-based enterprises lead in the use of AI to automate repetitive tasks (44%) and employee retention (42%).

These and many other fascinating insights are from a recent study completed by Harris Interactive in collaboration with Eightfold titled Talent Intelligence And Management Report 2019-2020, which provides insights into how CHROs are adopting AI today and in the future. You can download a copy here. A total of 1,350 CEOs and CHROs from the U.S., France, Germany, and the U.K. responded to the survey. One of the most noteworthy findings is how U.S-based CEOs and CHROs lead the world in prioritizing and taking action on improving their teams and their own AI skills. The more expertise they and their teams have with AI, the more effective they will be achieving operational improvements while taming the bias beast. The following graphic provides insights into how the four nations surveyed vary by their CEOs’ and CHROs’ perception of new technologies having had positive impacts, their plans for using AI in three years, and employee’s concerns about AI:

Predicting How AI Will Improve Talent Management In 2020

Predicting The Future Of AI In Talent Management

Four leading experts who are actively advising clients, implementing, and using AI to solve talent management challenges shared their predictions of how AI will improve talent management in 2020. The panel includes Kelly O. Kay, Partner, Heidrick & Struggles, Jared Lucas, Chief People Officer at MobileIron, Mandy Sebel, Senior Vice President, People at UiPath and David Windley CEO, IQTalent Partners. Mr. Kay leads the Software Practice for Heidrick & Struggles, a leading executive search and consulting firm commented: “As we all know, the talent crisis of 2019 is real and Eightfold’s application of AI on today is the most impactful approach I’ve seen and the outcomes they deliver eliminate unconscious bias, increases transparency and improves matching supply and demand of talent.” The following are their predictions of how AI will improve the following areas of talent management in 2020:

  • “Pertaining to talent attraction & acquisition-as adoption of intelligent automation and AI tools increases hiring managers and recruiters more easily uncover and surface overlooked talent pools,” said Mandy Sebel, Senior Vice President, People at UiPath.
  • “I predict that AI will become a requirement for companies in the screening of candidates due to the pervasive need to find higher-quality candidates at a faster pace,” said Jared Lucas, Chief People Officer at MobileIron.
  • “I believe the use of AI in the talent acquisition space will begin to hit critical mass in 2020. We are still in the early adopter phase, but the use of AI to match potential candidates to job profiles is catching on. Especially the use of AI for rediscovering candidates in ATS systems of larger corporations. Companies like Eightfold, Hiretual, and Atipica are leading the way,” said David Windley CEO, IQTalent Partners.
  • “Fear of job replacement will also subside, and more focus on job/role evolution as teams are experiencing firsthand how respective task elimination allows them to do more meaningful work,” commented Mandy Sebel, Senior Vice President, People at UiPath.
  • AI will provide the insights needed for CHROs to retain and grow their best talent, according to Jared Lucas, Chief People Officer at MobileIron. “I predict that AI will drive better internal mobility and internal candidate identification as companies are better able to mine their internal talent to fill critical roles,” he said.
  • Having gained credibility for executive and senior management recruiting, AI platforms’ use will continue to proliferate in 2020. “Private Equity is beginning to commercialize how AI can help select executives for roles based on competencies and experiences, which is exciting!” said Kelly O. Kay, Partner, Heidrick & Struggles.

Top 25 AI Startups Who Raised The Most Money In 2019

Top 25 AI Startups Who Raised The Most Money In 2019

  • $10.7B was invested in AI startups this year in their seed, early-stage venture, or late-stage venture funding rounds.
  •  Over half, or 57.9% of all AI startup financing rounds where either seed or pre-seed, 21.2% are Series A, 11.8% are Series B, and all others comprise 9% of all funding rounds.
  • The median AI startup funding round generated $4M with the average being $14.6M and the maximum, $319M, obtained by Vacasa.

These and many other fascinating insights are from an analysis of AI startups’ funding rounds in 2019 using Crunchbase Pro research. AI startups who have had seed, early-stage venture or late-stage venture funding since December 31, 2018, and are U.S.-based are included in the analysis which is provided here. Crunchbase Pro found 499 startups meeting the search criteria as of today.

Top 25 AI Startups Who Have Raised The Most Money In 2019

  1. Vacasa – Raised $319M from a Series C round on October 29th, Vacasa is creating and using AI-driven tools to improve their customers’ experiences renting vacation homes around the world. Their AI strategies include improving every aspect of the customer’s lifecycle from pricing through scheduling post-stay cleans. The company manages a growing portfolio of more than 14,000 vacation homes in the U.S, Europe, Central, and South America, and South Africa.
  2. Samsara – Raised $300M from a Series F round on September 10th. Samsara is an IoT platform combining hardware, software, and cloud to bring real-time visibility, analytics, and AI to operations. Samsara’s portfolio of Internet of Things (IoT) solutions combine hardware, software, and cloud to bring real-time visibility, analytics, and AI to operations. Their core strengths include vehicle telematics, driver safety, mobile workflow and compliance, asset tracking, and industrial process controls all in an integrated, open, real-time platform.
  3. TripActions – Raised $250M from a Series D round on June 27th. TripActions is a business travel platform that combines the latest AI-driven personalization with inventory and 24×7 365 live human support to serve employees, finance leaders, and travel managers alike all while empowering organizations to seize travel as a strategic lever for growth.
  4. ThoughtSpot – Raised $248M from a Series E round on August 22nd. ThoughtSpot’s AI-Driven analytics platform enables business analyst to capitalize on the expertise and shared knowledge of experienced data scientists. With ThoughtSpot, business analysts can analyze data or automatically get trusted insights pushed to you with a single click. ThoughtSpot connects with any on-premise, cloud, big data, or desktop data source. Business Intelligence and Analytics teams have used ThoughtSpot to cut reporting backlogs by more than 90% and make more than 3 million decisions and counting.
  5. CloudMinds – Raised $186M from a Series B round on February 23rd. Founded in 2015, CloudMinds’ unique Cloud Robot Service Platform consists of Human Augmented Robotics Intelligence with Extreme Reality (HARIX), a Secure virtual backbone network (VBN over 4G/5G), and Robot Control Unit (RCU). Designed by CloudMinds, XR-1 Robot is the first commercial humanoid service robot powered by our Smart Compliant Actuator (SCA) technology with precise and compliant grasping capability. Their AI Cloud Brain platform (HARIX) is designed to enable robotic intelligence through a secured network over 4G/5G. CloudMinds is focused on several core technologies, including Smart Vision, Smart Voice, Smart Motion and Human Augmentation. The following is an overview of their architecture:

Top 25 AI Startups Who Raised The Most Money In 2019

  1. Icertis – Raised $115M from a Series E round on July 17th. Icertis is an enterprise contract management platform in the cloud that solves contract management problems using AI. Using advanced algorithms, Icertis helps its customers accelerate business cycles by increasing contract velocity, protecting against risk by ensuring regulatory and policy compliance and optimizing the commercial relationships by maximizing revenue and reducing costs. 3M, Airbus, Cognizant, Daimler, Microsoft, and Roche who rely on Icertis to manage 5.7 million contracts in 40+ languages across 90+ countries, are all customers. The following is an overview of the Icertis Contract Management Platform:

Top 25 AI Startups Who Raised The Most Money In 2019

  1. SparkCognition – Raised $100M from a Series C round on October 8th. SparkCognition builds artificial intelligence systems focused on the needs of its customers in the aviation, cybersecurity, defense, Financial Services, manufacturing, maritime, and Utilities industries. SparkCognition offers four main products: DarwinTM, DeepArmor, SparkPredict, and DeepNLPTM. One of their most noteworthy products is DeepArmor, an AI-powered endpoint security solution that has trained on millions of malicious and benign files and provides industry-leading protection against a broad spectrum of threats. With millions of new malware variants showing up each month, DeepArmor uses AI to assess risk levels and thwart malware and break attempts. DeepArmor’s dashboard is shown below:

Top 25 AI Startups Who Raised The Most Money In 2019

  1. Vectra AI – Raised $100M from a Series E round on June 10th. Vectra specializes in network detection and response – from cloud and data center workloads to user and IoT devices. Its Cognito platform accelerates threat detection and investigation using artificial intelligence to collect, store, and enrich network metadata with the right context to detect, hunt and investigate known and unknown threats in real-time.
  2. Globality – Raised $100M from a Series D round on January 22nd. The January round enabled Globality to accelerate its growth through investment in its AI technology, increasing business capacity by hiring additional members of its engineering, product, and client teams, and expanding its Marketing and Sales programs. Through its AI-powered Platform, Globality is automating the procurement of B2B services and improving the RFP process. Globality efficiently matches companies with service providers that meet their specific needs, cutting the sourcing process from months to hours, and delivering savings of 20% or more for companies.
  3. Black Sesame Technologies – Raised $100M from a Series B round on April 12th.  Black Sesame Technologies is an AI digital imaging technology firm provides solutions for image processing and computing images, as well as embedded sensing platforms. The firm specializes in algorithms for smartphones, autonomous driving, and other consumer electronics. Its R & D teams are actively working on core algorithm development, ASIC design, software system, and ADAS engineering applications.
  4. Scale – Raised $100M from a Series C round on August 5th. Scale accelerates the development of AI applications by helping computer vision teams generate high-quality ground truth data. Our advanced LiDAR, video, and image annotation APIs allow self-driving, drone, and robotics teams at companies like Waymo, OpenAI, Lyft, Zoox, Pinterest, and Airbnb focus on building differentiated models vs. labeling data. Scale’s greatest strength is its API for training data, providing access to human-powered data for a multitude of use cases.
  5. AutoX – Raised $100M from a Series A round on September 16th. AutoX is a self-driving car startup that uses AI to fine-tune Location-Based Services with camera-first autonomous driving technology. In July of this year, AutoX announced a partnership with NEVS, the Swedish holding company, and electric vehicle manufacturer that bought Saab’s assets out of bankruptcy, to deploy a robotaxi pilot service in Europe by the end of 2020.
  6. DISCO – Raised $83M from a Series E round on January 24th. DISCO is a legal technology company that applies artificial intelligence and cloud computing to legal problems to help lawyers and legal teams improve legal outcomes for clients. Corporate legal departments, law firms, and government agencies around the world use DISCO as an ediscovery solution for compliance, disputes, and investigations. The company is looking to reinvent legal technology to automate and simplify complex and error-prone tasks that distract from practicing law.
  7. QOMPLX – Raised $78.6M from a Series A round on July 23rd. QOMPLX makes it faster and easier for organizations to integrate disparate internal and external data sources across the enterprise via a unified analytics infrastructure that supports better decision-making using AI at scale. This enterprise data-fabric is called QOMPLX OS: an enterprise operating system that powers QOMPLX’s decision platforms in cybersecurity, insurance, and quantitative finance. The following is an example of how the QOMPLX OS automates data management while providing greater contextual intelligence to data:

Top 25 AI Startups Who Raised The Most Money In 2019

  1. Galileo Financial Technologies – Raised $77M from a Series A round on October 17th. Galileo’s APIs are used widely throughout the neobank, payments, gig economy, investing and SaaS market segments. As of September 2019, Galileo was managing over $26B in annual payments volume, a 130% increase over September 2018. Galileo’s latest round, a $77M investment led by venture capital firm Accel with participation from Qualtrics Co-Founder & CEO Ryan Smith. The company, which is already profitable and growing rapidly, plans to use the funds to accelerate growth, including expansion into Latin America, the UK, and Europe, and for continued product expansion.
  2. BlackThorn Therapeutics – Raised 76M from a Series B round on June 13th. BlackThorn Therapeutics, Inc., is a clinical-stage neurobehavioral health company pioneering the next generation of AI technologies to advance its pipeline of targeted therapeutics for treating brain disorders. The company has engineered PathFinder, a cloud-based computational psychiatry and data platform, to enable the collection, integration, and analysis of multimodal data at great speed and scale. BlackThorn applies its data-driven approaches to create an understanding of the core underlying pathophysiology of neurobehavioral disorders and uses these insights to generate objective neuromarkers, which support drug target identification, patient stratification, and objective clinical trial endpoints.
  3. Highspot – Raised $75M from a Series D round on December 3rd. Highspot is a sales enablement platform that relies on AI technologies to elevate and add value to companies’ conversations with their customers and drive strategic growth. The platform combines intelligent content management, training, contextual guidance, customer engagement, and actionable analytics. Revenue teams use Highspot to deliver a unified buying experience that increases revenue, customer satisfaction and retention. Highspot has attained a 90% average monthly recurring usage rate and has global support across 125 countries. It’s available on the Salesforce AppExchange, Microsoft Store, Google Play and Apple AppStore.
  4. Moveworks – Raised $75M from a Series B round on November 11th. Moveworks is a cloud-based AI platform designed for large enterprises’ IT support and service desk challenges. Instead of just tracking issues, Moveworks uses advanced AI to solve IT support and service problems automatically, often with no human intervention. Customers include AutoDesk, Broadcom, Nutanix and many other Fortune 500 companies. Moveworks is backed by Bain Capital Ventures and Lightspeed Venture Partners and is headquartered in Mountain View, California.
  5. Reonomy – Raised $60M from a Series D round on November 7th. Reonomy is an AI-powered data platform for the commercial real estate industry. The goal of the company’s platform is to leverage big data, partnerships, and machine learning to connect the fragmented world of commercial real estate. Reonomy products enable individuals, teams, and companies to unlock new insights from property intelligence. By constantly aggregating and organizing up-to-the-minute marketplace data, Reonomy offer investors and brokers the opportunity to research nuanced property characteristics that indicate the likelihood of a future sale. Below is an example of an analysis of the San Francisco neighborhood using AI-based filtering technology:

Top 25 AI Startups Who Raised The Most Money In 2019

  1. Clari – Raised $60M from a Series D round on October 10th. Clari is a connected revenue operations platform that uses automation and AI to unlock all the activity data captured in key business systems such as marketing automation, CRM, email, calendar, phone, content management, and conversations. It automatically aligns that data to accounts and opportunities to deliver visibility, forecasting, and apply predictive insights, which results in more insight, less guesswork, and more predictable revenue. Clari helps companies by changing their revenue operations to be more connected, efficient, and predictable. Clari’s platform is used by hundreds of sales, marketing, and customer success teams at B2B companies such as Qualtrics, Lenovo, Adobe, Dropbox, and Okta to control pipeline, audit deals and accounts, forecast the business, and reduce churn. The following is an example of a Clari dashboard:

Top 25 AI Startups Who Raised The Most Money In 2019

  1. People.ai – Raised $60M from a Series C round on May 21st. People.ai is an artificial intelligence (AI) platform for enterprise revenue. People.ai helps sales, marketing, and customer success teams uncover every revenue opportunity from every customer by capturing all customer contacts, activity, and engagement to drive actionable insights across all revenue teams. People.ai enables sales leaders to be more effective at managing their teams and growing revenue by giving them a complete picture of sales activities and leveraging AI to deliver sales performance analytics, personalized coaching, one-on-one feedback, and pipeline reviews. The People.ai platform identifies and targets the buying group, and gives marketers a clear visualization of whom sales have spoken with, and which campaign has been successful in each opportunity. Using this information, marketers are able to build personas and deal models in order to better target their marketing efforts and get better campaign ROI. Customer success and services teams use People.ai to ensure they are engaging with the right people when the customer is handed off to them, but more importantly, these post-sales teams are constantly looking to align their effort and activities with the right opportunities and customers, tracking the true cost to support each customer. The following graphic illustrates the People.ai platform automatically capture all contact and customer activity data, dynamically update your CRM, and provide actionable intelligence to realize the full potential of customer-facing teams. The following graphic illustrates the People.ai platform:

Top 25 AI Startups Who Raised The Most Money In 2019

 

  1. Invoca – Raised $56M from a Series C round on October 17th. Invoca is an AI-powered call tracking and analytics platform that helps marketers drive inbound calls and turn them into sales. The platform delivers real-time call analytics to help marketers take informed actions based on data generated before and during a phone conversation. It also allows marketers to understand, in real-time, the factors affecting consumers’ intent to buy, like competitive promotional campaigns. Marketers can put the data to work directly in the platform by automating customer experience workflows during, before, and after each call. Invoca’s platform integrates with Google Marketing Platform, Facebook, Adobe Experience Cloud, and Salesforce Sales and Marketing Clouds. Invoca’s investors include Accel Partners, H.I.G. Growth Partners, Upfront Ventures, Morgan Stanley Alternative Investment Partners, Salesforce Ventures, and Rincon Venture Partners. The following is an example of an Invoca dashboard used for measuring Google AdWords effectiveness:

Top 25 AI Startups Who Raised The Most Money In 2019

  1. Clinc – Raised $52M from a Series B round on May 20th.  Clinc is a conversational AI platform that enables enterprises to build “human-in-the-room” level, next-gen, virtual assistants. In contrast to a speech-to-text word matching algorithm, Clinc analyzes dozens of factors from the user’s input including wording, sentiment, intent, tone of voice, time of day, location, and relationships, and uses those factors to deliver an answer that represents a composite of knowledge extracted from its trained brain. Clinc’s underlying technology is based on state-of-the-art machine learning and deep neural networks (DNN)-as-a-service developed by computer science professors at the University of Michigan. Clinc is a standalone “trained brain” that has been given an initial deep knowledge of the financial and banking industry. Its machine learning capabilities enable it to expand its knowledge with every query and to then draw from that knowledge for each subsequent customer query.
  2. Biz2Credit – Raised $52M from a Series D round on June 4th. Biz2Credit is a hub connecting small business owners with lenders and service providers, and seek solutions based on their online profiles. Biz2X uses a streamlined user interface, AI-driven analytics, and a customizable white label environment to help banks enhance their core services such as offering focused customer service, growing their portfolio, and increasing the use of their products. With enhanced loan management, servicing, risk analytics and a configurable customer journey, Biz2X is helping banks like these run their lending operations at scale.
  3. Uniphore – Raised $51M from a Series C round on August 13th. Uniphore is a global Conversational AI technology company that offers a customer service platform that is powered by AI and automation technologies. The Company’s vision is to bridge the gap between people and machines through voice. Uniphore enables businesses globally to deliver transformational customer service by providing a platform of Conversational Analytics, Conversational Assistant, and Conversational Security that changes the way enterprises engage their consumers, build loyalty and realize efficiencies.

 

AI Skills Among The Most In-Demand For 2020

AI Skills Among The Most In-Demand For 2020

Python, React (web), Angular, machine learning, and Docker will be the five most popular tech skills in 2020.

  • TensorFlow is the most popular tech skill of the last three years, exponentially increasing between 2016 and 2019 based on Udemy’s
  • Udemy sees robust demand for AI and data science skills, in addition to web development frameworks, cloud computing, and IT certifications, including AWS, CompTIA & Docker.
  • SAP expertise is projected to be the fastest-growing process-related skill set in 2020.

These and many other fascinating insights are from Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future (48 pp., PDF, opt-in).  The report provides compelling evidence of how important it is to prepare workforces for the future of work in an AI-enabled world. Udemy predicts 2020 will be the year AI goes mainstream. The report states that “In the world of finance, investment funds managed by AI and computers account for 35% of America’s stock market today,” citing a recent article in The Economist, The rise of the financial machines. The following are the key insights from the report:

  • Python, React (web), Angular, machine learning, and Docker will be the five most popular tech skills in 2020. TensorFlow, OpenCV, and neural networks are the foundational skills many data scientists are pursuing and perfecting today to advance their AI-based career strategies. Mastering those three skills is essential for understanding and developing AI apps and platforms. TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks. The following is a comparison of the top 10 most popular tech skills in 2020 and the top 10 tech skills that grew in popularity between 2016 and 2019.
AI Skills among the Most In-Demand For 2020

Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future

  • The top 10 emerging tech skills in 2020 will be web development, quantum computing, and Internet of Things IoT). Udemy analyzed the emerging skills that over 40M people are learning on Udemy today, and found that Gatsby.js, a new web development framework tool, is gaining rapid adoption. Additional web development tools include React Hooks, Next.js, and SwiftUI, a user interface tool for Apple apps. Entirely new skills, including quantum computing and ESP32, used in the IoT development, are also among the top 1 emerging tech skills of 2020.
AI Skills among the Most In-Demand For 2020

Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future

  • SAP enterprise software expertise, knowledge of the ISO/IEC 27001 standard, information security, and Microsoft Dynamics 365 are projected to be the four of the fastest-growing process and tools skills in 2020. Udemy also found a strong interest in Robotic Process Automation (RPA) and Business Process Management (BPM). Robotic Process Automation (RPA) refers to the use of process automation tools to quickly replicate how human beings perform routine daily office work using popular productivity apps, including Microsoft Excel, databases, or web applications.
AI Skills among the Most In-Demand For 2020

Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future

 

  • Chef Software expertise, network security, penetration testing, Linux security, and AWS Certified Cloud are predicted among the fastest-growing skills for IT professionals in 2020. Chef software is prevalent in IT organizations and is used for streamlining the task of configuring & maintaining a company’s servers. Chef has invested in integrating with many of the most popular cloud-based platforms, including Rackspace, Microsoft Azure, and Amazon Elastic Compute Cloud, to automatically provision and configure new machines.
AI Skills among the Most In-Demand For 2020

Udemy for Business’ 2020 Workplace Learning Trends Report: The Skills of the Future

 

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