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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.

 

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

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

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

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

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

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

Sources:

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

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

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

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

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

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

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

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

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

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

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

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

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

What Matters Most In Business Intelligence, 2019

  • Improving revenues using BI is now the most popular objective enterprises are pursuing in 2019.
  • Reporting, dashboards, data integration, advanced visualization, and end-user self-service are the most strategic BI initiatives underway in enterprises today.
  • Operations, Executive Management, Finance, and Sales are primarily driving Business Intelligence (BI) adoption throughout enterprises today.
  • Tech companies’ Operations & Sales teams are the most effective at driving BI adoption across industries surveyed, with Advertising driving BI adoption across Marketing.

These and many other fascinating insights are from Dresner Advisory Associates’ 10th edition of its popular Wisdom of Crowds® Business Intelligence Market Study. The study is noteworthy in that it provides insights into how enterprises are expanding their adoption of Business Intelligence (BI) from centralized strategies to tactical ones that seek to improve daily operations. The Dresner research teams’ broad assessment of the BI market makes this report unique, including their use visualizations that provide a strategic view of market trends. The study is based on interviews with respondents from the firms’ research community of over 5,000 organizations as well as vendors’ customers and qualified crowdsourced respondents recruited over social media. Please see pages 13 – 16 for the methodology.

Key insights from the study include the following:

  • Operations, Executive Management, Finance, and Sales are primarily driving Business Intelligence (BI) adoption throughout their enterprises today. More than half of the enterprises surveyed see these four departments as the primary initiators or drivers of BI initiatives. Over the last seven years, Operations departments have most increased their influence over BI adoption, more than any other department included in the current and previous survey. Marketing and Strategic Planning are also the most likely to be sponsoring BI pilots and looking for new ways to introduce BI applications and platforms into use daily.

  • Tech companies’ Operations & Sales teams are the most effective at driving BI adoption across industries surveyed, with Advertising driving BI adoption across Marketing. Retail/Wholesale and Tech companies’ sales leadership is primarily driving BI adoption in their respective industries. It’s not surprising to see the leading influencer among Healthcare respondents is resource-intensive HR. The study found that Executive Management is most likely to drive business intelligence in consulting practices most often.

  • Reporting, dashboards, data integration, advanced visualization, and end-user self-service are the most strategic BI initiatives underway in enterprises today. Second-tier initiatives include data discovery, data warehousing, data discovery, data mining/advanced algorithms, and data storytelling. Comparing the last four years of survey data, Dresner’s research team found reporting retains all-time high scores as the top priority, and data storytelling, governance, and data catalog hold momentum. Please click on the graphic to expand for easier reading.

  • BI software providers most commonly rely on executive-level personas to design their applications and add new features. Dresner’s research team found all vertical industries except Business Services target business executives first in their product design and messaging. Given the customer-centric nature of advertising and consulting services business models, it is understandable why the primary focus BI vendors rely on in selling to them are customer personas. The following graphic compares targeted users for BI by industry.

  • Improving revenues using BI is now the most popular objective in 2019, despite BI initially being positioned as a solution for compliance and risk management. Executive Management, Marketing/Sales, and Operations are driving the focus on improving revenues this year. Nearly 50% of enterprises now expect BI to deliver better decision making, making the areas of reporting, and dashboards must-have features. Interestingly, enterprises aren’t looking to BI as much for improving operational efficiencies and cost reductions or competitive advantages. Over the last 12 to 18 months, more tech manufacturing companies have initiated new business models that require their operations teams to support a shift from products to services revenues. An example of this shift is the introduction of smart, connected products that provide real-time data that serves as the foundation for future services strategies. Please click on the graphic to expand for easier reading.

  • In aggregate, BI is achieving its highest levels of adoption in R&D, Executive Management, and Operations departments today. The growing complexity of products and business models in tech companies, increasing reliance on analytics and BI in retail/wholesale to streamline supply chains and improve buying experiences are contributing factors to the increasing levels of BI adoption in these three departments. The following graphic compares BI’s level of adoption by function today.

  • Enterprises with the largest BI budgets this year are investing more heavily into dashboards, reporting, and data integration. Conversely, those with smaller budgets are placing a higher priority on open source-based big data projects, end-user data preparation, collaborative support for group-based decision-making, and enterprise planning. The following graphic provides insights into technologies and initiatives strategic to BI at an enterprise level by budget plans.

  • Marketing/Sales and Operations are using the greatest variety of BI tools today. The survey shows how conversant Operations professionals are with the BI tools in use throughout their departments. Every one of them knows how many and most likely which types of BI tools are deployed in their departments. Across all industries, Research & Development (R&D), Business Intelligence Competency Center (BICC), and IT respondents are most likely to report they have multiple tools in use.

Tech Leaders Look To IoT, AI & Robotics To Fuel Growth Through 2021

  • 30% of tech leaders globally predict blockchain will disrupt their businesses by 2021.
  • IoT, Artificial Intelligence (AI) and Robotics have the greatest potential to digitally transform businesses, making them more customer-centered and efficient.
  • 26% of global tech leaders say e-Commerce apps and platforms will be the most disruptive new business model in their countries by 2021.
  • IDC predicts worldwide IoT spending will reach $1.1T by 2021.

These and many other insights are from KPMG’s recent research study Tech Disruptors Outpace The Competition. The study can be downloaded here (PDF, 42 pp., no opt-in.).  The methodology is based on interviews with 750 global technology industry leaders, 85% of whom are C-level executives. For additional details on the methodology, please see pages 32 and 33 of the study. The study found that the three main benefits of adopting IoT, AI, and robotics include improved management of personal information, increased personal productivity, and improved customer experience through personalized real-time information. Key insights gained from the study include the following:

  • IoT, Artificial Intelligence (AI) and Robotics have the greatest potential to digitally transform businesses, making them more customer-centered and efficient. Tech leaders also see these three core technologies enabling the next indispensable consumer technology and driving the greatest benefit to life, society, and the environment. KPMG’s research team found that tech companies are integrating these three technologies to create growth platforms for new business ventures while digitally transforming existing business processes. Tech leaders in the U.K. (21%), Japan (20%) and the U.S. (16%) lead all other nations in their plans for IoT digitally transforming their businesses by 2021. Please click on the graphic below to expand for easier reading.

  • 30% of tech leaders globally predict blockchain will disrupt their businesses by 2021. 50% of Japanese tech leaders predict that blockchain will digitally transform their industries and companies by 2021, leading all nations included in the survey.  IoT processes and the rich, real-time data stream sensors and systems are capable of delivering is predicted by tech leaders to be the primary catalyst that will enable blockchain to digitally transform their businesses. 27% of tech leaders globally expect IoT data and applications combined with blockchain to redefine their companies, supply chains and industries. Identity authentication (24%), automated trading (22%) and contracts (14%) are the 2nd through fourth-most disruptive aspects of blockchain by 2021 according to tech leaders. Please click on the graphic below to expand for easier reading.

  • 26% of global tech leaders say e-Commerce apps and platforms will be the most disruptive new business model in their countries by 2021. 19% see social media platforms creating the majority of new business models, followed autonomous vehicle platforms (14%) and entertainment platforms (11%).  KPMG’s analysis includes a ranking of top business models by country, with e-Commerce dominating four of the five regions included in the survey.

  • 50% of tech leaders expect media, transportation, healthcare, and transportation to experience the greatest digital transformation in the next three years.  Respondents most mentioned Amazon, Netflix, Alibaba, Uber, Google, and Facebook as examples of companies who will digitally transform their industries by 2021.  The following table provides insights into which industries by country will see the greatest digital transformations in the next three years. Entertainment platforms are predicted by tech leaders to have the greatest potential to digitally transform the media industry in the U.S. by 2021.

  • Tech leaders predict IoT’s greatest potential for adoption by 2021 is in consumer products, education, services, industrial manufacturing, and telecom. AI’s greatest potential to digitally transform business models is in healthcare and industrial manufacturing (both 11%), consumer products, financial, and services (10% each).  As would be expected, Robotics’ adoption and contribution to digitally transforming businesses will be most dominant in industrial manufacturing (15%), followed by healthcare (11%) and consumer, financial and services (10%). Please click on the graphic to expand for easier reading.

How To Close The Talent Gap With Machine Learning

  • 80% of the positions open in the U.S. alone were due to attrition. On an average, it costs $5,000 to fill an open position and takes on average of 2 months to find a new employee. Reducing attrition removes a major impediment to any company’s productivity.
  • The average employee’s tenure at a cloud-based enterprise software company is 19 months; in the Silicon Valley this trends to 14 months due to intense competition for talent according to C-level executives.
  • Eightfold.ai can quantify hiring bias and has found it occurs 35% of the time within in-person interviews and 10% during online or virtual interview sessions.
  • Adroll Group launched nurture campaigns leveraging the insights gained using Eightfold.ai for a data scientist open position and attained a 48% open rate, nearly double what they observed from other channels.
  • A leading cloud services provider has seen response rates to recruiting campaigns soar from 20% to 50% using AI-based candidate targeting in the company’s community.

The essence of every company’s revenue growth plan is based on how well they attract, nurture, hire, grow and challenge the best employees they can find. Often relying on manual techniques and systems decades old, companies are struggling to find the right employees to help them grow. Anyone who has hired and managed people can appreciate the upside potential of talent management today.

How AI and Machine Learning Are Revolutionizing Talent Management

Strip away the hype swirling around AI in talent management and what’s left is the urgent, unmet needs companies have for greater contextual intelligence and knowledge about every phase of talent management. Many CEOs are also making greater diversity and inclusion their highest priority. Using advanced AI and machine learning techniques, a company founded by former Google and Facebook AI Scientists is showing potential in meeting these challenges. Founders Ashutosh Garg and Varun Kacholia have over 6000+ research citations and 80+ search and personalization patents. Together they founded Eightfold.ai as Varun says “to help companies find and match the right person to the right role at the right time and, for the first time, personalize the recommendations at scale.” Varun added that “historically, companies have not been able to recognize people’s core capabilities and have unnecessarily exacerbated the talent crisis,” said Varun Kacholia, CTO, and Co-Founder of Eightfold.ai.

What makes Eightfold.ai noteworthy is that it’s the first AI-based Talent Intelligence Platform that combines analysis of publicly available data, internal data repositories, Human Capital Resource Management (HRM) systems, ATS tools and spreadsheets then creates ontologies based on organization-specific success criteria. Each ontology, or area of talent management interest, is customizable for further queries using the app’s easily understood and navigated user interface.

Based on conversations with customers, its clear integration is one of the company’s core strengths. Eightfold.ai relies on an API-based integration strategy to connect with legacy back-end systems. The company averages between 2 to 3 system integrations per customer and supports 20 unique system integrations today with more planned. The following diagram explains how the Eightfold Talent Intelligence Platform is constructed and how it works.

For all the sophisticated analysis, algorithms, system integration connections, and mathematics powering the Eightfold.ai platform, the company’s founders have done an amazing job creating a simple, easily understood user interface. The elegant simplicity of the Eightfold.ai interface reflects the same precision of the AI and machine learning code powering this platform.

I had a chance to speak with Adroll Group and DigitalOcean regarding their experiences using Eightfold.ai. Both said being able to connect the dots between their candidate communities, diversity and inclusion goals, and end-to-end talent management objectives were important goals that the streamlined user experience was helping enable. The following is a drill-down of a candidate profile, showing the depth of external and internal data integration that provides contextual intelligence throughout the Eightfold.ai platform.

Talent Management’s Inflection Point Has Arrived 

Every interaction with a candidate, current associate, and high-potential employee is a learning event for the system.

AI and machine learning make it possible to shift focus away from being transactional and more on building relationships. AdRoll Group and DigitalOcean both mentioned how Eightfold.ai’s advanced analytics and machine learning helps them create and fine-tune nurturing campaigns to keep candidates in high-demand fields aware of opportunities in their companies. AdRoll Group used this technique of concentrating on insights to build relationships with potential Data Scientists and ultimately made a hire assisted by the Eightold.ai platform. DigitalOcean is also active using nurturing campaigns to recruit for their most in-demand positions. “As DigitalOcean continues to experience rapid growth, it’s critical we move fast to secure top talent, while taking time to nurture the phenomenal candidates already in our community,” said Olivia Melman, Manager, Recruiting Operations at DigitalOcean. “Eightfold.ai’s platform helps us improve operational efficiencies so we can quickly engage with high quality candidates and match past applicants to new openings.”

In companies of all sizes, talent management reaches its full potential when accountability and collaboration are aligned to a common set of goals. Business strategies and new business models are created and the specific amount of hires by month and quarter are set. Accountability for results is shared between business and talent management organizations, as is the case at AdRoll Group and DigitalOcean, both of which are making solid contributions to the growth of their businesses. When accountability and collaboration are not aligned, there are unpredictable, less than optimal results.

AI makes it possible to scale personalized responses to specific candidates in a company’s candidate community while defining the ideal candidate for each open position. The company’s founders call this aspect of their platform personalization at scale. “Our platform takes a holistic approach to talent management by meaningfully connecting the dots between the individual and the business. At Eightfold.ai, we are going far beyond keyword and Boolean searches to help companies and employees alike make more fulfilling decisions about ‘what’s next, “ commented Ashutosh Garg, CEO, and Co-Founder of Eightfold.ai.

Every hiring manager knows what excellence looks like in the positions they’re hiring for. Recruiters gather hundreds of resumes and use their best judgment to find close matches to hiring manager needs. Using AI and machine learning, talent management teams save hundreds of hours screening resumes manually and calibrate job requirements to the available candidates in a company’s candidate community. This graphic below shows how the Talent Intelligence Platform (TIP) helps companies calibrate job descriptions. During my test drive, I found that it’s as straightforward as pointing to the profile of ideal candidate and asking TIP to find similar candidates.

Achieving Greater Equality With A Data-Driven Approach To Diversity

Eightfold.ai can quantify hiring bias and has found it occurs 35% of the time within in-person interviews and 10% during online or virtual interview sessions. They’ve also analyzed hiring data and found that women are 11% less like to make it through application reviews, 19% less likely through recruiter screens, 12% through assessments and a shocking 30% from onsite interviews. Conscious and unconscious biases of recruiters and hiring managers often play a more dominant role than a woman’s qualifications in many hiring situations. For the organizations who are enthusiastically endorsing diversity programs yet struggling to make progress, AI and machine learning are helping to accelerate them to the goals they want to accomplish.

AI and machine learning can’t make an impact in this area quickly enough. Imagine the lost brainpower from not having a way to evaluate candidates based on their innate skills and potential to excel in the role and the need for far greater inclusion across the communities companies operate in. AdRoll Group’s CEO is addressing this directly and has made attaining greater diversity and inclusion a top company objective for the year. Daniel Doody, Global Head of Talent at AdRoll Group says “We’re very deliberate in our efforts to uncover and nurture more diverse talent while also identifying individuals who have engaged with our talent brand to include them” he said. Daniel Doody continued, “Eightfold.ai has helped us gain greater precision in our nurturing campaigns designed to bring more diverse talent to Adroll Group globally.”

Kelly O. Kay, Managing Partner, Global Managing Partner, Software & Internet Practice at Heidrick & Struggles agrees. “Eightfold.ai levels the playing field for diversity hiring by using pattern matching based on human behavior, which is fascinating,” Mr. Kay said. He added, “I’m 100% supportive of using AI and machine learning to provide everyone equal footing in pursuing and attaining their career goals.” He added that the Eightfold.ai’s greatest strength is how brilliantly it takes on the challenge of removing unconscious bias from hiring decisions, further ensuring greater diversity in hiring, retention and growth decisions.

Eightfold.ai has a unique approach to presenting potential candidates to recruiters and hiring managers. They can remove any gender-specific identification of a candidate and have them evaluated purely on expertise, experiences, merit, and skills. And the platform also can create gender-neutral job descriptions in seconds too. With these advances in AI and machine learning, long-held biases of tech companies who only want to hire from Cal-Berkeley, Stanford or MIT are being challenged when they see the quality of candidates from just as prestigious Indian, Asian, and European universities as well. Daniel Doody of Adroll Group says the insights gained from the Eightfold.ai platform “are helping to make managers and recruiters more aware of their own hiring biases while at the same time assisting in nurturing potential candidates via less obvious channels.”

How To Close The Talent Gap

Based on conversations with customers, it’s apparent that Eightfold.ai’s Talent Intelligence Platform (TIP) provides enterprises the ability to accelerate time to hire, reduce the cost to hire and increase the quality of hire. Eightfold.ai customers are also seeing how TIP enables their companies to reduce employee attrition, saving on hiring and training costs and minimizing the impact of lost productivity. Today more CEOs and CFOs than ever are making diversity and talent initiatives their highest priority. Based on conversations with Eightfold.ai customers it’s clear their TIP provides the needed insights for C-level executives to reach their goals.

Another aspect of the TIP that customers are just beginning to explore is how to identify employees who are the most likely to leave, and take proactive steps to align their jobs with their aspirations, extending the most valuable employees’ tenure at their companies. At the same time, customers already see good results from using TIP to identify top talent that fits open positions who are likely to join them and put campaigns in place to recruit and hire them before they begin an active job search. Every Eightfold.ai customer spoken with attested to the platform’s ability to help them in their strategic imperatives around talent.

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