73% of enterprises (over 500 employees) accelerated their cloud migration plans to support the shift to remote working across their organizations due to the pandemic.
81% of enterprises accelerated their IT modernization processes due to the pandemic.
48% of all companies surveyed have accelerated their cloud migration plans, 49% have sped up their IT modernization plans because of Covid-19.
32% of large-scale enterprises, over 500 employees, are implementing more automation using artificial intelligence-based tools this year.
These and many other insights are from a recent survey of IT leaders completed by CensusWide and sponsored by Centrify. The survey’s objectives on understanding how the dynamics of IT investments, operations and spending have shifted over the last six months. The study finds that the larger the enterprise, the more important it is to secure remote access to critical infrastructure to IT admin teams. Remote access and updating privacy policies and notices are two of the highest priorities for mid-size organizations to enterprises today. The methodology is based on interviews with 215 IT leaders located in the U.S.
Key insights from the survey include the following:
The overwhelming majority of enterprises have transformed their cybersecurity approach over the last six months, with 83% of large-scale enterprises leading all organizations. It’s encouraging to see small and medium-sized businesses adjusting and improving their approach to cybersecurity. Reflecting how digitally-driven many small and medium businesses are, cybersecurity adjustments begin in organizations with 10 to 49 employees. 60% adjusted their cloud security postures as a result of distributed workforces.
48% of all organizations had to accelerate cloud migration due to the pandemic, with larger enterprises leading the way. Enterprises with over 500 employees are the most likely to accelerate cloud migration plans due to the pandemic. 73.5% of enterprises with more than 500 employees accelerated cloud migration plans to support their employees’ remote working arrangements, leading all organization categories. This finding reflects how cloud-first the largest enterprises have become this year. It’s also consistent with many other surveys completed in 2020, reflecting how much the cloud has solidly won the enterprise.
49% of all organizations and 81% of large-scale enterprises had to accelerate their IT modernization process due to the pandemic. For the largest enterprises, IT modernization equates to digitizing more processes using cloud-native services (59%), maintaining flexibility and security for a partially remote workforce (57%) and revisiting and adjusting their cybersecurity stacks (40%).
51% of enterprises with 500 employees or more are making remote, secure access their highest internal priority. In contrast, 27% of all organizations’ IT leaders say that providing secure, granular access to IT admin teams, outsourced IT and third-party vendors is a leading priority. The larger the enterprise, the more important remote access becomes. The survey also found organizations with 250 – 500 employees are most likely to purchase specific cybersecurity tools and applications to meet compliance requirements.
Conclusion & Wrap-Up
IT leaders are quickly using the lessons learned from the pandemic as a crucible to strengthen cloud transformation and IT modernization strategies. One of every three IT leaders interviewed, 34%, say their budgets have increased during the pandemic. In large-scale enterprises with over 500 employees, 59% of IT leaders have seen their budgets increase.
All organizations are also keeping their IT staff in place. 63% saw little to no impact on their teams, indicating that the majority of organizations will have both the budget and resources to maintain or grow their cybersecurity programs. 25% of IT leaders indicated that their company plans to keep their entire workforce 100% remote.
It’s encouraging to see IT leaders getting the support they need to achieve their cloud transformation and IT modernization initiatives going into next year. With every size of organization spending on cybersecurity tools, protecting cloud infrastructures needs to be a priority. Controlling administrative access risk in the cloud and DevOps is an excellent place to start with a comprehensive, modern Privileged Access Management solution. Leaders in this field, including Centrify, whose cloud-native architecture and flexible deployment and management options, deliver deep expertise in securing cloud environments.
Instead of relying on data-driven signals of past accomplishments, Eightfold.ai is using AI to discover the innate capabilities of people and matching them to new opportunities in their own companies.
Bottom Line: Eightfold.ai’s innovative approach of combining their own AI and virtual hackathons to create and launch new additions to their Project Marketplace rapidly is a model enterprises need to consider emulating.
Eightfold.ai was founded with the mission that there is a right career for everyone in the world. Since its founding in 2016, Eightfold.ai’s Talent Intelligence Platform continues to see rapid global growth, attracting customers across four continents and 25 countries, supporting 15 languages with users in 110 countries. Their Talent Intelligence Platform is built to assist enterprises with Talent Acquisition and Management holistically.
What’s noteworthy about Eightfold.ai’s approach is how they have successfully created a platform that aggregates all available data on people across an enterprise – from applicants to alumni – to create a comprehensive Talent Network. Instead of relying on data-driven signals of past accomplishments, Eightfold.ai is using AI to discover the innate capabilities of people and matching them to new opportunities in their own companies. Eightfold’s AI and machine learning algorithms are continuously learning from enterprise and individual performance to better predict role, performance and career options for employees based on capabilities.
How Eightfold Sets A Quick Pace Innovating Their Marketplace
Recently Eightfold.ai announced Project Marketplace, an AI-based solution for enterprises that align employees seeking new opportunities and companies’ need to reskill and upskill their employees with capabilities that line up well with new business imperatives. Eightfold wanted to provide employees with opportunities to gain new skills through experiential learning, network with their colleagues, join project teams and also attain the satisfaction of helping flatten the unemployment curve outside. Project Marketplace helps employers find hidden talent, improve retention strategies and gain new knowledge of who has specific capabilities and skills. The following is a screen from the Marketplace that provides employees the flexibility of browsing all projects their unique capabilities qualify them for:
Employees select a project of interest and are immediately shown how strong of a match they are with the open position. Eightfold provides insights into relevant skills that an employee already has, why they are a strong match and the rest of the project team members – often a carrot in itself. Keeping focused on expanding employee’s capabilities, Eightfold also provides guidance of which skills an employee will learn. The following is an example of what an open project positions looks like:
Employee applicants can also view all the projects they currently have open from the My Projects view shown below:
Project Marketplace is the win/win every employee has yearned for as they start to feel less challenged in their current position and start looking for a new one, often outside their companies. I recently spoke with Ashutosh Garg, CEO and Co-Founder and Kamal Ahluwalia, Eightfold’s President, to see how they successfully ran a virtual hackathon across three continents to keep the Marketplace platform fresh with new features and responsive to the market.
How to Run A Virtual Hackathon
Starting with the hackathon, Eightfold relied on its own Talent Intelligence Platform to define the teams across all three continents, based on their employees’ combined mix of capabilities. Ashutosh, Kamal and the senior management team defined three goals of the hackathon:
Solve problems customers are asking about with solutions that are not on the roadmap yet.
Accelerate time to value for customers with new approaches no one has thought of before.
Find new features and unique strengths that further strengthen the company’s mission of finding the right career for everyone in the world.
It’s fascinating to see how AI, cybersecurity and revenue management software companies continue to innovate at a fast pace delivering complex apps with everyone being remote. I asked Ashutosh how he and his management team approached the challenge of having a hackathon spanning three continents deliver results. Here’s what I learned from our discussion and these lessons are directly applicable to any virtual hackathon today:
Define the hackathon’s purpose clearly and link it to the company mission, explaining what’s at stake for customers, employees and the millions of people looking for work today – all served by the Talent Intelligence Platform broadening its base of features.
Realize that what you are building during the hackathon will help set some employees free from stagnating skills allowing them to be more employable with their new capabilities.
The hackathon is a chance to master new skills through experiential learning, further strengthening their capabilities as well. And often learning from some of the experts in the company by joining their teams.
Reward risk-taking and new innovative ideas that initially appear to be edge cases, but can potentially be game changers for customers.
I’ve been interviewing CEOs from startups to established enterprise software companies about how they kept innovation alive during the lockdown. CEOs have mentioned agile development, extensive use of Slack channels and daily virtual stand-ups. Ashutosh Garg is the only one to mention how putting intrinsic motivation into practice, along with these core techniques, binds hackathon teams together fast. Dan Pink’s classic TED Talk, The Puzzle of Motivation, explains intrinsic motivators briefly and it’s clear they have implications on a hackathon succeeding or not.
Measuring Results Of the Hackathon
Within a weekend, Project Marketplace revealed several new rock stars amongst the Eightfold hackathon teams. Instead of doing side projects for people who had time on their hands, this Hackathon was about making Eightfold’s everyday projects better and faster. Their best Engineers and Services team members took a step back, re-looked at the current approaches and competed with each other to find better and innovative ways. And they all voted for the most popular projects and solutions – ultimate reward in gaining the respect of your peers. As well as the most “prolific coder” for those who couldn’t resist working on multiple teams.
Remote work is creating daunting challenges for individuals at home as well as for companies. Business models need to change and innovation cannot take a back seat while most companies have employees working from home for the foreseeable future. Running a hackathon during a global lockdown and making it deliver valuable new insights and features that benefit customers now is achievable as Eightfold’s track record shows. Project marketplace may prove to be a useful ally for employees and companies looking to stay true to their mission and help each other grow – even in a pandemic. This will create better job security, a culture of continuous learning, loyalty and more jobs. AI will change how we look at our work – and this is a great example of inspiring innovation.
Customers’ expectations, preferences, changing patterns in how and why they purchase need to be the core of any digital transformation effort. With it, digital transformation projects flourish and take on a life of their own. Without it, I’ve seen digital transformation projects become myopic, narrowly focused, substituting internal metric gains for measures that matter most to customers.
Digital Maturity Drives Revenue
Anyone who has worked on a digital transformation project quickly sees how the most digitally mature organizations can turn their investments in transformation into revenue by overwhelming customers with value. Initiatives that put customers first can serve to generate greater confidence among C-level executives and board members, leading to more funding. This is because business cases for customer-centric digital transformation projects are easier to create, more defensible and best of all, point to revenue gains and cost reductions.
Deloitte Insights’ recent survey uncovering the connection between digital maturity and financial performance accurately reflects the true state of customer-centric digital transformation. The article explains how the more digitally mature an organization is, the more achievable gains are in diversity and inclusion, Corporate Social Responsibility (CSR), customer satisfaction, product quality, gross margin and long-term financial performance. Deloitte’s latest study finds a strong correlation between the digital maturity of an enterprise and its net revenue and net profit margin. The following graphic makes clear how valuable pursuing digital maturity is, with customers being at the center of all transformation efforts. This contributes to greater net revenue and net profit margin growth:
A fascinating point regarding Deloitte Insights’ research is the correlation it uncovered between an organization’s digital transformation maturity and the benefits they gain in efficiency, revenue growth, product/service quality, customer satisfaction and employee engagement. They found a hierarchy of pivots successful enterprises make to keep pursuing more agile, adaptive organizational structures combined with business model adaptability, all driven by customer-driven innovation. The most digitally mature organizations can adopt new frameworks that prioritize market responsiveness, customer-centricity and have analytics and data-driven culture with actionable insights embedded in their DNA.
Mastering Data & Removing Roadblocks Are Key To Driving Customer Value
The two highest-payoff areas for accelerating digital maturity and achieving its many benefits are mastering data and creating more intelligent workflows. Deloitte Insights’ research team looked at the seven most effective digital pivots enterprises can make to become more digitally mature. The pivots that paid off the best as measured by revenue, margin, customer satisfaction, product/service quality and employee engagement combined data mastery and improving intelligent workflows. The following graphic shows how 51% of revenue growth can be explained by these two factors alone and 49% of improved customer satisfaction.
Data mastery and intelligent workflows are among the easiest areas to measure and include in a business case for digital transformation projects aimed at delivering a transcendent customer experience. Choosing to excel on the dimension of customer-centric data mastery gives enterprises the insights they need to create their unique omnichannel platforms. Adding in intelligent workflows that give customers the freedom to buy how, where and when they choose across any digital platform is the cornerstone of entirely new digital business models today. Capturing the voice of the customer and combining data mastery and intelligent workflows to gain an accurate, true 360-degree view of customers is invaluable for every aspect of go-to-market strategies.
Achieving Digital Maturity Requires A Framework
Enterprises that have customer centricity and a data-driven mindset are the most likely to succeed with a digital transformation initiative. As the Deloitte Insights study inferred, the most digitally mature organizations are continually adapting to customer and market dynamics. They’re prioritizing market responsiveness, striving to improve customer-centricity and have data-driven cultures with actionable insights as part of their DNA. Enterprises who see new digital business model opportunities and act on them capitalize on these three areas of organizational strength. They’re also able to combine their data mastery and intelligent workflows to identify areas of competitive opportunity to help them excel for their customers.
Consider how cybersecurity is now part of any customer experience, for good and bad. Multi-factor Authentication (MFA) and many other forms of identity verification secure customer transactions, yet they can also cause dissatisfaction. For any digitally mature enterprise, integrating cybersecurity into their existing framework is a challenge. The growth of new frameworks designed to empower greater customer-centricity, agility and actionable insights across every facet of a business is a fascinating area of watch.
One of the more interesting is BMC’s Autonomous Digital Enterprise (ADE) framework, which is shown below. Mapping Deloitte Insights’ top investment priorities for the next 12 months across all digital maturity levels to the ADE framework shows why frameworks like BMC’s are gaining adoption, particularly as organizations look to run and reinvent themselves with new digital business models built around AI/ML capabilities. The following graphic provides insights into how Deloitte’s top investment priorities are integral to BMC’s Autonomous Digital Enterprise Framework and its many contributions to the success of new digital business growth.
Quantifying the impact of having a customer-centric digital transformation strategy has proved elusive until recently. Deloitte Insights’ research shows how digital maturity enables greater gains from customer-centric digital transformation efforts. What’s fascinating about their research is how the progression of digital pivots leads to improved margin, revenue, customer satisfaction, diversity and inclusion and product quality gains. Equally interesting is the growing utility of frameworks like BMC’s, which are designed to enable long-standing enterprises to seamlessly embrace new digital business models, so they can flex and change with the world around them.
Bottom Line: Capitalizing on AI and machine learning’s inherent strengths to create contextual intelligence in real-time, LogicMonitor’s early warning and failure prevention systems reflect where AIOps is delivering results today.
LogicMonitor’s track record of making solid contributions to their customers’ ability to bring greater accuracy, insight, and precision into monitoring all IT assets is emerging as a de facto industry standard. Recently I was speaking with a startup offering Hosted Managed Services of a variety of manufacturing applications, and the must-have in their services strategy is LogicMonitor LM Intelligence. LogicMonitor’s AIOps platform is powered by LM Intelligence, enabling customers’ businesses to gain early warning into potential trouble spots in IT operations stability and reliability. LogicMonitor does the hard work for you with automated alert thresholds, AI-powered early warning capabilities, customizable escalation chains, workflows, and more.
Engineers who are working at the Hosted Managed Services provider I recently spoke with say LM Intelligence is the best use case of AI and machine learning to provide real-time alerts, contextual insights, discover new patterns in data, and make automation achievable. The following is an example of the LM Intelligence dashboard:
How LogicMonitor’s Architecture Supports AIOps
One of the core strengths LogicMonitor continues to build on is integration, which they see as essential to their ability to excel at providing AIOps support for their customers. Their architecture is shown below. By providing real-time integration to public cloud platforms, combined with control over the entire IT infrastructure structure along with over 2,000 integrations from network to cloud, LogicMonitor excels at unifying diverse IT environments into a single, cohesive AIOps-based intelligence system. The LogicMonitor platform collects cloud data through our cloud collectors. These collectors retrieve metrics such as the cloud provider health and billing information by making API calls to the cloud services. The collector is a Windows Service or background process that is installed in a virtual machine. This collector then pulls metrics from the different devices using a variety of different methods, including SNMP, WMI, perf Mon JMX, APIs, and scripts.
Using AIOps To Monitor, Analyze, Automate
LogicMonitor has created an architecture that’s well-suited to support the three dominant dimensions of AIOps, including Monitoring, Analytics (AIOps), and Automating. Their product and services strategies in the past have reflected a strong focus on Monitoring. The logic of prioritizing Monitoring as a product strategy area was to provide the AI and machine learning models with enough data to train on so they could identify anomalies in data patterns faster. Their 2018/2019 major releases in the Monitor area reflect how the unique strength they have of capturing and making use of any IT asset that can deliver a signal is paying off. Key Monitor developers recently include the following:
Public Cloud Monitoring
LogicMonitor’s core strengths in AIOps are in the Anomaly Detection and Early Warning System areas of their product strategy. Their rapid advances in the Early Warning System development show where AIOps is delivering solid results today. Supporting the Early Warning System, there are Dynamic Thresholds and Root Cause Analysis based on Dependencies as well.
The Automate area of their product strategy shows strong potential for future growth, with the ServiceNow integration having upside potential. Today Alert Chaining and Workflow support integrations to Ansible, Terraform, Slack, Microsoft, Teama, Putter, Terraform, OpsGenie, and others.
LogicMonitor’s platform handles 300B metrics on any given day and up to 10B a month, with over 28K collectors deployed integrated with approximately 1.4M devices being monitored. Putting AI and machine learning to work, interpreting the massive amount of data the platform captures every day to fine-tune their Early Warning and Failure Prevention Systems, is one of the most innovative approaches to AIOps today. Their AIOps Early Warning System is using machine learning Algorithms to fine-tune Root Cause Analysis and Dynamic Thresholds continually. AIOps Log Intelligence is also accessing the data to complete Automatic Log Anomaly Detection, Infrastructure change detection, and Log Volume Reduction to Signal analysis.
Enterprises who are increasing the average number of endpoint security agents from 9.8 last year to 10.2 today aren’t achieving the endpoint resilience they need because more software agents create more conflicts, leaving each endpoint exposed to a potential breach.
1 in 3 enterprise devices is being used with a non-compliant VPN, further increasing the risk of a breach.
60% of breaches can be linked to a vulnerability where a patch was available, but not applied. Windows 10 devices in enterprises are, on average, 95 days behind on patches.
CIOs, CISOs and cybersecurity teams say autonomous endpoint security is the most challenging area they need to strengthen in their cybersecurity strategy today. Software agents degrade faster than expected and conflict with each other, leaving endpoints exposed. Absolute’s 2020 State of Endpoint Resilience Report quantifies the current state of autonomous endpoint security, the scope of challenges CISOs face today and how elusive endpoint resiliency is to achieve with software agents. It’s an insightful read if you’re interested in autonomous endpoint security.
Endpoint Security Leads CISOs’ Priorities In 2020
With their entire companies working remotely, CIOs and CISOs I’ve spoken with say autonomous endpoint security is now among their top three priorities today. Cutting through the endpoint software clutter and turning autonomous endpoint security into a strength is the goal. CISOs are getting frustrated with spending millions of dollars among themselves only to find out their endpoints are unprotected due to software conflicts and degradation. Interested in learning more, I spoke with Steven Spadaccini, Vice President, Sales Engineering at Absolute Software and one of the most knowledgeable autonomous endpoint cybersecurity experts I’ve ever met. Our conversation delved into numerous cybersecurity challenges enterprise CIOs and CISOs are facing today. My interview with him is below:
The Seven Toughest Questions the C-Suite Is Asking About Endpoint Security
Louis: Thank you for your time today. I have seven questions from CIOs, CISOs and their teams regarding endpoint security. Let’s get started with their first one. What happens if an endpoint is compromised, how do you recover, encrypt, or delete its data?
Steven: It’s a challenge using software agents, both security and/or management, to do this as each agents’ tools and features often conflict with each other, making a comprised endpoints’ condition worse while making it virtually impossible to recover, encrypt, delete and replace data. The most proven approach working for enterprises today is to pursue an endpoint resilience strategy. At the center of this strategy is creating a root of trust in the hardware and re-establishes communication and control of a device through an unbreakable digital tether. I’m defining Endpoint Resilience as an autonomous endpoint security strategy that ensures connectivity, visibility and control are achieved and maintained no matter what is happening at the OS or application level. Taking this approach empowers devices to recover automatically from any state to a secure operational state without user intervention. Trust is at the center of every endpoint discussion today as CIOs, CISOs and their teams want the assurance every endpoint will be able to heal itself and keep functioning
Louis: Do endpoint software security solutions fail when you lose access to the endpoint, or is the device still protected at the local level?
Steven: When they’re only protected by software agents, they fail all the time. What’s important for CISOs to think about today is how they can lead their organizations to excel at automated endpoint hygiene. It’s about achieving a stronger endpoint security posture in the face of growing threats. Losing access to an endpoint doesn’t have to end badly; you can still have options to protect every device. It’s time for enterprises to start taking a more resilient-driven mindset and strategy to protecting every endpoint – focus on eliminating dark endpoints. One of the most proven ways to do that is to have endpoint security embedded to the BIOS level every day. That way, each device is still protected to the local level. Using geolocation, it’s possible to “see” a device when it comes online and promptly brick it if it’s been lost or stolen.
Louis: How can our cybersecurity team ensure compliance that all cybersecurity software is active and running on all endpoints?
Steven: Compliance is an area where having an undeletable tether pays off in a big way. Knowing what’s going on from a software configuration and endpoint security agent standpoint – basically the entire software build of a given endpoint – is the most proven way I’ve seen CISOs keep their inventory of devices in compliance. What CISOs and their teams need is the ability to see endpoints in near real-time and predict which ones are most likely to fail at compliance. Using a cloud-based or SaaS console to track compliance down to the BIOS level removes all uncertainty of compliance. Enterprises doing this today stay in compliance with HIPAA, GDPR, PCI, SOX and other compliance requirements at scale. It’s important also to consider how security automation and orchestration kicks on to instantly resolve violations by revising security controls and configurations, restoring anti-malware, or even freezing the device or isolating it from data access. Persistent visibility and control give organizations what they need to be audit-ready at every moment.
Having that level of visibility makes it easy to brick a device. Cybersecurity teams using Absolute’s Persistence platform can lead to humorous results for IT teams, who call the bricking option a “fun button as they watch hackers continually try to reload new images and right after they’re done, re-brick the device again. One CIO told the story of how their laptops had been given to a service provider who was supposed to destroy them to stay in compliance with the Health Insurance Portability and Accountability Act (HIPAA) and one had been resold on the black market, ending up in a 3rd world nation. As the hacker attempted to rebuild the machine, the security team watched as each new image was loaded at which time they would promptly brick the machine. After 19 tries, the hacker gave up and called the image rebuild “brick me.”
Louis: With everyone working remote today, how can we know, with confidence where a given endpoint device is at a moments’ notice?
Steven: That’s another use case where having an undeletable tether pays off in two powerful ways: enabling autonomous endpoint security and real-time asset management. You can know with 100% confidence where a given endpoint device is in real-time so long as the device is connected to a permanent digital tether . Even if the device isn’t reachable by your own corporate network it’s possible to locate it using the technologies and techniques mentioned earlier. CIOs sleep better at night knowing every device is accounted for and if one gets lost or stolen, their teams can brick it in seconds.
Louis: How can our IT and cybersecurity teams know all cybersecurity applications are active and protecting the endpoint?
Steven: By taking a more aggressive approach to endpoint hygiene, it’s possible to know every application, system configuration and attributes of user data on the device. It’s important not to grow complacent and assume the gold image IT uses to configure every new or recycled laptop is accurate. One CIO was adamant they had nine software agents on every endpoint, but Absolute’s Resilience platform found 16, saving the enterprise from potential security gaps. The gold image is an enterprise IT team was using had inadvertently captured only a subset of the total number of software endpoints active on their networks. Absolute’s Resilience offering and Persistence technology enabled the CIO to discover gaps in endpoint security the team didn’t know existed before.
Louis: How can we restrict the geolocations of every endpoint?
Steven: This is an area that’s innovating quickly in response to the needs enterprises have to track and manage assets across countries and regions. IP tracking alone isn’t as effective as the newer techniques, including GPS tracking, Wi-Fi triangulation, with both integrated into the Google Maps API. Enterprises whose business relies on Personal Identifiable Information (PII) is especially interested in and adopting these technologies today. Apria Healthcare is currently using geofencing for endpoint security and asset management. They have laptops in use today across Indonesia, the Philippines and India. Given the confidential nature of the data on those devices and compliance with local government data protection laws, each laptop needs to stay in the country they’re assigned to. Geofencing gives Apria the power to freeze any device that gets outside of its region within seconds, averting costly fines and potential breaches.
Louis: How can our IT team immediately validate an endpoint for vulnerabilities in software and hardware?
Steven: The quickest way is to design in audit-ready compliance as a core part of any endpoint resilience initiative. Endpoint resilience to the BIOS level makes it possible to audit devices and find vulnerabilities in real-time, enabling self-healing of mission-critical security applications regardless of complexity. The goal of immediately validating endpoints for current security posture needs to be a core part of any automated endpoint hygiene strategy. It’s possible to do this across platforms while being OS-agnostic yet still accessible to over 500M endpoint devices, deployed across Microsoft Windows, macOS via a Mac Agent and Chrome platforms.
Knowing if their autonomous endpoint security and enterprise-wide cybersecurity strategies are working or not is what keeps CIOs up the most at night. One CISO confided to me that 70% of the attempted breaches to his organization are happening in areas he and his team already knew were vulnerable to attack. Bad actors are getting very good at finding the weakest links of an enterprises’ cyber defenses fast. They’re able to look at the configuration of endpoints, see which software agents are installed, research known conflicts and exploit them to gain access to corporate networks. All this is happening 24/7 to enterprises today. Needing greater resilient, persistent connections to every device, CISOs are looking at how they can achieve greater resilience on every endpoint. Capitalizing on an undeletable tether to track the location of the device, ensure the device and the apps on that device have self-healing capabilities and gain valuable asset management data – these are a few of the many benefits they’re after.
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:
Achieve as few false positives as possible by making real-time updates to machine learning algorithms and fine-tuning merchant responses.
Reduce the number of manual reviews for fraud analysts consistently by applying AI and machine learning to provide early warning of anomalies.
Minimize the number of chargebacks to merchant partners.
Reduce the friction and challenges merchants experience with legacy fraud prevention systems by streamlining the purchasing experience.
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:
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.
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.
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.
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.
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.
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.
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.
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 & Control, 28(11/12), 873-876.
96% of the employees of the 15 highest rated machine learning startups would recommend their company to a friend looking for a new job, and 98% approve of their CEOs.
Across all machine learning startups with Glassdoor ratings, 74% of employees would recommend the startup they work for to a friend, and 81% approve of their CEO.
There are over 230 cities globally who have one or more machine learning startups in operation today with Crunchbase finding 144 in San Francisco, 60 in London, 69 in New York, 82 in Tel Aviv, 22 in Toronto, 20 in Paris, 18 in Seattle and the remainder distributed over 223 global locations.
These and many other insights are from a Crunchbase Pro analysis completed today using Glassdoor data to rank the best machine learning startups to work for in 2020. Demand reminds high for technical professionals with machine learning expertise. According to Indeed, Machine Learning Engineer job openings grew 344% between 2015 to 2018 and have an average base salary of $146,085 according to their Best Jobs In The U.S. Study. You can read the study shows that technical professionals with machine learning expertise are in an excellent position to bargain for the average base salary of at least $146,085 or more.
In response to readers’ most common requests of which machine learning startups are the best to work for, a Crunchbase Pro query was created to find all machine learning startups who had received Seed, Early Stage Venture, or Late Stage Venture financing. The 2,682 machine learning startups Crunchbase is tracking were indexed by Total Funding Amount by startup to create a baseline.
Next, Glassdoor scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO were used to find the best startups to work for. 79 of the 150 machine learning startups have 15 or more Glassdoor reviews and are included in the analysis. 41 have less than 15 reviews and 30 have no reviews. The table below is a result of the analysis, and you can find the original Microsoft Excel data set here.
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’s2020 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:
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.
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.
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.
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.
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.
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.
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.
Zest predicts Fintechswill 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.
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.
Credit unions will adopt ML in 2020 to automate routine tasks and free up human underwriters to focus on providing more personalized services, including improvements in inquiry resolution & dispute and fraud management. Credit unions are built on an annuity-based business model that delivers successively higher profitability the longer a member is retained. Credit unions will capitalize on ML by driving up loan approvals with no added risk and automating more of the loan approval process. By the end of 2020, according to a Fannie Mae survey of mortgage lenders, 71% of credit unions plan to investigate, test, or fully implement AI/ML solutions – up from just 40% in 2018. AI and ML will also be adopted across credit unions to improve inquiry resolution & dispute and fraud management while improving multichannel customer experiences. Providing real-time, relevant responses to customers to expedite inquiries and dispute resolutions using AI and ML is going to become commonplace in 2020. AI and ML are predicted to make a significant contribution to automating anomaly detection and borrower default risk assessment as the graphic below from Fannie Mae’s Mortgage Lender Sentiment Survey® How Will Artificial Intelligence Shape Mortgage Lending? Q3 2018 Topic Analysis illustrates:
$10.7B 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
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.
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.
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.
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.
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:
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:
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:
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.
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.
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.
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.
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.
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.
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:
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.
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.
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.
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.
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:
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:
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:
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:
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.
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.
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.