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10 Ways Asset Intelligence Improves Cybersecurity Resiliency And Persistence

By on January 23, 2020

10 Ways Asset Intelligence Improves Cybersecurity Resiliency And Persistence 1

Bottom Line: By securing every endpoint with a persistent connection and the resiliency to autonomously self-heal, CIOs are finding new ways to further improve network security by capitalizing on each IT assets’ intelligence.

Capturing real-time data from IT assets is how every organization can grow beyond its existing boundaries with greater security, speed, and trust. Many IT and cybersecurity teams and the CIOs that lead them, and with whom I’ve spoken with, are energized by the opportunity to create secured perimeterless networks that can flex in real-time as their businesses grow. Having a persistent connection to every device across an organizations’ constantly changing perimeter provides invaluable data for achieving this goal. The real-time data provided by persistent device connections give IT and cybersecurity teams the Asset Intelligence they need for creating more resilient, self-healing endpoints as well.

How Asset Intelligence Drives Stronger Endpoint Security 

Real-time, persistent connections to every device in a network is the foundation of a strong endpoint security strategy. It’s also essential for controlling device operating expenses (OPEX) across the broad base of device use cases every organization relies on to succeed. Long-term persistent connections drive down capital expenses (CAPEX) too, by extending the life of every device while providing perimeterless growth of the network. By combining device inventory and analysis, endpoint data compliance with the ability to manage a device fleet using universal asset management techniques, IT and cybersecurity teams are moving beyond Asset Management to Asset Intelligence. Advanced analytics, benchmarks, and audits are all possible across every endpoint today. The following are the 10 ways Asset Intelligence improves cybersecurity resiliency and persistence:

  • Track, trace and find lost or stolen devices on or off an organizations’ network in real-time, disabling the device if necessary. Every device, from laptops, tablets, and smartphones to desktops and specialized use devices are another threat surface that needs to be protected. Real-time persistent connections to each of these devices make track-and-trace possible, giving CIOs and their teams more control than had been possible before. Real-time track-and-trace data combined with device condition feedback closes security blind spots too. IT and cybersecurity teams can monitor every device and know the state of hardware, software, network and use patterns from dashboards. Of the endpoint providers in this market, Absolute’s approach to providing dashboards that provide real-time visibility and control of every device on a network is considered state-of-the-art. An example of Absolute’s dashboard is shown below:

10 Ways Asset Intelligence Improves Cybersecurity Resiliency And Persistence 2

  • Asset Intelligence enables every endpoint to autonomously self-heal themselves and deliver constant persistence across an organization’s entire network. By capitalizing on the device, network, threat, and use data that defines Asset Intelligence, endpoint agents learn over time how to withstand breach attempts, user errors, and malicious attacks, and most importantly, how to return an endpoint device to its original safe state. Asset Intelligence is the future of endpoint security as it’s proving to be very effective at enabling self-healing persistence across enterprise networks.
  • Asset Intelligence solves the urgent problem created from having 10 or more agents installed on a single endpoint that collide, conflict and decay how secure the endpoint is. Absolute Software’s 2019 Endpoint Security Trends Report found that the more agents that are added to an endpoint, the greater the risk of a breach. Absolute also found that a typical device has ten or more endpoint security agents installed, often colliding and conflicting with the other. MITRE’s Cybersecurity research practice found there are on average, ten security agents on each device, and over 5,000 common vulnerabilities and exposures (CVEs) found on the top 20 client applications in 2018 alone.
  • Asset Intelligence sets the data foundation for achieving always-on persistence by tracking every devices’ unique attributes, identifiers, communication log history and more. Endpoint security platforms need a contextually-rich, real-time stream of data to know how and when to initialize the process of autonomously healing a given endpoint device. Asset Intelligence provides the centralized base of IT security controls needed for making endpoint persistence possible.
  • Having a real-time connection to every device on a perimeterless network contributes to creating a security cloud stack from the BIOS level that delivers persistence for every device. CIOs and CISOs interested in building secured perimeterless networks are focused on creating persistent, real-time connections to every device as a first step to creating a security cloud stack from each devices’ BIOS level. They’re saying that the greater the level of Asset Intelligence they can achieve, the broader they can roll out persistence-based endpoints across their networks that have the capacity to self-diagnose and self-heal.
  • Device fleets are churning 20% a year or more, increasing the urgency CIOs have for knowing where each device is and its current state, further underscoring Asset Intelligence’s value. Gavin Cockburn of ARUP is the global service lead for workplace automation and endpoint management, including how the firm acquires devices, manages and reclaims them. ARUP is using the Absolute Persistence platform for managing the many high-value laptops and remote devices their associates use on global projects. During a recent panel discussion he says that device replacements “become part of our budgeting process in that 33% of devices that we do replace every year, we know where they are.” Gavin is also using API calls to gain analytical data to measure how devices are being used, if the hard drive is encrypted or not and run Reach scripts to better encrypt a device if there is not enough security on them.
  • The more Asset Intelligence an organization has, the more they can predict and detect malware intrusion attempts, block them and restore any damage to any device on their perimeter. When there’s persistent endpoint protection across a perimeterless network, real-time data is enabling greater levels of Asset Intelligence which is invaluable in identifying, blocking and learning from malware attempts on any device on the network. Endpoint protection platforms that have persistence designed in are able to autonomously self-heal back to their original state after an attack, all without manual intervention.
  • Persistent endpoints open up the opportunity of defining geofencing for every device on a perimeterless network, further providing valuable data Asset Intelligence platforms capitalize on. Geofencing is proving to be a must-have for many organizations that have globally-based operations, as their IT and cybersecurity teams need to track the device location, usage, and compliance in real-time. Healthcare companies are especially focused on how Asset Intelligence can deliver geofencing at scale. Janet Hunt, Senior Director, IT User Support at Apria Healthcare recently commented during a recent panel discussion that “our geo-fencing is extremely tight. I have PCs that live in the Philippines. I have PCs that live in India. I have one PC or actually two PCs that live in Indonesia. If somebody goes from where they say that they’re going to be to another part of Indonesia, that device will freeze because that’s not where it’s supposed to be and that’s an automatic thing. Don’t ask forgiveness, don’t ask questions, freeze the device and see what happens. It’s one of the best things we’ve done for ourselves.”  Gavin Cockburn says, “We actually do some kind of secretive work, government work and we have these secure rooms, dotted around the organization. So we know if we put a device in that room, what we do is, what we say is this device only works in this area and we can pinpoint that to a pretty decent accuracy.”  From healthcare to secured government contracting, geofencing is a must-have in any persistent endpoint security strategy.
  • Automating customer and regulatory audits and improving compliance reporting by relying on Asset Intelligence alleviates time-consuming tasks for IT and cybersecurity teams. When persistent endpoint protection is operating across an organization’s network, audit and compliance data is captured in real-time and automatically fed into reporting systems and dashboards. CIOs and their cybersecurity teams are using dashboards to monitor every device’s usage patterns, audit access, and application activity, and check for compliance to security and reporting standards. Audits and compliance reporting are being automated today using PowerShell, BASH scripts and API-based universal asset commands. Gavin Cockburn of ARUP mentioned how his firm gives customers the assurance their data is safe by providing them ongoing audits while project engagements are ongoing. “We need to show for our clients that we look after their data and we can prove that. And we show that again and again. I mean similar story, we’ve seen machines go missing, either breaking into cars, re-image three times. We wipe it every time. Put a new hard drive in, think it might be a hard drive issue, it wipes again. We never see it come online again, “ he said.
  • Asset Intelligence improves data hygiene, which has a direct effect on how effective all IT systems are and the customer experiences they deliver. CIOs and their teams’ incentives center on how effective IT is at meeting internal information needs that impact customer experiences and outcomes. Improving data hygiene is essential for IT to keep achieving their incentive plans and earning bonuses. As Janet Hunt, Senior Director, IT User Support at Apria Healthcare said, “right now we are all about hygiene and what I mean by that is we want our data to be good. We want all the things that make IT a valued partner with the business operation to be able to be reliable.” The more effective any organization is at achieving and sustaining a high level of data hygiene, the more secure their perimeterless network strategies become.

 

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Introducing the Future of Work Global Index

By on January 16, 2020

Over the last few years, a lot has been written about “The Future of Work.” Everyone from McKinsey to the World Economic Forum have made predictions regarding skills shortages, the need for training and skills development, and the future of jobs.

But what about the technology that is enabling the massive disruption in work. Last quarter, we set out to explore the financial performance of publicly-traded “Future of Work” companies, or those companies focused on enabling technology to managing people, pay, productivity, and progression.

Our work has resulted in the creation of the Future of Work Global Index. The Future of Work Global Index is an index of 31 publicly-traded technology companies globally focused on delivering cloud solutions that address the changing workforce. The index including companies from 10 countries with public exchange listings in 7 countries including Japan, Australia, Canada, and even Norway.

How has the Future of Work Global Index performed in the market, you ask? It is safe to say the technology companies enabling the “Future of Work” have witnessed hypergrowth. Over the last 5 years, the Future of Work Global Index is up over 365%. The performance is 6x better than the S&P 500 over that same period and 4x better than the NASDAQ composite.

In the last year alone, while the S&P 500 grew an astounding 29% growth, the Future of Work Global Index gained over 52%.

We believe the convergence of significant macro-trends in the market have attributed to the strong growth of “Future of Work” companies including:

  1. digital transformation and the rapid adoption of cloud technologies in the enterprise
  2. low unemployment forcing companies to investment in technology solution to more effectively streamline critical HR processes
  3. workforce globalization requiring new investments collaboration and productivity tools
  4. rapid automation of jobs and the need to reskill the global workforce

For more details, please visit The Future of Work Global Index. Over the coming weeks we will be further dissecting the Future of Work Global Index companies to better understand the underlying insights behind Future of Work companies.

 

(Cross-posted @ Arcadian Insights - Jason Corsello)

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The Best Machine Learning Startups To Work For In 2020 Based On Glassdoor

By on January 13, 2020

Late night work session

  • Duolingo, HOVER, Ironclad, Orbital Insight, People.ai, Dataiku, DeepMap, Cobalt, Aktana, Chorus.ai, Noodle Analytics, Inc. (Noodle.ai), Signal AI, Augury, SparkCognition, and KONUX are the most likely to be recommended by their employees to friends looking for a machine learning startup to work for in 2020.
  • 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.

Methodology

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.

The Best Machine Learning Startups To Work For In 2020 Based On Glassdoor

 

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10 Ways AI Is Going To Improve Fintech In 2020

By on January 11, 2020

10 Ways AI Is Going To Improve Fintech In 2020

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

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

How AI & Machine Learning Will Improve Fintech In 2020

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

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

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

10 Ways AI Is Going To Improve Fintech In 2020 part 2

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If they sold enterprise software on television …

By on January 6, 2020

It was either the late night spicy burrito or falling asleep while the television was still on but I could have sworn I saw an infomercial pitching enterprise software. Worse, I could have sworn it was colleague Jon Reed and I selling that stuff! Here’s my recollection of what that interminable, obnoxious ad contained:

Brian: “Hi, Brian and Jon here to tell you about the biggest news to hit the software space today!”

Jon: “That’s right, Astounding ERP is now available without a prescription. It’s gonna change how you feel about work – and – the way you think of enterprise software!”

This miracle, space-age technology is not just ERP:  it’s four software tools in one! Now you can harness the power of Astounding ERP through this special TV offer!

Brian: Watch as we take this ordinary accounting department and insert Astounding ERP! Why in just seconds, paradigms will shift and business cases will automagically come true!

The Astounding ERP software saves money because of its unique atomic clarification technology. It eliminates software audits, contract negotiations and those pesky software user conferences.  It even sucks up any IT budget, no matter how big or small! And it works in any situation!

But how great is Astounding ERP? We put our ERP to the test with real users not actors!”

(voice of an ERP customer): “After 14 years, it came as no surprise that we needed a new ERP. Our new Astounding ERP installed in just seconds! We couldn’t believe they put it up in the cloud just for us!”

(voice of another ERP customer): “We liked how our Astounding ERP software developers used the rare Konjac root to build the product. It even restored the luster to our multi-columnar spreadsheets! With Astounding ERP, you don’t need a Boot Camp and you will lose up to 4X your old ERP software code – 78% of that old code was just ‘fat’!”

Jon: “New Astounding ERP will slice, dice and even make Julienne fries out of your old backup tapes! Its all-copper construction locks in nutrients while powering the bot revolution!”

(voice of a third ERP customer): “And, should you ever need help, just press this button and an Astounding ERP representative will assess your situation and get help on its way!”

Brian: “Astounding ERP is made right here in the USA! People were not asked to change their lifestyle when using the Astounding ERP software. That’s right! People, processes, forms and workflows never have to change with Astounding ERP! Let the power of the Astounding ERP do it all for you!”

Jon: “To show you the power of Astounding ERP, we cut this data center in half. After applying our patented, space-age cloud technology, this company’s business is better than ever!”

(both Brian & Jon simultaneously): “We liked the Astounding ERP technology so much, we bought the company!

(Prospective customer): “So, how can I get Astounding ERP?”

Jon: “Call or go online now! And, if you call in the next 30 minutes, we’ll throw in multi-tenancy at no additional cost!  But that’s not all! Order right now and we’ll double your user count and upgrade you to the untested beta version! WE’LL EVEN TOSS IN SOME GDPR! YES, YOU HEARD THAT RIGHT FOLKS! All you have to pay is a separate shipping and handling fee. Be sure and act now! Use the special code “Software Audit”! Operators are on duty.”

Brian: “And remember at Astounding ERP, we se habla accounting!

Jon: “But there’s more! If you call in the next 30 minutes, you’ll not only get the Astounding ERP, you’ll also get a set of Ginzu knives, a bamboo wok steamer and this one-time special bonus: out of the box Salesforce integration! Don’t miss this chance to get the integrations others only dream about along with a one year subscription to Time Magazine!

(unseen announcer whispering the fine print): “Your results may vary. Don’t use Astounding ERP if you’re allergic to software salespeople, scope creep or financial losses. Discontinue use of Astounding ERP if you experience diarrhea, convulsions, heart attacks, nightmares about user licensing, mood swings or death. See your doctor right away if you experience any of these symptoms after implementing Astounding ERP.  I’m a non-attorney spokesman for Astounding ERP.”

Jon: “I’m Jon Reed and I approve of this message.”

 

Was it a late-night infomercial, or just a bad user conference I had tried not to remember? Yeah, I might have been dreaming but I kept on channel surfing, in the hope that someday I’ll find Astounding ERP on sale again.

 

Jon Reed was a contributing writer on this piece. He’s an informercial fan boy from the way back.

 

 

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Challenges of a New Decade

By on January 6, 2020

Arab Spring showed what technology could do in society. Getty Image.

It finally struck me over the break that it wasn’t simply another year end, but the end of a decade. Having lived through the end of a millennium, I guess I could be forgiven for taking a decade in stride. But then it occurred to me that more things happen in a decade these days than happened in all of a century, say the 12th as an example.

That got me musing about things typical at a milestone like this, as well as some that aren’t quite as typical…

The decade began in the aftermath of one of the worst global economic catastrophes in history, rivalling the Great Depression (that began in 1929). It drove discontent among the global middle-classes in ways we couldn’t have predicted pre-social media.

The Arab Spring happened in 2011 when people subjugated by decades of totalitarian rule in the Middle East took to the streets armed only with cell phones, demanding better and fairer treatment. The Arab Spring was made possible through social media and internet technology and they were front and centre for much of what happened throughout the rest of the decade. More importantly though, these technologies, and several more, have gone from wondrous curiosities that seemed to be bending history’s curve toward justice, to the core elements of what looks like growing repression. In short, the past decade revealed tech’s dark underbelly.

Unknowns

My retrospective analysis started when I began reading Shoshana Zouboff’s new book, . I was a fan of her 2002 book The Support Economy, and I referenced her in a paper I wrote in 2004, titled The New Garage, which said in part,

Enterprise applications that merely track transaction data are ubiquitous and rather than bringing the vendor and customer closer together, as CRM promised, according to Harvard Business School professor, Shoshana Zouboff[1], customers are more alienated and show less loyalty than ever before.

A real balck swan. Powertopursuade.org.au

In retrospect, that seems like my awakening to the dark underside of social. We knew enterprise software needed more than what we entered the century with, more data, analysis, and communications, but we had no idea what additional technologies would cost. It’s amazing to me now that we never thought to ask what the cost was and we certainly could have.

Asking about the potential underside is an idea brought to the forefront in 2007 by Nassim Nicolas Taleb in “The Black Swan” from which many of us first got accustomed to the idea of unknown unknowns. But Zouboff points out that rather than doing that, i.e. looking for the unknowns, more often than not, we humans instead try to normalize the unknown so that it is amenable to analysis by conventional thinking. In the end applying conventional thinking to sui generis phenomena is just plain lazy.

Not to get political but (okay, just for a moment) we watched as someone ascended to political power by outright unconventional means, portending things that were antithetical to our traditions. But rather than asking about the possible unknown consequences we normalized it all by saying things like he’ll become presidential when he’s president. Still waiting on that, but I use this as a metaphor and not a screed.

So, what do Zouboff’s and Taleb’s ideas have to teach us in the new decade? Perhaps what many already sense, that we’re in for a wild ride and not just politically but in technology and CRM especially. It’s easy expanding and claiming new land but much harder bringing law and order to the newly settled territory and that’s what the next decade in tech looks to have in store.

Law and Order

Economic cycles get started by technological disruptions and both pass predictable milestones. At the beginning we see birthing and early adoption and later, as the disruption matures, commoditization and market saturation but also in many cases we see regulation.

Regulation happens when a disruption is so big and its economic consequences so profound that all people must have it and the rules of sharing that resource need to be written down and enforced. We saw this with the road system, telephone and electricity but also with such basics as water and sanitation services. Some disruptions skirt or avoid the regulation trap and society is not necessarily well served. The internet and its most heavily used apps like social media are not regulated but maybe they should be. They’re certainly undergoing a robust wild west phase.

This brings us back to social media and machine-based analytics. Once the darling of the tech-literati but now the bane of democratic governments these technologies, if left unchecked, continue to change the social dynamic around the world, and not in a good way. They are becoming the tools of strongmen for manipulating populations crowding out sensible discourse with the verbal equivalent of mudwrestling.

They are the underside of the tech era. They are also too valuable to be exiled from society, which is why they need regulation. As recently as November, Salesforce CEO Marc Benioff likened social media to nicotine addiction, telling journalist Kara Swisher, that Facebook is the new cigarettes. He went on, “You know, it’s addictive. It’s not good for you. There’s people trying to get you to use it that even you don’t understand what’s going on. The government needs to step in. The government needs to really regulate what’s happening.”

Getting regulation right

He’s right, of course, but there’s regulation and then there’s regulation and it should concern all of us that the regulation we seek is sensible and easily enforced. That’s why I have been championing to idea of regulating social media through licensing and certification, which we already use in professions and industries like medicine, law, construction and personal services like hair styling.

In all of these areas, practitioners must demonstrate proficiency through certification and licensing and practitioners in social media and its use with analytics and machine learning should have to do the same. But there’s a fine line we establish in regulating these societal necessities. For example, we can all represent ourselves in small claims court, take over the counter medications, even cut our own hair and the world will not bother us. But when we try to do any of these things for others, we act as professionals who should have ethics which drives the need for credentials.

Oliver Wendell holmes, Jr., National Portrait Gallery, Smithsonian Institution

We need similar balance with social media and how we use other people’s data. We can’t go on letting social moguls pontificate about free speech without accepting a duty to first do no harm, something the medical profession figured out a long time ago. To my mind that threshold was asset 100 years ago by Supreme Court Justice, Oliver Wendell Holmes, Jr.

In Schenck v. United States Holmes articulated the free speech standard that has held for over a century. There is no absolute right to free speech, Holmes observed. You can’t falsely shout “fire” in a theater, cause a panic and expect the First Amendment to bail you out because you were just exercising your right. In Schenck Holmes first announced the “clear and present danger” test which basically said that free speech can’t be the refuge of individuals intent on causing harm with their words. Schenck was announced as a unanimous decision of the court.

Something like that should apply to social media and the easiest way to get there is by requiring those who use it professionally to be, well, certified professionals. So far social media companies have gotten a free pass by trotting out the same tired arguments that other would-be monopolists have always used: It would clobber the economy, or the industry, or prices would spike, or your rights are being violated. But nothing is further from the truth.

Conclusion

In the next decade, we’re going to have to clean up some of the mess made by cloud computing, mobile devices, the wireless internet, social media and other gee-wiz tech since the turn of the century. The good news is that it’s a predictable journey and a path well-traveled. There are economic waves, like K-waves, that explain what we’re going through in more detail. Regulation is a key component but so should be taking a hard look at business models and while we’re at it developing global conventions on data privacy and a treaty on cyber warfare.

If the treaty idea seems far-fetched keep in mind that the US just budgeted for a Space Force akin to the Army, Navy and Air Force. Many people think this is nuts. It would be great not to need to militarize outer space and that may depend on how we regulate cyber-attacks or fail to because we need to determine when a cyber-attack is really an act of war. It’s certain that we’ve turned a corner on more than the calendar. Technology’s childhood, its innocence, ended in the decade just passed and now we need to figure out how to ensure a long, prosperous and sane adulthood.

 

 

(Cross-posted @ Denis Pombriant | Medium)

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Kellblog’s 10 Predictions for 2020

By on January 6, 2020

As I’ve been doing every year since 2014, I thought I’d take some time to write some predictions for 2020, but not without first doing a review of my predictions for 2019.  Lest you take any of these too seriously, I suggest you look at my batting average and disclaimers.

Kellblog 2019 Predictions Review

1.  Fred Wilson is right, Trump will not be president at the end of 2019.  PARTIAL.  He did get impeached after all, but that’s a long way from removed or resigned.

2.  The Democratic Party will continue to bungle the playing of its relatively simple hand.  HIT.  This is obviously subjective and while I think they got some things right (e.g., delaying impeachment), they got others quite wrong (e.g., Mueller Report messaging), and continue to play more left than center which I believe is a mistake.

3.  2019 will be a rough year for the financial markets.  MISS.  The Dow was up 22% and the NASDAQ was up 35%.  Financially, maybe the only thing that didn’t work in 2019 were over-hyped IPOs.  Note to self:  avoid quantitative predictions if you don’t want to risk ending up very wrong.  I am a big believer in regression to the mean, but nailing timing is the critical (and virtually impossible) part.  Nevertheless, I do use tables like these to try and eyeball situations where it seems a correction is needed.  Take your own crack at it.

 

4.  VC tightens.  MISS.  Instead of tightening, VC financing hit a new record.  The interesting question here is whether mean reversion is relevant.  I’d argue it’s not – the markets have changed structurally such that companies are staying private far longer and thus living off venture capital (and/or growth-stage private equity) in ways not previously seen.  Mark Suster did a great presentation on this, Is VC Still a Thing, where he explains these and other changes in VC.  A must read.

5. Social media companies get regulated.  PARTIAL.  While “history may tell us the social media regulation is inevitable,” it didn’t happen in 2019.  However, the movement continued to gather steam with many Democratic presidential candidates calling for reform and, more notably, none other than Facebook investor Roger McNamee launching his attack on social media via his book Zucked: Waking Up To The Facebook Catastrophe.  As McNamee says, “it’s an issue of ‘right vs. wrong’ not ‘right vs. left.’”

 

6. Ethics make a comeback.  HIT.  Ethics have certainly been more discussed than ever and related to the two reasons I cited:  the current administration and artificial intelligence.  The former forces ethics into the spotlight on a daily basis; the later provokes a slew of interesting questions, from questions of accidental bias to the trolley car problem.  Business schools continue to increase emphasis on ethics.  Mark Benioff has led a personal crusade calling for what he calls a new capitalism.

7.  Blockchain, as an enterprise technology, fades away.  HIT.  While I hate to my find myself on the other side of Ray Wang, I’m personally not seeing much traction for blockchain in the enterprise.  Maybe I’m running with the wrong crowd.  I have always felt that blockchain was designed for one purpose (to support cybercurrency), hijacked to another, and ergo became a vendor-led technology in search of a business problem.  McKinsey has a written a sort of pre-obituary, Blockchain’s Occam Problem, which was McKinsey Quarterly’s second most-read article of the year.  The 2019 Blockchain Opportunity Summit’s theme was “Is Blockchain Dead?  No. Industry Experts Join Together to Share How We Might Not be Using it Right” which also seems to support my argument.

8.  Oracle enters decline phase and is increasingly seen as a legacy vendor.  HIT.  Again, this is highly subjective and some people probably concluded it years ago.  My favorite support point comes from a recent financial analyst note:  “we believe Oracle can sustain ~2% constant currency revenue growth, but we are dubious that Oracle can improve revenue growth rates.”  That pretty much says it all.

9.  ServiceNow and/or Splunk get acquired.  MISS.  While they’re both great businesses and attractive targets, they both so expensive only few could make the move – and no one did.  Today, Splunk is worth $24B and ServiceNow a whopping $55B.

10.  Workday succeeds with its Adaptive Insights agenda.  HIT.  Changing general ledgers is a heart transplant while changing planning systems is a knee replacement.  By acquiring Adaptive, Workday gave itself another option – and a far easier entry point – to get into corporate finance departments.  While most everyone I knew scratched their head at the enterprise-focused Workday acquiring a more SMB-focused Adaptive, Workday has done a good job simultaneously leaving Adaptive alone enough to not disturb its core business while working to get the technology more enterprise-ready for its customers.  Whether that continues I don’t know, but for the first 18 months at least, they haven’t blown it.  This remains high visibility to Workday as evidenced by the Adaptive former CEO (and now Workday EVP of Planning) Tom Bogan’s continued attendance on Workday’s quarterly earnings calls.

With the dubious distinction of having charitably self-scored a 6.0 on my 2019 predictions, let’s fearlessly roll out some new predictions for 2020.

Kellblog 2020 Predictions

1.  Ongoing social unrest. The increasingly likely trial in the Senate will be highly contentious, only to be followed by an election that will be highly contentious as well.  Beyond that, one can’t help but wonder if a defeated Trump would even concede, which could lead to a Constitutional Crisis of the next level. Add to all that the possibility of a war with Iran.  Frankly, I am amazed that the Washington, DC continuous distraction machine hasn’t yet materially damaged the economy.  Like many in Silicon Valley, I’d like Washington to quietly go do its job and let the rest of us get back to doing ours.  The reality TV show in Washington is getting old and, happily, I think many folks are starting to lose interest and want to change the channel.

2.  A desire for re-unification.  I remain fundamentally optimistic that your average American – Republican, Democrat, or the completely under-discussed 38% who are Independents — wants to feel part of a unified, not a divided, America.  While politicians often try to leverage the most divisive issues to turn people into single-issue voters, the reality is that far more things unite us as Americans than divide us.  Per this recent Economist/YouGov wide-ranging poll, your average American looks a lot more balanced and reasonable than our political party leaders.  I believe the country is tired of division, wants unification, and will therefore elect someone who will be seen as able to bring people together.  We are stronger together.

3.  Climate change becomes the new moonshot.  NASA’s space missions didn’t just get us to the moon; they produced over 2,000 spin-off technologies that improve our lives everyday – from emergency “space” blankets to scratch-resistant lenses to Teflon-coated fabrics.  Instead of seeing climate change as a hopeless threat, I believe in 2020 we will start to reframe it as the great opportunity it presents.  When we mobilize our best and brightest against a problem, we not only will solve it, but we will create scores to hundreds of spin-off technologies that benefit our everyday lives in the process.  See this article for information on 10 startups fighting climate change, this infographic for an overview of the kinds of technologies that could alleviate it, or this article for a less sanguine view on the commitment required and extent to which we actually can de-carbonize the air.

4.  The strategic chief data officer (CDO).  I’m not a huge believer in throwing an “O” at every problem that comes along, but the CDO role is steadily becoming mainstream – in 2012, just 12% of F1000 companies reported having a CDO; in 2018 that’s up to 68%.  While some of that growth was driven by defensive motivations (e.g., compliance), increasingly I believe that organizations will define the CDO more strategically, more broadly, and holistically as someone who focuses on data, its cleanliness, where to find it, where it came from, its compliance with regulations as to its usage, its value, and how to leverage it for operational and strategic advantage.   These issues are thorny, technical, and often detail-oriented and the CIO is simply too busy with broader concerns (e.g., digital transformation, security, disruption).  Ergo, we need a generation of chief data officers who want to play both offense and defense, focused not just tactically on compliance and documentation, but strategically on analytics and the creation of business value for the enterprise. This is not a role for the meek; only half of CDOs succeed and their average tenure is 2.4 years.  A recent Gartner CDO study suggests that those who are successful take a more strategic orientation, invest in a more hands-on model of supporting data and analytics, and measure the business value of their work.

5.  The ongoing rise of DevOps.   Just as agile broke down barriers between product management and development so has DevOps broken down walls between development and operations.  The cloud has driven DevOps to become one of the hottest areas of software in recent years with big public company successes (e.g., Atlassian, Splunk), major M&A (e.g., Microsoft acquiring GitHub), and private high-flyers (e.g., HashiCorp, Puppet, CloudBees).  A plethora of tools, from configuration management to testing to automation to integration to deployment to multi-cloud to performance monitoring are required to do DevOps well.  All this should make for a $24B DevOps TAM by 2023 per a recent Cowen & Company report.  (I’d say the creation of strong DevOps and Customer Success functions were two of the biggest changes to software companies in the past decade.)  Ironically though, each step forward in deployment is often a step backward in developer experience, why is one reason why I decided to work with Kelda in 2019.

6. Database proliferation slows.  While 2014 Turning Award winner Mike Stonebraker was right over a decade ago when he argued in favor of database specialization (One Size Fits All:  An Idea Whose Time Has Come and Gone), I think we may now too much of a good thing.   DB Engines now lists 350 different database systems of 14 different types (e.g., relational, graph, time series, key-value). Crunchbase lists 274 database (and database-related) startups.  I believe the database market is headed for consolidation.  One of the first big indicators of a resurgence in database sanity was the failure of the (Hadoop-based) data lake, which happened in 2018-2019 and was the closest thing I’ve seen to déjà vu in my professional career – it was as if we learned nothing from the Field of Dreams enterprise data warehouse of the 1990s (“build it and they will come”).  Moreover, after a decade of developer-led database selection, developers and now re-realizing what database people knew along – that a lot of the early NoSQL movement was akin to throwing out the ACID transaction baby with the tabular schema bathwater.

7.  A new, data-layer approach to data loss prevention (DLP).  I always thought DLP was a great idea, especially the P for prevention.  After all, who wants tools that can help with forensics after a breach if you could prevent one from happening at all — or at least limit one in progress?  But DLP doesn’t seem to work:  why is it that data breaches always seem to be measured not in rows, but in millions of rows?  For example, Equifax was 143M and Marriott was 500M.  DLP has many known limitations.  It’s perimeter-oriented in a hybrid cloud world of dissolving perimeters and it’s generally offline, scanning file systems and database logs to find “misplaced data.”  Wouldn’t a better approach be to have real-time security monitored and enforced at the data layer, just the same way as it works at the network and application layer?  Then you could use machine learning to understand normal behavior, detect anomalous behavior, and either report it — or stop it — in real time.  I think we’ll see such approaches come to market in 2020, especially as cloud services like Snowflake, RDS, and BigQuery become increasingly critical components of the data layer.

8. AI/ML continue to see success in highly focused applications.  I remain skeptical of vendors with broad claims around “enterprise AI”(e.g., C3)  and remain highly supportive of vendors applying AI/ML to specific problems (e.g., Moveworks and Astound who both provide AI/ML-based trouble-ticket resolution).  In the end, AI and ML are features, not apps, and while both technologies can be used to build smart applications, they are not  applications unto themselves.  In terms of specificity, the No Free Lunch Theorem reminds us that any two optimization techniques perform equivalently when averaged across all possible problems – meaning that no one modeling technique can solve everything and thus that AI/ML is going to be about lots of companies applying different techniques to different problems.   Think of AI/ML more as a toolbox than a platform.  There will not be one big winner in enterprise AI as there was in enterprise applications or databases.  Instead there will be lots of winners each tackling specific problems.  The more interesting battles will those between systems of intelligence (e.g., Moveworks) and systems of record (e.g., ServiceNow) with the systems-of-intelligence vendors running Trojan Horse strategies against systems-of-record vendors (first complementing them but eventually replacing them) while the system-of-record vendors try to either acquire or build systems of intelligence alongside their current offerings.  My bets are on the systems-of-intelligence vendors in these battles.

9.  Series A rounds remain hard.  I think many founders are surprised by the difficulty of raising A rounds these days.  Here’s the problem in a nutshell

  • Seed capital is readily available via pre-seed and seed-stage investments from angel investors, traditional early-stage VCs, and increasingly, seed funds.  Simply put, it’s not that hard to raise seed money.
  • Companies are staying in the seed stage longer (a median of 1.6 years), increasingly extending seed rounds, and ergo raising more money during seed stage (e.g., $2M to $4M).
  • Such that, companies are now expected to really have achieved something in order to raise a Series A.  After all, if you have been working at it for 2 years and spent $3M you better have an MVP product, a handful of early customers, and some ARR to show for it – not just a PowerPoint deck talking about a great opportunity.

Moreover, you should be making progress roughly in line with what you said at the outset and, if you took seed capital from a traditional VC, then they better be prepared to lead your A round otherwise you face signaling risk that could impede or preclude your Series A.

Simply put, Series A is the new chokepoint.  Or, as Suster likes to say, the Series A and B funnel hasn’t really changed – we’ve just inserted a new seed funnel atop it that is 3 times larger than it used to be.

10.  Autonomy’s former CEO gets extradited.  Silicon Valley is not generally not a place of long memories, but I saw the unusual news last month that the US government is trying to extradite Autonomy founder and former CEO Mike Lynch from the UK to face charges.  You might recall that HP, in the brief era under Leo Apotheker, acquired enterprise search vendor Autonomy in August, 2011 for a whopping $11B only to write-off about $8.8B under subsequent CEO Meg Whitman a little more than a year later in November, 2012.  Computerworld provides a timeline of the saga here, including a subsequent PR war, US Department of Justice probe, UK Serious Fraud Office investigation (later dropped), shareholder lawsuits, proposed settlements, more lawsuits including Lynch’s suing HP for $150M for reputation damages, and HP’s spinning-off the Autonomy assets.  Subsequent to Computerworld’s timeline, this past May Autonomy’s former CFO was sentenced to five years in prison.  This past March, the US added criminal charges of securities fraud, wire fraud, and conspiracy against Lynch.  Lynch continues to deny all wrongdoing and blame the failed acquisition on HP.  I don’t have any special legal knowledge or specific knowledge of this case, but I do believe that if the US government is still fighting this case, still adding charges, and now seeking extradition, that they aren’t going to give up lightly, so my hunch is that Lynch does come to the US and face these charges.

More broadly, regardless of how this particular case works out, in a place so prone to excess, where so much money can be made so quickly, frauds will periodically happen and it’s probably the most under-reported class of story in Silicon Valley.  Even this potentially huge headline case – the proposed extradition of a British billionaire tech mogul —  never seems to make page one news.  Let’s talk about Loft’s $175M Series C instead.

To finish this up, I’ll add a bonus prediction:  Dave doesn’t get a traditional job in 2020.  While I continue to look at VC-backed startup and/or PE-backed CEO opportunities, I am quite enjoying my work doing a mix of boards, advisory relationships, and consulting gigs.  While I remain interested in looking at great CEO opportunities, I am also interested in adding a few more boards to my roster, working on stimulating consulting projects, and a few more advisory relationships as well.

I wish everyone a happy, healthy, and above-plan 2020.

 

(Cross-posted @ Kellblog)

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Predicting How AI Will Improve Talent Management In 2020

By on December 31, 2019

Predicting How AI Will Improve Talent Management In 2020

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

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

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

Adoption of AI has Positive Impacts

Predicting The Future Of AI In Talent Management

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

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

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Top 25 AI Startups Who Raised The Most Money In 2019

By on December 28, 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 improve 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), 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 enable robotic intelligence through 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:

  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 their 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 relies 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:

  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 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 their 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:

  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:

  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:

  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 has 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:

  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:

  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.

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The Most Innovative Tech Companies Based On Patent Analytics

By on December 23, 2019

The Most Innovative Tech Companies Based On Patent Analytics

  • Microsoft, Apple, and IBM lead the world in hardware & software patent innovation according to PatentSight.
  • Samsung, Johnson & Johnson, LG Electronics. Alphabet, Qualcomm, Ford, Intel, Microsoft, Sony, and VW are the ten most innovative companies in the world, according to PatentSight’s patent analytics research.
  • Ford leads the global automotive industry in patent innovation, due in large part to successful R&D efforts in autonomous driving.

These and many other fascinating insights are from Swiss consulting firm EconSight’s patent analytics research that first identified all the patents that are supposed to protect particularly relevant innovations – in this case, defined as innovations for the digitization of applied technologies – using the PatentSight database. Companies not only have to maintain their innovative strength; they also have to continue to expand in comparison to previous years to take a leading position in the ranking. For additional details on the methodology and to request the rank of your company, please visit the PatentSight Innovation Ranking 2019 site here.

Key insights from EconSight’s patent analytics research defining the most innovative companies globally include the following:

  • 38 of the most innovative companies in the world are based in the U.S, 21 in China, and 15 from Europe. Chinese followed by Japanese-based companies lead the world in electronics innovation as measured by the uniqueness of patents produced. U.S. companies lead the world in medical technology patent innovation. The following graphic compares the number of companies within the global top 100 ranking by country and industry for 2019.

  • In the U.S., tech companies dominate the top 10 most innovative companies of 2019. Alphabet, Qualcomm, Intel, Microsoft, Honeywell, Apple, and GE are producing the most unique, differentiated and value-adding patents based on EconSight’s methodology. Medical technology companies show the greatest growth in innovative patent production as the graphic below illustrates:

The Most Innovative Tech Companies Based On Patent Analytics

  • The world’s most innovative medical technology companies’ patent focus is on biosensors, surgical robotics, shortening the time-to-market for pharmaceutical drugs, and funding startup incubators that yield new patents. Johnson & Johnson’s (J&J) multifaceted innovation strategy reflects the broader strategic vision of every medical technology company pursuing new intellectual property (IP) that leads to patent leadership. J&J acquired Auris Health and Verb Surgical, which is managed as a joint venture with Alphabet’s Verily medical division, which gives them a patent portfolio in healthcare intelligence. J&J has in total acquired over 300 companies in the medical technology industry according to PatentSight’s analysis. These acquisitions have moved them into biosensors, surgical robotics, and startups performing drug research.

The Most Innovative Tech Companies Based On Patent Analytics

  • Japanese robotics manufacturer Fanuc is the world’s most innovative automation technology company based on patent analysis. Since 2019 Fanuc has jumped 42 places in the ranking, from 61st to 19th. The global labor shortage in manufacturing is a contributing factor to the strong market demand Fanuc is seeing for all its robotics products and systems. The following are the top 25 robotics companies of 2019:

The Most Innovative Tech Companies Based On Patent Analytics

  • Ford leads the global automotive industry in patent innovation, while Volkswagen doubles down on patents over the last two years. Ford leads the world in patent innovation due to its rapid advances in autonomous vehicle development. Volkswagen’s rapid ascent in the automotive industry rankings has made them the most innovative company in Germany based on patent analytics this year. VW is investing in autonomous vehicles, and the networking of mobility participants, setting a solid foundation for future vehicle models today.

PatentSight Background

PatentSight – A Lexis Nexis company– specializes in cleaning and refining patent data and providing advanced patent analytics. Publicly available patent data simply cannot be used without qualitative preparation and correction. Due to the sheer mass (about 3.3 million new registrations in 2018 alone), all available patents cannot be viewed manually. Publications in many different languages and often very abstract contents make a manual review and evaluation difficult not only for laymen but also for experts. A further challenge is to level out the widely differing citation practices of national patent offices or to document the legal status of patents.

PatentSight, through manually supervised and scientifically developed algorithms, has best-in-class information on ownership data, going far beyond the testing standards recommended by the World Intellectual Property Organization (WIPO).

Moreover, PatentSight‘s proprietary patent valuation metrics reveal which patents are key, and which are superfluous. Based on citations, global protection, and several correction factors, EconSight leveraged these metrics to determine the most innovative companies.

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