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

  • Worldwide enterprise application software revenue totaled more than $193.6B in 2018, a 12.5% increase from 2017. CRM made up nearly 25% of the entire enterprise software revenue market.
  • 72.9% of CRM spending was on software as a service (SaaS) in 2018, which is expected to grow to 75% of total CRM software spending in 2019.
  • Worldwide spending on customer experience and relationship management (CRM) software grew 15.6%, from $41.7B in 2017 to $48.2B in 2018, and is projected to reach $55.2B in 2019.
  • Salesforce dominated the worldwide CRM market with a 19.5% market share in 2018, over double its nearest rival, SAP, at 8.3% share, according to Gartner’s market share estimates.
  • CRM revenue in 2018 is comprised of software and services revenue from Customer Service and Support (35.7%), Sales (25.9%), Marketing (25.4%), and Digital Commerce (13%). These four categories together comprise the customer experience and relationship management market, according to Gartner.

New technologies are proliferating across the CRM landscape, driven by the need every business has to understand, communicate, serve, and strengthen customer relationships. Gartner’s decision to create its first-ever Hype Cycle for CRM Sales Technology, 2019, reflects the widening spectrum of new technologies being introduced to improve sales effectiveness while improving operational efficiency. Gartner’s Hype Cycle for CRM Sales Technology, 2019 is based on an update to their Hype Cycle for CRM Sales, 2018.  Gartner’s definition of Hype Cycles includes five phases of a technology’s lifecycle and is explained here.  The Gartner Hype Cycle for CRM, 2019, is shown below:

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

  • Four new technologies are on the Hype Cycle for CRM, reflecting enterprises’ need for greater integration of diverse systems and the demand for more predictive and prescriptive analytics-based insights. The four technologies include the following:
    • Blockchain for lead generation. Gartner sees the potential for blockchain to provide a decentralized peer-to-peer network model that supports exchanging data to the highest bidders using smart contracts. Gartner predicts this approach reduces or in some cases eliminates the need for a centralized authority such as a data intelligence solution. It also allows for a new ecosystem of managing, sharing and monetizing data for revenue-generating purposes.
    • Knowledge graphs for sales. The ability to build an AI-enabled knowledge model of real-world entities and their relationships to one another, expressed in a data schema, shows potential to increase sales effectiveness. Gartner predicts this emerging technology provides organizations with the ability to create data-driven sales organizations using graphs arranged in a network of nodes rather than in tables of rows and columns. The significance is the ability to correlate sales activities and benchmark against performance metrics in a more digestible and insightful way, which is often too complex for human analysis.
    • Digital adoption solutions. Gartner sees potential in this technology to improve the adoption of multiple tools across a selling and marketing organization. Digital adoption solutions enable sellers to onboard more quickly and improve productivity.
    • Relationship intelligence. By relying on machine learning, sales organizations can map out their universe of network connections, both internal and external, to identify potential avenues of engagement with any prospect or client. Gartner sees this as useful in its ability to provide warm introductions or even referrals for revenue-generating activities while reducing sales cycles.
  • Gartner predicts the following five technologies will deliver the most significant transformational benefits to selling organizations in 2 years or less. The five most transformation technologies in the near term are the following according to Gartner:
    • CPQ Application Suites
    • Digital Content Management for Sales
    • Lead Management
    • Partner Relationship Management (PRM)
    • Price Optimization and Management for B2B
  • The following CRM technologies have gained wide usage and adoption in the last year, as reflected by their position on this year’s Hype Cycle. Data intelligence solutions for sales, CPQ application suites, digital content management for sales, and sales KPI analytics are among the most adopted mature technologies on the hype cycle today.
  • Visual configurators have moved at a much faster pace to mainstream adoption along the Hype Cycle this year. Gartner credits visual configurators’ rapid adoption rate to how the majority of them are now embedded or easily integrated with configure, price, quote (CPQ) applications, or in digital commerce sites. State-of-the-art visual configurator are enabling engineering, production, and sales to become real-time collaborators in creating new products. For additional insights into visual configurators, please see How To Make Complex CPQ Selling Simple With Visual Configurators published earlier this week.
  • Algorithmic guided selling is now listed as obsolete. Gartner has re-assigned this technology as it’s now an embedded core capability in many CPQ and sales force automation (SFA) applications. By doing this, Gartner is saying it is doubtful algorithmic guided selling applications will be sold stand-alone in the future.
  • Social for sales and predictive B2B marketing analytics are off the CRM Hype Cycle. Gartner has chosen to merge them into the data intelligence solutions for sales market. Social for sales is more of a process, not a technology market. The majority of social for sales-based strategies are executed over social networks that have the audience and scale to make them succeed, with LinkedIn being an example. Gartner believes the predictive B2B marketing analytics vendor landscape has shrunk and is not a viable market long term, as they have seen inquiries regarding market share in this area steadily drop in this area since 2016.
  • Gartner is seeing two main drivers of investment and innovation in CRM in 2019 and beyond. The first is digital optimization or a process and program of using digital technology to maximize existing operating processes and business models. The second is predictive/prescriptive-enabled technology or technology using capabilities such as machine learning that provides predictive signals and prescriptive “next best action” recommendations. Please see their research note, 4 Key Insights from the Gartner Hype Cycle for CRM Sales Technology, 2019, for additional details.

Sources:

4 Key Insights From the Gartner Hype Cycle for CRM Sales Technology, 2019, published October 2, 2019

Hype Cycle for CRM Sales Technology 2019, July 10, 2019 (Client access required)

Scaling Cloud Services Is Key To Growing A Digital Business

  • 93% of enterprises are securing remote locations with a centralized approach that rarely scales to secure every endpoint and identity of remote branch locations, leaving an enterprise more vulnerable to a breach.
  • Enabling network security is the greatest challenge enterprises face when managing a highly distributed network with numerous remote locations.
  • In an era of cloud-first networks, 9 out of 10 companies are still relying on centrally managed networks that don’t scale for remote system users, creating productivity bottlenecks.
  • 75% of enterprises experience branch and remote location network interruptions several times a year or more frequently, costing an organization thousands of dollars an hour in lost productivity.

The challenges of scaling cloud services to grow a digital business are many and are well-explained in the recent research report, Remote Office Networks Pose Business and Reliability Risk A Survey of IT Professionals (27 pp., PDF, no opt-in), published on August 2019 by Dimensional Research in collaboration with Infoblox. This report provides valuable insights into why scaling cloud services is essential for growing a digital business. The study’s findings reflect how remote branch and production locations’ lack of IT security and site personnel are one of the most challenging constraints to overcome and keep growing their business. Please see page 22 of the study for specifics on the methodology.

99% or nearly all enterprises with distributed operations suffer adverse business impacts from network interruptions. Of the many causes of network disruption, one of the most common is not directing traffic to the closest point of entry into cloud platforms. Taking a software-based approach to wide-area networking (SDWAN) is proving effective in improving cloud-based application performance, including Microsoft Office 365 cloud-based application performance. The report shows how SD-WAN is replacing outdated centralized IT models that lack the scale to flex and support new digital business models.

Key insights from the research report include the following:

  • Enterprises realize the model of relying on centralized IT security isn’t scaling to support and protect the proliferation of user devices with internet access, leaving branch offices less secure than ever before. Every IT architect, IT Director, or CIO needs to consider how taking an SDWAN-based approach to network management reduces the risk of a breach and data exfiltration. 93% of enterprises are securing remote locations with a centralized approach that rarely scales to secure every endpoint and identity of remote branch locations, leaving an enterprise more vulnerable to a breach. Enterprises are upgrading their core network services, including DNS, DHCP, and IP address management, on cloud-based DDI platforms to bring greater security scale and reliability across their enterprise networks. Enterprises are also devising Zero Trust Security (ZTS) frameworks to secure every network, cloud, and on-premise platform, operating system, and application across their branch offices. Chase Cunningham of Forrester, Principal Analyst, is the leading authority on Zero Trust Security, and his recent video, Zero Trust in Action, is worth watching to learn more about how enterprises can secure their IT infrastructures. You can find his blog here.

  • 75% or the majority of an enterprises’ branch offices experience network interruptions several times a year, with 49% of them requiring three or more hours to resolve remote office network outages. Enterprises continue to pay a very high price in lost productivity due to network interruptions and the time it takes to troubleshoot them and get a branch or remote location back online. Enterprises are upgrading their core network services, including DNS, DHCP, and IP address management, on cloud-based DDI platforms to bring greater scale and reliability across their enterprise networks. Cloud-based DDI platforms enable enterprises to manage networking for hundreds to thousands of remote sites with unprecedented cost-efficiency.

  • Relying on centralized IT creates many challenges and security threats for remote offices, with the most costly not having IT staff at remote sites. Network security at remote locations is the greatest challenge enterprises face when managing a highly distributed network with numerous remote locations. A contributing factor to security being the leading challenge of managing a highly distributed network is the lack of IT employees at remote branches. 65% of enterprises are routinely sending IT employees to remote branches to resolve networking issues alone. Travel costs combined with lost productivity from having to send IT technicians out for a week or longer to solve network performance issues is another reason why enterprises are adopting cloud-based DDI platforms.

  • Enterprises are adopting cloud-based DDI platforms that enable enterprises to simplify the management of highly distributed remote networks as well as to optimize the network performance of cloud-based applications. Dimensional Research’s study reflects how enterprises are meeting the challenge of increasingly complex, distributed networks that have a proliferating number of remote locations and endpoints. The majority of enterprises, 71%, are looking to integrate core network services, DNS, DHCP, and IP address management, into a single cloud-based DDI platform. The problem is, conventional DDI solutions for branch locations are too slow or complicated for a cloud-first world. The following graphic from the study shows what motivating enterprises to adopt SD-WAN today is.

How To Make Complex CPQ Selling Simple With Visual Configurators

Bottom Line: Realizing visual configurators’ full potential starts by enabling engineering, production, and sales to become real-time collaborators in creating new products.

2D, 3D, Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR) visual configurators are proliferating across the Configure, Price, and Quote (CPQ) landscape today. Manufacturing marketing teams say they are the most effective lead generation technology they have, responsible for 40%+ growth in Marketing Qualified Leads (MQLs) this year alone. Sales VPs and Chief Revenue Officers (CROs) are seeing from 9% to 30% improvements in deal close rates and over 90% increases in quote accuracy. Visual configurators deliver shock-and-awe to prospects and drive more leads and deals.

Product Models Need To Scale, Driving Greater Collaboration

The good test of any product configurator is whether it can scale from assemble-to-order (ATO) to Engineer-To-Order (ETO) while enabling real-time collaboration between engineering, production, and sales. A given products’ many attributes and options defined by engineering in their PLM system need to be consistent with manufacturing’s work instructions and Bill of Materials (BOM) in their ERP system. And the visual configurator sales & marketing is using needs to reflect, in real-time, what engineering defined in PLM and what manufacturing’s ERP system can build. Product models serve as the master data that enables real-time collaboration between engineering, manufacturing, and sales.

Visual configurators need to push beyond the veneer of delivering shock-and-awe and enable real-time collaboration between PLM, ERP, and CRM & CPQ systems to achieve their full potential. Visual configuration providers need to pursue the goal of enabling engineering, manufacturing, and sales to be collaborators in creating accurate products and challenge themselves to deliver the following:

  • Improve sales performance while increasing margin per deal by providing only the options that are the most buildable at the lowest cost.
  • Eliminate disconnects between what engineering designed and what manufacturing can produce leads to more sales at higher gross margins.
  • Close product configuration gaps and improving fulfillment speed and product quality, creating greater customer loyalty and follow-on sales.
  • Automatically propagate product and design changes across all functional areas to accelerate new products to market while improving product quality.
  • Real-time fine-tuning of new product features to the model level that specific customers want becomes possible when engineering, manufacturing, and sales are collaborating in real-time.
  • Update work instructions and BOMs in real-time based on changes customers make in product visualizations.
  • Improve the balance of revenue across configurable products to sell higher-margin models based on real-time collaboration between PLM, ERP, CRM, and CPQ systems.
  • See in real-time how changes in product design, Bill of Materials (BOM), and delivery dates impact the financial performance of a manufacturer.

Predicting Visual Configuration’s Future

Shortening cycle times from product concept to completed product is the secret to succeeding with visual configuration. And when each manufacturing cycle time has its cadence or speed depending on how little or much a customer wants a product customized, visual configurators need to flex and deliver what customers want when.

Companies defining the future of visual configuration today include CDSDERWID, and SAP Visual Enterprise. These three companies are defining the future of visual configuration by enabling real-time integration between PLM, ERP, CRM, and CPQ systems.  I recently spoke with John Major, CEO of CDS to get his insights into what’s driving visual configuration’s success today.  “What we’re seeing in the marketplace now are two things. One is the clients want to understand how our visual configuration solution is going to fit into their change management as it’s rooted in PLM, because to any manufacturer, PLM reigns supreme,” he said. He continued, “The second is about staffing. When you’re a manufacturing company, and you buy a visual configurator toolkit that requires you to create your app, a few things happen. You need to staff up a software team to now run that toolkit and write development. So your long-term cost is fairly significant versus a company that can deliver an entire solution at scale.” 

CDS is partnering with eLogic, who is regarded as the leading system integration partner in CPQ and product configuration and is considered a global leader in delivering business solutions for manufacturers across SAP configuration technologies and Microsoft Dynamics 365, Power Platform & Azure. Together they are delivering next-generation visual configuration solutions for their shared clients. Examples of the work they are doing are shown below:

  • Real-time model updates keep engineering, manufacturing, and sales in sync. When customers are designing a new product in a CPQ session, the model is updated in real-time and saved, so engineering, manufacturing, and sales can see how their changes affect the product. An example of this is shown below:

  • When the product is configured “to scale,” 2D proposal drawings are automatically generated, and the product model is updated in real-time, making augmented reality visualizations possible. 3D models are also made available in a variety of CAD formats. Additionally, an Augmented Reality model is created that can be placed in any virtual environment. What’s noteworthy is that while the model’s appearance is changing, all relevant changes to the work instructions and BOM are happening in real-time using the SAP Visual Enterprise

  • When product models are the catalyst enabling real-time collaboration between engineering, manufacturing, and sales, selling into the aftermarket becomes profitable. Aftermarket selling has a complexity all its own. Taking on the challenge of shortening cycle times from product concept to completed products in the market is what’s needed today. The example below shows a piece of equipment selected in CPQ, then rotated, zoomed in, and exploded to see the internal components. Internal parts can now be selected, quoted, ordered and delivered for replacement.

Conclusion

Visual configurators are capable of so much more than they are delivering today. It’s time to graduate beyond the shock-and-awe stage, which has been very successful in driving leads, generating MQLs, and closing deals. It’s time to get down to the hard work of making all those impressive models buildable at scale and profitable. And that comes by doubling down efforts at shortening cycle times from product concept to completed product. That’s the true north of this market and the secret to succeeding. Getting engineering, manufacturing, and sales collaborating using product models as a single source of truth is the best place to start.

How To Improve Your CPQ Pricing Strategies

Manufacturers can get more than their fair share of channel sales and margins by improving price management for every dealer, distributor, and reseller they sell-through. It’s possible to expand earnings by 50% on slight increases in volume when pricing is consistent channel-wide. McKinsey’s latest research on the topic, Pricing: Distributors’ Most Powerful Value-Creation Lever, shows how the highest performing distributors use pricing to create value. For manufacturers competing for more sales through distributors, they share with competitors, improving their channel partners’ margins is the single best strategy to win more sales and long-term loyalty.

  • A 1% price increase yields a 22% increase in Earnings before Interest & Taxes (EBITDA) margins for distribution-based businesses.
  • It would take a 7.5% reduction in fixed costs to achieve the same 22% increase in EBITDA that a 1% increase in pricing achieves.
  • A distribution-based business would need to increase volume by 5.9% while holding operating expenses flat to achieve the same impact as a 1% price increase.
  • Channel partners are more loyal to margin than manufacturers, which is why price management needs urgent attention on CPQ roadmaps.

CPQ Strategies Need To Deliver More Margin Back To The Channel

The typical manufacturer who has over $100M in sales generates 40% or more of their sales through indirect channels. The channel partners they recruit and sell through are also reselling 12 other competitive products on average. Which factors most influence a distributor or channel partner’s decision to steer a sale to one manufacturer versus another?  The following are the steps manufacturers can take now to improve price management and drive more channel sales:

  • Upgrade the pricing module in CPQ to deliver more than configurable price lists to include pricing waterfalls, automated approval levels for pricing requests, and discounts. Distributors drive more deals to manufacturers whose CPQ systems are designed to give them greater freedom in tailoring pricing to every customer and selling situation they have. Automating approval levels using machine learning-based supervised algorithms that serve as pricing guardrails on every quote a channel partner creates is proving effective at delivering a 1% price increase which drives margin back to resellers. The more a manufacturer can make margins flow back to its channel partners, the faster the channel partners can grow. The following graphic from McKinsey’s latest pricing research illustrates why.

  • Distributors will drive more deals to manufacturers who automate pricing approvals, guiding their sales teams to the largest and most profitable deals first. One of the best ways to compete and win more deals through channel partners is to achieve the ambitious goal of delivering pricing approval within seconds on a 24/7 basis. Pricing needs to provide guardrails that guide channel sales reps to the largest, most profitable, and most ready-to-buy new and aftermarket sales opportunities. Manufacturers capturing more channel sales are relying on machine learning-based pricing systems that optimize price approvals while recommending only those new and aftermarket deals that will drive a 1% or greater price increase. Machine learning is making solid contributions to automating pricing approvals. It’s proving most effective when it is balanced with the flexibility of responding to subjective competitive situations where pricing on specific products need discounts to win deals in aggregate. The following workflows from Deloitte explain how this is being accomplished today:

  • Helping distributors solve sales compensation problems by improving price management drives more deals in the short-term and keep distributors in business long-term. Distributors start out building their sales comp plans on volume and growth alone. The problem is comp plans reward revenue growth at the expense of profits. That’s making it harder every year for distributors to stay in business. Manufacturers delivering new pricing management and optimization apps in their CPQ platforms need to provide real-time guidance on margin potential by the deal, pricing waterfall logic that includes margins, contract pricing overrides for margins and more if they are going to help their distributors stay in business.

Conclusion – Pricing Is the Engine Powering CPQ’s Market Growth Today

Manufacturers who excel at growing indirect product and services revenue through channels realize that every one of their channel partners is more loyal to pricing and margins than any specific vendor they resell. Providing a CPQ application or platform they can personalize, and automate workflows is just the beginning. The bottom line is that manufacturers need to put more intensity into improving pricing today if they’re going to hold onto the distributors they have and attract new ones.

Pricing is the primary catalyst driving the CPQ market’s growth as well. According to Gartner, the CPQ grew 36% in 2017, reaching $1.084B with the majority of growth attributable to cloud-based solutions. It’s no wonder CPQ is considered one of the hottest CRM technologies for the foreseeable future, projected to grow at a 25% Compound Annual Growth Rate (CAGR) through 2020. Supervised machine learning algorithms capable of providing guardrails in real-time for every potential deal a reseller sales representative has is what’s needed to protect a distributor’s margins. Winning more deals with channel partners starts by respecting how vital margins are to their success and improving pricing management as part of a broader CPQ strategy that delivers results.

Sources:

Configure, Price, and Quote (CPQ) Capabilities: Why the right CPQ capability is key to transitioning to a flexible consumption model, 8 pp., PDF, no opt-in, Deloitte, 2019.

Pricing: Distributors’ Most Powerful Value-Creation Lever, McKinsey & Company, September 2019.

It’s Time To Solve K-12’s Cybersecurity Crisis

It's Time To Solve K-12's Cybersecurity Crisis

  • There were a record 160 publicly-disclosed security incidents in K-12 during the summer months of 2019, exceeding the total number of incidents reported in all of 2018 by 30%.
  • 47% of K-12 organizations are making cybersecurity their primary investment, yet 74% do not use encryption.
  • 93% of K-12 organizations rely on native client/patch management tools that have a 56% failure rate, with 9% of client/patch management failures never recovered.

These and many other fascinating insights are from Absolute’s new research report, Cybersecurity and Education: The State of the Digital District in 2020​, focused on the state of security, staff and student safety, and endpoint device health in K-12 organizations. The study’s findings reflect the crisis the education sector is facing as they grapple with high levels of risk exposure – driven in large part by complex IT environments and a digitally savvy student population – that have made them a prime target for cybercriminals and ransomware attackers. The methodology is based on data from 3.2M devices containing Absolute’s endpoint visibility and control platform, active in 1,200 K-12 organizations in North America (U.S. and Canada). Please see the full report for complete details on the methodology.

Here’ the backdrop:

  • K-12 cybersecurity incidents are skyrocketing, with over 700 reported since 2016 with 160 occurring during the summer of 2019 alone. Educational IT leaders face the challenge of securing increasingly complex IT environments while providing access to a digitally savvy student population capable of bypassing security controls. Schools are now the second-largest pool of ransomware victims, just behind local governments and followed by healthcare organizations. As of today, 49 school districts have been hit by ransomware attacks so far this year.

“Today’s educational IT leaders have been tasked with a remarkable feat: adopting and deploying modern learning platforms, while also ensuring student safety and privacy, and demonstrating ROI on security and technology investments,” said Christy Wyatt, CEO of Absolute.

Research from Absolute found:

K-12 IT leaders are now responsible for collectively managing more than 250 unique OS versions, and 93% are managing up to five versions of common applications. The following key insights from the study reflect how severe K-12’s cybersecurity crisis is today:

  • Digital technologies’ rapid proliferation across school districts has turned into a growth catalyst for K-12’s cybersecurity crisis. 94% of school districts have high-speed internet, and 82% provide students with school-funded devices through one-to-one and similar initiatives. Absolute found that funding for educational technology has increased by 62% in the last three years. The Digital Equity Act goes into effect this year, committing additional federal dollars to bring even more technology to the classroom. K-12 IT leaders face the daunting challenge of having to secure on average 11 device types, 258 unique operating systems versions and over 6,400 unique Chrome OS extensions and more, reflecting the broad scale of today’s K-12 cybersecurity crisis. Google Chromebooks dominate the K-12 device landscape. The following graphic illustrates how rapidly digital technologies are proliferating in K-12 organizations:

  • 42% of K-12 organizations have staff and students regularly bypass security endpoint controls using web proxies and rogue VPN apps, inadvertently creating gateways for malicious outsiders to breach their schools’ networks. Absolute found that there are on average 10.6 devices with web proxy/rogue VPN apps per school and 319 unique web proxy/rogue VPN apps in use today, including “Hide My Ass” and “IP Vanish.”  Many of the rogue VPN apps originate in China, and all of them are designed to evade web filtering and other content controls. With an average of 10.6 devices per school harboring web proxies and rogue VPN apps, schools are also at risk of non-compliance with the Children’s Internet Protection Act (CIPA).

  • While 68% of education IT leaders say that cybersecurity is their top priority, 53% rely on client/patch management tools that are proving ineffective in securing their proliferating IT infrastructures. K-12 IT leaders are relying on client/patch management tools to secure the rapidly proliferating number of devices, operating systems, Chrome extensions, educational apps, and unique application versions. Client/patch management agents fail 56% of the time, however, and 9% never recover. There are on average, nine daily encryption agents’ failures, 44% of which never recover. The cybersecurity strategy of relying on native client/patch management isn’t working, leading to funds being wasted on K-12 security controls that don’t scale:

“Wyatt continued, this is not something that can be achieved by simply spending more money… especially when that money comes from public funds. The questions they each need to be asking are if they have the right foundational security measures in place, and whether the controls they have already invested in are working properly. Without key foundational elements of a strong and resilient security approach in place – things like visibility and control, it becomes nearly impossible to protect your students, your data, and your investments.”

  • Providing greater device visibility and endpoint security controls while enabling applications and devices to be more resilient is a solid first step to solving the K-12 cybersecurity crisis. Thwarting the many breach and ransomware attacks K-12 organizations receive every day needs to start by considering every device as part of the network perimeter. Securing K-12 IT networks to the device level delivers asset management and security visibility that native client/patch management tools lack. Having visibility to the device level also gives K-12 IT administrators and educators insights into how they can tailor learning programs for broader adoption. The greater the visibility, the greater the control. K-12 IT administrators can ensure internet safety policies are being adhered to while setting controls to be alerted of suspicious activity or non-compliant devices, including rogue VPNs or stolen devices. Absolute’s Persistence platform provides a persistent connection to each endpoint in a K-12’s one-to-one program, repairing or replacing critical apps that have been disabled or removed.

You can download the full Absolute report here.

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

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

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

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

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

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

Sources:

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

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

10 Ways AI And Machine Learning Are Improving Endpoint Security

  • Gartner predicts $137.4B will be spent on Information Security and Risk Management in 2019, increasing to $175.5B in 2023, reaching a CAGR of 9.1%. Cloud Security, Data Security, and Infrastructure Protection are the fastest-growing areas of security spending through 2023.
  •  69% of enterprise executives believe artificial intelligence (AI) will be necessary to respond to cyberattacks with the majority of telecom companies (80%) saying they are counting on AI to help identify threats and thwart attacks according to Capgemini.
  •  Spending on AI-based cybersecurity systems and services reached $7.1B in 2018 and is predicted to reach $30.9B in 2025, attaining a CAGR of 23.4% in the forecast period according to Zion Market Research.

Traditional approaches to securing endpoints based on the hardware characteristics of a given device aren’t stopping breach attempts today. Bad actors are using AI and machine learning to launch sophisticated attacks to shorten the time it takes to compromise an endpoint and successfully breach systems. They’re down to just 7 minutes after comprising an endpoint and gaining access to internal systems ready to exfiltrate data according to Ponemon. The era of trusted and untrusted domains at the operating system level, and “trust, but verify” approaches are over. Security software and services spending is soaring as a result, as the market forecasts above show.

AI & Machine Learning Are Redefining Endpoint Security

AI and machine learning are proving to be effective technologies for battling increasingly automated, well-orchestrated cyberattacks and breach attempts. Attackers are combining AI, machine learning, bots, and new social engineering techniques to thwart endpoint security controls and gain access to enterprise systems with an intensity never seen before. It’s becoming so prevalent that Gartner predicts that more than 85% of successful attacks against modern enterprise user endpoints will exploit configuration and user errors by 2025. Cloud platforms are enabling AI and machine learning-based endpoint security control applications to be more adaptive to the proliferating types of endpoints and corresponding threats. The following are the top ten ways AI and machine learning are improving endpoint security:

  • Using machine learning to derive risk scores based on previous behavioral patterns, geolocation, time of login, and many other variables is proving to be effective at securing and controlling access to endpoints. Combining supervised and unsupervised machine learning to fine-tune risk scores in milliseconds is reducing fraud, thwarting breach attempts that attempt to use privileged access credentials, and securing every identity on an organizations’ network. Supervised machine learning models rely on historical data to find patterns not discernable with rules or predictive analytics. Unsupervised machine learning excels at finding anomalies, interrelationships, and valid links between emerging factors and variables. Combining both unsupervised and supervised machine learning is proving to be very effective in spotting anomalous behavior and reducing or restricting access.
  • Mobile devices represent a unique challenge to achieving endpoint security control, one that machine learning combined with Zero Trust is proving to be integral at solving.  Cybercriminals prefer to steal a mobile device, its passwords, and privileged access credentials than hack into an organization. That’s because passwords are the quickest onramp they have to the valuable data they want to exfiltrate and sell. Abandoning passwords for new techniques including MobileIron’s zero sign-on approach shows potential for thwarting cybercriminals from getting access while hardening endpoint security control. Securing mobile devices using a zero-trust platform built on a foundation of unified endpoint management (UEM) capabilities enables enterprises to scale zero sign-on for managed and unmanaged services for the first time. Below is a graphic illustrating how they’re adopting machine learning to improve mobile endpoint security control:
  • Capitalizing on the core strengths of machine learning to improve IT asset management is making direct contributions to greater security.  IT Management and security initiatives continue to become more integrated across organizations, creating new challenges to managing endpoint security across each device. Absolute Software is taking an innovative approach to solve the challenge of improving IT asset management, so endpoint protection is strengthened at the same time. Recently I had a chance to speak with Nicko van Someren, Ph.D. and Chief Technology Officer at Absolute Software, where he shared with me how machine learning algorithms are improving security by providing greater insights into asset management. “Keeping machines up to date is an IT management job, but it’s a security outcome. Knowing what devices should be on my network is an IT management problem, but it has a security outcome. And knowing what’s going on and what processes are running and what’s consuming network bandwidth is an IT management problem, but it’s a security outcome. I don’t see these as distinct activities so much as seeing them as multiple facets of the same problem space. Nicko added that Absolute’s endpoint security controls begin at the BIOS level of over 500M devices that have their endpoint code embedded in them. The Absolute Platform is comprised of three products: Persistence, Intelligence, and Resilience—each building on the capabilities of the other. Absolute Intelligence standardizes the data around asset analytics and security advocacy analytics to allow Security managers to ask any question they want. (“What’s slowing down my device? What’s working and what isn’t? What has been compromised? What’s consuming too much memory? How does this deviate from normal performance?”). An example of Absolute’s Intelligence providing insights into asset management and security is shown below:
  • Machine learning has progressed to become the primary detection method for identifying and stopping malware attacks. Machine learning algorithms initially contributed to improving endpoint security by supporting the back-end of malware protection workflows. Today more vendors are designing endpoint security systems with machine learning as the primary detection method. Machine learning trained algorithms can detect file-based malware and learn which files are harmful or not based on the file’s metadata and content. Symantec’s Content & Malware Analysis illustrates how machine learning is being used to detect and block malware. Their approach combines advanced machine learning and static code file analysis to block, detect, and analyze threats and stop breach attempts before they can spread.
  • Supervised machine learning algorithms are being used for determining when given applications are unsafe to use, assigning them to containers, so they’re isolated from production systems. Taking into account an applications’ threat score or reputation, machine learning algorithms are defining if dynamic application containment needs to run for a given application. Machine learning-based dynamic application containment algorithms and rules block or log unsafe actions of an application based on containment and security rules. Machine learning algorithms are also being used for defining predictive analytics that define the extent of a given applications’ threat.
  •  Integrating AI, machine learning, and SIEM (Security Information and Event Management) in a single unified platform are enabling organizations to predict, detect, and respond to anomalous behaviors and events. AI and machine learning-based algorithms and predictive analytics are becoming a core part of SIEM platforms today as they provide automated, continuous analysis and correlation of all activity observed within a given IT environment. Capturing, aggregating, and analyzing endpoint data in real-time using AI techniques and machine learning algorithms is providing entirely new insights into asset management and endpoint security. One of the most interesting companies to watch in this area is LogRhythm. They’ve developed an innovative approach to integrating AI, machine learning, and SIEM in their LogRhythm NextGen SIEM Platform, which delivers automated, continuous analysis and correlation of all activity observed within an IT environment. The following is an example of how LogRhythm combines AI, machine learning, and SIEM to bring new insights into securing endpoints across a network.
  • Machine learning is automating the more manually-based, routine incident analysis, and escalation tasks that are overwhelming security analysts today. Capitalizing on supervised machine learnings’ innate ability to fine-tune algorythms in milliseconds based on the analysis of incidence data, endpoint security providers are prioritizing this area in product developnent. Demand from potential customers remains strong, as nearly everyone is facing a cybersecurity skills shortage while facing an onslaught of breach attempts.  “The cybersecurity skills shortage has been growing for some time, and so have the number and complexity of attacks; using machine learning to augment the few available skilled people can help ease this. What’s exciting about the state of the industry right now is that recent advances in Machine Learning methods are poised to make their way into deployable products,” Absolute’s CTO Nicko van Someren added.
  • Performing real-time scans of all processes with an unknown or suspicious reputation is another way how machine learning is improving endpoint security. Commonly referred to as Hunt and Respond, supervised and unsupervised machine learning algorithms are being used today to seek out and resolve potential threats in milliseconds instead of days. Supervised machine learning algorithms are being used to discover patterns in known or stable processes where anomalous behavior or activity will create an alert and pause the process in real-time. Unsupervised machine learning algorithms are used for analyzing large-scale, unstructured data sets to categorize suspicious events, visualize threat trends across the enterprise, and take immediate action at a single endpoint or across the entire organization.
  • Machine learning is accelerating the consolidation of endpoint security technologies, a market dynamic that is motivating organizations to trim back from the ten clients they have on average per endpoint today. Absolute Software’s 2019 Endpoint Security Trends Report found that a typical device has ten or more endpoint security agents installed, each often conflicting with the other. The study also found that enterprises are using a diverse array of endpoint agents, including encryption, AV/AM, and Endpoint Detection and Response (EDR). The wide array of endpoint solutions make it nearly impossible to standardize a specific test to ensure security and safety without sacrificing speed. By helping to accelerate the consolidation of security endpoints, machine learning is helping organizations to see the more complex and layered the endpoint protection, the greater the risk of a breach.
  • Keeping every endpoint in compliance with regulatory and internal standards is another area machine learning is contributing to improving endpoint security. In regulated industries, including financial services, insurance, and healthcare, machine learning is being deployed to discover, classify, and protect sensitive data. This is especially the case with HIPAA (Health Insurance Portability and Accountability Act) compliance in healthcare. Amazon Macie is representative of the latest generation of machine learning-based cloud security services. Amazon Macie recognizes sensitive data such as personally identifiable information (PII) or intellectual property and provides organizations with dashboards, alerts, and contextual insights that give visibility into how data is being accessed or moved. The fully managed service continuously monitors data access activity for anomalies and generates detailed alerts when it detects the risk of unauthorized access or inadvertent data leaks. An example of one of Amazon Macie’s dashboard is shown below:
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