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Posts from the ‘SaaS’ Category

74% Of Data Breaches Start With Privileged Credential Abuse

Centrify’s survey shows organizations are granting too much trust and privilege, opening themselves up to potential internal and externally-driven breaches initiated with compromised privileged access credentials. Photo credit: iStock

Enterprises who are prioritizing privileged credential security are creating a formidable competitive advantage over their peers, ensuring operations won’t be interrupted by a breach. However, there’s a widening gap between those businesses protected from a breach and the many who aren’t. In quantifying this gap consider the typical U.S.-based enterprise will lose on average $7.91M from a breach, nearly double the global average of $3.68M according to IBM’s 2018 Data Breach Study.

Further insights into how wide this gap is are revealed in Centrify’s Privileged Access Management in the Modern Threatscape survey results published today. The study is noteworthy as it illustrates how wide the gap is between enterprises’ ability to avert and thwart breaches versus their current levels of Privileged Access Management (PAM) and privileged credential security. 74% of IT decision makers surveyed whose organizations have been breached in the past, say it involved privileged access credential abuse, yet just 48% have a password vault, just 21% have multi-factor authentication (MFA) implemented for privileged administrative access, and 65% are sharing root or privileged access to systems and data at least somewhat often.

Addressing these three areas with a Zero Trust approach to PAM would make an immediate difference in security.

“What’s alarming is that the survey reveals many organizations, armed with the knowledge that they have been breached before, are doing too little to secure privileged access. IT teams need to be taking their Privileged Access Management much more seriously, and prioritizing basic PAM strategies like vaults and MFA while reducing shared passwords,” remarked Tim Steinkopf, Centrify CEO. FINN Partners, on behalf of Centrify, surveyed 1,000 IT decision makers (500 in the U.S. and 500 in the U.K.) online in October 2018. Please see the study here for more on the methodology.

How You Choose To Secure Privileged Credentials Determines Your Future 

Identities are the new security perimeter. Threats can emerge within and outside any organization, at any time. Bad actors, or those who want to breach a system for financial gain or to harm a business, aren’t just outside. 18% of healthcare employees are willing to sell confidential data to unauthorized parties for as little as $500 to $1,000, and 24% of employees know of someone who has sold privileged credentials to outsiders, according to a recent Accenture survey.

Attackers are increasingly logging in using weak, stolen, or otherwise compromised credentials. Centrify’s survey underscores how the majority of organizations’ IT departments have room for improvement when it comes to protecting privileged access credentials, which are the ‘keys to the kingdom.’ Reading the survey makes one realize that forward-thinking enterprises who are prioritizing privileged credential security gain major cost and time advantages over their competitors. They’re able to keep their momentum going across every area of their business by not having to recover from breaches or incur millions of dollars on losses or fines as the result of a breach.

One of the most promising approaches to securing every privileged identity and threat space within and outside an organization is Zero Trust Privilege (ZTP). ZTP enables an organizations’ IT team to grant least privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment.

Key Lessons Learned from the Centrify Survey

How wide the gap is between organizations who see identities as the new security perimeter and are adopting a Zero Trust approach to securing them and those that aren’t is reflected in the results of Centrify’s Privileged Access Management in the Modern Threatscape surveyThe following are the key lessons learned of where and how organizations can begin to close the security gaps they have that leave them vulnerable to privileged credential abuse and many other potential threats:

  • Organizations’ most technologically advanced areas that are essential for future growth and attainment of strategic goals are often the most unprotected. Big Data, cloud, containers and network devices are the most important areas of any IT infrastructure. According to Centrify’s survey, they are the most unprotected as well. 72% of organizations aren’t securing containers with privileged access controls. 68% are not securing network devices like hubs, switches, and routers with privileged access controls. 58% are not securing Big Data projects with privileged access controls. 45% are not securing public and private cloud workloads with privileged access controls. The study finds that UK-based businesses lag U.S.-based ones in each of these areas as the graphic below shows:

  • Only 36% of U.K. organizations are very confident in their company’s current IT security software strategies, compared to 65% in the U.S. The gap between organizations with hardened security strategies that have a higher probability of withstanding breach attempts is wide between U.K. and U.S.-based businesses. 44% of U.K. respondents weren’t positive about what Privileged Access Management is, versus 26% of U.S. respondents. 60% of U.K. respondents don’t have a password vault.

  • Just 35% of U.S. organizations and 30% of those in the UK are relying on Privileged Access Management to manage partners’ access to privileged credentials and infrastructure. Partners are indispensable for scaling any new business strategy and expanding an existing one across new markets and countries. Forward-thinking organizations look at every partner associates’ identity as a new security perimeter. The 35% of U.S.-based organizations doing this have an immediate competitive advantage over the 65% who aren’t. By enforcing PAM across their alliances and partnerships, organizations can achieve uninterrupted growth by eliminating expensive and time-consuming breaches that many businesses never fully recover from.
  • Organizations’ top five security projects for 2019 include protecting cloud data, preventing data leakage, analyzing security incidents, improving security education/awareness and encrypting data. These top five security projects could be achieved at scale by having IT teams implement a Zero Trust-based approach to Privileged Access Management (PAM). The time, cost and scale advantages of getting the top five security projects done using Zero Trust would free up IT teams to focus on projects that deliver direct revenue gains for example.

Conclusion

Centrify’s survey shows organizations are granting too much trust and privilege, opening themselves up to potential internal and externally-driven breaches initiated with compromised privileged access credentials. It also reveals that there is a strong desire to adhere to best practices when it comes to PAM (51% of respondents) and that the reason it is not being adequately implemented rarely has to do with prioritization or difficulty but rather budget constraints and executive buy-in.

The survey also shows U.K. – and U.S.-based organizations need to realize identity is the new security perimeter. For example, only 37% of respondents’ organizations are able to turn off privileged access for an employee who leaves the company within one day, leaving a wide-open exposure point that can continue to be exploited.

There are forward-thinking organizations who are relying on Zero Trust Privilege as a core part of their digital transformation efforts as well. The survey found that given a choice, respondents are most likely to say digital transformation (40%) is one of the top 3 projects they’d prefer to work on, followed by Endpoint Security (37%) and Privileged Access Management (28%). Many enterprises see digital transformation’s missing link being Zero Trust and the foundation for redefining their businesses by defining every identity as a new security perimeter, so they can securely scale and grow faster than before.

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Vodafone’s 2019 IoT Barometer Reflects Robust Growth In The Enterprise

  • 85% of enterprises who develop deep expertise with IoT succeed at driving revenue faster than competitors.
  • 81% of enterprises say Artificial Intelligence streamlines interpreting and taking action on data insights gained from IoT systems and sensors.
  • 68% of enterprises are using IoT to track the security of physical assets, making this use case the most common across enterprises today.
  • Transport & Logistics and Manufacturing & Industrials saw the most significant increase in adoption between 2018 and 2019.

These and many other fascinating insights are from the 6th annual Vodafone IoT Barometer, 2019.  The entire report can be downloaded here (PDF, 32 pp., e-mail opt-in). The methodology is based on 1,758 interviews distributed across the Americas (22%), EMEA (49%) and Asia-Pacific (29%). Eight vertical markets were included with manufacturing (22%), healthcare and wellness (14%) and retail, leisure, and hospitality (14%) being the three most represented markets.  Vodaphone is making an interactive tool available here for exploring the results.

Key insights from Vodafone’s 2019 IoT Barometer include the following:

  • 34% of global businesses are now using IoT in daily operations, up from 29% in 2018, with 95% of IoT adopters are already seeing measurable benefits. 81% of IoT adopters say their reliance on IoT has grown, and 76% of adopters say IoT is mission-critical to them. 58% are using analytics platforms to get more insights from their IoT data to improve decision making. 71% of enterprises who have adopted IoT expect their company and others like them will start listing data resources on their balance sheets as assets within five years.

  • 95% of enterprises adopting IoT are achieving tangible benefits and positive ROI. 52% of enterprises report significant returns on their IoT investments. 79% say IoT is enabling positive outcomes that would have been impossible without it, further reflecting robust growth in the enterprise. Across all eight vertical markets reducing operating costs (53%) and gaining more accurate data and insights (48%) are the most common benefits. Transitioning an IoT pilot to production based on cost reduction and improved visibility creates a compelling ROI for many enterprises. The following graphic compares IoT’s benefits to enterprises. Please click on the graphic to expand for easier reading.

  • Transport & Logistics and Manufacturing & Industrials saw the greatest increase in adoption between 2018 and 2019. Transport and Logistics had the highest IoT adoption rate at 42% followed by Manufacturing and Industrials at 39%. Manufacturers are facing the challenges of improving production efficiency and product quality while accelerating time-to-market for next-generation smart, connected products. IoT contributes to productivity improvements and creates opportunities for services-based business models, two high priorities for manufacturers in 2019 and beyond.  The following graphic from the interactive tool compares IoT adoption by industry based on Vodaphone’s IoT barometer data over the last six years:

  • 89% of most sophisticated enterprises have multiple full-scale projects in production, orchestrating IoT with analytics, AI and cloud, creating a technology stack that delivers real-time insights. Enterprises who lead IoT adoption in their industries rely on integration to gain scale and speed advantages quickly over competitors. The greater the real-time integration, the greater the potential to digitally transform an enterprise and remove roadblocks that get in the way of growing. 95% of adopters where IoT is fully integrated say it’s enabling their digital transformation, compared with 55% that haven’t started integration. The following graphics reflect how integrated enterprises’ IoT projects are with existing business systems and processes and the extent to which enterprises agree that IoT is enabling digital transformation.

  • 68% of enterprises are using IoT to track the security of physical assets, making this use case the most common across enterprises today. 57% of all enterprises are using IoT to manage risk and compliance. 53% are using it to increase revenue and cut costs, with 82% of high performing enterprises rely on IoT to manage risk and compliance. The following graphic compares the types of variables enterprises are using IoT to track today and plan to in the future.

  • IoT adoption is soaring in Americas-based enterprises, jumping from 27% in 2018 to 40% in 2019. The Americas region leads the world in terms of IoT usage assessed by strategy, integration, and implementation of IoT deployments. 73% of Americas-based enterprises are the most likely to report significant returns from their IoT investments compared to 47% for Asia-Pacific (APAC) and 45% for Europe, Middle East and Africa (EMEA).
  • 52% of IoT-enabled enterprises plan to use 5G when it becomes available. Enterprises are looking forward to 5G’s many advantages including improved security via stronger encryption, more credentialing options, greater quality of service management, more specialized services and near-zero latency. Vodafone predicts 5G will be a strong catalyst of growth for emerging IoT applications including connected cars, smart cities, eHealth and industrial automation.

 

Digital Transformation’s Missing Link Is Zero Trust

    • Enterprises will invest $2.4T by 2020 in digital transformation technologies including cloud platforms, cognitive systems, IoT, mobile, robotics, and integration services according to the World Economic Forum.
    • Digital transformation software and services revenue in the U.S. is predicted to reach $490B in 2025, soaring from $190B in 2019, attaining a Compound Annual Growth Rate (CAGR) of 14.49% according to Grand View Research published by Statista.
    • IDC predicts worldwide spending on the technologies and services that enable the digital transformation of business practices, products, and organizations will reach $1.97T in 2022.
    • Legacy approaches to Privileged Access Management (PAM) don’t protect the new threatscapes digital transformation initiatives create, making Zero Trust Privilege essential for enterprises.

B2B customers, including manufacturers looking to replace legacy production equipment with smart, connected machines, have high expectations when it comes to product quality, ease of integration, and intuitive user experiences. Replacing factories full of legacy assets with smart, connected machinery is one of the most powerful catalysts driving digital transformation today. Innovative smart, connected machinery and the performance gains they provide are the oxygen that keeps customer relationships alive. That’s why digital transformation forecasts from the World Economic Forum, Grand View ResearchIDC, and many others predict perennial growth. The many forecasts reflect a fundamental truth: digital transformation done with intensity creates a customer-driven renaissance for any business.

Businesses digitally transforming themselves are succeeding because they’ve made themselves accountable and transparent to customers. Earning and protecting that trust is the heartbeat of any business’ growth. 51% of enterprises invest in digital transformation to capture growth opportunities in new markets, with 46% investing to stay in front of evolving customer behaviors and preferences. Brian Solis’ excellent report, The State of Digital Transformation, 2018 – 2019 Edition (31 pp., PDF, opt-in) shows how digitally transforming any business with the customer first leads to greater growth. The graphic from his study illustrates this point:

 

Closing The Digital Transformation Gap With Zero Trust

Gaps exist between the results digital transformation initiatives are delivering today, and the customer-driven value they’re capable of. According to Gartner, 75% of digital transformation projects are not aligned internally today, leading to delayed new product launches, mediocre experiences, and greater security risks than ever before. Interactive, IoT-enabled experiences and products are expanding the threatscape of enterprises to include Big Data, cloud, containers, DevOps, IoT systems, and more. With that comes a host of new exposure points, many of which allow access to sensitive data that must be protected with modern Privileged Access Management solutions that reduce risk in these modern enterprise use cases.

The new security perimeter is identity. Forrester estimates that 80% of data breaches are caused by privileged access abuse. Every smart, connected machine that replaces legacy production equipment is another identity that defines a manufacturer’s security perimeter.

As the use cases and adoption of smart, connected machines proliferate, so too does the urgency that manufacturers need to replace their legacy approaches to Privileged Access Management (PAM). Relying on outdated strategies for protecting administrative access to all machines needs to be replaced with a “never trust, always verify, enforce least privilege” approach.

IT needs to improve how they’re protecting the most privileged access credentials, the ‘keys to the kingdom,’ by granting just-enough, just-in-time privilege. Of the many cybersecurity approaches available today, Zero Trust Privilege (ZTP) enables IT to grant least privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment.

The more diverse any digital transformation strategy, the greater the risk of privileged credential abuse. Thwarting privileged credential abuse needs to start with a least privilege access approach, minimizing each attack surface, improving audit and compliance visibility while reducing risk, complexity, and costs. Leaders in Zero Trust include CentrifyMobileIronPalo Alto Networks, and others. Of these companies, Centrify’s approach to Zero Trust to prevent privileged access abuse shows the greatest potential for securing digital transformation initiatives and strategies.

How To Secure Digital Transformation Strategies

IDG Research found in their Security Priorities for 2018 study that 71% of security-focused IT decision-makers are aware of the Zero Trust model and 18% of enterprises are either running pilots or have implemented Zero Trust.

Zero Trust Privilege (ZTP) is the force multiplier digital transformation initiatives need to reach their true potential by securing administrative access to the complex mix of machinery and infrastructure – and the sensitive data they hold and use – that manufacturers rely on daily.

Starting with a strategic perspective, ZTP’s contribution to securing digital transformation deployments apply to every area of planning, pilots, platforms, product, and service data being designed to stop the leading cause of breaches, which is privileged credential abuse. The following graphic illustrates how ZTP needs to span every aspect of an enterprise’s digital transformation capabilities.

Source: World Economic Forum, Digital Transformation Initiative, May 2018

Conclusion

By 2020, 30% of Global 2000 companies will have allocated capital budget equal to at least 10% of revenue to fuel their digital transformation strategies according to IDC.  European spending on technologies and services that enable the digital transformation of business practices, products, and organizations is forecasted to reach $378.2B in 2022. The perennial growth these forecasts promise is predicated on enterprises delivering new experiences and innovative products, which create the oxygen that keeps their customer relationships alive.

Amidst all the potential for growth, enterprises need to realize every new infrastructure element, machine, or connected production asset is a new identity that collectively comprises the fabric of their security perimeter. Legacy cybersecurity approaches won’t scale to protect the proliferating number of smart machines being put into use today. Relying entirely on legacy approaches to PAM, where privileged access to systems and resources only inside the network are secure, is failing today. Smart, connected machinery and the products and experiences they deliver require an entirely new cybersecurity strategy, one based on a “never trust, always verify, enforce least privilege” approach. Centrify Zero Trust Privilege shows potential to meet this challenge by granting least privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment.

How Machine Learning Improves Manufacturing Inspections, Product Quality & Supply Chain Visibility

Bottom Line: Manufacturers’ most valuable data is generated on shop floors daily, bringing with it the challenge of analyzing it to find prescriptive insights fast – and an ideal problem for machine learning to solve.

Manufacturing is the most data-prolific industry there is, generating on average 1.9 petabytes of data every year according to the McKinsey Global Insititute. Supply chains, sourcing, factory operations, and the phases of compliance and quality management generate the majority of data.

The most valuable data of all comes from product inspections that can immediately find exceptionally strong or weak suppliers, quality management and compliance practices in a factory. Manufacturing’s massive problem is in getting quality inspection results out fast enough across brands & retailers, other factories, suppliers and vendors to make a difference in future product quality.

How A Machine Learning Startup Is Revolutionizing Product Inspections

Imagine you’re a major brand or retailer and you’re relying on a network of factories across Bangladesh, China, India, and Southeast Asia to produce your new non-food consumer goods product lines including apparel. Factories, inspection agencies, suppliers and vendors that brands and retailers like you rely on vary widely on ethics, responsible sourcing, product quality, and transparency. With your entire consumer goods product lines (and future sales) at risk based on which suppliers, factories and product inspection agencies you choose, you and your companies’ future are riding on the decisions you make.

These career- and company-betting challenges and the frustration of gaining greater visibility into what’s going on in supply chains to factory floors led Carlos Moncayo Castillo and his brothers Fernando Moncayo Castillo and Luis Moncayo Castillo to launch Inspectorio. They were invited to the Target + Techstars Retail Accelerator in the summer of 2017, a competition they participated in with their cloud-based inspection platform that includes AI and machine learning and pervasive support for mobile technologies. Target relies on them today to bring greater transparency to their supply chains. “I’ve spent years working in non-food consumer goods product manufacturing seeing the many disconnects between inspections and suppliers, the lack of collaboration and how gaps in information create too many opportunities for corruption – I had to do something to solve these problems,” Carlos said. The many problems that a lack of inspection and supply chain visibility creates became the pain Inspectorio focused on solving immediately for brands and retailers. The following is a graphic of their platform:

Presented below are a few of the many ways the combining of a scalable inspection cloud platform combined with AI, machine learning and mobile technologies are improving inspections, product quality, and supply chain visibility:

  • Enabling the creation of customized inspector workflows that learn over time and are tailored to specific products including furniture, toys, homeware and garments, the factories they’re produced in, quality of the materials used. Inspectorio’s internal research has found 74% of all inspections today are done manually using a pen and paper, with results reported in Microsoft Word, Excel or PDFs, making collaboration slow and challenging. Improving the accuracy, speed and scale of inspection workflows including real-time updates across production networks drive major gains in quality and supply chain performance.
  • Applying constraint-based algorithms and logic to understand why there are large differences in inspection results between factories is enabling brands & retailers to manage quality faster and more completely. Uploading inspections in real-time from mobile devices to an inspection platform that contains AI and machine learning applications that quickly parse the data for prescriptive insights is the future of manufacturing quality. Variations in all dimensions of quality including factory competency, supplier and production assembly quality are taken into account. In a matter of hours, inspection-based data delivers the insights needed to avert major quality problems to every member of a production network.
  • Reducing risk, the potential for fraud, while improving the product and process quality based on insights gained from machine learning is forcing inspection’s inflection point. When inspections are automated using mobile technologies and results are uploaded in real-time to a secure cloud-based platform, machine learning algorithms can deliver insights that immediately reduce risks and the potential for fraud. One of the most powerful catalysts driving inspections’ inflection point is the combination of automated workflows that deliver high-quality data that machine learning produces prescriptive insights from. And those insights are shared on performance dashboards across every brand, retailer, supplier, vendor and factory involved in shared production strategies today.
  • Matching the most experienced inspector for a given factory and product inspection drastically increases accuracy and quality. When machine learning is applied to the inspector selection and assignment process, the quality, and thoroughness of inspections increase. For the first time, brands, retailers, and factories have a clear, quantified view of Inspector Productivity Analysis across the entire team of inspectors available in a given region or country. Inspections are uploaded in real-time to the Inspectorio platform where advanced analytics and additional machine learning algorithms are applied to the data, providing greater prescriptive insights that would have ever been possible using legacy manual methods. Machine learning is also making recommendations to inspectors on which defects to look for first based on the data patterns obtained from previous inspections.
  • Knowing why specific factories and products generated more Corrective Action/Preventative Action (CAPA) than others and how fast they have been closed in the past and why is now possible. Machine learning is making it possible for entire production networks to know why specific factory and product combinations generate the most CAPAs. Using constraint-based logic, machine learning can also provide prescriptive insights into what needs to be improved to reduce CAPAs, including their root cause.

10 Ways Machine Learning Is Revolutionizing Sales

  • Sales teams adopting AI are seeing an increase in leads and appointments of more than 50%, cost reductions of 40%–60%, and call time reductions of 60%–70% according to the Harvard Business Review article Why Salespeople Need to Develop Machine Intelligence.
  • 62% of highest performing salespeople predict guided selling adoption will accelerate based on its ability rank potential opportunities by value and suggest next steps according to Salesforces’ latest State of Sales research study.
  • By 2020, 30% of all B2B companies will employ AI to augment at least one of their primary sales processes according to Gartner.
  • High-performing sales teams are 4.1X more likely to use AI and machine learning applications than their peers according to the State of Sales published by Salesforce.
  • Intelligent forecasting, opportunity insights, and lead prioritization are the top three AI and machine learning use cases in sales.

Artificial Intelligence (AI) and machine learning show the potential to reduce the most time-consuming, manual tasks that keep sales teams away from spending more time with customers. Automating account-based marketing support with predictive analytics and supporting account-centered research, forecasting, reporting, and recommending which customers to upsell first are all techniques freeing sales teams from manually intensive tasks.

The Race for Sales-Focused AI & Machine Learning Patents Is On

CRM and Configure, Price & Quote (CPQ) providers continue to develop and fine-tune their digital assistants, which are specifically designed to help the sales team get the most value from AI and machine learning. Salesforces’ Einstein supports voice-activation commands from Amazon Alexa, Apple Siri, and Google. Salesforce and other enterprise software companies continue aggressively invest in Research & Development (R&D). For the nine months ended October 31, 2018, Salesforce spent $1.3B or 14% of total revenues compared to $1.1B or 15% of total revenues, during the same period a year ago, an increase of $211M according to the company’s 10Q filed with the Securities and Exchange Commission.

The race for AI and machine learning patents that streamline selling is getting more competitive every month. Expect to see the race of sales-focused AI and machine learning patents flourish in 2019. The National Bureau of Economic Research published a study last July from the Stanford Institute For Economic Policy Research titled Some Facts On High Tech Patenting. The study finds that patenting in machine learning has seen exponential growth since 2010 and Microsoft had the greatest number of patents in the 2000 to 2015 timeframe. Using patent analytics from PatentSight and ipsearchIAM published an analysis last month showing Microsoft as the global leader in machine learning patents with 2,075.  The study relied on PatentSight’s Patent Asset Index to rank machine learning patent creators and owners, revealing Microsoft and Alphabet are dominating today. Salesforce investing over $1B a year in R&D reflects how competitive the race for patents and intellectual property is.

10 Ways Machine Learning Is Revolutionizing Sales

Fueled by the proliferation of patents and the integration of AI and machine learning code into CRM, CPQ, Customer Service, Predictive Analytics and a wide variety of Sales Enablement applications, use cases are flourishing today. Presented below are the ten ways machine learning is most revolutionizing selling today:

 

  1. AI and machine learning technologies excel at pattern recognition, enabling sales teams to find the highest potential new prospects by matching data profiles with their most valuable customers. Nearly all AI-enabled CRM applications are providing the ability to define a series of attributes, characteristics and their specific values that pinpoint the highest potential prospects. Selecting and prioritizing new prospects using this approach saves sales teams thousands of hours a year.
  2. Lead scoring and nurturing based on AI and machine learning algorithms help guide sales and marketing teams to turn Marketing Qualified Leads (MQL) into Sales Qualified Leads (SQL), strengthening sales pipelines in the process. One of the most important areas of collaboration between sales and marketing is lead nurturing strategies that move prospects through the pipeline. AI and machine learning are enriching the collaboration with insights from third-party data, prospect’s activity at events and on the website, and from previous conversations with salespeople. Lead scoring and nurturing relies heavily on natural language generation (NLG) and natural-language processing (NLP) to help improve each lead’s score.
  3. Combining historical selling, pricing and buying data in a single machine learning model improves the accuracy and scale of sales forecasts. Factoring in differences inherent in every account given their previous history and product and service purchasing cycles is invaluable in accurately predicting their future buying levels. AI and machine learning algorithms integrated into CRM, sales management and sales planning applications can explain variations in forecasts, provided they have the data available. Forecasting demand for new products and services is an area where AI and machine learning are reducing the risk of investing in entirely new selling strategies for new products.
  4. Knowing the propensity of a given customer to churn versus renew is invaluable in improving Customer Lifetime Value. Analyzing a diverse series of factors to see which customers are going to churn or leave versus those that will renew is among the most valuable insights AI and machine learning is delivering today. Being able to complete a Customer Lifetime Value Analysis for every customer a company has provides a prioritized roadmap of where the health of client relationships are excellent versus those that need attention. Many companies are using Customer Lifetime Value Analysis as a proxy for a customer health score that gets reviewed monthly.
  5. Knowing the strategies, techniques and time management approaches the top 10% of salespeople to rely on to excel far beyond quota and scaling those practices across the sales team based on AI-driven insights. All sales managers and leaders think about this often, especially in sales teams where performance levels vary widely. Knowing the capabilities of the highest-achieving salespeople, then selectively recruiting those sales team candidates who have comparable capabilities delivers solid results. Leaders in the field of applying AI to talent management include Eightfold whose approach to talent management is refining recruiting and every phase of managing an employee’s potential. Please see the recent New York Times feature of them here.
  6. Guided Selling is progressing rapidly from a personalization-driven selling strategy to one that capitalized on data-driven insights, further revolutionizing sales. AI- and machine learning-based guided selling is based on prescriptive analytics that provides recommendations to salespeople of which products, services, and bundles to offer at which price. 62% of highest performing salespeople predict guided selling adoption will accelerate based on its ability rank potential opportunities by value and suggest next steps according to Salesforces’ latest State of Sales research study.
  7. Improving the sales team’s productivity by using AI and machine learning to analyze the most effective actions and behaviors that lead to more closed sales. AI and machine learning-based sales contact and customer predictive analytics take into account all sources of contacts with customers and determine which are the most effective. Knowing which actions and behaviors are correlated with the highest close rates, sales managers can use these insights to scale their sales teams to higher performance.
  8. Sales and marketing are better able to define a price optimization strategy using all available data analyzing using AI and machine learning algorithms. Pricing continues to be an area the majority of sales and marketing teams learn to do through trial and error. Being able to analyze pricing data, purchasing history, discounts are taken, promotional programs participated in and many other factors, AI and machine learning can calculate the price elasticity for a given customer, making an optimized price more achievable.
  9. Personalizing sales and marketing content that moves prospects from MQLs to SQLs is continually improving thanks to AI and machine learning. Marketing Automation applications including HubSpot and many others have for years been able to define which content asset needs to be presented to a given prospect at a given time. What’s changed is the interactive, personalized nature of the content itself. Combining analytics, personalization and machine learning, marketing automation applications are now able to tailor content and assets that move opportunities forward.
  10. Solving the many challenges of sales engineering scheduling, sales enablement support and dedicating the greatest amount of time to the most high-value accounts is getting solved with machine learning. CRM applications including Salesforce can define a salesperson’s schedule based on the value of the potential sale combined with the strength of the sales lead, based on its lead score. AI and machine learning optimize a salesperson’s time so they can go from one customer meeting to the next, dedicating their time to the most valuable prospects.

Predicting The Future Of Digital Marketplaces

  • The U.S. B2B eCommerce market is predicted to be worth $1.2T by 2022 according to Forrester.
  • 75% of marketing executives say that reaching customers where they prefer to buy is the leading benefit a company gains from selling through an e-commerce marketplace according to Statista.
  • 67% strongly agree to the importance of B2B e-commerce being critical to their business’s advantages and results in their industry.

Digital Marketplaces are flourishing today thanks to the advances made in Artificial Intelligence (AI), machine learning, real-time personalization and the scale and speed of the latest generation of cloud platforms including the Google Cloud Platform. Today’s digital marketplaces are capitalizing on these technologies to create trusted, virtual trading platforms and environments buyers and sellers rely on for a wide variety of tasks every day.

Differentiated from B2B exchanges and communities from the 90s that often had high transaction costs, proprietary messaging protocols, and limited functionality, today’s marketplaces are proving that secure, trusted scalability is achievable on standard cloud platforms. Kahuna recently partnered with Brian Solis of The Altimeter Group to produce a fascinating research study, The State (and Future) of Digital Marketplaces. The report is downloadable here (PDF, 14 pp., opt-in). A summary of the results is presented below.

Kahuna Digitally Transforms Marketplaces With Personalization

The essence of any successful digital transformation strategy is personalization, and to the extent, any organization can redefine every system, process, and product to that goal is the extent to which they’ll grow. Digital marketplaces are giving long-established business and startups a platform to accelerate their digital transformation efforts by delivering personalization at scale.

Kahuna’s approach to solving personalization at scale across buyers and sellers while creating trust in every transaction reflects the future of digital marketplaces. They’ve been able to successfully integrate AI, machine learning, advanced query techniques and a cloud platform that scales dynamically to handle unplanned 5x global traffic spikes. Kahuna built its marketplace platform on Google App EngineGoogle BigQuery, and other Google Cloud Platform (GCP).

Kahuna’s architecture on GCP has been able to scale and onboard 80+ million users a day without any DevOps support, a feat not possible with the exchange and community platforms of the 90s. By integrating their machine learning algorithms designed to enhance their customers’ ability to personalize marketing messages with Google machine learning APIs to drive TensorFlow, Kahuna has been able to deliver fast response times to customers’ inquiries. Their latest product,  Kahuna Subject Line Optimization, analyzes the billions of emails their customers use to communicate with customers to see what has and hasn’t worked in the past.  Marketplace customers will receive real-time recommendations as they are in the email editor composing an email subject line. Kahuna scores the likely success of the subject lines in appealing to target audiences so that marketers can make adjustments on the fly.

The State (And Future) Of Digital Marketplaces

Digital marketplaces are rapidly transforming from transaction engines to platforms that deliver unique, memorable and trusted personal experiences.
Anyone who has ever used OpenTable to get a last-minute reservation with friends at popular, crowded restaurant has seen the power of digitally enabled marketplace experiences in action. Brian Solis noted futurist, author, and analyst with The Altimeter Group recent report,  The State (and Future) of Digital Marketplaces is based on 100 interviews with North American marketing executives across eight market segments.
Key insights and lessons learned from the study include the following:

  • Altimeter found that 67% of marketplaces are generating more than $50M annually and 32% are generating more than $100M annually with the majority of marketplaces reporting a Gross Merchandise Volume (GMV) of between $500M to $999M. When the size of participating companies is taken into account, it’s clear digital marketplaces are one form of new digital business models larger organizations are adopting, piloting and beginning to standardize on. It can be inferred from the data that fast-growing, forward-thinking smaller organizations are looking to digital marketplaces to help augment their business models. Gross merchandise volume (GMV) is the total value of merchandise sold to customers through a marketplace.
  • 59% of marketing executives say new product/service launches are their most important marketplace objective for 2019. As marketplaces provide an opportunity to create an entirely new business model, marketing executives are focused on how to get first product launches delivering revenue fast. Revenue growth (55%), customer acquisition (54%) and margin improvement (46%) follow in priority, all consistent with an organizations’ strategy of relying on digital marketplaces as new business models.

  • Competitive differentiation, buyer retention, buyer acquisition, and social media engagement and the four most common customer-facing challenges marketplaces face today. 39% of marketing execs say that differentiating from competitors is the greatest challenge, followed by buyer retention (32%), buyer acquisition (29%) and effective social media campaigns (29%) Further validation that today’s digital marketplaces are enabling greater digital transformation through personalization is found in just 22% of respondents said customer experience is a challenge.
  • Marketplaces need to scale and provide a broader base of services that enable “growth as a ” to keep sellers engaged. Marketplaces need to continually be providing new services and adding value to buyers and sellers, fueling growth-as-a-service. The three main reasons sellers leave a marketplace are insufficient competitive differentiation (46%), insufficient sales (33%) and marketplace service fees (31%). Additionally, sellers claim that marketing costs (28%) and the lack of buyers (26%) are critical business issues.
  • Lack of sellers who meet their needs (53%) is the single biggest reason buyers leave marketplaces. Buyers also abandon marketplaces due to logistical challenges including shipping costs and fees added by sellers (49%) and large geographic distances between buyers and sellers (39%). These findings underscore why marketplaces need to be very adept at creating and launching new value-added services and experiences that keep buyers active and loyal. Equally important is a robust roadmap of seller services that continually enables greater sales effectiveness and revenue potential.

Which CRM Applications Matter Most In 2018

 

According to recent research by Gartner,

  • Marketing analytics continues to be hot for marketing leaders, who now see it as a key business requirement and a source of competitive differentiation
  • Artificial intelligence (AI) and predictive technologies are of high interest across all four CRM functional areas, and mobile remains in the top 10 in marketing, sales and customer service.
  • It’s in customer service where AI is receiving the highest investments in real use cases rather than proofs of concept (POCs) and experimentation.
  • Sales and customer service are the functional areas where machine learning and deep neural network (DNN) technology is advancing rapidly.

These and many other fascinating insights are from Gartner’s What’s Hot in CRM Applications in 2018 by Ed Thompson, Adam Sarner, Tad Travis, Guneet Bharaj, Sandy Shen and Olive Huang, published on August 14, 2018. Gartner clients can access the study here  (10 pp., PDF, client access reqd.).

Gartner continually tracks and analyzes the areas their clients have the most interest in and relies on that data to complete their yearly analysis of CRM’s hottest areas. Inquiry topics initiated by clients are an excellent leading indicator of relative interest and potential demand for specific technology solutions. Gartner organizes CRM technologies into the four category areas of Marketing, Sales, Customer Service, and Digital Commerce.

The following graphic from the report illustrates the top CRM applications priorities in Marketing, Sales, Customer Service, and Digital Commerce.

Key insights from the study include the following:

  • Marketing analytics continues to be hot for marketing leaders, who now see it as a key business requirement and a source of competitive differentiation. In my opinion and based on discussions with CMOs, interest in marketing analytics is soaring as they are all looking to quantify their team’s contribution to lead generation, pipeline growth, and revenue. I see analytics- and data-driven clarity as the new normal. I believe that knowing how to quantify marketing contributions and performance requires CMOs and their teams to stay on top of the latest marketing, mobile marketing, and predictive customer analytics apps and technologies constantly. The metrics marketers choose today define who they will be tomorrow and in the future.
  • Artificial intelligence (AI) and predictive technologies are of high interest across all four CRM functional areas, and mobile remains in the top 10 in marketing, sales and customer service. It’s been my experience that AI and machine learning are revolutionizing selling by guiding sales cycles, optimizing pricing and enabling CPQ to define and deliver smart, connected products. I’m also seeing CMOs and their teams gain value from Salesforce Einstein and comparable intelligent agents that exemplify the future of AI-enabled selling. CMOs are saying that Einstein can scale across every phase of customer relationships. Based on my previous consulting in CPQ and pricing, it’s good to see decades-old core technologies underlying Price Optimization and Management are getting a much-needed refresh with state-of-the-art AI and machine learning algorithms, which is one of the factors driving their popularity today. Using Salesforce Einstein and comparable AI-powered apps I see sales teams get real-time guidance on the most profitable products to sell, the optimal price to charge, and which deal terms have the highest probability of closing deals. And across manufacturers on a global scale sales teams are now taking a strategic view of Configure, Price, Quote (CPQ) as encompassing integration to ERP, CRM, PLM, CAD and price optimization systems. I’ve seen global manufacturers take a strategic view of integration and grow far faster than competitors. In my opinion, CPQ is one of the core technologies forward-thinking manufacturers are relying on to launch their next generation of smart, connected products.
  • It’s in customer service where AI is receiving the highest investments in real use cases rather than proofs of concept (POCs) and experimentation. It’s fascinating to visit with CMOs and see the pilots and full production implementations of AI being used to streamline customer service. One CMO remarked how effective AI is at providing greater contextual intelligence and suggested recommendations to customers based on their previous buying and services histories. It’s interesting to watch how CMOs are attempting to integrate AI and its associated technologies including ChatBots to their contribution to Net Promoter Scores (NPS). Every senior management team running a marketing organization today has strong opinions on NPS. They all agree that greater insights gained from predictive analytics and AI will help to clarify the true value of NPS as it relates to Customer Lifetime Value (CLV) and other key metrics of customer profitability.
  • Sales and customer service are the functional areas where machine learning and deep neural network (DNN) technology is advancing rapidly.  It’s my observation that machine learning’s potential to revolutionize sales is still nascent with many high-growth use cases completely unexplored. In speaking with the Vice President of Sales for a medical products manufacturer recently, she said her biggest challenge is hiring sales representatives who will have longer than a 19-month tenure with the company, which is their average today.  Imagine, she said, knowing the ideal attributes and strengths of their top performers and using machine learning and AI to find the best possible new sales hires. She and I discussed the spectrum of companies taking on this challenge, with Eightfold being one of the leaders in applying AI and machine learning to talent management challenges.

Source: Gartner by Ed Thompson, Adam Sarner, Tad Travis, Guneet Bharaj,  Sandy Shen and Olive Huang, published on August 14, 2018.

How Blockchain Can Improve Manufacturing In 2019

  • The business value-add of blockchain will grow to slightly more than $176B by 2025, then exceed $3.1T by 2030 according to Gartner.
  • Typical product recalls cost $8M, and many could be averted with improved track-and-traceability enabled by blockchain.
  • Combining blockchain and IoT will revolutionize product safety, track-and-traceability, warranty management, Maintenance, Repair & Overhaul (MRO), and lead to new usage-based business models for smart, connected products.
  • By 2023, 30% of manufacturing companies with more than $5B in revenue will have implemented Industry 4.0 pilot projects using blockchain, up from less than 5% today according to Gartner.

Blockchain’s greatest potential to deliver business value is in manufacturing. Increasing visibility across every area of manufacturing starting with suppliers, strategic sourcing, procurement, and supplier quality to shop floor operations including machine-level monitoring and service, blockchain can enable entirely new manufacturing business models. Supply chains are the foundation of every manufacturing business, capable of making use of blockchain’s distributed ledger structure and block-based approach to aggregating value-exchange transactions to improve supply chain efficiency first. By improving supplier order accuracy, product quality, and track-and-traceability, manufacturers will be able to meet delivery dates, improve product quality and sell more.

Capgemini Research Institute’s recent study, Does blockchain hold the key to a new age of supply chain transparency and trust? provide valuable insights into how blockchain can improve supply chains and manufacturing. A copy of the study is available here (PDF, 32 pp., no opt-in). Capgemini surveyed 731 organizations globally regarding their existing and planned blockchain initiatives. Initial interviews yielded 447 organizations who are currently experimenting with or implementing blockchain. Please see pages 25 & 26 of the study for additional details regarding the methodology.

Key takeaways of the study include the following:

  • Typical product recalls cost $8M, and many could be averted with improved track-and-traceability enabled by blockchain. Capgemini found that there was 456 food recalls alone in the U.S. last year, costing nearly $3.5B. Blockchain’s general ledger structure provides a real-time audit trail for all transactions secured against modifications making it ideal for audit and compliance-intensive industries.

  • Gaining greater cost savings (89%), enhancing traceability (81%) and enhancing transparency (79%) are the top three drivers behind manufacturer’s blockchain investments today. Additional drivers include increasing revenues (57%), reducing risks (50%), creating new business opportunities (44%) and being more customer-centric (38%). The following graphic from the study illustrates the manufacturer’s priorities for blockchain. Capgemini finds that improving track-and-traceability is a primary driver across all manufacturers, consistent with the broader trend of manufacturers adopting software applications that improve this function today. That’s also understandable given how additional regulatory compliance requirements are coming in 2019 and those manufacturers competing in highly regulated industries including aerospace & defense, medical devices, and pharma are exploring how blockchain can give them a competitive edge now

  • Digital marketplaces, tracking critical supply chain parameters, tracking components quality, preventing counterfeit products, and tracking asset maintenance are the five areas Capgemini predicts blockchain will see the greatest adoption. Based on interviews with industry experts and startups, Capgemini found 24 blockchain use cases which are compared by level of adoption and complexity in the graphic below. The use cases reflect how managing supplier contracts is already emerging as one of the most popular blockchain use cases for manufacturing organizations today and will accelerate as compliance becomes even more important in 2019.

  • Manufacturers have the most at-scale deployments of blockchain today, leading all industries included in the study. Blockchain adoption is still nascent across all industries included in the study, with 6% of manufacturers having at-scale implementations today. Customer products manufacturers lead in pilots, with 15% actively [purusing blockchain in limited scope today. And retailers trail all industries with 91% having only proofs of concept.

  • Combining IoT and blockchain at the shipping container level in supply chains increases authenticity, transparency, compliance to product and contractual requirements while reducing counterfeiting. In highly regulated industries including Aerospace & Defense (A&D), Consumer Packaged Goods (CPG), medical devices, and pharma, combining IoT and blockchain provides real-time data on the shipping container conditions, tamper-proof storage, each shipment’s locational history and if there have been changes in temperature and product condition. Capgemini sees use cases where a change in a shipment’s temperature as measured by a sensor change sends alerts regarding contractual compliance of perishable meats and produce, averting the potential of bad product quality and rejected shipments once they reach their destination.

  • Capgemini found that 13% of manufacturers are Pacesetters and are either implementing blockchain at scale or have pilots in at least one site. Over 60% of Pacesetters believe that blockchain is already transforming the way they collaborate with their partners. Encouraged by these results, Pacesetters are set to increase their blockchain investment by 30% in the next three years. They lead early stage experimenters and all implementers on three core dimensions of organizational readiness. These include end-to-end visibility across functions, detailed and defined supportive processes, and availability of the right talent to succeed.

  • Lack of a clear ROI, immature technology and regulatory challenges are the top three hurdles Pacesetter-class manufacturers face in getting blockchain initiatives accepted and into production. All implementations face these three challenges in addition to having to overcome the lack of complementary IT systems at the partner organizations. The following graphic compares the hurdles all manufacturers face in getting blockchain projects implemented by the level of manufacturers adoption success (Pacesetter, early-stage experimenters, all implementers).

Source: Capgemini Research Institute, Does blockchain hold the key to a new age of supply chain transparency and trust? October, 2018

2018 Roundup Of Cloud Computing Forecasts And Market Estimates

Cloud computing platforms and applications are proliferating across enterprises today, serving as the IT infrastructure driving new digital businesses. The following roundup of cloud computing forecasts and market estimates reflect a maturing global market for cloud services, with proven scale, speed and security to support new business models.

CIOs who are creating compelling business cases that rely on cloud platforms as a growth catalyst is the architects enabling these new business initiatives to succeed. The era of CIO strategist has arrived. Key takeaways include the following:

  • Amazon Web Services (AWS) accounted for 55% of the company’s operating profit in Q2, 2018, despite contributing only 12% to the company’s net sales. In Q1, 2018 services accounted for 40% of Amazon’s revenue, up from 26% three years earlier. Source: Cloud Business Drives Amazon’s Profits, Statista, July 27, 2018.

  • 80% of enterprises are both running apps on or experimenting with Amazon Web Services (AWS) as their preferred cloud platform. 67% of enterprises are running apps on (45%) and experimenting on (22%) the Microsoft Azure platform. 18% of enterprises are using Google’s Cloud Platform for applications today, with 23% evaluating the platform for future use. RightScale’s 2018 survey was included in the original data set Statista used to create the comparison. Source: Statista, Current and planned usage of public cloud platform services running applications worldwide in 2018. Please click on the graphic to expand for easier viewing.

  • Enterprise adoption of Microsoft Azure increased significantly from 43% to 58% attaining a 35% CAGR while AWS adoption increased from 59% to 68%. Enterprise respondents with future projects (the combination of experimenting and planning to use) show the most interest in Google (41%). Source: RightScale 2018 State of the Cloud Report. Please click on the graphic to expand for easier viewing.

  • Wikibon projects the True Private Cloud (TPC) worldwide market will experience a compound annual growth rate of 29.2%, reaching $262.4B by 2027. The firm predicts TPC growth will far outpace the infrastructure-as-a-service (IaaS) growth of 15.2% over the same period. A true private cloud is distinguished from a private cloud by the completeness of the integration of all aspects of the offering, including performance characteristics such as price, agility, and service breadth. Please see the source link for additional details on TPC. Source: Wikibon’s 2018 True Private Cloud Forecast and Market Shares. Please click on the graphic to expand for easier viewing.

  • Quality Control, Computer-Aided Engineering, and Manufacturing Execution Systems (MES) are the three most widely adopted systems in the cloud by discrete and process The survey also found that 60% of discrete and process manufacturers say their end users prefer the cloud over on-premise. Source: Amazon Web Services & IDC: Industrial Customers Are Ready For The Cloud – Now (PDF, 13 pp., no opt-in, sponsored by AWS). Please click on the graphic to expand for easier viewing.

  • The Worldwide Public Cloud Services Market is projected to grow by 17.3 3% in 2019 to total $206.2B, up from $175.8B in 2018 according to Gartner. In 2018 the market will grow a healthy 21% up from $145.3B in 2017 according to the research and advisory firm. Infrastructure-as-a-Service (IaaS) will be the fastest-growing segment of the market, forecasted to grow by 27.6% in 2019 to reach $39.5B, up from $31B in 2018. By 2022, Gartner expects that 90% of enterprises purchasing public cloud IaaS will do so from an integrated IaaS and Platform-as-a-Service (PaaS), and will use both the IaaS and PaaS capabilities from that provider. Source: Gartner Forecasts Worldwide Public Cloud Revenue to Grow 17.3 Percent in 2019.

  • More than $1.3T in IT spending will be directly or indirectly affected by the shift to cloud by 2022. 28% of spending within key enterprise IT markets will shift to the cloud by 2022, up from 19% in 2018. The largest cloud shift before 2018 occurred in application software, particularly driven by customer relationship management (CRM) software, with Salesforce dominating as the market leader. CRM has already reached a tipping point where a higher proportion of spending occurs in the cloud than in traditional software. Source: Gartner Says 28 Percent of Spending in Key IT Segments Will Shift to the Cloud by 2022.

  • IDC predicts worldwide Public Cloud Services Spending will reach $180B in 2018, an increase of 23.7% over 2017. According to IDC, the market is expected to achieve a five-year compound annual growth rate (CAGR) of 21.9% with public cloud services spending totaling $277B in 2021. The industries that are forecast to spend the most on public cloud services in 2018 are discrete manufacturing ($19.7B), professional services ($18.1B), and banking ($16.7B). The process manufacturing and retail industries are also expected to spend more than $10B each on public cloud services in 2018. These five industries will remain at the top in 2021 due to their continued investment in public cloud solutions. The industries that will see the fastest spending growth over the five-year forecast period are professional services (24.4% CAGR), telecom (23.3% CAGR), and banking (23.0% CAGR). Source: Worldwide Public Cloud Services Spending Forecast to Reach $160 Billion This Year, According to IDC.
  • Discrete Manufacturing is predicted to lead all industries on public cloud spending of $19.7B in 2018 according to IDC. Additional industries forecast to spend the most on public cloud services this year include Professional Services at $18.1B and Banking at $16.7B. The process manufacturing and retail industries are also expected to spend more than $10B each on public cloud services in 2018. According to IDC, these five industries will remain at the top in 2021 due to their continued investment in public cloud solutions. The industries that will see the fastest spending growth over the five-year forecast period are Professional Services with a 24.4% CAGR, Telecommunications with a 23.3% CAGR, and banking with a 23% CAGR. Source: Worldwide Public Cloud Services Spending Forecast to Reach $160 Billion This Year, According to IDC.

Additional Resources:

Google Needs To Make Machine Learning Their Growth Fuel

  • In 2017 Google outspent Microsoft, Apple, and Facebook on R&D spending with the majority being on AI and machine learning.
  • Google needs new AI- and machine learning-driven businesses that have lower Total Acquisition Costs (TAC) to offset the rising acquisition costs of their ad and search businesses.
  • One of the company’s initial forays into AI and machine learning was its $600M acquisition of AI startup DeepMind in January 2014.
  • Google has launched two funds dedicated solely to AI: Gradient Ventures and the Google Assistant Investment Program, both of which are accepting pitches from AI and machine learning startups today.
  • On its Q4’17 earnings call, the company announced that its cloud business is now bringing in $1B per quarter. The number of cloud deals worth $1M+ that Google has sold more than tripled between 2016 and 2017.
  • Google’s M&A strategy is concentrating on strengthening their cloud business to better compete against Amazon AWS and Microsoft Azure.

These and many other fascinating insights are from CB Insight’s report, Google Strategy Teardown (PDF, 49 pp., opt-in). The report explores how Alphabet, Google’s parent company is relying on Artificial Intelligence (AI) and machine learning to capture new streams of revenue in enterprise cloud computing and services. Also, the report looks at how Alphabet can combine search, AI, and machine learning to revolutionize logistics, healthcare, and transportation. It’s a thorough teardown of Google’s potential acquisitions, strategic investments, and partnerships needed to maintain search dominance while driving revenue from new markets.

Key takeaways from the report include the following:

  • Google needs new AI- and machine learning-driven businesses that have lower Total Acquisition Costs (TAC) to offset the rising acquisition costs of their ad and search businesses. CB Insights found Google is experiencing rising TAC in their core ad and search businesses. With the strategic shift to mobile, Google will see TAC escalate even further. Their greatest potential for growth is infusing greater contextual intelligence and knowledge across the entire series of companies that comprise Alphabet, shown in the graphic below.

  • Google has launched two funds dedicated solely to AI: Gradient Ventures and the Google Assistant Investment Program, both of which are accepting pitches from AI and machine learning startups today. Gradient Ventures is an ROI fund focused on supporting the most talented founders building AI-powered companies. Former tech founders are leading Gradient Ventures, assisting in turning ideas into companies. Gradient Venture’s portfolio is shown below:

  • In 2017 Google outspent Microsoft, Apple, and Facebook on R&D spending with the majority being on AI and machine learning. Amazon dominates R&D spending across the top five tech companies investments in R&D in 2017 with $22.6B. Facebook leads in percent of total sales invested in R&D with 19.1%.

  • Google AI led the development of Google’s highly popular open source machine software library and framework Tensor Flow and is home to the Google Brain team. Google’s approach to primary research in the fields of AI, machine learning, and deep learning is leading to a prolific amount of research being produced and published. Here’s the search engine for their publication database, which includes many fascinating studies for review. Part of Google Brain’s role is to work with other Alphabet subsidiaries to support and lead their AI and machine learning product initiatives. An example of this CB Insights mentions in the report is how Google Brain collaborated with autonomous driving division Waymo, where it has helped apply deep neural nets to vehicles’ pedestrian detection The team has also been successful in increasing the number of AI and machine learning patents, as CB Insight’s analysis below shows:

  • Mentions of AI and machine learning are soaring on Google quarterly earnings calls, signaling senior management’s prioritizing these areas as growth fuel. CB Insights has an Insights Trends tool that is designed to analyze unstructured text and find linguistics-based associations, models and statistical insights from them. Analyzing Google earnings calls transcripts found AI and machine learning mentions are soaring during the last call.

  • Google’s M&A strategy is concentrating on strengthening their cloud business to better compete against Amazon AWS and Microsoft Azure. Google acquired Xively in Q1 of this year followed by Cask Data and Velostrata in Q2. Google needs to continue acquiring cloud-based companies who can accelerate more customer wins in the enterprise and mid-tier, two areas Amazon AWS and Microsoft Azure have strong momentum today.

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