• Hiring companies nationwide miss out on 50% or more of qualified candidates and tech firms incorrectly classify up 80% of candidates due to inaccuracies and shortcomings of existing Applicant Tracking Systems (ATS), illustrating how faulty these systems are for enabling hiring.
• It takes on average 42 days to fill a position, and up to 60 days or longer to fill positions requiring in-demand technical skills and costs an average $5,000 to fill each position.
• Women applicants have a 19% chance of being eliminated from consideration for a job after a recruiter screen and 30% after an onsite interview, leading to a massive loss of brainpower and insight every company needs to grow.

It’s time the hiring process gets smarter, more infused with contextual intelligence, insight, evaluating candidates on their mastery of needed skills rather than judging candidates on resumes that reflect what they’ve achieved in the past. Enriching the hiring process with greater machine learning-based contextual intelligence finds the candidates who are exceptional and have the intellectual skills to contribute beyond hiring managers’ expectations. Machine learning algorithms can also remove any ethic- and gender-specific identification of a candidate and have them evaluated purely on expertise, experiences, merit, and skills.

The hiring process relied on globally today hasn’t changed in over 500 years. From Leonardo da Vinci’s handwritten resume from 1482, which reflects his ability to build bridges and support warfare versus the genius behind Mona Lisa, Last Supper, Vitruvian Man, and a myriad of scientific discoveries and inventions that modernized the world, the approach job seekers take for pursuing new positions has stubbornly defied innovation. ATS apps and platforms classify inbound resumes and provide rankings of candidates based on just a small glimpse of their skills seen on a resume. When what’s needed is an insight into which managerial, leadership and technical skills & strengths any given candidate is attaining mastery of and at what pace.  Machine learning broadens the scope of what hiring companies can see in candidates by moving beyond the barriers of their resumes. Better hiring decisions are being made, and the Return on Investment (ROI) drastically improves by strengthening hiring decisions with greater intelligence. Key metrics including time-to-hire, cost-to-hire, retention rates, and performance all will improve when greater contextual intelligence is relied on.

Look beyond resumes to win the war for talent

Last week I had the opportunity to speak with the Vice President of Human Resources for one of the leading technology think tanks globally. He’s focusing on hundreds of technical professionals his organization needs in six months, 12 months and over a year from now to staff exciting new research projects that will deliver valuable Intellectual Property (IP) including patents and new products.

Their approach begins by seeking to understand the profiles and core strengths of current high performers, then seek out matches with ideal candidates in their community of applicants and the broader technology community. Machine learning algorithms are perfectly suited for completing the needed comparative analysis of high performer’s capabilities and those of candidates, whose entire digital persona is taken into account when comparisons are being completed. The following graphic illustrates the eightfold.ai Talent Intelligence Platform (TIP), illustrating how integrated it is with publicly available data, internal data repositories, Human Capital Resource Management (HRM) systems, ATS tools. Please click on the graphic to expand it for easier reading.

The comparative analysis of high achievers’ characteristics with applicants takes seconds to complete, providing a list of prospects complete with profiles. Machine learning-derived profiles of potential hires meeting the high performers’ characteristics provided greater contextual intelligence than any resume ever could. Taking an integrated approach to creating the Talent Intelligence Platform (TIP) yields insights not available with typical hiring or ATS solutions today. The profile below reflects the contextual intelligence and depth of insight possible when machine learning is applied to an integrated dataset of candidates. Please click on the graphic to expand it for easier reading. Key elements in the profile below include the following:

• Career growth bell curve: Illustrates how a given candidate’s career progressions and performance compares relative to others.
• Social following on public sites: Provides a real-time glimpse into the candidate’s activity on Github, Open Stack, and other sites where technical professionals can share their expertise. This also provides insight into how others perceive their contributions.

• Highlights of background that is relevant to job(s) under review: Provides the most relevant data from the candidate’s history in the profile so recruiters and managers can more easily understand their strengths.

• Recent publications: Publications provide insights into current and previous interests, areas of focus, mindset and learning progression over the last 10 to 15 years or longer.

• Professional overlap that makes it easier to validate achievements chronicled in the resume: Multiple sources of real-time career data validate and provide greater context and insight into resume-listed accomplishments.

The key is understanding the context in which a candidate’s capabilities are being evaluated. And a 2-page resume will never give enough latitude to the candidate to cover all bases. For medium to large companies – doing this accurately and quickly is a daunting task if done manually – across all roles, all the geographies, all the candidates sourced, all the candidates applying online, university recruiting, re-skilling inside the company, internal mobility for existing employees, and across all recruitment channels. This is where machine learning can be an ally to the recruiter, hiring manager, and the candidate.

Five reasons why machine learning needs to make resumes obsolete

Reducing the costs and time-to-hire, increasing the quality of hires and staffing new initiatives with the highest quality talent possible all fuels solid revenue growth. Relying on resumes alone is like being on a bad Skype call where you only hear every tenth word in the conversation. Using machine learning-based approaches brings greater acuity, clarity, and visibility into hiring decisions.

The following are the five reasons why machine learning needs to make resumes obsolete:

Resumes are like rearview mirrors that primarily reflect the past: What needed is more of a focus on where someone is going, why (what motivates them) and what are they fascinated with and learning about on their own. Resumes are rearview mirrors and what’s needed is an intelligent heads-up display of what their future will look like based on present interests and talent.

By relying on a 500+-year-old process, there’s no way of knowing what skills, technologies and training a candidate is gaining momentum in: The depth and extent of mastery in specific areas aren’t reflected in the structure of resumes. By integrating multiple sources of data into a unified view of a candidate, it’s possible to see what areas they are growing the quickest in from a professional development standpoint.

It’s impossible to game a machine learning algorithm that takes into account all digital data available on a candidate, while resumes have a credibility issue: Anyone who has hired subordinates, staff, and been involved in hiring decisions has faced the disappointment of finding out a promising candidate lied on a resume. It’s a huge let-down. Resumes get often gamed with one recruiter saying at least 60% of resumes have exaggerations and in some cases lies on them. Taking all data into account using a platform like TIP shows the true candidate and their actual skills.

It’s time to take a more data-driven approach to diversity that removes unconscious biases: Resumes today immediately carry inherent biases in them. Recruiter, hiring managers and final interview groups of senior managers draw their unconscious biases based on a person’s name, gender, age, appearance, schools they attended and more. It’s more effective to know their skills, strengths, core areas of intelligence, all of which are better predictors of job performance.

Reduces the risk of making a bad hire that will churn out of the organisation fast: Ultimately everyone hires based in part on their best judgment and in part on their often unconscious biases. It’s human nature. With more data the probability of making a bad hire is reduced, reducing the risk of churning through a new hire and costing thousands of dollars to hire then replace them. Having greater contextual intelligence reduces the downside risks of hiring, removes biases by showing with solid data just how much a person is qualified or not for a role, and verifies their background strengths, skills, and achievements. Factors contributing to unconscious biases including gender, race, age or any other factors can be removed from profiles, so candidates are evaluated only on their potential to excel in the roles they are being considered for.

Bottom line: It’s time to revolutionize resumes and hiring processes, moving them into the 21st century by redefining them with greater contextual intelligence and insight enabled by machine learning.

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

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

How AI and machine learning are revolutionising talent management

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

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

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

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

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

Talent management’s inflection point has arrived 

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

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

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

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

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

Achieving greater equality with a data-driven approach to diversity

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

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

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

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

How to close the talent gap

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

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

• Cloud BI adoption is soaring in 2018, nearly doubling 2016 adoption levels.
• Over 90% of sales and marketing teams say that cloud BI is essential for getting their work done in 2018, leading all categories in the survey.
• 66% of organizations that consider themselves completely successful with business intelligence (BI) initiatives currently use the cloud.
• Financial Services (62%), technology (54%), and education (54%) have the highest Cloud BI adoption rates in 2018.
• 86% of Cloud BI adopters name Amazon AWS as their first choice, 82% name Microsoft Azure, 66% name Google Cloud, and 36% identify IBM Bluemix as their preferred provider of cloud BI services.

These and other many other fascinating insights are from Dresner Advisory Services 2018 Cloud Computing and Business Intelligence Market Study (client access reqd.) of the Wisdom of Crowds® series of research. The goal of the 7th annual edition of the study seeks to quantify end-user deployment trends and attitudes toward cloud computing and business intelligence (BI), defined as the technologies, tools, and solutions that employ one or more cloud deployment models. Dresner Advisory Services defines the scope of business intelligence (BI) tools and technologies to include query and reporting, OLAP (online analytical processing), data mining and advanced analytics, end-user tools for ad hoc query and analysis, and dashboards for performance monitoring. Please see page 10 of the study for the methodology. The study found the primary barriers to greater cloud BI adoption are enterprises’ concerns regarding data privacy and security.

Key takeaways from the study include the following:

Cloud BI’s importance continues to accelerate in 2018, with the majority of respondents considering it an important element of their broader analytics strategies

The study found that mean level of sentiment rose from 2.68 to 3.22 (above the level of “important”) between 2017 and 2018, indicating the increased importance of cloud BI over the last year. By region, Asia-Pacific respondents continue to be the strongest proponents of cloud computing regarding both adjusted mean (4.2 or “very important”) and levels of criticality. The following graphic illustrates cloud BI’s growing importance between 2012 and 2018.

Over 90% of sales and marketing teams say cloud BI apps are important to getting their work done in 2018, leading all respondent categories in the survey

The study found that cloud BI importance in 2018 is highest among sales/marketing and executive management respondents. One of the key factors driving this is the fact that both sales & marketing and executive management are increasingly relying on cloud-based front office applications and services that are integrated with and generate cloud-based data to track progress towards goals.

Cloud BI is most critical to financial services and insurance, technology, and retail and wholesale trade industries

The study recorded its highest-ever levels of cloud BI importance in 2018. Financial services has the highest weighted mean interest in cloud BI (3.8, which approaches “very important” status shown in the figure below). Technology organizations, where half of the respondents say cloud BI is “critical” or “very important,” are the next most interested. Close to 90% of retail/wholesale respondents say SaaS/cloud BI is at least “important” to them. As it has been over time, healthcare remains the industry least open to managed services for data and business intelligence.

Cloud BI adoption is soaring in 2018, nearly doubling 2016 adoption levels

The study finds that the percentage of respondents using cloud BI in 2018 nearly doubled from 25% of enterprise users in 2016. Year over year, current use rose from 31% to 49%. In the same time frame, the percentage of respondents with no plans to use cloud BI dropped by half, from 38% to 19%. This study has been completed for the last seven years, showing a steady progression of cloud BI awareness and adoption, with 2018 being the first one showing the most significant rise in adoption levels ever.

Sales and marketing leads all departments in current use and planning for cloud BI applications

Business Intelligence Competency Centers (BICC) are a close second, each with over 60% adoption rates for cloud BI today. Operations including manufacturing and supply chains and services are the next most likely to use cloud BI currently. Marketing and BICC lead current adoption and are contributing catalysts of cloud BI’s soaring growth between 2016 and 2018. Both of these departments often have time-constrained and revenue-driven goals where quantifying contributions to company growth and achievement are critical.

Financial services (62%), technology (54%), and education (54%) industries have the highest cloud BI adoption rates in 2018

The retail/wholesale industry has the fourth-highest level of Cloud BI adoption and the greatest number of companies who are currently evaluating Cloud BI today. The least likely current or future users are found in manufacturing and security-sensitive healthcare organizations, where 45% respondents report no plans for cloud-based BI/analytics.

Dashboards, advanced visualization, ad-hoc query, data integration, and self-service are the most-required cloud BI features in 2018

Sales and marketing need real-time feedback on key initiatives, programs, strategies, and progress towards goals. Dashboards and advanced visualization features’ dominance of feature requirements reflect this department’s ongoing need for real-time feedback on the progress of their teams towards goals. Reporting, data discovery, and end-user data blending (data preparation) make up the next tier of importance.

Manufacturers have the greatest interest in dashboards, ad-hoc query, production reporting, search interface, location intelligence, and ability to write to transactional applications

Education respondents report the greatest interest in advanced visualization along with data integration, data mining, end-user data blending, data catalog, and collaborative support for group-based analysis. Financial services respondents are highly interested in advanced visualization and lead all industries in self-service. Healthcare industry respondents lead interest only in in-memory support. Retail/wholesale and healthcare industry respondents are the least feature interested overall.

Interest in cloud application connections to Salesforce, NetSuite, and other cloud-based platforms has increased 12% this year

Getting end-to-end visibility across supply chains, manufacturing centers, and distribution channels requires cloud BI apps be integrated with cloud-based platforms and on-premises applications and data. Expect to see this accelerate in 2019 as cloud BI apps become more pervasive across marketing and sales and executive management, in addition to operations including supply chain management and manufacturing where real-time shop floor monitoring is growing rapidly.

Retail/wholesale, business services, education and financial services and insurance industries are most interested in Google Analytics connectors to obtain data for their cloud BI apps

Respondents from technology industries prioritize Salesforce integration and connectors above all others. Education respondents are most interested in MySQL and Google Drive integration and connectors. Manufacturers are most interested in connectors to Google AdWords, SurveyMonkey, and The healthcare industry respondents prioritize SAP cloud BI services and also interested in ServiceNow connectors.

By enabling enterprises to scale security with user behaviour-based, contextual intelligence, next-gen access strategies are delivering Zero Trust Security (ZTS) enterprise-wide, enabling the fastest companies to keep growing strong.

Every digital business is facing a security paradox today created by their proliferating amount of applications, endpoints and infrastructure on the one hand and the need to scale enterprise security without reducing the quality of user experiences on the other. Businesses face a continual series of challenges to growth, the majority of which are scale-based. Scaling security takes a multidimensional approach that accurately interprets user behavior, risk and threat predictions, and assesses data use and access patterns.

How enterprises are solving the security paradox with next-gen access

Security defies simple, scale-based solutions because its processes are ingrained in many different systems across a company. Each of the many systems security relies on and protects have their cadence, speed, and scale. When a company is growing fast, core systems including accounting, CRM, finance, pricing, sales, services, supply chain and human resources become security-constrained. It’s common for companies experiencing high growth to choose expediency over security. 32% of enterprises are sacrificing security for expediency and business performance, leaving many areas of their core infrastructure unsecured according to the Verizon Mobile Security Index 2018 Report.

The hard reality for any growing business is the faster they grow; the more sophisticated and strong they need to become at security. Protecting intellectual property (IP), all data assets and eradicating threats assures uninterrupted, profitable growth. Adding new suppliers, sales teams, distribution partners and service centers can’t be slowed down by legacy-based approaches to user authentication and system access.  The challenge is the faster a business is growing, the slower its legacy approaches to security reacts, slowing down sales cycles, supplier qualifications, and pipelines.

Next-gen access solves the security paradox of fast-growing businesses, enabling Zero Trust Security (ZTS) enterprise-wide by solving the following major challenges of a high growth business:

Quit relying on brute-force multi-factor authentication (MFA) techniques that deliver mediocre user experiences and slow down productivity

Any company can still attain Zero Trust Security (ZTS) without reverting to brute-force approaches to MFA. Get away from the idea of having MFA challenges be for every user on every device they use to access every resource. Instead look to next-gen access (NGA) to quantify context, device, and behavioral patterns and derive risk scores for each user.

Begin to rely on next-gen access, risk-aware MFA, and risk scores to quantify trust and set the foundation of a Zero Trust Security (ZTS) enterprise-wide strategies

The goal is to keep growth going strong, uninterrupted by any security event or breach. Next-Gen Access (NGA) provides behavioral, contextual intelligence indexed as a risk score for each user, enabling more secure and efficient user experiences. NGA is built on a platform that includes identity as a service (IDaaS), enterprise mobility management (EMM) and privileged access management (PAM). They are also the essential components for creating and fine-tuning Zero Trust Security (ZTS) across fast-growing businesses. Taken together in a concerted strategy, ZTS delivers greater control and visibility over every resource in a company.

Identify potential security risks on a per-user basis to the device level and limiting access while asking for identity verification without impacting user experiences

NGA takes contextual and user intelligence into account when deciding which resources will be available to a given user based on their previous login and system use actions and behaviors quantified in their risk score. Machine learning algorithms are used to find patterns in user behavior that could signal a potential security risk. Based on the risk score, conditional access is provided or not. All of this is done in seconds and doesn’t impact the user experience.

Rely on more NGA that learns users' behavioural patterns over time and improves the user experience, scaling Zero Trust Security enterprise-wide

Solving the paradox of scaling security in fast-growing companies needs to start with a machine learning-based approach to finding and acting on user’s behavioral and contextual activity. As NGA “learns” how valid users interact with security, updating risk scores and performing identity verification, the quality of a user’s experience improves. In fast-growing companies adding new employees, partners, and suppliers, this is invaluable as every new user will generate a risk score. Quantifying trust using NGA, the foundation of any ZTS strategy makes fast, secure profitable growth possible.

The era of ZTS has arrived, and it is accentuating the importance of partnering with security providers who excel at offering next-gen access solutions

ZTS will continue to revolutionise every aspect of an organisation’s security strategy, enabling digital businesses to grow faster and more securely over time. Next-Gen Access solutions are the foundations enabling enterprises to scale ZTS strategies across their businesses. Key Next-Gen access providers enabling the era of ZTS include Palo Alto Networks for firewalls and Centrify for Access. Over the next 18 months, ZTS will redefine the cybersecurity landscape as digital businesses look to Next-Gen Access solutions to securely scale their companies and grow.

• $86bn was spent on security in 2017, yet 66% of companies have still been breached an average of five or more times.
• Just 55% of CEOs say their organizations have experienced a breach, while 79% of CTOs acknowledge breaches have occurred. One in approximately four CEOs (24%) aren’t aware if their companies have even had a security breach.
• 62% of CEOs inaccurately cite malware as the primary threat to cybersecurity.
• 68% of executives whose companies experienced significant breaches in hindsight believe that the breach could have been prevented by implementing more mature identity and access management strategies.

These and many other fascinating findings are from the recently released Centrify and Dow Jones Customer Intelligence study, CEO Disconnect is Weakening Cybersecurity (31 pp, PDF, opt-in).

One of the most valuable findings from the study is how CEOs can reduce the risk of a security breach meltdown by rethinking their core cyber defense strategy by maturing their identity and access management strategies.

However, 62% of CEOs have the impression that multi-factor authentication is difficult to manage. Thus, their primary security concern is primarily driven by how to avoid delivering poor user experiences. In this context, machine learning can assist in strengthening the foundation of a multi-factor authentication platform to increase effectiveness while streamlining user experiences.

Five ways machine learning saves companies from security breach meltdowns

Machine learning is solving the security paradox all enterprises face today. Spending millions of dollars on security solutions yet still having breaches occur that are crippling their ability to compete and grow, enterprises need to confront this paradox now. There are many ways machine learning can be used to improve enterprise security. With identity being the primary point of attacks, the following are five ways machine learning can be leveraged in the context of identity and access management to minimize the risk of falling victim to a data breach.

Thwarting compromised credential attacks by using risk-based models that validate user identity based on behavioral pattern matching and analysis

Machine learning excels at using constraint-based and pattern matching algorithms, which makes them ideal for analyzing behavioral patterns of people signing in to systems that hold sensitive information. Compromised credentials are the most common and lethal type of breach. Applying machine learning to this challenge by using a risk-based model that “learns’ behavior over time is stopping security breaches today.

Attaining Zero Trust Security (ZTS) enterprise-wide using risk scoring models that flex to a businesses’ changing requirements

Machine learning enables Zero Trust Security (ZTS) frameworks to scale enterprise-wide, providing threat assessments and graphs that scale across every location. These score models are invaluable in planning and executing growth strategies quickly across broad geographic regions. CEOs need to see multi-factor authentication as a key foundation of ZTS frameworks that can help them grow faster. Machine learning enables IT to accelerate the development of Zero Trust Security (ZTS) frameworks and scale them globally. Removing security-based roadblocks that get in the way of future growth needs to be the highest priority CEOs address. A strong ZTS framework is as much a contributor to revenue as is any distribution or selling channel.

Streamlining security access for new employees by having persona-based risk model profiles that can be quickly customized by IT for specific needs

CEOs most worry about security’s poor user experience and its impacts on productivity. The good news is that the early multi-factor authentication workflows that caused poor user experiences are being redefined with contextual insights and intelligence based on more precise persona-based risk scoring models. As the models “learn” the behaviors of employees regarding access, the level of authentication changes and the experience improves. By learning new behavior patterns over time, machine learning is accelerating how quickly employees can gain access to secured services and systems.

Provide predictive analytics and insights into which are the most probable sources of threats, what their profiles are and what priority to assign to them

CIOs and the security teams they manage need to have enterprise-wide visibility of all potential threats, ideally prioritized by potential severity. Machine learning algorithms are doing this today, providing threat assessments and defining which are the highest priority threats that CIOs and their teams need to address.

Stop malware-based breaches by learning how hackers modify the code bases in an attempt to bypass multi-factor authentication

One of the favourite techniques for hackers to penetrate an enterprise network is to use impersonation-based logins and passwords to pass malware onto corporate servers. Malware breaches can be extremely challenging to track. One approach that is working is when enterprises implement a ZTS framework and create specific scenarios to trap, stop and destroy suspicious malware activity.

• Improving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimising fab operations is achievable with machine learning.
• Reducing supply chain forecasting errors by 50% and lost sales by 65% with better product availability is achievable with machine learning.
• Automating quality testing using machine learning is increasing defect detection rates up to 90%.

Bottom line: Machine learning algorithms, applications, and platforms are helping manufacturers find new business models, fine-tune product quality, and optimise manufacturing operations to the shop floor level.

Manufacturers care most about finding new ways to grow, excel at product quality while still being able to take on short lead-time production runs from customers. New business models often bring the paradox of new product lines that strain existing ERP, CRM and PLM systems by the need always to improve time-to-customer performance. New products are proliferating in manufacturing today, and delivery windows are tightening. Manufacturers are turning to machine learning to improve the end-to-end performance of their operations and find a performance-based solution to this paradox.

The ten ways machine learning is revolutionising manufacturing in 2018 include the following:

Improving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimising fab operations are is achievable with machine learning

Attaining up to a 30% reduction in yield detraction in semiconductor manufacturing, reducing scrap rates based on machine learning-based root-cause analysis and reducing testing costs using AI optimisation are the top three areas where machine learning will improve semiconductor manufacturing. McKinsey also found that AI-enhanced predictive maintenance of industrial equipment will generate a 10% reduction in annual maintenance costs, up to a 20% downtime reduction and 25% reduction in inspection costs. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.

Asset management, supply chain management, and inventory management are the hottest areas of artificial intelligence, machine learning and IoT adoption in manufacturing today

The World Economic Forum (WEF) and A.T. Kearney’s recent study of the future of production find that manufacturers are evaluating how combining emerging technologies including IoT, AI, and machine learning can improve asset tracking accuracy, supply chain visibility, and inventory optimisation. Source: Technology and Innovation for the Future of Production: Accelerating Value Creation (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney.

Manufacturer’s adoption of machine learning and analytics to improve predictive maintenance is predicted to increase 38% in the next five years according to PwC

Analytics and MI-driven process and quality optimisation are predicted to grow 35% and process visualisation and automation, 34%. PwC sees the integration of analytics, APIs and big data contributing to a 31% growth rate for connected factories in the next five years. Source: Digital Factories 2020: Shaping the future of manufacturing (48 pp., PDF, no opt-in) PriceWaterhouseCoopers

McKinsey predicts machine learning will reduce supply chain forecasting errors by 50% and reduce lost sales by 65% with better product availability

Supply chains are the lifeblood of any manufacturing business. Machine learning is predicted to reduce costs related to transport and warehousing and supply chain administration by 5 to 10% and 25 to 40%, respectively. Due to machine learning, overall inventory reductions of 20 to 50% are possible. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company.

Improving demand forecast accuracy to reduce energy costs and negative price variances using machine learning uncovers price elasticity and price sensitivity as well

Honeywell is integrating AI and machine-learning algorithms into procurement, strategic sourcing and cost management. Source: Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.

Automating inventory optimisation using machine learning has improved service levels by 16% while simultaneously increasing inventory turns by 25%

AI and machine learning constraint-based algorithms and modeling are making it possible scale inventory optimisation across all distribution locations, taking into account external, independent variables that affect demand and time-to-customer delivery performance. Source: Transform the manufacturing supply chain with Multi-Echelon inventory optimisation, Microsoft, March 1, 2018.

Combining real-time monitoring and machine learning is optimising shop floor operations, providing insights into machine-level loads and production schedule performance

Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimising the best possible set of machines for a given production run is now possible using machine learning algorithms. Source: Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile, (43 pp., PDF, no opt-in) Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D

Improving the accuracy of detecting costs of performance degradation across multiple manufacturing scenarios reduces costs by 50% or more

Using real-time monitoring technologies to create accurate data sets that capture pricing, inventory velocity, and related variables gives machine learning apps what they need to determine cost behaviors across multiple manufacturing scenarios. Source: Leveraging AI for Industrial IoT (27 pp., PDF, no opt-in) Chetan Gupta, Ph.D. Chief Data Scientist, Big Data Lab, Hitachi America Ltd. Date: Sept. 19th, 2017

A manufacturer was able to achieve a 35% reduction in test and calibration time via accurate prediction of calibration and test results using machine learning

The project’s goal was to reduce test and calibration time in the production of mobile hydraulic pumps. The methodology focused on using a series of machine learning models that would predict test outcomes and learn over time. The process workflow below was able to isolate the bottlenecks, streamlining test and calibration time in the process. Source: The Value Of Data Science Standards In Manufacturing Analytics (13 pp., PDF, no opt-in) Soundar Srinivasan, Bosch Data Mining Solutions And Services

Improving yield rates, preventative maintenance accuracy and workloads by the asset is now possible by combining machine learning and Overall Equipment Effectiveness (OEE)

OEE is a pervasively used metric in manufacturing as it combines availability, performance, and quality, defining production effectiveness. Combined with other metrics, it’s possible to find the factors that impact manufacturing performance the most and least. Integrating OEE and other datasets in machine learning models that learn quickly through iteration are one of the fastest growing areas of manufacturing intelligence and analytics today. Source: TIBCO Manufacturing Solutions, TIBCO Community, January 30, 2018

Additional reading:

Artificial Intelligence (AI) Delivering Breakthroughs in Industrial IoT (26 pp., PDF, no opt-in) Hitachi

Artificial Intelligence and Robotics and Their Impact on the Workplace (120 pp., PDF, no opt-in) IBA Global Employment Institute

Artificial Intelligence: The Next Digital Frontier? (80 pp., PDF, no opt-in) McKinsey and Company

Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing (20 pp., PDF, no opt-in), Applied Materials, Applied Global Services

Connected Factory and Digital Manufacturing: A Competitive Advantage, Shantanu Rai, HCL Technologies (36 pp., PDF, no opt-in)

Demystifying AI, Machine Learning, and Deep Learning, DZone, AI Zone

Digital Factories 2020: Shaping the future of manufacturing (48 pp., PDF, no opt-in) PriceWaterhouseCoopers

Emerging trends in global advanced manufacturing: Challenges, Opportunities, And Policy Responses (76 pp., PDF, no opt-in) University of Cambridge

Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile, (43 pp., PDF, no opt-in) Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D

Get started with the Connected factory preconfigured solution, Microsoft Azure

Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.

Impact of the Fourth Industrial Revolution on Supply Chains (22 pp., PDF, no opt-in) World Economic Forum

Leveraging AI for Industrial IoT (27 pp., PDF, no opt-in) Chetan Gupta, Ph.D. Chief Data Scientist, Big Data Lab, Hitachi America Ltd. Date: Sept. 19th, 2017

Machine Learning & Artificial Intelligence Presentation (14 pp., PDF, no opt-in) Erik Hjerpe Volvo Car Group

Machine Learning Techniques in Manufacturing Applications & Caveats, (44 pp., PDF, no opt-in), Thomas Hill, Ph.D. | Exec. Director Analytics, Dell

Machine learning: the power and promise of computers that learn by example (128 pp., PDF, no opt-in) Royal Society UK

Predictive maintenance and the smart factory (8 pp., PDF, no opt-in) Deloitte

Priore, P., Gómez, A., Pino, R., & Rosillo, R. (2014). Dynamic scheduling of manufacturing systems using machine learning: An updated review. Ai Edam, 28(1), 83-97.

Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (52 pp., PDF, no opt-in) McKinsey & Company

Technology and Innovation for the Future of Production: Accelerating Value Creation (38 pp., PDF, no opt-in) World Economic Forum with A.T. Kearney

The Future of Manufacturing; Making things in a changing world (52 pp., PDF, no opt-in) Deloitte University Press

The transformative potential of AI in the manufacturing industry, Microsoft, by Sanjay Ravi, Managing Director, Worldwide Discrete Manufacturing, Microsoft, September 25, 2017

The Value Of Data Science Standards In Manufacturing Analytics (13 pp., PDF, no opt-in) Soundar Srinivasan, Bosch Data Mining Solutions And Services

TIBCO Manufacturing Solutions, TIBCO Community, January 30, 2018

Transform the manufacturing supply chain with Multi-Echelon inventory optimisation, Microsoft, March 1, 2018.

Turning AI into concrete value: the successful implementers’ toolkit (28 pp., PDF, no opt-in) Capgemini Consulting

Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016). Machine learning in manufacturing: advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23-45.

• 84% of marketing organizations are implementing or expanding AI and machine learning in 2018.
• 75% of enterprises using AI and machine learning enhance customer satisfaction by more than 10%.
• 3 in 4 organizations implementing AI and machine learning increase sales of new products and services by more than 10% according to Capgemini.

Measuring marketing’s many contributions to revenue growth is becoming more accurate and real-time thanks to analytics and machine learning. Knowing what’s driving more Marketing Qualified Leads (MQLs), Sales Qualified Leads (SQL), how best to optimize marketing campaigns, and improving the precision and profitability of pricing are just a few of the many areas machine learning is revolutionizing marketing.

The best marketers are using machine learning to understand, anticipate and act on the problems their sales prospects are trying to solve faster and with more clarity than any competitor. Having the insight to tailor content while qualifying leads for sales to close quickly is being fueled by machine learning-based apps capable of learning what’s most effective for each prospect and customer. Machine learning is taking contextual content,  marketing automation including cross-channel marketing campaigns and lead scoring, personalization, and sales forecasting to a new level of accuracy and speed.

The strongest marketing departments rely on a robust set of analytics and Key Performance Indicators (KPIs) to measure their progress towards revenue and customer growth goals. With machine learning, marketing departments will be able to deliver even more significant contributions to revenue growth, strengthening customer relationships in the process.

The following are 10 ways machine learning is revolutionizing marketing today and in the future:

57% of enterprise executives believe the most significant growth benefit of AI and machine learning will be improving customer experiences and support

44% believe that AI and machine learning will provide the ability to improve on existing products and services. Marketing departments and the Chief Marketing Officers (CMOs) running them are the leaders devising and launching new strategies to deliver excellent customer experiences and are one of the earliest adopters of machine learning. Orchestrating every aspect of attracting, selling and serving customers is being improved by marketers using machine learning apps to more accurately predict outcomes. Source: Artificial Intelligence: What’s Possible for Enterprises In 2017 (PDF, 16 pp., no opt-in), Forrester, by Mike Gualtieri, November 1, 2016. Courtesy of The Stack.

58% of enterprises are tackling the most challenging marketing problems with AI and machine learning first, prioritizing personalized customer care, new product development

These “need to do” marketing areas have the highest complexity and highest benefit. Marketers haven’t been putting as much emphasis on the “must do” areas of high benefit and low complexity according to Capgemini’s analysis. These application areas include Chatbots and virtual assistants, reducing revenue churn, facial recognition and product and services recommendations. Source:  Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. 2017. (PDF, 28 pp., no opt-in).

By 2020, real-time personalized advertising across digital platforms and optimized message targeting accuracy, context and precision will accelerate

The combined effect of these marketing technology improvements will increase sales effectiveness in retail and B2C-based channels. Sales Qualified Lead (SQL) lead generation will also increase, potentially reducing sales cycles and increasing win rates. Source: Can Machines be Creative? How Technology is Transforming Marketing Personalization and Relevance, IDC White Paper Sponsored by Gerry Brown, July 2017.

Analyze and significantly reduce customer churn using machine learning to streamline risk prediction and intervention models

Instead of relying on expensive and time-consuming approaches to minimize customer churn, telecommunications companies and those in high-churn industries are turning to machine learning. The following graphic illustrates how defining risk models help determine how actions aimed at averting churn affect churn impact probability and risk. An intervention model allows marketers to consider how the level of intervention could affect the probability of churn and the amount of customer lifetime value (CLV). Source: Analyzing Customer Churn by using Azure Machine Learning.

Price optimization and price elasticity are growing beyond industries with limited inventories including airlines and hotels, proliferating into manufacturing and services

All marketers are increasingly relying on machine learning to define more competitive, contextually relevant pricing. Machine learning apps are scaling price optimization beyond airlines, hotels, and events to encompass product and services pricing scenarios. Machine learning is being used today to determine pricing elasticity by each product, factoring in channel segment, customer segment, sales period and the product’s position in an overall product line pricing strategy. The following example is from Microsoft Azure’s Interactive Pricing Analytics Pre-Configured Solution (PCS). Source: Azure Cortana Interactive Pricing Analytics Pre-Configured Solution.

Improving demand forecasting, assortment efficiency and pricing in retail marketing have the potential to deliver a 2% improvement in Earnings Before Interest & Taxes (EBIT), 20% stock reduction and 2 million fewer product returns a year

In Consumer Packaged Goods (CPQ) and retail marketing organizations, there’s significant potential for AI and machine learning to improve the entire value chain’s performance. McKinsey found that using a concerted approach to applying AI and machine learning across a retailer’s value chains has the potential to deliver a 50% improvement of assortment efficiency and a 30% online sales increase using dynamic pricing. Source:  Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)

Creating and fine-tuning propensity models that guide cross-sell and up-sell strategies by product line, customer segment, and persona

It’s common to find data-driven marketers building and using propensity models to define the products and services with the highest probability of being purchased. Too often propensity models are based on imported data, built in Microsoft Excel, making their ongoing use time-consuming. Machine learning is streamlining creation, fine-tuning and revenue contributions of up-sell and cross-sell strategies by automating the entire progress. The screen below is an example of a propensity model.

Lead scoring accuracy is improving, leading to increased sales that are traceable back to initial marketing campaigns and sales strategies

By using machine learning to qualify the further customer and prospect lists using relevant data from the web, predictive models including machine learning can better predict ideal customer profiles. Each sales lead’s predictive score becomes a better predictor of potential new sales, helping sales prioritize time, sales efforts and selling strategies. The following two slides are from an excellent webinar Mintigo hosted with Sirius Decisions and Sales Hacker. It’s a fascinating look at how machine learning is improving sales effectiveness. Source: Give Your SDRs An Unfair Advantage with Predictive (webinar slides on Slideshare).

Identifying and defining the sales projections of specific customer segments and microsegments using RFM (recency, frequency and monetary) modeling within machine learning apps is becoming pervasive

Using RFM analysis as part of a machine learning initiative can provide accurate definitions of the best customers, most loyal, biggest spenders, almost lost, lost customers and lost cheap customers.

Optimizing the marketing mix by determining which sales offers, incentive and programs are presented to which prospects through which channels is another way machine learning is revolutionizing marketing

Specific sales offers are created supported by contextual content, offers, and incentives. These items are made available to an optimization engine which uses machine learning logic to continually try to predict the best combination of marketing mix elements that will lead to a new sale, up-sell or cross-sell. Amazon’s product recommendation feature is an example of how their e-commerce site is using machine learning to increase up-sell, cross-sell and recommended products revenue.

Data sources on machine learning’s impact on marketing:

4 Ways to Use Machine Learning in Marketing Automation, Medium, March 30, 2017

84 percent of B2C marketing organizations are implementing or expanding AI in 2018. Infographic. Amplero.
AI, Machine Learning, and their Application for Growth, Adelyn Zhou. SlideShare/LinkedIn.  Feb. 8, 2018.

AI: The Next Generation of Marketing Driving Competitive Advantage throughout the Customer Life Cycle (PDF, 10 pp., no opt-in), Forrester, February 2017.

An Executive’s Guide to Machine Learning, McKinsey Quarterly. June 2015.

Artificial Intelligence for Marketers 2018: Finding Value beyond the Hype, eMarketer. (PDF, 20 pp., no opt-in). October 2017

Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)

Artificial Intelligence: The Ultimate Technological Disruption Ascends, Woodside Capital Partners. (PDF, 111 pp., no opt-in). January 2017.

AWS Announces Amazon Machine Learning Solutions Lab, Marketing Technology Insights

B2B Predictive Marketing Analytics Platforms: A Marketer’s Guide, (PDF, 36 pp., no opt-in) Marketing Land Research Report.
Four Use Cases of Machine Learning in Marketing, June 28, 2018, Martech Advisor,
How Artificial Intelligence and Machine Learning Will Reshape Small Businesses, SMB Group (PDF, 8 pp., no opt-in) May 2017.

How Machine Learning Helps Sales Success (PDF, 12 pp., no opt-in) Cognizant

Inside Salesforce Einstein Artificial Intelligence A Look at Salesforce Einstein Capabilities, Use Cases and Challenges, Doug Henschen, Constellation Research, February 15, 2017

Machine Learning for Marketers (PDF, 91 pp., no opt-in) iPullRank

Machine Learning Marketing – Expert Consensus of 51 Executives and Startups, TechEmergence. May 15, 2017.

Marketing & Sales Big Data, Analytics, and the Future of Marketing & Sales, (PDF, 60 pp., no opt-in), McKinsey & Company.

Sizing the prize – What’s the real value of AI for your business and how can you capitalize? (PDF, 32 pp., no opt-in) PwC, 2017.

The New Frontier of Price Optimization, MIT Technology Review. September 07, 2017.

The Power Of Customer Context, Forrester (PDF, 20 pp., no opt-in) Carlton A. Doty, April 14, 2014. Provided courtesy of Pegasystems.

Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. 2017. (PDF, 28 pp., no opt-in)

Using machine learning for insurance pricing optimization, Google Cloud Big Data and Machine Learning Blog, March 29, 2017

What Marketers Can Expect from AI in 2018, Jacob Shama. Mintigo. January 16, 2018.

• Machine learning patents grew at a 34% compound annual growth rate (CAGR) between 2013 and 2017, the third-fastest growing category of all patents granted
• International Data Corporation (IDC) forecasts that spending on AI and ML will grow from $12bn in 2017 to $57.6bn by 2021
• Deloitte Global predicts the number of machine learning pilots and implementations will double in 2018 compared to 2017, and double again by 2020

These and many other fascinating insights are from the latest series of machine learning market forecasts, market estimates, and projections.

Machine learning’s potential impact across many of the world’s most data-prolific industries continues to fuel venture capital investment, private equity (PE) funding, mergers, and acquisitions all focused on winning the race of Intellectual Property (IP) and patents in this field. One of the fastest growing areas of machine learning IP is the development of custom chipsets. Deloitte Global is predicting up to 800K machine learning chips will be in use across global data centers this year.

Enterprises are increasing their research, investment, and piloting of machine learning programs in 2018. And while the methodologies all vary across the many sources of forecasts, market estimates, and projections, all reflect how machine learning is improving the acuity and insights of companies on how to grow faster and more profitably. Key takeaways from the collection of machine learning market forecasts, market estimates and projections include the following:

Within the business intelligence (BI) & analytics market, data science platforms that support machine learning are predicted to grow at a 13% CAGR through 2021

Data science platforms will outperform the broader BI & analytics market, which is predicted to grow at an 8% CAGR in the same period. Data Science platforms will grow in value from $3B in 2017 to $4.8B in 2021. Source: An Investors’ Guide to Artificial Intelligence, J.P. Morgan. November 27, 2017 (110 pp., PDF, no opt-in).

Machine learning patents grew at a 34% CAGR between 2013 and 2017, the third-fastest growing category of all patents granted

IBM, Microsoft, Google, LinkedIn, Facebook, Intel, and Fujitsu were the seven biggest ML patent producers in 2017. Source: IFI Claims Patent Services (Patent Analytics) 8 Fastest Growing Technologies SlideShare Presentation.

61% of organizations most frequently picked machine learning / artificial intelligence as their company’s most significant data initiative for next year

Of those respondent organizations indicating they actively use Machine Learning (ML) and Artificial Intelligence (AI), 58% percent indicated they ran models in production. Source: 2018 Outlook: Machine Learning and Artificial Intelligence, A Survey of 1,600+ Data Professionals (14 pp., PDF, no opt-in).

Tech market leaders including Amazon, Apple, Google, Tesla, and Microsoft are leading their industry sectors by a wide margin in machine learning (ML) and AI investment

Each is designing ML into future-generation products and using ML and AI to improve customer experiences and improve the efficiency of selling channels. Source: Will You Embrace AI Fast Enough? AT Kearney, January 2018.

SAS, IBM, and SAP lead the predictive analytics and machine learning market based on 23 evaluation criteria applied to 14 vendors by Forrester in 2017

Forrester predicts the Predictive Analytics & Machine Learning (PAML) market will grow at a 21% CAGR through 2021 as evidenced by the increase in client inquiries and purchasing activity they are seeing with clients. Source: Data Science Association, Predictive Analytics & Machine Learning Vendors, 2017 and The Forrester Wave: Predictive Analytics And Machine Learning Solutions, Q1 2017 courtesy of SAP.

Deloitte Global predicts the number of machine learning pilots and implementations will double in 2018 compared to 2017, and double again by 2020

Factors driving the increasing pace of ML pilots include more pervasive support of Application Program Interfaces (APIs), automating data science tasks, reducing the need for training data, accelerating training and greater insight into explaining results. Source: Deloitte Global Predictions 2018 Infographics.

60% of organizations at varying stages of machine learning adoption, with nearly half (45%) saying the technology has led to more extensive data analysis & insights

35% can complete faster data analysis and increased the speed of insight, delivering greater acuity to their organizations. 35% are also finding that machine learning is enhancing their R&D capabilities for next-generation products. Source: Google & MIT Technology Review study: Machine Learning: The New Proving Ground for Competitive Advantage (10 pp., PDF, no opt-in).

McKinsey estimates that total annual external investment in AI was between $8B to $12B in 2016, with machine learning attracting nearly 60% of that investment

McKinsey estimates that total annual external investment in AI was between $8B to $12B in 2016, with machine learning attracting nearly 60% of that investment. Robotics and speech recognition are two of the most popular investment areas. Investors are most favoring machine learning startups due to quickness code-based start-ups have at scaling up to include new features fast. Software-based machine learning startups are preferred over their more cost-intensive machine-based robotics counterparts that often don’t have their software counterparts do. As a result of these factors and more, Corporate M&A is soaring in this area. The following graphic illustrates the distribution of external investments by category from the study. Source: McKinsey Global Institute Study, Artificial Intelligence, The Next Digital Frontier (80 pp., PDF, free, no opt-in).

Deloitte Global is predicting machine learning chips used in data centres will grow from a 100k to 200k run rate in 2016 to 800k this year

At least 25% of these will be Field Programmable Gate Arrays (FPGA) and Application Specific Integrated Circuits (ASICs). Deloitte found the Total Available Market (TAM) for Machine Learning (ML) Accelerator technologies could potentially reach $26bn by 2020. Source: Deloitte Global Predictions 2018.

Amazon is relying on machine learning to improve customer experiences in key areas of their business including product recommendations, substitute product prediction, fraud detection, meta-data validation and knowledge acquisition

For additional details, please see the presentation, Machine Learning At Amazon, Amazon Web Services (47 pp., PDF no opt-in).

​Other statistics

• International Data Corporation (IDC) forecasts that spending on AI and ML will grow from $12B in 2017 to $57.6B by 2021. Source: Machine learning: things are getting intense. Deloitte (6 pp., PDF. No opt-in)
• The global machine learning market is expected to grow from $1.41B in 2017 to $8.81B by 2022, attaining a 44.1% compound annual growth rate (CAGR). Factors contributing to the market’s global rapid growth include new technologies that excel at data aggregation, integration and analysis, and more scalable cloud platforms. Source: Machine Learning Market – Global Forecast to 2022 – Market Overview & Industry Trends.
• Worldwide revenues for cognitive and artificial intelligence (AI) systems will increase from $12.5B in 2017 to more than $46B in 2020. Source: Worldwide Spending on Cognitive and Artificial Intelligence Systems Forecast to Reach $12.5 Billion This Year, According to New IDC Spending Guide.

Sources of market data on machine learning:

2018 Outlook: Machine Learning and Artificial Intelligence, A Survey of 1,600+ Data Professionals. MEMSQL. (14 pp., PDF, no opt-in)

Advice for applying Machine Learning, Andrew Ng, Stanford University. (30 pp., PDF, no opt-in)

An Executive’s Guide to Machine Learning, McKinsey Quarterly. June 2015

An Investors’ Guide to Artificial Intelligence, J.P. Morgan. November 27, 2017 (110 pp., PDF, no opt-in)

Artificial intelligence and machine learning in financial services Market developments and financial stability implications, Financial Stability Board. (45 pp., PDF, no opt-in)

Big Data and AI Strategies Machine Learning and Alternative Data Approach to Investing, J.P. Morgan. (280 pp., PDF. No opt-in).

Google & MIT Technology Review study: Machine Learning: The New Proving Ground for Competitive Advantage (10 pp., PDF, no opt-in).

Hitting the accelerator: the next generation of machine-learning chips, Deloitte. (6 pp., PDF, no opt-in).

How Do Machines Learn? Algorithms are the Key to Machine Learning. Booz Allen Hamilton. (Infographic)

IBM Predicts Demand For Data Scientists Will Soar 28% By 2020, Forbes. May 13, 2017

Machine Learning At Amazon, Amazon Web Services (47 pp., PDF no opt-in).

Machine Learning Evolution (infographic). PwC. April 17, 2017 Machine learning: things are getting intense. Deloitte (6 pp., PDF. No opt-in)

Machine Learning: The Power and Promise Of Computers That Learn By Example. The Royal Society’s Machine Learning Project (128 pp., PDF, no opt-in)

McKinsey Global Institute Study, Artificial Intelligence, The Next Digital Frontier (80 pp., PDF, free, no opt-in)

McKinsey’s State Of Machine Learning And AI, 2017, Forbes, July 9, 2017

Predictions 2017: Artificial Intelligence Will Drive The Insights Revolution. Forrester, November 2, 2016 (9 pp., PDF, no opt-in)

Risks And Rewards: Scenarios around the economic impact of machine learning, The Economist Intelligence Unit. (80 pp., PDF, no opt-in)

Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? Digital/McKinsey & Company. (52 pp., PDF, no opt-in)

So What Is Machine Learning Anyway?  Business Insider. Nov. 23, 2017

The 10 Most Innovative Companies In AI/Machine Learning 2017, Wired

The Business Impact and Use Cases for Artificial Intelligence. Gartner (28 pp., PDF, no opt-in)

The Build-Or-Buy Dilemma In AI, Boston Consulting Group. January 4, 2018.

The Next Generation of Medicine: Artificial Intelligence and Machine Learning, TM Capital (25 pp., PDF, free, opt-in)

The Roadmap to Enterprise AI, Rage Networks Brief based on Gartner research. (17 pp., PDF, no opt-in)

Will You Embrace AI Fast Enough? AT Kearney. January 2018

• Enterprise investments in machine learning will nearly double over the next three years, reaching 64% adoption by 2020.
• International Data Corporation (IDC) is forecasting spending on artificial intelligence (AI) and machine learning will grow from $8B in 2016 to $47B by 2020.
• 89% of CIOs are either planning to use or are using machine learning in their organizations today.
• 53% of CIOs say machine learning is one of their core priorities as their role expands from traditional IT operations management to business strategists.
• CIOs are struggling to find the skills they need to build their machine learning models today, especially in financial services.

These and many other insights are from the recently published study, Global CIO Point of View. The entire report is downloadable here (PDF, 24 pp., no opt-in). ServiceNow and Oxford Economics collaborated on this survey of 500 CIOs in 11 countries on three continents, spanning 25 industries. In addition to the CIO interviews, leading experts in machine learning and its impact on enterprise performance contributed to the study. For additional details on the methodology, please see page 4 of the study and an online description of the CIO Survey Methodology here.

Digital transformation is a cornerstone of machine learning adoption. 72% of CIOs have responsibility for digital transformation initiatives that drive machine learning adoption. The survey found that the greater the level of digital transformation success, the more likely machine learning-based programs and strategies would succeed. IDC predicts that 40% of digital transformation initiatives will be supported by machine learning and artificial intelligence by 2019.

Key takeaways from the study include the following:

90% of CIOs championing machine learning in their organizations today expect improved decision support that drives greater topline revenue growth

CIOs who are early adopters are most likely to pilot, evaluate and integrate machine learning into their enterprises when there is a clear connection to driving business results. Many CIO compensation plans now include business growth and revenue goals, making the revenue potential of new technologies a high priority.

89% of CIOs are either planning to use or using machine learning in their organizations today

The majority, 40%, are in the research and planning phases of deployment, with an additional 26% piloting machine learning. 20% are using machine learning in some areas of their business, and 3% have successfully deployed enterprise-wide. The following graphic shows the percentage of respondents by stage of their machine learning journey.

Machine learning is a key supporting technology leading the majority finance, sales and marketing, and operations management decisions today

Human intervention is still required across the spectrum of decision-making areas including Security Operations, Customer Management, Call Center Management, Operations Management, Finance and Sales & Marketing. The study predicts that by 2020, machine learning apps will have automated 70% of Security Operations queries and 30% of Customer Management ones.

Automation of repetitive tasks (68%), making complex decisions (54%) and recognizing data patterns (40%) are the top three most important capabilities CIOs of machine learning CIOs are most interested in

Establishing links between events and supervised learning (both 32%), making predictions (31%) and assisting in making basic decisions (18%) are additional capabilities CIOs are looking for machine learning to accelerate. In financial services, machine learning apps are reviewing loan documents, sorting applications to broad parameters, and approving loans faster than had been possible before.

Machine learning adoption and confidence by CIOs varies by region, with North America in the lead (72%) followed by Asia-Pacific (61%)

Just over half of European CIOs (58%) expect value from machine learning and decision automation to their company’s overall strategy. North American CIOs are more likely than others to expect value from machine learning and decision automation across a range of business areas, including overall strategy (72%, vs. 61% in Asia Pacific and 58% in Europe). North American CIOs also expect greater results from sales and marketing (63%, vs. 47% Asia-Pacific and 38% in Europe); procurement (50%, vs. 34% in Asia-Pacific and 34% in Europe); and product development (48%, vs. 29% in Asia-Pacific and 29% in Europe).

CIOs challenging the status quo of their organization’s analytics direction are more likely to rely on roadmaps for defining and selling their vision of machine learning’s revenue contributions

More than 70% of early adopter CIOs have developed a roadmap for future business process changes compared with just 33% of average CIOs. Of the CIOs and senior management teams in financial services, the majority are looking at how machine learning can increase customer satisfaction, lifetime customer value, improving revenue growth. 53% of CIOs from our survey say machine learning is one of their core priorities as their role expands from traditional IT operations to business-wide strategy.

Sources: CIOs Cutting Through the Hype and Delivering Real Value from Machine Learning, Survey Shows

• Data scientist has been named the best job in America for three years running, with a median base salary of $110,000 and 4,524 job openings.
• DevOps engineer is the second-best job in 2018, paying a median base salary of $105,000 and 3,369 job openings.
• There are 29,187 software engineering jobs available today, making this job the most popular regarding Glassdoor postings according to the study.

These and many other fascinating insights are from Glassdoor’s 50 Best Jobs In America For 2018. The Glassdoor Report is viewable online here. Glassdoor’s annual report highlights the 50 best jobs based on each job’s overall Glassdoor Job Score. The Glassdoor Job Score is determined by weighing three key factors equally: earning potential based on median annual base salary, job satisfaction rating, and the number of job openings. Glassdoor’s 2018 report lists jobs that excel across all three dimensions of their Job Score metric. For an excellent overview of the study by Karsten Strauss of Forbes, please see his post, The Best Jobs To Apply For In 2018.

LinkedIn’s 2017 U.S. Emerging Jobs Report found that there are 9.8 times more Machine Learning Engineers working today than five years ago with 1,829 open positions listed on their site as of last month. Data science and machine learning are generating more jobs than candidates right now, making these two areas the fastest growing tech employment areas today.

Key takeaways from the study include the following:

Six analytics and data science jobs are included in Glassdoor’s 50 best jobs In America for 2018

These include Data Scientist, Analytics Manager, Database Administrator, Data Engineer, Data Analyst and Business Intelligence Developer. The complete list of the top 50 jobs is provided below with the analytics and data science jobs highlighted along with software engineering, which has a record 29,817 open jobs today:

• Median base salary of the 50 best jobs in America is $91,000 with the average salary of the six analytics and data science jobs being $94,167.
• Across all six analytics and data science jobs there are 16,702 openings as of today according to Glassdoor.
• Tech jobs make up 20 of Glassdoor’s 50 Best Jobs in America for 2018, up from 14 jobs in 2017.

Source: Glassdoor Reveals the 50 Best Jobs in America for 2018