Mobile devices are popular with hackers because they're designed for quick responses based on minimal contextual information. Verizon's 2020 Data Breach Investigations Report (DBIR) found that hackers are succeeding with integrated email, SMS and link-based attacks across social media aimed at stealing passwords and privileged access credentials. And with a growing number of breaches originating on mobile devices according to Verizon's Mobile Security Index 2020, combined with 83% of all social media visits in the United States are on mobile devices according to Merkle's Digital Marketing Report Q4 2019, applying machine learning to harden mobile threat defense deserves to be on any CISOs' priority list today. Google's use of machine learning to thwart the skyrocketing number of phishing attacks occurring during the Covid-19 pandemic provides insights into the scale of these threats. During a typical week in April of this year, Google's G-Mail Security team saw 18M daily malware and phishing emails related to Covid-19.

Bottom Line: This year's hard reset is amplifying how vital customer relationships are and how much potential AI has to find new ways to improve them. The hard reset every company is going through today is making senior management teams re-evaluate every line item and expense, especially in marketing. Spending on Customer Experience is getting re-evaluated as are supporting AI, analytics, business intelligence (BI), and machine learning projects and spending. Marketers able to quantify their contributions to revenue gains are succeeding the most at defending their budgets. Knowing if and by how much CX initiatives and strategies are paying off has been elusive.

Traditional approaches to securing endpoints based on the hardware characteristics of a given device aren't stopping breach attempts today. Bad actors are using AI and machine learning to launch sophisticated attacks to shorten the time it takes to compromise an endpoint and successfully breach systems. The era of trusted and untrusted domains at the operating system level, and "trust, but verify" approaches are over. Security software and services spending is soaring as a result, as the market forecasts above show. AI and machine learning are proving to be effective technologies for battling increasingly automated, well-orchestrated cyberattacks and breach attempts.

Bottom Line: AI and machine learning are enabling omnichannel strategies to scale by providing insights into the changing needs and preferences of customers, creating customer journeys that scale, delivering consistent experiences. For any omnichannel strategy to succeed, each customer touchpoint needs to be orchestrated as part of an overarching customer journey. That's the only way to reduce and eventually eliminate customers' perceptions of using one channel versus another. What makes omnichannel so challenging to excel at is the need to scale a variety of customer journeys in real-time as customers are also changing. AI and machine learning are being used to close these gaps with greater intelligence and knowledge.

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. 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.

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.

These and other findings are from the McKinsey Global Institute Study, and discussion paper, Artificial Intelligence, The Next Digital Frontier (80 pp., PDF, free, no opt-in) published last month. McKinsey Global Institute published an article summarizing the findings titled How Artificial Intelligence Can Deliver Real Value To Companies. McKinsey interviewed more than 3,000 senior executives on the use of AI technologies, their companies' prospects for further deployment, and AI's impact on markets, governments, and individuals. McKinsey Analytics was also utilized in the development of this study and discussion paper.

Bottom Line: By enabling enterprises to scale security with user behavior-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. 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.

Prospects of universal AI ethics seem slim. However the five design pillars will serve organizations well beyond the social fads and fears. The goal – build controls that will identify biases, show attribution, and enable course correction as needed. Ready to roll out your plans for AI? Do you understand the business model implications? Who will you partner with for AI? Add your comments to the blog or reach me via email: R (at) ConstellationR (dot) com or R (at) SoftwareInsider (dot) org.

We have had many previous hype cycles around AI. As I wrote in Silicon Collar: "Since the 1950s! That is when Alan Turing defined his famous test to measure a machine's ability to exhibit intelligent behavior equivalent to that of a human. In 1959, we got excited when Allen Newell and his colleagues coded the General Problem Solver. In 1968, Stanley Kubrick sent our minds into overdrive with HAL in his movie, 2001: A Space Odyssey. We applauded when IBM's Deep Blue supercomputer beat Grandmaster Garry Kasparov at chess in 1997. We were impressed in 2011 when IBM's Watson beat human champions at Jeopardy! and again in 2016 when Google's AlphaGo showed it had mastered Go, the ancient board game. Currently, we are so excited about Amazon's Echo digital assistant/home automation hub and its ability to recognize the human voice, that we are saying a machine has finally passed the Turing Test. The good news is over the seven decades, the AI community has gifted us a wide range of big words like deep learning, neural networks, cognitive computing and natural language processing. Yale computer science professor David Gelernter thinks we have only scratched the surface. In his book The Tides of Mind, he calls it "the spectrum of consciousness," which is "essentially a range of mental states through which all humans cycle each day.