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Posts tagged ‘Louis Columbus’ blog’

10 Charts That Will Change Your Perspective Of Microsoft Azure’s Growth

  • Microsoft Azure revenue grew 50% year-over-year in fiscal Q2, 2021, contributing to a 26% increase in Server products and cloud services revenue.
  • According to the latest earnings call, more than 1,000 Microsoft customers now use Azure Arc to simplify hybrid management and run Azure services across on-premises, multi-cloud and at the edge.
  • Commercial cloud gross margins increased to 71% in the latest quarter, up from 67% a year earlier.
  • There are now over 60 Azure regions globally, strengthening Microsoft’s competitive global position versus Amazon Web Services.
  • Microsoft reported $43.08 billion in the second fiscal quarter ended Dec. 31, up from $36.91 billion a year earlier,

These and many other insights are from Microsoft’s Fiscal Year 2021 Second Quarter Earnings Conference Call and related research. Microsoft’s early decision to double down on expanding their cloud platform by accelerating new product and services development and Azure region expansion is paying off. Azure’s revenue growth shows Microsoft is an innovation machine when it comes to the cloud.  

In their latest fiscal quarter, Microsoft announced hundreds of new services and updates to Microsoft Azure alone. The most noteworthy are improvements to Microsoft Cloud for Healthcare, Azure Defender for SQL, Password spray detection in Azure AD Identity Protection, Azure Stack HCI, Azure Stack Edge, Azure Data Factory now being available in five new regions and many more. All Azure updates are available in an online index that provides options for finding those now available, in preview, or in development.  

The following ten charts will change your perspective of Microsoft Azure’s growth:

  • Intelligent Cloud delivered the highest operating income of all segments in the 2nd quarter at $6.4 billion or 36% of total consolidated operating income. This quarter, Microsoft’s success with indirect channel sales combined with more enterprise customers accelerating their cloud-first initiatives contributed to Intelligent Cloud leading all segments in operating income. The following is from the Q2, FY21 Earnings Call.
10 Charts That Will Change Your Perspective Of Microsoft Azure's Growth
  • Synergy Research Group’s latest cloud market analysis finds that Amazon and Microsoft are over 50% of the global cloud provider market, with Microsoft reaching 20% worldwide market share for the first time. Q4, 2020 enterprise spending on cloud infrastructure services was just over $37 billion, $4 billion higher than the previous quarter and up 35% from the fourth quarter of 2019. Synergy Research notes that it has taken just nine quarters for the market to double in size.
10 Charts That Will Change Your Perspective Of Microsoft Azure's Growth
  • 63% of enterprises are currently running apps on Microsoft Azure, second only to AWS.  Azure is narrowing the gap with AWS in both the percentage of enterprises using it and the number of virtual machines (VMs) enterprises are running on it. 6% of enterprises are spending at least $1.2 million annually on Microsoft Azure. Source: Statista and Flexera 2020 State of the Cloud Report, page 50.
10 Charts That Will Change Your Perspective Of Microsoft Azure's Growth
  • 2020 total cloud infrastructure services spending grew 33% to $142 billion from $107 billion in 2019, according to Canalys, with Microsoft’s indirect channel business fueling their 20% market share growth. Microsoft’s dominance of indirect selling channels is evident in the level of sales enablement, sales and technical support they provide resellers. Canalys’ Chief Analyst Alastair Edwards says that “organizations are turning to trusted business partners to advise, implement, support and manage their cloud journeys and articulate the real business value of cloud migration.”
10 Charts That Will Change Your Perspective Of Microsoft Azure's Growth
  • 19% of enterprises expect to invest significantly more on Microsoft Azure in 2021, leading all other cloud vendors this year. Microsoft Azure leads all vendors when compared to the percentage change in spending this year. It’s noteworthy that 61% of all enterprises interviewed expect to increase their investments in Microsoft Azure this year, second only to Microsoft SaaS software. Source: 2021 Flexera State of Tech Report, January 2021.
10 Charts That Will Change Your Perspective Of Microsoft Azure's Growth
  • Microsoft Azure Stack is the second most-used private cloud platform by enterprises, with 35% of them currently running apps today. Azure Stack also leads all others in experimentation, with one in five enterprises, or 21%, currently in that phase of deployment. 67% of all enterprises interviewed in the 2020 Flexera State of the Cloud Report are either running Azure apps or are considering it.
10 Charts That Will Change Your Perspective Of Microsoft Azure's Growth
  • Microsoft’s centerpiece for their intelligence investment is the Microsoft Intelligent Security Graph, which processes over 630 billion authentications across our cloud services each month. Microsoft relies on the Security Graph to gain insights into normal behavior, including sign-ins and authentications and abnormal behavior, including attempted bypasses to two-factor authentication. Microsoft blocks more than 5 billion distinct malware threats per month, providing a great deal of useful data to analyze endpoints across customers’ networks. Source: Microsoft CISO Workshop 1 – Cybersecurity Briefing.
10 Charts That Will Change Your Perspective Of Microsoft Azure's Growth
  • 44.5% of enterprises say Microsoft Azure is their preferred provider for Cloud Business Intelligence (BI). Azure is considered 27% more critical to an enterprises’ Cloud BI requirements and preferences than Amazon Web Services. It’s noteworthy that 96.5% of all enterprises have a preference for Microsoft Azure BI versus its main competitors, including Google Cloud, IBM BlueMix, or Alibaba.   
10 Charts That Will Change Your Perspective Of Microsoft Azure's Growth
  • Microsoft Azure is the leading IoT platform worldwide by end-to-end capabilities with a total score of 276 according to Counterpoint Research. According to the methodology Counterpoint used for ranking IoT platforms, Microsoft Azure is considered a global leader in edge data processing, an increasingly important feature of IoT platforms worldwide. The ability to deliver IoT capabilities from the cloud to the edge helped Microsoft’s platform rank high in this category. Source; Statista and CounterPointResearch.com.
10 Charts That Will Change Your Perspective Of Microsoft Azure's Growth
  •  Microsoft Azure is the foundation for a Digital Supply Chain Platform that integrates supply chain partner, corporate, data & advanced analytics platforms and supply chain core transaction systems.  The ongoing pandemic is putting continued pressure on supply chains. Most manufacturing executives say that employee safety, data security, remote worker access, supply chain visibility and insights visibility are high priorities. In response to these market needs, Microsoft Supply Chain (MSC) was created on the Azure platform. The diagram below explains how Azure is integral to the Digital Supply Chain platform.
10 Charts That Will Change Your Perspective Of Microsoft Azure's Growth

76% Of Enterprises Prioritize AI & Machine Learning In 2021 IT Budgets

  • 43% of enterprises say their AI and Machine Learning (ML) initiatives matter “more than we thought,” with one in four saying AI and ML should have been their top priority sooner.
  • 50% of enterprises plan to spend more on AI and ML this year, with 20% saying they will be significantly increasing their budgets.
  • 56% of all enterprises rank governance, security and auditability issues as their highest-priority concerns today.
  • In just over a third of enterprises surveyed (38%), data scientists spend more than 50% of their time on model deployment.   

Enterprises accelerated their adoption of AI and machine learning in 2020, concentrating on those initiatives that deliver revenue growth and cost reduction. Consistent with many other surveys of enterprises’ AI and machine learning accelerating projects last year, Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning finds enterprises expanding into a wider range of applications starting with process automation and customer experience. Based on interviews with 403 business leaders and practitioners who have insights into their company’s machine learning efforts, the study represents a random sampling of industries across a spectrum of machine learning maturity levels. Algorithmia chose to limit the survey to only those from enterprises with $100M or more in revenue. Please see page 34 of the study for additional details regarding the methodology.   

Key insights from the research include the following:

  • 76% of enterprises prioritize AI and machine learning (ML) over other IT initiatives in 2021. Six in ten (64%) say AI and ML initiatives’ priorities have increased relative to other IT priorities in the last twelve months. Algorithmia’s survey from last summer found that enterprises began doubling down on AI & ML spending last year. The pandemic created a new sense of urgency regarding getting AI and ML projects completed, a key point made by CIOs across the financial services and tech sectors last year during interviews for comparable research studies.
76% Of Enterprises Prioritize AI & Machine Learning In 2021 IT Budgets
Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning
  • 83% of enterprises have increased their budgets for AI and machine learning year-over-year from 2019 to 2020. 20% of enterprises increased their budget by over 50% between 2019 and 2020. According to MMC Ventures’ The State of AI Divergence Study, one in ten enterprises now uses ten or more AI applications with chatbots, process optimization and fraud analysis leading all categories. A recent Salesforce Research report, Enterprise Technology Trends, found that 83% of IT leaders say AI & ML is transforming customer engagement and 69% say it is transforming their business. The following compares year-over-year AI and ML budget changes between FY 2018 – 2019 and FY 2019 – 20.
76% Of Enterprises Prioritize AI & Machine Learning In 2021 IT Budgets
Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning
76% Of Enterprises Prioritize AI & Machine Learning In 2021 IT Budgets
Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning
  • Improving customer experiences to drive greater revenue growth and automating processes to reduce costs are the two most popular use cases or application areas for AI and ML in enterprises today. It’s noteworthy that seven of the top 20 use cases are customer-centric, nearly half of all use cases tracked in Algorithmia’s survey.  46% of enterprises are using AI & ML to combat fraud, which will most likely grow given the growth and severity of breaches, including the SolarWinds cyberattack. Capgemini’s recent study of AI adoption in cybersecurity found network, data and endpoint security are the three leading use cases of AI in cybersecurity today, with each predicted to get more funding in 2021, according to CISOs interviewed for the report.
76% Of Enterprises Prioritize AI & Machine Learning In 2021 IT Budgets
Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning
  • AI and ML business cases that provide greater customer revenue growth, reduced costs and greater financial visibility have the highest priority of being funded inside any enterprise today. The combination of improving customer experiences, automating processes (to reduce costs) and generating financial insights (for greater financial visibility) is the ideal combination for getting a proof of concept started for an AI or ML project. The proliferation of AI and ML use cases shown in the graphic below is attributable to how each contributes to enterprises achieving a tangible, positive ROI by combining them to solve specific business problems.
76% Of Enterprises Prioritize AI & Machine Learning In 2021 IT Budgets
Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning

The Best Tech Companies For Remote Jobs In 2021 According To Glassdoor

  • Glassdoor shows 3,937 companies in the middle of a hiring surge during Covid-19, 960 of which are in information technology.
  • Leading software companies going through a hiring surge right now include Aha! Software, Appen, Clevertech, CrowdStrike, Datadog, Dataiku, Fastly, Hashicorp, Leidos, Liveops, Netskope, Proofpoint, Rackspace, Zapier and Zendesk.   
  • Modern Tribe, Dataiku, Zapier, PartnerCentric, Slack, Fuse, ScienceLogic and SAP are the highest rated companies by their employees on Glassdoor who offer remote jobs today.
  • Between Glassdoor, Indeed, LinkedIn and Monster, there are over 16,500 open remote-based software technical professional jobs available today. Companies with open, remote-based solutions include Aha!, Box, Cloudera, DemandBase, Jobot,  Red Hat, NTT Data, Salesforce and many others.   
  • Freshworks currently has 161 openings, the majority of which are remote. Check out their open positions here on Glassdoor.
  • GitLab alone has 79 remote full-time positions open today and is widely considered a leader in creating a productive, positive remote working culture, with 88% of employees saying they would recommend the company to a friend.  

These and many other useful insights are based on comparing the leading tech companies who offer remote, work-from-home job positions by their Glassdoor scores. Leading tech companies are ranked on the percentage of employees who would recommend their company to a friend and the percent of employees who approve of the CEO. The total number of open job positions by company is in the third column of the table. Hiring companies of note include the following:

PowerToFly has had an impressive growth year and is the go-to remote job search engine for women professionals. The company was launched in 2014 by Milena Berry and Katharine Zaleski to connect Fortune 500 companies, startups and growing companies with women looking to work for businesses that value gender diversity and inclusion. PowerToFly’s number of available remote jobs has soared from 994 earlier this year to over 2,500 open remote positions today. 94% of employees would recommend working at PowerToFly to a friend and 93% approve of their CEOs.  

The best tech companies for remote jobs in 2021 table is shown below. You can download the original Excel data set here. Please click on the image to expand it for easier reading.

  • Angelist has 2,700 enterprise software-related remote positions on their website today with companies including Auth0, Arctic Wolf Networks, Confluent, Couchbase, HackerOne, Slack, MindTickle, MongoDB, Sendoso, Tanium and many others.  
  • FlexJobs has 5,566 remote-based software jobs that include full-time, part-time and freelance positions. Open positions include Senior Software Engineers, DevOps Engineers, Product Managers, Project Managers, Full Stack Developers and more. 
  • Remotive provides a curated list of 192 startups, many of which have open remote-based positions on December 1, 2020.
  • StackOverflow has 815 open remote-based job positions available today, including Canonical (39 open jobs), Octane AI, Shield AI and many others.
  • Torch Capital’s Talent Connect Portal has 980 positions open today, including several from DoubleVerify, Electric, Lexis Nexis, Nexon America, Shopify, Tesla and others.  
  • Working Nomads site currently has 11,216 remote, work-from-home development jobs advertised. There are also 2,021 marketing, 1,922 management, 1,873 system administration, 1,592 design and 1,164 sales remote, work-from-home job postings.  

Software Dominates Deloitte’s 2020 Tech Fast 500 With 71% Of All Companies

  • Software companies continue to deliver the highest growth rates for the 25th straight year, representing 71% of the entire list, the highest-ever percentage in the history of the rankings.
  •  353 of the 500 fastest-growing companies in North America are in the software industry according to Deloitte’s 2020 Tech Fast 500, the most ever in the history of their rankings and a 3% increase over last year.
  • Two of the ten fastest-growing companies over the last three years specialize in cybersecurity, OneTrust and Transmit Security.
  • Notable software companies ranked in Deloitte’s 2020 Tech Fast 500 include Bolt, Illumio, LogicMonitor and Seeq.
  • Biotechnology/pharmaceutical companies are the second most prevalent sector, comprising 14% of all companies, followed by digital content/media/entertainment (5%) and medical devices (4%).  

It’s fascinating to look at the emerging trends in Deloitte’s 2020 North America Technology Fast 500 Rankings as leading predictors of innovation. This year’s report is a quick read and provides a glimpse into the fastest-growing companies between 2016 and 2019. Deloitte chooses Technology Fast 500 awardees based on percentage fiscal year revenue growth from 2016 to 2019. Overall, the 2020 Technology Fast 500 companies achieved revenue growth ranging from 175% to 106,508% over the three-year time frame, with a median growth rate of 450%.

Key insights from the rankings include the following:

  • Five of the top ten winners are software companies, including Branch Metrics, OneTrust, Transmit Security, Drift and CharterUP. It’s noteworthy that cybersecurity is well-represented in the top ten fastest-growing companies between 2016 and 2019. OneTrust and Transmit Security is in the top five fastest-growing companies between 2016 and 2019, accentuating how critical cybersecurity is becoming in all businesses. The following graphic lists the top ten Deloitte 2020 North America Technology Fast 500 winners.
Software Dominates Deloitte's 2020 Tech Fast 500 With 71% Of All Companies
Deloitte’s 2020 North America Technology Fast 500 Rankings
  •  Digital platform and enterprise infrastructure & productivity dominate software companies are dominating software sub-sectors with 56% of all companies. Deloitte’s ranking reflects the increasing urgency all organizations have to launch, scale and excel at new digital selling channels. The pandemic accelerated the urgency faster than the most compelling business case ever could. Having over 50% of all software companies in these categories quantifies the cloud as the platform of choice across enterprises.  
Software Dominates Deloitte's 2020 Tech Fast 500 With 71% Of All Companies
Deloitte’s 2020 North America Technology Fast 500 Rankings
  • Electronic devices/hardware, energy tech and software & SaaS are the three sectors generating the fastest growing businesses over the last three years. Edge computing and the quick pace of innovation in intelligent sensor development and adoption for the Internet of Things (IoT) and Industrial Internet of Things (IIoT) use cases are catalysts driving the 683% growth rate. Sustainability’s bottom-line benefits, including its positive impact on lean manufacturing, help drive to 525% growth rate in energy tech. Software and SaaS median growth rate of 465% shows enterprise software’s evolution is nascent and just getting started.
Software Dominates Deloitte's 2020 Tech Fast 500 With 71% Of All Companies
Deloitte’s 2020 North America Technology Fast 500 Rankings
Software Dominates Deloitte's 2020 Tech Fast 500 With 71% Of All Companies
Deloitte’s 2020 North America Technology Fast 500 Rankings

How An AI Platform Is Matching Employees And Opportunities

How An AI Platform Is Matching Employees And Opportunities

Instead of relying on data-driven signals of past accomplishments, Eightfold.ai is using AI to discover the innate capabilities of people and matching them to new opportunities in their own companies.

Bottom Line: Eightfold.ai’s innovative approach of combining their own AI and virtual hackathons to create and launch new additions to their Project Marketplace rapidly is a model enterprises need to consider emulating.

Eightfold.ai was founded with the mission that there is a right career for everyone in the world. Since its founding in 2016, Eightfold.ai’s Talent Intelligence Platform continues to see rapid global growth, attracting customers across four continents and 25 countries, supporting 15 languages with users in 110 countries. Their Talent Intelligence Platform is built to assist enterprises with Talent Acquisition and Management holistically.

What’s noteworthy about Eightfold.ai’s approach is how they have successfully created a platform that aggregates all available data on people across an enterprise – from applicants to alumni – to create a comprehensive Talent Network. Instead of relying on data-driven signals of past accomplishments, Eightfold.ai is using AI to discover the innate capabilities of people and matching them to new opportunities in their own companies. Eightfold’s AI and machine learning algorithms are continuously learning from enterprise and individual performance to better predict role, performance and career options for employees based on capabilities.

How Eightfold Sets A Quick Pace Innovating Their Marketplace

Recently Eightfold.ai announced Project Marketplace, an AI-based solution for enterprises that align employees seeking new opportunities and companies’ need to reskill and upskill their employees with capabilities that line up well with new business imperatives. Eightfold wanted to provide employees with opportunities to gain new skills through experiential learning, network with their colleagues, join project teams and also attain the satisfaction of helping flatten the unemployment curve outside. Project Marketplace helps employers find hidden talent, improve retention strategies and gain new knowledge of who has specific capabilities and skills. The following is a screen from the Marketplace that provides employees the flexibility of browsing all projects their unique capabilities qualify them for:

How An AI Platform Is Matching Employees And Opportunities

Employees select a project of interest and are immediately shown how strong of a match they are with the open position. Eightfold provides insights into relevant skills that an employee already has, why they are a strong match and the rest of the project team members – often a carrot in itself. Keeping focused on expanding employee’s capabilities, Eightfold also provides guidance of which skills an employee will learn. The following is an example of what an open project positions looks like:

How An AI Platform Is Matching Employees And Opportunities

How An AI Platform Is Matching Employees And Opportunities

Employee applicants can also view all the projects they currently have open from the My Projects view shown below:

How An AI Platform Is Matching Employees And Opportunities

Project Marketplace is the win/win every employee has yearned for as they start to feel less challenged in their current position and start looking for a new one, often outside their companies. I recently spoke with Ashutosh Garg, CEO and Co-Founder and Kamal Ahluwalia, Eightfold’s President, to see how they successfully ran a virtual hackathon across three continents to keep the Marketplace platform fresh with new features and responsive to the market.

How to Run A Virtual Hackathon

Starting with the hackathon, Eightfold relied on its own Talent Intelligence Platform to define the teams across all three continents, based on their employees’ combined mix of capabilities. Ashutosh, Kamal and the senior management team defined three goals of the hackathon:

  1. Solve problems customers are asking about with solutions that are not on the roadmap yet.
  2. Accelerate time to value for customers with new approaches no one has thought of before.
  3. Find new features and unique strengths that further strengthen the company’s mission of finding the right career for everyone in the world.

It’s fascinating to see how AI, cybersecurity and revenue management software companies continue to innovate at a fast pace delivering complex apps with everyone being remote. I asked Ashutosh how he and his management team approached the challenge of having a hackathon spanning three continents deliver results. Here’s what I learned from our discussion and these lessons are directly applicable to any virtual hackathon today:

  1. Define the hackathon’s purpose clearly and link it to the company mission, explaining what’s at stake for customers, employees and the millions of people looking for work today – all served by the Talent Intelligence Platform broadening its base of features.
  2. Realize that what you are building during the hackathon will help set some employees free from stagnating skills allowing them to be more employable with their new capabilities.
  3. The hackathon is a chance to master new skills through experiential learning, further strengthening their capabilities as well. And often learning from some of the experts in the company by joining their teams.
  4. Reward risk-taking and new innovative ideas that initially appear to be edge cases, but can potentially be game changers for customers.

I’ve been interviewing CEOs from startups to established enterprise software companies about how they kept innovation alive during the lockdown. CEOs have mentioned agile development, extensive use of Slack channels and daily virtual stand-ups. Ashutosh Garg is the only one to mention how putting intrinsic motivation into practice, along with these core techniques, binds hackathon teams together fast. Dan Pink’s classic TED Talk, The Puzzle of Motivation, explains intrinsic motivators briefly and it’s clear they have implications on a hackathon succeeding or not.

Measuring Results Of the Hackathon

Within a weekend, Project Marketplace revealed several new rock stars amongst the Eightfold hackathon teams. Instead of doing side projects for people who had time on their hands, this Hackathon was about making Eightfold’s everyday projects better and faster. Their best Engineers and Services team members took a step back, re-looked at the current approaches and competed with each other to find better and innovative ways. And they all voted for the most popular projects and solutions – ultimate reward in gaining the respect of your peers. As well as the most “prolific coder” for those who couldn’t resist working on multiple teams.

Conclusion

Remote work is creating daunting challenges for individuals at home as well as for companies. Business models need to change and innovation cannot take a back seat while most companies have employees working from home for the foreseeable future. Running a hackathon during a global lockdown and making it deliver valuable new insights and features that benefit customers now is achievable as Eightfold’s track record shows. Project marketplace may prove to be a useful ally for employees and companies looking to stay true to their mission and help each other grow – even in a pandemic. This will create better job security, a culture of continuous learning, loyalty and more jobs. AI will change how we look at our work – and this is a great example of inspiring innovation.

 

What’s New In Gartner’s Hype Cycle For Endpoint Security, 2020

What’s New In Gartner’s Hype Cycle For Endpoint Security, 2020

  • Remote working’s rapid growth is making endpoint security an urgent priority for all organizations today.
  • Cloud-first deployment strategies dominate the innovations on this year’s Hype Cycle for Endpoint Security.
  • Zero Trust Security (ZTNA) is gaining adoption in enterprises who realize identities are the new security perimeter of their business.
  • By 2024, at least 40% of enterprises will have strategies for adopting Secure Access Service Edge (SASE) up from less than 1% at year-end 2018.

These and many other new insights are from Gartner Hype Cycle for Endpoint Security, 2020 published earlier this year and the recent announcement, Gartner Says Bring Your Own PC Security Will Transform Businesses within the Next Five Years. Gartner’s definition of Hype Cycles includes five phases of a technology’s lifecycle and is explained here.  There are 20 technologies on this year’s Hype Cycle for Endpoint Security. The proliferation of endpoint attacks, the rapid surge in remote working, ransomware, fileless and phishing attacks are together, creating new opportunities for vendors to fast-track innovation. Cloud has become the platform of choice for organizations adopting endpoint security today, as evidenced by the Hype Cycle’s many references to cloud-first deployment strategies.  The Gartner Hype Cycle for Endpoint Security, 2020, is shown below:

What’s New In Gartner’s Hype Cycle For Endpoint Security, 2020

 

Details Of What’s New In Gartner’s Hype Cycle for Endpoint Security, 2020

  • Five technologies are on the Hype Cycle for the first time reflecting remote working’s rapid growth and the growing severity and sophistication of endpoint attacks. Unified Endpoint Security, Extended Detection and Response, Business E-Mail Compromise Protection, BYOPC Security and Secure Access Service Edge (SASE) are the five technologies added this year. Many organizations are grappling with how to equip their remote workforces with systems, devices and smartphones, with many reverting to have employees use their own. Bring your PC (BYOPC) has become so dominant so fast that Gartner replaced BYOD on this year’s Hype Cycle with the new term. Gartner sees BYOPC as one of the most vulnerable threat surfaces every business has today. Employees’ devices accessing valuable data and applications continues to accelerate without safeguards in place across many organizations.
  • Extended detection and response (XDR) are on the Hype Cycle for the first time, reflecting the trend of vendor consolidation across cybersecurity spending today. Gartner defines XDR as a vendor-specific, threat detection and incident response tool that unifies multiple security products into a security operations system. XDR and its potential to reduce the total cost and complexity of cybersecurity infrastructures is a dominant theme throughout this year’s Hype Cycle. XDR vendors are claiming that their integrated portfolios of detection and response applications deliver greater accuracy and prevention than stand-alone systems, driving down Total Cost of Ownership (TCO) and increasing productivity. Key vendors in XDR include Cisco, FireEye, Fortinet, McAfee, Microsoft, Palo Alto Networks, Sophos, Symantec and Trend Micro.
  • Business email compromise (BEC) protection is on the Hype Cycle for the first time this year. Phishing attacks cost businesses $1.8B in 2019, according to the FBI, underscoring the need for better security in the area of business email. Gartner defines business email compromise (BEC) protection as a series of solutions that detect and filter malicious emails that fraudulently impersonate business associates to misdirect funds or data. There have been many instances of business email compromise attacks focused on C-level executives, hoping that a fraudulent directive from them to subordinates leads to thousands of dollars being transferred to outside accounts or being sent in gift cards. Gartner found that fraudulent invoices accounted for 39% of such attacks in 2018, posing an internal risk to organizations and reputation risk.
  • Unified Endpoint Security (UES) is being driven by IT organizations’ demand for having a single security console for all security events. Gartner notes that successful vendors in UES will be those that can demonstrate significant productivity gains from the integration of security and operations and those that can rapidly process large amounts of data to detect previously unknown threats. CIOs and CISOs are looking for a way to integrate UES and Unified Endpoint Management (UEM), so their teams can have a single, comprehensive real-time console of all devices that provides alerts of any security events. The goal is to adjust security policies across all devices. Absolute’s approach to leveraging their unique persistence, resilience and intelligence capabilities are worth watching. Their approach delivers unified endpoint security by relying on their Endpoint Resilience platform that includes a permanent digital tether to every endpoint in the enterprise. By having an undeletable digital thread to every device, Absolute is enabling self-healing, greater visibility and control. Based on conversations with their customers in Education and Healthcare, Absolute’s unique approach gives IT complete visibility into where every device is at all times and what each device configuration looks like in real-time.
  • Unified Endpoint Management (UEM) is expanding rapidly beyond managing PCs and mobile devices to provide greater insights from endpoint analytics and deeper integration Identity and Access Management. Gartner notes interest in UEM remains strong and use-case-driven across their client base. UEM’s many benefits, including streamlining continuous OS updates across multiple mobile platforms, enabling device management regardless of the connection and having an architecture capable of supporting a wide range of devices and operating systems are why enterprises are looking to expand their adoption of UEM. Another major benefit enterprises mention is automating Internet-based patching, policy, configuration management. UEM leaders include MobileIron, whose platform reflects industry leadership with its advanced unified endpoint management (UEM) capabilities. MobileIron provides customers with additional security solutions integrated to their UEM platform, including passwordless multi-factor authentication (Zero Sign-On) and mobile threat defense (MTD). MTD is noteworthy for its success at MobileIron customers who need to validate devices at scale, establish user context, verify network connections, then detect and remediate threats.
  •  Gartner says ten technologies were either removed or replaced in the Hype Cycle because they’ve evolved into features of broader technologies or have developed into tools that address more than security. The ten technologies include protected browsers, DLP for mobile devices, managed detection and response, user and entity behavior analytics, IoT security, content collaboration platforms, mobile identity, user authentication, trusted environments and BYOD being replaced by BYOPC.

 

10 Ways AI Is Improving New Product Development

10 Ways AI Is Improving New Product Development

  • Startups’ ambitious AI-based new product development is driving AI-related investment with $16.5B raised in 2019, driven by 695 deals according to PwC/CB Insights MoneyTree Report, Q1 2020.
  • AI expertise is a skill product development teams are ramping up their recruitment efforts to find, with over 7,800 open positions on Monster, over 3,400 on LinkedIn and over 4,200 on Indeed as of today.
  • One in ten enterprises now uses ten or more AI applications, expanding the Total Available Market for new apps and related products, including chatbots, process optimization and fraud analysis, according to MMC Ventures.

From startups to enterprises racing to get new products launched, AI and machine learning (ML) are making solid contributions to accelerating new product development. There are 15,400 job positions for DevOps and product development engineers with AI and machine learning today on Indeed, LinkedIn and Monster combined. Capgemini predicts the size of the connected products market will range between $519B to $685B this year with AI and ML-enabled services revenue models becoming commonplace.

Rapid advances in AI-based apps, products and services will also force the consolidation of the IoT platform market. The IoT platform providers concentrating on business challenges in vertical markets stand the best chance of surviving the coming IoT platform shakeout. As AI and ML get more ingrained in new product development, the IoT platforms and ecosystems supporting smarter, more connected products need to make plans now how they’re going to keep up. Relying on technology alone, like many IoT platforms are today, isn’t going to be enough to keep up with the pace of change coming.   The following are 10 ways AI is improving new product development today:

  • 14% of enterprises who are the most advanced using AI and ML for new product development earn more than 30% of their revenues from fully digital products or services and lead their peers is successfully using nine key technologies and tools. PwC found that Digital Champions are significantly ahead in generating revenue from new products and services and more than a fifth of champions (29%) earn more than 30% of revenues from new products within two years of information. Digital Champions have high expectations for gaining greater benefits from personalization as well. The following graphic from Digital Product Development 2025: Agile, Collaborative, AI-Driven and Customer Centric, PwC, 2020 (PDF, 45 pp.) compares Digital Champions’ success with AI and ML-based new product development tools versus their peers:

10 Ways AI Is Improving New Product Development

 

  • 61% of enterprises who are the most advanced using AI and ML (Digital Champions) use fully integrated Product Lifecycle Management (PLM) systems compared to just 12% of organizations not using AI/ML today (Digital Novices). Product Development teams the most advanced in their use of AL & ML achieve greater economies of scale, efficiency and speed gains across the three core areas of development shown below. Digital Champions concentrate on gaining time-to-market and speed advantages in the areas of Digital Prototyping, PLM, co-creation of new products with customers, Product Portfolio Management and Data Analytics and AI adoption:

10 Ways AI Is Improving New Product Development

  • AI is actively being used in the planning, implementation and fine-tuning of interlocking railway equipment product lines and systems.  Engineer-to-order product strategies introduce an exponential number of product, service and network options. Optimizing product configurations require an AI-based logic solver that can factor in all constraints and create a Knowledge Graph to guide deployment. Siemens’ approach to using AI to find the optimal configuration out of 1090 possible combinations provides insights into how AI can help with new product development on a large scale. Source: Siemens, Next Level AI – Powered by Knowledge Graphs and Data Thinking, Siemens China Innovation Day, Michael May, Chengdu, May 15, 2019.

10 Ways AI Is Improving New Product Development

  • Eliminating the roadblocks to getting new products launched starts with using AI to improve demand forecast accuracy. Honeywell is using AI to reduce energy costs and negative price variance by tracking and analyzing price elasticity and price sensitivity as well. Honeywell is integrating AI and machine-learning algorithms into procurement, strategic sourcing and cost management getting solid returns across the new product development process. Source: Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.

10 Ways AI Is Improving New Product Development

  • Relying on AI-based techniques to create and fine-tune propensity models that define product line extensions and add-on products that deliver the most profitable cross-sell and up-sell opportunities by product line, customer segment and persona. It’s common to find data-driven new product development and product management teams 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. AI 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 created in Microsoft Power BI.

10 Ways AI Is Improving New Product Development

  • AI is enabling the next generation of frameworks that reduce time-to-market while improving product quality and flexibility in meeting unique customization requirements on every customer order. AI is making it possible to synchronize better suppliers, engineering, DevOps, product management, marketing, pricing, sales and service to ensure a higher probability of a new product succeeding in the market. Leaders in this area include BMC’s Autonomous Digital Enterprise (ADE). BMC’s ADE framework shows the potential to deliver next-generation business models for growth-minded organizations looking to run and reinvent their businesses with AI/ML capabilities and deliver value with competitive differentiation enabled by agility, customer centricity and actionable insights. The ADE framework is capable of flexing and responding more quickly to customer requirements than competitive frameworks due to the following five factors: proven ability to deliver a transcendent customer experience; automated customer interactions and operations across distributed organizations; seeing enterprise DevOps as natural evolution of software DevOps; creating the foundation for a data-driven business that operates with a data mindset and analytical capabilities to enable new revenue streams; and a platform well-suited for adaptive cybersecurity. Taken together, BMC’s ADE framework is what the future of digitally-driven business frameworks look like that can scale to support AI-driven new product development. The following graphic compares the BMC ADE framework (left) and the eight factors driving digital product development as defined by PwC (right) through their extensive research. For more information on BMC’s ADE framework, please see BMC’s Autonomous Digital Enterprise site. For additional information on PwC’s research, please see the document Digital Product Development 2025: Agile, Collaborative, AI-Driven and Customer Centric, PwC, 2020 (PDF, 45 pp.).

10 Ways AI Is Improving New Product Development

  • Using AI to analyze and provide recommendations on how product usability can be improved continuously. It’s common for DevOps, engineering and product management to run A/B tests and multivariate tests to identify the usability features, workflows and app & service responses customers prefer. Based on personal experience, one of the most challenging aspects of new product development is designing an effective, engaging and intuitive user experience that turns usability into a strength for the product. When AI techniques are part of the core new product development cycle, including usability, delivering enjoyable customer experiences, becomes possible. Instead of a new app, service, or device is a chore to use, AI can provide insights to make the experience intuitive and even fun.
  • Forecasting demand for new products, including the causal factors that most drive new sales is an area AI is being applied to today with strong results. From the pragmatic approaches of asking channel partners, indirect and direct sales teams, how many of a new product they will sell to using advanced statistical models, there is a wide variation in how companies forecast demand for a next-generation product. AI and ML are proving to be valuable at taking into account causal factors that influence demand yet had not been known of before.
  • Designing the next generation of Nissan vehicles using AI is streamlining new product development, trimming weeks off new vehicle development schedules. Nissan’s pilot program for using AI to fast-track new vehicle designs is called DriveSpark. It was launched in 2016 as an experimental program and has since proven valuable for accelerating new vehicle development while ensuring compliance and regulatory requirements are met. They’ve also used AI to extend the lifecycles of existing models as well. For more information, see the article, DriveSpark, “Nissan’s Idea: Let An Artificial Intelligence Design Our Cars,” September 2016.
  • Using generative design algorithms that rely on machine learning techniques to factor in design constraints and provide an optimized product design. Having constraint-optimizing logic within a CAD design environment helps GM attain the goal of rapid prototyping. Designers provide definitions of the functional requirements, materials, manufacturing methods and other constraints. In May 2018, General Motors adopted Autodesk generative design software to optimize for weight and other key product criteria essential for the parts being designed to succeed with additive manufacturing. The solution was recently tested with the prototyping of a seatbelt bracket part, which resulted in a single-piece design that is 40% lighter and 20% stronger than the original eight component design. Please see the Harvard Business School case analysis, Project Dreamcatcher: Can Generative Design Accelerate Additive Manufacturing? for additional information.

Additional reading:

2020 AI Predictions, Five ways to go from reality check to real-world payoff, PwC Consulting

Accenture, Manufacturing The Future, Artificial intelligence will fuel the next wave of growth for industrial equipment companies (PDF, 20 pp., no opt-in)

AI Priorities February 2020 5 ways to go from reality check to real-world pay off, PwC, February, 2020 (PDF, 16 pp.)

Anderson, M. (2019). Machine learning in manufacturing. Automotive Design & Production, 131(4), 30-32.

Bruno, J. (2019). How the IIoT can change business models. Manufacturing Engineering, 163(1), 12.

Digital Factories 2020: Shaping The Future Of Manufacturing, PwC DE., 2017 (PDF, 48 pp.)

Digital Product Development 2025: Agile, Collaborative, AI Driven and Customer Centric, PwC, 2020 (PDF, 45 pp.)

Enabling a digital and analytics transformation in heavy-industry manufacturing, McKinsey & Company, December 19, 2019

Global Digital Operations 2018 Survey, Strategy&, PwC, 2018

Governance and Management Economics, 7(2), 31-36.

Greenfield, D. (2019). Advice on scaling IIoT projects. ProFood World

Hayhoe, T., Podhorska, I., Siekelova, A., & Stehel, V. (2019). Sustainable manufacturing in industry 4.0: Cross-sector networks of multiple supply chains, cyber-physical production systems and AI-driven decision-making. Journal of Self-

Industry’s fast-mover advantage: Enterprise value from digital factories, McKinsey & Company, January 10, 2020

Kazuyuki, M. (2019). Digitalization of manufacturing process and open innovation: Survey results of small and medium-sized firms in japan. St. Louis: Federal Reserve Bank of St Louis.

‘Lighthouse’ manufacturers lead the way—can the rest of the world keep up?  McKinsey & Company, January 7, 2019

Machine Learning in Manufacturing – Present and Future Use-Cases, Emerj Artificial Intelligence Research, last updated May 20, 2019, published by Jon Walker

Machine learning, AI are most impactful supply chain technologies. (2019). Material Handling & Logistics

MAPI Foundation, The Manufacturing Evolution: How AI Will Transform Manufacturing & the Workforce of the Future by Robert D. Atkinson, Stephen Ezell, Information Technology and Innovation Foundation (PDF, 56 pp., opt-in)

Mapping heavy industry’s digital-manufacturing opportunities, McKinsey & Company, September 24, 2018

McKinsey, AI in production: A game changer for manufacturers with heavy assets, by Eleftherios Charalambous, Robert Feldmann, Gérard Richter and Christoph Schmitz

McKinsey, Digital Manufacturing – escaping pilot purgatory (PDF, 24 pp., no opt-in)

McKinsey, Driving Impact and Scale from Automation and AI, February 2019 (PDF, 100 pp., no opt-in).

McKinsey, ‘Lighthouse’ manufacturers, lead the way—can the rest of the world keep up?,by Enno de Boer, Helena Leurent and Adrian Widmer; January, 2019.

McKinsey, Manufacturing: Analytics unleashes productivity and profitability, by Valerio Dilda, Lapo Mori, Olivier Noterdaeme and Christoph Schmitz, March, 2019

McKinsey/Harvard Business Review, Most of AI’s business uses will be in two areas,

Morey, B. (2019). Manufacturing and AI: Promises and pitfalls. Manufacturing Engineering, 163(1), 10.

Preparing for the next normal via digital manufacturing’s scaling potential, McKinsey & Company, April 10, 2020

Reducing the barriers to entry in advanced analytics. (2019). Manufacturing.Net,

Scaling AI in Manufacturing Operations: A Practitioners Perspective, Capgemini, January, 2020

Seven ways real-time monitoring is driving smart manufacturing. (2019). Manufacturing.Net,

Siemens, Next Level AI – Powered by Knowledge Graphs and Data Thinking, Siemens China Innovation Day, Michael May, Chengdu, May 15, 2019

Smart Factories: Issues of Information Governance Manufacturing Policy Initiative School of Public and Environmental Affairs Indiana University, March 2019 (PDF, 68 pp., no opt-in)

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

Team predicts the useful life of batteries with data and AI. (2019, March 28). R & D.

The AI-powered enterprise: Unlocking the potential of AI at scale, Capgemini Research, July 2020

The Future of AI and Manufacturing, Microsoft, Greg Shaw (PDF, 73 pp., PDF, no opt-in).

The Rise of the AI-Powered Company in the Postcrisis World, Boston Consulting Group, April 2, 2020

Top 8 Data Science Use Cases in Manufacturing, ActiveWizards: A Machine Learning Company Igor Bobriakov, March 12, 2019

Walker, M. E. (2019). Armed with analytics: Manufacturing as a martial art. Industry Week

Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156.

Zulick, J. (2019). How machine learning is transforming industrial production. Machine Design

Why Cybersecurity Is Really A Business Problem

Why Cybersecurity Is Really A Business Problem

Bottom Line: Absolute’s 2020 Endpoint Resilience Report illustrates why the purpose of any cybersecurity program needs to be attaining a balance between protecting an organization and the need to keep the business running, starting with secured endpoints.

Enterprises who’ve taken a blank-check approach in the past to spending on cybersecurity are facing the stark reality that all that spending may have made them more vulnerable to attacks. While cybersecurity spending grew at a Compound Annual Growth Rate (CAGR) of 12% in 2018, Gartner’s latest projections are predicting a decline to only 7% CAGR through 2023. Nearly every CISO I’ve spoken with in the last three months say prioritizing cybersecurity programs by their ROI and contribution to the business is how funding gets done today.

Cybersecurity Has Always Been A Business Decision

Overcoming the paradox of keeping a business secure while fueling its growth is the essence of why cybersecurity is a business decision. Securing an entire enterprise is an unrealistic goal; balancing security and ongoing operations is. CISOs speak of this paradox often and the need to better measure the effectiveness of their decisions.

This is why the findings from Absolute’s 2020 State of Endpoint Resilience Report​  are so timely given the shift to more spending accountability on cybersecurity programs. The report’s methodology is based on anonymized data from enterprise-specific subsets of nearly 8.5 million Absolute-enabled devices active across 12,000+ customer organizations in North America and Europe. Please see the last page of the study for additional details regarding the methodology.

Key insights from the study include the following:

  • More than one of every three enterprise devices had an Endpoint Protection (EP), client management or VPN application out of compliance, further exposing entire organizations to potential threats. More than 5% of enterprise devices were missing one or more of these critical controls altogether. Endpoints, encryption, VPN and Client Management are more, not less fragile, despite millions of dollars being spent to protect them before the downturn. The following graphic illustrates how fragile endpoints are by noting average compliances rate alongside installation rates:
  • When cybersecurity spending isn’t being driven by a business case, endpoints become more complex, chaotic and nearly impossible to protect. Absolute’s survey reflects what happens when cybersecurity spending isn’t based on a solid business decision, often leading to multiple endpoint security agents. The survey found the typical organization has 10.2 endpoint agents on average, up from 9.8 last year. One of the most insightful series of findings in the study and well worth a read is the section on measuring Application Resilience. The study found that the resiliency of an application varies significantly based on what else it is paired with. It’s interesting to see that same-vendor pairings don’t necessarily do better or show higher average compliance rates than pairings from different vendors. The bottom line is that there’s no guarantee that any agent, whether sourced from a single vendor or even the most innovative vendors, will work seamlessly together and make an organization more secure. The following graphic explains this point:
  •  60% of breaches can be linked to a vulnerability where a patch was available, but not applied. When there’s a compelling business case to keep all machines current, patches get distributed and installed. When there isn’t, operating system patches are, on average, 95 days late. Counting up the total number of vulnerabilities addressed on Patch Tuesday in February through May 2020 alone, it shows that the average Windows 10 enterprise device has hundreds of potential vulnerabilities without a fix applied – including four zero-day vulnerabilities. Absolute’s data shows that Post-Covid-19, the average patch age has gone down slightly, driven by the business case of supporting an entirely remote workforce.
  • Organizations that had defined business cases for their cybersecurity programs are able to adapt better and secure vulnerable endpoint devices, along with the sensitive data piling up on those devices, being used at home by employees. Absolute’s study showed that the amount of sensitive data – like Personal Identifiable Information (PII), Protected Health Information (PHI) and Personal Financial Information (PFI) data – identified on endpoints soared as the Covid-19 outbreak spread and devices went home to work remotely. Without autonomous endpoints that have an unbreakable digital tether to ensure the health and security of the device, the greater the chance of this kind of data being exposed, the greater the potential for damages, compliance violations and more.

Conclusion

Absolute’s latest study on the state of endpoints amplifies what many CISOs and their teams are doing today. They’re prioritizing cybersecurity endpoint projects on ROI, looking to quantify agent effectiveness and moving beyond the myth that greater compliance is going to get them better security. The bottom line is that increasing cybersecurity spending is not going to make any business more secure, knowing the effectiveness of cybersecurity spending will, however. Being able to capable of tracking how resilient and persistent every autonomous endpoint is in an organization makes defining the ROI of endpoint investments possible, which is what every CISO I’ve spoken with is focusing on this year.

10 Ways Enterprises Are Getting Results From AI Strategies

10 Ways Enterprises Are Getting Results From AI Strategies

  • One in 10 enterprises now use 10 or more AI applications; chatbots, process optimization, and fraud analysis lead a recent survey’s top use cases according to MMC Ventures.
  • 83% of IT leaders say AI & ML is transforming customer engagement, and 69% say it is transforming their business according to Salesforce Research.
  • IDC predicts spending on AI systems will reach $97.9B in 2023.

AI pilots are progressing into production based on their combined contributions to improving customer experience, stabilizing and increasing revenues, and reducing costs. The most successful AI use cases contribute to all three areas and deliver measurable results. Of the many use cases where AI is delivering proven value in enterprises today, the ten areas discussed below are notable for the measurable results they are providing.

What each of these ten use cases has in common is the accuracy and efficiency they can analyze and recommend actions based on real-time monitoring of customer interactions, production, and service processes. Enterprises who get AI right the first time build the underlying data structures and frameworks to support the advanced analytics, machine learning, and AI techniques that show the best potential to deliver value. There are various frameworks available, with BMC’s Autonomous Digital Enterprise (ADE) encapsulating what enterprises need to scale out their AI pilots into production. What’s unique about BMC’s approach is its focus on delivering transcendent customer experiences by creating an ecosystem that uses technology to cater to every touchpoint on a customer’s journey, across any channel a customer chooses to interact with an enterprise on.

10 Areas Where AI Is Delivering Proven Value Today

Having progressed from pilot to production across many of the world’s leading enterprises, they’re great examples of where AI is delivering value today. The following are 10 areas where AI is delivering proven value in enterprises today

  • Customer feedback systems lead all implementations of AI-based self-service platforms. That’s consistent with the discussions I’ve had with manufacturing CEOs who are committed to Voice of the Customer (VoC) programs that also fuel their new product development plans. The best-run manufacturers are using AI to gain customer feedback better also to improve their configure-to-order product customization strategies as well. Mining contact center data while improving customer response times are working on AI platforms today. Source: Forrester study, AI-Infused Contact Centers Optimize Customer Experience Develop A Road Map Now For A Cognitive Contact Center.
  • McKinsey finds that AI is improving demand forecasting by reducing forecasting errors by 50% and reduce lost sales by 65% with better product availability. Supply chains are the lifeblood of any manufacturing business. McKinsey’s initial use case analysis is finding that AI can reduce costs related to transport and warehousing and supply chain administration by 5% to 10% and 25% to 40%, respectively. With AI, 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? McKinsey & Company.

10 Ways Enterprises Are Getting Results From AI Strategies

  • The majority of CEOs and Chief Human Resource Officers (CHROs) globally plan to use more AI within three years, with the U.S. leading all other nations at 73%. Over 63% of all CEOs and CHROs interviewed say that new technologies have a positive impact overall on their operations. CEOs and CHROs introducing AI into their enterprises are doing an effective job at change management, as the majority of employees, 54%, are less concerned about AI now that they see its benefits. C-level executives who are upskilling their employees by enabling them to have stronger digital dexterity skills stand a better chance of winning the war for talent. Source: Harris Interactive, in collaboration with Eightfold Talent Intelligence And Management Report 2019-2020 Report.

10 Ways Enterprises Are Getting Results From AI Strategies

  • AI is the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems. AI-based techniques are the foundation of a broad spectrum of next-generation logistics and supply chain technologies now under development. The most significant gains are being made where AI can contribute to solving complex constraints, cost, and delivery problems manufacturers are facing today. For example, AI is providing insights into where automation can deliver the most significant scale advantages. Source: McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus.

10 Ways Enterprises Are Getting Results From AI Strategies

  • AI sees the most significant adoption by marketers working in $500M to $1B companies, with conversational AI for customer service as the most dominant. Businesses with between $500M to $1B lead all other revenue categories in the number and depth of AI adoption use cases. Just over 52% of small businesses with sales of $25M or less are using AI for predictive analytics for customer insights. It’s interesting to note that small companies are the leaders in AI spending, at 38.1%, to improve marketing ROI by optimizing marketing content and timing. Source: The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte, and American Marketing Association. (71 pp., PDF, free, no opt-in).
  • A semiconductor manufacturer is combining smart, connected machines with AI to improve yield rates by 30% or more, while also optimizing fab operations and streamlining the entire production process. They’ve also been able to reduce supply chain forecasting errors by 50% and lost sales by 65% by having more accurate product availability, both attributable to insights gained from AI. They’re also automating quality testing using machine learning, increasing defect detection rates up to 90%. These are the kind of measurable results manufacturers look for when deciding if a new technology is going to deliver results or not. These and many other findings from the semiconductor’s interviews with McKinsey are in the study, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? . The following graphic from the study illustrates the many ways AI and machine learning are improving semiconductor manufacturing.

10 Ways Enterprises Are Getting Results From AI Strategies

  • AI is making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer. By definition, propensity models rely on predictive analytics including machine learning to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. Every business excelling at omnichannel today rely on propensity models to better predict how customers’ preferences and past behavior will lead to future purchases. The following is a dashboard that shows how propensity models work. Source: customer propensities dashboard is from TIBCO.
  • AI is reducing logistics costs by finding patterns in track-and-trace data captured using IoT-enabled sensors, contributing to $6M in annual savings. BCG recently looked at how a decentralized supply chain using track-and-trace applications could improve performance and reduce costs. They found that in a 30-node configuration, when blockchain is used to share data in real-time across a supplier network, combined with better analytics insight, cost savings of $6M a year is achievable. Source: Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, December 18, 2018, by Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra.
  • Detecting and acting on inconsistent supplier quality levels and deliveries using AI-based applications is reducing the cost of bad quality across electronic, high-tech, and discrete manufacturing. Based on conversations with North American-based mid-tier manufacturers, the second most significant growth barrier they’re facing today is suppliers’ lack of consistent quality and delivery performance. Using AI, manufacturers can discover quickly who their best and worst suppliers are, and which production centers are most accurate in catching errors. Manufacturers are using dashboards much like the one below for applying machine learning to supplier quality, delivery, and consistency challenges. Source: Microsoft, Supplier Quality Analysis sample for Power BI: Take a tour.

10 Ways Enterprises Are Getting Results From AI Strategies

  • Optimizing Shop Floor Operations with Real-Time Monitoring and AI is in production at Hitachi today. Combining real-time monitoring and AI to optimize shop floor operations, providing insights into machine-level loads and production schedule performance, is now in production at Hitachi. Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimizing the best possible set of machines for a given production run is now possible using AI.  Source: Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics, and AI Are Making Factories Intelligent and Agile, Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D.

10 Ways Enterprises Are Getting Results From AI Strategies

Additional reading:

15 examples of artificial intelligence in marketing, eConsultancy, February 28, 2019

4 Positive Effects of AI Use in Email Marketing, Statista, March 1, 2019

4 Ways Artificial Intelligence Can Improve Your Marketing (Plus 10 Provider Suggestions), Forbes, Kate Harrison, January 20, 2019

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,

DHL Trend Research, Logistics Trend Radar, Version 2018/2019 (PDF, 55 pp., no opt-in)

2018 (43 pp., PDF, free, no opt-in).

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

How To Win Tomorrow’s Car Buyers – Artificial Intelligence in Marketing & Sales, McKinsey Center for Future Mobility, McKinsey & Company. February 2019. (44 pp., PDF, free, no opt-in)

How Top Marketers Use Artificial Intelligence On-Demand Webinar with Vala Afshar, Chief Digital Evangelist, Salesforce and Meghann York, Director, Product Marketing, Salesforce

In-depth: Artificial Intelligence 2019, Statista Digital Market Outlook, February 2019 (client access reqd).

bes Insights and Quantcast Study (17 pp., PDF, free, opt-in),

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

McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus

McKinsey & Company, Notes from the AI frontier: Modeling the impact of AI on the world economy, September 2018 By Jacques Bughin, Jeongmin Seong, James Manyika, Michael Chui, and Raoul Joshi

Papadopoulos, T., Gunasekaran, A., Dubey, R., & Fosso Wamba, S. (2017). Big data and analytics in operations and supply chain management: managerial aspects and practical challenges. Production Planning & Control28(11/12), 873-876.

Powerful pricing: The next frontier in apparel and fashion advanced analytics, McKinsey & Company, December 2018

Winning tomorrow’s car buyers using artificial intelligence in marketing and sales, McKinsey & Company, February 2019

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

World Economic Forum, Supply Chain 4.0 Global Practices, and Lessons Learned for Latin America and the Caribbean (PDF, 44 pp., no opt-in)

Worldwide Spending on Artificial Intelligence Systems Will Grow to Nearly $35.8 Billion in 2019, According to New IDC Spending Guide, IDC; March 11, 2019

 

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