AI and machine learning’s potential to drive greater visibility, control, and insight across shop floors while monitoring machines and processes in real-time continue to attract venture capital. $62 billion is now invested in 5,396 startups concentrating on the intersection of AI, machine learning, manufacturing, and Industry 4.0, according to Crunchbase.
PwC’s broader tech sector analysis shows a 30% year-over-year growth in funding rounds that reached $293.2 billion in 2021. Smart manufacturing startups are financed by seed rounds at 52%, followed by early-stage venture funding at 33%. The median last funding amount was $1.6 million, with the average being $9.93 million.
Abundant AI startup opportunities in smart manufacturing and industry 4.0
According to Gartner, “The underlying concept of Industry 4.0 is to connect embedded systems and smart production facilities to generate a digital convergence between industry, business, and internal functions and processes.” As a result, Industry 4.0 is predicted to grow from $84.59 billion in 2020 to $334.18 billion by 2028. AI and machine learning adoption in manufacturing are growing in five core fields: smart production, products and services improvements, business operations and management, supply chain, and business model decision-making. Deloitte’s survey on AI adoption in manufacturing found that 93% of companies believe AI will be a key technology to drive growth and innovation.
Machine intelligence (MI) is one of the primary catalysts driving increased venture capital investment in smart manufacturing. Startup CEOs and their customers want AI and machine learning models based on actual data, and machine intelligence is helping to make that happen. An article by McKinsey & Company provides valuable insights into market gaps for new ventures. McKinsey’s compelling data point is that those leading companies using MI achieve 3X to 4X the impact of their peers. However, 92% of leaders also have a process to track incomplete or inaccurate data – which is another market gap startups need to fill.
Based on the uplift MI creates for new smart manufacturing startup funding and the pervasive need manufacturers have to improve visibility & control across shop floors, startups have many potential opportunities. The following are five that AI and machine learning is helping to create:
AI-enabled Configure, Price, and Quote (CPQ) systems that can factor in supply chain volatility on product costs are needed. Several startups are already using AI and machine learning in CPQ workflows, and they compete with the largest enterprise software providers in the industry, including Salesforce, SAP, Microsoft, and others. However, no one has taken on the challenge of using AI to factor in how supply chain volatility changes standard and actual costs in real-time. For example, knowing the impact of pricing changes based on an allocation, how does that impact standard costs per unit on each order? Right now, an analyst needs to spend time doing that. AI and machine learning could take on that task so analysts could get to the larger, more complex, and costly supply chain problems impacting CPQ close rates and revenue.
Using AI-enabled real-time data capture techniques to identify anomalies in throughput as an indicator of machine health. The aggregated data manufacturing operations produced every day holds clues regarding each machine’s health on the shop floor. Automated data capture can identify scrap rates, yield rates and track actual costs. However, none of them can analyze the slight variations in process flow product outputs to warn of possible machine or supply chain issues. Each process manufacturing machine runs at its cadence or speed, and having an AI-based sensor system track and analyze why speeds are off could save thousands of dollars in maintenance costs and keep the line running. In addition, adding insight and intelligence to the machine’s real-time data feeds frees quality engineers to concentrate on more complex problems.
Industrial Internet of Things (IIoT) and edge computing data can be used for fine-tuning finite scheduling in real-time. Finite scheduling is part of the broader manufacturing systems organizations rely on to optimize shop floor schedules, machinery, and staff scheduling. It can be either manually intensive or automated to provide operators with valuable insights. A potential smart manufacturing opportunity is a finite scheduler that relies on AI and machine learning to keep schedules on track and make trade-offs to ensure resources are used efficiently. Finite schedulers also need greater accuracy in factoring in frequent changes to delivery dates. AI and machine learning could drive greater on-time delivery performance when integrated across all the shop floors a manufacturer relies on.
Automated visual inspections and quality analysis to improve yield rates and reduce scrap. Using visual sensors to capture data in real-time and then analyze them for anomalies is in its nascent stages of deployment and growth. However, this is an area where captured data sets can provide machine learning algorithms with enough accuracy to identify potential quality problems on products before they leave the factory. Convolutional neural networks are an effective machine learning technique for identifying patterns and anomalies in images. They’re perfect for the use case of streamlining visual inspection and in-line quality checks in discrete, batch, and process manufacturing.
Coordinated robotics (Cobots) to handle assemble-to-order product assembly. The latest cobots can be programmed to stay in sync with each other and perform pick, pack, ship, and place materials in warehouses. What’s needed are advanced cobots that can handle simple product assembly at a more competitive cost as manufacturers continue to face chronic labor shortages and often run a shift with less than half the teams they need.
Talent remains an area of need
Manufacturers’ CEOs and COOs say that recruiting and retaining enough talent to run all the production shifts they need is the most persistent issue. In addition, those manufacturers located in remote regions of the world are turning to robotics to fulfill orders, which opens up opportunities for integrating AI and machine learning to enable cobots to complete assemble-to-order tasks. The unknown impact of how fast supply chain conditions change needs work from startups, too, especially in tracking actual cost performance. These are just a few opportunities for startups looking to apply AI and machine learnings’ innate strengths to solve complex supply chain, manufacturing, quality management, and compliance challenges.
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.
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:
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:
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.
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.
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.
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.).
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.
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-
Microsoft, Apple, and IBM lead the world in hardware & software patent innovation according to PatentSight.
Samsung, Johnson & Johnson, LG Electronics. Alphabet, Qualcomm, Ford, Intel, Microsoft, Sony, and VW are the ten most innovative companies in the world, according to PatentSight’s patent analytics research.
Ford leads the global automotive industry in patent innovation, due in large part to successful R&D efforts in autonomous driving.
These and many other fascinating insights are from Swiss consulting firm EconSight’s patent analytics research that first identified all the patents that are supposed to protect particularly relevant innovations – in this case, defined as innovations for the digitization of applied technologies – using the PatentSight database. Companies not only have to maintain their innovative strength; they also have to continue to expand in comparison to previous years to take a leading position in the ranking. For additional details on the methodology and to request the rank of your company, please visit the PatentSight Innovation Ranking 2019 site here.
Key insights from EconSight’s patent analytics research defining the most innovative companies globally include the following:
38 of the most innovative companies in the world are based in the U.S, 21 in China, and 15 from Europe. Chinese followed by Japanese-based companies lead the world in electronics innovation as measured by the uniqueness of patents produced. U.S. companies lead the world in medical technology patent innovation. The following graphic compares the number of companies within the global top 100 ranking by country and industry for 2019.
In the U.S., tech companies dominate the top 10 most innovative companies in 2019. Alphabet, Qualcomm, Intel, Microsoft, Honeywell, Apple, and GE are producing the most unique, differentiated and value-adding patents based on EconSight’s methodology. Medical technology companies show the greatest growth in innovative patent production as the graphic below illustrates:
The world’s most innovative medical technology companies’ patent focus is on biosensors, surgical robotics, shortening the time-to-market for pharmaceutical drugs, and funding startup incubators that yield new patents. Johnson & Johnson’s (J&J) multifaceted innovation strategy reflects the broader strategic vision of every medical technology company pursuing new intellectual property (IP) that leads to patent leadership. J&J acquired Auris Health and Verb Surgical, which is managed as a joint venture with Alphabet’s Verily medical division, which gives them a patent portfolio in healthcare intelligence. J&J has in total acquired over 300 companies in the medical technology industry according to PatentSight’s analysis. These acquisitions have moved them into biosensors, surgical robotics, and startups performing drug research.
Japanese robotics manufacturer Fanuc is the world’s most innovative automation technology company based on patent analysis. Since 2019 Fanuc has jumped 42 places in the ranking, from 61st to 19th. The global labor shortage in manufacturing is a contributing factor to the strong market demand Fanuc is seeing for all its robotics products and systems. The following are the top 25 robotics companies of 2019:
Ford leads the global automotive industry in patent innovation, while Volkswagen doubles down on patents over the last two years. Ford leads the world in patent innovation due to its rapid advances in autonomous vehicle development. Volkswagen’s rapid ascent in the automotive industry rankings has made them the most innovative company in Germany based on patent analytics this year. VW is investing in autonomous vehicles, and the networking of mobility participants, setting a solid foundation for future vehicle models today.
PatentSight – A Lexis Nexis company– specializes in cleaning and refining patent data and providing advanced patent analytics. Publicly available patent data simply cannot be used without qualitative preparation and correction. Due to the sheer mass (about 3.3 million new registrations in 2018 alone), all available patents cannot be viewed manually. Publications in many different languages and often very abstract contents make a manual review and evaluation difficult not only for laymen but also for experts. A further challenge is to level out the widely differing citation practices of national patent offices or to document the legal status of patents.
PatentSight, through manually supervised and scientifically developed algorithms, has best-in-class information on ownership data, going far beyond the testing standards recommended by the World Intellectual Property Organization (WIPO).
Moreover, PatentSight‘s proprietary patent valuation metrics reveal which patents are key, and which are superfluous. Based on citations, global protection, and several correction factors, EconSight leveraged these metrics to determine the most innovative companies.
Cloud computing platforms and applications are proliferating across enterprises today, serving as the IT infrastructure driving new digital businesses. The following roundup of cloud computing forecasts and market estimates reflect a maturing global market for cloud services, with proven scale, speed and security to support new business models.
CIOs who are creating compelling business cases that rely on cloud platforms as a growth catalyst is the architects enabling these new business initiatives to succeed. The era of CIO strategist has arrived. Key takeaways include the following:
80% of enterprises are both running apps on or experimenting with Amazon Web Services (AWS) as their preferred cloud platform. 67% of enterprises are running apps on (45%) and experimenting on (22%) the Microsoft Azure platform. 18% of enterprises are using Google’s Cloud Platform for applications today, with 23% evaluating the platform for future use. RightScale’s 2018 survey was included in the original data set Statista used to create the comparison. Source: Statista, Current and planned usage of public cloud platform services running applications worldwide in 2018. Please click on the graphic to expand for easier viewing.
Enterprise adoption of Microsoft Azure increased significantly from 43% to 58% attaining a 35% CAGR while AWS adoption increased from 59% to 68%. Enterprise respondents with future projects (the combination of experimenting and planning to use) show the most interest in Google (41%). Source: RightScale 2018 State of the Cloud Report. Please click on the graphic to expand for easier viewing.
Wikibon projects the True Private Cloud (TPC) worldwide market will experience a compound annual growth rate of 29.2%, reaching $262.4B by 2027. The firm predicts TPC growth will far outpace the infrastructure-as-a-service (IaaS) growth of 15.2% over the same period. A true private cloud is distinguished from a private cloud by the completeness of the integration of all aspects of the offering, including performance characteristics such as price, agility, and service breadth. Please see the source link for additional details on TPC. Source: Wikibon’s 2018 True Private Cloud Forecast and Market Shares. Please click on the graphic to expand for easier viewing.
Quality Control, Computer-Aided Engineering, and Manufacturing Execution Systems (MES) are the three most widely adopted systems in the cloud by discrete and process The survey also found that 60% of discrete and process manufacturers say their end users prefer the cloud over on-premise. Source: Amazon Web Services & IDC: Industrial Customers Are Ready For The Cloud – Now (PDF, 13 pp., no opt-in, sponsored by AWS). Please click on the graphic to expand for easier viewing.
The Worldwide Public Cloud Services Market is projected to grow by 17.3 3% in 2019 to total $206.2B, up from $175.8B in 2018 according to Gartner. In 2018 the market will grow a healthy 21% up from $145.3B in 2017 according to the research and advisory firm. Infrastructure-as-a-Service (IaaS) will be the fastest-growing segment of the market, forecasted to grow by 27.6% in 2019 to reach $39.5B, up from $31B in 2018. By 2022, Gartner expects that 90% of enterprises purchasing public cloud IaaS will do so from an integrated IaaS and Platform-as-a-Service (PaaS), and will use both the IaaS and PaaS capabilities from that provider. Source: Gartner Forecasts Worldwide Public Cloud Revenue to Grow 17.3 Percent in 2019.
More than $1.3T in IT spending will be directly or indirectly affected by the shift to cloud by 2022. 28% of spending within key enterprise IT markets will shift to the cloud by 2022, up from 19% in 2018. The largest cloud shift before 2018 occurred in application software, particularly driven by customer relationship management (CRM) software, with Salesforce dominating as the market leader. CRM has already reached a tipping point where a higher proportion of spending occurs in the cloud than in traditional software. Source: Gartner Says 28 Percent of Spending in Key IT Segments Will Shift to the Cloud by 2022.
IDC predicts worldwide Public Cloud Services Spending will reach $180B in 2018, an increase of 23.7% over 2017. According to IDC, the market is expected to achieve a five-year compound annual growth rate (CAGR) of 21.9% with public cloud services spending totaling $277B in 2021. The industries that are forecast to spend the most on public cloud services in 2018 are discrete manufacturing ($19.7B), professional services ($18.1B), and banking ($16.7B). The process manufacturing and retail industries are also expected to spend more than $10B each on public cloud services in 2018. These five industries will remain at the top in 2021 due to their continued investment in public cloud solutions. The industries that will see the fastest spending growth over the five-year forecast period are professional services (24.4% CAGR), telecom (23.3% CAGR), and banking (23.0% CAGR). Source: Worldwide Public Cloud Services Spending Forecast to Reach $160 Billion This Year, According to IDC.
Discrete Manufacturing is predicted to lead all industries on public cloud spending of $19.7B in 2018 according to IDC. Additional industries forecast to spend the most on public cloud services this year include Professional Services at $18.1B and Banking at $16.7B. The process manufacturing and retail industries are also expected to spend more than $10B each on public cloud services in 2018. According to IDC, these five industries will remain at the top in 2021 due to their continued investment in public cloud solutions. The industries that will see the fastest spending growth over the five-year forecast period are Professional Services with a 24.4% CAGR, Telecommunications with a 23.3% CAGR, and banking with a 23% CAGR. Source: Worldwide Public Cloud Services Spending Forecast to Reach $160 Billion This Year, According to IDC.
Gartner added eight new technologies to the Hype Cycle this year including 5G, Artificial General Intelligence, Deep Learning, Edge Computing, Serverless PaaS.
Virtual Personal Assistants, Personal Analytics, Data Broker PaaS (dbrPaaS) are no longer included in the Hype Cycle for Emerging Technologies.
The Hype Cycle for Emerging Technologies, 2017 provides insights gained from evaluations of more than 2,000 technologies the research and advisory firms tracks. From this large base of technologies, the technologies that show the most potential for delivering a competitive advantage over the next five to 10 years are included in the Hype Cycle.
The eight technologies added to the Hype Cycle this year include 5G, Artificial General Intelligence, Deep Learning, Deep Reinforcement Learning, Digital Twin, Edge Computing, Serverless PaaS and Cognitive Computing. Ten technologies not included in the hype cycle for 2017 include 802.11ax, Affective Computing, Context Brokering, Gesture Control Devices, Data Broker PaaS (dbrPaaS), Micro Data Centers, Natural-Language Question Answering, Personal Analytics, Smart Data Discovery and Virtual Personal Assistants.
The three most dominant trends include Artifical Intelligence (AI) Everywhere, Transparently Immersive Experiences, and Digital Platforms. Gartner believes that key platform-enabling technologies are 5G, Digital Twin, Edge Computing, Blockchain, IoT Platforms, Neuromorphic Hardware, Quantum Computing, Serverless PaaS and Software-Defined Security.
Key takeaways from this year’s Hype Cycle include the following:
Heavy R&D spending from Amazon, Apple, Baidu, Google, IBM, Microsoft, and Facebook is fueling a race for Deep Learning and Machine Learning patents today and will accelerate in the future – The race is on for Intellectual Property (IP) in deep learning and machine learning today. The success of Amazon Alexa, Apple Siri, Google’s Google Now, Microsoft’s Cortana and others are making this area the top priority for R&D investment by these companies today. Gartner predicts deep-learning applications and tools will be a standard component in 80% of data scientists’ tool boxes by 2018. Amazon Machine Learning is available on Amazon Web Services today, accessible here. Apple has also launched a Machine Learning Journal. Baidu Research provides a site full of useful information on their ongoing research and development as well. Google Research is one of the most comprehensive of all, with a wealth of publications and research results. IBM’s AI and Cognitive Computing site can be found here. The Facebook Research site provides a wealth of information on 11 core technologies their R&D team is working on right now. Many of these sites also list open positions on their R&D teams.
5G adoption in the coming decade will bring significant gains for security, scalability, and speed of global cellular networks – Gartner predicts that by 2020, 3% of network-based mobile communications service providers (CSPs) will launch 5G networks commercially. The Hype Cycle report mentions that from 2018 through 2022 organizations will most often utilize 5G to support IoT communications, high definition video and fixed wireless access. AT&T, NTT Docomo, Sprint USA, Telstra, T-Mobile, and Verizon have all announced plans to launch 5G services this year and next.
Artificial General Intelligence is going to become pervasive during the next decade, becoming the foundation of AI as a Service – Gartner predicts that AI as a Service will be the enabling core technology that leads to the convergence of AI Everywhere, Transparently Immersive Experiences and Digital Platforms. The research firm is also predicting 4D Printing, Autonomous Vehicles, Brain-Computer Interfaces, Human Augmentation, Quantum Computing, Smart Dust and Volumetric Displays will reach mainstream adoption.
Public Cloud spending is predicted to grow at quickly, attaining 16% year-over-year growth in 2017.
Cowen’s AWS segment model is predicting Revenue and EBITDA to grow 25% and 26.8% annually from 2017 to 2022.
Microsoft Azure is viewed as the platform that customers would most likely purchase or renew going forward (28% of total vs. AWS at 22%, GCP at 15%, and IBM at 10%).
These and many other fascinating insights are from Cowen’s study published this week, Public Cloud V: AWS And Azure Still Leading The Pack (58 pp., PDF, client access reqd.). Cowen partnered with Altman Vilandrie & Company to complete the study. The study relies on a survey sample of 551 respondents distributed across small, medium and enterprises who are using Public Cloud platforms and services today. For purposes of the survey, small businesses have less than 500 employees, medium-sized businesses as 500 to 4,999 employees, and enterprises as more than 5,000 employees. The study provides insight on a range of topics including cloud spending trends, workload migration dynamics, and vendor positioning. Please see pages 5,6 & 7 for additional details regarding the methodology.
The more AWS and Azure compete to win customers, the greater the innovation and growth in public cloud adoption as the following key takeaways illustrate:
Existing Public Cloud customers predict spending will grow 16% year-over-year in 2017. Existing mid-market Public Cloud customers predict spending will increase 18% this year. SMBs who have already adopted Public Cloud predict a 17% increase in spending in 2017, and enterprises, 13%. Public Cloud providers are the most successful upselling and cross-selling mid-market companies this year as many are relying on the cloud to scale their global operations to support growth.
AWS dominates awareness levels with SMBs who have existing Public Cloud deployments, with Microsoft Azure the most known and considered in enterprises. Consistent with many other surveys of Public Cloud adoption, IBM SoftLayer scored better in enterprises than any other segment including SMBs (71% vs. 58%). Google Cloud Platform has its strongest awareness levels in SMBs, attributable to the adoption of their many cloud-based applications in this market segment. They trail AWS, Azure, and SoftLayer in the enterprise, however. Across all existing companies who have adopted Public Cloud, the majority are most aware of AWS and Microsoft Azure. The second graphic provides an overview of awareness across the entire respondent base.
Microsoft is the most-used Public Cloud and the most likely to be purchased or renewed by 28% of all respondents. While AWS is the most reviewed Public Cloud across all respondents, Microsoft Azure is the most used. When asked which Public Cloud provider they are likely to purchase or renew, the majority of respondents said Microsoft Azure (28%), followed by AWS (22%), Google Cloud Platform (15%) and IBM SoftLayer (10%). The following graphic compares awareness, reviewed and use levels by Public Cloud platform.
Only 37% of current Azure users expect to add or replace their Public Cloud provider, compared to 53% of current AWS users and 50% of GCP users. The study found that approximately 40% of respondents expect to add or replace their cloud provider in the next two years, compared to 43% who predicted that last year. Companies who have adopted Microsoft Azure are least likely to replace/add other vendors, as only 37% of current Azure users expect to add or replace, compared to 53% of current AWS users and 50% of GCP users.
AWS and Azure dominate all seven facets of user experience included in the survey. AWS has the best User Interface, API Complexity, and Reporting & Billing. Microsoft Azure leads all Public Cloud providers globally in the areas of Management & Monitoring, Software & Data Integration, Technical Support and Training & Google Cloud Platform is 3rd on all seven facts of user experience.
18% of workloads are supported by Public Cloud today with SMBs and mid-market companies slightly leading enterprises (16%). Overall, 38% of all workloads are supported with on-premise infrastructure and platforms, increasing to 43% for enterprises. The following graphic illustrates the percentage of workloads supported by each infrastructure type.
77% of existing Public Cloud adopters are either likely or very likely to add a SaaS workload in the next two years, led by mid-market companies (81%). SMBs (76%) and enterprises (73%) are also likely/very likely to add SaaS workloads in the next two years. The majority of these new SaaS workloads will be in the areas of Testing & Development, Web Hosting, and e-mail and communications.
Cowen’s AWS segment model is predicting Revenue and EBITDA to have a five-year Compound Annual Growth Rate (CAGR) of 25% and 26.8% from 2017 to 2022. AWS Net Income is predicted to increase from $2.7B in 2017 to $8.2B in 2022, attaining a projected 24.5% CAGR from 2017 to 2022. Revenue is predicted to soar from an estimated $16.8B in 2017 to $51.5B in 2022, driving a 25% CAGR in the forecast period.
Worldwide customer relationship management (CRM) software totaled $26.3B in 2015, up 12.3% from $23.4B in 2014.
SaaS revenue grew 27% yr-over-yr, more than double overall CRM market growth in 2015.
Asia/Pacific grew the fastest of all regions globally, increasing 9% 2015, closely followed by greater China with 18.4% growth.
These and many other insights into the current state of the global CRM market are from Gartner’s Market Share Analysis: Customer Relationship Management Software, Worldwide, 2015 (PDF, client access) published earlier this month. The top five CRM vendors accounted for 45% of the total market in 2015. Salesforce dominated in 2015, with a 21.1% annual growth rate and absolute growth of over $902M in CRM revenue, more than the next ten providers combined. Gartner found that Salesforce leads in revenue in the sales and customer service and support (CSS) segments of CRM, and is now third in revenue in the marketing segment. Gartner doesn’t address how analytics are fundamentally redefining CRM today, which is an area nearly every C-level and revenue team leader I’ve spoken with this year is prioritizing for investment. The following graphic and table compare 2015 worldwide CRM market shares.
Adobe, Microsoft, and Salesforce Are Growing Faster Than The Market
Adobe grew the fastest between 2014 and 2015, increasing worldwide sales 26.9%. Salesforce continues to grow well above the worldwide CRM market average, increasing sales 21.1%. Microsoft increased sales 20% in the last year. The worldwide CRM market grew 12.3% between 2014 and 2015.
Analytics, Machine Learning, and Artifical Intelligence Are The Future Of CRM
Advanced analytics, machine learning and artificial intelligence (AI) will revolutionize CRM in the next three years. Look to the five market leaders in 2015 to invest heavily in these areas with the goal of building patent portfolios and increasing the amount of intellectual property they own. Cloud-based analytics platforms offer the scale, speed of deployment, agility, and ability to rapidly prototype analytics workflows that support the next generation of CRM workflows. My recent post on SelectHub, Selecting The Best Cloud Analytics Platform: Trends To Watch In 2016, provides insights into how companies with investments in CRM systems are making decisions on cloud platforms today. Based on insights gained from discussions with senior management teams, I’ve put together an Intelligent Cloud Maturity Model that underscores why scalability of a cloud-based analytics platform is a must-have for any company.
To keep the rankings and analysis completely impartial and fair, the latest Computer Reseller News list, The 100 Coolest Cloud Computing Vendors Of 2016 is the basis of the rankings. Cloud computing companies are among the most competitive there are about salaries, performance and sign-on bonuses and a myriad of perks and benefits. They are also attracting senior management teams that have strong leadership skills, many of whom are striving to create distinctive company cultures. The most popular request from Forbes readers are for recommendations of the best cloud computing companies to work for, and that’s what led to this analysis.
Using the 2016 CRN list as a baseline to compare the Glassdoor.com scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO, the table below is provided. You can find the original data set here. There are many companies listed on the CRN list that doesn’t have than many or any entries on Glassdoor, and they are excluded from the rankings shown below but are in the original data set. If the image below is not visible in your browser, you can view the rankings here.
The highest rated CEOs on Glassdoor as of February 3rd, 2016 include the following:
SaaS is the most pervasive cloud technology used today with a presence in 77.3% of all organizations, an increase of 9% since 2014.
IT is moving significant processing to the cloud with 85.9% of web content management, 82.7% of communications, 80% of app development and 78.9% of disaster recovery now cloud-based.
Seeking simple and clear relationships, over 50% of enterprises opt for online purchasing or direct to provider purchasing of cloud services. Online buying is projected to increase over the next two years up to 56%.
Vendor leadership/consolidation continues to take hold with 75% of enterprises using fewer than ten
These and many other insights are from North Bridge Growth Equity and Venture Partners’Future of Cloud Computing Survey published on December 15th. North Bridge and Wikibon collaborated on the study, interviewing 952 companies across 38 different nations, with 65% being from the vendor community and 35% of enterprises evaluating and using cloud technologies in their operations The slide deck is accessible on SlideShare here:
Key takeaways from the study include the following:
Wikibon forecasts the SaaS is worth $53B market today and will grow at an 18% Compound Annual Growth Rate (CAGR) from 2014 to 2026. By 2026, the SaaS market will be worth $298.4B according to the Wikibon forecast. The fastest growing cloud technology segment is Platform-as-a-Service (PaaS), which is valued at $2.3B today, growing at a CAGR of 38% from 2014 to 2026. Infrastructure-as-a-Service (IaaS) has a market value of $25B and is growing at a 19% CAGR in the forecast period. Please see the graphic from the report below and a table from Wikibon’s excellent study, Public Cloud Market Forecast 2015-2026 by Ralph Finos published in August.
Cloud-based applications are becoming more engrained in core business processes across enterprises. The study found that enterprises are migrating significant processing, systems of engagement and systems of insight to the cloud beyond adoption levels of the past. 81.3% of sales and marketing, 79.9% of business analytics, 79.1% of customer service and 73.5% of HR & Payroll activities have transitioned to the cloud. The impact on HR is particularly noteworthy as in 2011; it was the third least likely sector to be disrupted by cloud computing.
78% of enterprises expect their SaaS investments to deliver a positive Return on Investment (ROI) in less than three months. 58% of those enterprises who have invested in Platform-as-a-Service (PaaS) expect a positive ROI in less than three months.
Top inhibitors to cloud adoption are security (45.2%), regulatory/compliance (36%), privacy (28.7%), lock-in (25.8%) and complexity (23.1%). Concerns regarding interoperability and reliability have fallen off significantly since 2011 (15.7% and 9.9% respectively in 2015).
Total private financing for cloud and SaaS startup has increased 4X over the last five years. North Bridge and Wikibon found that average deal size rose 1.8X in the same period. The following graphic provides an overview of cloud and SaaS finance trends from 2010 to present.
15% of U.S. tech leaders see biotech/digital health/healthcare IT as the most disruptive consumer-driven technology in the next three years.
13% of U.S. tech leaders predict data and analytics will be the most disruptive enterprise technology in three years.
Global tech leaders predict cloud computing (11%), mobile platforms and apps (9%), Internet of Things (IoT)/machine-to-machine (M2M) (9%) and data and analytics (9%) will be the most disruptive technologies over the next three years.
These and many other insights are from the fourth annual 2015 Global Technology Innovation Survey released via webcast by KPMG last month. KPMG surveyed 832 technology industry business leaders globally, with the majority of being C-level executives (87%). Respondents were selected from a broad spectrum of businesses including tech industry startups, mid- and large-scale enterprises, angel investors and venture capital firms. For an in-depth explanation of the survey methodology, please see slides 6 and 7 of the webinar presentation. The goals of the survey include spotting disruptive technologies, identifying tech innovation barriers and opportunities, and tracking emerging tech innovation hubs.
The five insights and predictions from the report include the following:
Global tech leaders predict cloud computing (11%), mobile platforms and apps (9%), Internet of Things (IoT)/M2M (9%) and data and analytics (9%) will be the most disruptive technologies over the next three years.U.S. tech leaders predict biotech/digital health/healthcare IT (15%), data and analytics (14%) and cloud computing (14%) will be the three most disruptive technologies over the next three years. Chinese tech leaders predict artificial intelligence/cognitive computing (15%) will be the most disruptive technology impacting the global business-to-consumer (B2C) marketplace.
The three most disruptive technologies predicted to drive business transformation in enterprises over the next three years in the U.S. include cloud computing (13%), data and analytics (13%), and cyber security (10%). Japanese tech leaders predict artificial intelligence/cognitive computing will have the greatest effect (23%), and 14% of Chinese tech leaders predict the Internet of Things/M2M (14%) will have the greatest impact on business transformation in their country. The following table compares global tech leader’s predictions of which technologies will disrupt enterprises the most and drive business transformation over the next three years.
Improving business efficiencies/higher productivity, and faster innovation cycles (both 20%) are top benefits tech leaders globally are pursuing with IoT strategies. The point was made on the webinar that in Asia, consumers are driving greater adoption of IoT-based devices to a richer contextual customer experience. Greatest challenges globally to adopting IoT is technology complexity (22%), lack of experience in the new technology or business model (16%), and both displacement of the existing tech roadmap and security (both 13%).
Analytics are most often adopted to gain faster innovation cycles (25%), improved business efficiencies and higher productivity (17%) and more effective R&D (13%). The greatest challenges are technology complexity (20%) and lack of experience in the new technology or business model (19%),
Tech leaders predict the greatest potential revenue growth for IoT in the next three years is in consumer and retail markets (22%). IoT/M2M is also expected to see significant revenue growth in technology industries (13%), aerospace and defense (10%), and education (9%). The following graphic compares tech leader’s predictions of the industries with the greatest potential revenue growth (or monetization potential) in the next three years.