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Posts tagged ‘Software-as-a-Service’

Analytics Will Revolutionize Supply Chains In 2018

  • While 94% of supply chain leaders say that digital transformation will fundamentally change supply chains in 2018, only 44% have a strategy ready.
  • 66% of supply chain leaders say advanced supply chain analytics are critically important to their supply chain operations in the next 2 to 3 years.
  • Forecast accuracy, demand patterns, product tracking traceability, transportation performance and analysis of product returns are use cases where analytics can close knowledge gaps.

These and other insights are from The Hackett Group study, Analytics: Laying the Foundation for Supply Chain Digital Transformation (10 pp., PDF, no opt-in). The study provides insightful data regarding the increasing importance of using analytics to drive improved supply chain performance. Data included in the study also illustrate how analytics is enabling business objectives across a range of industries. The study also provides the key points that need to be considered in creating a roadmap for implementing advanced supply chain analytics leading to digital transformation. It’s an interesting, insightful read on how analytics are revolutionizing supply chains in 2018 and beyond.

Key takeaways from the study include the following:

  • 66% of supply chain leaders say advanced supply chain analytics are critically important to their supply chain operations in the next 2 to 3 years. The Hackett Group found the majority of supply chain leaders have a sense of urgency for getting advanced supply chain analytics implemented and contributing to current and future operations. The majority see the value of having advanced analytics that can scale across their entire supplier network.

  • Improving forecast accuracy, optimizing transportation performance, improving product tracking & traceability and analyzing product returns are the use cases providing the greatest potential for analytics growth. Each of these use cases and the ones that are shown in the graphic below has information and knowledge gaps advanced supply chain analytics can fill. Of these top use cases, product tracking and traceability are one of the fastest growing due to the stringent quality standards defined by the US Food & Drug Administration in CFR 21 Sec. 820.65 for medical products manufacturers.  The greater the complexity and cost of compliance with federally-mandated reporting and quality standards, the greater potential for advanced analytics to revolutionize supply chain performance.

  • Optimizing production and sourcing to reduce total landed costs (56%) is the most important use case of advanced supply chain analytics in the next 2 to 3 years. The Hackett Group aggregated use cases across the four categories of reducing costs, improving quality, improving service and improving working capital (optimizing inventory). Respondents rank improving working capital (optimizing inventory) with the highest aggregated critical importance score of 39%, followed by reducing costs (29.5%), improving service (28.6%) and improving quality (25.75%).

  • 44% of supply chain leaders are enhancing their Enterprise Resource Planning (ERP) systems’ functionality and integration to gain greater enterprise and supply chain-wide visibility. Respondents are relying on legacy ERP systems as their main systems of record for managing supply chain operations, and integrating advanced supply chain analytics to gain end-to-end supply network visibility. 94% of respondents consider virtual collaboration platforms for internal & external use the highest priority technology initiative they can accomplish in the next 2 to 3 years.

  • The majority of companies are operating at stages 1 and 2 of the Hackett Group’s Supply chain analytics maturity model. A small percentage are at the stage 3 level of maturity according to the study’s results. Supply chain operations and performance scale up the model as processes and workflows are put in place to improve data quality, provide consistent real-time data and rely on a stable system of record that can deliver end-to-end supply chain analytics visibility. Integrating with external data becomes critically important as supply networks proliferate globally, as does the need to drive greater predictive analytics accuracy.

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53% Of Companies Are Adopting Big Data Analytics

  • Big data adoption reached 53% in 2017 for all companies interviewed, up from 17% in 2015, with telecom and financial services leading early adopters.
  • Reporting, dashboards, advanced visualization end-user “self-service” and data warehousing are the top five technologies and initiatives strategic to business intelligence.
  • Data warehouse optimization remains the top use case for big data, followed by customer/social analysis and predictive maintenance.
  • Among big data distributions, Cloudera is the most popular, followed by Hortonworks, MAP/R, and Amazon EMR.

These and many other insights are from Dresner Advisory Services’ insightful 2017 Big Data Analytics Market Study (94 pp., PDF, client accessed reqd), which is part of their Wisdom of Crowds® series of research. This 3rd annual report examines end-user trends and intentions surrounding big data analytics, defined as systems that enable end-user access to and analysis of data contained and managed within the Hadoop ecosystem. The 2017 Big Data Analytics Market Study represents a cross-section of data that spans geographies, functions, organization size, and vertical industries. Please see page 10 of the study for additional details regarding the methodology.

“Across the three years of our comprehensive study of big data analytics, we see a significant increase in uptake in usage and a large drop of those with no plans to adopt,” said Howard Dresner, founder and chief research officer at Dresner Advisory Services. “In 2017, IT has emerged as the most typical adopter of big data, although all departments – including finance – are considering future use. This is an indication that big data is becoming less an experimental endeavor and more of a practical pursuit within organizations.”

Key takeaways include the following:

  • Reporting, dashboards, advanced visualization end-user “self-service” and data warehousing are the top five technologies and initiatives strategic to business intelligence.  Big Data ranks 20th across 33 key technologies Dresner Advisory Services currently tracks.  Big Data Analytics is of greater strategic importance than the Internet of Things (IoT), natural language analytics, cognitive Business Intelligence (BI) and Location intelligence.

  • 53% of companies are using big data analytics today, up from 17% in 2015 with Telecom and Financial Services industries fueling the fastest adoption. Telecom and financial services are the most active early adopters, with Technology and Healthcare being the third and fourth industries seeing big data analytics Education has the lowest adoption as 2017 comes to a close, with the majority of institutions in that vertical saying they are evaluating big data analytics for the future. North America (55%) narrowly leads EMEA (53%) in their current levels of big data analytics adoption. Asia-Pacific respondents report 44% current adoption and are most likely to say they “may use big data in the future.”

  • Data warehouse optimization is considered the most important big data analytics use case in 2017, followed by customer/social analysis and predictive maintenance. Data warehouse optimization is considered critical or very important by 70% of all respondents. It’s interesting to note and ironic that the Internet of Things (IoT) is among the lowest priority use cases for big data analytics today.

  • Big data analytics use cases vary significantly by industry with data warehouse optimization dominating Financial Services, Healthcare, and Customer/social analysis is the leading use case in Technology-based companies. Fraud detection use cases also dominate Financial Services and Telecommunications. Using big data for clickstream analytics is most popular in Financial Services.

  • Spark, MapReduce, and Yarn are the three most popular software frameworks today. Over 30% of respondents consider Spark critical to their big data analytics strategies. MapReduce and Yarn are “critical” to more than 20 percent of respondents.

  • The big data access methods most preferred by respondents include Spark SQL, Hive, HDFS and Amazon S3. 73% of the respondents consider Spark SQL critical to their analytics strategies. Over 30% of respondents consider Hive and HDFS critical as well. Amazon S3 is critical to one of five respondents for managing big data access. The following graphic shows the distribution of big data access methods.

  • Machine learning continues to gain more industry support and investment plans with Spark Machine Learning Library (MLib) adoption projected to grow by 60% in the next 12 months. In the next 24 months, MLib will dominate machine learning according to the survey results. MLib is accessible from the Sparklyr R Package and many others, which continues to fuel its growth. The following graphic compares projected two-year adoption rates by machine learning libraries and frameworks.

The Best Software Companies To Work For In 2018, According To Glassdoor

These and other findings are based on an analysis of Glassdoor rankings of Software Magazine’s 2017 Software 500 list of the leading software companies globally. An Excel spreadsheet was first created using the 2017 Software 500 list as the basis of the Glassdoor company comparisons. Rankings from Glassdoor were added today for the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO.The Software 500 list was used to preserve impartiality in the rankings.  The original data set the analysis is based on is available for download here in Microsoft Excel format.

To gain greater insights into the data sets a series of cross-tabulations and correlation analyses were done using IBM SPSS Statistics Version 25. The analysis shows CEOs have an even greater impact on improving their company’s recommendation scores, rising to 82% this year from 70% in 2015. The analysis also showed that companies who flood Glassdoor with fake reviews hit a wall around 10 posts, down from 15 in 2015. This doesn’t stop some companies from offering cash, prizes, and merchandise to their employees in exchange for positive reviews. Relying on Glassdoor and ideally in-office visits to see how a company culture is and how your potential boss treats others is ideal.

The following are the highest rated software companies to work for in 2018, based the (%) of employees who would recommend the company to a friend:

The following companies scored between 80% and 89% on the rating % of employees who would recommend this company to a friend:

Please see the entire data set for the rankings of all companies included in the Software Magazine 500 here in Microsoft Excel format.

Cloud Computing Market Projected To Reach $411B By 2020

  • Worldwide public cloud services market revenue is projected to grow 18.5% in 2017 reaching $260.2B, up from $219.6B in 2016.
  • 2016 worldwide SaaS revenue exceeded Gartner’s previous forecast by $48.2B.
  • SaaS revenue is expected to grow 21% in 2017 reaching $58.6B by the end of this year.
  • Infrastructure as a Service (IaaS) is projected to grow 36.6% in 2017 alone, reaching $34.7B this year making this area the fastest growing of all cloud services today.

Gartner’s latest worldwide public cloud services revenue forecast published earlier this month predicts Infrastructure-as-a-Service (IaaS), currently growing at a 23.31% Compound Annual Growth Rate (CAGR), will outpace the overall market growth of 13.38% through 2020. Software-as-a-Service (SaaS) revenue is predicted to grow from $58.6B in 2017 to $99.7B in 2020. Taking into account the entire forecast period of 2016 – 2020, SaaS is on pace to attain 15.65% compound annual growth throughout the forecast period, also outpacing the total cloud market. The following graphic compares revenue growth by cloud services category for the years 2016 through 2020. Please click on the graphic to expand it for easier reading.

Catalysts driving greater adoption and correspondingly higher CAGRs include a shift Gartner sees in infrastructure, middleware, application and business process services spending. In 2016, Gartner estimates approximately 17% of the total market revenue for these areas had shifted to the cloud. Gartner predicts by 2021, 28% of all IT spending will be for cloud-based infrastructure, middleware, application and business process services. Another factor is the adoption of Platform-as-a-Service (PaaS). Gartner notes that enterprises are confident that PaaS can be a secure, scalable application development platform in the future.  The following graphic compares the compound annual growth rates (CAGRs) of each cloud service area including the total market. Please click on the graphic to expand it for easier reading.

Source: Gartner Forecasts Worldwide Public Cloud Services Revenue to Reach $260 Billion in 2017

Gartner’s Hype Cycle for Emerging Technologies, 2017 Adds 5G, Edge Computing For First Time

  • 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 JournalBaidu 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.

Sources:

Gartner Identifies Three Megatrends That Will Drive Digital Business Into the Next Decade

Gartner Hype Cycle for Emerging Technologies, 2017 (client access required)

6 Ways Cloud ERP Is Revolutionizing How Services Deliver Results

  • Cloud ERP is the fastest growing sector of the global ERP market with services-based businesses driving the majority of new revenue growth.
  • Legacy Services ERP providers excel at meeting professional & consulting services information needs yet often lack the flexibility and speed to support entirely new services business models.
  • Configure-Price-Quote (CPQ) is quickly emerging as a must-have feature in Services-based Cloud ERP suites.

From globally-based telecommunications providers to small & medium businesses (SMBs) launching new subscription-based services, the intensity to innovate has never been stronger. Legacy Services ERP and Cloud ERP vendors are responding differently to the urgent needs their prospects and customers have with new apps and suites that can help launch new business models and ventures.

Services-based Cloud ERP providers are reacting by accelerating improvements to Professional Services Automation (PSA), Financials, and questioning if their existing Human Capital Management (HCM) suite can scale now and in the future. Vertical industry specialization is a must-have in many services businesses as well.  Factoring all these customer expectations and requirements along with real-time responsiveness into a roadmap deliverable in 12 months or less is daunting.  Making good on the promises of ambitious roadmaps that includes biannual release cycles is how born-in-the-Cloud ERP providers will gain new customers including winning many away from legacy ERP providers who can’t react as fast.

The following key takeaways are based on ongoing discussions with global telecommunications providers, hosters and business & professional services providers actively evaluating Cloud ERP suites:

  • Roadmaps that reflect a biyearly release cadence complete with user experience upgrades are the new normal for Cloud ERP providers. Capitalizing on the strengths of the Salesforce platform makes this much easier to accomplish than attempting to create entirely new releases every six months based on unique code lines. FinancialForceKenandy and Sage have built their Cloud ERP suites on the Salesforce platform specifically for this reason. Of the three, only FinancialForce has provided detailed product roadmaps that specifically call out support for evolving services business models, multiple user interface (UI) refreshes and new features based on customer needs. FinancialForce is also one of the only Cloud ERP providers to publish their Application Programming Interfaces (APIs) already to support their current and next generation user interfaces.
  • Cloud ERP leaders are collaborators in the creation of new APIs with their cloud platform provider with a focus on analytics, integration and real-time application response. Overcoming the challenges of continually improving platform-based applications and suites need to start with strong collaboration around API development. FinancialForce’s decision to hire Tod Nielsen, former Executive Vice President, Platform at Salesforce as their CEO in January of this year reflects how important platform integration and an API-first integration strategy is to compete in the Cloud ERP marketplace today. Look for FinancialForce to have a break-out year in the areas of platform and partner integration.
  • Analytics designed into the platform so customers can create real-time dashboards and support the services opportunity-to-revenue lifecycle. Real-time data is the fuel that gets new service business models off the ground. When a new release of a Cloud ERP app is designed, it has to include real-time Application Programming Interface (API) links to its cloud platform so customers can scale their analytics and reporting to succeed. What’s most important about this from a product standpoint is designing in the scale to flex and support an entire opportunity-to-revenue lifecycle.
  • Having customer & partner councils involved in key phases of development including roadmap reviews, User Acceptance Testing (UAT) and API beta testing are becoming common.  There’s a noticeable difference in Cloud ERP apps and suites that have gone through UAT and API beta testing outside of engineering.  Customers find areas where speed and responsiveness can be improved and steps saved in getting workflows done. Beta testing APIs with partners and customers forces them to mature faster and scale further than if they had been tested in isolation, away from the market. FinancialForce in services and IQMS in manufacturing are two ERP providers who are excelling in this area today and their apps and suites show it.
  • New features added to the roadmap are prioritized by revenue potential for customers first with billing, subscriptions, and pricing being the most urgent. Building Cloud ERP apps and suites on a platform free up development time to solve challenging, complex customer problems. Billing, subscriptions, and pricing are the frameworks many services businesses are relying on to start new business models and fine-tune existing ones. Cloud ERP vendors who prioritize these have a clear view of what matters most to prospects and customers.
  • Live and build apps by the mantra “own the process, own the market”. Configure-Price-Quote (CPQ) and Quote-to-Cash (QTC) are two selling processes services and manufacturing companies rely on for revenue daily and struggle with. Born-in-the-cloud CPQ and QTC competitors on the Salesforce platform have the fastest moving roadmaps and release cadences of any across the platform’s broad ecosystem. The most innovative Services-focused Cloud ERP providers look to own opportunity-to-revenue with the same depth and expertise as the CPQ and QTC competitors do.

Artificial Intelligence Will Enable 38% Profit Gains By 2035

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  • By 2035 AI technologies have the potential to increase productivity 40% or more.
  • AI will increase economic growth an average of 1.7% across 16 industries by 2035.
  • Information and Communication, Manufacturing and Financial Services will be the top three industries that gain economic growth in 2035 from AI’s benefits.
  • AI will have the most positive effect on Education, Accommodation and Food Services and Construction industry profitability in 2035.

Today Accenture Research and Frontier Economics published How AI Boosts Industry Profits and Innovation. The report is downloadable here (28 pp., PDF, no opt-in).The research compares the economic growth rates of 16 industries, projecting the impact of Artifical Intelligence (AI) on global economic growth through 2035. Using Gross Value Added (GVA) as a close approximation of Gross Domestic Product (GDP), the study found that the more integrated AI is into economic processes, the greater potential for economic growth.  One of the reports’ noteworthy findings is that AI has the potential to increase economic growth rates by a weighted average of 1.7% across all industries through 2035. Information and Communication (4.8%), Manufacturing (4.4%) and Financial Services (4.3%) are the three sectors that will see the highest annual GVA growth rates driven by AI in 2035. The bottom line is that AI has the potential to boost profitability an average of 38% by 2035 and lead to an economic boost of $14T across 16 industries in 12 economies by 2035.

Key takeaways from the study include the following:

  • AI will increase economic growth by an average of 1.7% across 16 industries by 2035 with Information and Communication, manufacturing and financial services leading all industries. Accenture Research found that the Information and Communication industry has the greatest potential for economic growth from AI. Integrating AI into legacy information and communications systems will deliver significant cost, time and process-related savings quickly. Accenture predicts the time, cost and labor savings will generate up to $4.7T in GVA value in 2035. High growth areas within this industry are cloud, network, and systems security including defining enterprise-wide cloud security strategies.

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  • AI will most increase profitability in Education, Accommodation and Food Services and Construction industries in 2035. Personalized learning programs and automating mundane, routine tasks to free up colleges, universities, and trade school instructors to teach new learning frameworks will accelerate profitability in the education through 2035.  Accommodation & Food Services and Construction are industries with manually-intensive, often isolated processes that will benefit from the increased insights and contextual intelligence from AI throughout the forecast period.

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  • Manufacturing’s adoption of Industrial Internet of Things (IIoT), smart factories and comparable initiatives are powerful catalysts driving AI adoption. Based on the proliferation of Industrial Internet of Things (IIoT) devices and the networks and terabytes of data they generate, Accenture predicts AI will contribute an additional $3.76T GVA to manufacturing by 2035. Supply chain management, forecasting, inventory optimization and production scheduling are all areas AI can make immediate contributions to this industry’s profits and long-term economic

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  • Financial Services’ greatest gains from AI will come automating and reducing the errors in mundane, manually-intensive tasks including credit scoring and first-level customer inquiries. Accenture forecasts financial services will benefit $1.2T in additional GVA in 2035 from AI. Follow-on areas of automation in Financial Services include automating market research queries through intelligent bots, and scoring and reviewing mortgages.

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  • By 2035 AI technologies could increase labor productivity 40% or more, doubling economic growth in 12 developed nations. Accenture finds that AI’s immediate impact on profitability is improving individual efficiency and productivity. The economies of the U.S. and Finland are projected to see the greatest economic gains from AI through 2035, with each attaining 2% higher GVA growth.The following graphic compares the 12 nations included in the first phase of the research.

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Sources:

How Artificial Intelligence Is Revolutionizing Enterprise Software In 2017

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  • 81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI).
  • Cowen predicts AI will drive user productivity to materially higher levels, with Microsoft at the forefront.
  • Digital Marketing/Marketing Automation, Salesforce Automation (CRM) and Data Analytics are the top three areas ripe for AI/ML adoption.
  • According to angel.co, there are 2,200+ Artificial Intelligence start-ups, and well over 50% have emerged in just the last two years.
  • Cowen sees Salesforce ($CRM), Adobe ($ADBE) and ServiceNow ($NOW) as well-positioned to deliver and monetize new AI-based application services.

These and many other fascinating insights are from the Cowen and Company Multi-Sector Equity Research study, Artificial Intelligence: Entering A Golden Age For Data Science (142 pp., PDF, client access reqd). The study is based on interviews with 146 leading AI researchers, entrepreneurs and VC executives globally who are involved in the field of artificial intelligence and related technologies. Please see the Appendix of the study for a thorough overview of the methodology. This study isn’t representative of global AI, data engineering and machine learning (ML) adoption trends. It does, however, provide a glimpse into the current and future direction of AI, data engineering, and machine learning.  Cowen finds the market is still nascent, with CIOs eager to invest in new AI-related initiatives. Time-to-market, customer messaging, product positioning and the value proposition of AI solutions will be critical factors for winning over new project investments.

Key takeaways from the study include the following:

  • Digital Marketing/Marketing Automation, Salesforce Automation (CRM) and Data Analytics are the top three areas ripe for AI/ML adoption. Customer self-service, Enterprise Resource Planning (ERP), Human Resource Management (HRM) and E-Commerce are additional areas that have upside potential for AI/ML adoption. The following graphic provides an overview of the areas in software that Cowen found the greater potential for AI/ML investment.

Artificial Intelligence: Entering A Golden Age For Data Science

  • 81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI). Based on the study, CIOs have a new mandate to integrate AI into IT technology stacks. The study found that 43% are evaluating and doing a Proof of Concept (POC) and 38% are already live and planning to invest more.  The following graphic provides an overview of company readiness for machine learning and AI projects.

How Artificial Intelligence Is Revolutionizing Enterprise Software In 2017

  • Market forecasts vary, but all consistently predict explosive growth. IDC predicts that the Cognitive Systems and AI market (including hardware & services) will grow from $8B in 2016 to $47B in 2020, attaining a Compound Annual Growth Rate (CAGR) of 55%. This forecast includes $18B in software applications, $5B in software platforms, and $24B in services and hardware. IBM claims that Cognitive Computing is a $2T market, including $200B in healthcare/life sciences alone. Tractica forecasts direct and indirect applications of AI software to grow from $1.4B in 2016 to $59.8B by 2025, a 52% CAGR.

Artificial Intelligence: Entering A Golden Age For Data Science

  • According to CBInsights, the number of financing transactions to AI start-ups increased 10x over the last six years, from 67 in 2011 to 698 in 2016. Accenture states that the total number of AI start-ups has increased 20-fold since 2011. The top verticals include FinTech, Healthcare, Transportation and Retail/e-Commerce. The following graphic provides an overview of the AI annual funding history from 2011 to 2016.

Artificial Intelligence: Entering A Golden Age For Data Science

  • Algorithmic trading, image recognition/tagging, and patient data processing are predicted to the b top AI uses cases by 2025. Tractica forecasts predictive maintenance and content distribution on social media will be the fourth and fifth highest revenue producing AI uses cases over the next eight years. The following graphic compares the top 10 uses cases by projected global revenue.

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  • Machine Learning is predicted to generate the most revenue and is attracting the most venture capital investment in all areas of AI. Venture Scanner found that ML raised $3.5B to date (from 400+ companies), far ahead of the next category, Natural Language Processing, which has seen just over $1Bn raised to date (from 200+ companies). Venture Scanner believes that Machine Learning Applications and Machine Learning Platforms are two relatively early stage markets that stand to have some of the greatest market disruptions.

Artificial Intelligence: Entering A Golden Age For Data Science

  • Cowen predicts that an Intelligent App Stack will gain rapid adoption in enterprises as IT departments shift from system-of-record to system-of-intelligence apps, platforms, and priorities. The future of enterprise software is being defined by increasingly intelligent applications today, and this will accelerate in the future. Cowen predicts it will be commonplace for enterprise apps to have machine learning algorithms that can provide predictive insights across a broad base of scenarios encompassing a company’s entire value chain. The potential exists for enterprise apps to change selling and buying behavior, tailoring specific responses based on real-time data to optimize discounting, pricing, proposal and quoting decisions.

Artificial Intelligence: Entering A Golden Age For Data Science

  • According to angel.co, there are 2,200+ Artificial Intelligence start-ups, and well over 50% have emerged in just the last two years. Machine Learning-based Applications and Deep Learning Neural Networks are experiencing the largest and widest amount of investment attention in the enterprise.
  • Accenture leverages machine learning in 40% of active Analytics engagements, and nearly 80% of proposed Analytics opportunities today. Cowen found that Accenture’s view is that they are in the early stages of AI technology adoption with their enterprise clients.  Accenture sees the AI market growing exponentially, reaching $400B in spending by 2020. Their customers have moved on from piloting and testing AI to reinventing their business strategies and models.

Roundup Of Cloud Computing Forecasts, 2017

  • Cloud computing is projected to increase from $67B in 2015 to $162B in 2020 attaining a compound annual growth rate (CAGR) of 19%.
  • Gartner predicts the worldwide public cloud services market will grow 18% in 2017 to $246.8B, up from $209.2B in 2016.
  • 74% of Tech Chief Financial Officers (CFOs) say cloud computing will have the most measurable impact on their business in 2017.

Cloud platforms are enabling new, complex business models and orchestrating more globally-based integration networks in 2017 than many analyst and advisory firms predicted. Combined with Cloud Services adoption increasing in the mid-tier and small & medium businesses (SMB), leading researchers including Forrester are adjusting their forecasts upward. The best check of any forecast is revenue.  Amazon’s latest quarterly results released two days ago show Amazon Web Services (AWS) attained 43% year-over-year growth, contributing 10% of consolidated revenue and 89% of consolidated operating income.

Additional key takeaways from the roundup include the following:

  • Wikibon is predicting enterprise cloud spending is growing at a 16% compound annual growth (CAGR) run rate between 2016 and 2026. The research firm also predicts that by 2022, Amazon Web Services (AWS) will reach $43B in revenue, and be 8.2% of all cloud spending. Source: Wikibon report preview: How big can Amazon Web Services get?
Wikibon Worldwide Enterprise IT Projection By Vendor Revenue

Wikibon Worldwide Enterprise IT Projection By Vendor Revenue

Rapid Growth of Cloud Computing, 2015–2020

Rapid Growth of Cloud Computing, 2015–2020

Worldwide Public Cloud Services Forecast (Millions of Dollars)

Worldwide Public Cloud Services Forecast (Millions of Dollars)

  • By the end of 2018, spending on IT-as-a-Service for data centers, software and services will be $547B. Deloitte Global predicts that procurement of IT technologies will accelerate in the next 2.5 years from $361B to $547B. At this pace, IT-as-a-Service will represent more than half of IT spending by the 2021/2022 timeframe. Source: Deloitte Technology, Media and Telecommunications Predictions, 2017 (PDF, 80 pp., no opt-in).
Deloitte IT-as-a-Service Forecast

Deloitte IT-as-a-Service Forecast

  • Total spending on IT infrastructure products (server, enterprise storage, and Ethernet switches) for deployment in cloud environments will increase 15.3% year over year in 2017 to $41.7B. IDC predicts that public cloud data centers will account for the majority of this spending ( 60.5%) while off-premises private cloud environments will represent 14.9% of spending. On-premises private clouds will account for 62.3% of spending on private cloud IT infrastructure and will grow 13.1% year over year in 2017. Source: Spending on IT Infrastructure for Public Cloud Deployments Will Return to Double-Digit Growth in 2017, According to IDC.
Worldwide Cloud IT Infrastructure Market Forecast

Worldwide Cloud IT Infrastructure Market Forecast

  • Platform-as-a-Service (PaaS) adoption is predicted to be the fastest-growing sector of cloud platforms according to KPMG, growing from 32% in 2017 to 56% adoption in 2020. Results from the 2016 Harvey Nash / KPMG CIO Survey indicate that cloud adoption is now mainstream and accelerating as enterprises shift data-intensive operations to the cloud.  Source: Journey to the Cloud, The Creative CIO Agenda, KPMG (PDF, no opt-in, 14 pp.)
Cloud investment by type today and in three years

Cloud investment by type today and in three years

AWS Segment Financial Comparison

AWS Segment Financial Comparison

  • In Q1, 2017 AWS generated 10% of consolidated revenue and 89% of consolidated operating income. Net sales increased 23% to $35.7 billion in the first quarter, compared with $29.1 billion in first quarter 2016. Source: Cloud Business Drives Amazon’s Profits.
Comparing AWS' Revenue and Income Contributions

Comparing AWS’ Revenue and Income Contributions

  • RightScale’s 2017 survey found that Microsoft Azure adoption surged from 26% to 43% with AWS adoption increasing from 56% to 59%. Overall Azure adoption grew from 20% to 34% percent of respondents to reduce the AWS lead, with Azure now reaching 60% of the market penetration of AWS. Google also increased adoption from 10% to 15%. AWS continues to lead in public cloud adoption (57% of respondents currently run applications in AWS), this number has stayed flat since both 2016 and 2015. Source: RightScale 2017 State of the Cloud Report (PDF, 38 pp., no opt-in)
Public Cloud Adoption, 2017 versus 2016

Public Cloud Adoption, 2017 versus 2016

  • Global Cloud IT market revenue is predicted to increase from $180B in 2015 to $390B in 2020, attaining a Compound Annual Growth Rate (CAGR) of 17%. In the same period, SaaS-based apps are predicted to grow at an 18% CAGR, and IaaS/PaaS is predicted to increase at a 27% CAGR. Source: Bain & Company research brief The Changing Faces of the Cloud (PDF, no opt-in).
60% of IT Market Growth Is Being Driven By The Cloud

60% of IT Market Growth Is Being Driven By The Cloud

  • 74% of Tech Chief Financial Officers (CFOs) say cloud computing will have the most measurable impact on their business in 2017. Additional technologies that will have a significant financial impact in 2017 include the Internet of Things, Artificial Intelligence (AI) (16%) and 3D printing and virtual reality (14% each). Source: 2017 BDO Technology Outlook Survey (PDF), no opt-in).
CFOs say cloud investments deliver the greatest measurable impact

CFOs say cloud investments deliver the greatest measurable impact

Cloud investments are fueling new job throughout Canada

Cloud investments are fueling new job throughout Canada

  • APIs are enabling persona-based user experiences in a diverse base of cloud enterprise As of today there are 17,422 APIs listed on the Programmable Web, with many enterprise cloud apps concentrating on subscription, distributed order management, and pricing workflows.  Sources: Bessemer Venture Partners State of the Cloud 2017 and 2017 Is Quickly Becoming The Year Of The API Economy. The following graphic from the latest Bessemer Venture Partners report illustrates how APIs are now the background of enterprise software.
APIs are fueling a revolution in cloud enterprise apps

APIs are fueling a revolution in cloud enterprise apps

Additional Resources:

Five Ways CPQ Is Revolutionizing Selling Today

CPQ, Salesforce CPQ, enosiX SAP to Salesforce Integration Configure-Price-Quote (CPQ) continues to be one of the hottest enterprise apps today, fueled by the relentless need all companies have to increase sales while delivering customized orders profitably and accurately. Here are a few of the many results CPQ strategies are delivering today:

  • Companies relying on CPQ are growing profit margins at a 57% greater rate year-over-year compared to non-adopters.
  • 89% improvement in turning Special Pricing Requests (SPRs) into sales by automating them using a cloud-based CPQ system.
  • 67% reduction in reworked orders at a leading specialty vehicle manufacturer due to quotes reflecting exactly what customers wanted to buy.
  • 23% improvement in upsell and cross-sell revenue by having the CPQ system intelligently recommend the optimal product or service that has the highest probability of purchase and best possible gross margin.
  • CPQ strategies excel when they are designed to reach challenging selling, pricing, revenue and operational performance goals versus automating existing selling workflows.

Another factor fueling CPQs’ rapid growth is how quickly results of a pilot can be measured and used for launching a successful company-wide launch.  Pilots often concentrate on quote creation time, quoting accuracy, sales cycle reduction, automating Special Pricing Requests (SPRs), up-sells and cross-sells, perfect order performance, margin improvements and best of all, winning new customers. These are the baseline metrics many companies use to measure their CPQ performance. Throughout 2017 these metrics across industries are accelerating. There is a revolution going on in selling today.

5 Ways CPQ Is Revolutionizing Selling Today

Cloud- and SaaS-based CPQ solutions are quicker to implement, easier to customize to customers’ requirements, and available 24/7 on any Internet-enabled device, anytime. Many are designed to integrate into Salesforce, further accelerating adoption seamlessly.  The following five factors are the primary catalysts revolutionizing selling today:

  1. Designing in excellent user experiences (UX) is the new normal for CPQ apps – CPQ vendors are competing with the quality of user experiences they deliver in 2017, moving beyond packing every feature possible into app releases. This is having a corresponding impact on adoption, increasing the number of sales representatives and entire teams who can get up and running fast with a new CPQ app. The net result is reduced sales cycles, growing pipelines, and more sales reps actively using CPQ apps to increase their selling effectiveness.
  2. Integrating with legacy CRM, ERP and pricing systems in real-time are using service-oriented frameworks gives sales teams what they need to close deals faster – Legacy CPQ systems in the past often had very precise field mappings to 3rd party legacy CRM, ERP and pricing systems. They were brittle and would break very easily, slowing down sales cycles and making sales reps resort to manually-based approaches from decades before. In 2017 there are service-oriented frameworks that make brittle, easily broken mappings thankfully an integration practice in the past. With a loosely coupled service framework, real-time integration between CRM and ERP systems can be quickly be implemented and sales teams can get out and close more deals. Leaders in the area include enosiX, who are enabling their customers’ sales forces to enter sales orders into SAP directly from Salesforce, saving valuable selling time and increasing order accuracy.
  3. Competing for deals using Artificial Intelligence (AI), machine learning and Intelligent Agents are force multipliers driving greater salesSalesforce’s Einstein is an example of the latest generation of AI applications that are enabling sales reps and teams to gain insights that weren’t available before. Combining customer data with these advanced predictive data analytics technologies yields insights into how selling strategies for different accounts can customize to specific prospect needs. Selling strategies are more effective and focused when AI, machine learning, and Intelligent Agents are designed in to guide quoting, pricing and product configuration in real-time.
  4. CPQ apps optimized for mobile devices are enabling sales reps to drastically reduce quote creation times, sales cycles and increase sales win rates – For many companies whose sales teams are in the field calling on accounts the majority of the time, mobile-based CPQ apps are how they get the majority of their work done. Salesforce’s Force.com is one of the leading platforms CPQ software companies are relying on to create mobile apps, further capitalizing on the already-established levels of familiarity sales teams have with the Salesforce platform.
  5. The vision many companies have of synchronizing multichannel and omnichannel selling as part of their CPQ strategies is now attainable – One of the greatest challenges of expanding sales channels is ensuring a consistently high-quality customer experience across each. With on-premise CPQ, CRM and ERP selling systems, this is very challenging as there are often multiple database systems supporting each. This is a breakout year for omnichannel selling as cloud-based CPQ systems and the platforms they are built on can securely scale across all selling channels a company chooses to launch. Being able to track which CPQ deals emanated from which marketing program, and which channels are the most effective in closing sales is now possible.
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