• 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 startups, 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 eCommerce 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.

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.

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.

According to CBInsights, the number of financing transactions to AI startups 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/eCommerce. The following graphic provides an overview of the AI annual funding history from 2011 to 2016.

Algorithmic trading, image recognition/tagging, and patient data processing are predicted to be the 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.

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.

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.

Other forecasts

• According to angel.co, there are 2,200+ artificial intelligence startups, 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.

• 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 startups, 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 eCommerce 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.

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.

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.

According to CBInsights, the number of financing transactions to AI startups 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/eCommerce. The following graphic provides an overview of the AI annual funding history from 2011 to 2016.

Algorithmic trading, image recognition/tagging, and patient data processing are predicted to be the 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.

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.

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.

Other forecasts

• According to angel.co, there are 2,200+ artificial intelligence startups, 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.

Software testers are often viewed as the “last mile” investment in a tech startup. A young, early-stage software company is undeniably focused on feature development that will drive sales. There is real pressure to develop the product quickly and spin out new features to beat the competition, especially if you have outside investors tapping their fingers.

Before long, things can take off and you’ve got a couple dozen developers churning out code. Suddenly, you aren’t prepared when quality issues creep into the equation, and it becomes a process just to find out who may have written the suspect code. Existing customers start to ramp up their complaints and calls into the help desk due to bugs and application errors: that’s not a trend you want to continue. You may find your business is losing customers as fast as it’s acquiring new ones, bringing growth to a standstill.

When over-investment in development is the root of the problem, the answer is not to hire more or better developers. It’s time to start adding testers. If you have waited too long, as many well-meaning companies have, you are now stuck playing a game of catch-up. It’s difficult to integrate these testers into your company: developers may think they can do it all and senior management sees these new testers as a bottleneck generating unnecessary costs. The testers are already behind on their work from day one. So, what to do if your lean startup wants to succeed in the market yet also values high-quality products?

The first piece of advice is to be more proactive about QA. Don’t wait until you have 30 or more developers to hire dedicated testers. In fact, a company that doesn’t see the need to scale up to that many developers might hire testers soon after the core development team is in place.

There’s no right answer, but here are some factors to consider when determining the optimal timing for hiring testers:

• An established or regulated business typically hires one tester for every two to three developers when at full scale (more than 500 developers)
• For a startup with 20 developers, it’s time to get in place at least one person dedicated primarily to testing/quality
• Consider the risk of revenue without quality checks. A startup can often grow a $6-$10 million-dollar revenue stream in 3-4 years, but with no testers, it can all be lost with one major bug or outage. Savvy investors have little tolerance for poor customer retention and unhappy customers
• How many testers you will need depends upon the development culture in place. If developers are testing savvy and located in near proximity to the business staff, they will be in a better position to take on some of the testing load
• Conversely, a company with a large, outsourced development team which has little interaction with business staff at headquarters won’t have adequate institutional knowledge to test the application adequately for end user needs. There is clearly a need in these cases to hire some dedicated testers early and ensure they have a good line of communication with the business

Software testers are often viewed as the “last mile” investment in a tech startup. A young, early-stage software company is undeniably focused on feature development that will drive sales. There is real pressure to develop the product quickly and spin out new features to beat the competition, especially if you have outside investors tapping their fingers.

Before long, things can take off and you’ve got a couple dozen developers churning out code. Suddenly, you aren’t prepared when quality issues creep into the equation, and it becomes a process just to find out who may have written the suspect code. Existing customers start to ramp up their complaints and calls into the help desk due to bugs and application errors: that’s not a trend you want to continue. You may find your business is losing customers as fast as it’s acquiring new ones, bringing growth to a standstill.

When over-investment in development is the root of the problem, the answer is not to hire more or better developers. It’s time to start adding testers. If you have waited too long, as many well-meaning companies have, you are now stuck playing a game of catch-up. It’s difficult to integrate these testers into your company: developers may think they can do it all and senior management sees these new testers as a bottleneck generating unnecessary costs. The testers are already behind on their work from day one. So, what to do if your lean startup wants to succeed in the market yet also values high-quality products?

The first piece of advice is to be more proactive about QA. Don’t wait until you have 30 or more developers to hire dedicated testers. In fact, a company that doesn’t see the need to scale up to that many developers might hire testers soon after the core development team is in place.

There’s no right answer, but here are some factors to consider when determining the optimal timing for hiring testers:

• An established or regulated business typically hires one tester for every two to three developers when at full scale (more than 500 developers)
• For a startup with 20 developers, it’s time to get in place at least one person dedicated primarily to testing/quality
• Consider the risk of revenue without quality checks. A startup can often grow a $6-$10 million-dollar revenue stream in 3-4 years, but with no testers, it can all be lost with one major bug or outage. Savvy investors have little tolerance for poor customer retention and unhappy customers
• How many testers you will need depends upon the development culture in place. If developers are testing savvy and located in near proximity to the business staff, they will be in a better position to take on some of the testing load
• Conversely, a company with a large, outsourced development team which has little interaction with business staff at headquarters won’t have adequate institutional knowledge to test the application adequately for end user needs. There is clearly a need in these cases to hire some dedicated testers early and ensure they have a good line of communication with the business

DevOps at Cloud Expo, taking place October 31 – November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA, is co-located with 21st Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world.
The widespread success of cloud computing is driving the DevOps revolution in enterprise IT. Now as never before, development teams must communicate and collaborate in a dynamic, 24/7/365 environment. There is no time to wait for long development cycles that produce software that is obsolete at launch. DevOps may be disruptive, but it is essential.

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For organizations that have amassed large sums of software complexity, taking a microservices approach is the first step toward DevOps and continuous improvement / development. Integrating system-level analysis with microservices makes it easier to change and add functionality to applications at any time without the increase of risk. Before you start big transformation projects or a cloud migration, make sure these changes won’t take down your entire organization.

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Rackspace has confirmed it has completed the acquisition of TriCore Solutions, a managed application and infrastructure provider.

The move was first announced in May, at a similar time to the San Antonio-based managed cloud provider announcing Joe Eazor, formerly of EarthLink, as the company’s new CEO. Mark Clayman, CEO of TriCore, will head up the newly created enterprise applications division at Rackspace, reporting directly to president Jeff Cotten.

“We acquired TriCore in response to customer demand,” said Matt Bradley, Rackspace vice president of strategy and corporate development. “Customers want a managed application expert to deliver a high quality service experience at a lower total cost for these complex applications and TriCore has been doing this for nearly two decades – they’ve done an incredible job and now our customers will be able to benefit from their experiences.”

Rackspace was recently named as a niche player in the most recent Gartner Magic Quadrant for cloud infrastructure as a service, alongside companies including Virtustream, CenturyLink, and Fujitsu. Yet as David Linthicum pointed out in an InfoWorld article, Rackspace’s position as a managed services ‘enabler’, for clouds such as Amazon Web Services, Microsoft, and Google, sets them apart from the competition in that particular square, which he describes as ‘niche IaaS providers to enterprises.’

In its analysis, Gartner praised Rackspace’s ‘evolution’ from OpenStack-based public cloud IaaS to its roots in managed services, making it “well-positioned to deliver hybrid and multi-cloud solutions”, but noting public cloud IaaS customers may expect more of its offerings.

The completed acquisition of TriCore, alongside the continued recognition from Gartner, will augur well as Eazor settles down in the CEO role. “Let me emphasise how excited I am by the huge market opportunity that Rackspace has in front of it, as companies move out of their corporate data centres and into multiple clouds,” he wrote in an introductory blog last month. “Rackspace is uniquely well positioned to take advantage of this trend, as the only provider who can deliver expertise and exceptional customer service for all of the leading public and private clouds, along with managed hosting.”

Eazor officially started work at Rackspace on June 12. But whither previous CEO Taylor Rhodes? Rhodes did not have a long period away from the hot seat – he has since become chief executive of SMS Assist, a multisite property management provider.

Picture credit: Rackspace Afterparty TechStars Boulder 2011, by Andrew Hyde, used under CC BY / Modified from original

In the race to move to hybrid cloud, enterprises are looking at containers to increase efficiency and developer productivity, according to a new study.

Of the more than 200 enterprise IT decision makers polled by CoreOS and 451 Research, their usage of containers was relatively even across the board. A quarter (24%) said they had a broad implementation of production applications, while 17% said they were still as the discovery and evaluation stage.

When it came to the drivers of container usage, greater efficiency (33%) and hybrid cloud and cross-cloud integration (32%) were the most frequently cited. The most popular benefit was developer productivity, cited by 30% of respondents, just ahead of efficiency (29%), but these numbers not surprisingly swung for DevOps (55% developer productivity) and infrastructure (37% efficiency) workers respectively.

Docker Swarm was cited as the most popular orchestration platform overall (36%), ahead of report sponsors CoreOS (27%), Kubernetes (22%) and Mesos (14%). For the largest organisations – those with 5,000 or more employees – Kubernetes saw a rise to 27%, with CoreOS going down to 19%.

The report also found that twice as many IT operations workers were identified as the primary users of containers – in other words, not developers – while a similar trend occurred with purchasing, albeit noting a ‘growing developer influence’ on this area.

“Organisations are looking for ways to create a consistent developer deployment model across on-premises and hybrid clouds,” said Jay Lyman, principal analyst at 451 Research in a statement. “The need to empower developers, the push to realise the benefits of public clouds and need to support mission critical on-premise applications have created multiple computing environments within the enterprise.

“In order to manage this increasingly chaotic IT infrastructure and avoid the mistakes of the past, organisations are turning to container software to deliver a single platform for application deployment across clouds and operationalised efficiency across the organisation,” Lyman added.

According to a report from NetEnrich last July surveying 200 IT professionals, two thirds of respondents described learning the likes of Docker, Kubernetes and Mesos as ‘moderately challenging’. CoreOS was the most popular main container technology cited by 33%, ahead of Kubernetes (32%), Docker (25%) and Joyent (11%).

As a result, it can be assumed that the technology’s usage is proliferating, if a major player has not quite burst through yet. 451 Research concludes that “given these survey results and the strong, growing production use not only of containers, but also container management and orchestration software at this early stage of the market, we expect the container market’s momentum to continue through 2017.”