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Posts tagged ‘enterprise analytics’

How Artificial Intelligence Is Revolutionizing Enterprise Software In 2017

future-of-artificial-intelligence-and-big-data

  • 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.

ai-use-cases

  • 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.

Cloud Predictive Analytics Most Used To Gain Customer Insight

AnalyticsUsing analytics to better understand customer satisfaction, profitability, retention and churn while increasing cross-sell and up-sell are the most dominant uses of cloud-based analytics today.

Jim Ericson and James Taylor presented the results of Decision Management Solutions’ cloud predictive analytics survey this week in the webinar Predictive Analytics in the Cloud 2013 – Opportunities, Trends and the Impact of Big Data.  The research methodology included 350 survey responses, with a Web-based survey used for data collection.  The survey centered on the areas of pre-packaged cloud-based solutions, cloud-based predictive modeling, and cloud deployment of predictive analytics.  You can see a replay of the webinar at this link.

Key takeaways of the study results released during the webinar include the following:

  • Customer Analytics (72%), followed by supply chain, business optimization, marketing optimization (57%), risk and fraud (52%), and marketing (58%) are the areas in which respondents reported the strongest interest.
  • When the customer analytics responses were analyzed in greater depth they showed most interest in customer satisfaction (50%) followed by customer profitability (34%), customer retention/churn (32%), customer management (30%), and cross-sell/up-sell (26%).
  • Adoption was increasingly widespread and growing, with over 90% of respondents reporting that they expected to deploy one or more type of predictive analytics in the cloud solution.
  • Industries with the most impact from predictive analytics include retail (13% more than average), Financial Services (12%) and hardware/software (4%). Lagging industries include health care delivery (-9%), insurance -11%) and (surprisingly) telecommunications (-33%).  The following graphic illustrates the relative impact of cloud-based predictive analytics applications by industry.

Adoption of Cloud-based Predictive Analytics by Industry

  • The most widespread analytics scenarios include prepackaged solutions (52%), cloud-based analytics modeling (47%) and cloud-based analytic embedding of applications (46%).  Comparing the 2011 and 2013 surveys showed significant gains in all three categories, with the greatest being in the area of cloud-based analytic modeling.  This category increased from 51% in 2011 to 75% in 2013, making it the most likely analytics application respondents are going to implement this year.

Comparison of Analytics Applications Most Likely To Deploy, 2011 versus 2013

  • 63% of respondents report that when predictive analytics are tightly integrated into operations using Decision Management, enterprises have the intelligence they need to transform their businesses.

Impact of Predictive Analytics Integration Across The Enterprise

  • Data security and privacy (61%) followed by regulatory compliance (50%) are the two most significant concerns respondent companies have regarding predictive analytics adoption in their companies.  Compliance has increased as a concern significantly since 2011, probably as more financial services firms are adopting cloud computing for mainstream business strategies.

Concerns of Enterprises Who Are Using Cloud-based Predictive Analytics Today

  • Internal cloud deployments (41%) are the most common approach to implementing central cloud platforms, followed by managed vendor clouds (23% and hybrid clouds (23%). Private and managed clouds continue to grow as preferred platforms for cloud-based analytics, as respondents seek greater security and stability of their applications.  The continued adoption of private and managed clouds are a direct result of respondents’ concerns regarding data security, stability, reliability and redundancy.

Approach To Cloud Deployment

  • The study concludes that structured data is the most prevalent type of data, followed by third party data and unstructured data.
  • While there was no widespread impact on results from Big Data, predictive analytics cloud deployments that have a Big Data component are more likely to contribute to a transformative impact on their organizations’ performance.  Similarly those with more experience deploying predictive analytics in the cloud were more likely to use Big Data.
  • In those predictive analytics cloud deployments already operating or having an impact, social media data from the cloud, voice or other audio data, and image or video data were all much more broadly used as the following graphic illustrates.

Which Data Types Deliver The Most Positive Impact In A Big Data Context

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