(click here to download presentation as a PDF)

Robert B. Cohen, Senior Fellow, Economic Strategy Institute, February 5, 2018
(click here to download the full report in PDF format)

How are Pervasive Cloud Ecosystems with the Internet of Things, Artificial Intelligence and Machine Learning Different from IT as we have known it? In 1995, Frances C. Cairncross1 proposed the term the “death of distance” to define how the Internet ties the world together so closely that the limitations of distance are overcome. Cairncross also argued that the Internet would support new types of innovation and be incorporated into many new products. She accurately described the early trajectory of the digital revolution.

As we discuss below, the “death of time” is a suitable, though strong, term for the next phase in the digital revolution. In this phase, several things change the relationship between software and data analysis and time. For one, firms more rapidly create and revise software. They use continuous integration and continuous delivery (CI/CD), microservices and other techniques that shorten these times. Target is a good example of this change2. Target was using “silos and processes that slowed innovation” and needed to “keep up with the pace of change” especially during the holidays when it received over 7 million orders from its stores. To address this, Target moved from a more traditional configuration management tool to an agile model that accelerated software development between 2015 and 2016. Target shortened its time to develop new software from 1440 minutes to 5 minutes, increased the number of applications it was using from 3 to 42 and expanded the number of software deployments it did each day from 2 or 3, to 90. In Appendix 1, we depict the enormous reduction in the time required to create new software in recent years. This places Target’s effort in a more general context. The decline we illustrate there is a 10,000-times reduction in the time to create new software over a 14-year period. Facebook, Google and Yahoo are other firms that have risen to the top of their fields by adopting new processes and building up internal skilled teams to speed software development.

Proposed by Robert B. Cohen, PhD, Senior Fellow, Economic Strategy Institute, March 7, 2018

We have developed a classification of three groups of emerging digital occupations:

1. Data analytics – data scientists and jobs in data governance, predictive analytics, process management, and data center functions.
2. Software development and deployment – jobs with skills in software engineering, DevOps, Docker/Containers, microservices and serverless computing.
3. “Intelligence” for analytics, computing and networks – jobs in artificial intelligence and machine learning, business intelligence, cybersecurity, and network virtualization.

The skills needed for these jobs are shaped by firms’ perceived greater value in the data they collect from their web-based and internal operations. It is now more critical to capture and analyze this data in “real-time”. This insures that unwanted intrusions do not persist and cause operational problems. Firms also want to know that new software programs are functioning properly.

This means that human judgement about how to act on information becomes critical. The demand for employees to monitor and respond to data, especially large data streams, is likely to expand demand for highly paid professionals. It will also provide jobs that help firms employ data for analysis. As firms increase their dependence on data, they will need to oversee and monitor complex systems for manufacturing, service delivery and analytics. These jobs will demand human judgement and new skills to support “data-related” functions in the modern firm.

Robert B. Cohen, Senior Fellow, Economic Strategy Institute, June 18, 2018

Challenge:

We examine the shift to AI and ML as part of an increasing sophistication in the analysis of processes and operations. We focus on this area since we believe that software plays a central role in shaping the direction and size of the economic benefits associated with how Cloud computing, AI and ML affect businesses. Hardware innovations are certainly part of the changes we expect to quantify.

Our goal is to estimate the magnitude and connection between software and hardware innovations and interactions between different types of software in the many areas that affect the way firms are using cloud computing, AI and ML. We believe if we can successfully develop this estimate, it will be possible to compare it to estimates of the impact of robotics and automation on the economy in terms of output and jobs.

There are several unique challenges to quantify the benefits that we want to measure:

Robert B. Cohen, PhD
Senior Fellow

Economic Strategy Institute 

(click here to download presentation as a PPT)

Click here or on image to start the presentation. Use the left/right arrows to navigate the slides

Robert B. Cohen, PhD, Senior Fellow, Economic Strategy Institute, March 15, 2018
(click here to download the full report in PDF format)

Changes in the Macro Economy and the Rise of Big Data

Previous analysis1 predicted that nearly half of U.S. jobs would be at risk of being displaced due to the automation of more routine skills. A shortcoming of this work was its inability to estimate how the introduction of digital processes and operations built upon innovative computing and software might have more complementary impacts on key parts of the economy. This analysis addresses this gap.

As firms’ operations rely in a much greater way on data analytics and data-insights and Big Data, more intelligent analytics and improvements in software development processes are becoming more central to the success of operations and a key determinant of competitiveness. Once there is wider use of machine learning (ML), artificial intelligence (AI), visualization and other tools for sophisticated analytics, the performance improvements achieved by this new focus on data are likely to be more apparent:

1. 1. Firms will need to analyze how new processes impact their operations. This will put a premium on their ability to capture and interpret data in “real time”.
2. Big Data will become indispensable. Firms’ now focus on sharpening their perception of what data are critical to business decisions, but this will reinforce it. They also will amass crucial data sets and to hire those with skills to manage and oversee them. Data and how it is harnessed will help determine a firm’s competitiveness.
3. To strengthen the benefits of data analysis, firms will redesign how they develop software to assess data and interpret the performance of processes. This will accelerate the deployment of more streamlined tools.

Robert B. Cohen, Ph.D., Senior Fellow, Economic Strategy Institute, May 11, 2018
(click here to download the full report in PDF format)

1. An Initial Estimate of the Impact of AI and ML on the U.S. economy

The impact of AI and ML on the economy is becoming clear through corporate presentations about how they are introducing these technologies. First, AI and ML will alter how firms operate and manage automation within their environments, whether that means manufacturing or services. Second, AI and ML will support a substantial evolution in software, infrastructure and computing for businesses, based largely on innovations in computing such as the move to Cloud Native (where no operating system is required on computers used to work in clouds). Part of this evolution includes virtualized infrastructure as well as the emergence of orchestration tools such as Kubernetes that accelerate the pace of application development. Third, with these enhancements, businesses can make more effective use of AI and ML applications to manage machines on the factory floor as well as support data analysis. They can also better manage human resources, supply chains, procurement, and other business functions.