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Holy Book of AI, Automation and Analytics Opened for 2018 – Dive in!

In a recent McKinsey survey of more than 500 executives representing companies across the spectrum of industries, regions, and sizes, more than 85% acknowledged that they were only somewhat effective at meeting goals they set for their data and analytics initiatives.

Holy Book Dive

Innovations in digitization, analytics, artificial intelligence, and automation are creating performance and productivity opportunities for business and the economy, even as they reshape employment and the future of work.

Rapid technological advances in digitization and data and analytics have been reshaping the business landscape, supercharging performance, and enabling the emergence of new business innovations and new forms of competition. At the same time, the technology itself continues to evolve, bringing new waves of advances in robotics, analytics, and artificial intelligence (AI), and especially machine learning. Together they amount to a step change in technical capabilities that could have profound implications for business, for the economy, and more broadly, for society.

The opportunity available now

Some companies are gaining a competitive edge with their use of data and analytics, which can enable faster and larger-scale evidence-based decision making, insight generation, and process optimization. But there is room to catch up and to excel. Harnessing digitization’s potential is similarly uneven.

Data and analytics are transformational, yet many companies are capturing only a fraction of their value

Leading companies are using their capabilities not only to improve their core operations but also to launch entirely new business models. The network effects of digital platforms are creating a winner-take-most dynamic in some markets. Yet while the volume of available data has grown exponentially in recent years, most companies are capturing only a fraction of the potential value in terms of revenue and profit gains.

Effective data and analytics transformations have several components:

  • Asking fundamental questions to shape the strategic vision: What will data and analytics be used for? How will the insights drive value? Which data sets are most useful for the insights needed?
  • Solving for the problems in the way data is generated, collected, and organized. Many incumbents struggle to switch from legacy data systems to a more nimble and flexible architecture that can get the most out of big data and analytics. They may also need to digitize their operations more fully in order to capture more data from their customer interactions, supply chains, equipment, and internal processes.
  • Acquiring the skills needed to derive insights from data; organizations may choose to add in-house capabilities or outsource to specialists.
  • Changing business processes to incorporate data insights into the actual workflow. This is a common stumbling block. It requires getting the right data insights into the hands of decision makers—and making sure that these executives and mid-level managers understand how to use data-driven insights.

In a recent McKinsey survey of more than 500 executives representing companies across the spectrum of industries, regions, and sizes, more than 85% acknowledged that they were only somewhat effective at meeting goals they set for their data and analytics initiatives.

Data and analytics are disrupting business models and bringing performance benefits

Disruptive data-driven models and capabilities are reshaping some industries, and could transform many more. Certain characteristics of a given market open the door to disruption by those using new data-driven approaches, including:

  • inefficient matching of supply and demand
  • prevalence of underutilized assets
  • dependence on large amounts of demographic data when behavioral data is now available
  • human biases and errors in a data-rich environment

One of the most powerful uses is micro-segmentation based on behavioral characteristics of individuals. This is changing the fundamentals of competition in many sectors, including education, travel and leisure, media, retail, and advertising.

Digitization, more broadly, is also progressing unevenly among companies, sectors, and economies

The corporate world’s broader embrace of digitization is similarly uneven. Our use of the term digitization (and our measurement of it), encompasses:

  • Assets, including infrastructure, connected machines, data, and data platforms, etc.,
  • Operations, including processes, payments and business models, customer and supply chain interactions and
  • The workforce, including worker use of digital tools, digitally-skilled workers, new digital jobs, and roles. In measuring each of these various aspects of digitization, we find relatively large disparities even among big companies (Exhibit 1).

Holy Book Automation

There are also disparities between sectors in terms of degree of digitization:

In the United States, the information and communications technology (ICT) sector, media, financial services, and professional services are surging ahead, while utilities, mining, and manufacturing, among others, are in the early stages of digitizing. In labor-intensive industries such as retail and health care, substantial parts of their large workforces do not use technology extensively.

The next wave of opportunity

Coming over the horizon is a new wave of opportunity related to the use of robotics, machine learning, and AI. Companies that deploy automation technologies can realize substantial performance gains and take the lead in their industries, even as their efforts contribute to economy-level increases in productivity.

The idea of AI is not new, but the pace of recent breakthroughs is. Three factors are driving this acceleration:

  • Machine-learning algorithms have progressed in recent years, especially through the development of deep learning and reinforcement-learning techniques based on neural networks.
  • Computing capacity has become available to train larger and more complex models much faster. Graphics processing units (GPUs), originally designed to render the computer graphics in video games, have been repurposed to execute the data and algorithm crunching required for machine learning at speeds many times faster than traditional processor chips. More silicon-level advances beyond the current generation of GPUs are already emerging, such as Tensor Units.  This compute capacity has been aggregated in hyper-scalable data centers and is accessible to users through the cloud.
  • Massive amounts of data that can be used to train machine learning models are being generated, for example through daily creation of billions of images, online click streams, voice and video, mobile locations, and sensors embedded in the Internet of Things.

Holy Book Automation Analytics

Several key factors will influence the pace and extent of automation. These include:

  • Technical feasibility of automation, a critical first step that will depend on sustained breakthrough innovation, but alone is not sufficient;
  • Cost of developing and deploying solutions;
  • Labor market dynamics, including supply and demand, and costs of human labor as an alternative to automation;
  • Business and economic benefits, not merely labor substitution benefits but also benefits from new capabilities that go beyond human capabilities;
  • Regulatory, user and social acceptance, which can affect the rate of adoption even when deployment makes business and economic sense.

Holy Book


BiS Team

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