Artificial intelligence – how advanced analytics and smart machines will change the way we work

Artificial intelligence (AI) continues to be a subject of controversy – is it our doom or a new dawn?

The idea of computer-based AI dates back to 1950, when Alan Turing proposed an experiment that is now commonly referred to as the Turing test: “A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.”

What does it take for a machine to pass the Turing test and convince human operators it is one of their kind? There are three constitutive elements of AI, sensing, thinking and acting.

How will AI impact the economy?

AI itself is not new, but the pace of recent breakthroughs is. Four factors are driving this acceleration:

  1. Machine-learning algorithms have progressed in recent years, especially through the development of deep-learning and reinforcement-learning techniques based on neural networks.
  2. Exponentially increasing computing capacity has become available to train larger and more complex AI models much faster.
  3. Machine-learning models can be trained using the growing amount of data generated every day (e.g., images, click streams, voice and video, mobile locations, and data gathered by sensors embedded in the Internet of Things).
  4. Improved processors and calculation systems allow for new AI applications, such as advanced machine learning algorithms. Quantum computing is one of the technologies that drives these applications.

One in two jobs could soon be done by a machine

The McKinsey Global Institute (MGI) has carried out a comprehensive study of the automation potential in a wide range of industries.

The top five industries in terms of automation potential are hospitality, manufacturing, logistics, agriculture, and retail. The average across all industries is 53 percent. In other words, one in two jobs could soon be carried out by a machine. The wages associated with technically automatable activities come to a total of USD 14.6 trillion globally. According to the MGI’s projection, the average automation potential will reach 90 percent by 2055.

Technical potential for automation across sectors varies depending on mix of activity types

Benefits go beyond labor substitution

Automation will not only render some jobs redundant, but it will also yield performance benefits in many industries. AI has already attracted billions of dollars in investment.

Machine learning received the most investment, although boundaries between technologies are not clear-cut

What should companies do?

The stakes are high, and the pace of change is swift. To benefit from the opportunities AI will bring, companies need to start preparing today for the dawning age of smart machines. Even if the current impact of AI on their business is limited, incumbents cannot afford to sit and wait.

Disrupt or be disrupted

To ready themselves for an age of AI-driven value creation, companies need to think beyond technology. A new IT server, software package, or even task force of tech heads will not solve the problem. To remain relevant in an age of smart machines, CEOs need to start asking some fundamental questions:

  • What is our future business model?
  • What are our prospective sources of growth?
  • Which capabilities will we need to succeed?

Approach for tech-enabled transformations

Four key elements of successful technology-enabled transformations

  1. Vision. Successful transformations begin with a clear, inspiring vision that is aligned with a company’s business strategy and relevant use cases.
  2. Enablers.
    • Wizards. You need a new breed of people to take advantage of new technology, scientists who are comfortable working with self-learning algorithms and neural networks.
    • Use cases. There is nothing as powerful as a successful business application to make people see the value of new technology. Look for quick wins to build buy-in and set off a virtuous cycle.
    • Data and technology. To take advantage of big data, you need the right hardware and software to merge multiple data sources and derive the kinds of insights that will drive sustainable growth.
    • Partners/ecosystems. Cross-sector ecosystems have the potential to create new opportunities for growth, often driven by data. See our lead article about technology-enabled transformations for more information on emerging new ecosystems.
  3. Translators. Additionally, you need people who understand both the business and the technology to act as intermediaries.
  4. Change management and operating model. Data-driven decision making typically requires structural adjustments and new, adaptive business processes. Change management helps ensure that new ways of working are rolled out to the entire organization.