What is Artificial Intelligence?
Machines have been able to perceive and sense for a long time. With a dramatic increase in processing power, it is now economical to equip them with many more sensors and collect a wider array of data from those sensors. In terms of experiences and knowledge, the universe of accessible data is growing exponentially and once again, the rapid rise of computing capability makes it possible to harness that vast data universe more easily. The most critical breakthrough in giving machines the semblance of intelligence comes in their ability to interpret the information. Progress in natural language processing, the ability to interpret unstructured data, the rapid advance of self-optimizing algorithms, are only some examples to illustrate how machines are increasingly capable of replicating complex cognitive processes while relying on less guidance to perform these tasks. And we are quickly becoming used to the machines communicating back to us in what feels like a natural form of interaction (Siri, Alexa, etc.). It is the combination of these advances that drive the impact of Artificial Intelligence. We will soon come to expect that “suggestions” we receive by sites or digital assistants (ranging from where to dine and what to buy to what to invest in) feel “intuitively right” as they are based on the broadest available data set about our preferences and behaviors and presented in a human like, conversational style of interaction, supported by facts if desired.
Who will be impacted by AI?
Forrester estimates that 25% of all jobs, globally, will be impacted by AI in some form by 2019. Three years is a very short timeline to prepare for that change. AT Kearney is estimating that 13% or 19 million jobs in the U.S. will be replaced by 2024. Many impacted jobs are going to be “cognitive” jobs, ranging from customer service to sales, decision making and advisory jobs in banking, insurance, and investment management, and in many other industries. A rapid and comprehensive wave of job displacements will leave scores of workers scrambling to refocus their careers, retrain for new expectations, or adjust to longer periods of unemployment between active work periods. If that change comes upon us quickly, how will we build educational resources, adjust on the job training, and deal with fundamentally altered workforce dynamics?
“As a society, we are entering uncharted territory.” (Marc Benioff, Salesforce CEO, 18 January 2016)
“Accelerating artificial intelligence (AI) capabilities will enable automation of some tasks that have long required human labor. These transformations will open up new opportunities for individuals, the economy, and society, but they have the potential to disrupt the current livelihoods of millions of Americans. “ (Office of the President of the United States, “Artificial Intelligence, Automation and the Economy” December 2016)
But with these changes come not only threats, but also opportunities for increased competitiveness of those who embrace AI and adapt to the new environment faster than others. Every CEO should have the following questions on top of their agenda:
- How will AI change the competitive dynamics of my industry?
- Where can we take advantage of AI in our products and services?
- How can we leverage AI to optimize our internal processes and capabilities?
- Where should we be investing to be well positioned in an AI based future?
And these questions and the actions they trigger are relevant NOW, not in a distant future. In a recent survey by RAGE Frameworks, a Boston based AI specialty provider, to C-level executives, 82% of respondents indicated that AI will be part of their organization’s overall IT spending in 2017. The most important driver for that investment was Revenue Generation (80%+ of those who will invest in AI quoted Revenue increase as a motivation) followed by Cost Reduction, Making Better Management and Strategic Decisions, and Driving Greater Business Intelligence and Insights.
How exactly does AI create this type of impact?
The power of AI comes from three distinct pillars. (1) Insight Generation, (2) Customer Engagement, and (3) Business Acceleration.
Insight generation is achieved by accessing large amounts of data, both structured and unstructured (the ability to “read and understand” unstructured information is one of the hallmarks of “intelligent platforms”), and to derive insights from that data based on patterns related to an individual consumer or based on patterns comparing that individual to many other “similar” individuals. The availability of data on the web in general, and our tendency to “share” much of our lives in social channels specifically has increased the transparency of patterns. This “data haven” combined with exponentially increased computing power is the basis for intelligent insight generation.
Customer engagement is facilitated through increasingly intuitive ways of interacting with the digital world. We speak to Siri or Alexa and we increasingly expect to be understood and to receive adequate responses and actions. Natural language abilities enable AI driven platforms to deliver their insights in a way that feels increasingly human-like, which in turn drives our acceptance and enables the elimination of the actual human interface. Chatbot capabilities are advancing from clumsy tools to assistants and will soon reach “expert and savant” levels, reducing the need and the desire to “speak to a real person”.
Business acceleration occurs through the deployment of a range of tools which optimize business outcomes. Capabilities include Machine Learning (complex algorithms that leverage large sets of data and excel at pattern matching), Causal/Semantic solutions (large knowledge bases that enable systems to understand how concepts are related to each other), Voice to Text translation, Character Recognition, and Vision (the ability to extract content and meaning from images), among others. It is the combination of these tools and solutions in business applications that creates value through higher speed, higher accuracy, and dramatically lower cost. Advanced software tools and code platforms allow developers to effectively and efficiently apply these capabilities in targeted solutions.
The goal is typically not to entirely replace the human aspect, but to empower the decision with data and insights, taking away the need for “lower level cognitive tasks” such as data collection and analysis, research, identification of options, relaying of information, etc. and focusing the human task on applying pattern recognition, experience, and judgment. AI supported medical diagnostic tools for example can dramatically reduce error rates when combined with human judgment.
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