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PHOTO: Aaron Barnaby

In its simplest form, automation includes any improvement to a process that reduces human labor while resulting in an outcome that’s the same or better than that of the previous process. The goals of automation include improvements not only in productivity but also in quality and consistency.

Most of us are familiar with information systems that automate and streamline decision-making using tools to aggregate, extract and analyze information. All of that has been around for a while. But newer systems — based on cognitive automation — now include natural language processing, machine learning algorithms, real-time computing, big data analytics and evidence-based-learning. This level of automation opens up exciting possibilities.

Cognitive automation — basically, the intersection of artificial intelligence (AI) and cognitive computing — has become one of the fastest-moving technologies because of the rise of the digital and connected workforce. According to one forecast, the global cognitive robotic process automation market will generate revenue of $50 million in 2017 and will expand at a compound annual growth rate of 60.9 percent from 2017 to 2026.

Artificial Intelligence Buzz: C-Suite Interest?

The earliest successes in the field of cognitive automation involved products such as warehouse robots powered by AI, automated restocking systems, self-driving cars and systems that predict electricity demand. However, despite the influx of billions of gigabytes of data and vast investments, deployment of AI is still relatively low — though it appears that, thanks to the current wave of AI buzz, C-level executives are taking an interest in how investments in AI can result in greater success.

In a Harvard Business Review article, researchers from the McKinsey Global Institute reported that only 20 percent of respondents to their survey said they use one or more AI technologies at scale, while 41 percent said they were experimenting or piloting AI. Nonetheless, a McKinsey report sees AI as a new disruptor that will accelerate “shifts in market share, revenue, and profit pools.”

McKinsey identifies early adopters as digitally mature larger businesses that will use AI in core activities through multiple technologies, and that focus on growth over savings.

Why So Much Excitement Around AI?

Ultimately, the power of AI lies in making a direct connection between potential and benefit. Because the AI labor market is tight, with just a small pool of AI experts, emerging efforts to develop and adopt AI need to be tightly aligned with compelling use cases such as:

  • Existing technology solutions that scale and provide immediate benefits.
  • Emerging technology that has clear business case value.
  • High-impact use cases that will provide early adopters with competitive advantages.

Whether through smarter forecasting, better means of production, marketing or customer experiences, AI impacts value. It’s quickly gaining traction as a way of facilitating human reasoning and planning (predictive analytics), perception (image identification), motion (robotics and self-driving vehicles), natural language (commerce recommendations, sentiment analysis, machine translation, natural language generation) and even emotion.

Real-World Examples of Automation at Work

AI and machine learning are already being used across an array of industries. The success of these technologies is driven by real-world use cases, such as:

  • The Roomba: This robotic vacuum cleaner uses AI to scan a room and look for blocking objects. It even remembers the best route possible and determines the amount of cleaning based on room area.
  • Digital assistants: AI helps applications like Alexa, Cortona and Siri support users with everyday tasks, from setting alarms to playing music and making purchases. These systems use speech recognition and natural language processing and offer the additional benefit of providing relevant results through personalization.
  • Marketing automation: Automation is already used for customer segmentation, campaign management and product promotion, and it is integrated in most customer relationship management systems. Customer data analysis, sentiment analysis and hyperlink intelligence provide marketing organizations with the ability to provide increasingly targeted content.

What About Digital Experiences?

When it comes to customer engagement and driving business, AI facilitates true personalization by optimizing recommendations, dynamic content generation and assembly, search optimization and tagging and sentiment analytics. It also connects content with customer data. The possibilities are almost endless. Following the customer journey further, AI also promises to improve self-service and offer preemptive support through predictions, chatbots and speech recognition.

The quality of data will drive the success of systems that rely on machine learning. Even the best algorithms will not produce results if the quality of input is lacking. Human input and creativity will ultimately drive the AI that then supports human intelligence.

In the future AI promises to drive self-creating, self-organizing, agile, hypertargeted content, and it will also present opportunities for greater security. Early adopters will create the new normal that makes tomorrow’s AI conversations as standard as today’s internet. It will differentiate the survivors of this latest disruptive force.