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For many years, the hype surrounding artificial intelligence (AI) overshadowed its real-world impact. More recently, AI is delivering on its much discussed promise. This shift has resulted from an exponential increase in both computing power and available data. Today, 40% of marketing and sales teams say that machine learning algorithms are critical to their success. And by 2030, PwC expects AI to contribute up to $15.7 trillion to the global economy.
However, our analysis is that businesses will need to more thoroughly understand how AI can drive real business impact to reach that lofty growth projection. For example, Gartner reports that only half of organizations currently utilize AI in some form, and even those deploying the technology do so on a limited scale. With better knowledge of how AI can be applied in the workplace, we believe that companies will rapidly understand how it can be leveraged to increase productivity, improve decision-making, and drive more effective customer engagement.
How AI Is Used in the Workplace
AI broadly refers to computer systems able to perform tasks that normally require human intelligence, including visual perception, speech recognition, and decision-making. Under this umbrella lie subset applications like natural language processing, machine vision, and deep learning.
These subsets support a variety of use cases within the office. Generally speaking, the application of AI can be sorted into three categories by the hierarchy of its effect:
These categories utilize different AI models, outlined below, which together encompass the large majority of AI uses across industries.
The Subsets of AI
Different AI models are better suited to achieve specific organizational outcomes. The following four AI fields currently have the greatest impact within the workplace. 1. Machine Learning: Machine learning (ML) algorithms learn from historical data and then extrapolate that data to answer new questions. This is employed in three distinct forms:
These four subsets often overlap and are combined to power a given product. As such, the definitions above are guidelines and not strict delineations. That’s especially true for NLP and computer vision, which require both machine learning and deep learning algorithms. For businesses, the application of the various AI subsets are broad and will continue to evolve.
Three AI Use Cases at the Organizational, Employee, and Customer Level
1. Predictive Maintenance for More Productive Operations
At the organizational level, machine learning-powered predictive analytics and machine vision identify system components at risk of imminent failure. This proactive AI application increases efficiency, saves money and time, and reduces required human intervention at scale.
Predictive maintenance is applicable to any large system, including assembly lines, factories, data centers, and even airplanes. It relies on deep neural networks that analyze large amounts of data to alert business leaders of potential failures before they can cause negative ripple effects across an entire system.
By minimizing downtime and operating costs, one study found that predictive maintenance could theoretically save between one and two percent of the value of an organization’s total sales.
2. Process Automation Tools for Happier Employees
At the employee level, AI can augment rather than eliminate the role of human workers. The most prominent example today is intelligent process automation, where manual processes are automated by a digital bot. This subset of automation fuses machine learning with robotic process automation for what’s known as intelligent or cognitive automation.
Previously, robotic process automation was limited to rule-based tasks like data entry. But advancements in machine learning enable cognitive automation bots to complete more complex tasks. These include everything from report generation and invoice handling to advanced analytics.
This results in a trickle down effect, where employee productivity and satisfaction increase and workers are freed from simple, yet stressful tasks. According to the Institute of Robotic Process Automation, the automation of these types of tasks can reduce the average 10% human error rate, improving productivity by over 1000% in some cases and providing measurable ROI.
3. Machine Learning and Natural Language Processing for More Effective Customer Engagement
For as much impact as AI has within an organization, its greatest benefits are with the customer experience. AI enables excellent customer experience management, removing high-friction moments in the customer journey through improved customer support, and creating new high points through advanced personalization.
AI can fill a variety of customer support roles. For example, it can create automated workflows that prioritize customer support tickets by levels of urgency, enabling staff to focus on the cases that most require their attention. AI-powered chatbots and virtual assistants can also provide customers with efficient, real-time self-service communication channels. A case study of this use of AI is BMW, which uses a customer support tool nicknamed DigitalGenius that is underpinned by NLP and deep learning. The tool achieved a 1.2 second average customer response time and a 99.5% accuracy rate.
Machine learning also assists with customer engagement transformation through both personalization and prediction services. Organizations like Netflix and Amazon use recommendation engines to suggest similar shows to watch or products to purchase. On a more sophisticated level, prices and promotions can be customized based on consumer demographic data, increasing conversion and retention rates. For businesses exploring customer engagement transformation, AI opens exciting doors to enhance the overall customer experience and increase customer retention.
The AI frontier offers opportunities to simultaneously improve both business operations and customer experience. In fact, the two are inextricably linked. The continual investment in AI technology will improve legacy business processes and enable companies to better serve their customers.
In today’s market, the average company currently utilizing AI has just four AI-driven projects deployed. Gartner expects that number to rise by over 300% within the next three years. However, reaching that point will require companies to find the talent and knowledge necessary for successful, large-scale AI deployments— a task that has so far proved challenging. The companies poised to fully realize AI’s vast potential are those who invest early in piloting the AI solutions that are most suited to their unique business needs.