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:
Organizational: At a high level, AI informs decisions, helps with demand forecasting, improves resource management and planning, and optimizes business processes.
Employee Productivity and Experience: AI eliminates repetitive tasks that derail employee productivity, while providing insights that can actually increase worker efficiencies. AI can also serve as a knowledge base for employee onboarding, workforce training, help desk tickets, and FAQs.
: AI’s largest impact is arguably in customer engagement transformation. It can improve and automate customer support and build customer relationships while providing personalization and predictive services. It can also help build data-informed customer journey maps through real-time customer sentiment analysis.
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:
Supervised Learning: In this training method, the AI agent learns from known data sets that include both an input and output. For example, an algorithm trained with height and weight data of a human population could predict one variable if given the other.
Unsupervised Learning: This is when an algorithm is given only one input and instead learns by identifying patterns within the data. For example, an advertising platform could use unsupervised learning algorithms to segment the U.S. population into smaller groups with similar demographics and interests.
Reinforcement Learning: This application allows an algorithm to achieve a complex objective over many steps. Correct moves are reinforced with a reward, while incorrect ones are penalized. This type of learning is behind IBM Watson’s defeat of former Jeopardy! Champions and other instances where AI has defeated a human in a complex game, like chess.
2. Deep Learning: Deep learning is related to machine learning but uses neural networks with multiple layers to perform human-like tasks without human involvement. It combines both supervised and unsupervised learning to train an AI agent and is the primary technology behind self-driving cars.
3. Natural Language Processing (NLP): NLP is the ability of an AI agent to understand and process human language. Underpinned by machine learning algorithms, NLP powers virtual assistant services like Siri or Alexa.
4. Computer Vision: Computer vision is a subset of AI that focuses on enabling computers or machines to analyze and understand an image. This technology is behind red light cameras, quality assurance solutions, and other visual recognition tools.
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 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.