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Lately, it seems everyone is talking about Artificial Intelligence (AI). From golden promises of potential to fear of looming doom, we’re saturated in speculation. This article aims to provide context and shed some light on what AI is, highlight some types of AI being deployed in businesses today, and consider what the future may hold for AI in the workplace.
A growing number of businesses are eagerly seeking ways to reap the rewards of deploying AI. At the same time, many have concerns over the consequences of AI in the workplace, especially its potential to eliminate jobs.
A recent Pew Research Center survey finds that many Americans have low trust in AI in the workplace and many are even worried about AI taking their jobs and/or being applied to decisions they feel people should make. For instance, they oppose AI’s use in making final hiring decisions by a 71% - 7% margin, and a majority also opposes AI analysis being used in making firing decisions. Additionally, many workers do not support the idea of using AI to track workers’ movements while they are at work or keeping tabs on whether office workers are at their desks. However, AI has already been applied to these business use cases, some successfully, and others not so successfully.
In March 2023, technology firm OpenAI released a report that found at least 80% of the U.S. labor force could have at least 10% of their work-related tasks affected by the introduction of Generative Pre-training Transformer (GPT) technology, while another 19% of employees may see at least 50% of these work-related tasks impacted. While GPT potentially affects all wage levels, higher-income jobs potentially face the greatest exposure, concludes OpenAI.
Some of the current mistrust may be simply because AI is a newer technology that many people are unfamiliar with, and they aren’t clear how it’s going to be leveraged in the workplace, particularly its direct impact in their company. With this in mind let’s focus on a couple of different types of AI currently being implemented in many businesses, and assess what that might mean for the workplace of the future.
Let’s first look at how artificial intelligence is defined and then look at some of the different types of AI being used today.
Artificial Intelligence (AI)
The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Based on its dictionary definition alone, it’s easy to see why people might be worried about AI replacing people. Let’s dive a little deeper into a couple of the different types of AI already being applied in the workplace.
Artificial Intelligence and Machine Learning
Artificial Intelligence (AI) and Machine Learning (ML) are often used interchangeably, but ML is a subset of the broader category of AI. AI refers to the general ability of computers to emulate human thought and perform tasks in real-world environments, while ML refers to the technologies and algorithms that enable systems to identify patterns, make decisions, and improve themselves through experience and data.
Software developers enable computers to analyze data and solve problems — essentially, they create artificial intelligence systems — by applying tools such as:
Machine Learning (ML)
Deep learning
Neural networks
Natural Language Processing (NLP)
More About Machine Learning
ML is a pathway to AI. This subcategory of AI uses algorithms to automatically learn insights and recognize patterns from data, applying that learning to make increasingly better decisions. By analyzing and experimenting with ML, programmers explore how much they can improve the perception, cognition, and action of a computer system.
Deep learning, an advanced method of ML, goes a step further. Deep learning models use large neural networks—networks that function like a human brain to logically analyze data—to learn complex patterns and make predictions independent of human input.
Generative AI/ML
Generative AI (GenAI) is a form of AI that’s capable of generating text, images, or other media, using generative models. Generative AI is a class of algorithms that enable machines to create new content that closely resemble human-generated data. Unlike traditional AI models that rely on pre-existing data, Generative AI can generate original content based on patterns and insights learned from a training dataset.
For example, while ChatGPT falls under the umbrella of generative AI, it’s important to understand that generative AI encompasses a wider range of applications beyond just conversational interactions.
Examples of Generative AI in the Workplace
Content Creation
Many small businesses are currently leveraging generative AI for creating realistic images and writing creative marketing content and campaigns in a fraction of the time. Generative AI is already influencing the copywriting profession, with some commentators noting that AI can augment workflow efficiency, SEO, and grammatical consistency. Online writing assistant, Grammarly, already features generative AI-based assistance.
Virtual Reality and Augmented Reality
Generative AI is vital in developing Virtual Reality (VR), Augmented Reality (AR), and gaming applications. While VR refers to a simulated, immersive, interactive three-dimensional environment, AR is the integration of digital information and content into the user’s real-world environment in real time.
AR/VR also lends itself to effective workplace training applications. By generating realistic environments, characters, and interactive elements, generative AI enhances the immersive experience for training participants. VR training companies can leverage this technology to create dynamic, adaptive real-world environments and scenarios (e.g. safety training for the construction industry), while providing participants with unique, personalized experiences.
ZERORISK HR is working on deploying this type of technology for emotional intelligence and empathy training for workers.
Examples of AI/ML in the Workplace
One example of how AI can help businesses is in manufacturing and automating their business processes by applying data analytics and machine learning to applications such as identifying equipment errors or monitoring production machines to predict needed maintenance.
In the banking industry, data privacy and security are especially critical. Financial services leaders can keep customer data secure while increasing efficiencies using AI/ML in several ways:
Detecting and preventing fraud and cybersecurity attacks
Using biometrics and computer vision to authenticate user identities
Implementing chat bots to automate routine customer service interactions
Additional Business Use Cases for AI/ML
The range of potential use cases and applications for AI/ML is seemingly limitless. Examples include predictive analytics for forecasting, understanding customer behavior and market trends, Natural Language Processing (NLP) for chatbots for customer service, and image and video recognition for security and e-commerce.
Benefits of AI/ML
The benefits of machine learning are many, but the core benefits to businesses fall within the following categories:
Business efficiency: More production in less time.
Business quality: Consistent, reliable output.
Business innovation: Opens up new opportunities and markets.
Here’s some recent data on how AI is viewed by many business leaders. The “2023 AI and Machine Learning Research Report” from Rackspace Technology found that 72% of the 1,400-plus respondents said AI and ML are already part of their IT and business strategies. Some 69% of respondents described AI/ML as a high priority.
Those who have adopted AI report using it to improve existing processes (67%), predict business performance and industry trends (60%), and reduce risk (53%).
Additionally, American Express released its first Small Business Financial Confidence Report recently, which found that 41% of small business owners said they are currently prioritizing AI in assisting with business decisions. Nearly 39% of respondents who utilize AI said they use the tools to help save them time, 21% said they improve data security, and 20% credited AI with boosting the efficiency of their customer service. According to AmEx, among all businesses using AI, the top two applications of the tools were for customer service and marketing, at 19% and 14% respectively.
While many businesses are finding AI can result in improved efficiencies, quality, and innovation, nonetheless, there is mixed sentiment when it comes to AI’s impact in the workplace overall. A Goldman Sachs study in March found generative AI could replace and affect 300 million jobs around the world. An article from Challenger, Gray & Christmas reported that AI chatbot ChatGPT could replace at least 4.8 million American jobs.
Five years ago, billionaire entrepreneur Mark Cuban warned America of the impending “Job Terminator” known as AI, saying “Literally, who you work for, how you work, the type of work you do is going to be completely different than your parents within the next 10 to 15 years.”
While there are legitimate reasons to be concerned about the effects of a potentially “disruptive” technology like AI-GPT in the longer term, the projections for near-term widespread “displacement” of jobs or occupations will occur are less likely for those employees embracing AI-GPT tools as a complement to their existing skill sets - thus evolving their job scope and opportunities by creating new value-added skill sets.
The customer service representatives of today might become more of a customer behavior analyst tomorrow, relying on the data from machine learning to direct their focus on key customers or pressing business issues. Workers have successfully embraced similar technological challenges in the past, and they will most likely adapt to this latest occupational challenge.
In conclusion, generative AI and ML are two powerful branches of AI that have already significantly transformed numerous industries. While Generative AI focuses on creating new and original content, ML emphasizes learning from data and making predictions. Both technologies have many business use cases.
While some automation will certainly displace some workers, there are skills that AI just won’t be able to replace, such as the soft skills and emotional intelligence of human beings. Workers that focus on adapting their technological knowledge, and who also see the importance of those soft skills and emotional intelligence competencies, will be able to adjust to these technology changes more easily.