Generative AI is a powerful automation tool that can make human jobs more creative and fulfilling. It can write code, design products, create marketing content and strategies, streamline operations, analyze legal documents, and provide customer service through chatbots.
With these tools, humans can be freed up to focus on high-value tasks like creativity and problem-solving, as well as collaboration with others.
1. Generative AI
Generative AI uses a machine learning model to learn from the data it collects and then generates new content. Depending on the model, it may sample from a latent space or through a generator network to produce output. The generated content can be further refined and tweaked for specific applications or tasks.
The technology is transforming content creation and delivery. Marketing organizations can save time and amp up creativity by using generative AI tools to draft marketing copy, visuals and other content that is personalized for each customer. Generative AI can also power chatbots and virtual assistants with customized responses based on the context of each interaction. Automation, data analysis, and customer service have all been transformed through the use of artificial intelligence. In fact, more businesses are using AI to streamline operations, enhance customer experiences, and drive innovation.
In addition, generative AI can help create natural-sounding speech and audio for voice-enabled AI chatbots and digital assistants, as well as original music that mimics the structure and sound of professional compositions. It can also aid software developers by generating code snippets, autocompleting text and translating between programming languages.
Despite the potential to improve productivity and efficiency, generative AI models can be complex to train and require substantial resources to operate. The availability of high-quality, unbiased data is critical to the success of these models. Without it, the models might produce results that are imaginary, inaccurate or offensive.
Moreover, generative AI can harm workers beyond their livelihood risks, including through violations of intellectual property, surveillance and monitoring with little or no consent, algorithmic decision making and management, and discrimination and bias. Workers should be able to participate in the design and development of these systems, as well as the oversight and governance structures that govern them. This is one of the most important ways to build worker voice in the future of work.
2. Deep Learning
Artificial intelligence is improving the way we work by delivering more accurate, more effective results. But the question remains: will this technology eliminate jobs altogether? The answer is no, AI is designed to act as a partner and collaborator in the workplace, enhancing employee performance and boosting productivity. This creates new opportunities for jobs like data analysis, machine learning and algorithm understanding. It also creates more demand for skilled positions like data scientists and information security analysts.
The technology behind deep learning is reshaping how machines “see” the world. This includes computer vision, which is used to unlock your phone with just a glance, or in healthcare, where it can identify tumors on medical scans and predict patient outcomes with uncanny accuracy. Similarly, it is the force behind autonomous cars, identifying objects on the road and navigating crowded streets. It’s also powering fraud detection, helping financial institutions spot and prevent fraudulent transactions faster.
It can help with a wide range of tasks, including text generation: generative models like ChatGPT can generate human-like conversations that mimic proper grammar and style. And in customer service, it is empowering businesses to personalize their customer experience and offer targeted offers.
In other industries, AI is accelerating decision-making and driving operational efficiency. In logistics, it’s optimizing delivery routes to save millions of gallons of fuel each year. In energy, predictive maintenance powered by AI is cutting downtime and reducing maintenance costs. And in retail, it’s enabling online platforms to serve up the products you want before you even know you need them.
While these uses of AI are making significant contributions, there’s a growing concern over the ethical accountability of this technology. The large amount of data required to train AI models raises concerns about how this information is collected and processed. It can also lead to biased algorithms that perpetuate inequities, but companies using this technology are addressing these concerns by considering and measuring fair outcomes for different groups and incorporating fairness metrics into the algorithm design process.
3. Robotic Process Automation
While the word “robot” in Robotic Process Automation (RPA) may bring to mind images of physical machines, these systems are not meant to replace humans. Instead, they help automate repetitive manual computer or business process tasks. RPA software records the steps required to perform a task and then replicates those steps without human intervention.
For example, when employees have to move data from one system to another, manually copy contact information from a spreadsheet into a CRM or populate a form with the results of an analysis, robotic process automation can record these tasks and then execute them in parallel, freeing up employee time for higher-value work. It can also automate processes end-to-end and provide a robust audit trail for each step in the process, which is often a critical requirement for meeting regulatory compliance standards.
These automated systems can be more accurate than human workers and are less prone to error. The ability to run processes in parallel means that they can perform tasks at a much faster pace than human workers and eliminate bottlenecks in the workflow. This allows businesses to complete more work and improve productivity.
However, automation and AI technology do have their limitations. Some of these are technical, such as the need for massive amounts of training data and the difficulty of “generalizing” algorithms across use cases. New innovations are starting to address these challenges.
Ultimately, the future of work is about collaborating with intelligent machines that can assist us with tasks and allow our minds to focus on creative, strategic, and complex work. This requires a different type of workforce. In addition to traditional skills like coding and data analytics, there will be a growing need for people who understand how AI tools fit into business strategies and how they can be used to drive innovation and growth.
4. Predictive Analytics
Predictive analytics involves using data analysis to predict future events or outcomes. It can be used in a wide range of industries and business processes, such as marketing, credit risk assessment, insurance, banking, fraud detection, healthcare, manufacturing, government operations including law enforcement, and more.
The key advantage of predictive analytics is that it allows companies to take proactive steps to avoid costly or dangerous situations. For example, businesses can use predictive models to identify customer churn or anticipate seasonal sales trends so they can be prepared for spikes in demand. The ability to identify possible friction points or issues before they occur also allows businesses to take action to minimize them, providing customers with more seamless experiences and increasing loyalty and retention.
In the energy sector, predictive analytics can be used to model resource requirements and identify equipment failures or maintenance needs before they happen, minimizing downtime and associated costs. For example, Salt River Project uses predictive analytics to analyze sensor data to determine when power-generating turbines need maintenance, preventing downtime and optimizing efficiency and safety.
Predictive analytics is a critical tool for HR professionals, who can use it to predict attrition rates and enable proactive hiring and training initiatives that are far less costly than reacting to employee departures. These tools can also be used to analyze employee performance and engagement levels, identifying issues before they lead to problems or conflicts. In addition, predictive analytics can be used to detect early signs of a potentially life-threatening allergic reaction like anaphylaxis and automatically trigger an injection of epinephrine before the individual has a full-blown reaction. This technology can save lives in a matter of seconds, compared to the minutes it would take for someone to administer a shot manually.
5. Artificial Intelligence as a Service
Artificial intelligence is becoming integral to modern work, enhancing productivity and efficiency in multiple industries. In logistics and warehousing, AI-powered automation handles tedious tasks such as sorting, packing and inventory management, eliminating manual processes and reducing costs. In customer service, AI can handle routine questions and free up human resources to focus on more complex issues. AI also improves decision-making and analytics in many sectors, accelerating processes and increasing accuracy. In addition, the integration of AI into the workplace enhances the quality of the office environment by enabling workers to collaborate more effectively.
In the long run, the adoption of AI could lead to job displacement in some roles and new opportunities in others. However, the impact of this transformation is dependent on how quickly companies embrace the technology and adapt their business models to take advantage of its capabilities. As with previous high-impact innovations such as power looms, steam engines and electricity, the direction of technological change depends on the decisions we make about how to harness its power.
The growth of accessible AI tools has the potential to upend dozens of industries and affect jobs typically considered immune to automation. Generative AI, for example, is capable of writing content and creating visual outputs that would previously require human labor with years of training. But the use of this type of AI comes with ethical concerns about data collection and its impact on consumers.
In order to redirect the trajectory of AI away from its anti-labor tendencies, a broad coalition must recognize that the technology is moving in a harmful direction. The same types of organizations that pushed tech giants like Apple to reconsider their anti-labor practices in the past—labor groups, government regulation and civil society—will play a critical role in pushing for a fundamental course correction.
