With plenty of hype around artificial intelligence and new advances to improve the technology, it’s important to understand what’s fact and fiction. Technology advancements, including AI, have become a mainstay for employees looking to streamline their day-to-day work. In fact, more than 50% of workers trust AI more than they trust a human HR professional, according to our survey about how AI empowers the employee journey. With this mass trust and dependence on AI platforms, everyone must understand the basic facts.
An AI agent is an autonomous system designed to gather data, make decisions, and execute tasks to achieve predefined goals. It adapts to new information, learns over time, and can manage a wide range of tasks, from simple repetitive actions to complex problem-solving.
To build and deploy AI agents within a technology stack that works for your company’s needs, you’ll have to personally overcome AI myths you’ve heard. Then, continue debunking these AI myths among your stakeholders—especially employees and customers. Your employees will more willingly use the technology when they know it’ll help them complete tasks more efficiently and empower them to do more without replacing them. Customers will accept AI use when it’s accompanied by faster, more accurate service and the knowledge that their data is protected.
Here are eight AI myths debunked to increase your knowledge of how the technology works:
While AI agents can respond to complex queries, make decisions, and take action independently, they are still dependent on data to form any understanding. Therefore, AI cannot think like humans.
Human thoughts and decisions are based on a general awareness combined with past experiences. These are influenced by feelings, intuition, and common sense to help us make decisions. All of this develops throughout our lifetime and allows us to reflect and take the time to make an informed choice. For example, in marketing, a human worker can discover an emotional connection with current events to make your brand stand out and go viral, while an AI marketer can predict market trends based on current events.
On the other hand, AI uses the data it was trained on to make rapid decisions based on patterns identified in its data analysis. Its choices aren’t influenced by emotion and delivery.
For businesses, this means you can rely on a well-trained AI worker to analyze data and provide solution-based options for improvement based on their findings. However, human workers are still necessary for a true emotional connection and complex or creative problem-solving.
Since AI is trained to rely on data to operate, if that data is biased, then the AI platform itself will also exhibit bias. A great example of this is historical hiring data. Historically, many men have held leadership positions, so AI trained on this information analyzing resumes for a higher position would favor men.
Even with unbiased data, an AI algorithm can also introduce biases. Usually, algorithmic biases appear through human choices in the design process or during the developmental process. For example, if an AI algorithm is designed to determine loan lending risk and developers include features that crawl social network connections, the system could inadvertently discriminate against individuals from certain demographics. Ensure you train your technology to prioritize the ethics of AI in CX to prevent bias in all places, especially when working with customers.
To prevent bias from infiltrating your AI systems, be sure to monitor and reevaluate the technology for biased information regularly. This involves scheduling a regular audit of your data and algorithms to ensure up-to-date, clear, reputable, and bias-free information. Include user feedback and implement any corrections regularly. ServiceNow is committed to the development of responsible AI that is safe, unbiased, and secure for people and businesses.
One of the most common misconceptions about AI is that it will completely erase the need for human workers in many industries. While the technology will undoubtedly transform the job market, it’ll likely change the types of tasks assigned to human workers and the way they do their jobs instead of leading to mass layoffs.
Many AI platforms help automate simple tasks, which allows human workers time to be more creative and work on escalated tasks. In the customer service industry, this means human workers will tackle complex issues and deliver empathy, while customers can receive rapid service for basic needs like reporting missing items in an order.
AI also helps create new jobs, specifically in technological roles. While jobs focused on more monotonous tasks, like data entry and customer intake, will become automated, new roles focused specifically on AI will open. Examples include AI training and development, data science, and AI maintenance. Therefore, workers should focus on reskilling or upskilling to remain competitive in the job market as AI expands.
There is also the benefit of putting AI to work for people to boost capabilities. While AI processes and AI agents handle tedious and time-consuming tasks, human workers have more bandwidth to focus on strategy and creative tasks.
We already know that an AI system is limited by the data you train it on. Therefore, its knowledge is also limited by that data, meaning AI is not all-knowing since it can’t access information outside of that specific dataset. In fact, if there are inaccuracies in the data, then AI can even make mistakes and provide false information.
For example, online information found on search engines relies on vast amounts of text data used by large language models (LLMs). If false information is presented as factual and repeated multiple times, AI will conclude the information is correct and present it as fact. This can lead to the mass spread of misinformation in areas of consequential importance like health, law, politics, and finance.
Whether you’re working with one of the biggest AI companies or building your own system, it’s important to use AI responsibly and be aware of its potential limitations.
The truth about AI is that it’s helpful to various industries beyond those focused specifically on tech. One specific example is the popularity of agentic AI, which deploys AI Agents that can not only assist by providing information but also take action, interact with other AI Agents and human employees, and make informed decisions.
Some examples of non-tech-based industries where AI is helpful include:
- Healthcare: AI is used throughout the healthcare industry to provide more accurate diagnoses, deliver more personalized patient care, and develop new drugs and treatment options. An example of this is AI being used in medical imaging, like mammograms to detect breast cancer or MRIs to analyze brain tumors and other neurological conditions. Using this technology leads to increased accuracy in diagnosis, earlier detection, and the potential for more personalized treatment plans.
- Manufacturing: AI helps scale manufacturing production processes, improve quality control, and predict failures in equipment and systems. For example, AI provides predictive maintenance for manufacturing systems by monitoring data input from multiple sensors for anomalies. Predictive maintenance will save manufacturing companies time and money versus traditional preventative or reactive maintenance measures, which can shut down lines for long periods.
- Finance: AI in finance detects fraud sooner, provides personalized financial advice, manages risk, and recommends investments. When detecting fraud, AI machine learning analyzes bank transactions in real time, allowing it to instantaneously notice unusual spending patterns, transactions made in unfamiliar locations, multiple rapid transactions, and spending outside usual patterns. This benefits banks and consumers by preventing them from facing losses based on fraud, increasing security, and receiving more accurate fraud flags.
- Retail: AI creates a more personalized customer experience, monitors pricing trends, and manages store inventory. For example, AI algorithms with machine learning models will analyze customer data to understand an individual customer's preferences and habits. The model can then display and send tailored recommendations to these individuals, leading to increased sales and revenue, improved customer engagement and loyalty, and more efficient inventory management.
- Agriculture: AI can help improve crop yields by sharing farming data, optimizing irrigation systems, and monitoring the health of plants and animals. Farmers can input data from drone images, sensors, and weather patterns to identify areas of their land that need immediate attention to prevent diseased crops and weed infestation and optimize irrigation or fertilization. This will lead to increased crop yields, reduced and sustainable use of resources, reduced labor costs, and earlier detection of crop issues.
The potential benefits of AI are massive and continue to grow as the technology continues to develop, regardless of the industry you’re working in and looking to optimize.
While developing new AI systems and algorithms requires people with highly skilled technical expertise, actually using AI tools to turn innovation into action can be done by virtually anyone. Actually, the average person probably uses AI daily without even knowing it.
Some of the everyday uses of AI include:
- Spelling and grammar checkers to improve client communication
- Voice assistants (i.e., Siri or Alexa) for quick answers to questions
- Facial recognition for added security measures
- Personalized recommendations on streaming services
For career-based uses, basic AI tools and platforms being developed by companies are often user-friendly with intuitive interfaces. Companies should offer AI-specific training to current and new employees when used at work to ensure the technology is within the business’ standards.
One fact about AI is that it can significantly boost productivity, but it cannot guarantee increased productivity. AI is a powerful tool, but unless you use it effectively, it won’t automatically lead to a more productive workflow.
AI can be used to increase productivity via optimized efficiencies like supply chain optimization, logistical route planning, and managing energy consumption in buildings. However, AI can also deter productivity when, for example, it creates a “black box” situation that no human can understand. This leads to mistrust and a need to double-check the work AI is outputting, which wastes time.
Some key tips for effective implementation of AI tools include:
- Utilize proper planning and execution by taking the time to understand where AI is helpful.
- Get stakeholder buy-in from top to bottom, including executives, employees, customers, and investors.
- Set realistic and data-backed expectations of how AI can help your business improve.
- Deploy training to ensure everyone in your company knows how to use AI.
- Train it on quality data to prevent misinformation, inaccuracies, and bias
- Provide human oversight of all AI systems to ensure the technology is accurate and effective
In short, it’s all about how you use AI tools and how they integrate into your company to lead to boosted productivity.
The truth about the cost of AI is that it depends on your approach to implementing it into your business. Developing an AI model from scratch and continuing to test new algorithms to stay on the cutting edge of advancements can be costly. However, a more economical approach involves using existing tools or working with an AI company to develop a custom interface based on their platform.
Partnering with an AI provider instead of building your own platform can help you become accustomed to the technology and find the best options for your specific needs. With the widespread availability of newer AI tools and platforms, these providers have made the technology more accessible and affordable.
As you begin implementing different AI tools into your business, it’s important to dispel these common AI myths to avoid technology misuse and ensure employee buy-in. Investing in a single platform that allows you to bring the power of AI and AI agents to every corner of your business is the most effective way to see the full benefits of your investment and ensure effectiveness.
Interested in learning more about how to make AI work for your company? Schedule a ServiceNow demo or contact us today to learn more.
Continue reading below to answer more questions about AI myths:
Whether you, your stakeholders, or your customers believe common AI myths, they can hurt your business if not debunked. To experience the transformative power of AI, you need to be transparent with all parties involved in your company—internally and externally—by explaining in non-technical terms how you’re using AI, how the technology works, and why it’s beneficial. In these explanations, you should also use the opportunity to dispel common AI myths and share interesting facts about AI.
While it’s difficult to identify the biggest problem, many connected challenges require attention to ensure that AI is being developed and used responsibly. These challenges include:
- Preventing bias and fairness
- Providing transparency and explainability
- Preventing misinformation and manipulation
- Providing realistic job market and skills impacts
- Preventing data and privacy leaks
- Providing accessibility and equity
- Preventing lack of diversity
- Providing controls to ensure safety
To get started with AI, uncover which current AI trends would be helpful and create a positive change in your company. Then, consult with a trusted AI company, like ServiceNow, to meet your specific needs.