Explore Business Uses and Risks of Generative AI

Learning Objectives

After completing this unit, you’ll be able to:

• Describe common business uses for generative artificial intelligence (gen AI).
  
• Explain how generative AI works.
  
• Identify risks of using gen AI.
  

Explore How Gen AI Is Used Across Business Tasks

Have you ever had to create a social-media post for work at the last minute? Did you ever struggle to tailor a presentation for different audiences? Maybe you’re looking for a quicker way to handle customer questions without being glued to your inbox. In moments when the pressure is on or deadlines are fast approaching, many professionals are turning to generative AI to help them work faster, better, or under less stressful circumstances.

Let’s explore some more workplace examples.

Scenario 1: Create Last-Minute Marketing Materials

Jordan runs a boutique soap company and just learned about a local street fair happening this weekend. With only a day to prepare, Jordan needs to quickly create attention-grabbing promotional materials. Instead of spending hours brainstorming or hiring a freelance copywriter, Jordan turns to her AI tool. Within minutes, the tool suggests catchy headlines, a product description tailored to the audience, and a short email blast. Jordan tweaks a few phrases and is ready to launch the campaign within an hour.

Scenario 2: Streamline HR Interviews

Alex, an HR manager for a growing tech startup, is hiring a new project coordinator. Typically, this would mean digging through old interview questions and rewriting them to fit the new job description. This time, Alex uses a generative AI tool to develop a first draft of customized behavioral questions based on the specific skills listed in the job announcement. Within minutes, Alex has a full set of relevant, structured questions that he can edit and use to guide the interview and evaluate candidates.

Scenario 3: Scale Customer Support

Sam manages a midsize ecommerce business and wants to reduce response times for customer questions. Previously, a team of three handled email and calls during working hours. Currently, Sam integrates an AI chatbot into the company’s website. The bot answers basic questions 24/7 and hands off more complex ones to human representatives. Customers get quicker responses, and the support team is freed up to spend time on more serious issues.

These examples show how generative AI is becoming part of daily business operations. But what exactly is generative AI?

Understand Generative AI

At its core, generative AI is sophisticated software running on powerful servers. It uses large amounts of data and advanced math to generate text, images, code, or other content in response to prompts. The “brain” of gen AI is a complex mathematical model, often a large neural network.

It can seem intelligent or personal, especially when it calls you by name or compliments your ideas, but that’s part of its programming—not evidence of self-awareness. It’s not thinking; it’s predicting the next-best word or phrase based on patterns it has previously learned.

When interacting with gen AI, it’s important to remember that you’re working with a software tool just like email, spreadsheets, or search engines. Its output can be useful, but it still needs a human mind to review and guide it because, unlike the other software tools, AI can sometimes hallucinate or make things up. That’s why the more you understand how it works, the easier it is to use its response without being misled.

Evaluate the Tradeoff Between Speed and Oversight

In most cases, AI makes things faster and often more efficient. It gives businesses the ability to do more with less. But speed and efficiency come with tradeoffs. If you’re producing content quickly with AI but not reviewing, you’re likely introducing errors. These errors often fall into two categories: hallucinations and biases.

Hallucinations happen when AI generates content that sounds correct but isn’t grounded in fact. It may invent fake quotes, make up legal cases, or create realistic-sounding statistics that don’t exist. This happens because generative AI generates text based on patterns, not facts. As described in this article, often the results of hallucinations can have real consequences. For example, one airline’s chatbot gave customers false refund information, which ended up costing the company in court.

Bias is also a hidden risk. AI systems are trained on massive amounts of data. With much of that data coming from the internet, bias can be common. Because AI seems neutral, biased output can appear to be more objective than it‌ is.

Unchecked, hallucinations and bias can harm a business in the following ways:

• Incorrect information may reach customers or stakeholders.
  
• Biases can shape hiring or decision-making.
  
• Trust can erode if customers receive generic, copy-paste responses to inquiries.
  
• Legal and compliance risks can grow if AI says something it shouldn't.
  

AI doesn’t know your values, context, or mission. It doesn’t understand the impact of its words—it just predicts what to say next based on patterns. If no human steps in to guide the output, it’s like leaving a plane on autopilot with no one in the cockpit. It can stay on course for a while, but who is there if something unexpected happens?

Explore the Reasons We Use AI

We don’t just use generative AI because it’s fast. We use it because it helps us fill in gaps, especially in the workplace. When we’re unsure how to say something, short on time, or trying to sound more professional, gen AI can help. But we’re most vulnerable when we’re rushed, uncertain, or eager to move on quickly, and it tends to be those times that we take whatever the tool gives us without question.

The table lists some of the feelings that compel us to reach for gen AI, and how it typically responds.

These are real work pressures, and AI can help solve them quickly. But when a tool steps in to fill emotional or mental gaps, it can also start to shape your message, tone, and judgment. That’s why it still needs guidance from someone who knows the bigger picture: not just how to say something, but when it matters, how it’s perceived, and what it means for your business. Without that oversight, AI can unintentionally introduce bias or harmful language, especially if it’s drawing from data that leaves people out or reflects negative behavior. To learn more about how to spot and reduce bias and toxicity in AI, check out Responsible Creation of Artificial Intelligence.

Utilize Tools That Make AI Decisions Easier to Understand

Some businesses are already beginning to build human oversight into their generative AI tools. For example, Agentforce drafts content and suggests actions, then places drafts in front of a human to make the final decision. Open-source tools like Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) help businesses see why AI made a certain decision or provided a particular output.

• LIME offers a simplified explanation by building a temporary, human readable model around a single decision. LIME’s explanation process is like zooming in on one result and saying, “Here are the factors that mattered most.”
  
• SHAP takes a more rigorous approach by looking at all the inputs and calculating how much each one contributed to the final decision. It’s like saying, “This decision was mostly driven by this one detail, and here’s how much it mattered.”
  

These tools make AI easier to understand, and they enhance human oversight by providing the clarity needed to question results and override them when needed. This clarity is a security measure because more transparency translates to earlier detection, better judgment, and fewer chances for errors or misuse to slip through unnoticed.

Understand the Risks of Using AI with Sensitive Data

While this module focuses on secure use of gen AI in supporting everyday tasks across an organization, it’s important to call out a different level of care when AI is used in work that involves sensitive data (for example, customer records, internal strategies, security documents). If you’re part of a risk management team, work in a security operations center, or handle sensitive or confidential data, your use of AI carries additional cybersecurity risks that go beyond typical use. In these cases, it’s important to understand how AI can expose critical systems or information if not handled carefully.

All AI use should follow your organization’s cybersecurity policies. But work involving sensitive or security-related content requires heightened awareness and oversight. For more on this, check out Artificial Intelligence and Cybersecurity for insights on AI security and trustworthiness. You can also read the World Economic Forum’s report, Artificial Intelligence and Cybersecurity: Balancing Risks and Rewards for concrete steps on managing risk and aligning AI use with your organization's cybersecurity policies.

Sum It Up

In this unit, you explored how generative AI shows up in everyday business tasks, what it can and can’t do, and why speed without oversight can introduce risks. You also identified key skills and actions that help keep human judgment involved in AI-driven workflows. In the next unit, explore what human oversight looks like and what it means to keep a human-in-the-loop when working with AI workflows.

Resources

• Trailhead: Get Started with Artificial Intelligence
• Trailhead: Explore Generative AI Tools
• External Link: Datacamp: Explainable AI–Understanding and Trusting Machine Learning Models