Defining artificial intelligence (AI)

Artificial intelligence, or AI, is technology that can take on tasks that require understanding, problem-solving, and decision-making. This could include learning from experience, spotting patterns or making decisions based on what it’s seen before. Instead of requiring step-by-step instructions, AI can adjust as it goes.

One way this happens is through reinforcement learning. The system tries different approaches, sees what works and uses that feedback to get better over time. 

You might have seen this in action if you use an AI tool like ChatGPT or Gemini. If it’s ever asked you to rate a response or choose between two options, that’s reinforcement learning. It uses your feedback to help the system learn what to do next time.

This ability to adjust can help make routine workflow steps more manageable while leaving important decisions to humans.

Defining automation

In contrast with AI, automation is designed to complete specific tasks based on a fixed set of rules. Once it’s set up, it follows instructions exactly and doesn’t take input along the way.

The goal of automation is to reduce the need for manual work, especially for repetitive or time-consuming tasks. Because automation follows the same steps every time, it’s often used when consistency and reliability are important.

While it doesn’t adjust or learn from feedback like AI, automation can be useful for completing routine tasks efficiently and with fewer errors.

Automation vs. AI: Key differences employers should know

AI and automation often come up in the same conversation, and for good reason. Both are designed to streamline tasks and improve efficiency. Some of the ways they achieve this include:

• Supporting routine or repetitive tasks
• Reducing manual input
• Helping improve speed and consistency
• Taking on steps that don’t always require human judgment

While AI and automation often operate alongside each other, the difference between automation and AI can be seen largely in how they operate. In some cases, though, they don’t need to be separate. AI can actually work alongside automation to make it more flexible. Below, we outline some of their key differences:

Automation:

• Executes tasks based on a fixed set of rules
• Always follows the same process unless manually updated
• Does not learn or improve on its own
• Is often used when the task stays the same every time

AI:

• Uses data to learn and adjust its behavior over time
• Improves performance as it processes more information
• Adapts to changes in patterns, inputs or context
• Supports tasks that benefit from flexibility and decision-making
• Is often used in situations where outcomes may vary
• Builds on automation by adding a layer of adaptability

How AI enhances automation

AI can make automation more efficient, adaptable and flexible using machine learning (ML) to constantly update and refine its processes.

Let’s illustrate this with an example. Say you’re using automation to move candidates through your hiring process. When someone applies, they automatically get a confirmation email. If they meet certain criteria, they’re automatically moved to the next stage. Using automation can make the process more reliable and may save time.

But what happens if a strong candidate leaves a key field blank? Or applies late in the process but fits the role? An automated system might pass them over.

AI can learn from patterns in past hiring activity and use that context to make more flexible decisions. It might recognize that a missing field in an application doesn’t matter, or that a candidate who’s slightly outside the standard filter is still worth reviewing.

In general, AI can enhance automation by:

• Making decisions based on data rather than fixed rules
• Adjusting to changes in input or behavior
• Flagging exceptions or outliers that automation might miss
• Prioritizing tasks based on the likelihood of success or importance
• Reducing the need for manual review without removing human oversight

This flexibility is especially useful in tools like chatbots, where the back-and-forth isn’t always predictable.

Traditional chatbots vs. AI assistants

In the late ’90s and early 2000s, Microsoft integrated an early attempt at a digital assistant into its Office product called Clippy. Clippy, an animated paperclip, would pop up in Microsoft Word to offer help. This traditional chatbot did not use AI, which meant it followed a fixed script and offered generic suggestions based on specific user actions. 

Clippy’s ability to offer personalized help to users was limited. Today, modern AI-based assistants use conversational technology to contextualize information and respond naturally. Instead of sticking to a programmed script, these AI assistants adjust their responses based on user input.

For example, Indeed Talent Scout is an intelligent, conversational agent that integrates with select ATSs and across Indeed’s products. You can interact with it just like you would in a natural conversation, and it provides AI-driven support to help you hire more efficiently.

Through these simple, back-and-forth conversations, Talent Scout can:

• Give you a list of top applicants based on your employer match profile
• Show profiles for prospective candidates who match your job description, so you can invite them to apply*
• Align on the criteria used to match candidates with your job requirements and preferences
• Refine match criteria by learning from intake notes or a sample resume you provide
• Summarize how each candidate matches your job requirements
• Provide recommendations to optimize job content, including titles, descriptions and benefits, to attract the right candidates
• Generate personalized outreach messages at scale while you focus on interviewing
• Provide market and job-specific insights to help you stand out

*Only available with Premium Sponsored Jobs or Smart Sourcing subscription

This adaptability comes from how AI systems learn. Machine learning helps them improve based on past interactions. Reinforcement learning helps them refine responses over time using your feedback.

And to get the most out of AI-powered tools, understanding how to engage with them plays a big part.

How to engage with an AI assistant vs. a chatbot 

Traditional chatbot agents follow a script. If your input doesn’t match what they’re programmed to expect, the conversation can stall or loop. 

For example, imagine you ask a chatbot a question, and it provides a help center article link in response. However, it’s not quite the help you’re looking for. You state this, but the chatbot replies by asking what other help you need. Since you didn’t get the help you needed from the help center article, you’re stuck, as the chatbot can’t provide a more specific answer that addresses your needs. 

Unlike chatbots, AI assistants, such as Talent Scout, are built to adapt. They use what you type, learn from the exchange, and adjust as they go. But to produce quality outputs, the information input needs to be quality as well. If you give AI incomplete or incorrect information, the AI tool will likely provide a flawed result. 

Here are a few ways to interact with an AI assistant:

• Share key information. For example, if you’re asking it to narrow down a list of candidates, you’ll need to share the job description, candidate requirements and any must-haves like certifications you want it to look for.
• Provide clear requests and feedback. This may require you to confirm that the initial output is correct or clarify where you feel it missed something. 
• Similar to a conversation to get the result you’re looking for, you may need to provide further context as you analyze the output. For example, you may ask it to narrow down a list of candidates further by looking for those with experience working in a specific environment or on a specific shift. 

Key benefits of AI in recruitment

When asked what benefits they expected from using AI, 72% of hiring professionals said a reduction in time-to-hire. While results may vary, AI-powered tools can help teams move faster by taking on time-consuming steps in the hiring process.

Hiring teams using AI-powered systems may see benefits such as:

• A reduction in time spent on repetitive tasks like sourcing, screening and scheduling
• More efficient work and increased output, allowing employees to prioritize human-centric tasks 
• Lower overall hiring costs through decreased manual effort by saving time on tasks like screening and scheduling
• Faster resume review
• Transparent and understandable insights to help you make informed hiring decisions

AI-powered tools don’t replace recruiters. Instead, they free up time for conversations, assessments and final hiring decisions that rely on human judgment.

Indeed’s AI recruiting tools you should know about

AI-powered recruitment tools are designed to make hiring more efficient while keeping people in control of the process. At Indeed, several recruiting tools already use AI to support hiring teams. 

These include:

• Indeed Talent Scout, which is an intelligent, conversational agent that helps you build your applicant shortlist, connect with hard-to-find talent and optimize your job postings, saving you time to focus on more important tasks.
• AI Job Description Generator, which helps create clear, tailored job postings in seconds
• Smart Sourcing, which has three distinct AI-powered features: job-based matched candidates, AI-generated outreach messages and candidate highlights

These tools support human decision-making, not replace it. You stay in control of the process, from editing job descriptions to reviewing candidates and sending final messages.

To learn more about how these features work, you can check out our guide on How Indeed uses AI to enhance hiring.

Addressing common concerns about AI and automation

It’s natural for employers to have questions about AI and automation, especially when it comes to fairness, transparency and control. These tools can be helpful but only when used thoughtfully and with clear boundaries.

Bias and lack of transparency

Employers may worry that AI systems can reflect bias from the data they’re trained on. Without transparency, it’s harder to catch issues or understand why certain candidates did or didn’t make it through a filter. 

Involving human recruiters can potentially help reduce bias in resume screening by adding oversight. They can help identify patterns that may seem unfair or inconsistent and make sure that decisions are grounded in context, not just data.

Job displacement
Some might be concerned that AI will replace human judgment. But these tools aren’t built to make final decisions. They assist with repetitive tasks and offer suggestions, while hiring teams continue to guide the process and make the decisions that matter. In fact, according to Indeed Hiring Lab, out of more than 2,800 work skills assessed by Indeed, none were deemed “very likely” to be replaced by GenAI. 

Compliance
AI tools often raise questions about compliance, especially as data laws evolve. Employers may worry about whether these systems meet legal standards or how to ensure that candidate data is handled properly. Staying on top of regulations and using well-governed tools can help reduce that risk.

Data privacy
Hiring teams may be concerned about how AI systems collect and use personal data, especially when it comes to sensitive candidate information. Without clear safeguards, there’s a risk of mishandling or misuse. Strong privacy practices help prevent that and build trust with job seekers.

With these concerns in mind, Indeed stays committed to using AI responsibly and developing technology that blends AI with human oversight.

Responsible and trustworthy AI

When hiring teams have concerns about bias and privacy, the next question is often about trust. Can the system be relied on? Can your team feel confident using AI to support hiring decisions?

Trusting artificial intelligence depends on more than performance. It also requires users to understand how it works and feel confident in the logic behind its suggestions.

Algorithms can also carry over bias from the data they’re trained on. If left unchecked, those patterns can influence how candidates are evaluated. It helps to raise ethical concerns early and take a closer look at how tools are tested and applied.

One way to build trust in AI is to involve a broad set of members in how AI tools are chosen and used in hiring. Recruiters, legal teams, analysts and hiring managers may each spot different risks or ask different questions. 

You can play a role too by taking part in tool reviews and sharing your perspective.

What are some future trends in AI recruiting?

As AI tools continue to develop, new use cases in AI and recruiting emerge. Your team might already be looking at how it can support parts of your recruitment process using AI-powered tools.

Generative AI, in particular, is starting to play a larger role in areas like writing job descriptions, engaging candidates and reviewing applications.

Here are a few current uses of AI in recruiting that may continue to shape future trends:

• Drafting or customizing job descriptions based on market trends and role details
• Suggesting tailored outreach messages for different candidate profiles
• Reviewing applications or resumes to surface candidates who match key criteria
• Providing candidate summaries or insights to help with early evaluation
• Answering candidate questions in real time through AI-powered chat
• Helping predict which roles may take longer to fill or where bottlenecks might occur

While adoption will vary by team and organization, AI continues to play a growing role in many hiring processes.

Seeing where AI and automation fit in your hiring process

Automation has long played a role in recruiting by helping teams handle repetitive tasks more consistently. AI builds on that foundation by offering adaptability and support for decisions that aren’t always clear-cut. 

When used thoughtfully, it can reduce time spent on repetitive work without removing people from the process. The key is knowing where these tools fit and staying involved as they develop. That way, you can use them to support your hiring goals without losing focus on what still needs human oversight.

FAQs about AI and automation

What are Indeed’s responsible AI principles?

Indeed’s Responsible AI principles outline how Indeed designs and uses AI tools to support fairness, transparency and human oversight. These were developed by our Responsible AI team and are available as a complete set of principles on our site.

How does Indeed ensure data security?

Indeed follows a Zero Trust security philosophy, which includes limiting access to production data, monitoring corporate and production environments, and providing ongoing security training for our Research and Development teams. You can learn more at the Indeed Trust Center. 

How can I identify trustworthy artificial intelligence?

It can be difficult to identify fully trustworthy AI upfront. While large language models are powerful, they can occasionally return outputs that are incomplete or inaccurate. 

The best approach may be to use good practices rather than take AI outputs at face value. Reviewing outputs for accuracy and completeness before acting on them can be a good way to stay in control of decisions and build trust in how the technology supports your team.

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