Power to the People: The Role of Humans in AI

Editor’s Note: This post was originally published in March 2024. We’ve updated the content to reflect the latest information and best practices so you can stay up to date with the most relevant insights on the topic.

Artificial intelligence (AI) is transforming modern business in ways we never thought possible just a few short years ago. But as new, AI-powered technologies and LLM-based AI agents make big promises, touting benefits such as greater operational efficiency, competitive advantage, personalized customer experiences, and revenue growth, many businesses are realizing that technology alone is not enough to succeed in this AI-driven era. Organizations must also consider the role of humans in AI, including the valuable role human expertise plays in safeguarding the integrity and reliability of AI and guiding its ethical use to drive business outcomes. 

What Role Do Humans Play in AI?

As AI tools and technologies integrate into modern business processes, it’s important to understand the distinct role AI and AI agents play versus the roles only humans can fill. While some doomsayers claim that AI can (and will) take over any task a human can perform, the reality is not quite that bleak.

Arguably, artificial intelligence is assuming responsibility for some of the work previously owned by people. But similar to how machines replaced manual labor during the Industrial Revolution, AI is replacing humans in areas where speed and efficiency matter. 

Think about it. AI has the capacity to work faster than a human. And it doesn’t tire because, well, it’s a machine. That’s why we’re seeing AI take over straightforward, repetitive tasks that are time-consuming for humans. A perfect example is entity resolution. 

Entity resolution is the process businesses use to integrate and match data across disparate systems and silos in order to create a “golden record” that represents the best version of a critical business entity such as a customer, supplier, product, or location. Resolving entities across a company’s ever-growing, ever-evolving data set would take a person, a team of people, or even traditional technology such as rules-based master data management (MDM) an inordinate amount of time. And every time the data changed or new data entered the systems, the process would reboot. But when AI is put to the task, entity resolution becomes exponentially more efficient. Organizations can employ advanced AI and machine learning-driven matching models to quickly and easily resolve the entities and create golden records that represent the best version of the data. And when data changes or new data enters the system, the models can quickly and easily adapt, without the need to rewrite or redefine the rules. As a result, companies see results in days or weeks, not months or years. 

In this example, AI assumes the heavy lift required to compare and resolve data sets. And as new data enters the system organically or through data enrichment, AI can quickly add it to the mix and resolve it against existing entities as well. It is essentially replacing a human or outdated technology, but in the best possible way, making a repetitive, time-consuming task more efficient. But because AI and AI agents only operate based on the data that trains them, on their own, they simply are not enough. 

While AI can match and resolve entities based on what it’s learned from the data that trained it, humans still have a role to play: fixing errors, making judgment calls on ambiguous cases, or providing additional context that the agent might not have considered. But even that process can be time-consuming, especially when the data in question falls within the “last mile” of enterprise data—the part that addresses the idiosyncrasies and complex edge cases that are close to consumption and difficult to decipher. That’s where agentic data curation comes in. 

Agentic data curation is a new approach pioneered by Tamr that uses LLM-based AI agents to automate more of the data curation process by capturing and acting on the contextual insights needed to make confident curation decisions. These agents are particularly helpful when it comes to cleaning, curating, managing, and refining the last mile of data. By comparing outputs of entity matches and explaining the reasoning behind why records are (or are not) a match, AI agents can provide the preliminary analysis humans need to determine if they trust the AI-produced output or if they need to tune the model further. 

Involving humans in the process is crucial to ensuring the highest level of accuracy and reliability, not only in data curation and identity resolution, but also in the golden records themselves. When paired with AI, humans spend less time on tedious, rote tasks and more time adding value, context, and perspective through feedback—which, in turn, improves the integrity and trustworthiness of the golden records. AI, AI agents, and human refinement are a powerful combination: You get AI’s efficiency and scalability while maintaining human expertise, emotion, and empathy.

Why Is Collaboration Between Humans and AI Critical?

While artificial intelligence can and will transform business, the importance of human oversight and collaboration cannot be understated. Where AI’s strengths lie in processing speeds and advanced algorithms, humans shine in the application of empathy, creativity, and contextual relevance. 

Taking accountability for ethical decision-making and responsible use of AI is critical—and it’s something businesses must prioritize. Data leaders must ask themselves: Are we doing the right things as we adopt and use AI—and are we doing them in the right way? Promoting collaboration between humans and AI is a good place to start. 

When humans are in the loop, they can help to expose potential biases, add context, and introduce new considerations that not only strengthen the integrity of the AI, but also hold organizations accountable for its ethical use. Without this human refinement, organizations run the risk of poor decisions or reputational harm.

Below are three risks organizations face when they fail to include human oversight as part of their AI processes. 

1. Exposing potential bias

Data powers AI. And when the data is bad, so, too, are the results AI and AI agents deliver. But bad data can lead to other consequences as well. When training data is incomplete, incorrect, out-of-date, or skewed, the results can be biased, leading users to make incorrect assumptions or flawed decisions. Humans can help reduce the risk of bias by challenging the models when results appear incorrect or distorted and adding insight and context that the AI may not have considered. 

2. Failing to consider new scenarios

Humans have the unique ability to imagine possibilities and create new scenarios based on their own distinct experiences. And because each human’s experiences and make-up are different, the scenarios they envision are distinct. AI and AI agents, on the other hand, do not yet have the ability to consider situations and scenarios beyond what they are taught. Therefore, they may inadvertently exclude potential use cases that only a human could conceive or fail to consider new or different scenarios that exist outside the realm of its training data. 

3. Ignoring contextual relevance

At face value, it’s possible for AI and AI agents to misinterpret data and return an invalid, inaccurate, or biased result. That’s why it is critical for humans to take accountability and refine the results by adding context based on their experiences and their history. Without this valuable insight, data can be misconstrued and misrepresented, leading to hallucinations, faulty decisions, or flawed outcomes. But when humans are involved, they can apply judgment and domain expertise by making judgment calls on ambiguous cases or providing additional, relevant context that the AI agent may not have considered. 

AI + AI Agents + Humans = Better, More Trustworthy Results

Clearly, humans play a critical role in the refinement of AI-driven results. Without their feedback, AI and AI agents run the risk of overlooking relevant context, excluding critical scenarios, or worse, exposing bias. However, when humans and AI collaborate, organizations can increase the integrity of the results and drive better outcomes for their business. 

To learn more about how AI agents will transform data management, download our ebook How Agentic Data Curation is Transforming Data Management: Perspectives from the Tamr Co-Founders.