The AI revolution is in full force. But how can you best use AI to improve CX? In this article, we’ll go into the human in the loop definition, its customer experience benefits, and what challenges you might face along the way.
By the way — if you’re a growth leader or CX specialist that’s looking for new and effective ways to solve for scale while staying lean, let us know. We’re both AI technophiles and experts at making customer moments matter, driving amazing CSAT.
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HITL is when a human steps into the machine learning (ML) process of an AI model in order to reduce errors and increase learning speed. It is placing humans in the loop of AI learning.
ML systems like generative AI don’t have beliefs, opinions, or knowledge beyond the extent of their datasets. Their entire “being” is dependent on processing factual information that’s provided by humans. Because of that, humans are needed to train, test, and supervise how an AI is using the data.
For example: imagine that a computer vision AI model thinks that a lion and a panther are the same animal because they share a similar feline shape. With a human in the loop AI learning is improved — the human teaches the AI why lions and panthers are different.
Through the HITL model, the AI can adjust its judgment and predictive analyses in the future — even for data it hasn’t encountered before. In our example, the AI might now identify a tiger correctly — or at least recognize that it’s neither a panther nor a lion.
HITL is used for:
- Text generation
- Image generation
- Image labeling
- Audio transcription
- AI content moderation
- Natural language processing (NLP)
- Computer vision
- and more
Human in the loop has three main parts:
- Data labeling
One reason the HITL process is important — it provides certainty for rare datasets and ensures safety standards.
In the CX space, HITL brings benefits such as:
- Improved accuracy and efficiency
- Enhanced personalization
- Increased customer satisfaction
- More productive workflows
- Consistently great AI outputs
- and more
If you want to get started with HITL, build a better CX infrastructure, or improve how you use AI, we can help you achieve efficiency.
The human in the loop approach is centered around the idea of continuous supervision of the machine learning (ML) process. Yes, AI has become amazing in a short span of time. Yes, the very idea of ML is to let the AI learn by itself based on the data that you feed it.
However, it’s important to keep in mind that these deep learning mechanisms were still designed by humans. Humans aren’t perfect, and so, at least for now, artificial intelligence isn’t either. And one of the top fears of those concerned with the dangers of AI is the loss of human influence — HITL avoids that risk.
What is human in the loop?
While AI models learn based on data provided by humans, the human element is still required long-term to:
- Keep the information updated (especially if your datasets are likely to change based on new research, new knowledge, or new inputs).
- Correct the way that the AI interprets and uses the data, as its understanding of information isn’t guaranteed to be 100% accurate.
And so, human “in the loop” is quite literal — referring to the presence of a human decision-maker in the loop of AI learning.
Machine learning systems like generative AI don’t have opinions or beliefs. They simply relay and process factual information that’s given to them by humans via datasets. Still, just as children don’t always grasp new information the first time around, the same can be true for AI models.
As such, humans are needed to train, test, and supervise how the machine is using the datasets provided. This continuous feedback process teaches the AI to interpret the data correctly.
Human in the loop examples
If the AI model keeps interpreting a picture of a bus as a van, then a person can keep correcting until it identifies the vehicle properly.
What about a customer experience example, like content moderation? If the AI consistently fails to flag a particular type of behavior as unacceptable, you adjust its “view” on the matter through HITL.
In reality, the name “human in the loop” is slightly misleading. It infers that the human plays a minor role in the AI process. In reality, the human is the guiding hand; without the human, the AI wouldn’t fulfill its function properly all the time.
HITL is an integral part of:
- Text generation
- Image generation
- Image labeling
- Audio transcription
- AI content moderation
- Natural language processing (NLP) — a core competency of tools like chatbots, which allows the AI to interpret, manipulate, and comprehend words, as well as their meaning within a context.
- Computer vision — deriving correct information from any visual media, such as images, videos, or other visual inputs.
- and more
How does HITL work?
AI models base their functionality on datasets of information provided by a human. Then, through the human in the loop optimization process, the human helps the AI avoid mistakes, such as misinterpreting the datasets.
Largely speaking, an AI “understands” data through statistics. This means that it cannot be absolutely certain of facts, with the exception of 3 cases:
- It’s been provided with biased information, lacking in perspectives or context.
- It’s dealing with information which is universally accepted as objective across all of its sources. Example: a cat is an animal.
- It has gone through a human in the loop workflow, where it’s been aided by humans to correctly discern and use information.
Helping the machine learning process through HITL follows three steps:
Data labeling means adding a helpful label to raw images, videos, text files, or other data. This provides the AI model with more information about what it’s “looking at,” as well as the context. For example, an AI could succeed in recognizing a yellow coat on a white background every time, but struggle if the background was yellow too.
By labeling the data, you’re helping the AI grasp the contents of an image better, even if the elements are blending together.
With your datasets labeled, the AI is then “set loose” to discover insights, unveil patterns, and create relationships between items in the dataset. However, because labeling datasets can take a lot of time, you might start training before you have all the data you actually wanted — letting the AI learn more through actual practice.
Training AI via labeled data helps it develop the intended functions faster. By looking at the examples you’ve given it, the AI can map out new examples for itself via ML. Its assumptions are then vetted through the human in the loop system. Thanks to this, the AI becomes more capable of interpreting unlabeled data too.
Once the AI knows what it’s supposed to be doing, HITL optimization corrects any inaccuracies as they appear. This builds more confidence for the AI’s logic patterns, helping it judge data more effectively.
HITL is even more important if you’re working with a large dataset of unlabeled data, since this increases the likelihood of erroneous conclusions on the AI’s part.
For example, you could provide it with 10 labeled examples of how a smartphone under warranty will receive free repairs for software issues.
However, it’s possible that at some point, the AI will encounter a customer who’s had their device repaired 3 times already, but it keeps malfunctioning. The customer is demanding a refund now. The thing is, if the AI wasn’t trained with labeled data around this scenario, it may not perform as desired.
Why is HITL used for machine learning?
In order for an AI to be truly independent, it would need a staggering amount of labeled data in order to cover any possible scenario. That's practically impossible. Accurate results only happen consistently if the AI is familiar with the data, or if its wrong judgment has been corrected.
Yes, the AI will map out potentially differing examples from the labeled data it's been provided — but you can never be sure it’ll get it right every time. There’s no way for the AI to know if its “guess” is right or wrong in the absence of human input.
So the human in the loop approach is used to:
- Provide certainty for rarer datasets — In the U.S., “chips” universally means potato chips. However, someone from the U.K. might ask your AI what varieties of “crisps” you sell. But neither word will mean anything to your AI if they weren’t part of its training data, or if it’s not explained via HITL. The same can apply to “potato”, versus “taters”. This concept also extends to image processing and beyond.
- Ensure safety standards — Imagine using AI in the medical field, banking, manufacturing, or any industry where safety standards and regulations continually change. And as fast as the world is changing these days, this really applies anywhere. Without a human in the loop approach, the AI’s dataset ends up being outdated, to the detriment of customers and staff.
The debate over whether AI will replace customer support agents continues. However, in order to provide the highest level of service, the best solution is to combine the approaches via human in the loop.
AI has the incredible ability to create scale instantly, and to seamlessly enable a digital-first business model. AI frees up agents to perform higher-value tasks, while the AI handles repetitive, typically costly, time-consuming tasks.
This also means a shift in mentality in the CX space. With AI, human-powered support will become less task-oriented and more outcome-oriented.
Improved accuracy and efficiency
Have you noticed the rise in AI Data Scientist job listings? Giants like Amazon and Adobe are filling these roles for good reason. Automation relies on laser-focused accuracy — and that type of accuracy needs continuous feedback from a human in the loop model.
With dedicated professionals constantly analyzing the performance of an AI model, you ensure that its predictive power remains in top shape. This is important in the training and testing phases, yes, but not just there. The deployment phase is where the AI will actually encounter the massive amounts of data essential to improving its efficiency.
Once an AI is “set loose in the wild,” it’ll start encountering misrepresented or under-represented situations. Human intervention is key here. Agents must confirm whether the AI’s existing training data was enough for it to correctly predict the scenario, and whether the AI handled it appropriately.
Then the agent sends feedback that either confirms that the ML process was a success, or that the prediction of the AI model should be retrained.
Especially in B2C (but in B2B as well), companies collect mountains of big data on their customers. This includes purchase history, wish lists, communication preferences, past support interactions, and more.
In order to deliver bespoke CX, an AI needs access to those datasets and training regarding what it should do with them. Essentially, the same things you would teach a human agent.
However, AI has the ability to apply that personalization at an incredible scale — instantly. But that means that the chances of mistakes are higher as well.
If the AI model doesn’t fully understand what’s happening in the customer’s journey at that point, then proper personalization isn’t guaranteed. Add in complex customizations — like AI-powered pricing or AI proximity marketing that combines loyalty apps, geolocation, and in-store sensors to measure dwell time, sentiment, and even what’s in your cart — and HITL becomes critical.
Through a human in the loop system, the AI can logically understand where the customer’s coming from, and what their relationship with the brand is at the moment. The result? The AI model should be able to convincingly simulate the type of “personal connection” that customers want from businesses.
Increased customer satisfaction
The lack of human empathy is often cited as a detractor for using AI in customer support — for good reason. Customers want to feel understood. For example, in a CX poll, 57% of respondents said that businesses should keep offering live phone support. Why? Because phone support imitates the in-person experience the best.
By hearing another person’s voice, you get a better feeling that someone’s on your side and that they care about what you need.
Additionally, in another CX poll, 77% of respondents said they don’t trust AI to solve context-rich support issues.
It’s clear that people have their doubts and hesitations about AI technology. In turn, that means that any mistake an AI system might make would only only go to aggravate customers further — perhaps even more than if a human agent would make a mistake.
HITL enhances the predictive and analytical capabilities of AI with the power of emotions.
It’s a transformative process whereby an agent can judge whether the AI is acting human enough for the customer to feel appreciated and understood.
The potential range of customer inquiries is wide (to say the least). AIs can struggle with language nuances or complex scenarios. At the same time, you also risk customers feeling frustrated that they can’t reach a human agent.
It’s important that your staff truly understand how to use AI to improve CX the right way. Some roadbumps you might face include:
Finding the right balance
AI’s great! But…it’s not for everything. Even with the aid of a human in the loop workflow, it can come short in specific areas. The more you need an AI to go “off script” and use its proverbial human side, the more complicated things become.
The key lies in figuring out what’s a repetitive process which can be handed off to AI, while freeing internal teams for more complex objectives.
Keep both agents and AI focused on support, but strike a balance. Ensure that automation serves a specific purpose, and the human touch serves a different one.
For example, we leverage AI as a support mechanism for our agents.
Another example: AI has a natural knack for discerning data patterns, but it’s still up to a human to make sense of them in a way that aids business strategy. Your AI-powered analytics will uncover trends and preferences, but you still need product development and marketing specialists to make something useful out of them.
Training and managing human agents
Human in the loop experts power some of the most interesting technology we’ve seen in the last decade. They’re not just labeling text, images, or videos so that an AI can understand them better. Sure, they do that too, but it’s all in the context of a much bigger picture.
HITL agents need to understand the AI model itself — the purpose of the training, the intended results, the optimization work likely needed post-deployment, the edge cases.
What’s more, agents must also understand what type of data might actually confuse the AI model, or worst of all, cause biases.
If the AI model is the child, then the HITL specialists are the parents. Each judgment error on the AI’s part must be tracked back to a previous step in the “raising” process, and corrected appropriately.
In other words, it can be difficult to find a team you can entrust with the vision that you have for your AI. But, it’s not impossible.
Transparency is critical. Companies must make it clear when customers are interacting with AI, not a person. By being upfront, you’re showing the customer that you respect them and their time, creating a bond of trust.
Another aspect is data privacy. Sensitive data used for training must adhere to strict guidelines to avoid breaches. At the same time, AI models may develop bias based on the data they’re given access to. Regular audits of fairness, bias, and accuracy are a must. Audits should be mandatory.
Last but not least, always give your customers the option of escalating to a human agent. Some complex issues require human assistance, while other customers would rather interact with a human even for simple matters.
We’ve recently launched NinjaBot — our own AI solution for improving CX. NinjaBot delivers instant, accurate responses for seamless support, streamlining workloads by automating routine queries. Thanks to the team behind NinjaBot (HITL experts and more!) we can now provide even faster resolutions and smoother customer journeys.
Moreover, AI-powered analytics are a game-changer for SupportNinja. These insights help us plan a strategic trajectory that’s based on customer needs and preferences. Our growth-fueling solutions are even more tailored, enhancing CX and keeping us ahead of the curve. AI is raising the quality of our service across the board, helping our clients free up resources, extend capacity, and solve for scale.
We currently assist over 200 businesses to set their sights higher, by having our Ninjas safeguard what they’ve already built. We’d be thrilled to help your company grow profitably too.
One of the biggest challenges in the AI and HITL space is staying up to date. Is your process properly outlined?
Without a proper process, AI won’t be an amplifier of success, but a tool that will leave people scratching their heads.
Here’s how you can improve the value that AI brings to the table:
- Clearly define your goals — what do you want to achieve with AI? Hash that out, and go from there.
- Start small — Simply put, AI will disrupt your business as usual if you suddenly, drastically switch SOPs one day. Test out pilot projects first and get feedback — both from employees and from customers.
- Prioritize transparency — Set clear expectations for your customers. Inform them they’re interacting with AI, and explain how to reach human support if needed.
- Invest in team training — You need both capable HITL professionals and support staff that’s ready to replace the AI in situations where its capabilities might not be sufficient. Both require training.
- Monitor and adapt — It’s called machine learning after all, and learning is a never-ending process. Your AI will keep being exposed to new datasets, new requests, new interactions, changing customer needs, and more. HITL staff need to react accordingly.
If you’d like to skip steps straight to success, you can always reach out to us. We’ll help you create customer delight and set up a robust human in the loop CX infrastructure.