When Should You Use AI Interpreting?

AI interpreting has come a long way from clunky word-for-word translations. But here’s the truth: knowing when to use it (and when not to) can make the difference between smooth communication and costly misunderstandings. Whether you’re running an event, handling client calls, or managing cross-border teams, the right choice can save time, money, and your reputation.

The demand for effective, speedy, and scalable interpreting solutions has never been greater. Whether it is international conferences, live customer support, or telehealth visits, language should never limit communication opportunities. 

However, like all tools, the key question is determining when and in what circumstances to use it.  

AI interpreting has the unique advantages of speed, cost, and scalability, but it will not be the appropriate fit in every circumstance.  

It’s important for organizations and individuals that are making strategic communication decisions to know when AI interpreting is beneficial and when human interpreters are irreplaceable. 

But first, let’s understand what is AI interpreting and how it works. 

What is AI Interpreting?

AI interpreting refers to the process of interpreting spoken language using artificial intelligence, specifically machine learning algorithms and natural language processing (NLP), in real-time speech interpretation.  

The interpreting process is delivered using dedicated technologies, mobile applications, web-based software, integrated technologies (e.g., smart assistants), or integrated into online meeting platforms (e.g., Microsoft Teams, Zoom). 

Unlike traditional machine translation (text to text), or text-to-speech applications that would also be considered a form of AI interpreting, the AI interpreting process involves the processing of spoken language, the transcription of spoken language, the translation of spoken language, and the output of spoken translated content, all in seconds. 

 Best Use Cases for AI Interpreting

1. Routine and Repeated Situations 

AI interpreting does well not only in structured and predictable environments, and with a limited choice of vocabulary, but also when the modality is repetitive, formulaic, and not very nuanced. 

For example, guided tours, airport announcements, and instructions in warehouses. The language in those contexts is easier for AI models to reasonably predict because they use much of the same common language and structural phrasing. 

Use case examples: 

• Tourist assistance kiosks at airports 
   
   

• Pre-recorded public service announcements 
   
   

• Automated check-in and check-out in hospitality 
          

2. Time-Sensitive Situations 

AI interpreting enables an immediate response when there is limited or no access to a human interpreter. While not perfect, AI interpreting can provide a bridge and produce some level of mutual understanding. 

Use case examples: 

 

• Emergency response interactions 
   
   

• Ad hoc interpreting scenarios for virtual meetings 
   
   

• Crisis support in multiple languages during natural disasters 
    

3. Low-Stakes Conversations 

 

AI can be helpful in informal or low-risk environments. Users may be less concerned about the absence of linguistic perfection and would prioritize convenience over complete accuracy. 

 

Use case examples: 

 

• Travelers in leisure and adventure scenarios 
   
   

• Internal communication situations (e.g.: multilingual teams) 
   
   

• Customer support triage for global products 
    

4. Scalability and Cost Savings 

 

If you’re operating across multiple markets, especially with high volumes of multilingual content, AI interpreting offers readily scalable solutions because you won’t have to book interpreters for each conversation. It could also significantly reduce your costs and the effort of finding an interpreter depending on each situation. Further, it could be added directly into customer experience pipelines or operational workflows. 

Some use cases: 

 

• AI chat or voice support across geographies 
   
   

• Interpreting support in webinars or online learning platforms 
   
   

• Onboarding sessions for large global workforces 
    

5. Basic Accessibility 

AI interpreting also provides additional accessibility. For example, when translated subtitles are shown in real time during a live video event, non-native speakers have greater accessibility, which can be especially empowering in educational or workplace contexts. 

However, just because AI interpreting can handle some tasks, doesn’t mean it’s right for every situation. Sometimes you need to think twice.

Limitations of AI Interpreting

 

Although AI interpreting technologies are rapidly advancing, it is still fundamentally limited, specifically when it comes to human communication, which is inherently much more complex. There are situations in which using AI interpreting could create misunderstandings, inadvertently cause offense, and expose you to legal liability. 

 

1. High-Stakes Situations or Sensitive Contexts 

Nuance, tone, and cultural sensitivities are of utmost importance in most interactions.  In legal, diplomatic, or medical contexts, the significance of a relatively harmless misinterpretation could be catastrophic.  

Human interpreters not only recognize language, but also cultural underpinnings, tone, and emotion, all of which remain an area of difficulty for AI. 

Red flags in use cases: 

• Legal depositions or court scenarios 
   
   

• Mental health consultations 
   
   

• High-level diplomatic environments 
   
   

2. Contextually Sensitive Cultural Communication 

When it comes to translating content created in major world languages, AI systems are a step ahead. However, the nuances of idioms, jokes, and culturally-specific references can be very difficult for AI to figure out.  

Humans can mediate and help build the context for interpretation as they are interpreting in real time, something no algorithm can do. Human interpretation remains the gold standard in cases where cultural preservation and accuracy are critical to the community. 

3. Low-Resource Languages 

Although major world languages are increasingly well served by AI models, very low-resource or endangered languages typically do not have adequate training data to produce usable AI interpretation. Community interpreters are an especially important resource when little technology exists. 

4. Accents and Quality of Audio 

Many AI systems fail to handle things like background noise, multiple speakers, or strong regional accents, all of which can significantly affect accuracy. In complex live contexts, humans demonstrate greater flexibility and can use topical cues, which may fill what would be considered gaps or misinterpretations by an AI system. 

The Hybrid Model: AI + Human Interpreting 

One of the most exhilarating developments in the interpreting space is hybrid, which utilizes AI to augment human interpreters instead of eliminating them. AI takes care of the straight forward or repetitive parts of the dialogue, and humans intervene for quality assurance, complicated conversations, and nuanced interpretation. 

Examples of hybrid models: 

 

• AI provides a first-pass translation that the human interpreter continues to refine as the conversation proceeds 
   
   

• Human interpreters use AI tools to enable them to work efficiently, but still focus on high accuracy and quality 
   
   

• AI provides side support during hours when human interpreters are not working 
    

This combination allows for both speed and accuracy, especially in high volume and/or multi-language situations. 

Ethical Considerations 

Implementing AI interpreting will also require consideration for ethics and privacy. Any data generated through real-time language needs to be carefully vetted. You must ensure: 

 

• Data security: Is the conversation retained? Who has access to it? 

• Consent: Are the participants aware that AI is a part of their experience? 

• Bias: Has the AI been trained on large and broad datasets that are diverse and inclusive?  

 

In the end, it is up to the organization to be responsible for ensuring that any AI tools meet legal and regulatory compliance, such as GDPR, and not be susceptible to on any language or cultural biases. 

AI interpreting is not one size solution. It is, however, a useful tool. One that is most beneficial, when exercising caution, of course. It can improve reasonable access to services and efficiencies of operation and reduce language barriers in many sectors.  

However, it has limits that must be recognized. 

The right question to ask here is not if AI will replace human interpreting, but: "When is this the right tool?" 
 

Recognizing that critical difference, and then deliberately choosing, will influence the success, inclusivity, and, ultimately, global influence of communication strategies going forward. 

 

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