Overcoming Obstacles in AI Corporate Integration
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Artificial Intelligence (AI) stands at the forefront of technological innovation, promising to revolutionize the way companies operate. However, the path to integrating AI is fraught with challenges that can hinder its adoption.
In a LinkedIn survey we conducted not long ago, we posed a simple question to our audience: “What reasons prevent you from using artificial intelligence (AI) when working with technical texts?” The results revealed that the majority cited security issues within their company, while others mentioned personal opposition to AI, uncertainty about applicable tasks, or a lack of time to explore AI solutions:
Overall, the results underscore the necessity for improved communication regarding the benefits and capabilities of AI, alongside the importance of addressing security concerns and enhancing accessibility and user-friendliness of AI tools to foster adoption.
In this blog post, we will examine the obstacles that businesses face when using AI, encompassing challenges ranging from data quality intricacies and privacy concerns to corporate culture’s resistance to change. Understanding these barriers is the initial stride towards unlocking AI’s transformative potential in the corporate realm.
Technical writers may encounter various obstacles when considering the integration of AI into their daily work. The most prominent reasons preventing writers from using AI are outlined below.
Addressing these challenges involves enhancing the security, usability, and accessibility of AI tools, as well as providing education and resources to help technical writers understand and leverage AI in their work.
Now, let’s focus on the three reasons listed above in more detail.
When integrating Artificial Intelligence (AI) into the process of creating and managing technical documentation, several security concerns must be addressed to protect sensitive information and comply with data privacy laws. Here’s an overview of these concerns and potential solutions:
By addressing these concerns with careful planning and the right tools, organizations can leverage the power of AI in technical documentation while maintaining the security and privacy of their data.
Resistance to change is a common human response, especially when it comes to adopting new technologies like AI in the workplace. For example, many technical writers are comfortable with the tools and processes they currently use and may view new AI tools as unnecessary or disruptive to their established workflow. Besides, there may be concerns that AI could automate aspects of their job, leading to a fear of job loss or a reduction in the perceived value of their skills.
In addition, there are quality concerns. Technical writers often pride themselves on the accuracy and clarity of their work. They might be skeptical about an AI’s ability to match the nuanced understanding that a human brings to technical documentation.
Furthermore, writers may not trust AI to handle sensitive or complex information correctly, fearing errors or oversights that could have serious consequences.
Finally, people may often think about the time barrier. Indeed, the time and effort required to learn new AI tools and integrate them into daily tasks can be seen as a barrier, particularly if the benefits are not immediately clear.
Overcoming resistance to change involves addressing these concerns directly, demonstrating the value of AI as a complementary tool rather than a replacement, and providing adequate training and support during the transition period.
The complexity of technical language can indeed pose a significant barrier to the adoption of AI by technical writers in their daily work.
Technical documents often contain specialized terms and jargon that are industry-specific. AI systems may not have been trained on such niche vocabularies, leading to misunderstandings or inaccuracies in the content they generate or edit.
Contextual nuance is an issue closely connected to the previous one. The meaning of technical terms can vary greatly depending on context. AI might struggle to discern these subtleties, which can result in content that is technically correct but contextually inappropriate.
These two problems become obvious when it comes to using AI for translation/localization purposes. To illustrate the problem, we have asked three AI-based tools (Google Translate, DeepL Translate, and ChatGPT 3.5) to translate the meaning of a technical term “Dutch folding” referring to sheet metal machining (a technique used to create a strong, folded edge on a metal sheet) into French, German, and Italian. The results are given in the table below:
Google Translate | DeepL Translate | ChatGPT 3.5 | |
French | pliage hollandais | pliage en néerlandais | pliage hollandais |
German | holländisches Falten | niederländisch Falten | holländisches Falten |
Italian | piega olandese | piegatura olandese | piega olandese |
The results in the table show that, despite the slight variations, all the three systems give a verbatim translation without taking into consideration the possibility of “Dutch folding” being a set phrase in English or referring to a limited field of usage (as a technical term). These results are useless from a localization perspective, as there are no corresponding concepts in any of the languages considered.
However, to be objective, it’s worth noting that ChatGPT provided its translation results with the following remark:
Please note that the context in which this term is used could affect the translation, especially in technical or industry-specific texts. It’s always a good idea to consider the specific context to ensure the translation is accurate.
To mitigate these issues, AI tools need to be carefully trained on domain-specific datasets and continuously updated to handle the evolving language of technical fields. Additionally, there should be a collaborative workflow where AI assists technical writers by automating routine tasks, allowing them to focus on more complex aspects of technical writing that require human expertise.
In summarizing the discussion on the obstacles of AI integration in businesses, it’s clear that companies must navigate through a labyrinth of ethical, technical, and cultural barriers to harness the full potential of AI. Yet, those who persevere will find themselves at the vanguard of innovation, reaping the benefits of increased efficiency, deeper insights, and a competitive edge.
Good luck with your technical writing!
ClickHelp Team
Author, host and deliver documentation across platforms and devices
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