Ten months after OpenAi’s release of ChatGPT, the power, capabilities, and perceptions of AI have greatly changed.
After being launched on November 30, 2022, by OpenAI, ChatGPT took the world by storm with its rapid generative capabilities. While not entirely brand new, ChatGPT was popular for its clear interface, simplicity, and accessibility. The resource quickly gained popularity among students for its writing capabilities and would eventually become known to the professional world. Fast-forward to today, thanks to open-source collaborations, the number of professional artificial intelligence tools has increased immensely. Many companies have begun to explore the development of their own artificial intelligence tools in order to take advantage of their usefulness while simultaneously addressing data security and privacy concerns.
Despite the rise in awareness of how artificial intelligence can positively transform aspects of a business, there remain many companies that have yet to adopt AI technology. This is true even in areas where artificial intelligence can be easily adapted such as creative media. Inversely, most businesses that have not yet adopted generative AI tools have done so after concluding that it would not yield outcomes significant enough to justify the investment. Industry expectations and standards are naturally important factors when deducing how or if artificial intelligence is to be used. However, businesses must nevertheless recognize that artificial intelligence as a business tool is here to stay and will not be going away any time soon. On the contrary, AI business tools will continue to develop and improve, meaning that the quicker businesses are at adapting to these changes, the better their chances are of not falling behind their competitors.
Powerful tools evidently require careful handling and preparation before they can be used. Before a company can implement generative AI tools it must prioritize understanding what they are, their limitations, and how they can be optimally used.
Understanding what it is
Generative AI is a category of artificial intelligence that uses a specific range of data and programming concepts to generate data. Outputs come in the form of text, images, videos, and voice. Generative AI systems are able to do this by learning from existing data and then using what it has learned to generate content that resembles the original data’s structure and style. This is important to note when considering the limitations that generative AI systems often have, for example, specific data may be available up to a specific date which can lead to incoherencies in research. Well-known systems such as ChatGPT are trained with an extensive variety of data from the internet. This includes data from articles, blogs, books, and other forms of text-based content on an incredibly broad range of topics. This also means that the system can also return the URL of a source used when prompted by the user.
Advantages
- Virtual Assistants and Chatbots
Chatbots are programs that simulate human conversation with users. Natural Language Processing (NLP) is at the center of how virtual assistants of chatbots work. An algorithm processes the user’s input, obtains the meaning and context, and then outputs a response based on its perceived interpretation. Chatbots are best used as customer service tools as they can help isolate common service issues and process requests within a few seconds.
- Content Creation
Whether it be text, photos, or videos, generative AI tools have made phenomenal leaps in creative and artistic capabilities. Text generators can be used to write captions or drafts for blogs and articles. Image generators can be used for graphic designs and original clipart. Regardless of the content type, when assisted with generative AI tools, the creative process can be optimized and creators can overcome creative blocks.
- Data Analysis
Artificial Intelligence has already become a commonly used tool for data analysis among large companies. Large datasets are often difficult to handle and require tedious work at all levels. With the help of artificial intelligence algorithms, data can be automatically processed, cleaned, and transformed thus allowing businesses to analyze more detailed trends and correlations that could have potentially been unseen if done manually. This is crucial for predictive analysis and forecasting. Even with AI tools, data analysis should always be done by an analyst who understands the tool well as they can assess how and where it can benefit their research.
- Automation of repetitive tasks
Some jobs often don’t begin until more repetitive and dull tasks have been completed. An example is the extraction of data from invoices or the processing of an order or complaint. Because these tasks are high volume, businesses allocate significant resources to ensure that they are completed optimally but also accurately. Artificial intelligence uses advanced recognition technology that can extract data from images, documents, videos, and voice recordings and interpret the context of the data. When applied correctly the initial process of jotting down data or making a note of a customer’s complaint can be handled entirely by an AI algorithm allowing businesses to allocate their resources to areas of the company that require them the most.
Navigating the drawbacks of AI tools
While there is no doubt that artificial intelligence tools are impressive, it is important to note that they are not perfect. Output can sometimes be inaccurate or the quality standard is below user expectations. This is why having a basic grasp of how the technology works is important as it will not only allow users to spot errors but it will also help them understand the factors that contributed to the output. This includes data availability, the clarity of the prompt (instructions), and even the size of the expected output. If a business plans to implement AI tools into its day-to-day operations, it must approach the matter with the right perspective to manage expectations.
Perspective is key
It is crucial for businesses to view artificial intelligence as a tool before anything else. For the same reasons stated above, artificial intelligence in work settings requires personnel that is firstly able to understand the needs and expectations of the business and can secondly, understand common output issues and how to navigate them. Quality is an attribute that businesses cannot afford to put at risk, this is not exclusive to their products but also extends to the quality of research, online content, and internal procedures. If artificial intelligence is implemented irresponsibly as a quick replacement with poor planning, there will be a noticeable effect on the businesses’ respective areas.
In news and media
News and media companies have been experimenting with artificial intelligence since the mid-2010s. Their strategies have naturally evolved since its inception and it has been refined so as to ensure that the quality of the content being produced is up to par with the reputations earned. Media companies use artificial intelligence tools for the analysis of data-oriented subjects like financial reports or performance statistics. The AI will then use the data to output a well-articulated summary or article that can afterward be refined by an editor before publication or distribution.
Key takeaways
There are two key takeaways that businesses need to remember when considering implementing artificial intelligence tools into work operations:
- Understand the limitations
- Use it as a tool
AI tools are exactly that, tools. Implementing artificial intelligence to function as a replacement for an entire department or group of personnel will lead to issues of efficiency as well as quality. Businesses should seek to use artificial intelligence to improve existing areas of their operations before viewing it as a way to make changes in personnel.