How Productive AI (Artificial Intelligence) will contribute to us

Thanks to ChatGPT and DALL-E developed by Open AI, the concept of “productive artificial intelligence”, which we often hear these days, is used to define artificial intelligence applications that can produce new and original content in line with the directives given instead of analyzing or processing only existing data.

These applications, which are famous for their names, can produce original pictures, music, code, text and videos according to their interactions with users. For example, in ChatGPT, “I’m going to write a post about this. Can you prepare a professional-toned introduction?”, and within seconds you will be presented with an original text suitable for your request.

How Productive AI (Artificial Intelligence) will contribute to US

Similarly, when you log in to Copy.ai and request an e-mail text to inform your employees about a certain topic, or when you request the creation of a package design with Andy Warhol style for your product in DALL-E, it only takes seconds for the output to come to you.

Productive artificial intelligence applications seem to keep their place on the agenda alive in 2023 with the potential they contain. Different professional groups, especially those working in the creative sector, are discussing how productive AI applications will shape the future of their professions. On the other hand, companies are trying to discover the potential of these applications for their companies and their different areas of use.

How tools like ChatGPT

The McKinsey and Company article, “Productive AI is here: How tools like ChatGPT can change your business,” highlights how these apps, which leverage input (data and a user request) and experiences (new information and interactions with users that help them “learn” what’s right/wrong) to create original content, will enable individuals to do their jobs faster and better.

While productive AI applications are still in their infancy, they promise exciting advances in areas such as sales and marketing, operations, information technology, engineering, law, risk management and human resources. Based on McKinsey and Company’s article, let’s take a closer look at the pontificism these practices have for various processes of companies.

Sales and Marketing

One of the first areas of application that comes to mind is the creation of digital and traditional advertising content. It is possible to prepare text, images, videos or social media content in accordance with the goal determined by the user directives given to these applications. In addition, personalized messages can be produced by specifying the target audience of the brand and consumers can be contacted through this channel by selecting the appropriate communication channel. Moreover, advice can even be taken on the pricing strategy appropriate to the target market.

It is possible to say that the answer we received is quite impressive for a productive artificial intelligence application that is not yet developed enough to realize its potential compared to other questions.

Of course, it is important to remember that the responses to all these queries will vary according to the depth of the information given to the application, supported by additional questions and the way the application is directed by us, and that these applications are still in their infancy.

Productive AI and Operation

Generative AI applications can be used to create and audit task lists to efficiently execute operational activities. For example, production errors and defects can be identified through the images given to the system, the effectiveness of chatbots can be improved, the customer service process can be automated, both increasing efficiency and improving customer service.

Information Technology and Engineering

These applications can also be used for writing, documenting, and reviewing code. For example, JavaScript-language expressions can be easily converted to Python language, or synthetic data can be created to improve the accuracy and reliability of machine learning models trained with limited data.

In addition, productive AI applications can help find bugs in code, identify exceptions, and prepare test cases.

Productive AI and Risk Management and Law

Support can be obtained from these applications in the preparation and control of legal documents such as contracts and patent applications, summarizing changes in legal regulations and making inquiries about legal regulations, and assessing investment risks and operational risks.

Human Resources

For the interviews to be carried out before the recruitment, productive artificial intelligence applications can be applied in subjects such as creating questions or case examples focused on corporate culture, industry dynamics and qualifications required by the relevant position, receiving strategic support for employment conditions, designing employee trainings and even performing the pre-selection before the recruitment interview.

Productive AI and Communication

Productive artificial intelligence applications can be applied in subjects such as automating internal communication e-mail responses, editing the tone of texts, improving the words used and preparing presentations in a certain content. In addition, support can be obtained from these applications in the preparation of abstracts from meetings, texts or presentations and the classification of these summaries.

Although it seems that productive artificial intelligence applications will maintain their place on the agenda by developing usage scales with their exciting potential, it should not be forgotten that these applications, like all technological developments, bring a number of ethical and practical challenges. First of all, as we have seen in the examples above, it is too early to say that the outputs are always accurate and flawless.

It’s also important to note that there are no clear answers to intellectual property questions in machine learning based applications yet.