Artificial intelligence (AI) and other tools for automation have been town talk over the past few years. We’ve been hearing about their potential to produce widespread, meaningful change.
AI and other advanced tools’ influences aren’t only part of a broader digital transformation but also a practical necessity. In this article, we’ll focus on how they can influence or improve the productivity of different business sectors.
Here are five top AI trends in business this 2023.
Increasingly Democratized Data
The outset of 2023 is when complex data become widely accessible. Like any new technology, it won’t be limited to large companies with in-house data science teams and technology. It’s also likely to soon be available for anyone over time, which is a big deal.
Companies can effectively leverage high-volume data with machine learning (ML) and AI. By iterating on existing data, they can accelerate manual processes and remove the most burdensome hindrances from critical processes and workflows. This drives better-informed business decisions with less internal technical oversight and dramatically lower costs.
Generative or Creative AI
Generative AI is a sub-field of machine learning. It produces new data based on an existing data set to generate new content similar to the original, real-world input data. It uses deep learning algorithms to learn a data set’s features and patterns, including codes, texts, images, audio, and videos.
Generative Pre-trained Transformer 3 (GPT-3) is an example of Generative AI. It’s technically a language prediction model with around 175 billion machine-learning parameters. It has copywriting tools that can autocomplete any text, like topics, descriptions, or essays, based on the given context. To generate human-like written content, it studies millions of web pages and researches on the Internet.
The more popular version of GPT-3 is called ChatGPT. It’s a bot version and is a large language model that can give the “human style” of responding (i.e., performing instructions or answering questions) by learning human feedback (human conversations and internet) content. However, both GPT-3 and ChatGPT have recently been discovered to generate discriminating content against gender, race, and minority groups.
Many tests have also revealed their other big problems, such as security threats and privacy concerns. Experts are also mentioning that they’re challenging education and work. OpenAI, the company behind GPT-3 and ChatGPT, is now auditing their performances to prevent these issues in customer care settings.
Natural Language Processing
GPT-3 is also considered a Natural Language Processing (NLP), another subfield of AI where computer science meets linguistics. The difference between a Generative AI and NLP is that the former mimics cognitive capabilities, such as learning from examples and solving problems, while the latter is the one that helps AI understand and translate human language.
NLP is a powerful tool for companies to improve their operations and stay competitive in a rapidly evolving market. Let’s take CreditNinja.com cash advance loans that borrowers take as an example.
The company wants to get insights into how profitable these credit products are and whether there are areas they can improve, so it takes advantage of NLP to enhance its overall customer experience.
With NLP, the company can glean and analyze information from a high volume of unstructured text data (e.g., customer reviews or social media posts). The gained insights from customer behaviors then help businesses not only in automating customer service procedures but also in product development, marketing, and advertising.
GPT-4 is thought to be the most advanced NLP version to date. It’s the most recent and more flexible, adaptable, and human-like version of GPT-3. Specifically, its text production more closely resembles human behavior. Additionally, although not 100%, it solves most writing issues that GPT-3 and ChatGPT have.
Revolutionize UX Research
The democratization of AI will also transform user experience (UX) research through low- to no-code tools. These enable researchers to access previously inaccessible AI abilities, such as collecting, analyzing, and interpreting qualitative and quantitative data more efficiently.
For example, large language models like GPT can help transcribe and translate between languages faster and more accurately than other tools, making cross-linguistic research better. Then, NLP analyzes text data and performs entity extraction, topic modeling, and sentiment analysis.
With these, too, researchers can boost their capabilities and analyze and find out new insights and discoveries fast.
Ethics and Regulation
Amid AI’s many perks, people fear its misuse, particularly by cybercriminals. It’s possible that they can use this technology to commit revenge, blackmail, slander, fraud, coercion, or extortion. On top of all, it also has original and proprietary content issues. With this, the AI sector looks forward to users and clients demanding transparency, safety, and responsible practices.
For example, a law called “Local Law 144” has already been enacted by the Department of Consumer and Worker Protection (DCWP) in New York City, United States. It requires companies to meet bias audit requirements when evaluating job candidates before using AI or any automated tools.
The employers or hiring teams are also mandated to inform the job seekers of their use of AI tools for recruitment and job advertisements in advance.
Final Thoughts
Although the list shouldn’t stop here, these five AI trends in business have significantly influenced businesses and numerous industries globally. Given how prevalent they’ll become in our lives in the future, business leaders must stay on top of them and accurately forecast their direction of change.
