7 Popular Artificial Intelligence Trends In Business In 2024

What Is Artificial Intelligence?

We daily interact with artificial intelligence in our lives but we don’t realize it. The acceptance of AI is not a new concept to accept.

It took decades to develop the AI technology. In this article, we will explain the artificial intelligence trends in business and the future of AI.

Today, AI technology is available in every family in the form of different electronic gadgets. Amazon Alexa is one of the best examples of Artificial intelligence.

The development of AI models like ChatGPT is clear proof of artificial intelligence trends in business. We have put together 7 trends that we expect to emerge throughout the year some of them are broad and high level and some of them are a bit technical.

Related article: Discover the trend of Google’s NotebookLM and Gemini

Let’s explore some of the popular artificial intelligence trends in business around the world.

1. Generative AI Reality Expectations:

The year 2024 is the year of realistic expectations. When generative AI first hit mass attention, it was met with a breathless announcement. Everyone was messing around with ChatGPT, DALL E, and the like, and now the dust has settled.

artificial intelligence trends in business

We are starting to develop an awareness of what AI-powered solutions can do. Now, many AI generative tools are being implemented as integrated elements in artificial intelligence trends in business rather than stand-alone chatbots, and the like.

They enhance and complement existing tools rather than revolutionize or substitute them. So, think of Copilot features in Microsoft Office or generative fill in Adobe Photoshop.

Embedding AI into everyday workflows like these helps us to better understand what generative AI can and can not do in its current form and one area where generative AI is extending its abilities is in multi-model AI.

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2. Multi-Model Artificial Intelligence Trends In Business:

Now, AI multi-model models can take multiple layers of data as input and we already have interdisciplinary models today, like Open AI’s GPT-4V and Google Gemini, that can move freely between natural language processing and computer vision tasks.

For example, like ask about an image and then receive a natural language answer or they could ask out loud for instructions to, let’s say, repair something and receive visual aids alongside step-by-step text instructions.

New models are also bringing video into the fold and where this gets interesting is in how multi-model AI allows models to process more diverse data inputs.

Related article: What is Robotic Process Automation (RPA):

3. Artificial Intelligence Impact On Jobs:

The role of artificial intelligence is becoming a milestone in transforming jobs in almost every industry by cutting roles and increasing these job roles with some advanced capabilities.

4. Cloud cost and GPU:

The artificial intelligence trends in business toward smaller models are being driven as much by necessity as it is by entrepreneurial vigor.

The larger the model, the higher the requirement for GPUs for training and inference.

Relatively, few AI adopters maintain their infrastructure which puts upward pressure on cloud costs as providers update and optimize their infrastructure to meet AI demands. If only these models were a bit more optimized, they’d need less computing.

5. Customer Local Models:

Open-source models afford the development of powerful custom AI models. That means training on an organization’s proprietary data and fine-tuning it for their specific needs.

Keeping AI training and inference nearby avoids the threat of proprietary facts or sensitive personal statistics being used to train closed-source models or in any other case passing via to the hands of third parties after which the usage of things like Retrieval Augmented Generation to get relevant data rather than storing all of that records at once in the LLM itself that enables to reduce the model size.

6. Virtual Agents:

Now, that goes beyond the straightforward customer experience chatbot because the virtual agents relate to task automation where agents will get stuff done for you. They will make reservations, they will complete the checklist task, or they connect to other services.

7. Shadow AI:

It is the unofficial personal use of AI in the workplace by the employees. It’s about using gen AI without going through IT for approval or oversight.

Most employees use AI in the workplace but without corporate AI policies in place and, importantly, policies that are observed. This can lead to issues related to security, privacy, compliance, and that sort of thing.

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