A Review of AI Trends in 2022

Published on: 2022-12-20

Written by the Enlabeler team

A review of this year’s trends indicates that artificial intelligence continues to be a powerful tool to drive businesses forward. According to Deloitte’s fifth edition of the “State of AI in the Enterprise”, 94% of business leaders surveyed agree that AI is critical to success over the next five years. However, there was a 29% increase in respondents that are struggling to achieve meaningful AI outcomes. Top challenges associated with scaling are managing AI-related risk, lack of executive commitment, lack of maintenance and post launch support. 

Some notable trends this year have been:

AI Content Generator: 

AI has progressed exponentially where previously we only had an auto-correct feature that had a synonym database, to replace words already typed out. Now, it allows you to creatively generate the world around you by simple inputs such as text or previous data. Creatives are leveraging AI generators to help speed the creative process.

A popular OpenAI system this year was DALL-E version 2- an image generator that works with processing commands from a human and rendering any images together to create a final image. Compared to first version, DALL-E 2 allows for the following:

Creativity can no longer be claimed as a human skill as AI takes a leap forward in producing unique works of art. A great example of DALL-E 2 is when it created a photo of an “astronaut riding a horse”. What is marvelous about this image is that it took mere seconds to generate and would have probably taken an artist or a photoshop director much more time. This allows for artists, and creatives alike to generate concepts with ease, in time frames that will possibly evolve ideas like never before. 

Al generated content - DALL-E version 2.

Forecasting and Analysis for Business:

Using historical data, machine learning can significantly boost business forecasting and analysis for more accurate predictions. Numerous business sectors are already employing such a feature to reduce cost and increase profits. 

With climate change being a real concern the agriculture industry is integrating technology for precision growing and farming. The combination of computer model vision and satellite images allows farms to estimate yield and increase produce by early detection of underperforming farmlands, faulty equipment and health anomalies. 

The financial services industry is experiencing significant disruption with the rise of digital banks to market, leveraging data from multiple aspects of a customer’s life such as spending behaviors at local retailers, property values and levels of physical activity to determine risk profiles and offer tailored loans and transactional services. 

Low Code- No Code:

It is a challenge when employing any computer programming, skilled engineers are missing to design, write and deploy code.

As a result, codeless and low-code solutions have stepped in to create software functions with simple command prompts to design and write the code for you. The benefits being faster development, lower cost and increased automation. 

GitHub Copilot was able to quantify the impact on developer productivity and happiness. They conducted a survey with two groups, one that used GitHub Copilot and the other group without Copilot’s assistance. The group that used GitHub Copilot had a 78% task completion rate compared to 70% in the other group. The striking difference was that developers who used GitHub Copilot completed the task significantly faster–55% faster than the developers who didn’t. A clear indication that productivity can be increased with the assistance of AI. 


It’s no secret that humans need help with sustainability. AI began as a surveillance model that needed human interaction to monitor and assess satellite imagery that proved deforestation and bio-waste in regions not accessible to humans. Now, we are able to be conscientious about product development from the onset. AI can help to build structures that require less waste, or harmful carbon footprints. 

One great case study for China’s energy consumption displays the use of the Support Vector Regression model, which proved to have 98.7% accuracy when compared to other models. These models are suggested to help China reduce their coal consumption and implement more non-fossil fuel alternatives. Over time, we are certain that using AI for the precision and betterment of humanity will be the order of the day.

Machine learning and artificial intelligence is fueling transformations across all industries, but taking the first step requires getting access to unbiased structured data. Enlabeler partners with companies to realize their value and fulfill the computer model vision potential that drives their businesses forward.

Contact us at [email protected] to learn more.