Emerging AI trends to consider in 2020

January 15, 2020

AI has expanded in recent years and become widely accepted in businesses today. Implementing new AI tools enables businesses to remain efficient and gain a competitive edge in their selected market. Forbes recently released a number of top trends related to the AI market for 2020:

Predictive Analytics

Predictive analytics utilised a combination of machine learning, historical data and other systems to generate potential scenarios for the future. Using these tools enables businesses to study certain trends and implement these to enhance businesses’ performance across many levels.  Predictive analytics is growing rapidly and is helping businesses become more efficient thanks to several factors:

  • Simplistic and affordable options 
  • Larger volumes of available data and information from a range of innovative tools
  • Rising market saturation encouraging businesses to seek new ways to diversify

The predictive analytics market is expected to rise to nearly $11 billion by 2022 and has recorded over 20% compound growth for the last 3 years.

Rising Anomaly Detection Tools

There are daily occurring problems for businesses that are generally due to human-related errors and although relatively minor, they can collectively incur high costs for a business. Anomaly detection is becoming more popular to determine problems before they occur. This AI-focused system uses existing and historical data to determine certain datasets that really stand out, enabling a business to find certain errors in security, marketing and other parts of a business.

On the flip side, anomaly detection can also be used to discover beneficial gains for a business, such as determining the most efficient PPC strategy or targeted keywords to focus on SEO campaigns.

Machine Learning Cybersecurity Tools

Security continues to be a global concern and the potential of losing data is something that no business wishes to experience. According to Forbes, nearly 70% of small businesses experienced a cyberattack in 2018, hence the need for cybersecurity in machine learning systems. These services utilise AI to continuously monitor how a business operates, detecting any potential threats before they may cause damage. The tools allow a business to run smoothly and remain protected. For example, Microsoft Windows Defender Advanced Threat Protection (ATP) is a great example, deploying cloud AI and machine learning tools to identify threats and potential errors that could cause problems for a business.

Increased user-friendly AI platforms

For many businesses, AI can seem complicated and new technology can be more complex and slower to be integrated for many. This, however, is changing with Gartner suggesting that nearly 40% of businesses have implemented AI in their business, as this figure is rising. This is largely down to the rising release of user-friendly AI technology being released, enabling simplistic integration and data production. Users don’t necessarily need to be experts to use these tools and can successfully generate valuable information for their business.

AI for productivity and work balance

AI is generally regarded in terms of data and machines, but there is an increase in AI technology being applied to enhance the human side of a business. As described, AI can be utilised for anomaly detection and cybersecurity trends, allowing computer technology to do what it does best, whilst the people managing the business can focus on applying their expertise. PwC predicts AI will generate over $15 trillion to the global economy by 2030. They predict that most of this revenue will come from driving consumer behvaiour, boosting products and enhancing productivity. For example, the business VMware integrated AI-driven solutions to manage content and editing as it looked to scale further. The saved time enabled their writers to focus on training, onboarding new personnel and generating new and efficient systems.

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