In 2019, AI adoption among large companies has increased by 47% compared to 2018, according to the latest Artificial Intelligence Index report. AI has made its way into many enterprise applications, including customer relationship management software, recruiting services, workforce productivity, and resource planning tools.
According to the same report, global private AI investment exceeded $70 billion last year, highlighting the interest in AI software and startups. At the same time, AI and related subjects have attracted more university students around the world and made AI experts some of the most sought-after professionals in developed countries.
On a smaller scale, AI has made it into many people’s lives in one form or another, even if its presence is not always recognized. To better understand the state of AI, let’s take a closer look at the role AI plays in business and in many people’s lives even as we speak.
Summary of the
State of AI
- AI-enhanced technologies and solutions are now more widely available than before across industries, though they are not necessarily cheap to implement.
- Voice-based assistants are at the forefront of the AI adoption process in industries as diverse as IT, automotive, and retail.
- Smaller-scale AI as seen in chatbots enables smaller brands to save resources and improve customer satisfaction.
- An increasing number of AI-enhanced tools are available as software-as-a-service (SaaS).
- Mobile devices and apps have become one of the easiest ways to deploy AI technologies across industries, whether it’s in the form of voice assistants, smart monitoring and control, personalized shopping experiences, or warehouse management apps.
- AI is present in intelligent applications, neuronal networks, AI platforms as a service, and AI cloud services.
- Emerging AI technologies include augmented intelligence, which seeks to enhance human intelligence and edge AI, where AI algorithms are processed locally without the need for an internet connection (such as some forms of face recognition).
- According to the Artificial Intelligence Index Report, AI computing power keeps doubling every 3.4 months.
AI Implementation Examples
Intelligent machines that can replicate human behavior may not yet be widely available, but subsets of AI such as machine learning (ML) and deep learning (DL) have found a wide range of applications in both enterprise and everyday settings. Here are some of the best-known implementations of AI.
Automated customer support for online stores
In most cases, this translates to AI-powered assistants that can instantly answer basic questions, find products, or communicate the status of an order.
Personalized shopping experience including recommendations
Big brands use AI to process huge amounts of data about their customers in order to come up with product recommendations, custom content, and more.
Forecasts and predictions for financial services
Powerful financial analysis tools use machine learning to analyze more data faster and provide accurate predictions.
AI curation and recommendations
AI plays a part in what you see in your feed on networks like Facebook. AI curation takes into account your social media preferences and habits, as well as other dynamic data.
Face detection and security systems
AI has been implemented in advanced security and surveillance systems to both prevent threats and identify suspects.
Antivirus threat detection
Antivirus software from top providers uses AI in addition to other detection methods to identify new threats and contain them before they become a problem.
More effective warehouse management systems
AI can help warehouse staff find products faster by optimizing routes and the picking process.
Safety improvements in the automotive industry
AI gives cars predictive safety powers and allows for self-driving modes. In 2019, AI in the automotive industry has seen a larger application than before, most notably on Tesla cars.
Internet of Things
Smart appliances such as temperature sensors use AI to better understand your behavior and react to your actions and commands. Also, smart homes are now being controlled by increasingly intelligent smart voice assistants.
Examples of
Everyday Uses of AI
At first glance, it may seem that AI is out of the reach of the average person who doesn’t work with AI-enhanced enterprise-grade software or systems. But a closer look at today’s digital technologies can change all that.
We’ve already mentioned the role of AI curation on Facebook. Apple, Amazon, and Google are some of the other household brands that integrate AI into their products and services.
- Siri, Apple’s voice assistant, uses AI to better understand your commands and habits and become more helpful over time. It may not yet be as receptive as a real assistant, but its accuracy has been improving incrementally.
- Gmail uses machine learning for security reasons—to stop spam and other unwanted emails.
- Google Translate uses statistical analysis of language patterns to provide more accurate translations.
- Amazon uses AI to provide product suggestions and other predictions. These suggestions improve product awareness and can encourage shoppers to add more products to their shopping carts.
- Netflix taps into the power of AI to provide suggestions and trends. Netflix’s AI analyzes your records and ratings and considers dynamic factors such as time of day to understand your viewing habits.
- Google Maps uses AI to suggest better routes and means of transportation.
AI in the Software World
Open-source AI and machine learning projects such as TensorFlow, Torch, Accord.NET, and the Microsoft Cognitive Toolkit now enable developers to integrate machine learning, deep learning, image recognition, voice recognition, and other AI features into new software.
The powerful capabilities of these platforms free developers from the need to stick to proprietary frameworks and can reduce the overall cost of developing AI-enhanced software. The impact of these platforms can be seen in the widespread use of chatbots and the other implementations of machine learning.
Chatbots have become the face of AI. Their presence across social media, websites, and applications has become a familiar one—you’ve probably encountered more than one already.
Brands and organizations around the world implement chatbots with AI-capabilities to streamline their customer support and ordering processes. Today, chatbots with basic AI capabilities have become accessible even to small and medium-sized brands.
Machine learning, an application of AI that many chatbots rely on, is at the forefront of AI implementation in many industries. It uses mathematical models to find patterns in big data and extract information. It’s being used to provide dynamic pricing, provide personalized customer treatment, and identify problems and threats.
The capabilities of machine learning have become difficult to ignore. Early in 2019, a team of ML bots known as OpenAI Five beat the world champion Dota2 team, becoming the first esports bot team to defeat world champions. The resounding victory highlighted that AI can not only think, but also cooperate with other instances of itself, in a way that challenges simple assumptions about it.
And that’s not the only striking example from the gaming world of how smart AI bots have become—AlphaStar, a StarCraft II AI developed by DeepMind, has recently achieved grandmaster status and is now better than 99.8% of StarCraft II players.
Today, when, many companies have to deal with an increasingly large set of data coming in from multiple channels, ML promises to facilitate the data interpretation process in a way that enables them to understand their customers better.
Final Thoughts
From search engine algorithms and customer support chatbots to enterprise apps and tools, AI is becoming a dependable and versatile technology. Despite the costs and other challenges of successful implementation, AI holds too much potential for it to be ignored.
Companies big and small use AI successfully to improve processes, analyze big data faster, and reduce human errors and labor. The results include tangible benefits for their customers.
While AI adoption can vary considerably between countries and industries, 2019 has seen a growing interest in what remains one of the most exciting technologies in the software world.
At the same time, however, AI has also raised ethical questions that brands that adopt it on a large scale now have to consider more carefully than before. AI governance, or the process of creating policies that address the negative implications of AI, will likely continue to attract attention until the capabilities of AI and their impact on human lives are fully defined.
Whether you’re already using AI implementations or not, one thing’s clear—ignoring AI in 2020 is quite impossible if you use a mobile device, browse your Facebook news feed, or shop online.
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