The democratization of AI with cloud-based platforms and cognitive services has found its way across industry verticals such as BFSI, retail, healthcare and manufacturing.
Consequently, businesses are giving due importance to data warehousing, and dabbling in algorithms and deep learning models to achieve high efficiency in performance.
We are also seeing artificial intelligence being leveraged to free up human resources so employees make better use of their time. This is clearly seen in business domains that involve repetitive and mundane processes.
IDC research finds that global AI spending for 2019 in terms of industries will be led by retail sector with investments totaling up to 5.9 billion USD on solutions such as automated customer service agents, shopping advisors and product recommendations, supply and logistics solutions, etc.
This will be followed by banking with spending up to 5.6 billion USD on AI-based solutions including automated threat intelligence and prevention, fraud analysis and investigation systems. Other major sectors with large spending on AI include discrete manufacturing, process manufacturing and healthcare.
Core of AI adoption: User personalization and product experience
AI is becoming quite pervasive, evident by a rising use of voice-assisted personal assistants and chat bots in consumer touch points. The crux of today’s customer-facing AI is to help users get the best experience through personalization, or what some would say hyper-personalization.
In this scenario, it is seen that enterprises that have deployed AI in their end user applications, certainly enjoy an edge over their competitors. An important reason why user personalization is appealing to businesses, especially in the services industry is the ability it gives them to preemptively create customized solutions to achieve high levels of customer experience.
Additionally, AI algorithms are being leveraged to identify and get rid of the pain points in services. It is also expected that consumption of personalized recommendations using some form of AI would indirectly fuel more product innovation in the future, which emphasizes on user experience.
The best illustration of why user personalization is one of the great applications of AI is its use in medical diagnostics, where a patient's present and historic data is used to detect and predict serious health conditions.
Another instance where AI-based personalization is being extensively used is on the content platforms such as online streaming services like Netflix, Amazon Prime etc. Based on a user's unique consumption patterns, algorithms can recommend in advance fresh content the user is most likely to consume. The use of AI-based personal recommendations has been a game-changer for retail and finance industries quite similarly.
Leveraging AI to achieve the maximum business efficiency
The key process flows and decision making across various industries are being automated and augmented with the use of AI-based techniques.
The most common example is the use of chat bots to manage repetitive requests by customers. As bots are able to handle repetitive tasks at a much faster rate and with high accuracy, they are a perfect use case of AI.
The automated decision-making by AI bots is certainly enhancing productivity in instances where excessive time is consumed to engage with multiple stakeholders in the entire value chain of a business. The trend is only going to rise. According to Gartner, by the year 2023, 40 percent of infrastructure & operations (I&O) teams will deploy AI-augmented automation in large businesses.
In addition, monitoring processes with the help of AI-based tools is bringing in high levels of efficiency. To illustrate, in the manufacturing sector, production processes are being automated, monitored and integrated to create optimum use of resources. The staggering amount of data available in manufacturing processes create the ideal environment to help train AI models.
IoT data makes AI a necessary tool
One of the major factors for the rising AI trend is the vast amount of data collected by internet of things (IoT) devices used in various industries. Although leveraging analytics on top of the IoT sensor data has been used in predictive maintenance for a long time, it is no longer limited to data analytics.
For example, AI models developed using data generated at various stages of a supply chain with IoT sensors are helping in smooth integration as well as intelligent monitoring. The application of AI in logistics using IoT-collected data similarly helps in the improvement of processes as well as prediction of unwanted events as goods move from point A to B. The same analogy holds true for autonomous vehicles as it is the data points during the route which are used to train AI for a safe journey.
Adapt or Die: Fighting against AI is a losing battle
Artificial intelligence holds the power to disrupt the prevalent business models across all industries, and also create new ones. This is certainly one of the reasons why many businesses are apprehensive of the technology.
But, technological advancements do not care of the market impact. So whether legacy businesses like it or not, artificial intelligence will eventually push them to carefully identify the AI opportunities in their operations. Eventually, it is predicted that the competitive pressure will push most businesses to adapt and adopt AI in order to sustain themselves.
"While organizations see continuing challenges with staffing, data, and other issues deploying AI solutions, they are finding that they can help to significantly improve the bottom line of their enterprises by reducing costs, improving revenue, and providing better, faster access to information thereby improving decision making," according to David Schubmehl, research director, Cognitive/Artificial Intelligence Systems at IDC.
The roadmap for enterprises
According to IDC Worldwide Semiannual Artificial Intelligence Systems Spending Guide, global spending on artificial intelligence systems will climb all the way up to USD 35.8 billion in 2019, a 44 percent jump from the amount spent in 2018 on AI systems. The spending on AI systems will more than double to USD 79.2 billion in 2022 with a compound annual growth rate (CAGR) of 38.0% over the 2018-2022 forecast.
While spending on AI applications is only going to dramatically rise, the big task for enterprises will be to make sure they have the right talent, data and technological resources to make the best use of the AI opportunity.
The thing that today's dominant business players and IT leaders must keep in mind is how vulnerable their business models are to AI disruption. This will automatically propel them to embrace AI and explore the infinite opportunities that the technology offers to them.