Key AI and ML Trends to Watch out for in 2022


The year 2021 is passing by, and the pandemic has essentially changed everything. Organizations using traditional techniques have either been forced to change their working models overnight or have become obsolete and left behind in this digital revolution.

Artificial Intelligence and Machine Learning have influenced the tech industry greatly by driving critical decision making, improving business intelligence, and creating innovative products and services.

“As per Gartner reports, 75% enterprises will shift from piloting to operationalizing AI, causing a 5X increase in streaming data and analytics infrastructure.”

Also, the global AI market is predicted to reach $190.61 billion market value by 2025. Having looked at these stats, AI and ML jointly collaborating with existing data play an important role in creating value for businesses everywhere.

So, let’s look at some of the top AI and ML trends for the coming year that the leaders, data scientists, and engineers cannot afford to ignore:

1. AI-powered business intelligence

The integration of artificial intelligence frameworks and predictive insights are transforming business intelligence like never before. Organizations that invest in AI for business intelligence are already ahead in the curve.

For instance, natural language processing allows any business user to query large data sets and identify critical insights in improved visuals and framework. With AI integration, users can also cut down on the time spent working on tedious excels and focus their time on more productive use.

2. AI, ML, and Cybersecurity

With the use of AI and ML, organizations are now making cybersecurity more automated and less risky.

“As per Markets and Markets research, the use of AI and ML in cybersecurity is expected to reach USD 38.2 billion by 2026.”

The new age cyber enemy does not wear a standard uniform, a unique language, or look different. Hence, it has become difficult to identify the variables to differentiate the good from the bad. One needs to track thousands of data points and activities of the concerned to identify suspicious activity.

AI and ML are useful when the parameters in the system are varied, inadequately correlated, dynamic, large, and cannot be identified with simple frameworks. The cyber systems using AI/ML help build human-like intuition and threat identification models using behavioral analysis, thereby helping in preventing attacks effectively.

3. AI, ML, and Intelligent Process Automation

Companies today are increasingly relying on and working with data to generate useful insights. Data science i.e., the process of cleaning, aggregating, and manipulating the data to provide powerful insights is found everywhere.

But, this data extraction process requires automation which in turn requires the use of advanced technologies, and this is called Intelligent Process Automation. Technologies like Artificial Intelligence, Machine Learning, Robotic Process Automation (RPA), And Cognitive Process Automation are used to simplify, design, and automate processes across business functions to give the desired results.

For instance, companies are developing AI-powered conversational chatbots to give advanced technical/customer support that involves automatically responding to queries, building machine intelligence to improve the context in answers, and providing user-specific guidance.

4. Integration of AI, ML, and IoT

Users are relying increasingly on connectivity, with an interesting estimate stating, “127 devices get connected to the internet every second.” Following up on these stats, the Internet of Things global market is expected to grow around 1.6 trillion dollars by 2025.

In this context, the integration of AI and ML with IoT helps automatically identify patterns and detect anomalies in data generated by smart sensors and devices. For example, fitness and health trackers, smartwatches, smart home devices like smart speakers and TV, and smart grids.

5. AI, ML, and Cloud

“As per Gartner reports, public cloud services will be essential for 90% of data and analytics innovation.”

Data and analytics are moving to the cloud but the data scientists and leaders find it difficult to control the integration overhead. Hence, they must prioritize their workloads and exploit the cloud capabilities well.

Integrating AI tools to private and public clouds helps monitor, manage, and self-heal when an issue occurs. AI automates core workflows with analytical capabilities creating improved processes that are independent.

Bottom Line

Shortly, these trends will influence businesses and, CIOs should consider using these opportunities to scale their existing capabilities, using them to their advantage in different parts of the business.

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