Top 5 AI Automation Trends in 2022

Labor shortage has always been a problem that plagues many companies. There are plenty of entry-level positions available, most retail stores are hiring, and of course average salaries are rising along with them. Countries like Japan have long been present and are leading the way in leveraging AI to achieve higher levels of automation, with the goal of doing more with fewer people.

Here are some of the key trends in AI automation:

1. AI training and data quality

Ruben Orduz, senior developer advocate for the Great Expectations open data quality platform, said data engineers trained in artificial intelligence are taking the data quality process seriously. One problem is that real-world data is not neat or predictable. It may suffer from quality issues such as missing, truncated or invalid data, unexpected duplications and anomalies.

He said, "When messy data is used to train algorithms, the results can be catastrophic. Machine learning and artificial intelligence algorithms that rely on inference and adjustment based on input data Particularly vulnerable to poor data conditions."

For example, if a business sends and delivers goods, it receives a set of orders from a supplier that includes the name, address, and order number to be delivered. of goods. AI systems receive this data and use it to learn and plan delivery routes. If the number of delivered goods usually ranges from 1 to 30, but the data suddenly shows thousands of packages in the "number of packages", then this means that more delivery drivers will need to be hired, which indicates that the model is also biased. That’s why data engineers are working hard to understand the quality of their data and use data quality platforms to identify outliers and affected data. Doing this is now easier than ever. Engineers can discover, run pipeline tests as data streams come in, and catch outliers before entering the AI ​​training process.

2. AI Automation and Networking

Many companies such as Cisco, Juniper Networks, Gluware, and Splunk are investing in the automation of AI functions , to apply it to network troubleshooting and performance diagnostics. Network operations have traditionally required highly involved human resources. Artificial intelligence and data can automate many tasks.

The network is, after all, a complex system that encompasses a vast array of technologies, architectures and overlays, leading to failures and performance issues at many operating points. The manual aspects of the network are equally challenging. Despite some efforts to centralize operations to network controllers, network management remains largely manual and requires considerable expertise on the part of administrators. Furthermore, the work is largely exploratory, with managers trying to manually derive the root cause of the problem without advance insight or data. As a result, running a network in a traditional manner is costly, both in terms of service downtime or degradation, and in terms of the manpower required to implement the solution.

Stanislav Miskovic, Vice President of Artificial Intelligence at Gluware, said, “Leveraging AI to automate analytics is a huge opportunity and we are building a platform that will leverage and unify the entire network stack.

AI automation can help enterprises reduce network-related operational costs in a number of ways: Performing root cause analysis and localizing problematic sites, devices, and protocols ; Autonomous baselining of the infrastructure as a whole and all its components; Ranking the relevance of identified issues; For example, identifying supporting evidence and prescribing remediation to network engineers. But in the networked world, most of these functions still In its infancy.”

3. Artificial Intelligence in Cybersecurity

Just as networks generally need to be automated with more artificial intelligence, The same goes for cybersecurity. As threats become more sophisticated and the boundaries of enterprise networks become increasingly blurred, especially with the transition to cloud platforms, the amount of data that needs to be analyzed far exceeds the capabilities of manual inspection.

Miskovic said, “Artificial intelligence and analytics are key enablers for enhanced security. Today, security needs to cover a wider scope, which is not possible without the help of AI automation. Achieved. If the attack surface is too large and the amount of data is too large, it cannot be checked without the help of artificial intelligence."

This promotes the development of user and entity behavior analysis , which is a purely artificial intelligence-driven security field. Another AI-driven approach to security is the detection of zero-day attacks or unknown events, which would not be possible without AI automated baseline analysis and anomaly detection. Finally, AI automation helps security response teams by prioritizing security alerts, reducing alert fatigue and recommending corrective actions.

4. Process automation

Even with all the products that help monitor, manage, operate and secure enterprise infrastructure, the number of alerts they generate and the number of steps engineers need to take to resolve them becomes overwhelming. For this reason, AI-driven process automation is becoming an integral part of network operations, security operations, and ticket management.

Miskovic said, “Many AI-driven solutions have been developed to automate various aspects of alerting and problem-solving tasks. These AI automation solutions provide customizable Playbooks, which can perform many logistical or remedial tasks without human intervention. The system can autonomously learn the many playbook steps needed to solve a problem by recognizing patterns in what engineers have done to solve similar problems. These playbooks can also be performed by engineers Customized.”

5. Artificial Intelligence as a Service

AI engines and platforms are complex. Therefore, others will inevitably do the heavy lifting and offer AI-as-a-service as an alternative to AI-as-a-platform.

Frans Cronje, co-founder and CEO of DataProphet, said: “As enterprises and their data science teams have leveraged artificial intelligence platforms to leverage their strengths, they are beginning to build professional teams to do so. The added value of artificial intelligence, and the need for artificial intelligence systems to provide in-depth knowledge in the form of artificial intelligence as a service, will accelerate development."

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