Monday, November 28, 2022

Potential of AI Operations in Enterprises

By Swapnil Mishra - July 21, 2022 4 mins read

The combination of Wi-Fi 6 and 5G mobility and an increasingly wired and mobile world of internet of things (IoT) technologies is expected to bring billions of additional devices onto networks in the coming years. This will significantly affect future workplaces, going beyond the apparent trends of remote workers and hybrid workforces.

As workplaces become more sophisticated and remote becomes the norm, the world is approaching a time when a large number of individuals will be able to engage with coworkers virtually from any location. In addition, virtual reality and IoT sensors will make it possible to bring remote expertise to any location in the world.

Difficulties in the implementation of AIops

AI and AI operations are the next and last step in the progression of automation for jobs comparable to those performed by human domain experts. Thus, the advantages of AI are well-known and increasingly sought after by business leaders. Many firms impede their progress in assisting the successful implementation of AI. Typically, they fall short on at least one of three main obstacles: building technology stacks, preparing the people, and establishing AI governance.

Many firms fall short on their progress in the successful implementation of AI. Typically, they stumble on at least one of three main areas: building technology stacks, preparing the people, and establishing AI governance.

  1. AI-ready technology stacks

Artificial intelligence is only as good as the data it needs to learn; generating, cleaning, and managing datasets and feature engineering remains the most significant technological obstacles to AI’s mainstream use. Whether due to a lack of data quality specialists or insufficient computational resources, etc., getting data ML-ready is a difficult task.

This data is derived from continuous network performance, health, and security monitoring. Capturing the proper data rather than just large amounts is a crucial preparedness difficulty. And the amount of data can be enormous, such as every change to a network user’s state. AI projects frequently fail without clearly defining what is essential and what is being automated. The data journey begins with a clear understanding of the human domain expertise the organization seeks to automate and the questions it seeks to answer.

Also Read: How to Approach the Right Data Architecture for Successful AI

  1. Readying the workforce

The advent of the “AI Era” has created three unique workforce challenges. In other words, organizations must educate their current employees and recruit from a competitive and limited pool of highly skilled data scientists and data engineers.

To overcome the first two obstacles, it is necessary to invest appropriately in training and business culture. There will always be more opportunities than people for highly skilled tech jobs, especially in AI/ML. Still, firms will be astonished by how much they can build on their own if they set the proper foundation and regularly train their personnel. Moreover, investing in existing employees is a means of convincing people that artificial intelligence is here to supplement and improve labor, not to replace humans.

Implementing tools and offering opportunities for all employees to utilize newly gained AI skills in their everyday workflows helps to solidify the belief that AI is here to enhance their daily experiences. While not every employee is required to learn to code, it is crucial to express that the ability to engage with and utilize AIops effectively can have enormous benefits for many professions.

  1. AI governance

The data dilemma extends beyond the question of how to identify the appropriate data. Equally challenging is what to do with all the data, particularly regarding risk, compliance, and security. Various reputational, operational, and financial stakes are associated with AI, but these risks are frequently not considered due to the discrete and walled nature of many projects.

There is currently a governance gap in the company, which is one of the most significant risks to AI projects. Although most executives acknowledge that they are responsible for implementing compliance standards, implementing such governance and procedures is frequently one of their lowest priorities. Organizations may overcome this gap by incorporating executive leadership and cross-functional stakeholders to ensure that projects with wide-reaching effects are assessed from a company-wide perspective, not only through the lens of a single department. In addition, there is great value in hiring AI-specific leaders and establishing an internal AI center of excellence to ensure governance receives the proper level of attention and investment and to facilitate the creation of consistent standards across the business.

Check Out The New Enterprisetalk Podcast. For more such updates follow us on Google News Enterprisetalk News.


Swapnil Mishra

Swapnil Mishra is a Business News Reporter with OnDot Media. She is a journalism graduate with 5+ years of experience in journalism and mass communication. Previously Swapnil has worked with media outlets like NewsX, MSN, and News24.

Subscribe To Newsletter

*By clicking on the Submit button, you are agreeing with the Privacy Policy with Enterprise Talks.*