Saturday, November 26, 2022

Four Strategies for Enterprises to Bridge the Skills Gap for Successful AI Implementation

By Prangya Pandab - June 09, 2022 3 mins read

To achieve their AI goals, companies must upskill and hire the right people and invest in new technology and tools. Today’s low-code and self-service technologies can help bridge the gap by democratizing Artificial Intelligence and Machine Learning, placing these transformative capabilities in the hands of more individuals with varying levels of expertise.

According to IDC’s Worldwide Semiannual Artificial Intelligence Tracker, the AI market, which includes hardware, software, and services, is expected to grow 19.6% year over year in 2022, with revenues reaching USD 432.8 billion. It is estimated that by 2023, this amount will have surpassed USD 500 billion. 

AI continues to be a significant driver of IT spending, which in turn drives spending on related services to ensure widespread adoption. Client need for expertise in developing production-grade AI solutions fuels IT services growth, while the need to establish the necessary business process, governance, and people strategies fuels business services investment. 

What is Preventing Businesses from Fully Utilizing AI’s Potential?

Enterprises have yet to deploy platforms that enable data scientists to be productive, allowing them to communicate while accessing the tools, data, and infrastructure they need. They don’t have sophisticated ways for monitoring and maintaining existing AI systems, much less developing and implementing new ones. 

The growing skills gap is jeopardizing AI deployment. Even the most powerful tech corporations are unable to leverage even a small part of AI’s potential. As a result, now is the moment for businesses to take proactive steps to learn how to leverage this technology. With more AI professionals on board, the gap between skills and AI implementation can be bridged. Here are some suggestions for businesses looking to close the AI skills gap. 

Educate Employees

Large portions of every company’s operations are yet to be AI-enabled. Due to a lack of scalable infrastructure, professional-grade tools, competent data scientists, and immature procedures, even the most AI-advanced areas of companies are still constrained.

Professionals who are AI literate and know how to properly apply AI solutions in their businesses are scarce and precious resources in today’s business world. Employees should get knowledgeable about AI technologies so that they may begin experimenting with ways to integrate them into the company.

Also Read: Four Impediments to AI Adoption for Enterprises

Effective Upskilling, Reskilling, and Training

Effective upskilling, reskilling, and training will benefit both individuals and companies, aiding businesses in establishing a resilient, agile, and high-performing workforce that can expand and adapt to changing business needs and the growing skills gap in the present and future.

AI Frameworks 

IT professionals interested in learning and understanding more about AI and pursuing a career in this field need to start with the basics. Before digging into the subject’s complex algorithms, they should first learn about AI frameworks, which can help them build AI applications more easily and quickly.


Chatbots are being deployed in customer-facing apps and websites to assist customers with a variety of tasks, including navigating the buying process, accessing information, and submitting support requests, to name a few.

However, chatbots are capable of much more. Businesses use Chatbots to integrate employee self-service applications to aid in employee onboarding and training.

Professionals can use Digital Adoption Platforms (DAPs) in conjunction with apps to guide them through complex processes and activities while maintaining their productivity. They can also use anonymized chatbots data to better identify and fix employee skill shortages.

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Prangya Pandab

Prangya Pandab is an Associate Editor with OnDot Media. She is a seasoned journalist with almost seven years of experience in the business news sector. Before joining ODM, she was a journalist with CNBC-TV18 for four years. She also had a brief stint with an infrastructure finance company working for their communications and branding vertical.

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