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Four Pitfalls Businesses Need to Avoid while Adopting Artificial Intelligence

By Prangya Pandab - April 29, 2022 3 Mins Read

Four Pitfalls Businesses Need to Avoid while Adopting Artificial Intelligence

AI development is primed to drive more innovation with the arrival of 5G. Leaders who are aware of the possible drawbacks have the best chance of capitalizing on the tremendous enterprise potential of AI in this environment.

The explosion of Artificial Intelligence (AI) in the past decade has provided humanity with a plethora of useful tools and technologies. Enterprises are more efficient, decision-makers are more informed, and customers can have better experiences thanks to AI tools.

In response to the COVID-19 pandemic, businesses across industries are turning to AI and advanced analytics to improve supply chain resilience, agility, and efficiency. For many, this means implementing new AI-powered capabilities in their company, such as real-time inventory optimization, risk-based scenario planning, and predictive analytics that enable procurement and supply chain to work together efficiently. 

However, there have been cultural and organizational challenges, as well as four main AI implementation risks.

Also Read: Four AI Challenges Enterprises Face in Supply Chain

Lack of access to high-quality data

Because information is continually being assessed, analysed, and used for actions and suggestions, data quality is crucial in AI. If organizations don’t provide AI with adequate data, or if the data quality changes without their knowledge, AI can make wild predictions and choices based on that data. 

In order for a company to profit from Artificial Intelligence in their supply chain, it’s critical to collect good data from as many relevant sources as possible, such as warehouses, production locations, freight monitoring apps, and order management systems. Instead of establishing their own integrations, the right technologies will allow organizations to easily “plug in” and profit from partner data, ensuring that they are not only relying on their own limited dataset.

Failing to pair AI with change management

The introduction of AI may intimidate some employees. This apprehension may lead to a refusal to properly embrace technology, which could jeopardize the success of the company.

One strategy to persuade employees to accept new and perhaps frightening changes is to emphasize how technology makes basic and tiresome logistics or supply-chain management activities easier. Teams can use AI to do things that would normally take a lot of little calculations and a lot of time, allowing them to do a lot more.

Bringing attention to these positive human outcomes, as well as the ways AI can boost individual and departmental performance, can help with change management. 

No C-suite sponsor

Adding AI to any business capacity or function will necessitate organizational change. AI implementation must be in line with a company’s strategy and include significant business-driven objectives and KPIs. To successfully start and deliver projects aimed at integrating Artificial Intelligence, IT teams will need the backing of other departments as well as an executive sponsor.

AI-powered tools enable new ways of doing business; rather than throwing a new technology at a problem and hoping for the best, it’s about developing business skills and capabilities.

Also Read: Recognizing and Eliminating Bias in Artificial Intelligence

A lack of training

It is not necessary for non-technical positions to understand algorithms and Machine Learning. Business leaders, however, will need appropriate training to appraise the required investment and prospective ROI of AI. They must retrain their model if the model was trained on certain assumptions and something changes in the future. As a result, it’s critical to train those involved so they know what they’re putting themselves into.

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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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