Top Advantages and Disadvantages of Using an AI in Enterprise Risk Management

Enterprise Risk Management


Businesses are innovating with Artificial Intelligence (AI) applications to streamline their operations and gain a competitive edge. The use of this emerging technology in Enterprise Risk Management (ERM) is the latest innovation helping corporates identify potential risks and improve their operations.

As one of the leading emerging technologies, Artificial Intelligence (AI) is transforming businesses across industries. From market analytics, cybersecurity to self-driving cars and bots, enterprises are using AI in many different ways. In the last couple of years, organisations have also started integrating AI in their core corporate functions.

With the pandemic wreaking havoc across the global business landscape, businesses are more focused on their risk management strategies. AI is finding vast applications in Enterprise Risk Management (ERM) systems to help companies identify potential risks and improve their operations.

Should every business start looking for ways to use AI in their risk management strategy? Understand the advantages and disadvantages of AI integration in ERM can help make the right decision.

Advantages of Using AI in ERM

1. Smarter Data Processing

ERM processes often rely on the data collected from helpdesk tickets for gaining insights into the problems being faced by the customers, employees, and partners. But traditional systems and manual processes cannot analyse stored data as quickly or thoroughly as AI-enabled solutions can.

With an AI-powered system, businesses can view and analyse data from various departments in real-time to make critical business decisions.

2. Improved Forecasting Accuracy

With forecasting, organizations look for benchmarks based on which they can monitor business performance while also reducing the scope of uncertainty. One of the primary uses of technologies like machine learning and AI is to improve forecasting accuracy.

Be it analysing workforce needs, business demands, cash flow, or other core operations, AI-powered systems can minimise the gap between actual requirements and predictions.

3. Greater Agility

AI integrated systems make the core business processes more agile and streamlined. These systems can effectively track and respond to the changing market conditions to help an organisation explore opportunities and minimise risk.

Thanks to AI integration, these systems automate the routine manual tasks to provide your employees more time to focus on other core duties, leading to improved productivity and efficiency.

Disadvantages of Using AI in ERM

1. AI Bias

While AI is a robust tool for risk management, it also has its limitations. For instance, AI can also be affected by bias like humans. Without human oversight, an algorithm might use predictions that violate ethical or legal standards or are discriminatory in nature.

This makes it essential for businesses to rely on risk management professionals for building and implementing their ERM strategies.

2. System Dependence

Implementation of AI into Enterprise Risk Management often leads to dependency. Operations are upgraded around these solutions to use them as effectively as possible.

In case if the AI-powered system malfunctions, it can be challenging for the organisation to manage the consequences and continue its operations.

The Expertise of Enterprise Resource Management Advisors Can Help

Artificial Intelligence (AI) is a game-changer for ERM. But while there are several ways in which AI can improve risk management, like every technology, it has some limitations. The different ways in which the use of AI in risk management can backfire should be effectively analysed before it is integrated into any risk management strategy.

ERM advisors can make it easier for businesses to adopt this emerging technology in their risk management plan. The disadvantages or limitations can be effectively handled for specific use cases to enable organisations to utilise this innovative technology for boosting their risk management processes.

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