3 Ways In Which Machine Learning Streamlines Corporate Restructuring

 

The criticality of getting corporate restructuring right is hard to overstate. As you may know, restructuring is normally carried out when an organization is not in the best financial health. A complete overhaul of existing working methods and the overall structure of an organization to avoid financial crises and stabilize business performance necessitates the proper extraction and use of data and resources. Corporate restructuring involves adhering to a robust business strategy while carrying out SWOT analysis, creating new strategies for the future, adding and eliminating operations and resources depending on financial requirements and launching a new brand language, if necessary, to turn the fortunes of a failing business around.

Corporate restructuring is a data-driven process. To successfully implement it, businesses need to accurately evaluate continually changing data such as quarterly revenue records, purchase trends, personnel performance statistics, capital and revenue expenditure and many more. Analyzing large volumes of such data is impossible for humans or even basic computers, necessitating the presence of AI in the mix. The use of enterprise AI in organizations is not a novel concept, with businesses already using the technology for various purposes.

Involving machine learning in corporate restructuring can improve the following aspects of the process:

By Facilitating Improved Business Strategies and Structures

Normally, business restructuring begins with finding the business problems that plague an organization. The problem could be related to a specific aspect of an organization's operations, such as poor customer experience and grievance redressal, high overhead expenses, issues with meeting regulatory compliances, frequent logistics-related issues such as procurement bottlenecks and others. Finding such problems lets organizations arrest their falling ROI and work towards renewed growth. As stated above, detecting these problems is only possible after an organization has scanned through thousands of physical or digital documents and records. Machine learning algorithms are trained to identify underlying patterns in such documents and provide valuable inferences to those tasked with overseeing the restructuring process. For example, an enterprise AI-powered application can check two separate, seemingly-unrelated records—say, compliance-related losses and records of cyber-attacks faced by an organization—before informing the restructuring officers that several expenses are incurred due to inadequate data security measures in place.

One of the biggest reasons for restructuring failure is the incorrect identification of business problems. The steps of restructuring involve identifying problems across all the departments—accounting, HR and others—of an organization. In nearly all situations, such problems are not visible on the surface, making it necessary to have them “extracted” out from massive amounts of data. Enterprise AI is not perfect on its own, with commonly-associated issues related to algorithmic explainability constantly present. However, machine learning and AI streamline the process of SWOT analysis better than any other technology or resource.

Finding business problems precedes strategy formulation for the long term. The data and insights generated from the first step are used to reimagine business operations. For example, something as simple as lowering packaging costs can be achieved by reducing the number of boxing layers or packaging materials for products. Enterprise AI is also useful for future forecasting—for example, using past audience purchase history as a reference point before carrying out dynamic price cuts during certain times of the year. Predictive analysis uses several factors—market competition, strategies of competitors, amongst others—to enable organizations to make robust operational, legal, pricing, marketing and other strategies.

As stated earlier, corporate restructuring may also be involved when organizations introduce enterprise AI in their daily operations. The incorporation of enterprise AI will prompt businesses to alter their organizational structure for the better. The penetration of enterprise AI in business operations will make organizations change their HR departments, create new training mechanisms and make changes to the way they hire workers. Several businesses are already preparing their existing personnel for the future of automated operations. Businesses, and the general public at large, must understand that AI and automation will not put people out of employment extensively, but necessitate the creation of new roles and upskilling of employees. Enterprise AI causes organizations to work in a more team-oriented and collaborative way than the traditional superior-subordinate structure. This was corroborated by a 2021 study, which found that 32% of businesses are redesigning themselves to accommodate a more team-centric approach.

In short, enterprise AI positively influences corporate restructuring either in forecast-driven strategy formulation or by altering the hierarchical structure of an organization.

By Improving Employee Performance Management

Employees are a major part of corporate restructuring. In fact, several causes of restructuring failure rise from employee-related issues—such as not creating an all-pervasive employee culture, not keeping employees at all levels in the loop during corporate reconstruction and several others. Several restructuring decisions revolve around employees and the value they bring to an organization. Occasionally, tough decisions, such as laying off employees or diverting them to different roles, are also needed to be taken during restructuring, necessitating in-depth performance evaluation of every employee in an organization. Machine learning-based performance assessment and monitoring streamlines this process.

Enterprise AI can automate the process of employee performance assessment. Automation saves a large amount of time and resources for an organization and its employees. For example, employees and HR officials do not have to scramble to find data in different files and folders. AI involves bringing all data about an employee in a single database. All the stakeholders and executives in the leadership group can refer to the database while evaluating an employee's performance over the past year. Machine learning facilitates predictive analysis of an employee—essentially, assessing the performance, productivity, reliability and longevity of an employee in the past to provide forecasts of how they'd perform in the future.

Apart from forecasts, AI improves performance evaluation in multiple ways. Firstly, machine learning algorithms configured to evaluate employee productivity and performance can execute the task in real-time on a daily basis. Essentially, the evaluation data will keep getting compiled through the period of a year and later can be assessed by the management in a one-on-one interview. Automated performance reviews have their fair share of benefits over traditional ones. Firstly, because the evaluation is carried out by AI, human managers only have to review the assessment. This eliminates the possibility of employees being rated poorly due to biases and favoritism.

Enterprise AI lets organizations know the strengths and weaknesses of each employee. With this information, businesses can assign new roles to their workers if the old ones are automated or made redundant after the restructuring. In this way, employees are laid off only when, according to the organization, they bring no value either in the present or future. Thus, the possibility of mass firing is avoided. Also, AI is useful for suggesting upskilling or training courses for specific employees to sharpen their skills and knowledge and further improve on their strong points.

In simple words, employee performance and talent management can be optimized during corporate restructuring with enterprise AI and machine learning.

By Ensuring Optimal Cash Flow and Liquidity Management\

Liquidity is the main requirement for carrying out restructuring. As one can imagine, several funds are needed to successfully implement the restructuring. An organization may need to borrow money from banks or other financial institutions for this purpose. Such borrowings may entail compliance-related issues. Enterprise AI can help organizations avoid compliance-related problems regarding creditworthiness and other aspects while borrowing money for the task.

Apart from that, data monitoring and cash flow analysis lets companies optimize their cash flow performance. Enterprise AI-based applications also enable organizations to make accurate, data-driven decisions during investments. Machine learning scans the performance and investment-worthiness of stocks before a business can put its finances on an asset. AI completely eliminates human error and impulse-driven decision-making from cash flow management and records. Therefore, with machine learning, there will be fewer situations in which the cash flows are negative.

This, in turn, lets organizations manage their liquidity efficiently. This results in organizations having to rely less on borrowing money. Organizations that use their own funds for restructuring have greater chances of seeing the process through successfully for the long term.

Corporate restructuring is a long process that takes weeks, months and even years in some cases. During this phase, data keeps generating and evolving. Using this data in real-time to improve a business can be guaranteed exclusively by enterprise AI and machine learning.

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Article Source: https://www.forbes.com/sites/naveenjoshi/2022/03/28/3-ways-in-which-machine-learning-streamlines-corporate-restructuring/?sh=52892a6e2a7a