Harnessing AI for Operational Efficiency: Policy Pathways to Sustainable Advantage

Aditi Pangotra
Aditi Pangotra
Research Analyst, Policy & Advocacy, CyberPeace
PUBLISHED ON
Jan 30, 2025
10

A report by MarketsandMarkets in 2024 showed that the global AI market size is estimated to grow from USD 214.6 billion in 2024 to USD 1,339.1 billion in 2030, at a CAGR of 35.7%. AI has become an enabler of productivity and innovation. A Forbes Advisor survey conducted in 2023 reported that 56% of businesses use AI to optimise their operations and drive efficiency. Further, 51% use AI for cybersecurity and fraud management, 47% employ AI-powered digital assistants to enhance productivity and 46% use AI to manage customer relationships.

AI has revolutionised business functions. According to a Forbes survey, 40% of businesses rely on AI for inventory management, 35% harness AI for content production and optimisation and 33% deploy AI-driven product recommendation systems for enhanced customer engagement.  This blog addresses the opportunities and challenges posed by integrating AI into operational efficiency.

Artificial Intelligence and its resultant Operational Efficiency

AI has exemplary optimisation or efficiency capabilities and is widely used to do repetitive tasks. These tasks include payroll processing, data entry, inventory management, patient registration, invoicing, claims processing, and others. AI use has been incorporated into such tasks as it can uncover complex patterns using NLP, machine learning, and deep learning beyond human capabilities. It has also shown promise in improving the decision-making process for businesses in time-critical, high-pressure situations. 

AI-driven efficiency is visible in industries such as the manufacturing industry for predictive maintenance, in the healthcare industry for streamlining diagnostics and in logistics for route optimisation. Some of the most common real-world examples of AI increasing operational efficiency are self-driving cars (Tesla), facial recognition (Apple Face ID), language translation (Google Translate), and medical diagnosis (IBM Watson Health)

Harnessing AI has advantages as it helps optimise the supply chain, extend product life cycles, and ultimately conserve resources and cut operational costs. 

Policy Implications for AI Deployment 

Some of the policy implications for development for AI deployment are as follows:

  1. Develop clear and adaptable regulatory frameworks for the ongoing and future developments in AI. The frameworks need to ensure that innovation is not hindered while managing the potential risks. 
  2. As AI systems rely on high-quality data that is accessible and interoperable to function effectively and without proper data governance, these systems may produce results that are biased, inaccurate and unreliable. Therefore, it is necessary to ensure data privacy as it is essential to maintain trust and prevent harm to individuals and organisations.
  3. Policy developers need to focus on creating policies that upskill the workforce which complements AI development and therefore job displacement. 
  4. To ensure cross-border applicability and efficiency of standardising AI policies, the policy-makers need to ensure that international cooperation is achieved when developing the policies.

Addressing Challenges and Risks 

Some of the main challenges that emerge with the development of AI are algorithmic bias, cybersecurity threats and the dependence on exclusive AI solutions or where the company retains exclusive control over the source codes. Some policy approaches that can be taken to mitigate these challenges are:

  1. Having a robust accountability mechanism. 
  2. Establishing identity and access management policies that have technical controls like authentication and authorisation mechanisms. 
  3. Ensure that the learning data that AI systems use follows ethical considerations such as data privacy, fairness in decision-making, transparency, and the interpretability of AI models.

Conclusion 

AI can contribute and provide opportunities to drive operational efficiency in businesses. It can be an optimiser for productivity and costs and foster innovation for different industries. But this power of AI comes with its own considerations and therefore, it must be balanced with proactive policies that address the challenges that emerge such as the need for data governance, algorithmic bias and risks associated with cybersecurity. A solution to overcome these challenges is establishing an adaptable regulatory framework, fostering workforce upskilling and promoting international collaborations. As businesses integrate AI into core functions, it becomes necessary to leverage its potential while safeguarding fairness, transparency, and trust. AI is not just an efficiency tool, it has become a stimulant for organisations operating in a rapidly evolving digital world.

References

PUBLISHED ON
Jan 30, 2025
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