Efficiency Redefined: AI's Role In Optimising Workplace Processes

Facing cost pressure from various market forces, businesses are always looking for ways to streamline operations. Artificial Intelligence (AI) is proving indispensable in their journey of optimization. A 2024 survey by a global consulting firm found that generative AI is now used by 65 per cent of organisations, up from a third last year, in more than one function to reduce operational costs and improve revenue.

Advances in AI – predictive analytics, machine learning, natural language processing, image recognition and now, generative AI – have given it virtually universal applicability. Corporations around the world are adopting smart manufacturing to unlock value in different ways – for example, they are leveraging analytics for real-time visibility into operations to identify bottlenecks, improve production processes, optimise workflows and build supply chain resilience. 

They can use AI to build a digital twin of their factory – a virtual replica of a physical plant down to the last detail – to simulate any production process or asset, test-fit tooling, or improve a certain operation, before implementing it for real. Image recognition tools are redefining quality benchmarks by identifying product defects early in the manufacturing process, while sensors affixed to shop floor equipment are facilitating predictive/ preventive maintenance to save downtime and repair costs. Collaborative robots or cobots are working alongside human beings on the shop floor to enhance both worker productivity and safety. More recently, generative AI is enabling product developers to design products at a speed and efficiency that is impossible with traditional means.

Far from the world of manufacturing, financial institutions are turning to AI to optimise processes in credit scoring, risk management, compliance and fraud detection: machine learning and predictive analytics solutions analyse vast quantities of structured and unstructured data to conduct a more holistic credit evaluation of customers beyond traditional scoring, study transaction patterns to detect suspicious activity in real-time, identify various risks, and predict market behaviours.

Brand marketers are using AI to save cost, time and effort by automating tasks ranging from media buying to content creation. According to a leading provider of insights, by 2025, 30 per cent of marketing messages issued by large corporations will be AI-generated. By employing generative AI tools for conceptualising and generating the content, brands could free up 5 to 10 per cent of marketing bandwidth. 

They can also use AI to analyse marketing performance data and trigger an appropriate response in real-time. By making use of the full range of AI’s creative and cognitive capabilities, brands can optimize marketing campaigns to reap double-digit cost savings.

By streamlining workflows and automating repetitive jobs, such as administrative tasks, report writing, meeting summarisation and the like, AI is freeing up time to allow employees to focus on exercising their unique capabilities and judgement for solving key problems, building innovations, mentoring co-workers, etc. What’s more, generative AI is proving its worth even in high-skilled jobs by raising the performance of users by 40 per cent compared to non-users.

But AI is more than just efficiency

Optimisation is AI’s low-hanging fruit that all organizations will certainly pick. Once the providers in an industry are equally automated and efficient, consumers will choose the ones with the most innovative offerings or engaging experiences. Hence automation aside, enterprises should exploit AI for its rich insights, so they can make faster, better and more accurate decisions, innovate great products, and provide targeted recommendations and memorable experiences to customers. For example, investment advisory firms that have traditionally relied on relationship managers can use AI models trained on customer data – financial position, risk appetite, investment goals etc. – to deliver personalised advice on saving, investing and trading strategies. Brands can use the combined insights of Customer Data Platforms (CDPs) and AI to segment customers in new ways (by behaviour, for instance), offer targeted recommendations, and provide seamless service through natural language interactions. For example, when a global sporting goods retailer used an advanced AI platform to segment customers based on 5000 attributes, and also for personalised marketing and engagement, it was able to grow purchases by 35 per cent.

Be AI-first, but responsibly

As organisations transform their operations through AI, they should also work on transforming themselves into AI-first enterprises, always considering AI before any other solution to resolve a business problem. Of course, the adoption of an AI-first approach by an organization would be guided by its particular context – for example, highly regulated businesses, such as banks, would not want to use generative AI algorithms (which lack transparency and explainability) for risk and compliance management. Further, any application of AI should conform to responsible and ethical use, ensuring data security, privacy and confidentiality, true and accurate content, and unbiased, non-discriminatory algorithmic outcomes.