Measuring Business With AI: Unleashing The Power and Overcoming Challenges

In today’s rapidly evolving business landscape, companies face a multitude of challenges when it comes to measuring their performance and making data-driven decisions

With the rise of artificial intelligence (AI) technology, businesses now have access to powerful tools that can revolutionize the way they measure and analyze their operations.

As AI continues to evolve both in its technological capabilities and its practical applications, businesses find themselves at a critical juncture. The era of AI experimentation confined to test labs is fading away, as companies increasingly embrace large-scale deployment, leading to remarkable transformations. 

In the realm of AI, measuring business success becomes paramount. Metrics for evaluating AI performance, such as accuracy, efficiency, and customer satisfaction, must be established and monitored consistently. By aligning AI initiatives with well-defined objectives and regularly evaluating their impact, businesses can ensure that AI remains a valuable tool rather than a risky gamble.

“Now more than ever, businesses have vast amounts of data at their fingertips. Interpreting that data and making it useful for business growth and customer success is where AI can really shine.

Take for example contracts, something organizations use nearly everyday to conduct business. By definition, these are static documents. By integrating AI tools into such business processes, companies can take advantage of the wealth of data available in a meaningful way to inform strategic planning, mitigate risk, supercharge customer experience, and support sustainable growth. For leaders, this means being able to easily extract data and holistically view key risks and insights across their organizations in a dashboard format. This is how DocuSign sees the future of agreements and believes that the power of AI can drive business outcomes.”

Kartik Krishnamurthy, Area Vice President, Asia, DocuSign

DBS, a prominent financial institution, utilizes AI technology to measure businesses in various domains. In consumer banking, DBS employs hyper-personalized nudges to provide tailored recommendations to five million customers, helping them optimize their finances. In institutional and SME banking, DBS utilizes AI and data analytics to offer instant financing of up to SGD 300,000 with a streamlined application process. Additionally, DBS has introduced an AI/ML-powered career development platform called “iGrow” in their human resources department. This platform leverages Natural Language Processing to create personalized employee profiles, curate insights, and offer recommendations for career advancement and skill development within the organization. By embracing AI technology, DBS is enhancing customer experience, enabling efficient banking processes, and fostering employee growth and development.

 In this article, we will explore the role of AI in measuring businesses and provide examples of how AI is transforming various aspects of the measurement process.

“AI can assess vast amounts of data with greater accuracy and speed, enabling organizations to obtain more reliable performance and operational guidance. AVEVA Predictive Analytics, for instance, allows businesses to identify asset anomalies weeks or months before potential failure. It offers prescriptive advice — such as actions to remediate problems — to maximize asset reliability and prevent unplanned downtime. Furthermore, AI serves as a reliable measurement tool to help companies achieve their net-zero targets. Using predictive analytics and insights of industrial data collected allow organizations to track their carbon emission levels across operations, enabling them to explore innovative routes to achieve their sustainability goals with data. AI is playing an increasingly vital role in the world’s transition to greener operations.”

Jim Chappell, Global Head of AI and Advanced Analytics

AI-powered Data Analytics

AI’s ability to analyze vast amounts of data quickly and accurately is a significant contribution to measuring businesses. Unlike traditional methods, AI algorithms can process and interpret massive datasets at a fraction of the time it would take a human analyst. This enables the identification of patterns, prediction of customer behavior, and optimization of pricing strategies through machine learning.

“Tapping on Cognitive AI helps to ensure the accuracy and reliability of AI-generated measurements, as it can provide reliable recommendations even under circumstances with limited data availability or when data is contradictory, and in situations where making decisions can pose a high level of risk due to uncertainty.”

Leonard Lee, President, APAC, Beyond Limits

The emergence of AI has brought about a seismic shift in the marketing industry, transforming it beyond recognition. AI’s power lies in speeding up data analysis and making sense of it. Marketers can now leverage vast quantities of data, automate complex processes, personalize content, and make highly accurate predictions, revolutionizing their strategies.

An example of how Walmart utilizes AI in real-time data analytics is through AI smart pricing. By experimenting with Wi-Fi-enabled cameras in their meat aisles, Walmart can adapt prices in real-time to reduce spoilage and waste. This smart pricing approach has resulted in a remarkable 30% sales boost in the department, demonstrating the tangible benefits AI brings to businesses.

“Improved accuracy: AI algorithms can process large amounts of data and identify patterns, trends, and relationships with greater accuracy than human analysts. This leads to more reliable measurements and evaluations of businesses.

Enhanced efficiency: AI-powered tools can perform complex calculations and analyses at a vastly faster pace than humans, enabling businesses to make informed decisions more quickly and efficiently.”

John Collins, Managing Director, Data & Analytics, FTI Consulting, Singapore

Customer Sentiment Analysis

Customer sentiment analysis is a powerful tool that leverages natural language understanding, text analysis, and machine learning algorithms to extract subjective information from customer feedback. By determining overall sentiment towards a product, brand, or service, businesses can make informed decisions based on customer feelings. However, manual analysis of customer sentiment is both time-consuming and complex.

“AI allows businesses to sharpen business performance through better performance reporting and extract useful data to distill insights. These insights optimise business processes and help them be more customer-centric through the delivery of customised offers, diversify revenue by identifying new areas where the business can operate as well as manage businesses risks with fraud detection.”

Daniel Hand, Field CTO, APJ, Cloudera

This is where AI comes in, revolutionizing customer sentiment analysis. AI  enables automated real-time processing of large volumes of customer feedback, offering businesses valuable insights and actionable information on a larger scale. With AI algorithms, the analysis is not only faster but also more accurate, reducing the likelihood of mistakes.

“AI and machine learning can also help brands understand customer preferences and choose the best time and way to send messages, which can improve Return on Investment. With AI automation and optimization, brands can analyze customer behavior, find the right audience, and communicate with customers personally at every stage of the customer lifecycle.”

Yashwanth Kumar, CoFounder & CTO & CISO at MoEngage

Supply Chain Optimization

Supply chain management is crucial for company operations as it involves the network between a company and its suppliers. Any disruptions in the chain can lead to manufacturing, distribution, and delivery issues. Accurate inventory management is essential to maintain the flow of items in and out of a warehouse, preventing stock-outs and overstocking. AI-driven supply chain planning tools can analyze large datasets quickly, offering insights for forecasting supply and demand, predicting consumer habits, and minimizing costs.

“AI’s potential is endless. The technology has successfully redefined the way businesses work and its application continues to improve the lives of many. However, the future AI in business measurement will also require a shift in mindset, from a focus on quantifying past performance to predicting future outcomes and making strategic decisions based on these predictions.”

Nicolas Paris, Chief Data & Technology Officer, Aboitiz Data Innovation (ADI)

AI programs play a vital role in improving supply chain forecasting by anticipating customer demand and adjusting the capacity accordingly. This enables businesses to add capacity during busy periods and scale down during slower periods to reduce costs. Accurate inventory management is crucial for smooth warehouse operations, protecting against under- or overstocking. However, managing inventory involves various complex factors, making it time-consuming, expensive, and prone to errors. AI’s ability to handle vast amounts of data makes it highly effective in managing inventory and streamlining supply chain processes.

Employee Performance Analytics

AI enables real-time productivity reporting across individuals, teams, and the entire organization, ensuring projects are completed on time and allowing for proactive adjustments. AI-driven tools like MetaSpark provide insights by analyzing metadata, offering unprecedented visibility into employee contributions toward goals and OKRs.

“Assistive AI helps employees in their flow of daily work, enabling them to complete simpler, more repetitive tasks faster and with less effort, so they can focus on more challenging tasks that make better use of their creativity and domain expertise. In data center operations, for example, AI systems analyze huge volumes of alerts and failure messages from logs of servers, networks, services, and applications to identify a few plausible root causes. A data center administrator can then use that information to determine an appropriate fix.”

Wee Luen Chia, Managing Director, and Area Vice President Asia at ServiceNow

AI offers an alternative to intrusive surveillance software by providing productivity scores based on output, allowing managers to reward hard work and focus on critical business aspects. This approach improves morale and trusts while identifying high performers and areas that require support, ultimately enhancing overall productivity and timely task completion.

Financial Forecasting and Risk Management

“Generative AI can drive more possibilities by providing more angles for analyzing existing data and bringing diversity to predictive performance analysis. This drives value from post-performance analysis, and real-time monitoring to future predictions. In addition, human-bot collaboration has been found to maximize efficiency, as demonstrated in our real business cases leveraging LLMs.”

Ms. Jennifer Zhang, Co-Founder, and CEO, WIZ.AI

AI-powered financial forecasting enhances risk assessment and fraud detection capabilities, enabling businesses to minimize potential losses and reputational damage. By analyzing historical data and identifying patterns associated with fraudulent activities, AI models can flag suspicious transactions in real time and adapt to evolving fraud techniques. This proactive approach strengthens risk management practices, providing organizations with the ability to make reliable forecasts and stay flexible in the face of market volatility and economic uncertainty. AI platforms evaluate historic data related to supply and demand, sales figures, inventory management, and internal costs to generate accurate predictions and roadmaps for future sales and operational costs.

AI and machine learning assist businesses in navigating industry and government regulations while mitigating the risks of fraud and cyberattacks. Advanced software can review contracts, ensuring clarity and conformity to established regulations. Machine learning capabilities enable a comparison of contracts to the preferred language in the corporate database, assessing risk levels to support decision-making. These AI-powered tools provide businesses with efficient compliance practices, reducing legal risks and ensuring adherence to regulatory requirements.

“I foresee that AI will evolve rapidly in this field, be it for automation, prediction, or processing inputs such as languages, images, and sounds. Concurrently, we will currently observe a lot of collaboration between humans and AI in many tasks that were restricted to humans only, most notably ChatGPT. Therefore, I believe we are not too far from the days when we will see AI advisory boards supporting executive functions and decisions.”

Pascal Weiss, Regional Chief Information Officer for Asia Pacific, NTT Ltd.