Beyond the trends: Realising AI’s true value through practical applications

By Dr. Srinath Perera – Senior Vice President and Chief Architect at WSO2

Introduction
AI often garners attention for its sophisticated algorithms and futuristic potential. However, at the end of the day, the true measure of AI’s success is not in its complexity, but in the tangible value it brings to real-world applications. A common pitfall in AI implementation is the obsession with impressive application of technology that may not add real value. The following story from an adjacent field of data science provides a striking example. A data science team in a fishing company was asked to analyze its data, but to only show positive trends. After much work, they could only find one positive trend—the growth in the weight of fish over time. This trend is true, yet trivial. The key takeaway here is that AI should be judged by the business value it generates, not just by its technical achievements or flashy metrics. In many cases, the pressure to showcase rapid success leads to focus on dazzling technology, which may divert attention from truly impactful applications. Thus, it is important for businesses to move beyond the trends to leverage AI effectively, focusing on practical, value-driven implementations.

Value-driven AI implementation
We can realize AI’s true potential only by solving specific business problems effectively. One notable case is in apparel manufacturing, where a simple image processing system dramatically improved quality control. In this scenario, a camera-based system checked the dimensions of garments, ensuring they met specific standards before moving to the next stage of production. This relatively straightforward application of AI technology significantly reduces errors and improves the overall quality of the products.

This example illustrates that impactful AI solutions often come from straightforward applications that directly address operational challenges. By focusing on solving the most pressing problems, and then asking how AI may solve these challenges, we can gain significant value from AI even with relatively simple implementations. Businesses should prioritise use cases where AI can provide clear, measurable benefits, rather than pursuing complex solutions that often do not address core issues.

Key use cases of creating value with AI
Enhancing customer experience: AI can make digital interactions seamless through personalisation and anticipation of user needs, creating a smooth user experience. For example, predictive text and recommendation systems can greatly enhance user satisfaction by making interactions more intuitive. Personalisation engines, powered by AI, can analyze user behavior to tailor content and product suggestions, thereby improving engagement and conversion rates.

Optimising customer support: AI-driven solutions, particularly with large language models (LLMs), have revolutionised customer support, enabling the efficient handling of queries and improving customer satisfaction. Companies have successfully used AI to handle up to two-thirds of all customer inquiries, significantly reducing response times and improving service quality. These AI systems can provide accurate, instant responses to common questions, freeing up human agents to tackle more complex issues.

Digital twins and simulations: By creating digital replicas of physical systems, businesses can simulate and optimise operations, leading to significant improvements in efficiency and problem-solving capabilities. This technique allows for real-time monitoring and predictive maintenance, thereby reducing downtime and increasing productivity. For instance, digital twins can be used in manufacturing to simulate production lines and optimise processes, or in urban planning to model and improve city infrastructure.

Critical considerations for AI success
Successful AI implementation begins with identifying clear value. AI projects should be driven by well-defined business objectives and value propositions. This starts with pinpointing specific problems and understanding how AI can address them. A thorough assessment of business needs ensures that AI applications align strategically with company goals, making them both technologically advanced and practically beneficial.

Before diving into AI, evaluating alternatives is crucial. Often, simpler solutions can achieve the same results more efficiently. Straightforward automation tools or process improvements might solve problems without the complexity and cost of AI. Considering non-AI solutions first can lead to effective, cost-efficient improvements, avoiding unnecessary investment in complex technologies.

Continuous monitoring and validation are essential once AI systems are implemented. Regular testing and refinement ensure that AI performs as intended and adapts to changing conditions and new data. Monitoring performance metrics and user feedback helps identify areas for improvement, keeping AI systems effective over time. This ongoing oversight maintains the relevance and accuracy of AI applications, allowing for necessary adjustments and optimisations.

Managing risks is another critical aspect. AI systems can introduce biases and operational risks that need to be mitigated through robust risk management practices. Addressing data privacy, algorithmic bias, and regulatory compliance ensures the ethical use of AI. Implementing safeguards and regularly reviewing these practices prevent and manage risks, ensuring AI applications are both effective and ethical.

Conclusion
To truly benefit from AI, businesses must focus on practical applications that drive real value. We need to start with identifying urgent problems and pain points, and focus on how AI can solve those problems rather than applying AI where it is convenient or in instances where it may look impressive.

Embracing AI with a clear strategy and a focus on real-world impact allows businesses to not only innovate but also achieve tangible improvements in efficiency, customer satisfaction, and overall performance. By moving beyond the allure of cutting-edge technology and concentrating on practical implementations, companies can realise the true potential of AI and secure a competitive edge in their respective industries.