AI success relies on setting clear goals and measuring performance, adaptability, ethics and business value, yet many projects remain stuck in pilots due to risk concerns.
Trust and localisation are vital, with transparency to address the “black box” issue and sovereign models like Sea-Lion improving regional accuracy and relevance.
Continuous monitoring, fairness checks and real-time refinement are essential for businesses to unlock AI’s full long-term value.
As businesses across India and the world increase their investments in artificial intelligence (AI), a key question arises: How do we measure if it is really working? The answer is not simple because AI success depends on what each organisation is trying to achieve. Whether the goal is better customer service, smoother operations or faster and more accurate predictions, it is important to clearly define what success looks like. Without this clarity, it becomes difficult to evaluate results in a meaningful way.
To properly assess the impact of AI, companies should look at four broad areas. First is performance: Are the AI models consistently delivering accurate results? Second is adaptability: Can the system function well in real-life, unpredictable conditions? Third is ethics: Are we actively checking for and reducing any bias in how the AI behaves? Finally, business value: Is there a measurable return in terms of cost savings, revenue growth or customer satisfaction?
A major hurdle in realising AI’s potential is the gap between pilot projects and actual deployment. Many companies launch proof-of-concept models but do not move them into production due to concerns about risk and reliability. Since these risks are difficult to measure, companies often delay deployment. This means significant investments in AI across industries, platforms and hardware remain underutilised.
While AI tools show promise in automating repetitive tasks they work best when applied to structured processes or what engineers call “state machines” where rules and outcomes are clearly defined.
Opening the AI Black Box
Another concern is the so-called “black box” nature of AI. When people do not understand how a model arrives at its conclusions it is hard to trust the system. That is where techniques like input-sensitivity testing and neural activation analysis are helpful. They allow engineers to look inside AI systems, understand their behaviour and detect errors or inconsistencies before they reach customers. Beyond standard benchmarks companies need to use in-depth evaluations tailored to their actual business scenarios. For example, how does the AI handle a vague or confusing customer query? This kind of real-world testing is key to building reliable systems.
Making AI Local, Trusted and Evolving
Localisation is another crucial factor. Many AI models are trained mainly on English or Chinese data and fail to perform well in regions with different languages or cultural contexts. This is especially true in Southeast Asia and applies to parts of India as well. In response we are seeing the rise of “sovereign AI” models that are designed to reflect local realities. One example is Sea-Lion, a large language model created for Southeast Asia in a partnership between AI Singapore and Thoughtworks. It is trained in the region’s 11 official languages and better understands local use cases. Such models reduce bias and make AI more useful in the markets they serve.
Building trust in AI systems is critical. According to the 2025 Edelman Trust Barometer, an annual global trust survey conducted by the Edelman Trust Institute, there is still a significant trust gap. When AI agents are allowed to take action within systems, we need to be sure they are safe, fair and reliable. This means testing them with real-world data, using diverse training inputs, applying fairness checks and following clear ethical practices. Transparency builds trust and without trust adoption will always lag.
It is also important to understand that AI is not like traditional IT systems. It is not something you set up once and forget. AI needs regular monitoring, testing and adjustments. As the tasks evolve so must the AI models and the tools around them. This flexible, ongoing process is essential for long-term success. When it comes to the fear that AI will replace jobs we must be honest about what AI can and cannot do. In reality making AI easier to understand and use, just as web development was simplified through tools, will help more people adopt it and benefit from it.
For businesses looking to make the most of AI five steps are essential: define clear measurable goals, use both standard and practical evaluations, make AI decisions easier to understand, stress-test for fairness and reliability and constantly refine the system with real-time feedback. The real opportunity with AI lies not just in adopting it but in using it smartly and responsibly to drive growth and deliver real value.
