There is a pattern that repeats across enterprise AI deployments, and it rarely surfaces until it is too late. A system gets built, cleared through procurement, and deployed at scale. However, during the rush to production, there is often one very important question that is omitted: Who will this affect, and how do we know it will be fair for them?
Founder & CEO of
FireAI
In 2024, more than one-third of all enterprises will report experiencing negative consequences due to AI bias, resulting in loss of revenue and customers. By 2026, data regarding the performance of certain AI medical systems indicate that they have a 30% higher likelihood of producing an adverse outcome for specific patient populations as compared to others. These are not edge cases. They are the direct result of scaling AI without governance frameworks that can keep pace.
Why Governance Has Become a Business Imperative
While most technology leaders accept AI bias as a tangible risk, very few have built the infrastructure to manage it. A major 2026 state of the AI industry report reveals that only 34% of organisations are genuinely reimagining their operations through AI. For the rest, the barrier is not a lack of ambition but a lack of accountability structures.
The cost of this gap is high. Most estimates indicate that approximately 85% of enterprise-level AI initiatives fail; poor governance is repeatedly identified as a major factor in these failures. Additionally, mid-cycle abandonment rates for these projects doubled in one year. Even among enterprises conducting bias testing, 77% still uncover bias in production models. Point-in-time testing is clearly no longer enough to protect the enterprise.
India Is Building a Serious AI Governance Architecture
Regulators recognise this shortfall and are moving aggressively. While the EU AI Act set global expectations, India has constructed a formidable governance architecture of its own over the past twelve months. The RBI FREE AI framework, launched in August 2025, mandates strict lifecycle management and bias audits for financial institutions. This was a direct response to findings that fewer than 15% of banks monitor post-deployment model drift.
The positive trend of AI in India has continued with the AI Governance Guidelines from the Ministry of Electronics and Information Technology, and the introduction of the AI Ethics and Accountability Bill in the Lok Sabha, which includes, among other things, mandatory bias audits for High Risk AI systems. Most recently, at the AI Impact Summit in early 2026, the MANAV framework was launched, establishing a national benchmark for AI to be created ethically and legitimately. Companies that are forward-thinking should be working now with a clear, understandable set of regulations that are in place.
What Human-Centric AI Looks Like in Practice
To be successful in this evolving world of artificial intelligence, the application of human-centric approaches to artificial intelligence must be adopted. Human-centric artificial intelligence is often misperceived as conservative and safety-focused; it is simply the process of designing products or technologies around the human experience through an appropriate central design methodology. Operationally, this relies on three foundational pillars.
First is explainability. Every AI recommendation affecting a hiring outcome or credit decision must be traceable to an understandable reason. Enterprises simply cannot govern what they cannot explain. Second is accountability. AI accelerates human judgment but must never substitute for it. Every production system requires a named owner and a clear escalation path. It is also important for businesses to ensure that they are using a large number of diverse sources of training data; have the ability to test that training data across a wide variety of people; and have a diverse set of engineers working on new technology to make sure that their company operates inclusively, not just a wishful thinking goal.
What Enterprises Need to Do Differently
There has been a transformation occurring within leading organisations regarding how they will begin implementing their principles. Within the next couple of years, most of the leading organisations moving forward will be moving from defining their principles to demonstrating these principles via measurable structures and methods of rigorous evaluation. This conversion will start in the boardroom, as AI governed only by technology is inherently reactionary; rather, when the different leadership areas of legal, HR, risk and business share governance authority, governance is then performed at the correct strategic altitude.
A model is only the first step of this transitional phase. Models drift over time. Bias will show up within the produced model that is not visible when the model is tested. Continuous monitoring of the model, using pre-defined limits for when to close the loop with humans, is critical to continuing the trust placed in the model long after it was originally deployed.
Indian organisations are currently positioned in the middle of a transitional phase. As the scale of AI being deployed is enormous across banking, healthcare, agriculture and retail, when organisations make governance decisions today, they will impact millions of people. They will not have time to hesitate; instead, they will need to make decisions based on precision. Organisations that begin the process of embedding ethical governance into their DNA now will build the trust they require to enable continued adoption. In the AI era, trust is not a “soft” metric; it is the extremely hard foundation upon which everything will be built.
–Authored by Vipul Prakash, Founder & CEO of FireAI