The reality of insurance in 2025 is wrapped around AI – from claims to underwriting. But the value creation remains extremely limited- all because of the contradiction that the insurers are grappling with.

The Indian insurance industry is one of the fastest adopters of AI but is also one of the slowest to realize its potential value. Despite being the second industry in AI and Gen-AI adoption, the sector has been quietly stalled, not because of the lack of ambition but because of a widening execution gap.  

AI was meant to unlock a new era of underwriting intelligence, distribution efficiency, fraud mitigation, and customer experience, yet much of it remains manual intensive. Despite swift adoption, insurers are still trapped in what has been defined by a recent report by BCG – the AI Rut. This is a phase where organizations have heavily invested, widely experimented, but are still struggling to scale meaningfully.

This article intends to explore the five AI pitfalls which the insurers did not see coming that played a key role in pushing them into the AI rut despite strong investment and intent. 

Hidden in Plain Sight- The 5 Pitfalls within the AI Rut 

The top pitfalls within the AI rut were always within plain sight but were romanticized as normal operational hurdles. Here’s a deeper look at these pitfalls that the insurers had been ignoring within the AI Rut: 

 1 — The Pilot Trap: Too Many Flowers, No Garden 

AI is undoubtedly one of the best operational tools that the insurers can get their hands upon, but in 2025, they have fallen into the “thousand flower mindset.”- A state where the insurers launch AI pilots across every function without clearly understanding its relevance in the process. In the same report of BCG study on India Insurtech Landscape and Trends: Reimagining Insurance with AI 2025, it’s highlighted that 90% of the insurers have multiple pilots, yet only 7% manage to scale even one leading to a fragmented effort and almost no enterprise-wide ROI.  

This is the core issue: insurers confuse experimentation with progress. AI is not rewarding the number of pilots, but it’s rewarding the discipline to prioritize and scale out what truly matters. Adopting this attitude will pave the way towards a focused AI implementation within the AI rut.  

2 — The Talent Mirage: Undervaluing People Over Algorithms 

It’s observed in the BCG study that one of the common reasons for the insurers to remain stuck in the AI rut is the belief that better algorithms alone fuel transformation. This is not the truth, and the belief has led the Insurers to overinvest in models and underinvest in people. This is exactly where the 10-20-70 rule by BCG comes into place, which states that only 10% of AI’s value comes from algorithms, while 70% comes from the organization’s design, talent readiness and culture. But the problem is not solved here; most insurers treat AI as a technology project rather than an enterprise-wide capability. 

This gap is evident across the industry, with two in three companies struggling with AI talent, workflow redesign, and cultural change. Only 70% of Indian insurers lack cross-functional operating models that are needed for scaling with AI. Ultimately, AI is not a tech story-instead it’s a talent and transformation story. 

 3 — The Data Illusion: Scaling AI on Weak Foundations   

Data Illusion is one of the most common pitfalls that is acknowledged in the industry yet not acted upon. A major reason that AI stalls in Insurance is the assumption that the existing data is “good enough.” However, in reality, most of the insurers are trying to scale AI on fragmented systems, inconsistent claim records, and siloed data architectures which were never designed for intelligent automation. All these create an illusion of readiness, where conversely the foundation is too weak to support enterprise-scale AI. 

In the same study by BCG on India Insurtech Landscape and Trends: Reimagining Insurance with AI 2025 this gap is clearly chalked out- “Investing in data quality and intelligent layers is the single biggest unlock for Gen-AI value.” The proof is quite visible in modern ecosystems like AIKosh, which uses over 1000+ curated datasets for underwriting and claims. It forms a testimony to the way clean, shared, and intelligent data directly amplifies the model’s accuracy as well as decision outcomes. Without this backbone, AI becomes a guessing engine instead of a value driven engine.  

Acknowledging this gap and making the right shift will be the ultimate gamechanger- moving from data collection to data intelligence. This approach will empower the insurers to build unified, high-quality, continuously updated data layers which will allow the AI platform to be reliable. Only then will the insurers be able to break out the AI Rut and unlock meaningful, scalable impact. 

4 — The Ethics Gap: Governance Lagging Behind AI Speed   

As AI accelerates faster than regulation, insurers face a widening ethics gap- this is where the models advance rapidly, but governance, oversight and control fail to keep pace. This gap is widely exposing the insurers to bias, opaque decisioning and compliance risks, especially as AI begins influencing underwriting, claims decision, pricing and customer communications at scale. 

This urgency is highlighted by the BCG report- insurers must build “Responsible AI Guardrails” that includes fairness, explainability, and auditability that is banked in from the start, and not added as an afterthought. Yet, fewer than 10% of the insurers globally have embedded responsible AI frameworks, which leaves the majority vulnerable to regulatory scrutiny, reputational damage and customer mistrust.  

Here, the narrative is crystal clear- the next AI crisis in insurance will not be optional. Instead, it will be ethical. In 2025 and beyond, trust will be a key differentiator, and the insurers who lead will be the ones who treat AI responsibly, as it becomes a core business requirement and not just a compliance checkbox. 

5 — The GenAI Hype Trap: Mistaking Novelty for ROI 

The rapid shift towards Gen-AI and Agentic AI has sparked a huge level of excitement across the industry, questioning the dangerous tendency to chase novelty over value. Most of the insurers jumped into the Gen-AI bandwagon without assessing the use-case maturity, operating costs or the integration friction, that will be leading to an impressive demo but with limited real-world impact. 

In the BCG study India Insurtech Landscape and Trends: Reimagining Insurance with AI 2025, a caution was highlighted stating that the insurers need to combine the traditional AI for automation with Gen-AI for augmentation and create a power player to achieve a sustainable ROI. Additionally, another key point was highlighted that stated- only 30% of Gen-AI value comes from technology itself, while 70% depends upon the transformation of readiness. This includes processes, governance, and workforce enablement.  

Here, the lesson for the insurers remains quite clear- AI maturity isn’t about adopting the newest models, instead it’s about blending the right models for getting the right outcomes. The insurers who avoid the Gen-AI hype trap focus upon operational value, and not just novelty for building a hybrid AI stack which delivers measurable, scalable impact. 

These pitfalls are crucially the ones that hide in plain sight while quietly existing behind operational processes and threatening the AI scalability of insurers.  

Escaping the AI Rut 

The AI rut of 2025 isn’t just a failure of algorithms, instead, it’s the failure of imagination, integration, and organizational readiness. Despite the Indian insurance industry keeping a rapid pace with AI adoption, the real value will be unlocked not in more pilots or more codes, but in rethinking how the insurance industry operates and the way is built. 

The future leaders will be the ones who will choose transformation over experimentation, unlocking the road towards achieving the next $25B in insurance AI. 

So, it’s time to think hard- is your organization’s AI helping you earn trust—or risk it?