In April 2025, BluSmart, once hailed as India’s electric mobility pioneer, abruptly collapsed crippled by allegations of financial misconduct and governance failure. SEBI accused co-founders Anmol and Puneet Singh Jaggi (also promoters of Gensol Engineering Ltd.) of diverting ₹262 crore in government-backed EV loans toward personal luxuries, including a DLF Camellias apartment and a ₹26 lakh golf set.

Between 2021 and 2024, Gensol secured ₹978 crore from public lenders (IREDA, PFC) to finance 6,400 EVs. Only 4,704 were procured. ₹410 crore went missing some of it funneled through a web of related entities.

By Q2 2025, BluSmart had halted operations across Delhi-NCR, Bengaluru, and Mumbai. Over 10,000 drivers were stranded. Nearly 8,000 EVs sat idle. The collapse was a result not of market failure, but of governance failure.

This Isn’t New

From Satyam’s $1.47B accounting fraud to Luckin Coffee’s fabricated $300M in revenue and WeWork’s implosion under Adam Neumann, we’ve seen the pattern: centralized power, weak oversight, and unchecked ambition create systemic risk. Startups scale fast but governance often doesn’t. So, how do we solve this?

The Shift: AI as a Strategic Governance Layer

Governance must evolve from static oversight to dynamic, predictive intelligence. AI is no longer optional, it's the next layer of scalable, proactive risk management. Where AI Outperforms Traditional Governance:

1. Real-Time Financial Monitoring
   AI tracks fund flows continuously. It flags deviations from budgets and forecasts potential misuse using pattern recognition far earlier than audits.
2. Behavioral Anomaly Detection
   ML models detect sudden, non-standard decision-making like unauthorized fund transfers or personal expenses disguised as corporate spending.
3. AI-Driven Audits & Compliance Checks
   NLP scans contracts, logs, and communication for red flags, inconsistencies, or policy violations, flagging misconduct buried in data that auditors miss.
4. Fleet & Asset Utilization Monitoring
   Predictive AI models could have flagged underutilized vehicles or discrepancies between loan amounts and fleet size, long before shutdowns.
5. Insider Risk Surveillance
   Internal messages (emails, chats) can be analyzed using LLMs to detect patterns of fraud, conflict of interest, or unethical intent in near real-time.

‍Actual Implementation

Leading organizations are already putting AI-driven governance into practice:

• Microsoft has embedded responsible AI into its Azure and Copilot ecosystems, offering enterprises tools that not only streamline compliance but actively build ethical, transparent systems. Its partnerships with Cloud Software Group and Axel Springer SE underscore its commitment to governance-first AI development.
  
• IBM’s watsonx.governance product, delivers enterprise-grade AI governance capabilities, helping businesses manage AI risks while scaling responsibly. Collaborations with Telefónica Tech further extend IBM’s leadership in AI compliance innovation.
  
• SAP leverages AI governance in its industry cloud solutions, co-developed with AWS and IBM, ensuring that customers across sectors benefit from built-in oversight mechanisms tailored to their industries.
  

The advantages for companies adopting AI-powered governance are clear: faster, more confident decision-making; early risk detection; operational efficiency; and enhanced trust with investors, regulators, and customers. Of course, risks remain. Algorithmic surveillance, if unchecked, can threaten privacy, autonomy, and fairness. Without transparency and due process, AI governance can become a liability rather than an asset. Thoughtful design and continuous oversight are non-negotiable.

But the trajectory is clear: AI is not merely assisting governance; it is redefining it. Companies that treat AI as an active governance partner will set the new standard achieving transparency, agility, and sustainable growth where others fall behind.

Case Study: BluSmart – A Governance Failure in the Age of AI

While the BluSmart case may seem like a typical case of corporate malfeasance, it exposes a deeper issue an acute lack of transparency, oversight, and governance in fast-growing startups. The situation could have been mitigated, if not entirely prevented, through the implementation of AI-driven governance systems that could have caught red flags early in the process.

In retrospect, AI could have played a pivotal role in preventing the financial misconduct that sank BluSmart. Here's how AI-driven governance tools could have identified the signs of trouble early:

1. Real-Time Financial Monitoring
   AI could have tracked the movement of funds in real time, automatically flagging any transactions that deviated from the approved budget for fleet expansion. With predictive analytics, the system could have raised alerts for large-scale fund diversions, such as the croreS being redirected towards personal assets. These AI systems would monitor financial flows 24/7, catching anomalies long before they become systemic problems.
   
2. Behavioral Analytics and Insider Threat Detection
   Using machine learning to analyze patterns of financial decisions, AI could have detected unusual behavior or deviations from standard practices. For example, sudden spikes in luxury spending or financial transfers linked to company accounts could have been flagged as potential insider risks. By continuously analyzing decision-making behaviors, AI could have uncovered signs of fraud or corruption much earlier.
   
3. AI-Powered Auditing and Compliance Checks
   Manual audits are often slow and react after the damage is done. AI-driven auditing tools can automate compliance checks, scanning contracts, transactions, and internal communications for signs of mismanagement or non-compliance. If BluSmart had such a system in place, the misappropriation of funds might have been detected through AI's ability to cross-reference financial documents and transactions against company policies.
   
4. Real-Time Driver and Asset Monitoring
   AI-powered fleet management systems could have monitored the performance and utilization of the 8,000 electric vehicles. Predictive maintenance algorithms could have identified when assets were underused or misallocated, allowing executives to correct course before vehicles were left to gather dust. This would have ensured more effective use of resources, directly mitigating the operational failure.
   
5. Internal Communication Monitoring
   Advanced natural language processing models could have scanned internal communications emails, chat messages, and reports for language indicative of unethical behavior, collusion, or policy violations. These systems would have flagged exchanges between the co-founders and vendors, potentially exposing the illegal diversion of funds long before it escalated.
   

The Lessons Learned

The collapse of BluSmart serves as a powerful reminder of the importance of governance, especially in high-growth startups. As AI continues to reshape industries, businesses must ensure that their governance frameworks are equally advanced. AI doesn’t just enhance efficiency it serves as a guardrail, identifying risks and flagging them for human oversight.

Had BluSmart integrated AI into their operations, the company could have avoided catastrophic mismanagement. Rather than relying on human oversight, which can be compromised by bias, speed, or limited scope, AI could have provided an objective, constant level of vigilance.

The BluSmart case is a wake-up call not only for startups but for investors and regulatory bodies as well. In the future, proactive, AI-enabled governance systems will become not just an option, but a critical part of risk management in scaling businesses. If the company had built governance infrastructure into its operations early on—powered by AI—it might have saved not only millions in financial losses but also the trust and future of the entire venture.

References

1. Business Standard (2025, April 16). Gensol Engineering fraud: SEBI order alleges BluSmart promoters diverted loans. Read article
2. Inc42 (2025, April). Gensol’s lenders mull legal route to recover loans: Report. Read article
3. Financial Express (2025, April). BluSmart blues: Drivers left stranded amid sudden shutdown. Read article
4. Scientific Research Publishing (SCIRP) (2012). Satyam Scandal: A case study of India’s Enron. Read paper
5. U.S. Securities and Exchange Commission (SEC) (2020, December 16). SEC charges China-based company and executives with fraud (Luckin Coffee case). Read press release

‍