Do You AI? The Problem with Corporate AI Missteps
Artificial Intelligence (AI) is widely recognized as the defining technology of the modern era. From healthcare and logistics to finance and customer experience, AI continues to reshape the competitive landscape. Government programs such as IndiaAI, the Digital India Mission, and the National Strategy for Artificial Intelligence (NITI Aayog) have further catalyzed momentum. Indian enterprises, both large conglomerates and ambitious startups, are now investing heavily in AI to enhance automation, customer engagement, fraud detection, and supply chain optimization. Yet despite the energy and investment, a significant number of AI projects in India have failed to meet expectations.
The landscape reflects a global paradox: while the potential of AI is vast, the implementation gap remains wide. According to a 2024 NASSCOM report, more than 70% of Indian enterprises report limited or no measurable ROI from their AI initiatives. The root causes align with international patterns but carry a distinctly local flavour ranging from fragmented data ecosystems and legacy IT infrastructures to overhyped claims and academic overengineering.
Same story globally, as organizations worldwide ramp up investments in AI technologies, the global spending on AI systems is expected to reach $500 billion by 2027. According to IDC and McKinsey estimates, over 80% of large enterprises plan to implement some form of AI by 2025. However, despite this aggressive adoption curve, a large proportion of AI initiatives fail to meet expectations. Studies suggest that nearly two-thirds of corporate AI projects do not deliver the projected outcomes, with many failing to move beyond the pilot stage. A 2024 Gartner survey further revealed that 78% of enterprise buyers now express skepticism toward vendors’ AI claims, pointing to a widening gap between AI’s promise and its practical reality.
At the heart of this challenge lies a structural dichotomy in how businesses approach AI adoption. On one end of the spectrum is the phenomenon commonly referred to as “AI washing.” In an attempt to appear technologically progressive, many organizations overstate their use of AI. Basic automation scripts, business rule engines, and off-the-shelf analytics tools are frequently rebranded as “AI-powered” in marketing materials and product documentation. This strategic exaggeration, while initially effective in attracting attention or investment, often erodes trust among stakeholders once the underlying capabilities are revealed. Regulatory bodies such as the U.S. Securities and Exchange Commission have already begun scrutinizing these practices in public disclosures, emphasizing the growing concern around the misrepresentation of AI functionality in the market.
At the opposite end lies another equally counterproductive trend. i.e. the overcommitment to research-heavy, academic-style AI projects. Particularly in organizations with generous R&D budgets, there is a tendency to pursue theoretical innovations that are technically sophisticated but commercially unviable. These projects often focus on cutting-edge neural networks or complex architectures that generate impressive benchmarks in lab environments but struggle to demonstrate value in operational settings. This disconnect between research ambition and business reality frequently leads to stalled projects, cost overruns, and disillusioned stakeholders. Despite consuming years of development effort and significant financial resources, many of these initiatives fail to solve concrete problems or scale across the enterprise.
The root of both these failures “AI washing” and academic overreach that can be traced to a misalignment between technology implementation and business priorities. While marketing teams may chase AI labels to enhance product appeal, and research departments may pursue breakthrough innovation for prestige or experimentation, neither approach guarantees real-world success. What organizations need is a more pragmatic, outcome-driven AI strategy, one grounded in measurable goals, efficient execution, and adaptive scaling.
The Empty Promise of AI Washing
In the race to appear cutting-edge, a growing number of companies are engaging in what industry experts refer to as “AI washing”—a misleading marketing strategy where businesses exaggerate or fabricate the capabilities of their technologies by labelling them as “AI-powered.” At its core, AI washing involves passing off basic automation, scripted workflows, or rudimentary algorithms as sophisticated artificial intelligence. This deceptive branding tactic aims to capitalize on the hype and perceived value of AI, luring customers and investors with inflated claims of innovation and intelligence.
This trend has escalated to such an extent that regulatory bodies are beginning to intervene. In the United States, the Securities and Exchange Commission (SEC) has started scrutinizing and taking action against public companies that make unsubstantiated AI-related claims. The regulatory attention underscores the severity and widespread nature of the issue. From retail and real estate to finance and healthcare, industries across the board have embraced this misleading language, diluting the meaning and credibility of what constitutes real AI innovation.
The fallout from AI washing is significant and growing. On one hand, it erodes consumer and enterprise trust in the technology. Buyers and decision-makers, once optimistic about AI’s potential, are now increasingly wary of vendors’ claims. According to a 2024 Gartner survey, nearly 78% of enterprise technology buyers express distrust toward suppliers’ AI narratives, suspecting overstatement or outright fabrication. On the other hand, this skepticism creates a chilling effect for genuine AI innovators. Legitimate, high-impact AI solutions often struggle to differentiate themselves in a noisy market clouded by false promises. As a result, organizations risk overlooking truly transformative technologies, ultimately stalling meaningful progress in AI adoption.
AI washing not only undermines innovation but also raises ethical and compliance concerns. Companies that misrepresent their technologies may face legal risks, brand damage, and loss of investor confidence. More importantly, by focusing on marketing over substance, they divert attention and resources away from responsible AI development grounded in transparency, accountability, and actual performance.
The Ivory Tower Dilemma
At the other extreme of AI overhype lies a different but equally problematic phenomenon: the pursuit of academic AI initiatives that are largely disconnected from real-world business needs. While these projects are often backed by significant R&D funding and driven by the ambition to push the boundaries of theoretical innovation, they frequently fall short of delivering tangible value. Enterprises, especially large ones with deep pockets, are sometimes seduced by the allure of cutting-edge research by investing heavily in experimental models or abstract algorithms that look promising on paper but prove impractical in commercial environments.
These initiatives can span years of development, consuming considerable talent and capital. Yet, when it comes time to deploy these innovations in live operations, many crumble under the weight of real-world constraints such as data availability, infrastructure limitations, user adoption challenges, or integration issues with existing enterprise systems. The result is a trail of expensive prototypes, white papers, and tech demos that rarely evolve into scalable products or services.
At the heart of this issue is a disconnect between research objectives and business imperatives. Academic AI research naturally prioritizes novelty, technical sophistication, and publication-worthiness, often operating under idealized conditions. In contrast, enterprise AI must grapple with unpredictable customer behavior, regulatory environments, legacy systems, and immediate ROI expectations. When these two worlds fail to align, even the most advanced breakthroughs can end up as shelved experiments rather than transformative innovations.
This “Ivory Tower” dilemma poses a strategic risk: it diverts critical resources from actionable innovation toward intellectual prestige. For businesses to extract real value from AI, a middle path must be found, one that respects academic rigor while staying firmly rooted in practical application. Bridging this gap will require stronger collaboration between research labs and business units, better productization pathways, and a renewed emphasis on measurable outcomes over theoretical elegance.
Charting a Practical Path Forward
To navigate the challenges posed by AI hype and impractical innovation, Indian enterprises must adopt a more disciplined, business-aligned approach to AI deployment. With India poised to become a global hub for responsible and inclusive AI, the focus must shift from theoretical ambition to real-world utility. Leading organizations are already making this pivot by grounding their AI strategies in use cases that yield measurable improvements whether in operational efficiency, customer satisfaction, cost reduction, or revenue acceleration.
From digital lending platforms in Bengaluru to e-governance projects in Andhra Pradesh, Indian innovators are identifying high-impact, locally relevant use cases. These include fraud detection in UPI-based payments, crop yield prediction in precision agriculture, patient triage in public health systems, and dynamic pricing for online retailers. These projects often begin with rapid prototyping and iterative development, favouring early deployment over prolonged research cycles. This agile approach acknowledges a critical reality in India’s digital landscape that a modest, well-deployed AI solution can create far greater impact than a complex model stuck in development purgatory.
An increasing number of Indian organizations are embracing what is being referred to as “minimal viable AI”, a practical framework that emphasizes starting small with proven techniques. Rather than chasing deep learning or generative AI prematurely, they deploy simple yet robust models, such as decision trees, clustering algorithms, or logistic regression, to solve business-specific problems. For example, a logistics startup in Delhi might use clustering to optimize last-mile delivery routes in congested urban zones, while an edtech firm in Pune may leverage classification models to personalize content recommendations based on student performance.
This stepwise approach delivers early wins, stakeholder confidence, and scalability without overstretching infrastructure or resources especially critical in India, where compute capacity and data quality vary significantly across regions and sectors. More importantly, it reflects a maturing AI mindset that values reliability, explainability, and impact over novelty and academic complexity.
The most effective AI strategies whether in a Gurugram-based conglomerate or a Chennai-born startup are those that balance ambition with pragmatism. These organizations understand that AI’s true power lies not in showcasing technical sophistication, but in solving real-world problems under real-world constraints. Whether optimizing logistics in the supply chain-heavy Indian FMCG sector, automating compliance in regulated BFSI environments, enhancing citizen services through AI-powered chatbots in local languages, or improving demand forecasting in rural retail, AI generates its highest value when closely aligned with operational realities and business outcomes.
As AI technologies continue to evolve globally, the gap between hype and meaningful value creation is widening. Indian companies that root their innovation efforts in practical execution those that ask not “What’s the most advanced AI we can build?” but “What’s the most effective AI we can deploy now?” will be best positioned to lead.
They will rise above the noise, not by the sophistication of their models, but by the tangible outcomes they achieve for businesses, citizens, and communities. What Say?