AI-Washing in Indian Digital Marketing: Risks, Realities, and the Path Forward

Introduction

Artificial Intelligence (AI) has become a buzzword in digital marketing, promising everything from predictive analytics to intelligent customer engagement. In India’s vibrant tech ecosystem, companies across fintech and SaaS are eager to showcase “AI-powered” innovations. However, this rush has given rise to AI-washing – a practice of exaggerating or misrepresenting the use of AI to appear more innovative than one truly is. Much like greenwashing in environmental marketing, AI-washing can mislead stakeholders and erode trust. Indian digital marketers and CMOs face a dual challenge: leveraging genuine AI capabilities to stay competitive, while avoiding the temptation (and risk) of superficial AI hype. This paper explores the phenomenon of AI-washing in Indian digital marketing, with a focus on fintech and SaaS sectors, and offers strategies to navigate this landscape responsibly.

Understanding AI-Washing in Context

Defining AI-Washing: AI-washing refers to branding a product or service as “AI-driven” when its actual AI capabilities are minimal or nonexistent. Companies engage in this to capitalise on the AI trend and boost valuations or customer interest.

As Infosys co-founder N.R. Narayana Murthy observed, “it has become a fashion in India to talk of AI for everything,” with “several normal, ordinary programs touted as AI”. In practice, AI-washing often means using basic if-then rules or manual processes but marketing them as sophisticated machine learning.

Hype vs. Reality: The hype around AI has led to a surge of dubious claims. Venture capital investors report that 3 out of 10 startups exaggerate their AI usage, and in some estimates as many as 60–70% of purported “AI” systems are actually simple software. For example, one Indian SaaS startup pitched an “AI-powered” medical diagnosis tool that was later found to rely entirely on human experts behind the scenes. In another case, a so-called AI chatbot was revealed to be a set of manually scripted responses with no real natural language processing. These cases mirror global incidents – such as the U.S. startup Nate, which raised $42 million on claims of an AI shopping assistant but secretly used human staff for all taskslinkedin.comlinkedin.com. The founder of Nate now faces fraud charges for this deception, underscoring how what “started as marketing exaggeration” can become a “serious legal liability”.

Why It Happens: AI-washing is fueled by the gold rush mentality around AI. During the dot-com boom, merely adding “.com” boosted stock prices – today, slapping “AI” onto a pitch or product can attract investors and customers. In fintech and SaaS, where differentiation is key, vendors may feel pressure to overstate AI integration to seem cutting-edge. This is exacerbated by FOMO (fear of missing out) on the AI wave and the lack of AI literacy among some decision-makers, which allows buzzwords to impress more than substance. As one VC noted, “most founders are guilty of stretching the truth to sell“, and AI jargon can create an illusion of advanced capabilities even if the underlying tech is ordinary software.

The result is an inflated marketplace where AI promises outpace real-world results. While short-term gains from AI-washing might include funding or PR buzz, the long-term risks – regulatory penalties, loss of credibility, and consumer backlash – are significant. Indian marketers must therefore clearly distinguish genuine AI value from hollow hype.

Regulatory Concerns in India

AI-washing doesn’t just risk reputational damage; it can run afoul of emerging regulations. India is developing a framework to ensure AI is used ethically and transparently. Key concerns for marketers include data privacy, truth in advertising, and accountability for automated decisions:

• Data Protection (DPDP Act 2023): India’s Digital Personal Data Protection Act, 2023 places strict requirements on how personal data can be collected and used. If an AI tool processes personal data (for example, a fintech app profiling users for credit scoring), it must have consent and comply with purpose limitations. Using someone’s data to train AI models without consent could violate the law. Marketers cannot simply feed customer data into algorithms without safeguards. The DPDP Act compels organisations to adopt data minimisation, obtain clear consent, and protect data security. In essence, any AI-driven personalisation or analytics must respect user privacy. Negligence here not only damages trust but carries legal penalties.
• Advertising and Consumer Protection: Indian law already penalises misleading claims in marketing. The Consumer Protection Act, 2019 explicitly covers “misleading ads about the reliability or performance of an AI service.” In other words, if a digital marketer overstates what their “AI” can do, they risk regulatory action for false advertising. The Advertising Standards Council of India (ASCI) has also weighed in – in 2025, ASCI’s report “Adhyaari: The AI Edition” called for responsible AI adoption in advertising, stressing that transparency and building lasting trust with consumers are paramount. Given that Indian consumers are increasingly tech-savvy, regulators are on alert for “AI-powered” claims that might deceive users. Marketers should be prepared to substantiate any AI-related assertions about product performance or outcomes.
• AI Ethics Guidelines: While India has yet to enact an AI-specific law akin to the EU’s AI Act, it has issued ethics guidelines. NITI Aayog’s “Responsible #AIforAll” strategy emphasises principles of fairness, accountability, and transparency (the FAT framework) for AI systems. Sectoral regulators have also provided guidance – for instance, the Reserve Bank of India is exploring oversight on AI in financial services, and the insurance regulator IRDAI has urged fairness in AI-driven insurance underwriting. These guidelines, though not legally binding, shape best practices. A marketer at an Indian fintech should be aware that an algorithm making loan decisions must be fair and explainable to avoid future regulatory scrutiny for bias or opacity.
• Global Precedents (SEC/FTC Crackdown): It’s worth noting global regulatory trends, as many Indian startups have global investors or customers. U.S. regulators have begun cracking down on AI-washing: the Securities and Exchange Commission (SEC) and Federal Trade Commission (FTC) have issued warnings against deceptive AI marketing. The SEC now demands that companies have a “reasonable basis” for any AI-related claims and disclose how AI is actually used. The fact that a startup founder was prosecuted in 2025 for falsely marketing human work as AIlinkedin.com sends a strong message. Indian companies may soon face similar scrutiny, either domestically or when operating abroad. Moreover, India’s proposed Digital India Act is expected to address online harms and could include provisions on AI transparency and accountability.

In summary, the regulatory landscape is evolving to curb AI-washing. Indian digital marketers must navigate a patchwork of data protection mandates, advertising standards, and ethical guidelines. The safest course is to market AI features truthfully and responsibly – not only to comply with the law but to uphold the trust that brands build with their consumers.

Consumer Perception and Trust Issues

AI-washing directly impacts consumer trust. In digital marketing, trust is currency – and misusing “AI” can deplete it quickly. Understanding how Indian consumers perceive AI can help marketers avoid pitfalls:

Openness vs. Skepticism: Indian consumers are relatively tech-forward and open to AI-driven experiences. A recent industry study noted the “unique openness of Indian consumers towards AI-powered technologies, particularly in advertising,” which even positions India as a testbed for advanced AI strategies. For instance, many Indians readily interact with AI chatbots for customer service or use AI-based voice assistants. However, openness does not mean blind trust. According to PwC’s 2024 consumer survey, 57% of Indian consumers trust AI for low-risk tasks (such as getting product recommendations or basic info) but remain skeptical of AI handling high-stakes decisions. There is enthusiasm for the convenience of AI, yet a “strong preference for direct human interaction” persists in complex or sensitive situations.

Trust is Fragile: Consumers reward authenticity and punish deceit. If a fintech app advertises an “AI-powered fraud detection” but fails to catch a basic scam, users will feel misled. Worse, if it turns out no real AI was ever involved, the brand’s credibility could plummet. Indian consumers rank data protection as the #1 factor for trust (82% cite it as crucial), and they expect honesty in how their data and automation are used. AI-washing can thus backfire spectacularly. Overhyped AI features that don’t deliver create user frustration and erode confidence not just in one product, but in AI solutions broadly. For example, if a SaaS claims its AI marketing tool will boost a client’s sales by 50% but delivers no lift, the client may become cynical about all AI marketing promises.

Privacy and Ethical Concerns: Trust issues also stem from fears about AI’s implications. Many Indians worry about how AI might affect jobs and security – over 86% express concern about cyber risks or job losses due to AI. A consumer might question: “Is this AI tool handling my finances secure? Who can access my data? Will this ‘AI advisor’ misuse my information?” If a company is coy about its AI (perhaps because there isn’t much under the hood), it will struggle to answer these questions satisfactorily. Transparency is key. When consumers understand what an AI feature does and that the company is accountable for it, they are more likely to trust it. On the other hand, a vague “AI-powered” label with no explanation can breed suspicion.

In the Indian context, trust can be a competitive differentiator. Brands that deploy AI ethically and communicate about it clearly tend to earn consumer goodwill, whereas those caught AI-washing face public relations crises. Importantly, trust is hard to rebuild once broken – a lesson marketers should heed before overpromising with AI. As the PwC survey suggested, businesses must “balance AI with human interaction” and deploy AI responsibly to meet customer expectations. In practice, this could mean using AI to augment human service (not replace it entirely) and being upfront that, say, an algorithm provides initial recommendations but a human agent has the final say. Such approaches maintain transparency and reassure users that AI is enhancing, not undermining, their experience.

Vendor Accountability and Transparency

For digital marketers championing AI-based products, accountability and transparency are non-negotiable. When a company claims to use AI, it implicitly makes a promise about performance and innovation. Failing to live up to that promise can attract not only consumer ire but also intervention from investors and regulators. Here’s what accountability entails in the AI-washing context:

• Truth in Labelling: Marketers should ensure that any product touted as “AI-driven” truly employs artificial intelligence in a meaningful way. This means having a “reasonable basis” for AI claims. For example, if a SaaS platform says it uses AI to optimise advertising spend, it should have real machine learning models analysing data, not just a hard-coded script. Organisations are now expected to define clearly what “AI” means in their offering and where it’s used. Vague or broad statements (“powered by AI”) without specifics are a red flag. A good practice is to provide transparency through documentation or dashboards – e.g. explaining that an AI recommendation engine uses a customer’s purchase history and browsing behaviour to suggest products. By demystifying the AI, vendors build trust and set correct expectations.
• Accountability for Outcomes: Introducing AI doesn’t absolve a company of responsibility for results; in fact, it heightens it. If an AI tool makes a mistake (say, declines a legitimate loan application or produces an offensive ad creative), the company must own the error and address it. Vendor accountability means robust testing and validation of AI systems before they go live. Many Indian investors now insist on technical due diligence: reviewing a startup’s code and AI model performance to verify claims. Some even bring in domain experts to audit AI algorithms. This level of scrutiny should be mirrored in marketing – don’t market what hasn’t been rigorously verified. As one best-practice recommendation, firms should “establish a reasonable basis for AI-related claims by documenting and verifying actual capabilities before making any public disclosures.
• Transparency with Stakeholders: Transparency is a cornerstone of both ethical AI and effective marketing. Internally, leadership should communicate honestly with boards and investors about the state of AI development (preventing scenarios where the Sales Team is selling a dream the CTO can’t deliver). Externally, user-facing transparency builds credibility. This could mean publishing model accuracy rates, use cases, or limitations. For instance, a fintech might disclose: “Our AI scoring model improves loan default prediction by 20%, but it may be less accurate for thin credit history customers, where we use additional manual review.” Such candour can be refreshing to clients and regulators alike. Indeed, Indian VCs have begun to demand that portfolio companies “maintain transparency and do not misrepresent their AI capabilities” after investment. Globally too, the direction is clear – the SEC’s guidelines push companies to spell out details of AI usage to investors.
• Ethical Use and Governance: Accountability extends to ensuring AI is used ethically. Does the AI avoid discrimination? Is it secure against data breaches? Marketers, in collaboration with product teams, should champion responsible AI practices. Following frameworks like NITI Aayog’s principles or the OECD AI principles can serve as a guide. One concrete step is establishing an internal review for AI features – essentially an ethics and risk check – before launch. This might flag, for example, that an “AI hiring tool” could be biased against certain schools, giving a chance to rectify it before it becomes a PR problem. By proactively addressing ethical issues, companies signal that they are accountable for their AI’s impact.

In essence, transparency and accountability are the antidotes to AI-washing. Manisha Kapoor, CEO of ASCI, encapsulated it well: the power of AI in advertising (and by extension, marketing) “must be wielded responsibly, with a focus on transparency, responsibility, and building lasting trust with consumers.” For Indian CMOs, this means that every AI claim in a campaign or product needs to be backed by truth and a willingness to stand by the outcomes. The reward is a sustainable reputation and customer loyalty; the cost of failing in this is far higher than any short-term gain from a flashy AI claim.

Case Studies: Effective vs. Superficial AI in Fintech and SaaS

To illustrate the contrast between genuine AI adoption and AI-washing, let’s examine case studies from fintech and SaaS – domains at the forefront of AI integration in India. These examples highlight what effective use of AI looks like versus superficial implementations that amount to little more than marketing rhetoric.

Fintech Sector

Superficial AI – Hype-Only Trading Bots: The fintech boom in India has seen an explosion of apps claiming “AI-powered” investing and trading. However, many such claims have been skin-deep. As an example, Financial Express reports that numerous trading startups touted AI-driven stock prediction bots that were in reality based on simplistic rules. One app advertised its “proprietary AI” for timing the market, but upon investigation, its strategy boiled down to “buy when price dips 10%” – a static rule any novice could code. There was no machine learning or adaptive algorithm at work. Similarly, some robo-advisors have marketed AI personalization but delivered one-size-fits-all portfolios, betraying a lack of true AI insight. These are clear instances of AI-washing: using the aura of AI to attract users, without delivering AI’s real advantages. While such tactics might win downloads initially, they tend to falter as users notice the lack of intelligent behaviour (e.g., the bot fails to adjust to market changes). The end result is damaged trust and user churn.

Effective AI – Credit Scoring and Fraud Detection: In contrast, several Indian fintech companies have genuinely leveraged AI to solve critical problems. A notable case is the use of AI in lending to close the $400B credit gap for underserved borrowers. Fintech lenders like Slice and KreditBee analyse thousands of non-traditional data points using ML models – from smartphone usage patterns to e-commerce history – to assess creditworthiness of “thin-file” customers who lack formal credit scores. This alternate data approach, powered by AI, enabled over 8 million loans to be approved for new-to-credit customers in 2023. That’s real impact directly attributable to AI. Another success story is in fraud prevention: Indian banks and payment platforms now deploy AI systems that reduce false declines by 60%, reclaiming nearly $1.2 billion in annual revenue that was previously lost due to legitimate transactions being flagged as fraud. These systems continuously learn from transaction patterns to better distinguish fraud from normal behaviour. Likewise, AI-based default prediction models in India have achieved over 90% accuracy in forecasting loan delinquencies, helping lenders proactively cut non-performing assets by about 30% Importantly, these applications have been publicly validated – some by third-party studies (Experian, etc.) – and are not just claims. Consumers may not see the AI, but they experience it through faster loans, fewer fraud hassles, and more personalised financial services. Fintech players like Paytm and PolicyBazaar also illustrate effective AI: Paytm’s money app uses AI behavioral nudges to reduce impulsive stock trades (cutting user losses by 50%), and PolicyBazaar’s insurance platform deployed AI chatbots that handle ~70% of customer queries, vastly improving response time. These examples show AI’s tangible benefits when sincerely implemented.

One global contrast to note is the Nate case mentioned earlier. Nate’s AI-washing (manual processing masquerading as AI) stands in stark opposition to, say, PayPal’s fraud AI or Mastercard’s AI-driven network security, which have been credited with saving hundreds of millions of dollars by thwarting fraudulent activities worldwide. The latter have published success metrics and even academic papers to back their effectiveness. The lesson from fintech: AI-washing may grab headlines, but real AI solutions grab market share. Companies like those above that invested in robust AI R&D are now reaping rewards in scale and consumer trust, whereas those that faked it (Nate, and a few ill-fated trading apps) have vanished or are struggling to retain users.

SaaS Sector

Superficial AI – The Engineer.ai Saga: In the SaaS arena, a cautionary tale comes from Engineer.ai, an Indian startup that claimed its platform could use AI to automatically build mobile apps. The pitch was alluring – “Get your app made 80% by AI in one hour!” – and it attracted nearly $30 million in funding. However, investigative reports revealed that Engineer.ai did not have any such AI capability; it was relying on human developers behind the scenes to do the work, while publicly riding the AI hype. The founder even gave himself the whimsical title “Chief Wizard” and spoke at conferences about their AI, all while the actual product was mostly conventional software and manual effort. This blatant AI-washing led to a lawsuit from a former executive alleging the company misled investors, and it severely tarnished Engineer.ai’s reputation. The episode, widely covered by tech media in 2019, serves as a reminder that overstating AI in SaaS can lead to public embarrassment and legal troubles. Customers today are quick to detect when an “AI SaaS” behaves no smarter than a standard program. Any SaaS provider tempted to brand routine features as AI should heed the fallout that Engineer.ai experienced.

Effective AI – Freshworks’ Freddy AI: On the positive side, India’s SaaS success stories show how to integrate AI meaningfully. Freshworks, a Chennai-headquartered SaaS firm offering customer engagement software, introduced an AI engine named Freddy AI across its products. Rather than just a buzzword, Freddy has delivered concrete results. In October 2024, Freshworks announced that its new Freddy AI agent autonomously resolves ~45% of customer support requests and 40% of IT service requests for its clients on average. This level of automation in helpdesk workflows means huge efficiency gains – faster responses for customers and lower workload for support teams. Crucially, Freshworks built Freddy over years of R&D (via its Freddy Labs) and was transparent about what it can do. The company’s CEO even contrasted Freddy’s quick deployment and tangible ROI with competitors that take weeks to implement, subtly underscoring that their AI is not superficial. Another Indian SaaS leader, Zoho, has embedded an AI assistant, Zia, into its CRM and other apps. Zia can predict deal closures, suggest optimal sales actions, and even draft emails. While exact metrics are proprietary, Zoho’s users have reported improved sales forecasting accuracy thanks to Zia’s machine learning on historical data. These examples indicate that when AI is thoughtfully integrated into SaaS products, it enhances the user experience and outcomes – be it via smarter automation (as with Freddy) or data-driven insights (as with Zia).

On a global level, one can look at Salesforce’s Einstein AI or Adobe’s Sensei AI, which embed AI into CRM and creative software respectively. These have generally been successful in driving personalisation and automation at scale. They stand in contrast to some smaller players that simply slap “AI” in product names. The effective players often provide case studies or numbers (e.g., Salesforce showing how Einstein helped increase email open rates by X% for a client, or Adobe demonstrating how Sensei cut designers’ editing time). The key differentiator is evidence of impact. In the Indian SaaS context, companies like Freshworks and Zoho are following this playbook of proving AI value, which sets them apart from less scrupulous competitors that might be indulging in AI-washing.

Lessons from the Case Studies

Across fintech and SaaS, a pattern emerges: Substance wins over style in the long run. Effective AI implementations focus on solving a real problem (credit access, fraud reduction, support automation) and measure success in terms of user benefits. They also tend to be transparent – clients know what the AI is doing and see it working. Superficial uses of AI, conversely, chase the hype for its own sake. They may gain short-term attention but cannot sustain performance or trust. For marketers, these cases reinforce that authentic AI narratives – backed by data and results – are far more powerful than fluffy claims. Customers and investors have become adept at cutting through the noise; they reward companies that get AI right and call out those that mislead. Therefore, aligning one’s marketing message with actual technical capability isn’t just ethical, it’s smart business strategy.

Strategies to Avoid AI-Washing (and Leverage AI Responsibly)

To thrive in the current landscape, Indian digital marketers and CMOs should adopt strategies that emphasize genuine AI value and integrity. Below are key recommendations to avoid the AI-washing trap while still capitalising on AI’s real capabilities:

• Prioritise Real Value Over Hype: Before advertising an AI feature, ask what tangible benefit it delivers. Use AI to solve concrete customer pain points, not just for the sake of having “AI” on your brochure. As venture investors advised, focus on the core value proposition rather than chasing AI trends. If your service automates invoice processing, highlight efficiency gains (e.g. hours saved, errors reduced) instead of just the AI angle. Substance in value delivered will naturally shine through in marketing.
• Be Transparent and Specific: In your messaging, clearly explain what your AI does. Avoid grandiose or vague claims. For example, instead of saying “AI-powered analytics,” say “our AI model analyses your sales data to predict high-converting leads.” Provide context – if the AI only assists a human team, make that clear (e.g. “AI-backed recommendations that our experts validate”). Transparency builds trust. Remember that misleading or overstated claims can violate advertising laws, so it’s both safer and smarter to stick to accurate descriptions.
• Educate Your Audience: Demystify AI for your customers. Through blogs, webinars, or in-app tips, educate users on how your AI features work and how to use them optimally. This not only empowers customers but also signals that you have nothing to hide. For instance, a fintech app could include a explainer: “We use a machine learning model trained on 10 years of stock data to suggest portfolios – here’s what that means for you.” Users are more likely to trust and adopt AI-driven features when they understand them. Education also manages expectations, reducing the risk of disappointment.
• Ensure Robust Data Practices: Since trust in AI is intertwined with data trust, comply fully with data protection norms like the DPDP Act. Seek user consent for data use in AI, and communicate your privacy safeguards. Brands that highlight how they protect personal data while using AI responsibly will earn a credibility boost. For example, make it a policy to anonymize customer data in AI training, and mention this in your communications. Consumers appreciate brands that handle data ethically – 82% of Indian consumers say protecting their data is crucial to winning their trust.
• Align with Ethical AI Guidelines: Adopt frameworks such as fairness, accountability, transparency in your AI project lifecycle, and let your clients know you do so. Whether it’s NITI Aayog’s guidelines or global AI ethics principles, aligning with them can differentiate your brand. It shows you’re not just jumping on the AI bandwagon; you’re committed to doing it right. For instance, if your SaaS uses an AI algorithm, you might conduct bias testing and share a summary of results with enterprise customers as part of sales due diligence. These steps can prevent reputational issues and are good talking points for marketing (e.g. “Our AI is audited for fairness and explainability”).
• Verify Claims Internally: Create an internal check for any externally communicated AI claim. Involve your data science/engineering team to validate marketing statements. If the tech team cannot back a claim with evidence or if they seem uncomfortable, that’s a red flag to revise the messaging. Some companies set up an “AI fact sheet” – a living document that marketers and sales can reference, which details what each AI feature is, what data it uses, and its performance metrics. This ensures consistency and accuracy in all communications. As noted in one analysis, having documented proof of AI capabilities is crucial before making public claims.
• Foster Cross-Functional Collaboration: Encourage close collaboration between marketing and product development teams on AI initiatives. Marketers should be involved early when an AI feature is being developed, so they fully grasp its workings and limitations. Conversely, engineers should be aware of how the feature will be marketed. This mutual understanding can prevent scenarios where marketing oversells what tech can deliver. It also helps craft more insightful campaigns – for example, using actual model performance data (“our AI improved conversion by 20% in beta tests”) as marketing content.
• Leverage Authentic Case Studies: When promoting AI capabilities, use real case studies and testimonials. Instead of asserting how great your AI is, show how it helped a client achieve X result. This flips the narrative from self-praise to customer success, which is far more credible. For instance, a SaaS company can publish a case study like: “Client A reduced customer churn by 15% using our AI-driven predictive analytics.” Ensure these stories are truthful and ideally third-party verified. Over time, a portfolio of success stories becomes your strongest defence against skepticism – proof that your AI isn’t just theatre.
• Stay Updated and Adaptive: AI technology is evolving rapidly. What was cutting-edge last year might be standard now. Marketers should stay updated on AI trends to avoid making outlandish claims out of ignorance. If competitors are offering genuine AI features, don’t label something AI in your product that clearly isn’t – you’ll be called out quickly in the comparison. Instead, work with your product team on a roadmap to incorporate real AI where it makes sense. In marketing strategy, be ready to adapt messaging as the industry and regulations change. For example, if new guidelines require labeling AI-generated content (as some global regulations propose), comply proactively and turn it into a trust signal (“AI-generated content – reviewed by our team for quality”).

By implementing these strategies, marketers can harness AI’s power as a real growth driver. The goal should be to use AI as a tool to genuinely enhance customer experience or efficiency – and then market those enhancements, rather than marketing the mere presence of AI. This approach ensures you’re selling outcomes and benefits, not technology buzzwords. In doing so, you protect your brand’s integrity and build a sustainable competitive edge grounded in truth and innovation.

Conclusion

AI-washing represents the intersection of technology hype and marketing exuberance – a trend particularly salient in India’s dynamic fintech and SaaS sectors. As we have discussed, conflating ambition with achievement in AI is a risky gambit. Yes, AI is transforming digital marketing and business: from automating customer interactions to uncovering rich insights, its potential is immense. Yet, with great potential comes great responsibility. Indian companies are learning that merely saying you have AI will not suffice; you must show it in action and prove its value. Regulators in India (and abroad) are sharpening their gaze, consumers are parsing claims more critically, and investors are diligence-ing tech claims like never before.

The way forward for digital marketers and CMOs is clear. Authenticity must anchor AI adoption. By grounding AI initiatives in real capabilities and adhering to principles of transparency, marketers can avoid the perils of AI-washing. Instead of fearing regulations like the DPDP Act or ethical AI guidelines, organisations can embrace them as guardrails that ultimately build consumer trust. Effective AI use and honest storytelling about it can become a brand differentiator – especially in a market like India where trust and innovation go hand in hand in consumers’ minds.

In sum, AI-washing is a trap that promises a short-lived shine but delivers lasting tarnish. The antidote is a balanced strategy that celebrates real AI achievements and candidly acknowledges what is a work in progress. Indian fintech and SaaS companies that follow this path are likely to not only win customer loyalty at home but also emerge as credible players on the global stage. For CMOs, the message is: let AI be an enabler of meaningful customer value, and let your marketing be the honest narrator of that journey. In doing so, you will build brands that stand the test of time – hype cycles come and go, but trust and tangible value endure.