Artificial intelligence now sits at the centre of modern marketing. It selects audiences, personalises offers, writes ad copy, optimises bids, predicts churn, and determines how often a consumer sees a message. It operates faster than any campaign team ever could.
What has changed between 2023 and 2026 is not just adoption. It is autonomy. AI systems are no longer limited to drafting suggestions. They are making micro decisions in real time. Who sees an ad. What discount is shown. Which headline variant gets priority. How aggressively a user is nudged.
In that shift from assistance to decision making, ethics has moved from theory to operational risk.
Marketing budgets are one reason this shift is accelerating. According to Gartner’s 2024 CMO Spend Survey, marketing budgets declined to 7.7 percent of company revenue in 2024, down from 9.1 percent in 2023 and well below pre pandemic levels that averaged around 11 percent. Under financial pressure, automation becomes attractive. Efficiency becomes a priority.
Ewan McIntyre, VP Analyst and Chief of Research in Gartner’s Marketing Practice, described it as an “era of less,” where marketing leaders must deliver growth with constrained resources. That pressure explains why AI is being deployed deeper into execution.
But speed and scale also multiply risk.
A 2025 MMA Global India study found that 42 percent of Indian marketing organisations are still in experimentation mode with AI, while only around one fifth report high integration of AI across processes. More than half said AI adoption is not effectively understood across the organisation, and nearly 40 percent admitted they are still developing strategies to manage AI related risks.
This gap between usage and governance is where ethical failures often begin.
The first risk is data overreach. AI systems thrive on large volumes of behavioural and transactional data. Marketing teams have historically collected extensively for targeting and segmentation. When AI tools are layered on top, there is a temptation to repurpose data beyond its original intent.
India’s Digital Personal Data Protection framework has changed the baseline. The DPDP Rules operationalised expectations around consent, purpose limitation, minimisation, accuracy, and security safeguards. Penalties for violations are significant. For marketing teams, this means personalisation strategies must now demonstrate legal alignment, not just performance efficiency.
The second risk is bias and uneven outcomes. AI models optimise toward metrics such as click through rate or conversion probability. They do not automatically optimise for fairness. If historical data reflects uneven treatment across income groups, geography, or language, models can amplify those patterns.
Globally, ad delivery systems have faced scrutiny for skewed exposure in sensitive categories. In consumer marketing, uneven discount allocation or exclusionary targeting can erode trust, even when not intentional.
The third risk is misinformation. Generative AI produces persuasive content. It can also produce inaccuracies that appear credible. In sectors such as finance, health, education, and sustainability, a hallucinated claim can become a compliance issue within minutes if replicated across campaigns.
India has tightened scrutiny around misleading advertisements and greenwashing. Reports in 2025 indicated that over 1.6 lakh self declaration certificates were filed for ads in specific categories following court directives aimed at curbing misleading health and food claims. AI generated content does not exempt marketers from these standards.
The fourth risk is manipulation through design. AI driven optimisation can refine interfaces to maximise conversions. But dark pattern guidelines in India explicitly identify deceptive choice architecture as unfair trade practice. When models test and iterate automatically, boundaries must be clearly defined to avoid crossing regulatory lines.
The fifth risk is spam and outreach scale. AI powered campaign tools can multiply message volume quickly. Telecom regulators have strengthened enforcement around unsolicited commercial communications, and millions of telecom connections have reportedly been disconnected in recent years as part of enforcement drives. Automated marketing outreach that ignores consent frameworks can quickly become operational and reputational damage.
The sixth risk is cybersecurity. Marketing stacks integrate CRM systems, ad platforms, analytics tools, and external AI services. Each integration expands potential exposure. IBM’s 2024 Cost of a Data Breach report estimated that the average breach cost in India reached nearly INR 195 million, with costs rising steadily over the past several years. Globally, the average breach cost approached USD 4.9 million in 2024.
For marketing leaders, a breach is not only an IT incident. It is a brand trust event.
Trust data reinforces the point. The 2025 Edelman Trust Barometer India report found that trust in AI varies significantly across demographic groups. Comfort with businesses using AI declines among groups reporting higher grievance or institutional distrust. This means AI driven marketing may be received differently across audiences, even when legally compliant.
David Cohen, CEO of IAB, has stated that AI will soon power every aspect of media campaigns. That scale brings performance potential. It also raises accountability expectations.
Rajesh Nambiar, President of NASSCOM, has cautioned that AI systems are being deployed at a scale where they can learn, decide, persuade, and act faster than human judgement can realistically monitor in real time. That observation applies directly to marketing, where persuasion is the core function.
In the United States, Federal Trade Commission Chair Lina Khan has made clear that AI driven conduct is subject to the same consumer protection laws as any other technology. The tool does not dilute responsibility.
European regulators have taken a structured approach through the AI Act, which phases in obligations based on risk levels. While marketing applications are not always categorised as high risk, transparency and governance requirements are influencing vendor design globally.
Together, these developments signal a consistent message. AI does not create a regulatory vacuum. It intensifies scrutiny.
From an operational standpoint, ethical marketing in the AI era is increasingly about process discipline.
Many enterprises now classify AI use cases by risk level. Low risk tasks include internal drafting or data summarisation. Medium risk tasks include automated segmentation and variant testing. High risk tasks include personalised pricing, sensitive category targeting, sustainability claims, and health related messaging.
Human review is being retained for high impact outputs. Confidence thresholds are embedded in systems so that low certainty responses trigger escalation rather than automatic execution. Logs and audit trails are maintained for regulatory defence.
Another emerging practice is vendor due diligence. Marketing leaders are working more closely with legal and security teams before integrating AI tools that process personal data. Data residency, retention policies, and model training disclosures are becoming procurement questions.
Budget pressure, however, complicates implementation. Gartner’s budget figures show that marketing leaders are working with less relative funding than in previous years. Efficiency gains from AI are attractive. But the same budget squeeze can limit investment in governance infrastructure.
The tension is structural. Automation lowers cost per action but raises potential cost per failure.
Industry surveys suggest that marketing teams using AI report stronger performance outcomes. Yet correlation does not equal causation. High performing organisations may be more capable of implementing AI effectively and ethically.
The more difficult measurement is long term brand impact. Trust erosion from perceived misuse of AI may not appear immediately in conversion dashboards but can surface in reputation metrics over time.
Marketing leaders in 2026 therefore face a layered challenge. They must deliver measurable growth. They must integrate AI deeply enough to remain competitive. And they must demonstrate that AI was used responsibly.
Ethics in this context is not a philosophical debate. It is a control system.
The most resilient organisations are embedding ethics into workflow rather than relying on post campaign audits. Consent checks are automated before activation. Claims are linked to substantiation databases. Dark pattern detection scripts run on user interface variants. Security teams are looped into campaign data architecture.
In short, ethics is being treated as infrastructure.
AI will continue to expand in marketing. Budget constraints, competitive intensity, and consumer expectations make that trajectory unlikely to reverse. The real shift between 2023 and 2026 is that AI systems now operate inside the decision layer of marketing.
When machines determine who sees what, at what time, and at what price, the margin for error narrows.
Marketing leaders do not need to slow innovation. They need to ensure that performance optimisation does not outrun accountability.
In a marketplace defined by speed, the brands that sustain trust may be the ones that treat AI ethics not as a campaign checkpoint, but as a continuous operating standard.
Disclaimer: All data points and statistics are attributed to published research studies and verified market research. All quotes are either sourced directly or attributed to public statements.
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