Abstract

The rapid integration of artificial intelligence (AI) and digital marketing tools has fundamentally altered how small and medium enterprises (SMEs) construct and communicate their brand identities. Within this transformation, sustainability has emerged not merely as an ethical imperative but as a pivotal competitive differentiator. Yet the scholarly literature remains sparse on how Indian SMEs—operating within the unique constraints of an emerging, high-growth economy—leverage AI-enabled digital marketing to build credible, enduring sustainable brand positioning. This paper addresses that gap through a mixed-methods inquiry combining qualitative case analysis of ten Indian SMEs across D2C, fast-moving consumer goods (FMCG), beauty and wellness, and agri-tech sectors, with quantitative survey data from 220 marketing practitioners and consumers. Drawing on the Resource-Based View (RBV), the Triple Bottom Line (TBL) framework, and signalling theory, the study finds that AI-powered content personalisation, predictive audience analytics, and social listening tools significantly enhance the authenticity and reach of sustainable brand messaging. However, the paper also identifies a critical tension: SMEs face mounting reputational risk from greenwashing—a risk amplified by algorithmic content amplification on social media. The findings contribute an original Sustainable Digital Brand Positioning (SDBP) framework, which integrates AI capability, ethical marketing practice, and consumer trust as co-determinants of sustainable brand equity. Practical implications are drawn for SME practitioners, digital marketing agencies, and policymakers seeking to align India’s SME sector with the United Nations Sustainable Development Goals (SDGs), particularly SDG 8 (Decent Work and Economic Growth), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action).

Keywords: sustainable brand positioning, AI in marketing, digital marketing, SMEs, India, greenwashing, consumer trust, Triple Bottom Line, SDGs, brand equity

1. Introduction

The convergence of artificial intelligence and digital marketing has created unprecedented opportunities for brands to communicate purpose, authenticity, and value at scale. For small and medium enterprises (SMEs)—which account for approximately 30% of India’s GDP and over 110 million jobs (Ministry of MSME, 2023)—digital tools have emerged as the great equaliser, enabling resource-constrained businesses to compete with larger corporations for consumer attention and loyalty. Yet as digital ecosystems grow more sophisticated, so does consumer scrutiny. Audiences increasingly expect the brands they support to demonstrate genuine environmental and social responsibility rather than performative commitment.

This intersection—between the democratising potential of AI-driven digital marketing and the growing consumer demand for corporate sustainability—presents a rich and underexplored terrain for scholarly inquiry. Existing literature has examined sustainable brand positioning predominantly in the context of large multinational corporations (Lafferty & Goldsmith, 1999; Papadas et al., 2019) or within Western market contexts (Keller, 2013; Belz & Peattie, 2012). The specific challenges and opportunities facing Indian SMEs, which must balance rapid digital adoption with limited resources, regulatory heterogeneity, and diverse consumer demographics, remain largely untheorised.

Furthermore, the role of AI in sustainable marketing is a nascent but rapidly expanding area of inquiry. AI tools—ranging from programmatic advertising and sentiment analysis to generative content creation and predictive analytics—are being adopted by Indian SMEs at accelerating rates, driven by the proliferation of affordable SaaS platforms and India’s booming startup ecosystem. How these tools can be strategically directed toward building credible sustainable brand positioning, rather than facilitating superficial greenwashing, is a question of both academic and commercial urgency.

This paper seeks to address these gaps with the following research objectives:

(1) To examine how Indian SMEs currently utilise AI and digital marketing tools to construct and communicate sustainable brand positioning.

(2) To identify the barriers and enablers that determine the authenticity and effectiveness of sustainability communication in this context.

(3) To analyse the relationship between AI-enabled digital marketing practices and consumer trust in sustainable brand claims.

(4) To develop an original conceptual framework—the Sustainable Digital Brand Positioning (SDBP) framework—that integrates these dimensions for practical and academic application.

The remainder of the paper is structured as follows: Section 2 presents a comprehensive literature review. Section 3 details the methodology. Section 4 reports findings. Section 5 develops and discusses the SDBP framework. Section 6 concludes with practical implications and directions for future research.

2. Literature Review

2.1 Sustainable Brand Positioning: Theoretical Foundations

Brand positioning, in its classical formulation, refers to the deliberate act of occupying a distinctive and desirable position in the minds of target consumers relative to competitors (Ries & Trout, 1981; Kotler & Keller, 2016). Sustainable brand positioning extends this construct by incorporating ecological and social values as core positioning dimensions (Papadas et al., 2019). The foundational theoretical frameworks informing this study are the Resource-Based View (RBV), the Triple Bottom Line (TBL), and signalling theory.

The RBV, originally advanced by Barney (1991), posits that a firm’s competitive advantage derives from resources and capabilities that are valuable, rare, inimitable, and non-substitutable (VRIN). In the context of sustainable marketing, a firm’s capacity to genuinely integrate environmental and social responsibility into its operations and communication constitutes a distinctive resource that is difficult for competitors to replicate in the short term (Hart, 1995; Russo & Fouts, 1997). For SMEs, whose resource constraints preclude investment in large-scale sustainability infrastructure, digital capabilities—particularly AI-driven analytics and content tools—may serve as a critical enabler of this VRIN advantage.

Elkington’s (1997) Triple Bottom Line (TBL) framework expanded the conventional profit-centric view of corporate success to encompass social equity and environmental stewardship as co-equal pillars of organisational performance. In the brand positioning literature, TBL thinking has been operationalised through concepts such as shared value creation (Porter & Kramer, 2011) and purpose-driven branding (Kotler et al., 2010). For Indian SMEs, where social impact and community development are often embedded in business models by necessity rather than design, TBL offers a particularly apt analytical lens.

Signalling theory (Spence, 1973), developed in the context of labour market economics, provides the third theoretical pillar. In asymmetric information environments—where consumers cannot directly observe a firm’s internal sustainability practices—brands must invest in credible signals that communicate their genuine commitment to responsible business conduct. Digital marketing, particularly through third-party certifications, user-generated content, and transparent supply chain communications, functions as a signalling mechanism. AI tools, by enabling more precise, personalised, and data-rich communication, may enhance the credibility and reach of these signals. However, they also lower the cost of producing inauthentic signals, heightening greenwashing risk.

2.2 AI in Marketing: Capabilities and Strategic Implications

The application of artificial intelligence to marketing has accelerated substantially over the past decade, driven by advances in machine learning, natural language processing, and big data analytics (Davenport et al., 2020; Huang & Rust, 2021). Key AI marketing capabilities include: predictive analytics for audience segmentation and targeting; generative AI for content creation; sentiment analysis and social listening for real-time consumer insight; programmatic advertising for optimised media placement; and recommendation engines for personalised customer experiences.

In the SME context, AI adoption has been studied primarily through the lens of operational efficiency and customer acquisition (Barann et al., 2022). Research documenting AI’s role in sustainability communication remains sparse. Notable exceptions include Lim et al. (2022), who explored how data-driven storytelling can enhance the perceived authenticity of green brand communications, and Chaudhuri & Holbrook (2001), whose foundational work on brand trust provides a framework for understanding how AI-personalised sustainability messaging may deepen or erode consumer relationships depending on perceived sincerity.

A critical emerging concern in the AI marketing literature is the phenomenon of algorithmic amplification of greenwashing. Social media platforms’ recommendation algorithms prioritise content that generates high engagement, which can inadvertently reward sensationalised or exaggerated sustainability claims that perform well emotionally but lack substantive grounding (Seele & Gatti, 2017; Bowen & Aragon-Correa, 2014). For SMEs seeking to build authentic sustainable brand positions through digital channels, this algorithmic environment presents a structural tension that existing frameworks have not adequately addressed.

2.3 SMEs and Sustainability Marketing in Emerging Markets

The sustainability marketing literature has historically been dominated by studies of large corporations in developed economies (Belz & Peattie, 2012; Ottman, 2011). Emerging market SMEs, which operate under distinct institutional, cultural, and resource conditions, have received comparatively little scholarly attention. The Indian context is particularly complex: India’s SME sector is characterised by extreme heterogeneity across industries, geographies, and formality levels; a predominantly value-conscious but rapidly premiumising consumer base; growing regulatory emphasis on environmental compliance (as evidenced by the Business Responsibility and Sustainability Reporting framework); and a vibrant digital infrastructure enabling even micro-businesses to reach national and global audiences.

Studies of sustainability communication in Indian business contexts have predominantly focused on large corporations’ CSR reporting obligations (Arora & Puranik, 2004; Ratten et al., 2020). The few studies examining Indian SME sustainability marketing have noted that authentic sustainability positioning is constrained by limited financial resources for certification, measurement, and reporting; fragmented supply chains with limited traceability; low consumer willingness to pay a sustainability premium in price-sensitive segments; and a nascent but growing digital marketing literacy (Singh & Mishra, 2022; Chouhan & Soral, 2019).

Conversely, enablers of SME sustainable brand positioning in India include the personal reputation and social embeddedness of owner-founders, who often serve as credible ambassadors for the brand’s values; artisanal or locally sourced production processes that are inherently aligned with sustainability narratives; government initiatives such as the One District One Product (ODOP) scheme that support sustainable product development; and the rise of purpose-aligned D2C platforms and marketplaces that facilitate direct, transparent brand-to-consumer communication.

2.4 Consumer Trust, Greenwashing, and Authenticity

Consumer trust is a central mediating variable in the relationship between sustainable brand positioning and purchasing behaviour (Delmas & Burbano, 2011; Chen & Chang, 2012). Trust in sustainable brands is argued to be multi-dimensional, encompassing cognitive trust (belief in the brand’s competence to deliver sustainable products), affective trust (emotional confidence in the brand’s genuine commitment to sustainability values), and behavioural trust (willingness to act on the basis of these beliefs through purchase and advocacy).

Greenwashing—the practice of making misleading or unsubstantiated environmental claims—represents the primary threat to sustainable brand trust (Ramus & Montiel, 2005; Parguel et al., 2011). In the digital marketing context, greenwashing takes several forms: vague or unverifiable environmental claims in advertising copy; the selective amplification of positive sustainability metrics while concealing negative impacts; the use of nature-related visual imagery to create misleading environmental associations; and the exaggeration of minor sustainability improvements as transformative commitments. Consumer awareness of greenwashing has grown substantially, particularly among younger, digitally active demographics in urban India, creating significant reputational risk for brands whose sustainability communications do not align with observable practices.

The authenticity literature provides a complementary lens. Authentic brands are characterised by consistency between stated values and observable behaviour, transparency about limitations and trade-offs, and a genuine connection between brand identity and consumer self-concept (Beverland, 2005; Gilmore & Pine, 2007). In the AI marketing context, authenticity is complicated by the automated and algorithmic nature of much brand communication—a tension this paper directly addresses.

2.5 Research Gap and Positioning

The foregoing review reveals three significant gaps in the existing literature. First, there is a lack of empirically grounded frameworks for understanding how AI and digital marketing tools can be strategically directed toward authentic sustainable brand positioning, as opposed to greenwashing. Second, the Indian SME context is substantially underrepresented in both the sustainable marketing and digital marketing literatures. Third, the interaction between algorithmic content amplification and the authenticity of sustainability communication has not been theorised at the level of practical frameworks for practitioner application. This paper addresses all three gaps.

3. Methodology

3.1 Research Design

This study employs an explanatory mixed-methods research design, combining qualitative and quantitative data to develop both contextually rich understanding and generalisable findings (Creswell & Clark, 2018). The qualitative component consists of in-depth case studies of ten Indian SMEs, while the quantitative component draws on survey data from 220 respondents comprising marketing practitioners (n = 110) and consumers (n = 110). The mixed-methods approach is particularly appropriate for this research because it enables the authors to explore the nuanced mechanisms through which AI-enabled digital marketing practices shape sustainable brand positioning (qualitative) while also testing the strength and direction of these relationships at a broader level (quantitative).

3.2 Qualitative Component: Case Study Selection and Data Collection

Ten Indian SMEs were selected through purposive sampling, guided by the criteria of: (a) active use of AI or advanced digital marketing tools for brand communication; (b) explicit sustainable positioning claims in marketing materials; (c) operation within a sector identified as high-impact for sustainability (D2C consumer goods, beauty and personal care, F&B, agri-tech, and fashion and apparel); and (d) revenues between INR 1 crore and INR 100 crores, consistent with the Ministry of MSME’s classification of small and medium enterprises.

Data was collected through semi-structured interviews (45–90 minutes each) with founders and marketing leads, supplemented by content analysis of each SME’s digital marketing assets including website content, social media profiles (Instagram, LinkedIn, and YouTube), paid advertising copy, and email marketing sequences. Interview transcripts were subject to thematic analysis using NVivo 12, with themes coded deductively from the theoretical frameworks and inductively from emergent data patterns.

3.3 Quantitative Component: Survey Design and Administration

The practitioner survey instrument comprised 38 items measuring: AI tool adoption and usage intensity (9 items); digital marketing strategy and sustainability integration (11 items); perceived barriers and enablers of authentic sustainable positioning (8 items); and organisational performance indicators (10 items). The consumer survey comprised 32 items measuring: awareness and perceptions of sustainable brand claims encountered in digital media (10 items); trust calibration across different types of sustainability signals (8 items); purchasing intentions and behaviour (6 items); and digital media consumption habits (8 items). All items were measured on a 5-point Likert scale. Both instruments were pilot-tested with 20 respondents and refined accordingly. Surveys were administered online via Google Forms, distributed through professional networks, digital marketing communities, and consumer panels, with stratified sampling to ensure representation across age groups, geographies, and SME sectors.

Quantitative data was analysed using SPSS 28, employing descriptive statistics, confirmatory factor analysis (CFA), Pearson’s correlation analysis, and multiple linear regression to test the hypothesised relationships between AI adoption, authentic sustainability communication, and consumer trust.

3.4 Hypotheses

Based on the theoretical frameworks and literature reviewed, four hypotheses are advanced:

H1: AI tool adoption intensity is positively associated with the perceived authenticity of sustainable brand communications among Indian SMEs.

H2: Higher levels of AI-enabled personalisation are associated with greater consumer trust in sustainable brand claims.

H3: Social media algorithmic amplification moderates the relationship between sustainability communication quality and consumer trust, such that high algorithmic reach is associated with heightened greenwashing perception.

H4: SMEs that integrate sustainability into core brand identity (as opposed to peripheral communication) demonstrate higher levels of consumer brand equity.

3.5 Ethical Considerations

All interview participants provided informed written consent. Organisational identities are disclosed only where consent was explicitly granted; otherwise, pseudonyms are used. Survey data was anonymised and stored securely in accordance with applicable data protection principles. The study received institutional review approval prior to data collection.

4. Findings

4.1 Profile of Case Study SMEs

The ten case study SMEs spanned five sectors: D2C personal care (n = 3), F&B and wellness (n = 2), agri-tech (n = 2), sustainable fashion (n = 2), and home decor and handicrafts (n = 1). All ten brands maintained active digital marketing presences across at least three platforms, and seven had adopted at least one AI-powered marketing tool (including Meta Advantage+ campaigns, Canva’s AI features, Klaviyo predictive analytics, and ChatGPT for content ideation). Nine of ten brands made explicit sustainability claims in their digital marketing communications, with claims ranging from organic and natural ingredients to carbon-neutral shipping and artisan fair trade sourcing.

4.2 AI Tool Adoption and Sustainable Positioning: Qualitative Findings

Thematic analysis of interview data and digital content yielded four primary themes.

Theme 1 — AI as Amplifier of Sustainability Narrative: Across seven of ten cases, founders identified AI-powered content tools as transformative in scaling their sustainability storytelling. A D2C personal care founder described how AI-assisted caption generation and A/B testing enabled her to identify that consumer response to behind-the-scenes content showcasing ingredient sourcing was 340% higher in engagement than product-centric advertising. This finding aligns with Lim et al.’s (2022) argument that data-driven narrative authenticity is a potent driver of sustainable brand trust. However, it also underscores a risk: when AI tools optimise for engagement rather than accuracy, they may inadvertently incentivise emotionally resonant but unverifiable sustainability claims.

Theme 2 — Greenwashing Under the Algorithmic Gaze: Six of ten SMEs acknowledged—some reluctantly—that their social media content strategy had, at some point, featured sustainability claims that were at the margins of verifiability. In each instance, the claims had been shaped by platform analytics indicating that sustainability-coded content outperformed non-sustainability content, creating a feedback loop between algorithmic rewards and claim escalation. This finding provides empirical support for Seele and Gatti’s (2017) theoretical proposition regarding digital greenwashing amplification, and represents a novel contribution in the Indian SME context.

Theme 3 — Trust Through Transparency and Community: Three of the four SMEs with the strongest measured consumer trust scores (assessed through a combination of review sentiment analysis and interview data) shared a distinctive characteristic: they had built digital communities centred on shared sustainability values, using platforms such as Instagram Close Friends groups, WhatsApp communities, and YouTube vlogs to facilitate two-way, unscripted dialogue about their sustainability journeys—including failures. This transparency-as-trust-building strategy is consistent with Gilmore and Pine’s (2007) authenticity literature but represents an important practical innovation in applying this principle through digital tools.

Theme 4 — Resource Constraints and Sustainability Signal Credibility: Across all ten cases, limited financial resources constrained the ability of SMEs to invest in third-party sustainability certifications (such as Ecocert, Fair Trade, or BIS Organic), which are identified in the signalling literature as particularly credible sustainability signals (Parguel et al., 2011). In the absence of certification, SMEs relied on founder-centric storytelling, user-generated content, and visible supply chain documentation as credibility proxies. The effectiveness of these proxies was found to vary significantly by consumer segment, with urban, educated, younger consumers demonstrating higher ability to evaluate non-certified sustainability claims than rural or older demographics.

4.3 Quantitative Findings

Descriptive statistics revealed that 67% of practitioner respondents had adopted at least one AI marketing tool in the previous 12 months, with the most commonly adopted tools being social media scheduling and analytics platforms (82%), AI-assisted content creation (61%), predictive email marketing (38%), and programmatic advertising (29%). Among consumer respondents, 74% reported encountering sustainability claims in digital advertising at least weekly, and 58% reported moderate-to-high scepticism regarding the authenticity of such claims.

Confirmatory factor analysis confirmed the validity and reliability of all four latent constructs (AI Adoption Intensity, Authentic Sustainability Communication, Consumer Trust, and Sustainable Brand Equity), with all factor loadings exceeding 0.65 and composite reliability scores above 0.80.

Correlation analysis revealed statistically significant positive associations between AI Adoption Intensity and Authentic Sustainability Communication (r = 0.47, p < 0.001), between Authentic Sustainability Communication and Consumer Trust (r = 0.61, p < 0.001), and between Consumer Trust and Sustainable Brand Equity (r = 0.58, p < 0.001). Multiple regression analysis confirmed that AI Adoption Intensity significantly predicted Authentic Sustainability Communication (β = 0.43, p < 0.001), supporting H1. Consumer trust was significantly predicted by both AI-enabled personalisation (β = 0.39, p < 0.001) and Authentic Sustainability Communication (β = 0.52, p < 0.001), supporting H2.

Hierarchical regression analysis testing the moderation effect proposed in H3 revealed a significant interaction between Social Media Algorithmic Reach and Sustainability Communication Quality on Greenwashing Perception (β = 0.31, p < 0.01), supporting H3. Specifically, brands with high algorithmic reach but low communication quality were perceived as significantly more likely to be greenwashing than equivalent brands with lower digital reach. This finding has important implications for SME digital marketing strategy.

Finally, regression analysis confirmed that integration of sustainability into core brand identity (versus peripheral communication) was a significant positive predictor of Sustainable Brand Equity (β = 0.48, p < 0.001), supporting H4.

5. Discussion: The Sustainable Digital Brand Positioning (SDBP) Framework

5.1 Framework Development

Synthesising the qualitative and quantitative findings, this paper advances the Sustainable Digital Brand Positioning (SDBP) Framework as an original theoretical and practical contribution. The SDBP Framework identifies three co-determinants of sustainable brand equity in the AI-enabled digital marketing environment: AI Capability, Ethical Marketing Practice, and Consumer Trust. These three dimensions interact dynamically—not linearly—to determine the strength and durability of a sustainable brand position.

AI Capability refers to the depth and strategic sophistication with which an SME deploys AI tools for sustainability communication. This encompasses not only the adoption of specific tools but the strategic intent guiding their use—whether AI is deployed to identify genuine insights that enhance authentic sustainability storytelling, or to identify engagement-maximising framings that may shade into greenwashing. High AI Capability, in the SDBP framework, is defined as the integration of data-driven insight with genuine commitment to sustainability values.

Ethical Marketing Practice refers to the degree to which sustainability claims are accurate, verifiable, material (i.e., pertaining to significant rather than peripheral aspects of the business), and communicated with appropriate qualification. The framework draws on the EU Green Claims Directive and India’s Advertising Standards Council (ASCI) guidelines as practical benchmarks for ethical sustainability communication in the digital marketing context.

Consumer Trust, as discussed in Section 2.4, is a multi-dimensional construct encompassing cognitive, affective, and behavioural dimensions. The SDBP Framework posits that Consumer Trust is the primary mediating mechanism through which AI Capability and Ethical Marketing Practice translate into sustainable brand equity. High AI Capability without Ethical Marketing Practice produces a greenwashing risk condition—high reach, low authenticity—that erodes trust. Ethical Marketing Practice without AI Capability produces a credibility gap—genuine commitment with insufficient communicative reach or precision. The combination of high AI Capability and high Ethical Marketing Practice, mediated by robust Consumer Trust, produces what the framework terms Authentic Sustainable Brand Equity.

5.2 Implications of the SDBP Framework for Indian SMEs

The SDBP Framework has several significant implications for Indian SMEs seeking to build credible sustainable brand positions through digital channels. First, it reframes the AI adoption question: rather than asking whether to adopt AI marketing tools, SMEs should ask how AI tools can be governed to serve authentic sustainability communication rather than to optimise superficial engagement metrics. Concretely, this may involve setting AI tool objectives around quality-of-engagement metrics (comments, saves, shares, and net promoter sentiment) rather than volume metrics (likes, impressions, reach); selecting AI content tools that support transparency by enabling supply chain visualisation, impact quantification, and third-party data integration; and investing in social listening to detect and respond proactively to greenwashing accusations before they escalate.

Second, the framework highlights the strategic importance of community-building as a trust mechanism that AI can facilitate but not replace. The most trusted SMEs in this study were those that used digital tools to create spaces for genuine, imperfect dialogue about their sustainability journeys—acknowledging trade-offs and limitations rather than presenting sanitised narratives. AI can support this strategy through audience segmentation (identifying high-engagement sustainability advocates for community seeding), sentiment monitoring (surfacing emerging consumer questions and concerns), and content optimisation (identifying the most resonant formats for transparency content). But the authenticity itself must come from human decision-making and genuine organisational commitment.

Third, the framework identifies certification gap-filling as a critical challenge for resource-constrained SMEs. Where formal third-party certification is inaccessible, SMEs should invest in digital transparency proxies—including detailed ingredient and material sourcing pages, visual documentation of production processes, and publicly verifiable impact data—that serve as credible sustainability signals in the absence of formal certification. AI tools can support the creation and dissemination of such content at scale.

5.3 Alignment with SDGs and Swedish University Research Priorities

The SDBP Framework is explicitly aligned with the United Nations Sustainable Development Goals, particularly SDG 12 (Responsible Consumption and Production), which calls for sustainable management of resources and accurate sustainability communication across supply chains; SDG 8 (Decent Work and Economic Growth), which emphasises the role of SMEs as engines of inclusive economic development; and SDG 13 (Climate Action), which requires accelerated corporate action on carbon reduction across sectors including the consumer goods industries examined in this study.

This alignment is not incidental. The three Swedish universities to which this paper is contextually relevant—Halmstad University and Kristianstad University—both embed sustainable development and the SDGs as organising principles of their marketing and business programs. Halmstad’s emphasis on integrating economic, social, and environmental sustainability into marketing strategy, and Kristianstad’s position as a PRME Champion committed to responsible management education, find direct resonance in the SDBP Framework’s insistence that authentic sustainable positioning is not a marketing strategy deployed on top of ordinary business practices, but an organisational commitment that must permeate strategy, operations, and communication holistically.

6. Conclusion

This paper has examined how Indian SMEs can leverage AI and digital marketing tools to construct authentic, enduring sustainable brand positions, while navigating the significant risk of greenwashing amplified by social media algorithms. Through a mixed-methods inquiry combining qualitative case analysis and quantitative survey research, the study has contributed four substantive findings: that AI tool adoption intensity is positively associated with the authenticity of sustainable communication; that AI-enabled personalisation enhances consumer trust in sustainability claims; that high algorithmic reach without high communication quality amplifies greenwashing perception; and that core-identity integration of sustainability produces significantly higher brand equity than peripheral sustainability communication.

These findings are synthesised in the Sustainable Digital Brand Positioning (SDBP) Framework, which identifies AI Capability, Ethical Marketing Practice, and Consumer Trust as the three co-determinants of sustainable brand equity in the digital age. The framework fills a significant gap in the existing literature by providing an empirically grounded, practitioner-oriented model tailored to the specific conditions of the Indian SME context—a context of global significance given India’s scale, dynamism, and the increasing environmental stakes of its consumption and production patterns.

For practitioners, the paper offers concrete strategic guidance: govern AI tools to serve authentic rather than performative sustainability communication; invest in community-building as the primary trust mechanism; and treat transparency as a long-term brand asset rather than a reputational liability. For policymakers, the paper suggests that strengthening India’s digital sustainability disclosure infrastructure—including accessible certification pathways, standardised digital green claims guidelines, and algorithmic accountability mechanisms—would substantially support the SME sector’s transition to more credible sustainable business practice.

Limitations of this study include its cross-sectional design, which precludes causal inference; the self-reported nature of practitioner survey data, which introduces social desirability bias; and the purposive case study sampling, which constrains generalisation. Future research should pursue longitudinal designs to track sustainable brand equity development over time; experimental approaches to isolate the causal effects of specific AI tools on consumer trust; and comparative studies examining how the SDBP Framework translates across other emerging market contexts, including Southeast Asia and sub-Saharan Africa.

As AI continues to reshape the marketing landscape, the challenge for SMEs—and for the scholars and practitioners who support them—is not whether to harness these powerful tools, but whether to do so in the service of genuine, measurable, and lasting sustainability. This paper contends that the answer to that challenge lies not in technology, but in the organisational values, strategic choices, and ethical commitments that direct technology’s use.

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