AI-Driven Decision-Making in Programmatic Advertising (PA) for Sustainable Digital Marketing

Abstract

Unlike traditional advertising, which depends on human decision-making, programmatic advertising (PA) allows real-time, automated transactions between software platforms. This marks a fundamental shift in digital advertising, as PA continuously optimizes and purchases ad placements through machine-to-machine (M2M) communication, without direct human intervention. Artificial intelligence (AI), increasingly used across digital applications, further enhances PA by improving automation and data-driven decision-making. Building on a comprehensive literature review, this study examines how AI-driven PA not only boosts marketing performance but also promotes sustainability through thoughtful, data-driven insights. It highlights how AI-powered innovations in marketing help align short-term advertising goals with long-term environmental, social, and economic responsibilities. The findings show that AI-driven PA improves efficiency and personalization, creating more relevant experiences for consumers while opening pathways to responsible and sustainable advertising practices. At the same time, challenges remain. Issues such as transparency, data privacy, and ethical risks must be carefully managed to unlock the full potential of AI-driven PA for long-term sustainability. This study offers practical insights for leveraging AI-powered PA to achieve both stronger marketing outcomes and more responsible digital marketing practices.

Introduction

Programmatic advertising (PA) allows businesses to target consumers online in real time with personalized messages by automatically purchasing ads (Samuel et al., 2021). PA leverages real-time bidding, behavioral targeting, and cross-device analytics to precisely match ads with highly specific audience segments (Turow, 2023). Artificial intelligence (AI) enables more efficient and effective digital advertising than traditional online methods (Dörnyei, 2021). AI tools turn complex data into actionable insights for advanced personalization beyond demographics (Loureiro et al., 2021). AI helps marketers predict trends, improve customer groups, and manage budgets better (Davenport et al., 2020). AI chatbots give fast, easy help, improving customer satisfaction and loyalty (Adam et al., 2020).

AI content and SEO tools create relevant, high-quality content to boost online visibility (Wilson et al., 2024). AI also enhances ad creatives by analyzing performance data and suggesting optimizations to increase click-through rates and campaign effectiveness (Wilson et al., 2024). While these innovations boost marketing efficiency, they also introduce new responsibilities for marketers. AI systems can unintentionally strengthen biases, leading to unfair outcomes. Regular auditing of AI systems is therefore essential to mitigate these risks (Binns, 2020). Although AI offers valuable data-driven insights, human creativity and critical thinking remain vital for crafting impactful campaigns. Top results come from combining AI with human creativity and empathy (Rust, 2020).

PA has become a key driver of growth in the global digital advertising ecosystem, enabling businesses to quickly adapt to evolving consumer behaviors (Statista, 2024). AI-powered PA targets faster, cheaper, and more accurate, boosting efficiency and revenue (Chen, Chiang, & Storey, 2019). Technological advancements have significantly reshaped the digital marketing landscape, with AI technologies emerging as particularly transformative. AI has already revolutionized sectors such as finance, healthcare, education, and, notably, digital marketing. Simultaneously, the potential for AI to support broader sustainability goals has become an increasingly important area of focus. As digital advertising continues to expand in scale and complexity, aligning marketing strategies with environmental and social considerations is becoming more urgent.

Today, AI is transforming digital marketing while supporting sustainable practices (Brundtland, 1985). Since marketing is part of daily life, AI—used for social good—can strongly support sustainable development (Hermann, 2022). Sustainable marketing emphasizes that strategies must be environmentally sustainable, socially equitable, and economically viable (Martin & Schouten, 2014). By using sustainable marketing, companies boost their advantage and benefit society and the environment (Crittenden et al., 2011). Figure 1 shows that programmatically sold advertising was worth $546 billion in 2023, with projections indicating it will reach 779 billion by 2028. This significant growth highlights the increasing dominance of PA within the global digital marketing landscape. This rapid expansion during the 2000–2025 period underscores why this time frame is critical for studying the evolution and sustainability of PA. This period shows fast tech growth, rising market demand, and the need for sustainable, efficient, and responsible PA use. Understanding how new tech supports sustainable marketing is now a key priority. In this context, this study builds on existing reviews by examining how AI-driven PA not only improves marketing performance but also supports sustainable digital marketing practices through a thematic synthesis of current research.

Figure 1.

Source: Statista Research Department (2024).

The following sections are organized to provide a comprehensive analysis of this topic. The ‘Materials and Methods’ section outlines the qualitative research design and the systematic literature review process used to identify and analyze relevant academic studies. The ‘Findings’ section covers three topics: AI and PA in marketing, PA’s role in sustainability, and related challenges. The ‘Discussion’ section critically evaluates these findings, examining their theoretical and practical implications, along with current challenges. Finally, the ‘Conclusions’ section summarizes key insights and emphasizes the need for ethical, transparent, and sustainability-oriented approaches in the future of digital advertising.

Background on Programmatic Advertising

PA uses AI to buy ads in real time, cutting costs and errors for e-commerce companies (Busch, 2015). This process allows advertisers to make more informed decisions quickly and efficiently. PA is a data-driven system that enables real-time bidding for ad space and delivers personalized marketing to potential customers (Seitz & Zorn, 2016). It shows targeted ads instantly, helping consumers avoid ad overload (Busch, 2015). By providing relevant ads, PA helps reduce the likelihood of consumer disengagement. PA goes beyond automating online ads by analyzing consumer needs and preferences at every touchpoint with a fully consumer-centric approach (Seitz & Zorn, 2016). Retailers leverage consumer data to influence purchasing decisions and deliver personalized ads through the PA system (Aguirre et al., 2015). PA enables real-time targeting of consumers online with personalized messages, facilitated by the automated purchasing of ads (Samuel et al., 2021). PA’s programmatic buying links data management platforms (DMPs) and demand-side platforms (DSP) to serve the most relevant ads with programmatic creativity (Chen et al., 2019). PA requires extensive data collection to create detailed consumer profiles, which raises ethical concerns regarding the protection of personal data (Núñez-Barriopedro et al., 2023). This reliance on data has sparked discussions about privacy and the regulation of consumer information.

From 2007, ads shifted to PA-driven automation, mirroring finance’s electronic trading (Rayport, 2015). As a result, PA has rapidly gained traction as a key component of digital advertising. PA uses AI to target audiences, deliver real-time ads, and boost campaign performance (Choi & Lim, 2020). PA is increasingly used in digital advertising as a cost-effective, measurable, and scalable alternative, enabled by real-time data exchange between buyers, sellers, and intermediaries (Alaimo et al., 2017). Google and Meta play a central role in the PA market (Hardy, 2023). Busch (2015) argues that PA should be seen as a value-added principle in the digitalization of marketing, not just a new pricing method for ad space.

PA uses real-time bidding (RTB) to show the right ads to the right people (Palos-Sanchez et al., 2019). PA relies on DMPs, DSPs, and supply-side platforms (SSPs) to automate ad buying and placement across social, video, mobile, and display channels (Chen et al., 2019). Although PA offers significant advantages, such as scalability, efficiency, and improved targeting accuracy, it also raises critical challenges related to transparency, brand safety, and data privacy. Therefore, a comprehensive understanding of its socio-technical nature is essential for successful implementation in e-commerce contexts (Núñez-Barriopedro et al., 2023).

Although the literature on PA primarily focuses on the technological and economic advantages of AI-driven ad buying, there is limited discussion on how PA intersects with sustainability and AI in the context of advertising. As AI continues to advance, its role in PA extends beyond automated ad placements, influencing the sustainability of digital marketing practices. AI-powered systems enable advertisers to optimize campaigns in real time, improving targeting accuracy, reducing wasted ad spend, and enhancing return on investment (Busch, 2015). However, there is growing concern about the environmental impact of AI models, particularly the high energy consumption associated with large-scale data processing and machine learning (ML) algorithms (Aguirre et al., 2015). In addition, sustainability challenges are raised when it comes to the ethical implications of data usage, particularly regarding consumer privacy and transparency. Addressing these concerns, AI can also drive more responsible practices by ensuring ads are placed in contexts that align with sustainability goals, such as promoting eco-friendly products or services. Thus, the relationship between AI, PA, and sustainability is multidimensional and needs further exploration to understand how these factors combine to shape the future of digital advertising.

Materials and Methods

This study aims to explore the role of AI-driven PA in promoting sustainability within digital marketing through a systematic literature review. To ensure a comprehensive exploration of the topic, both theoretical and qualitative approaches were utilized in the research design. The core/main research question guiding this study is: “How can AI-driven PA contribute to the sustainability and efficiency of digital marketing?” This main question of the research is designed to address the overarching goal of understanding how PA can enhance sustainability in digital marketing.

The literature search was conducted in January 2025 across three databases: Google Scholar, Scopus, and Science Direct. The search used the keywords “Programmatic Advertising,” “Sustainability,” and “Digital Marketing” in TITLE-ABS-KEY fields, and articles related to “Artificial Intelligence” were subsequently included. Inclusion criteria focused on peer-reviewed journals and review articles published in English between 2000 and 2025. Exclusion criteria removed duplicates, non-English articles, and studies without full-text access. After screening titles, abstracts, and full texts, a final set of 60 articles was selected for analysis. The selected articles were analyzed using deductive thematic analysis to identify key themes regarding AI-driven PA and sustainability. The three main themes—(a) sustainability challenges, (b) ethical concerns, and (c) AI’s role in PA—were defined and analyzed to provide a structured framework for understanding the positive impacts and challenges of AI-driven PA in sustainable digital marketing. Following this process, key statistics and contextual information were added where relevant (e.g., Figure 2; fraud and invisible inventory statistics) to ensure clarity and credibility, including citation years where applicable.

Figure 2.

Literature Search, Screening, and Analysis Processes.

Findings

Programmatic Advertising and AI-Driven Digital Marketing

Through real-time auctions, PA helps marketers— especially in e-commerce—reduce errors, cut costs, and manage risks more effectively using data-driven decisions (Busch, 2015). This automation also improves personalization, delivering ads that closely match individual consumer preferences and addressing common challenges like ad fatigue or ad-blocking by showing more relevant content (Seitz & Zorn, 2016). Since its emergence in the late 2000s, PA has grown alongside technological advances similar to those in financial markets, such as algorithmic trading and electronic exchanges (Rayport, 2015). While definitions vary, PA is generally understood as an automated system for buying and selling digital ads. Its popularity continues to rise thanks to cost-effectiveness, scalability, and real-time measurability, supported by seamless data exchanges among advertisers, publishers, and intermediaries (Alaimo et al., 2017). Leading companies such as Google and Meta have secured dominant positions in the PA ecosystem, highlighting its efficiency, scalability, and performance focus (Hardy, 2023).

PA is more than an automation tool—it represents a fundamental shift in how digital marketing is designed and executed (Busch, 2015). AI has played a major role in this transformation, enhancing targeting, segmentation, and consumer engagement. By analyzing large and diverse datasets—from browsing behaviors to social media activity and purchase histories—AI systems create detailed consumer profiles. ML algorithms detect patterns that predict individual preferences with high accuracy, enabling highly personalized content that boosts satisfaction, loyalty, and conversion rates (Loureiro et al., 2021). Predictive analytics, a key AI function, further strengthens campaign planning by forecasting trends and user behavior more accurately than traditional methods (Davenport et al., 2020).

Beyond analytics, AI drives innovation across content creation, engagement strategies, and resource allocation (Emon, 2023). For instance, AI-powered chatbots and virtual assistants handle routine customer interactions efficiently, freeing human agents to focus on more complex or emotionally nuanced tasks (Adam et al., 2021). Natural language processing also improves SEO and branded content by ensuring linguistic consistency, brand alignment, and better search visibility (Ding et al., 2022).

PA exemplifies the integration of automation, data analytics, and AI-driven insights, empowering marketers to adapt strategies rapidly in response to market dynamics and to optimize return on investment (ROI) in real time (Chaffey & Smith, 2022). However, the implementation of PA and AI introduces several challenges, including data privacy concerns, algorithmic bias, transparency issues, and potential limitations in creative diversity. These remain active areas of inquiry in both academic and industry discussions (Rust, 2020). Moreover, implementing AI solutions often requires substantial financial investment, specialized expertise, and complex infrastructure—factors that may hinder adoption among small and medium-sized enterprises (SMEs). Nevertheless, advancements in cloud computing and the increasing availability of open-source AI platforms are gradually improving accessibility, enabling more organizations to incorporate AI into their marketing operations (Wilson et al., 2024). Advanced analytics tools offer marketers actionable insights into consumer behavior and campaign performance, fostering agile decision-making and greater accountability in an increasingly dynamic digital environment (Davenport et al., 2020). AI-driven PA thus transforms traditional marketing into a dynamic, data-centered ecosystem, enhancing personalization and operational agility while simultaneously raising new ethical, transparency, and accessibility concerns. The findings in this section suggest that while AI-driven PA significantly enhances the personalization and efficiency of digital marketing, it also presents challenges that must be addressed.

Programmatic Advertising’s Role in a Sustainable Digital Marketing Ecosystem

PA has become a cornerstone of modern digital marketing by automating and optimizing the ad-buying process through real-time algorithms and extensive data analysis (Chen et al., 2019). This shift moves marketing away from traditional media-centric models toward strategies that focus on the consumer, prioritizing audience targeting over simply purchasing media space (Lee & Cho, 2020). As a result, PA allows brands to engage more precisely and continuously with customers throughout their journey—from awareness to conversion—demonstrating greater sophistication and a stronger focus on consumer behavior (Gertz & McGlashan, 2015).

Supporting technologies such as DMPs, SSPs, and DSPs provide detailed insights into user demographics, behaviors, and preferences. These tools make personalized ad delivery possible while maintaining compliance with data privacy regulations (Gusic & Stallone, 2020). Real-time optimization of campaigns further improves efficiency, scalability, and overall effectiveness (Khan, 2024). Together, these technologies bring major benefits: precise targeting, automation, a smoother user experience, higher customer satisfaction, and stronger brand loyalty (Dawson & Lamb, 2016).

Despite these advantages, PA is not without challenges. Issues such as transparency, data privacy, brand safety, and ad fraud remain significant (Khan, 2024). When managed well, however, PA can foster sustainable growth by reaching new customer segments and attracting broader digital audiences (Kiran et al., 2020). In today’s complex marketing landscape, programmatic strategies offer a competitive edge and drive innovation (Nordman, 2022). Still, hurdles persist, including the lack of standardized best practices, cross-device targeting difficulties, rising ad-blocker use, stricter privacy rules, and an increasing focus on owned media and content marketing (Seitz & Zorn, 2016).

A key strength of PA lies in its ability to deliver highly precise targeting, which supports more focused and resource-efficient advertising (Meirezaldi, 2023). Addressing its challenges is essential for ensuring long-term effectiveness and sustainability. Research highlights inefficiencies in the ecosystem: around 57.4% of ad inventory is invisible, 14.5% is fraudulent, and 17.5% poses brand safety risks in networks and exchanges (Gusic & Stallone, 2020). These numbers underscore the need for stronger regulation, transparency, and security to maintain trust and ensure PA’s future viability. By leveraging real-time data, PA delivers targeted ads to specific audiences across digital platforms without requiring advertisers to buy space in advance (Turnbull et al., 2023). This approach not only reduces costs but also increases campaign efficiency (Cooper et al., 2023). The shift from broad media buying to data-driven targeting helps cut wasted spending and promotes more sustainable marketing practices.

In this context, the sustainability impacts can be categorized into three linked dimensions: environmental, economic, and social effects. From an environmental perspective, AI-driven PA reduces unnecessary impressions, minimizing energy use and carbon footprint (Yuan et al., 2023). Economically, PA increases cost efficiency, enhances ROI, and optimizes media spending (Meirezaldi, 2023). Socially, AI-powered personalization improves user experience, engagement, and ethical targeting, ensuring fairness and relevance in advertising. The emphasis on “target audience buying” improves both operational efficiency and ad accuracy. Beyond media-buying optimization, PA enables the seamless integration of creative brand messaging into personalized ads (Weisbrich & Owens, 2016). This reduces resource consumption, minimizes ad fatigue, and boosts consumer engagement. Automation also decreases the likelihood of human error in ad placement, improving campaign performance (Kiran & Arumugam, 2020). Advertisers can use behavioral data to develop tailored ads that increase user interaction (Feng et al., 2016). Continuous optimization based on behavioral metrics, click-through rates, and engagement levels sustains a dynamic and sustainable digital marketing ecosystem (Araujo et al., 2020). The involvement of multiple intermediaries—such as DSPs and SSPs—can limit advertiser visibility and control over ad spending (Ungureanu & Popescu, 2022). This complexity also heightens concerns over data protection and ethical implications, which are critical to the long-term sustainability of the system. The opaque nature of PA makes it susceptible to cyber threats (Stone-Gross et al., 2011) and contributes to perceptions of it as a “black box” among marketers (Kim & Huh, 2017). A lack of transparency—especially regarding fee structures and algorithmic operations—can erode trust and raise concerns about fairness in the marketplace (Goldberg et al., 2019). Studies show that personalized advertising positively influences consumer behavior, purchase intentions, and brand perception more than non-targeted ads (Watts, 2016). Ads aligned with specific consumer actions (e.g., abandoned shopping carts) are particularly effective in generating urgency and relevance (Bleier & Eisenbeiss, 2015). Matching ads to consumer preferences enhances engagement and reinforces purchase behavior (Van Doorn & Hoekstra, 2013), highlighting the value of data-driven personalization in promoting sustainable e-commerce.

AI plays a central role in enhancing PA’s capabilities by automating billions of daily ad placements and optimizing targeting and media revenue (Pärssinen et al., 2018). AI enables precise demographic targeting and supports emerging trends like the use of virtual influencers for enhanced engagement (Sands et al., 2024). ML—a core AI component—identifies patterns in large datasets to offer predictive insights and recommendations that improve marketing effectiveness across Web and social platforms (Lambrecht & Tucker, 2013). Within the automated PA ecosystem, data analytics and ML algorithms help identify user interests and behaviors, enabling relevant ad delivery while reducing cost per impression (Ciuchita et al., 2023). Bid requests contain contextual web page information (e.g., URL, topic) as well as user-specific data (e.g., cookies, IP address, location, interests), allowing advertisers to assess ad placement value with high accuracy (Tunuguntla & Hoban, 2021). This granular targeting reduces waste and supports more sustainable economic and environmental advertising practices (Meirezaldi, 2023). Further, advancements include programmatic creative technologies, which combine content management platforms (CMPs) with programmatic creative platforms (PCPs) to automate and optimize ad creation in real time, thereby enhancing consumer communication (Chen et al., 2019). SSPs automate functions such as frequency capping and facilitate ad space sales to DSPs via exchanges, with AI enhancing creative management processes (Gordón et al., 2019). DMPs integrate offline, online, and mobile data to build audience segments that connect seamlessly with DSPs and SSPs for efficient ad evaluation and purchase (Núñez-Barriopedro et al., 2023). By automating routine tasks, PA enables marketing teams to prioritize strategic initiatives and innovation, leading to reduced costs and improved operational efficiency (Ciuchita et al., 2023). This increased productivity supports the adoption of sustainable marketing strategies. The findings in this section demonstrate that PA plays a vital role in advancing sustainable digital marketing by enabling precise audience targeting, real-time campaign optimization, and resource-efficient ad delivery. However, persistent challenges related to transparency, data privacy, and industry standardization must be addressed to ensure the long-term viability and ethical evolution of PA ecosystems.

Performance, Transparency, and Sustainability in Programmatic Advertising

Several studies highlight both performance benefits and challenges associated with PA. For example, research indicates serious concerns: approximately 57.4% of ad space is invisible through networks and exchanges, 14.5% of inventory is fraudulent, and 17.5% is exposed to brand safety risks (Gusic & Stallone, 2020). These statistics underscore the urgent need for enhanced regulation, improved transparency, and stronger security mechanisms to ensure the long-term viability and trustworthiness of the PA ecosystem. Despite these challenges, PA offers measurable advantages in terms of efficiency and ROI. Real-time data enables precision targeting, allowing advertisers to reach highly specific audiences across digital platforms without the need for upfront media space purchases (Turnbull et al., 2023). AI-driven PA’s sustainability effects are considered across three dimensions: environmental (reducing energy and resource waste), economic (optimizing costs and ROI), and social (enhancing fairness and user engagement), providing a structured framework for interpreting findings. A core technological element of PA is RTB, which uses AI algorithms to select advertisements, identify target audiences, and determine bid prices based on extensive data inputs such as cookies and user browsing behavior (Yuan et al., 2023). By enabling a more accurate ad placement, RTB improves campaign efficiency and contributes to environmental sustainability by reducing unnecessary impressions. Personalized ads also help reduce ad fatigue and resource overconsumption, improving user engagement and interaction (Weisbrich & Owens, 2016). However, PA continues to face criticism regarding transparency and accountability. The involvement of multiple intermediaries—including DSPs and SSPs—adds complexity and often limits advertiser visibility into how budgets are spent (Ungureanu & Popescu, 2022). This lack of transparency raises concerns around data protection and ethics, threatening the long-term sustainability of PA. The complex structure of the ecosystem contributes to the perception that PA operates as a “black box,” leaving advertisers with a limited understanding of processes and outcomes (Kim & Huh, 2017). Additionally, hidden fees and inefficiencies across the value chain reduce both economic efficiency and ethical transparency (Goldberg et al., 2019).

Ads tailored to users’ real-time behaviors—such as those linked to items in shopping carts—tend to be more effective due to their relevance and urgency (Bleier & Eisenbeiss, 2015). Aligning ads with individual consumer preferences further enhances engagement and strengthens purchasing behavior (Van Doorn & Hoekstra, 2013), an important consideration in promoting sustainable digital commerce. AI technologies are central to the evolution of PA, offering tools to automate billions of ad placements and improve targeting accuracy (Pärssinen et al., 2018). One emerging application is the creation of virtual influencers, which extends marketing strategies by using digital personas to engage audiences in novel ways (Sands et al., 2024). ML algorithms analyze large datasets to uncover actionable insights, enhancing marketing performance across platforms such as Web search and social media (Lambrecht & Tucker, 2013). Within the PA’s automated ecosystem, ML and data analytics identify user interests and behaviors, enabling the delivery of highly relevant ads that optimize engagement and cost efficiency (Ciuchita et al., 2023). Bid requests include rich contextual and user data—such as web page topics, cookies, IP addresses, locations, and interests— allowing advertisers to accurately assess the value of ad placements (Tunuguntla & Hoban, 2021). This granularity supports more efficient use of advertising budgets and contributes to sustainable economic and environmental outcomes (Meirezaldi, 2023).

Programmatic creative technologies, which link CMPs with PCPs, automate and optimize ad creatives in real time, enhancing consumer communication (Chen et al., 2019). Tools such as SSPs and DMPs streamline campaign management and audience targeting, making PA a powerful tool for achieving marketing goals while maintaining resource efficiency (Núñez-Barriopedro et al., 2023). Automation also enables marketing teams to shift focus toward innovation and strategy, improving productivity, cost control, and sustainability (Ciuchita et al., 2023). The findings in this section confirm that PA offers significant performance advantages through precise targeting, real-time bidding, and AI-driven automation. These technologies collectively improve campaign efficiency and support sustainable marketing practices. However, persistent issues related to transparency, data security, and the complexity of intermediary structures highlight the need for stronger regulatory frameworks and technological advancements to ensure the continued trustworthiness and sustainability of the PA ecosystem.

The Impact and Challenges of Programmatic Advertising

PA presents significant opportunities for promoting sustainability in digital marketing by enabling efficiency, personalization, and resource optimization throughout the advertising process. Through its automation capabilities, PA reduces the need for manual operations, allowing advertisers to streamline campaign management and decrease operational overhead, thereby lowering energy consumption and resource use (Ciuchita et al., 2023). Its precision targeting tools minimize wasted impressions by delivering relevant ads only to selected audiences based on detailed demographic, behavioral, and geographic data (Meirezaldi, 2023). This contributes to a more efficient use of digital space and improves the return on advertising spend, reinforcing the financial sustainability of marketing activities (Malthouse et al., 2019). PA uses real-time analytics to optimize campaigns and reduce wasted ads (Greif, 2024). The scalability of PA allows advertisers to expand their reach without proportional increases in manual labor or infrastructure, supporting sustainable growth for e-commerce and online retail sectors (Colarossi, 2024). PA helps keep users engaged and loyal by showing ads that match their interests, supporting a healthy digital ecosystem (Chen et al., 2019). In this way, PA not only improves marketing outcomes but also contributes to the long-term sustainability of the digital advertising environment.

While PA offers numerous advantages, it also presents substantial sustainability-related challenges, primarily linked to transparency, data ethics, brand safety, and fraud. The PA ecosystem is complex, making it hard for advertisers to track spending, placements, and results (Kim & Huh, 2017). This lack of transparency undermines accountability and promotes inefficiencies that reduce long-term system trust and performance (Rogovskiy, 2018). Data privacy is a major concern, as PA collects large amounts of user data without always ensuring consent or fair use (Goldberg et al., 2019). In addition, brand safety issues arise when automated systems inadvertently place ads next to harmful or inappropriate content, risking serious reputational damage for brands and weakening the perceived reliability of the ecosystem (Makani, 2021). PA often amplifies the spread of disinformation due to its automated and opaque nature, creating significant challenges for transparency, accountability, and sustainability in digital marketing ecosystems (Ruiz, 2025, p. 232). Ad fraud—such as fake clicks, bots, and domain spoofing—remains a major threat, costing billions and over 10% of global ad spending each year (Sadeghpour & Vlajic, 2021). This not only distorts campaign effectiveness and key performance indicators but also increases distrust among stakeholders and diminishes the overall sustainability of the digital advertising environment (Alzahrani & Aljabri, 2022). The sustainability of PA relies on both technology and better transparency, fraud prevention, and ethical data practices across the ecosystem.

Discussion

Alignment of AI-Driven PA with Sustainable Marketing Principles

The findings indicate that AI-driven PA aligns closely with the core principles of sustainable digital marketing—environmental responsibility, economic efficiency, and social relevance. PA automates ad buying and targets audiences better, cutting wasted ads and saving energy. This is increasingly important as digital advertising faces scrutiny for its environmental impact, particularly the carbon footprint associated with data centers and RTB processes. From an economic perspective, PA increases ROI by streamlining decision-making and optimizing resource allocation, resulting in more cost-effective campaigns. Socially, AI’s capacity to personalize content and predict user behavior enhances the overall user experience and strengthens brand– consumer relationships. These qualities support ethical, user-centric marketing practices. Together, these features position AI-driven PA as a promising tool for advancing long-term sustainability within digital ecosystems.

Ethical and Regulatory Considerations

Despite its benefits, PA’s complexity and hidden algorithms raise major ethical and legal concerns. The ecosystem, which includes DSPs, SSPs, and DMPs, has been criticized for its lack of transparency. Programmatic systems hide how user data are used, causing distrust among advertisers and consumers. Key challenges include algorithmic bias, data privacy violations, and a lack of accountability. If these issues remain unregulated, they risk compromising user rights and the legitimacy of the PA model itself. Adherence to international standards, such as the General Data Protection Regulation and the California Consumer Privacy Act, is critical. To reduce ethical risks, stakeholders should ensure transparent, explainable, and audited algorithms for fairer advertising.

Strategic Implications for Marketers and SMEs

AI-driven PA brings both opportunities and challenges, especially for SME marketers. On the one hand, programmatic platforms provide access to sophisticated tools that were once exclusive to large corporations. Advances in cloud computing and open-source AI technologies are reducing entry barriers, enabling SMEs to compete more effectively in the digital marketplace. However, AI’s technical complexity, skill needs, and high costs can be major obstacles. Marketers should assess their readiness and combine human creativity with AI. Developing competencies in data analytics, AI ethics, and programmatic strategy will be essential for leveraging these technologies effectively and responsibly.

Reconciling Performance with Sustainability

A key tension revealed by this study is the challenge of balancing performance optimization with sustainability goals. While AI-driven PA increases efficiency, it may annoy users and cause overuse. Moreover, real-time data processing may raise energy use and environmental concerns. To reconcile these competing objectives, future advancements in PA should emphasize eco-efficiency and ethical personalization. For example, advertisers could incorporate sustainability scoring metrics into campaign evaluations to balance ROI with ecological impact. Using AI with ethical rules and environmental monitoring can create a more sustainable model. This approach aligns with the principles of Industry 5.0, which advocates for human-centric, ethical, and responsible use of emerging technologies.

The Dual Impact of AI and PA on Sustainability: Opportunities and Challenges

While AI-driven PA can boost marketing efficiency and support sustainability, it also raises ethical and environmental concerns. AI’s capacity for high-precision targeting, campaign automation, and real-time data analytics allows for more efficient ad delivery at a lower cost. However, this efficiency often comes at the expense of environmental costs.

Several studies have indicated that AI and PA can lower energy use and emissions. Yet, data processing and RTB can raise energy use and environmental impact. This tension between efficiency and environmental cost is key to AI-driven PA sustainability. Specifically, research into data centers and RTB processes suggests that, while AI-based PA offers significant opportunities, it also presents notable challenges for achieving sustainability in the digital marketing space.

In this context, it is crucial to develop strategies that balance environmental sustainability with the performance gains associated with AI and PA. For instance, using energy-efficient AI and low-carbon infrastructure can reduce PA’s environmental impact. Additionally, incorporating environmental monitoring tools to track the ecological impact of PA campaigns can further strengthen the sustainability agenda within digital marketing.

Therefore, AI-driven PA is efficient but raises ethical and environmental issues. Moving forward, transparent AI and sustainable practices are key for PA’s long-term impact.

Conclusions

This study examined how AI-driven PA can support more sustainable digital marketing strategies. The findings show that AI-powered PA improves marketing performance by enabling real-time automation, precise targeting, and data-driven decisions. These features not only boost ROI but also make resource use more efficient, helping PA align with environmental, economic, and social sustainability goals. In practice, this means that marketers and SMEs can leverage PA to run more effective campaigns while minimizing waste, protecting consumer data, and ensuring that their advertising strategies contribute positively to broader sustainability objectives.

Despite these benefits, the PA ecosystem faces notable challenges. Issues such as transparency, data privacy, algorithmic bias, and market concentration among dominant platforms raise important ethical and structural concerns. These challenges show that while AI can greatly improve efficiency, it must be balanced with responsible practices to ensure the long-term sustainability and trustworthiness of PA. The often complex and opaque nature of PA processes can create uncertainty around accountability and fairness, emphasizing the need for clear regulations, ethical guidelines, and technological solutions that enhance transparency.

AI-driven PA has great potential to transform digital marketing into a more sustainable and efficient ecosystem. To fully realize this potential, collaboration is essential among marketers, developers, policymakers, and platform providers, ensuring that technological advancements go hand in hand with ethical responsibility and sustainability commitments.

In practice, marketers and SMEs should use clear transparency practices in AI-driven PA. They should openly report campaign data and the decision-making processes of PA algorithms. They also need to follow ethical data standards to protect consumer privacy and prevent algorithmic bias. Additionally, they should invest in training staff on AI tools and sustainable digital marketing strategies. These actions can help businesses use AI-driven PA effectively while maintaining trust, accountability, and long-term sustainability.

Future research could explore ways to make PA campaigns more transparent, for example, by clearly showing how consumer data and AI algorithms are used, which could help build trust among stakeholders. Studies could also explore stronger data governance policies that prioritize privacy and ethical standards, reducing the risks of reputational damage from data misuse. Additionally, developing continuous monitoring and evaluation frameworks to track the environmental impact of PA campaigns could help identify inefficiencies and guide improvements that support both economic and ecological sustainability.