In 2018, a performance marketer could explain most campaign results by pointing to a familiar set of levers: audience targeting, bid caps, placements, frequency, creative rotation, and landing page tweaks. When cost per acquisition rose, teams could usually trace it to a tighter segment, a new competitor in the auction, or budget being pushed into inefficient hours.
In 2026, that kind of clarity is harder to maintain.
Performance marketing is increasingly being run through systems that do not just automate execution but also make decisions in real time. Platforms adjust bids at the auction level, expand audience definitions beyond manual inputs, combine creative assets dynamically, reallocate budget across channels, and model conversions when signals are incomplete. Marketers still define the objective, but much of the journey toward that objective is selected by machine learning systems.
This is not simply automation layered onto old workflows. It represents a structural shift in how performance marketing is designed, measured, and governed.
Three forces are accelerating this rewrite. First, digital advertising ecosystems have grown too complex for manual management at scale. Second, privacy changes and signal loss have reduced deterministic visibility, increasing reliance on modelling. Third, budget compression has pushed teams to prioritise efficiency and automation.
Gartner’s 2024 CMO Spend Survey illustrates the financial pressure. Marketing budgets fell to 7.7 percent of company revenue in 2024, down from 9.1 percent in 2023. Before the pandemic, the average stood closer to 11 percent. Paid media now accounts for 27.9 percent of the marketing budget, and digital represents 57.1 percent of paid media spend, up from 54.9 percent the previous year. Within digital, search leads at 13.6 percent, followed by social at 12.2 percent and display at 10.7 percent.
Ewan McIntyre, VP Analyst and Chief of Research for Gartner’s Marketing Practice, described the environment as an “era of less.” Under such constraints, AI-powered optimisation is less a strategic luxury and more an operational necessity.
The Indian market reflects this acceleration clearly through the expansion of programmatic buying. According to the dentsu exchange4media Digital Advertising Report 2026, programmatic accounted for 42 percent of India’s total digital media spend by the end of 2025, amounting to Rs 30,081 crore. This marked a 19 percent growth over 2024. The same report projects that by 2027, programmatic spend will reach Rs 42,435 crore, with its share rising to 43 percent and growing at a compound annual rate of 18.77 percent.
Programmatic is no longer a niche execution method. It has become the infrastructure layer of digital advertising. And AI is increasingly the intelligence layer within that infrastructure.
At the platform level, the shift is visible in product design. Google’s Performance Max allows advertisers to input conversion goals, budgets, and creative assets, after which the system automatically distributes ads across Search, YouTube, Display, Discover, Gmail, and Maps. Bidding and targeting are continuously optimised by machine learning. Generative AI features now assist in creating text and image assets inside campaigns.
Meta’s Advantage+ campaigns follow a similar philosophy. Manual segmentation is simplified, and optimisation is pushed into automated systems that test and scale combinations at speed. Retail media networks are also embedding automated bidding and dynamic targeting as ecommerce marketplaces expand their advertising businesses.
The role of the performance marketer has begun to change accordingly. Instead of adjusting bids daily or excluding placements manually, teams focus on setting guardrails, improving data quality, producing sufficient creative variations, and monitoring performance signals for anomalies.
David Cohen, CEO of IAB, has said that “AI will soon power every aspect of media campaigns.” The IAB State of Data 2025 report supports that trajectory, but it also highlights uneven adoption. Only 30 percent of organisations have fully integrated AI across the media campaign lifecycle. About half of those that have not yet done so expect to integrate by 2026. At the same time, roughly half of the industry lacks a clear AI roadmap, and 37 percent of professionals cite job security as a top concern.
Angelina Eng, VP of Measurement, Addressability and Data Center at IAB, has pointed out that AI can now assist in building media plans, generating audience segments, selecting partners, and forecasting outcomes. The technology is not limited to bidding. It is influencing strategy formation itself.
For Indian performance teams, these changes manifest in daily operations. Budget allocation is increasingly automated within platforms. Creative strategy has shifted from a few static assets to a library of dynamic variations designed to feed optimisation systems. Product feeds and structured data have become critical inputs. The marketer’s task is less about controlling every lever and more about managing system inputs and outputs.
Measurement has become the most complex layer of this transformation.
Privacy regulations and platform-level changes have reduced deterministic attribution. Conversion modelling, aggregated reporting, and probabilistic measurement are now standard. While dashboards remain detailed, the underlying mechanics are increasingly opaque. Explaining why a system delivered a specific outcome is more difficult than before.
Brands and agencies are adapting. Some are expanding incrementality testing through holdout groups. Others are revisiting marketing mix modelling to understand broader contribution. Many are investing in first-party data infrastructure to ensure conversion signals remain clean and compliant.
In India, regulatory frameworks such as the Digital Personal Data Protection rules have introduced stronger expectations around consent, purpose limitation, and data minimisation. Performance stack design now intersects with compliance considerations. Automated systems that scale outreach must also respect regulatory guardrails.
At the leadership level, the pressure is measurable. The IBM CMO Study 2025 found that 63 percent of Indian CMOs are directly accountable for profitability, and 53 percent are responsible for driving revenue growth. Yet readiness remains uneven. Only 26 percent of Indian CMOs reported having responsible AI guidelines established within their organisations. Around 44 percent believe their marketing function is ready to integrate agentic AI systems. Just 26 percent feel they have the necessary talent in place for the next two years. The study also noted that enterprises are tapping into only 1 percent of their available data, indicating that AI ambition is running ahead of foundational data maturity.
Tuhina Pandey, Director for APAC Communications and Marketing for IBM India and South Asia, said, “As AI radically transforms how businesses engage, operate, and grow, Indian CMOs are uniquely positioned to lead this shift by harnessing AI responsibly.” Her comment reflects the dual reality facing marketing leaders: acceleration and caution must coexist.
The tension is also strategic. As performance media becomes increasingly optimised toward measurable conversions, industry leaders are warning against an excessive short-term focus.
Sam Balsara, Chairman of Madison World, has publicly cautioned that marketers under pressure may over-rely on performance channels, potentially weakening long-term brand building. His argument is not anti-performance. It is a reminder that systems optimise toward defined goals, and those goals may not always capture long-term brand equity.
AI-driven performance marketing is highly efficient at capturing demand. It is less equipped to create demand on its own. If budgets compress into the lower funnel, acquisition costs may rise over time as audiences saturate and incremental reach narrows.
Retail media growth illustrates this dynamic. Marketplaces offer high purchase intent and clearer attribution, attracting performance budgets. Yet increased reliance on a few conversion-rich environments can heighten dependency and reduce strategic diversity.
The global pattern is similar. As platforms push deeper automation, marketers gain scale and speed but surrender some transparency. The process of running campaigns is becoming easier. The process of explaining them is becoming harder.
By 2026, performance marketing has shifted from manual optimisation to objective-led automation. Creative has moved from singular assets to combinational libraries. Measurement has evolved from deterministic tracking to modelling and experimentation. Teams have shifted from adjusting levers to configuring systems.
The definition of a strong performance marketer is changing accordingly. Technical literacy around how models learn, how signals degrade, and how incrementality is proven is becoming more important than granular platform manipulation.
The rewrite is still unfolding. Not every organisation has reached full integration, and experimentation continues. But the trajectory is evident. AI is not an add-on layer to performance marketing. It is becoming the operating system.
For Indian marketers in 2026, the central question is not whether AI should be used. In many cases, it already is embedded in campaign infrastructure. The more relevant question is how to balance speed with transparency, efficiency with trust, and short-term conversion with long-term value in a system increasingly shaped by algorithms.
Performance marketing is not disappearing. It is being redefined.
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.