Business-to-consumer marketing will face multiple challenges in 2026, such as price hikes, artificial intelligence-driven privacy breaches, and a decline in measurement confidence, according to a report from Forrester. Industry executives are gearing up for a year of volatility, with 52% predicting tighter budgets and 51% anticipating reduced headcounts. Overall, 64% of B2C marketing executives believe that 2026 will be more volatile than 2025.

"It's not great news in terms of the strife and headwinds that CMOs and other business leaders are going to face," said Mike Proulx, vice president, research director at Forrester. "Predictions 2026: B2C Marketing" was put together using input from analysts to evaluate potential trends in the following year. It is supported by data collected throughout the year, including from Forrester's Q3 CMO Pulse survey. The convergence of budget constraints, measurement uncertainty, and AI implementation challenges creates a perfect storm for marketing organizations already stretched thin from previous rounds of efficiency demands.

Price Sensitivity Will Break Brand Loyalty at Unprecedented Scale

As low price continues to be a top-purchase driver, price hikes in 2026 are expected to cause up to one-third of consumers to walk away from B2C brands in favor of better deals elsewhere. While 37% of online U.S. adults say they are willing to pay a higher price for a brand or product they love, the majority of consumers won't be as easy to convince. The solution, according to Forrester, is to create a consumer experience that makes them want to stay.

"Brands need to understand their consumers and their different segments that exist within their diverse target audiences, and ensure they are meeting them where they are, with regards to their own individual economic conditions," said Proulx. This represents a fundamental shift from the brand loyalty assumptions that dominated marketing strategy for decades. The presumption that emotional connections and brand preferences would insulate companies from price competition is collapsing under economic pressure that makes value proposition more important than brand affinity.

The strategic implication is that marketing can no longer rely on loyalty built through emotional appeals or brand positioning to defend premium pricing. Instead, brands must deliver tangible value—whether through product quality, customer experience, or price competitiveness—that justifies continued patronage despite cheaper alternatives. This shifts marketing from persuasion to value delivery, requiring closer collaboration with product development, operations, and pricing teams to ensure the total customer experience supports premium positioning. 

Measurement Confidence Decline Undermines Strategic Decision-Making

Consumer loyalty isn't the only thing that will face a reckoning in 2026. The measurement crisis is only going to get worse, with confidence predicted to slip 7%, according to the report. Despite confidence being high in 2025, with 79% of B2C marketers saying they feel confident in their ability to accurately measure the value of marketing, this will be temporary. Concerns around data transparency, especially as AI becomes increasingly entrenched in the measurement process, will erode the faith marketers have around measurement. The politicization of economic measurements will only exacerbate the matter.

The measurement confidence decline is particularly problematic because it arrives simultaneously with budget constraints. When resources are abundant, measurement uncertainty is tolerable—you can afford some waste while testing approaches. When budgets tighten, every dollar requires justification through demonstrable ROI. But if marketers don't trust their measurement systems to accurately attribute value, how do they justify any spending? This creates a vicious cycle where budget pressure demands better measurement just as measurement confidence collapses.

The AI integration into measurement systems compounds the problem. While AI promises more sophisticated attribution modeling and predictive analytics, it also introduces black-box decision-making that marketers struggle to interpret or validate. When attribution algorithms change recommendations but can't explain why in terms marketers understand, confidence in those recommendations naturally declines. The solution requires better measurement literacy among marketing teams and more transparent AI systems that can articulate their reasoning in human-comprehensible terms. Learn AI integration fundamentals that help you evaluate measurement systems critically rather than accepting algorithmic recommendations blindly.

AI Implementation Struggles Persist Despite Vendor Promises

AI's presence in the advertising industry has undoubtedly grown. In 2026, some of the excitement surrounding the new technology will begin to wane as some major issues become impossible to ignore. The upcoming year is already predicted to be a challenging one in terms of consumer confidence, and AI may only make that worse. For example, data breaches driven by the technology are expected to increase class action lawsuits by 20%, according to the report.

Implementation will also continue to be a struggle for marketers in 2026, according to the report. Only about a third of employees, 37%, feel confident adapting AI systems for work. These issues will cause agentic AI adoption to slow, predicts Forrester. B2C marketers should continue to clarify their desired levels of autonomy as well as ensuring alignment across data, tech, and the workforce. The gap between vendor promises and organizational capability remains enormous—tools claiming to automate complex marketing decisions require human expertise to implement, monitor, and interpret effectively.

"There are a lot of promises being made by third party vendors," said Proulx. "Any technology provider is going to tout their AI capabilities…what is pivotal is to ensure that as AI advances as a commonplace aid to getting work done, the human quotient cannot be removed." This caution reflects growing recognition that AI augments rather than replaces human judgment, particularly in strategic and creative contexts where nuance, brand understanding, and cultural sensitivity matter more than processing speed.

The Liability Exposure from AI-Driven Data Breaches

The expected 20% increase in class action lawsuits due to AI-driven data breaches represents more than just operational headaches—it's potential existential threat for companies without robust data governance. AI systems trained on customer data can inadvertently expose that data through training data extraction attacks, model inversion techniques, or simple configuration errors that leak information. The legal liability extends beyond regulatory fines to include class action damages, brand reputation harm, and customer churn.

Marketing organizations implementing AI must work closely with legal, security, and compliance teams to understand liability exposure and implement appropriate safeguards. This isn't purely IT's responsibility—marketers selecting AI vendors and defining use cases determine what data gets exposed to which systems. Without clear data governance frameworks specifying what customer information can be used for AI training and how models must be secured, marketing teams can inadvertently create massive legal liabilities while pursuing operational efficiency.

The challenge is that many AI vendors lack transparency about their data handling practices, security measures, and liability protections. When vendors experience breaches that expose customer data provided by multiple clients, who bears legal responsibility? The vendor? The client that provided the data? Both? These questions often lack clear contractual answers until lawsuits force judicial interpretation. Marketers should demand explicit liability protections and data handling commitments from AI vendors before entrusting customer information to their systems.

AI-Native Applications Will Launch Despite Implementation Challenges

However, the challenges won't completely derail AI's use in marketing. Three AI-native B2C marketing applications are expected to hit the market in 2026, according to Forrester. Marketers still have a strong desire to implement AI into their workflow, and AI companies are going to race to meet those needs. Marketers should evaluate each new technology carefully and ensure they align with their needs and values.

The term "AI-native" signals applications designed from the ground up around AI capabilities rather than traditional software with AI features bolted on. These applications will likely focus on areas where AI provides fundamental advantages—personalization at scale, predictive analytics, content generation, or automated optimization—rather than automating existing manual processes. The distinction matters because AI-native applications require different evaluation criteria than traditional marketing technology. Instead of asking "does this replicate our current workflow more efficiently," the question becomes "does this enable capabilities we couldn't achieve before."

The risk is that vendor hype around AI-native applications will obscure fundamental questions about accuracy, reliability, and business value. Just because an application uses sophisticated AI doesn't mean it solves actual business problems or integrates effectively with existing systems. Marketers should approach new AI-native applications with healthy skepticism, demanding proof of performance through trials and references rather than accepting vendor promises at face value. Explore how to build content systems that integrate AI capabilities strategically rather than adopting technology for its own sake.

Strategic Priorities for Navigating 2026 Volatility

The convergence of tighter budgets, declining measurement confidence, AI implementation challenges, and eroding consumer loyalty creates an environment where strategic discipline matters more than ever. The brands that thrive will be those that prioritize ruthlessly—cutting initiatives with unclear ROI, doubling down on proven approaches, and investing selectively in capabilities that provide durable competitive advantages rather than chasing every new trend.

This requires resisting pressure to match competitors' AI adoption, social media presence, or channel expansion if those investments don't demonstrably serve strategic priorities. It means accepting that doing fewer things better produces superior results to doing many things adequately. And it requires honest conversations with executive leadership about realistic expectations given resource constraints rather than promising outcomes that require budgets you don't have.

The measurement confidence decline makes this prioritization harder because you can't definitively prove which initiatives deliver value. This argues for combining quantitative measurement with qualitative assessment—customer feedback, brand health metrics, competitive positioning—that provides triangulating evidence when attribution data is uncertain. No single measurement system will provide perfect answers, but multiple imperfect signals can inform reasonable strategic decisions.

Prepare for Volatility at The Academy of Continuing Education

Forrester's research indicates that 2026 will test marketing organizations' resilience through budget constraints, measurement uncertainty, and implementation challenges. The marketers who succeed won't be those with the most resources—they'll be those who deploy limited resources most strategically, maintain measurement discipline despite uncertainty, and implement AI capabilities that actually solve business problems rather than chasing technological novelty.

Ready to develop the strategic frameworks and technical capabilities that help you navigate volatility and resource constraints? Join The Academy of Continuing Education and master the prioritization discipline and measurement literacy ambitious marketers need to thrive regardless of economic conditions or budget availability.