5 Emerging Tech Trends Brands Need to Know About in 2026

The technology conversation inside boardrooms has changed shape over the past year. Until recently, most brand discussions around innovation revolved around generative AI tools, faster content production, or automation at scale. But in 2026, the conversation has become broader and more structural. The focus is shifting from individual tools to entire ecosystems that influence how consumers discover products, how trust is built, how campaigns are measured, and how commerce happens across digital platforms.

For brands, this transition is creating a more complex environment. The number of martech platforms may still be growing, but the bigger change lies underneath the stack. According to Chiefmartec’s latest landscape report, the global martech ecosystem now includes more than 15,500 products. Yet the annual growth rate has slowed to below 1%, suggesting the industry is moving from expansion to consolidation and integration. Categories such as analytics, ecommerce infrastructure, governance, and generative engine optimisation are now seeing faster momentum than standalone campaign tools.

That shift is also visible in consumer behaviour. AI interfaces are changing how people search for products. Video commerce is blending entertainment with transactions. Brands are facing pressure to unify customer data as AI becomes embedded into marketing operations. At the same time, synthetic content and deepfakes are making authenticity a technology concern, not just a communications issue.

Taken together, five emerging technology trends are beginning to reshape how brands operate in 2026.

AI Agents are Becoming the New Customer Interface

One of the clearest shifts is the rise of AI agents and AI-powered assistants as part of the customer journey. These systems are no longer limited to back-end automation or internal productivity tools. Increasingly, they are becoming customer-facing interfaces that influence purchase decisions.

Research from Accenture shows that 90% of frequent AI users in North America are willing to switch from a preferred brand if an AI assistant recommends a better alternative. The same study found that nearly half of shoppers expect to move at least 50% of their online commerce activities into AI-mediated ecosystems over the next two years.

That changes the role of branding in digital commerce. Instead of optimising only for human search behaviour, companies now need to optimise for machine interpretation. Product descriptions, customer reviews, FAQs, specifications, and structured metadata all become important inputs for AI assistants deciding what to recommend.

Adobe’s 2026 AI and Digital Trends report found that one in four consumers already uses AI-powered platforms as their primary source for information discovery, recommendations, or purchase decisions. The report also found that 49% of consumers are open to using AI for personalised shopping recommendations, while 44% would use AI for instant customer support.

Scott Brinker, editor of Chiefmartec, recently observed that buyers are increasingly moving away from traditional search behaviour toward AI-assisted discovery. That shift matters because it changes where visibility happens. Search rankings alone may no longer determine discoverability. AI assistants are becoming an additional layer between the customer and the brand.

The effect is already visible in traffic patterns. Adobe Analytics data from the first quarter of 2026 found that AI-referred traffic to retail websites increased by nearly 400% year on year. More importantly, those visits converted 42% better than traditional traffic sources and generated higher average revenue per visit.

For brands, this creates practical challenges. A consumer electronics company now needs product information detailed enough for AI comparison tools. Travel platforms need itinerary and pricing systems that AI assistants can interpret accurately. Fashion and beauty brands need product attributes structured in ways that recommendation engines can process.

However, consumer comfort with AI remains selective. Adobe also found that 37% of customers would disengage if they realised they were interacting with AI when they expected a human response. Only 19% said AI agents could become their primary mode of brand interaction.

That suggests the future may not be entirely automated. Instead, brands may need to balance efficiency with transparency, ensuring customers know when AI is involved and when human support remains available.

Search is Shifting from Keywords to Answer Engines

The second major trend is closely connected to the rise of AI agents: the evolution of search itself.

Traditional search engine optimisation focused on rankings, keywords, and visibility on results pages. But generative AI is creating a new layer called answer engine optimisation and generative engine optimisation. Instead of users clicking through ten blue links, AI systems increasingly summarise information directly.

This transition is reshaping digital discovery strategies.

Chiefmartec’s latest martech landscape highlighted significant growth in AEO and GEO-related platforms this year. That reflects growing concern among brands about how they appear inside AI-generated answers.

Unlike conventional search, AI discovery systems rely heavily on structured and trusted information. A brand’s website architecture, documentation quality, customer reviews, and factual consistency all influence how AI assistants summarise or recommend products.

This matters because AI-driven search behaviour is accelerating quickly. Adobe’s data shows that AI-referred users spend 48% longer on websites, browse more pages, and engage more deeply with content than visitors from traditional channels.

The implications extend beyond ecommerce. B2B companies are also seeing changes in buyer behaviour. Procurement research, vendor evaluation, and software comparisons are increasingly being filtered through AI assistants before human decision-makers even visit a company website.

For marketers, this creates a new optimisation challenge. It is no longer enough to build content only for human readers. Brands now need information systems that are machine-readable, context-rich, and continuously updated.

The shift also increases the importance of trust signals. AI systems tend to prioritise sources that appear reliable, authoritative, and consistent. Brands with fragmented information or outdated product details may struggle to maintain visibility as answer engines become more dominant.

Data Quality is Becoming more Important than AI Adoption

While AI tools dominate headlines, many marketers are discovering that the real challenge lies beneath the technology layer.

The issue is data infrastructure.

Salesforce’s latest State of Data and Analytics report found that the average enterprise now operates nearly 900 applications, yet less than one-third of them are fully connected. The same study found that 70% of business leaders believe their most valuable insights remain trapped inside siloed systems.

This fragmentation is creating problems for AI deployment. While companies are investing aggressively in automation, many still lack clean, unified, and accessible customer data.

According to Salesforce, 84% of data and analytics leaders believe their current data strategy requires major restructuring before AI can deliver meaningful business value. Nearly 90% of leaders already using AI in production environments reported experiencing inaccurate or misleading outputs.

Michael Andrew, Chief Data Officer at Salesforce, recently said that AI is only as effective as the data foundation underneath it. That observation reflects a broader reality inside marketing departments.

Most brands today have access to AI-powered tools, but far fewer have unified customer intelligence. Marketing, commerce, sales, loyalty, and customer service data often sit in disconnected systems. As a result, brands struggle to deliver contextual experiences even when advanced AI layers are available.

Salesforce’s State of Marketing report found that 83% of marketers believe customers now expect two-way interactions with brands. Yet 69% admitted they cannot respond quickly because they lack complete customer context.

The contradiction is becoming increasingly visible. AI adoption rates are high, but campaign personalisation remains limited. Many brands continue to rely on broad segmentation because their underlying systems cannot connect real-time customer signals effectively.

This is pushing data architecture back into strategic discussions. Instead of asking which AI tool to adopt next, companies are focusing on how to unify consent management, customer profiles, inventory data, and engagement history across platforms.

The same issue is influencing measurement.

The Interactive Advertising Bureau’s State of Data 2026 report found that most marketers still believe current measurement systems lack sufficient rigour and transparency. Respondents cited problems with fragmented channel attribution, inconsistent metrics, and slow reporting cycles.

IAB estimates that AI-powered measurement improvements could unlock more than $26 billion in additional media value over the next two years.

For brands, this means the competitive advantage may no longer come from simply having AI. It may come from having cleaner, connected, and actionable data systems that allow AI to work properly.

Video commerce is becoming a performance channel

Another important shift in 2026 is the convergence of media, commerce, and creator ecosystems.

Video is no longer functioning only as an awareness tool. Increasingly, it is becoming a transactional environment where discovery, engagement, and purchasing happen simultaneously.

According to IAB’s latest Digital Video Ad Spend report, digital video ad spending in the United States is expected to cross $81 billion this year, accounting for the majority of total TV and video advertising expenditure for the first time.

Social video platforms are leading this growth. The report found that 69% of buyers now classify social video as a must-buy channel, ahead of connected television.

The reasons are tied directly to technology. AI-powered personalisation systems, integrated shopping tools, creator-led commerce, and automated optimisation are making social video easier to connect with measurable business outcomes.

Two-thirds of media buyers surveyed by IAB said they are either already using or actively testing agentic AI tools for video campaign management in 2026.

This reflects a wider shift in advertiser expectations. Brands increasingly want media environments that combine audience targeting, commerce capability, creator influence, and measurable attribution within the same platform.

The creator economy is also evolving alongside these changes. Influencer marketing is becoming more infrastructure-driven, with brands placing greater emphasis on audience authenticity, engagement quality, and verification systems.

That transition is partly a response to rising concerns around synthetic media.

Authenticity is Becoming a Technology Issue

As generative AI tools become easier to access, brands are facing a growing trust challenge.

Synthetic content is becoming harder to detect. AI-generated influencers, deepfake videos, voice cloning, and automated content production are now part of mainstream digital culture. That has increased pressure on brands to establish authenticity safeguards.

Gartner predicts that by 2027, brands will allocate half of influencer marketing budgets toward authenticity initiatives, including identity verification, provenance tracking, and anti-deepfake systems.

Consumer expectations are shifting as well. Gartner’s research found that 78% of consumers consider clear labelling of AI-generated content important for maintaining trust.

Adobe’s consumer studies reinforce the same point. While users are comfortable engaging with AI tools in defined scenarios, many still expect transparency about when AI is being used.

This creates a delicate balancing act for brands. AI can increase efficiency, scale, and personalisation. But excessive automation or unclear disclosure may damage credibility.

The issue extends beyond marketing campaigns. Trust now affects search visibility, creator partnerships, customer support interactions, and even commerce performance.

Platforms and regulators are also beginning to respond. Content provenance systems, watermarking standards, and AI disclosure policies are becoming more common across the digital ecosystem.

For brands, authenticity is no longer only a communications value. It is increasingly becoming operational infrastructure.

The Bigger Shift is Happening Underneath the Stack

What ties these trends together is that they are all connected by systems rather than individual tools.

AI agents rely on structured data. Answer engines depend on trusted information. Video commerce requires unified measurement. Authenticity systems depend on verification infrastructure.

That is why the technology conversation in 2026 is becoming less about novelty and more about integration.

The martech ecosystem may be stabilising in size, but its complexity is increasing. Brands are entering a phase where the connective layer between platforms matters more than the number of tools inside the stack.

The companies likely to adapt fastest may not necessarily be those adopting the most AI tools. Instead, they may be the ones building systems that connect discovery, commerce, customer intelligence, measurement, and trust into a unified operating model.

For marketers, the emerging challenge is not simply keeping pace with technology trends. It is understanding how those trends are beginning to reshape the structure of digital brand experiences altogether.