December 31, 2025
Matt Britton
CEO
Prompt shift: Top consumer AI trends reshaping search, shopping, and creativity in 2026
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The conversation around artificial intelligence has fundamentally shifted. It's no longer about technical capabilities confined to research labs or enterprise software departments.
In a special edition of the Speed of Culture Podcast released on December 31, 2025, Matt Britton, founder and CEO of Suzy, the AI-powered consumer intelligence platform, breaks down the consumer AI trends that are already reshaping how millions of people search, shop, create, and learn in real time.
This isn't speculative analysis. The behaviors described in Episode 228 are observable right now in consumer environments worldwide. People have moved beyond curiosity about AI tools and embraced them as integral to daily decision-making.
From health optimization to financial planning, from creative production to learning, consumer adoption is accelerating in ways that traditional business playbooks never anticipated. The implications for brands, retailers, creators, and enterprises are profound and immediate.
Britton's perspective comes from two decades of advising more than half of the Fortune 500 on emerging consumer trends. Through Suzy—a consumer insights platform that has raised over $120 million in venture capital and serves enterprise clients including Microsoft, Google, PepsiCo, and Procter & Gamble—he captures real-world consumer behavior at scale.
His analysis reveals a consumer landscape in motion, with expectations resetting faster than most organizations can adapt.
2026 marks a turning point where AI is no longer a background technology or specialized tool. Instead, it has become an active participant in how consumers make decisions, express creativity, and manage their lives.
For brands and leaders who understand these shifts, the opportunity is significant. For those who don't, the risk is equally real.
One of Britton's most compelling insights is that consumer AI adoption does not follow the enterprise playbook. In traditional technology adoption models, organizations adopt first, then consumers follow. But AI has inverted that pattern completely.
The personal use case is driving adoption far faster than corporate approvals or formal workplace implementations. People are experimenting freely with AI at home—managing finances, planning health routines, supporting their children's learning, and solving everyday problems without the approval layers or restrictions that govern workplace technology.
These hands-on experiences build intuition and confidence faster than any formal training program could deliver.
By the time AI shows up in professional settings, many consumers already know what it should be capable of. They understand its strengths and limitations based on months of personal experimentation.
This creates a significant gap: enterprise AI adoption is lagging behind personal AI adoption. Employees bring expectations shaped by personal efficiency into professional environments that often still operate with older processes and limitations.
This pattern mirrors the early iPhone era, when consumers embraced the device at home while organizations resisted it in the workplace. Eventually, employee expectations forced enterprises to catch up. The same dynamic is playing out with AI now, at an accelerated pace.
For brands, this signals a critical insight: consumers are no longer learning about AI in formal settings or waiting for official endorsements. They're becoming AI-fluent through experimentation, and their expectations are being shaped by the tools they use personally.
Any brand strategy that assumes consumers are still discovering AI is operating from outdated assumptions. Instead, leaders should assume their customers are already using advanced AI tools and building their brand experiences accordingly.
For nearly three decades, search has operated according to the same fundamental principle: users enter keywords, search engines return lists of ranked results, and users click through to find what they need. This model is dissolving rapidly.
AI-powered search fundamentally changes the discovery model. Instead of typing keywords and scanning lists of links, consumers now engage with conversational interfaces that carry memory, intent, and context forward through a dialogue.
The interaction is no longer one-shot; it's iterative. Consumers refine their needs over time, asking follow-up questions and receiving suggestions that evolve with their inputs. This approach pushes discovery deeper into the decision journey, moving consumers from broad exploration toward specific, outcome-oriented questions.
The implications for visibility and content strategy are substantial. In the traditional search model, ranking broadly was the objective. Companies built content designed to capture large keyword categories, betting on volume and traffic.
But AI-powered search rewards specificity and relevance far more than brand size or historical dominance.
A small company with highly specific, use-case-driven content can now surface alongside or even replace established incumbents. The difference is that visibility is no longer earned through rankings accumulated over time. Instead, it's earned through usefulness.
If AI systems can confidently surface a brand's content because it directly answers a specific consumer intent, that brand appears. If the content is vague, generic, or misaligned with real questions, it disappears.
This shift creates both a challenge and an opportunity for brands. The challenge is that existing content strategies built on broad keyword targeting become less effective.
The opportunity is that clarity and specificity are now competitive advantages. Brands that invest in understanding the precise, narrowly defined needs consumers have—and build content designed to address those specific moments—will gain visibility and relevance in ways that generic product pages and broad positioning never will.
For businesses selling complex products or services, this trend is particularly significant. Health, finance, education, and technology sectors are experiencing rapid shifts in how consumers discover solutions.
The brands winning in these spaces are those building content frameworks around specific decision moments, not around product categories or features.
The traditional purchase funnel—awareness, consideration, evaluation, decision—assumed that each stage involved separate actions across separate channels. Consumers might become aware of a brand through an ad, move to consideration through search, compare options on a review site, and finally make a purchase decision after visiting the retailer.
Chat-based shopping powered by AI is collapsing these steps into a single conversational flow. Within one interface, a consumer can research a product, ask clarifying questions, receive personalized recommendations based on their specific constraints and preferences, and complete a transaction without ever leaving the conversation.
This transforms the competitive dynamic entirely. In traditional e-commerce, visibility was built slowly through SEO, paid advertising, and brand recognition.
But in AI-driven conversational commerce, relevance to a specific intent can elevate a brand almost instantly.
A highly specific, use-case-driven product description that directly answers a consumer's exact question can surface as a recommendation in a chat interface, potentially replacing a more established competitor. The AI system doesn't care about the brand's size or historical marketing success; it cares about relevance to the specific question being asked.
This shift is particularly powerful for brands with specialized products, niche use cases, or strong value propositions tied to specific consumer problems.
Rather than competing on brand awareness alone, these companies can win by building content that makes it easy for AI systems to identify their product as the right solution at the moment of decision.
For larger, more established brands, the risk is real. Generic product pages and broad positioning fade into the background when an AI system is choosing between options.
Clarity, specificity, and use-case alignment become the deciding factors. This means significant portions of e-commerce strategy need to shift from building brand awareness to building clarity about who the product is for and what specific problems it solves.
For years, personalization has been promised as the future of marketing. Most implementations have fallen short, relying on segmentation—grouping customers into broad categories and delivering targeted messages to the segment rather than to the individual.
But AI makes true personalization possible at scale for the first time. Not segmented messaging. Not behavioral targeting. True one-to-one personalization that reflects an individual's specific preferences, context, and needs, often without the consumer explicitly asking for it.
When brands achieve this level of personalization, it feels seamless and natural. Consumers don't think about it; they simply notice that experiences are relevant to them.
But when personalization is absent—when a brand treats the consumer as an average or segment—it feels glaring and irrelevant. Over time, this creates a fundamental shift in baseline expectations.
Consumers increasingly expect every brand interaction to reflect understanding of them as individuals. They expect product recommendations tailored to their specific needs, not broad categories.
They expect communications that acknowledge their preferences and context, not generic messaging sent to millions. They expect experiences designed specifically for them, not adapted versions of mass-market offerings.
This expectation is reshaping customer experience across industries.
The brands and organizations that build this true personalization at scale will win customer loyalty and revenue growth. Those that continue relying on segmented approaches will increasingly struggle to retain attention and engagement.
For many organizations, this requires fundamental rethinking of how customer data is collected, integrated, and activated. The infrastructure must support individual-level understanding and response, not segment-level broadcasting.
One of Britton's most thought-provoking observations is that AI signals the end of the knowledge economy as it has been structured for the past fifty years.
For decades, professional success has been built on the foundation of memorization, procedural expertise, and repeated execution of known solutions.
Accounting firms have valued employees who knew tax codes and regulations. Law firms have valued attorneys who understood case law and legal procedures.
Medical practice has rewarded doctors who memorized diagnostic criteria and treatment protocols. Tech companies have valued engineers who understood multiple programming languages and architectures.
Education has rewarded students who could recall information accurately and quickly.
But AI absorbs much of that work beneath the surface. It can recall information instantly, apply known procedures accurately, and execute routine tasks faster than any human.
This fundamentally shifts the nature of professional value.
The advantage in an AI-enabled world is not knowing answers. Instead, the advantage is in defining the right questions to ask in the first place.
It's in interpreting the implications of answers and guidance that AI provides. It's in applying judgment, empathy, and human context to decisions that affect other people.
It's in understanding which problems matter and why.
This doesn't mean professions like accounting, law, and medicine disappear. But their value moves upstream.
Rather than providing answers and executing procedures, professionals increasingly add value by identifying what questions need to be answered, interpreting complex results, and making judgments about how guidance applies to specific human situations.
The same principle applies across knowledge work.
For individuals, this transition creates both risk and opportunity. The risk is that skills built on memorization and procedural expertise become less valuable.
The opportunity is that skills in judgment, synthesis, creativity, and human understanding become more valuable.
For organizations, the shift requires rethinking how talent is evaluated, how work is structured, and how professional development is approached.
Consumer adoption is far more advanced than many organizations realize. People are using AI for health tracking, financial planning, family support, and everyday problem-solving, often without waiting for official endorsements or formal training.
This adoption has happened within personal contexts where there are no approval layers or restrictions, allowing consumers to build confidence through experimentation faster than enterprises can through formal implementations.
Traditional keyword-based content strategy is becoming less effective. Instead, build content frameworks around specific decision moments and use cases.
Answer narrowly defined questions with clarity and specificity. Design content that makes it easy for AI systems to identify your product or service as the right solution for particular consumer intents.
Smaller brands have a significant advantage in AI-powered environments because relevance matters more than brand dominance.
By building highly specific, use-case-driven content and positioning, smaller companies can surface alongside incumbents. Focus on clarity about your unique value and the specific problems you solve rather than trying to compete on brand awareness alone.
True individual-level personalization, not segmented messaging. Consumers increasingly expect experiences designed specifically for them as individuals, reflecting their preferences, context, and needs.
Generic or segment-based experiences feel irrelevant. Organizations that can build this one-to-one personalization at scale will build stronger customer loyalty and competitive advantage.
Episode 228 of the Speed of Culture Podcast provides a map for understanding a consumer landscape in rapid motion. The trends Britton outlines are not speculative.
They are observable today in how millions of people are searching, shopping, deciding, and learning. The question for brands and organizations is not whether these shifts will happen, but how quickly they will adapt to them.
The good news is that adaptation is still possible. The brands that understand these shifts—that invest in specificity and relevance, that build true personalization, that recognize the end of the knowledge economy and begin rewarding judgment and creativity—will lead their industries forward.
They will earn customer loyalty, competitive advantage, and market share.
For more insights from Matt Britton on consumer trends, artificial intelligence, and what's coming next, visit:
