Gen AI as Infrastructure: 2026 Turns AI Into a Ubiquitous UI

Forrester’s new outlook on generative AI and consumers frames 2026 as the year when genAI moves from novelty to infrastructure — a ubiquitous, sometimes invisible layer that will rewire how people search, create, buy, and even socialize, and it places urgent demands on brands to treat AI not as a feature but as a new user interface and a governance problem to solve.

Background / Overview​

Three years after ChatGPT’s public debut, genAI’s consumer footprint is unmistakable: conversational assistants are already a mainstream answer engine, creative tools now live in the hands of everyday users, and a small but vocal group has begun to treat AI as a trusted companion. Forrester’s synthesis underscores a few headline dynamics: ChatGPT remains the most-recognized consumer touchpoint; genAI adoption is accelerating faster than historical consumer technology curves; and the real inflection will come when AI is embedded into devices, apps, and wearables — often without users thinking about the “AI” inside.
That acceleration is partly structural. Unlike early smartphone adoption, genAI’s barriers to entry are lower: many tools have free entry tiers, model access is bundled into services users already rely on, and developers can embed generative capabilities without shipping entire products from scratch. Forrester predicts this will drive faster diffusion than the smartphone era, and anticipates hardware catalysts — most notably AI glasses and other wearables — to accelerate the next wave of mainstream usage. Treat those expectations as directional and strategic: they reflect clear momentum but depend on product, privacy, and business-model execution.

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How consumers are actually using genAI​

Forrester organizes consumer uses of genAI into five practical pillars. These reflect how real-world behavior has diverged from hype and where brands should focus their efforts.

1. The new “answer engine”​

More than half of consumers in Forrester’s surveys already use genAI to find answers to questions. This is no incremental improvement on search; it represents a mode shift where conversational context replaces lists of links and users prize synthesized, actionable responses. Brands need to understand that visibility in LLM-driven answers means more than SEO — it requires machine-readable product truth and verified content that models can cite.

2. Content creation and creative augmentation​

GenAI moved quickly from workplace drafting to consumer content creation. Text-first use cases expanded into images, audio, and video. More than 40% of consumers report using AI tools to draft or create content, and that proportion is growing as multimodal capabilities become cheaper and faster. For marketers and content teams, that means both opportunity (scale creative output) and risk (accuracy, brand voice drift, and IP exposure).

3. Advice and recommendations​

GenAI is also a recommendation engine. Forrester notes that over 40% of consumers already report using genAI for advice and recommendations, and about one in five use it to plan travel. That creates fertile ground for conversational marketing, but also raises the stakes for transparency — consumers rely on recommendations; when those recommendations are monetized or manipulated, trust degrades quickly.

4. Delegation to agents (early, but rising)​

Agentic AI — systems that execute tasks autonomously — is the logical next step, but Forrester stresses that consumer-facing, truly agentic products are still limited. Moving from instruction to delegation demands robust safeguards around trust, control, and technical isolation before users will hand over complex workflows. Enterprises are building agent runtimes and marketplaces now; consumers will follow if and when controls and privacy are convincing.

5. Companionship (niche, fast-emerging)​

A smaller cohort already treats AI as a companion. While only about 12% of online adults in the US and UK describe genAI as “a friend/companion,” Forrester predicts sizable uptake among Gen Z and Gen Alpha, with steep adoption in countries where chatbots have already become social spaces. These uses are psychologically powerful and commercially sensitive — intimacy increases trust but also creates potential for manipulation and regulatory scrutiny.

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Why genAI growth could outpace mobile​

Forrester’s comparison to smartphone adoption is instructive: the smartphone became indispensable because it replaced multiple single-purpose devices and created new daily workflows over many years. GenAI could compress that adoption timeline because:

• It is available via software updates and integrated into platforms users already use.
• Entry costs are often negligible.
• Multimodal models scale quickly on cloud infrastructure and are being embedded in mainstream apps.

Still, adoption will not be frictionless. Hardware integration (AI wearables) and the normalization of agentic behaviors require improvements in latency, battery life, on-device inference, and — crucially — privacy-preserving architectures that minimize data leakage and unwelcome training of third-party models. Forrester’s scenario thinking is optimistic about speed, but it is conditional on industry competence in privacy, governance, and trustworthy AI design.

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What this means for brands: treat AI as a contextual UI, not just a new channel​

Forrester’s most actionable message for brands is blunt: don’t just “optimize” existing content for LLMs — rethink customer experiences around conversational flows and intent-driven interactions. Zero-click answers and synthesized recommendations will shrink traditional organic traffic patterns, and brands should prepare for three interlocking shifts:

• Optimize for AI discovery: convert product and service facts into machine-friendly schemas and canonical pages that agents can reliably cite.
• Design for conversational marketing: short, utility-first content and verified product cards that work inside assistant flows will outperform long-form pages that assume a click-through.
• Prepare for hybrid monetization: search-style ads will exist inside conversational responses, but new formats (sponsored follow-ups, agent defaults, or prioritized data feeds) are likely to emerge.

Concretely, brands should prioritize three investments immediately:

• Build a “model-ready” data layer: structured product metadata, availability, pricing, and canonical facts.
• Add provenance and authorship signals: machine-readable proofs, clear attribution, and first-party facts pages agents can rely on.
• Experiment with conversational experiences: prototype integrations into assistant platforms and measure session value, not just clicks.

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Responsible AI is now a business-critical capability​

Consumers worry about misinformation and privacy — these are not peripheral PR problems but core product risks. Forrester emphasizes that brands must “prioritize responsible AI” with practical steps:

• Implement transparency and disclosure for AI-generated outputs.
• Use non-training contractual clauses where appropriate and demand telemetry visibility from vendors.
• Treat agentic access as a new endpoint risk: apply isolation, permission-gating, and audit trails for agents that can perform actions.

The stakes are material. When assistants act (book flights, place orders, modify accounts), poor governance can lead to financial loss, legal exposure, and a rapid erosion of consumer trust. For regulated industries — finance, healthcare, public sector — these governance practices are procurement essentials. Brands that embed them early gain a durable trust advantage.

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The agentic inflection: opportunities and technical guardrails​

Agentic AI represents both the largest upside and the largest new class of risk. Forrester and related industry analyses outline what differentiates agents from chatbots: planning and decomposition, tool use (APIs, browser automation), memory, and execution with observability. Those capabilities enable agents to perform tasks end-to-end — scheduling, CRM updates, support triage — but they also multiply attack surfaces.
Key operational controls for enterprises:

• Least-privilege connectors: ensure agents access only the data needed for a task.
• Runtime isolation: run agents in sandboxes with explicit admin approval for write or execution privileges.
• Audit trails and human-in-the-loop checkpoints: log agent actions and make human review straightforward when decisions affect outcomes or rights.
• RAG (retrieval-augmented generation) hygiene: treat retrieved context as untrusted and normalize verification workflows to prevent hallucinated actions.

Vendors are racing to provide agent runtime tooling and marketplaces. That competition will make agent deployment easier, but it will also increase concentration risk: a few major stacks that combine models, connectors, and identity will dominate, inviting regulatory scrutiny. Brands should diversify where practical and demand contractual escape hatches and audit rights.

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The zero-click economy and measurement changes for marketers​

Generative assistants change attribution. When users receive a synthesized answer, traditional last-click metrics break down. Forrester and industry consultants argue marketers must move from click-centric metrics to session-level value and conversion-lift testing. That requires instrumenting server-side tracking and running controlled experiments that capture assistant-driven lift versus control cohorts.
Practical short-term tactics:

• Server-side logging of assistant referrals and structured events.
• Holdout experiments to quantify lift from conversational placements.
• Reworking creative to be concise, utility-oriented, and verifiable within assistant flows.
• Negotiating auditable reporting with platform partners; be cautious if platforms cannot provide verifiable lift metrics.

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Strengths and risks — a candid assessment​

Generative AI delivers clear strengths for consumers and brands:

• Faster access to synthesized knowledge and personalized recommendations.
• Scalable content production and new creative formats.
• Productivity gains in workplace and retail settings via task automation.
• New revenue models in assistant-native commerce and sponsorship.

But risks are profound and must be managed:

• Misinformation and hallucinations remain a real hazard when assistants synthesize answers without provenance.
• Privacy and data‑training exposure when user inputs become part of model training unless contractually prevented.
• Security risks from agentic capabilities that can execute commands or access systems.
• Concentration risks when a small set of platforms control distribution and agent defaults.

Where possible, brands should operationalize these trade-offs into a measurable program: define acceptable use cases for AI, set escalation rules for decisions agents may take, and build KPIs that measure both value and harm (e.g., conversion lift versus misinformation incidents).

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Recommendations for Windows users, IT teams, and product leaders​

For Windows consumers and professionals — and for product teams building into the Windows ecosystem — the same rules apply but with OS-specific nuance:

• Treat Copilot and integrated assistants as a primary discovery surface. Ensure your product metadata and enterprise integrations (APIs, Office connectors) are agent-ready.
• For IT/security teams: treat agent-enabled endpoints as new risk vectors. Apply isolation, admin approvals, and logging for any agent that can write or execute on devices. Audit connector permissions and enforce DLP to prevent leakage of sensitive data.
• For developer teams: design with multimodality and long-context in mind. Test models for hallucination rates, latency, and cost, and consider hybrid architectures that mix on-device inference with cloud-only fallback for sensitive workloads.
• For product and marketing: prototype conversational UX, instrument for session value, and prepare for hybrid monetization experiments inside assistant responses. Align legal, marketing, and product teams early to avoid ad hoc rollouts that harm trust.

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What’s likely — and what to treat cautiously​

Forrester’s report and supporting industry analyses indicate several plausible near-term scenarios:

• AI wearables and embedded multimodal assistants become mainstream catalysts for daily use.
• Agentic features expand from lab pilots to consumer-facing helpers for bookings, shopping, and routine financial tasks.
• Monetization will diversify: subscriptions, sponsored responses, and agent commerce deals will coexist with traditional ads.
• Regulatory attention will rise on disclosure, ranking transparency, and the auditability of agent decisions.

Treat large headline projections (e.g., “1 billion weekly users” for ChatGPT) as directional unless backed by public, audited metrics. Forrester suggests OpenAI could hit large-scale adoption milestones soon; that’s plausible given distribution and product integrations, but exact thresholds and timing are inherently uncertain and depend on product, pricing, and macro conditions. Marketers and product leaders should plan for rapid scale while building guardrails that survive both fast growth and regulatory scrutiny.

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Checklist: practical next steps brands can implement in 90 days​

• Audit product metadata and canonical facts pages; fill missing schema fields so agents can access verified data.
• Run a pilot conversational experience for a high-frequency task and instrument session value and verification errors.
• Negotiate or re-check vendor contracts: add non-training clauses, telemetry visibility, and deletion rights where appropriate.
• Implement DLP and admin gating for any agent that can access corporate systems.
• Create an incident playbook for misinformation or agent misbehavior that includes rollback and customer remediation steps.

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Final analysis: an inflection in user interface and brand responsibility​

Forrester’s assessment is both straightforward and sobering: generative AI is not just another technology trend — it is a fundamental shift in how users ask and receive information. That creates extraordinary commercial opportunities for brands that prepare their data, their conversational UX, and their governance models. It also creates existential risks for organizations that treat AI as a bolt-on feature without accountability. The winners in 2026 will be the teams that treat AI as a contextual user interface, build model-ready infrastructure, and pair rapid experimentation with ironclad consumer protections.
Be cautious with headline projections and with any solution that promises “full automation” without transparency. Agentic features are powerful but demand new risk controls, and the regulatory horizon is moving quickly. If you’re building products or marketing in the assistant era, move fast — but instrument, govern, and disclose faster.
Conclusion: genAI’s consumer moment is here. The path to value runs through thoughtful design, defensible governance, and a renewed focus on trustworthy, verifiable information. Brands that act now — structurally, not tactically — will shape how assistants choose, recommend, and act on behalf of consumers in the years to come.

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Source: Forrester The State Of GenAI And Consumers For 2026