Mobile AI Assistants Surge: Copilot, Gemini, ChatGPT Lead Comscore Growth

Mobile usage of AI assistants has taken a measurable lead over desktop in recent months, with Comscore reporting mobile reach for AI tools rising to 73.4 million users (a 5.3% increase) while PC usage fell roughly 11.1%, and the largest mobile growth rates concentrated in Microsoft Copilot, Google Gemini and — to a lesser degree — OpenAI’s ChatGPT.

Background / Overview​

Comscore introduced an AI usage tracker earlier this year that tracks visits across 117 AI tools in nine categories, measuring deduplicated audience reach on mobile and desktop. That dataset — sampled from a digital panel and aggregated across web and native apps — is the basis for the March–June window that produced the headlines about rapid mobile adoption and platform-specific growth rates.
Across the March–June snapshot Comscore reported:

• Total mobile reach (web + native apps): 73.4 million users (up 5.3%).
• Desktop/PC usage: a decline of approximately 11.1% in the same period.
• Mobile adoption of AI assistants from November 2024 to June 2025 grew 82% overall.
• Fastest mobile growth (March–June 2025): Microsoft Copilot +175%, Google Gemini +68%, OpenAI ChatGPT +17.9%.

Comscore’s cross-visitation and deduplication analysis also shows that more than 85% of heavy AI assistant users primarily use a single platform, and that OpenAI’s mobile user base appears comparatively more loyal than Google’s or Microsoft’s, whose users explore across platforms more often. Smriti Sharma of Comscore summarized the broader behavioral shift as users treating mobile AI assistants as personal, always-available companions and gravitating to tools that feel native to phones.

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What the numbers actually measure (methodology and caveats)​

Panel-based visitation vs other telemetry​

Comscore’s numbers are drawn from a panel-based measurement that captures visits and in‑app usage on a representative device panel. This method emphasizes deduplicated reach — unique users across devices — which answers a different question than referral or session-share trackers that count outbound web traffic. Independent tracking services that measure web referral and session share (for example, StatCounter) have historically shown ChatGPT commanding a dominant share of chatbot-driven web referrals, which explains why different trackers can tell complementary but distinct stories about market leadership.

Why percentage growth can mislead​

A headline like “Copilot up 175%” is dramatic but needs absolute context. High percentage growth from a smaller base can still be well below the raw audience of market incumbents. Comscore’s growth rates were first amplified through media partners that translated percentages into estimated absolute user counts (e.g., Copilot’s mobile user base reported in some writeups at roughly 8.8 million and ChatGPT at ~25.4 million for the same window), but those absolute figures come from secondary reporting and should be treated as estimates unless Comscore or the vendors publish explicit counts. Cross-checks with other telemetry vendors are essential to avoid misreading competitive positions.

What Comscore’s tracker covers — and what it doesn’t​

• Includes native apps and mobile web visits for a curated set of 117 AI tools across consumer and productivity categories.
• Produces deduplicated audience reach by platform and cross-visitation patterns.
• Does not directly measure API query volume, cloud inference counts, or paid-subscription revenue; those require vendor disclosures or cloud-provider financials for confirmation.
  Because of differences in definitions, readers should treat Comscore’s reach as a robust indicator of user behavior on devices rather than a full accounting of backend usage or revenue.

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The mobile pivot: Why phones are winning casual AI interactions​

Short sessions, multimodal inputs, and device-native UX​

Mobile sessions are characteristically shorter and more context-specific. Users increasingly employ assistants for quick productivity tasks — drafting emails, summarizing a meeting note, snapping a photo and asking for analysis — and those flows favor assistants that are optimized for voice, camera/image inputs, and tight OS integration. Comscore’s analysts attribute the mobile tilt to these behavioral patterns: convenience plus multimodal inputs make phones the natural home for many assistant interactions.

Performance and latency considerations​

Mobile-first AI experiences place a premium on latency, efficient on-device routing, and streamlined UI. Assistants that can give succinct results with minimal friction win quick-use scenarios. That means vendors will prioritize smaller, optimized models (or hybrid on-device/inference routing) and UX polish over raw model size in mobile product roadmaps.

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Platform dynamics: Copilot, Gemini, ChatGPT — growth drivers and strategic differences​

Microsoft Copilot — enterprise wedge, rapid mobile lift​

Microsoft’s Copilot shows the sharpest percentage gains on mobile in Comscore’s window. The explanation is largely structural: Copilot is not only a consumer app but also deeply embedded in Microsoft 365, Windows, Edge and enterprise workflows, which creates multiple low-friction distribution paths. Admin controls, single sign‑on, and enterprise licensing mean IT organizations can enable Copilot at scale across employee devices — including mobile — producing fast adoption in environments where the assistant is provisioned rather than discovered. That enterprise-first distribution is a core reason behind Copilot’s dramatic percentage growth.Key implications:

• Copilot benefits from bundling with productivity apps and OS-level integration.
• Enterprise enablement can convert into quick mobile adoption when organizations allow or push Copilot to employee devices.
• Rapid growth can continue as Microsoft ties Copilot into licensing and device management channels.

Google Gemini — device distribution and Android positioning​

Google’s Gemini growth is linked to device preloads and ecosystem placement (Pixel devices, Android integration, and Google Workspace touchpoints). Preinstallation and default presence on devices are powerful user-acquisition levers that can quickly translate into millions of mobile users without the same enterprise rollout mechanics Microsoft uses. Gemini’s strengths also include multimodal reasoning and long-context features that align well with creative and search-centric mobile use cases.

OpenAI ChatGPT — scale incumbent, high retention​

ChatGPT remains the scale leader in many independent referral and session-share trackers, even when Comscore shows faster percentage growth for competitors. Where Copilot and Gemini are growing quickly from smaller bases, ChatGPT’s absolute audience remains very large and sticky. Comscore’s cross-visitation data also indicates OpenAI mobile users show higher platform loyalty, meaning they are less likely to hop among assistants compared with some competitors’ users. That retention is a competitive moat: scale begets ecosystem effects (developer integrations, plugins, and content partnerships) that keep core audiences engaged.

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Distribution, bundling and the new user-acquisition economics​

Large vendors have weaponized distribution:

• Microsoft uses enterprise licensing and embedded productivity integrations to push Copilot.
• Google leverages device preloads and Android defaults to expand Gemini’s reach.
• OpenAI retains consumer mindshare via direct-to-consumer product experiences and a broad developer ecosystem.

These channel plays create varied acquisition economics. Device preloads and enterprise enablement can produce fast user counts and percentage growth, but they also change the nature of consumer consent and product discovery. The practical effect: an assistant can become the default experience inside a company or on a phone, creating switching costs that extend beyond simple product preference.

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User behavior: loyalty, cross-visitation, and habit formation​

Comscore’s deduplication shows heavy users frequently stick to one assistant: more than 85% of top users primarily use a single platform. That suggests AI assistants are evolving into habitual workflow components rather than novelty experiments. Habit formation favors:

• Assistants that are defaulted by the OS or IT.
• Assistants with deep integrations into frequently used apps.
• Experiences that feel personal and quickly solve common tasks.

The loyalty finding should be read with nuance: while top users tend to be platform-loyal, casual users still explore multiple tools, particularly when trying new multimodal features (image input, live camera analysis, or voice interfaces) that may be unique to specific assistants.

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Commercial and infrastructure implications​

The commercialization of assistant usage translates directly into cloud demand and vendor economics. Independent reporting tied the usage surge to larger financial dynamics: Azure’s reported growth and OpenAI’s subscription/API revenue milestones have been cited as evidence that usage is converting into meaningful commercial outcomes. Those business realities create incentives for deeper integrations and for vendors to prioritize features that increase cloud consumption, subscriptions, or enterprise seat attachments. That feedback loop accelerates feature rollouts and ecosystem engineering, but also concentrates bargaining power among platform owners who control distribution channels.

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Risks and governance concerns​

Measurement and reporting risks​

• Growth-rate headlines can obscure absolute scale; percentage figures require baseline context to be meaningful.
• Different measurement methods (panel reach vs session/referral tracking vs API/inference counts) can yield divergent narratives. Decision-makers should triangulate across trackers rather than rely on a single headline.

Privacy and data controls​

Mobile assistants often require camera, microphone, and location permissions to deliver rich multimodal experiences. Those permissions increase the attack surface for data misuse and raise complex compliance questions for regulated enterprises. Vendors and IT administrators must ensure:

• Clear consent flows for end users.
• Contractual clarity on data usage and retention.
• Configuration controls that prevent unintended data leakage from corporate apps to third-party services.

Platform lock-in and procurement risk​

The habit formation and distribution advantages enjoyed by big vendors can produce vendor lock-in, particularly for organizations that standardize on a single assistant across their productivity stack. Procurement teams must weigh short-term productivity gains against long-term strategic flexibility, focusing on data portability, auditability, and contract provisions that let them switch or dual‑footprint as requirements evolve.

Unverified and hyperbolic claims​

Some viral lists and rankings in the wider market have inflated absolute “user” numbers that are not verifiable with independent telemetry. These claims — often framed as cumulative or ambiguous “users” — should be flagged and treated cautiously until vendors or neutral samplers publish transparent methodologies. Where Comscore provides panel-derived reach, other independent trackers measure different slices (like web referrals), and some headline “billions of users” claims lack corroboration. Mark these as unverified in procurement or strategic analysis.

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What this means for Windows users, IT admins and enterprises​

For Windows and Microsoft‑centric environments​

• Copilot’s mobile growth is a strategic opportunity if the organization already standardizes on Microsoft 365: admins can enable Copilot to accelerate productivity gains across devices, including mobile.
• Governance remains critical: deploy with policies that restrict sensitive data exposure and ensure audit trails for assistant queries that touch corporate documents or identity systems.

Practical actions for IT teams​

• Inventory AI touchpoints: identify where assistants are embedded across apps (Outlook, Teams, Word, mobile apps).
• Establish clear governance: set DLP rules, retention policies, and role-based access for assistant features.
• Pilot & measure: run controlled pilots to instrument latency, hallucination rates, and productivity impact before broad rollouts.
• Contractual protections: require transparency on data usage, provide for portability, and include SLA metrics for uptime and model behavior when relevant.
• Consider multi-assistant strategies: maintain the ability to route sensitive workflows to vetted, auditable models while letting less-sensitive workflows use consumer-grade assistants for convenience.

For everyday Windows users​

• Expect more AI features to appear in mobile and desktop apps; learn how permission dialogs affect privacy and be cautious about sharing sensitive screenshots, files, or account credentials in assistant prompts.
• Use paid tiers or enterprise configurations when possible if data residency and stronger SLAs are priorities.

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How journalists, analysts and buyers should interpret the Comscore window​

• Treat the Comscore dataset as one high-quality panel-based lens on user behavior that is particularly valuable for mobile and app usage patterns.
• Complement Comscore with session/referral trackers (e.g., StatCounter-style telemetry) and vendor disclosures for a fuller picture: panel reach answers the “how many unique people used this on a device?” question while referral/session data answers “which assistant is sending traffic or producing outbound referrals?”
• Demand absolute figures where decisions depend on scale (for example: licensing costs, API capacity planning, or migration timelines), and insist that vendors provide transparent definitions for terms like “user”, “session”, and “visit.”

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Looking ahead — product and market outlook​

Mobile-first assistant design will accelerate:

• Vendors will optimize latency and build hybrid local/cloud inference strategies to improve responsiveness on phones.
• Multimodal capabilities (camera + voice + text) will become baseline expectations for mobile assistants.
• Distribution deals (preloads, carrier bundles, enterprise enablement) will remain the fastest path to user growth, so procurement and antitrust scrutiny are likely to follow if default placements materially disadvantage competitors.

At the same time, measurement sophistication must improve. The industry needs:

• Standardized definitions for user metrics across panels, referral trackers, and API counts.
• Independent audits of vendor-reported usage where those numbers materially affect procurement or public policy.
• Clearer privacy-preserving telemetry frameworks so product teams can measure adoption without compromising user data protections.

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Conclusion​

Comscore’s March–June snapshot provides a clear signal: mobile is now a dominant venue for many AI assistant interactions, and the ecosystem winners will be those that combine product excellence with distribution muscle and trustworthy governance. The headline growth figures — Copilot’s 175% surge, Gemini’s 68% increase, ChatGPT’s steadier 17.9% rise — are real and instructive, but they must be read alongside absolute scale, different telemetry methodologies, and the commercial incentives shaping where assistants live on devices and in enterprises. Decision-makers should use Comscore as a valuable behavioral lens, triangulate with other telemetry, and treat rapid mobile adoption as a call to harden privacy, governance, and procurement practices before default assistants become hard-to-replace defaults.

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Source: Research Live Mobile AI tool usage increasing, says Comscore | News | Research live

 

[ChatGPT]

Comscore’s latest dataset signals a decisive shift: consumers are moving many AI assistant interactions off desktops and onto phones, with mobile reach rising to 73.4 million users in the March–June window while PC usage declined — a trend driven by rapid mobile gains for Microsoft Copilot, Google Gemini and continued scale at OpenAI’s ChatGPT. (comscore.com)

Background / Overview​

Comscore expanded its measurement suite in May to include dedicated consumer AI tool usage tracking across 117 AI tools and nine categories, and its September release uses that dataset to compare platform reach and month‑to‑month momentum. The headline numbers: mobile reach increased by +5.3% (from 69.7M to 73.4M) while PC usage slipped -11.1% in the same three‑month window. Comscore’s longer‑range point shows mobile adoption of AI assistants up +82% between November 2024 and June 2025. (comscore.com)
Those percent changes are concentrated among a small set of high‑visibility assistants: Comscore reported Microsoft Copilot +175%, Google Gemini +68%, and OpenAI ChatGPT +17.9% for mobile growth in the March–June snapshot. Media partners that had access to Comscore’s figures translated growth rates into estimated audience counts for context; those absolute estimates are useful but require independent verification. (comscore.com, adweek.com)
Why this matters for readers: Comscore’s panel‑derived reach provides a deduplicated view of unique people using AI tools on each platform — a different lens from referral or session‑share trackers that instead measure traffic sources. Together, these complementary measurements reveal both velocity (who’s growing fastest) and scale (who has the largest established audience). (comscore.com, gs.statcounter.com)

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What the Comscore numbers actually measure​

Panel‑based reach vs referral/session metrics​

Comscore’s methodology relies on a digital panel and aggregated device telemetry to estimate deduplicated monthly unique visitors across apps and mobile web. That gives a robust answer to “how many unique people used this assistant on this device” rather than “how much referral traffic did it send” or “how many API calls were processed.” The company’s May dataset introduction explains the 117‑tool taxonomy and cross‑platform intent of the measurement. (comscore.com)
By contrast, trackers like StatCounter measure referral and session share — the proportion of outbound website traffic that originates from a given assistant — and show ChatGPT dominating that slice with roughly ~80% of chatbot referrals in mid‑2025. That illustrates a common pattern: ChatGPT remains the principal source of web referrals, while Copilot and Gemini are accelerating in device and in‑app reach. Use both lenses to avoid misreading competitive positions. (gs.statcounter.com)

Panel size, cadence and caveats​

Comscore’s March–June snapshot was compiled from its newly launched consumer AI tool usage dataset; reporting outlets referenced a panel of roughly a quarter‑million devices as the underlying telemetry sample. Panel design and deduplication rules are central to interpretation: percentage change looks different when the base is smaller, and rapid percentage growth often springs from a smaller absolute base. Look for absolute counts from primary providers when decisions hinge on scale. (adweek.com, proximic.com)
Flag: When media outlets translate percentage changes into absolute user counts (for example, Copilot’s mobile base estimated at ~8.8M and ChatGPT’s mobile at ~25.4M for the same window), treat those absolute counts as estimates derived from the Comscore dataset and secondary reporting — not vendor‑audited totals — unless explicit counts are published by the measurement vendor or the platform.

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The mobile pivot: behavioral and technical drivers​

Why mobile fits many assistant use cases​

Mobile sessions are typically shorter, more context‑specific and tightly integrated with device sensors. Users increasingly ask assistants to perform quick tasks — summarizing messages, drafting a reply, transcribing or analyzing a photo, or getting a rapid fact check. These micro‑tasks favor assistants that are responsive, multimodal and optimized for on‑device flows. Comscore’s analysts emphasize voice, camera and touch inputs as core mobile advantages. (comscore.com)
From a UX standpoint, mobile AI experiences demand:

• Low latency and concise answers that fit small screens.
• Tight OS and app integrations (notifications, share sheets, camera input).
• Clear permission flows for microphone, camera and location.
• Hybrid inference strategies that reduce round‑trip latency while preserving cloud capabilities.

Vendors that succeed on mobile will prioritize engineering work around latency, bandwidth resilience, and streamlined prompt flows rather than pure model size.

Multimodal inputs change the interaction model​

Mobile’s sensor stack — camera, mic, GPS — enables multimodal prompts (photo + voice + text) that transform how assistants are used. Features like image analysis, quick voice notes, and contextual followups turn assistants into companion apps rather than one‑off web pages. That behavioral shift is exactly what Comscore’s mobile adoption numbers reflect: people are interacting with AI differently on phones. (comscore.com)

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Platform dynamics: Copilot, Gemini, ChatGPT — what’s powering the growth​

Microsoft Copilot — enterprise distribution and embedding​

Copilot’s blistering mobile growth (+175%) is closely tied to Microsoft’s distribution muscle inside enterprises and its embedding across Microsoft 365, Windows, Edge and related apps. When administrators enable Copilot for employees or when Copilot features appear inside apps users already open daily, adoption can scale quickly through provisioning rather than discovery. This explains how Copilot posted large percentage gains in the March–June window. (adweek.com)
Key strengths:

• Enterprise provisioning and admin‑centric rollout paths.
• SSO and directory integration that reduce friction.
• Deep links into document and collaboration flows.

Risks:

• Growth driven by provisioning can create consent and governance questions for IT teams.
• Rapid embedding raises procurement tradeoffs: short‑term productivity gains vs long‑term vendor lock‑in.

Google Gemini — device defaults and Android positioning​

Gemini’s mobile gains (+68%) are attributable in part to device distribution (Pixel preloads and Android integration) and Google’s ability to place the assistant in high‑visibility touchpoints. Preinstallation and default settings remain powerful acquisition levers on mobile and can rapidly translate into reach lift without equivalent organic discovery. (adweek.com, proximic.com)
Strengths:

• OS and app ecosystem leverage on Android.
• Multimodal capabilities tuned for search and creative tasks.

Risks:

• Device defaulting invites scrutiny from regulators and customer choice advocates.
• Preloads don’t guarantee high engagement quality; retention and repeated utility matter.

OpenAI ChatGPT — scale incumbent and referral leader​

ChatGPT’s mobile growth (+17.9%) appears steadier but less dramatic because it started from a much larger base. StatCounter’s referral metrics show ChatGPT sending roughly 80% of chatbot‑originated links to websites, underlining its role as the primary public conversational gateway for web traffic. That referral dominance is a different kind of competitive advantage than the distribution levers used by Microsoft and Google. (gs.statcounter.com, proximic.com)
Strengths:

• Massive installed user base and developer ecosystem (plugins, integrations).
• High referral share that drives partner traffic and ecosystem effects.

Risks:

• Scale brings intense scrutiny on accuracy, content safety and moderation.
• A large public footprint is not the same as tightly integrated enterprise controls.

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Commercial and infrastructure implications​

Mobile usage increases translate directly into cloud demand, subscription economics and distribution leverage. Vendors pushing assistant features into devices and enterprise suites convert usage into monetizable metrics (API calls, subscription seats, cloud compute). Microsoft’s and OpenAI’s financial disclosures during 2024–2025 underscore how usage growth has real revenue consequences for platform owners. (adweek.com)
Enterprises should anticipate:

• Growing demand for edge and hybrid inference to meet mobile latency expectations.
• Contractual pressure to include data‑governance terms, audit logs and portability clauses.
• Procurement negotiations that must weigh bundled convenience against long‑term strategic flexibility.

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Measurement caveats and where to be cautious​

• Growth rates vs absolute scale: Percentage increases are easy to headline, but they can mislead when bases differ widely. Copilot’s 175% surge is dramatic — but it grew from a smaller mobile base than ChatGPT’s tens of millions. Always pair percentage moves with absolute counts where decisions depend on scale.
• Different trackers, different stories: Panel‑reach metrics (Comscore) and referral/session trackers (StatCounter) address different questions. Use both sets of data to triangulate market position instead of treating one as definitive. (comscore.com, gs.statcounter.com)
• Unverified absolute claims: Some media writeups or vendor statements publish cumulative or ambiguous “user” numbers that lack transparent methodology. Flag these as unverified until auditably defined.
• Privacy and permissions: Mobile assistants commonly request camera, microphone and location access. Those permissions expand the attack surface and complicate compliance for regulated data. Admins must require clear user consent flows and contractual data protections.
• Vendor lock‑in risk: Deep embedding into productivity stacks or default device placements can create switching costs. Procurement teams should insist on data portability, exit clauses, and auditability before broad commitments are made.

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Practical playbook for IT teams and decision makers​

Rapid assessment checklist​

• Define target use cases and map data sensitivity: classify which workflows are allowed to touch consumer AI assistants.
• Inventory AI touchpoints: list all apps and device surfaces that surface assistant features (Outlook mobile, Word, Pixel Assistant, Copilot app).
• Pilot & measure: run narrow pilots instrumented for latency, output accuracy and productivity gains.
• Insist on governance: require audit logs, DLP integration, contractual limits on training‑use of corporate data and SLAs for uptime.
• Build fallback plans: ensure business continuity if an assistant becomes unavailable or is de‑provisioned.

Recommended sequential steps (1. — 5.)​

• Identify 3–5 high‑value, low‑risk workflows (e.g., internal email drafting, meeting summarization) suitable for pilot testing with an assistant.
• Select pilot cohorts and enable assistants with conservative data‑sharing defaults and strong audit logging.
• Measure outcomes over 30–60 days: time saved, error rates, and user satisfaction.
• Evaluate vendor contracts for data retention, training use, and portability; add contractual clauses where absent.
• Scale cautiously: expand to larger teams only after meeting predefined governance and productivity thresholds.

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Product and market outlook: what to watch next​

• Mobile‑first assistant design will accelerate. Expect more features built specifically for short, multimodal flows and prioritized latency optimization.
• Distribution deals will matter. Preloads, carrier bundling and enterprise provisioning will remain the fastest paths to user growth — and they will attract regulatory scrutiny if defaults constrain choice.
• Measurement sophistication must improve. The industry would benefit from standardized metric definitions (unique users, sessions, visits) and third‑party audits of vendor claims.
• Multimodal capabilities will become baseline expectations. Image, voice and short‑video integration will push creative and social use cases further onto mobile.
• Interoperability and portability will be a bigger procurement battleground, as buyers demand escape hatches from supplier‑specific lock‑in.

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Strengths, opportunities and risks — a balanced assessment​

Notable strengths​

• Distribution leverages are real: Microsoft’s enterprise embedding and Google’s device defaults can unlock massive reach quickly.
• Mobile UX advantages: Sensors and personal context make phones a natural home for quick assistant tasks.
• Complementary measurement lenses: Panel reach plus referral metrics create a more complete picture when used together.

Strategic opportunities​

• Bundling assistants with devices or service plans offers a low‑friction route to user acquisition.
• Enterprises can use assistants to accelerate routine workflows (summaries, drafting) and reclaim time for higher‑value work.
• Developers and partners can build differentiated experiences tailored to mobile’s multimodal affordances.

Potential risks​

• Governance and privacy gaps: The mobile expansion increases exposure of sensitive data unless policy and contract guardrails keep pace.
• Misleading headlines: Percentage growth can be weaponized for PR without clarifying absolute reach.
• Vendor lock‑in: Strong defaults and provisioning paths can produce switching costs that reduce buyer flexibility.

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Cross‑checks and verification notes​

Comscore’s press release provides the primary panel‑reach numbers cited in this article (mobile 73.4M; PC decline 11.1%; growth rates for Copilot/Gemini/ChatGPT). Independent reporting (Adweek) obtained Comscore data to present estimates of absolute mobile audiences and panel size, while StatCounter’s referral dataset offers a divergent but complementary view that emphasises ChatGPT’s referral dominance. Readers should triangulate these different measurements when drawing firm conclusions about scale and market position. (comscore.com, adweek.com, gs.statcounter.com)
Flag: Some widely circulated absolute user totals reported in follow‑on media coverage are derived estimates and not directly published as audited vendor figures; treat those numbers with caution until verified by the measurement vendor or the platform.

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What this means for Windows users and power users​

For everyday Windows consumers, expect more Copilot‑powered features to surface across mobile and desktop Microsoft apps, with incremental convenience wins for drafting emails, summarizing content and managing schedules. For Windows‑centric IT teams, Copilot’s cross‑device embedding presents quick productivity wins but requires the same corporate governance rigour applied to any cloud service that can access corporate data.
Power users should think in terms of an assistant toolkit: choose the right assistant for the job — ChatGPT for broad conversational exploration and plugins, Copilot for Microsoft‑centric workflows, and Gemini where device integration on Android gives you a clear UX edge.

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Conclusion​

The Comscore snapshot is a high‑quality behavioral signal: mobile is where many AI assistant interactions are moving. That shift is driven by UX affordances, multimodal inputs and aggressive distribution strategies from major platform owners. The story it tells — of rapid percentage gains for Copilot and Gemini alongside enduring scale and referral leadership for ChatGPT — is coherent, but it must be read through the right measurement lens.
Decision‑makers should treat Comscore as an essential behavioral lens, triangulate with referral/session metrics and vendor disclosures, and prioritize governance and portability as they adopt assistant technology. When percentage headlines and absolute counts diverge, the safest operational posture is cautious experimentation combined with strict contractual and technical controls that protect data and preserve strategic flexibility. (comscore.com, adweek.com, gs.statcounter.com)

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Source: MarTech Cube Comscore Reports Surge in Mobile AI Assistant Usage