Google’s AI Search: Revolutionizing Information Access

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Google has launched a wave of AI-driven services, most notably a major overhaul of its search platform that promises to fundamentally transform how users access information online. Called ‘AI Mode’, the new search experience is designed to answer longer queries and generate full responses.

Sanchit Vir Gogia, Chief Analyst & CEO at Greyhound Research, said that 61 per cent of global CIOs are re-evaluating how internal and external users access information, preferring contextual, summarised responses over traditional links.

“Google’s move is not reactive—it aligns with evolving user expectations and the proliferation of AI-native information environments. We believe this is a foundational reset in how digital search is designed, governed, and monetised,” he said, adding that AI co-pilots, chat-based interfaces, and proprietary knowledge agents have redefined how users formulate queries and consume answers. This does two things: raise new questions around content provenance and result transparency, open up opportunities for better engagement in enterprise knowledge work.

On the other hand, Gogia said that Google’s platform is built to differentiate itself from unanchored AI chatbots by explicitly linking generative summaries back to live indexable pages. Closed models like ChatGPT often reference static training sets or rely on subscription plug-ins for source expansion.

“Google’s use of live web results offers a technical basis for real-time accuracy, but it also introduces a risk: the summarisation process may paraphrase or compress content in ways that raise IP and ethical concerns for news publishers. Google’s challenge now is not just technical—it’s political and editorial,” said Gogia.

As quoted in The Hindu Business Line, in an article authored by Gyana Swain published on May 21, 2025.

Beyond the Media Quote: Our View, In Full

Pressed for time? You can focus solely on the Greyhound Flashpoints that follow. Each one distills the full analysis into a sharp, executive-ready takeaway — combining our official Standpoint, validated through Pulse data from ongoing CXO trackers, and grounded in Fieldnotes from real-world advisory engagements.

Google’s AI-Enhanced Search: A Strategic Response to Changing Information Behaviours

Greyhound Flashpoint – Google’s decision to integrate generative AI into core search experiences reflects a broader industry pivot from keyword retrieval to intent-based interaction. Per the Greyhound CIO Pulse 2025, 61% of global CIOs say they are re-evaluating how internal and external users access information, with a growing preference for contextual, summarised responses over traditional links. Google’s move is not reactive—it aligns with evolving user expectations and the proliferation of AI-native information environments. At Greyhound Research, we believe this is a foundational reset in how digital search is designed, governed, and monetised.

Greyhound Standpoint – According to Greyhound Research, Google’s AI-led transformation of search should be seen as a strategic adaptation to a fragmented digital knowledge landscape. The rise of AI copilots, chat-based interfaces, and proprietary knowledge agents has redefined how users formulate queries and consume answers. Google’s integration of generative summaries and interactive follow-ups aims to keep the platform central to these changing behaviours. While this evolution introduces new questions around content provenance and result transparency, it also opens up fresh opportunities for richer engagement, particularly in enterprise knowledge work and research.

Greyhound Pulse – Greyhound CIO Pulse 2025 data shows that 61% of enterprise technology leaders expect AI-powered search to play a larger role in organisational productivity by 2026. However, only 42% are currently confident in the explainability and consistency of generative responses. Among digital marketing heads, 58% are actively exploring how new AI overlays will affect SEO dynamics and campaign measurement. There’s cautious optimism—but also a clear demand for better tooling around citation transparency, user controls, and enterprise-grade reliability. Adoption will hinge on how well these systems are governed, not just how intuitively they respond.

Greyhound Fieldnote – Per a recent Greyhound Fieldnote with a leading European banking group, the digital transformation team has begun piloting generative search assistants for internal knowledge navigation. While early results show promise in surfacing summarised regulatory guidance, the team flagged the need for audit trails and override options to meet compliance obligations. In parallel, a North American retail major is experimenting with AI-infused product discovery using Google’s Search Generative Experience (SGE), citing faster decision loops for merchandising. However, both engagements underscore a common theme: enthusiasm is tempered by the need for clear policies, especially around hallucination mitigation and data sensitivity.

Can Google’s Generative Search Resolve Trust, Transparency, and IP Concerns?

Greyhound Flashpoint – Google’s Search Generative Experience (SGE) attempts to balance generative speed with source integrity. Per Greyhound CIO Pulse 2025, 49% of enterprise digital leaders say they would adopt generative search more broadly if summaries offered transparent attribution and confidence scoring. While Google now anchors AI snapshots with linked sources, questions remain around how it distinguishes its outputs from third-party models and how well it aligns with publisher IP norms. At Greyhound Research, we believe the future of search lies not in the speed of response—but in its traceability, explainability, and consent-led ecosystem design.

Greyhound Standpoint – According to Greyhound Research, Google’s SGE is built to differentiate itself from unanchored AI chatbots by explicitly linking generative summaries back to live indexable pages. This is a marked departure from closed models like ChatGPT, which often reference static training sets or rely on subscription plug-ins for source expansion. Google’s use of live web results offers a technical basis for real-time accuracy, but it also introduces a risk: the summarisation process may paraphrase or compress content in ways that raise IP and ethical concerns for news publishers. While recent licensing deals (e.g., with Reddit) indicate a direction of travel, they don’t yet resolve the broader question of universal consent and attribution. Google’s challenge now is not just technical—it’s political and editorial.

Greyhound Pulse – Greyhound CIO Pulse 2025 finds that 52% of legal and compliance heads in media and publishing believe generative models—regardless of vendor—currently operate in a grey zone with respect to fair use. In parallel, 67% of CTOs in digital publishing have prioritised AI watermarking, consent protocols, and licensing enforcements for 2025. Notably, only 34% believe Google’s current SGE approach offers adequate recourse for when their content is used without formal licensing. This tension isn’t about visibility alone—it’s about ownership and editorial control. For AI-enhanced search to gain sustained enterprise and publisher support, governance layers will need to become as visible as the summaries themselves.

Greyhound Fieldnote – Per a recent Greyhound Fieldnote, multiple enterprises across publishing, research, and education sectors have raised concerns about how generative search engines summarise their content. Teams report that while summaries often reflect their work, they don’t always lead to corresponding traffic or attribution. In response, some have implemented stricter crawler policies and opened licensing dialogues with major AI platform providers. In parallel, we’ve observed organisations piloting alternative models like DeepSeek and Claude for internal knowledge workflows, appreciating their transparency in response sourcing—even if breadth occasionally lags behind mainstream engines. The overarching insight: enterprises are no longer assessing AI tools solely on fluency or accuracy. Trust now hinges on how clearly models disclose source lineage and how consistently they respect intellectual property norms.

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