The Future of Search: How AI Will Redefine Information Retrieval
Gyanesh S.
PM (PPO) - IBM || Coordinator - XSYS || MBA BM 2024-26 ll XIMB || Ex - Snapdeal || SRMIST ’23
Imagine waking up, coffee steaming beside your laptop, and instead of sifting through a pile of web links, you simply ask an AI assistant, ‘What’s the best ERP for my e-commerce startup?’ A crisp answer arrives—not just a list, but a confident, contextual recommendation crafted by machine intelligence. Sounds like magic. But it’s also a loaded question:
Whose voice is shaping that answer? Whose biases hide behind that intelligence?
Welcome to the reimagined world of search in 2025—a realm where information retrieval is no longer transactional, but conversational, generative, and riddled with profound implications for bias, trust, and human decision-making.
From Ten Blue Links to Conversational Intelligence
Once upon a digital time, search was simple:
type a query -> receive a ranked list of pages -> pick one -> repeat.
Efficiency was king but understanding was shallow; search engines prized keywords and hyperlinks over context and nuance. Today, we’re frescoing on a vastly different canvas.
Google’s Search Generative Experience (SGE) embodies this shift. It doesn’t merely surface relevant pages; it synthesizes knowledge across billions of documents, weaving responses that answer questions rather than list them. Microsoft’s Bing, powered by GPT-5, accepts written, verbal, and visual inputs, engaging users in dialogues, following up on clarifications, adapting answers in real time. Tools like ChatGPT and Perplexity.ai tailor responses with unprecedented fluidity, signaling a turn from search as “lookup” to search as “conversation”.
The User’s Journey: From Queries to Conversations
Let’s track a typical user journey. Meet Maya, a product manager scouting “best OMS for e-commerce.” Instead of combing through review sites, she queries Perplexity.ai. The AI ingests customer feedback, product specs, integration capabilities, market trends.
What Maya hears back is more than a list—it’s a nuanced briefing:
Maya drills deeper: “What about pricing and regional support?” The AI adapts, tailors answers based on priorities, transparency, and recent shifts.
Seamless. Powerful.
The Elephant in the Algorithm: Bias, Bias, Bias
But here, the story bends.
Artificial Intelligence is, in essence, a curator. It filters through oceans of data reflecting the human condition—laced with historical prejudices, blind spots, societal trends. Research published in ScienceDirect (Hanna et al., 2025) and Frontiers in Digital Health (Schilke, 2025) show the pervasiveness of systemic bias bleeding into AI models.
Same logic, applied to search: what Maya gets is shaped by:
The AI, opaque and complex, delivers “best” recommendations that may invisibly entrench inequities and gatekeeper effects.
The Academic Lens: Understanding and Mitigating Bias
Bias is a stubborn shadow. The comprehensive 2025 review from Aisnet highlights that bias emerges at multiple junctions—data collection, feature selection, and model design—with compounding effects on decision-making systems. Solutions like adversarial training, fairness metrics, and transparent algorithmic audits are promising but far from panaceas.
Stanford’s AI Index Report articulates a core tension: we demand both high-performing AI and ethical guarantees. “Transparency becomes a double-edged sword,” they warn—too little and trust evaporates, too much and users confront complexity paralysis.
User Behavior in the AI Search Era: The Data Tells A Story
Amid this complexity, what does user behavior reveal?
An Arc Intermedia case study (2025) shows that AI search compresses traditional “clicks” by 35%, but extends session duration by 12–15%—users dive deeper into layered, synthesized responses rather than skimming links. Riandhi’s behavioral research underscores a “trust margin,” where users increasingly accept AI-generated answers as authoritative, reducing exploratory behavior.
This redefines SEO dynamics. As I often say,
“Every backlog is a graveyard of good intentions.”
In 2025, SEO is less about gaming keyword density and more about crafting content that AI can surface with context, nuance, and clarity.
Giants and Mavericks: How Industry Leaders Shape AI Search
Risks and Challenges Ahead
A Glimpse into the Future
McKinsey forecasts AI will dominate over 70% of search traffic by 2030, moving beyond question-answering into knowledge creation and decision support.
As I say,
“The art of product is the art of questions.”
The internet’s new search engines are not just answering queries—they are shaping the questions themselves. The game isn’t merely finding answers; it’s framing knowledge, wielding it responsibly, and navigating biases baked into the algorithms.
Final Reflection: The Search For Trust in the Age of Intelligence
Maya’s OMS question is less a solitary query and more a turning point in human-computer partnership. The AI crafts answers, but those answers carry biases encoded in training data, reflecting histories and values imprinted by unseen hands.
The future of search demands more than smarter machines. It demands smarter us: users who question, researchers who illuminate bias, designers who inscribe transparency, and leaders who hold AI accountable.
Because, as I would remind you,
“Every AI answer is a fork in the road—a choice we must own collectively."
Sources I used earlier: