Search is invisible, and that’s part of the problem.
It powers nearly everything we touch online: our inboxes, our shopping carts, our code editors, our news feeds. We use it dozens of times a day, so seamlessly that we stop noticing it exists. And recent months with GenAI - almost every ChatGPT prompt will execute a search behind the scene. Agentic AI solutions rely on recent and relevant information. That information retrieval - is indeed Search.
And yet, for something so foundational to modern computing, few disciplines are more misunderstood or more distorted by hype. The history of search is a story of recurring illusions. First came the belief that better ranking formulas would “solve” relevance. Then machine learning promised automation. Now large language models are said to make search obsolete altogether. Each wave arrives with confidence. Each eventually collides with reality.
In our work building Pulse - the agentic cluster maintenance & support platform - we’ve spoken extensively with engineers, architects, and product leaders who are shaping modern search. These conversations have ranged from our Top Voices in Search interviews, Pulse product sessions, industry events, and more.
A question that came up repeatedly was: how will advances in AI and machine learning impact the search-tech industry in the coming year?
Across virtually all of these conversations, a consistent picture emerged: AI isn’t replacing search. It’s restructuring it. And over the coming year, that restructuring will likely accelerate in ways that are both powerful and (potentially) deeply constraining.
No one can ever predict the future with certainty, and certainly not when it comes to AI. But listening to seasoned industry veterans and technologists is about as close as one can get.
So we’ve packaged all of these collected insights into a single industry forecast for 2026.
Here’s what we can all expect going into the new year and beyond.
Search and AI Are Merging, But Not as Replacements
A few years ago, the dominant narrative was simple: LLMs would replace search engines. That fear (and excitement) has largely subsided, not because language models failed, but because practitioners discovered where they actually belong.
LLMs represent “both the present and the future of search, but not as replacements.” Instead, they are becoming embedded throughout the search stack, augmenting the parts search engines have historically struggled with.
LLMs excel at:
- rewriting queries to better express intent
- enriching documents with additional context
- generating summaries and answers from retrieved evidence
- assisting with relevance evaluation, one of search’s most persistent pain points
Search engines still retrieve. LLMs interpret, enrich, and explain.
What’s changing isn’t retrieval itself. It’s everything around it.
Crucially, much of this integration will remain invisible to users. They won’t experience “AI search.” They’ll just experience fewer dead ends, clearer answers, and systems that feel more forgiving when humans don’t know how to ask the perfect question.
RAG Is Becoming the Backbone, Not a Feature
Retrieval-Augmented Generation (RAG) has crossed an important threshold. It’s no longer an experiment bolted onto search systems. It’s rapidly becoming the default architecture.
High-quality retrieval pipelines now feed fresher, more specific evidence into language models, while those same models loop insights back into ranking, taxonomy creation, and personalization. The boundary between “search relevance” and “data insights” is dissolving.
This trajectory extends further into what’s often called agentic search - systems that don’t just answer questions, but reason about what information is needed, orchestrate retrieval across multiple sources, and iteratively refine their approach.
However, this autonomy introduces a quiet but serious constraint.
When systems reason dynamically, reproducibility becomes fragile. In regulated or high-stakes environments, the idea that the same query could produce different retrieval paths minutes apart complicates debugging, auditing, and accountability. Increasingly, this isn’t just an engineering concern, it’s a compliance one. In a world shaped by principles-based regulation, systems must be able to explain why specific information was retrieved and surfaced, not merely that it was.
Agentic search won’t fail because it’s ambitious, it will only struggle if autonomy outpaces explainability. In practice, this means the next generation of agentic systems will succeed not by reasoning more freely, but by pairing autonomy with strong observability, traceability, and replayable decision paths. Without those foundations, trust erodes quickly. Not because the system is wrong, but because teams can no longer understand or defend its behavior.
Search Is Becoming Multimodal by Default
If early search was about text, the next phase is about expression.
Systems already in production retrieve products based on images, handle queries spoken in mixed or imperfect language, and operate across text, vision, and speech simultaneously. For teams that spent years hand-tuning ranking signals to infer intent from sparse text, this feels transformative.
While multimodal capabilities feel revolutionary, many of the underlying challenges are familiar: language mismatch, document extraction, summarization. What’s changed is that AI finally makes these problems tractable at scale.
Search is no longer just about matching words. It’s about understanding how humans communicate — imperfectly, visually, contextually — and meeting them there.
The Stack Is Decoupling And Intelligence Is Moving Everywhere
One of the most consequential changes unfolding is architectural.
Historically, search engines were monolithic. Powerful, but rigid. Intelligence lived at the edges: query parsing on the way in, ranking on the way out. We’re now seeing a shift toward workflow-oriented systems, where intelligence is injected throughout the pipeline: small models rewriting queries evaluators judging result quality feedback loops that retry and rerank dynamically
Rather than treating the engine as a black box, future systems expose hooks — enabling intelligence at every stage. But there’s a constraint that can’t be ignored: latency.
Search has trained users to expect answers in tens of milliseconds. Even small LLMs introduce non-trivial inference cost, especially when chained together. Over the next year, the real innovation won’t be about adding more intelligence, it will be about deciding where intelligence is worth the cost, when to fall back to deterministic paths, and how to preserve the speed and invisibility users subconsciously expect. Selectivity will become the mark of maturity.
Semantic Power Is Rising But Lexical Search Isn’t Going Anywhere
As semantic and vector search mature, many assume lexical retrieval is becoming obsolete. The experts disagree.
Two forces are developing in parallel: unprecedented semantic capabilities alongside renewed appreciation for the precision, efficiency, and predictability of lexical search. Vector and multi-vector techniques will continue to grow in importance, but almost always as part of hybrid systems.
There’s also a less glamorous reason hybrid architectures persist: cost. At scale, storing and querying high-dimensional vectors for massive corpora is dramatically more expensive than maintaining classic inverted indices. For many teams, architecture isn’t chosen because it’s philosophically elegant, but because it’s economically survivable.
Lexical search remains unmatched in cost efficiency and latency. Hybrid systems aren’t a compromise, they’re a reflection of reality.
Evaluation Is Becoming the Center of Gravity
As systems grow more generative and autonomous, evaluation shifts from “best practice” to existential requirement. Innovation budgets flow toward AI initiatives only for teams to rediscover the same hard problems search has grappled with for decades: relevance, messy content, ranking under uncertainty. The difference now is risk.
When systems don’t just retrieve documents but synthesize answers, evaluation can no longer stop at ranked lists. Traditional metrics like precision, recall, and NDCG were designed to judge document ordering for human readers. Increasingly, the “user” of retrieval is another model — one that may misinterpret, over-generalize, or hallucinate even when given relevant inputs.
As a result, evaluation is expanding end-to-end. It’s no longer enough to retrieve the “right” document if the system fails to ground, synthesize, or attribute it correctly. Techniques like LLM-assisted judging and outcome-based evaluation are emerging not as replacements for classic IR metrics, but as necessary complements.
Evaluation isn’t slowing progress, it’s what makes progress safe.
Data Quality Is Still the Real Bottleneck
Much of the early disappointment with semantic search and RAG won’t come from model limitations, it will come from data.
Embeddings can’t fix duplicated documents, missing metadata, broken taxonomies, or inconsistent permissions. The teams seeing real gains from AI-powered search are the ones that invested heavily in the unglamorous work first: cleaning, structuring, and understanding their data.
Many organizations will experience a dip before improvement mistaking early semantic gains for lasting relevance while ignoring foundational issues.
AI accelerates relevance only when the foundation is sound.
Search Is Becoming the Backbone of AI Workloads
One of the most consistent patterns across interviews is how often search quietly underpins AI initiatives.
Across SaaS and enterprise platforms, teams are re-architecting systems to support agents, copilots, and ML workflows. In nearly every case, information retrieval sits at the center.
Whether building chatbots, copilots, or autonomous agents, teams rediscover the same truth: you can’t generate good answers without retrieving good information.
Search expertise is becoming a core AI competency even when teams don’t call it that.
The Economic Model of Search Is Under Pressure
AI-driven answers are breaking the “10 blue links” contract that once governed the web.
As systems summarize and answer directly, content providers lose traffic, and with it, revenue. The symbiotic relationship between search engines and publishers is under strain.
In enterprise environments, the disruption looks different but no less severe. There is no referral traffic to lose. Instead, the risk lies in permissions. When generative systems summarize across documents, document-level security must survive every transformation step. A single leaked sentence from a restricted HR or legal document isn’t a UX flaw. It’s a system failure.
Over the next year, permission-aware retrieval and generation will quietly become one of the hardest problems in applied AI search.
There’s another economic shift beginning to surface. As AI systems increasingly synthesize answers directly, visibility itself is being redefined. In 2026, many brands and content providers won’t be competing primarily for clicks, but for citation - the right to be referenced inside a generated answer. This shift raises a deeper question for the search ecosystem: if content creators no longer receive traffic, attribution, or measurable value, what incentive remains to keep their data indexable at all? Without new models of visibility and compensation, search systems risk creating their own data deserts, starved not by lack of intelligence, but by lack of permission.
Learning Is Accelerating But Mistakes Will Come First
AI dramatically lowers the barrier to experimentation. It accelerates learning. But it doesn’t eliminate the need for understanding.
Many teams will assume vectors or LLMs automatically improve relevance and will be surprised when results degrade. The fundamentals still matter: understanding what users care about, which signals matter, and how success is measured.
AI doesn’t replace expertise. It amplifies it for better or worse.
What 2026 Will Demand
Across all these perspectives, a few truths converge:
- Search and AI are becoming one system, not competing paradigms
- Hybrid retrieval is the stable end state
- Evaluation and observability define trust
- Data engineering remains some of the hardest work
- Search expertise is becoming foundational to AI strategy
The coming year won’t be about replacing search.
It will be about finally understanding what search really is and building systems worthy of that complexity.
