Search is being rebuilt right now, and for data science and management students, that means rethinking what “search skills” look like in a world where LLMs, AI assistants, and agentic workflows turn results pages into personalized recommendation layers across apps, feeds, and devices. Global ad spend is set to top $1.04 trillion in 2026, with digital claiming nearly 69% and algorithm-driven formats hitting 71.6%—rising to 76% by 2028 as AI handles targeting, bidding, and creative decisions. Paid search alone will exceed $218 billion, but its 3.1% growth signals a bigger shift: search is exploding horizontally across TikTok, Amazon, ChatGPT, and messaging apps, with queries growing longer, more emotional, and context-rich.
Algorithms move to the center
Global advertising spending is expected to rise past US$1 trillion in 2026, with digital channels capturing close to seven dollars out of every ten spent. Within that, algorithm‑driven formats already account for a clear majority and are on track to reach roughly three‑quarters of spend by the end of the decade as AI systems take on more of the work of targeting, bidding and creative selection.
In practice, this means that media planning is no longer an exercise in placing messages in fixed locations. It is a contest inside learning systems that are constantly reshaping what people see, click and buy. Retail media networks, social platforms and streaming services increasingly depend on machine‑learning models to decide which brands surface in front of which users, and when.
Search spreads across platforms
Traditional paid search remains a large line item, projected to exceed US$200 billion in 2026, but its growth is slowing even as marketing leaders say they will increase spending on search and AI‑driven tools. One explanation offered in the report is that “search” itself is no longer confined to a single engine or to short, transactional queries.
Consumers now look for information on social platforms, e‑commerce sites, AI chat assistants, app stores and even within messaging apps. Their questions are becoming longer, more subjective and more emotional—queries about how to feel, what to learn and which brands to trust, not only what to buy. For companies, the implication is that prominence in one search box is no longer enough; visibility must be built across many different environments where intent now shows up.
A broader idea of optimization
To respond, the report argues for a wider concept it calls “search experience optimization,” an approach that joins traditional search engine optimization with efforts to shape how brands appear inside language models, social search, retail search and mobile ecosystems. Instead of chasing individual keywords, marketers are urged to design an end‑to‑end discovery experience that algorithms can repeatedly recognize as authoritative and safe.
That makes brand websites and landing pages more important, not less, even as generative tools provide answers directly on the results page. These pages serve as source material that search engines and assistants consult when deciding what to surface, and they also feed newer ad formats that automatically generate copy from on‑page content.
Data as a competitive weapon
With older forms of ad targeting fading, first‑party data—information gathered directly from customers—becomes the primary way brands distinguish themselves inside auctions and recommendation systems. Clean, well‑structured data sets also matter for AI systems that try to interpret and connect information about a company’s products, services and content.
That elevates skills that, until recently, were often seen as back‑office work. Designing taxonomies, managing product feeds, building identity graphs and enforcing data quality controls now tie directly to whether a brand is visible inside AI‑mediated search results. For aspiring data scientists and managers, the signal is clear: understanding how data is modeled and governed is becoming central to marketing performance.
The rise of agents
The report also points to a rapid move toward AI “agents” that automate tasks such as campaign optimization, reporting and creative testing. Marketing leaders say they plan to invest in understanding generative AI use cases, building agent‑based tools and applying AI to analytics over the next year.
At the same time, the authors warn that launching many disconnected agents risks creating more complexity rather than less. They argue that successful organizations will define clear objectives and metrics for their agents, coordinate them through a central orchestration layer and enforce governance so that outputs stay compliant and on‑brand. In the search context, these agents might continuously test prompts, creatives and audiences across different platforms and feed the results back into strategy.
A new curriculum for search
Put together, these trends suggest that early‑career roles in “search” will look very different from the keyword‑driven jobs of a decade ago. Employers are likely to prize hybrid profiles that combine technical and strategic strengths:
- The ability to design content and experiences that language models can understand and reuse safely.
- Comfort with data engineering tasks, from tagging and schema design to building and maintaining product and content feeds.
- Fluency in experimentation, including designing tests across search, social, retail and AI‑assistant environments and interpreting real‑time feedback signals.
- Familiarity with agentic workflows, including when and how to hand decisions back to human oversight.
For students, that may require a quiet shift in how they think about their own training. The next wave of search work will not sit neatly in either marketing or engineering. It will borrow from both, and reward those who can talk to algorithms as comfortably as they talk to executives. In a world where AI systems filter so much of what people see, the ability to design for those systems may become one of the most valuable skills graduates can bring into the job market.
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