Searchlight is Aztek's marketing news roundup that brings together the week’s most relevant developments in marketing, search, AI, and digital strategy, all in one place. We update this article throughout the week with news we think is worth your time, along with context to help you understand what changed, why it matters, and what it could mean for your business.
This week's topics:
03/16: Why Customer Reviews Matter More in the Age of AI Search
Search has changed before, and it’s changing again. For years, most businesses treated reviews as a trust signal that helped someone feel better about buying a product or booking a service. Reviews usually sat near the bottom of the funnel, where they helped reinforce a decision that was already close to happening. They mattered, but they often weren’t treated like a meaningful part of discovery.
That mindset is starting to feel outdated. As AI tools become part of how people research products and validate decisions, customer reviews are beginning to influence the process much earlier. They’re no longer just there to reassure someone who has already landed on your site. In many cases, they can help shape how your business is understood before a prospect ever reaches your website.
This matters for marketing teams trying to keep up with the way search behavior is evolving, and it matters for business owners who want to stay visible and competitive as those habits continue to shift.
Search Is Becoming More Conversational
Traditional search has usually required users to do a fair amount of the work themselves. Someone enters a query, gets a list of links, and starts clicking through to compare options, gather context, and decide which source feels most useful. AI-driven search experiences are pushing things in a different direction. Instead of simply presenting a page of possible results, these tools are increasingly built to summarize information and answer more detailed questions in a more natural way.
That changes what gets noticed and what gets used. When someone asks an AI tool for the best software for a certain size team, the most reliable local provider for a specific project, or the differences between two competing options, the answer may be built from a mix of signals that includes:
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Brand websites
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Third-party content
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Public discussions
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Customer reviews
That means a prospect may form an impression of your business based on a wider information ecosystem, not just the copy you wrote for your homepage or service pages. Visibility still matters, but the conversation is becoming less about simply appearing and more about being represented clearly and credibly in the sources these systems rely on.
Reviews Help Answer the Questions Buyers Actually Ask
One reason reviews matter more in this environment is that they sound like real people because they are real people. Website copy has an important job to do, but it’s still brand-written and intentionally polished. Reviews, on the other hand, often contain the natural language customers use when they describe their problem, explain what they were looking for, share what nearly held them back, and talk about what made one option stand out over another.
That kind of language carries weight because it reflects real use cases and real outcomes in a way that polished brand messaging often can’t fully replicate. Reviews can:
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Surface details your marketing team might overlook
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Reveal the benefits customers care about most
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Reinforce the kinds of questions buyers are already asking
In that sense, reviews become more than social proof. They become useful context that helps shape how your business is interpreted. A detailed review that explains how responsive your team was, how easy the process felt, or what kind of outcome the customer saw can do more than build confidence at the point of conversion.
For marketers, that means reviews can strengthen messaging, inform content strategy, support SEO and local visibility efforts, and improve conversion assets across the funnel. For business owners, it means reviews can influence whether your company makes the shortlist at all, which is a different role than they used to play.
Reviews Are Moving Up the Funnel
It used to be easier to separate discovery from decision-making. A prospect might find your business through search, paid media, or a referral, then land on your site and look for trust signals like testimonials, ratings, or case studies before taking the next step. That still happens, but it’s no longer the full picture. Buyers are increasingly gathering validation while they research, not just after they’ve narrowed down their options.
That shift matters because people want fast clarity. They want answers that help them understand whether a product or provider fits their needs without spending time clicking through ten different pages and piecing everything together themselves. As a result, reviews are beginning to influence more than the final decision point. They’re helping shape perception during discovery and consideration, too. If your review presence is weak, stale, thin, or inconsistent, that gap can matter much earlier than it used to. What once looked like a missed conversion opportunity can now become a visibility problem.
It’s Not Just About Having Reviews
A lot of businesses hear this conversation and immediately assume the takeaway is simple: get more reviews. That’s part of the answer, but it’s not the whole answer. A pile of vague five-star reviews isn’t nearly as useful as a steady stream of specific feedback that reflects actual experiences. “Great company” and “highly recommend” may help at a glance, but they don’t tell a future customer much about what your business actually does well or why someone chose you in the first place.
Specificity matters because it creates stronger signals. A review that explains what problem was solved, what stood out in the process, or what result the customer experienced gives future buyers more to work with and gives your marketing team better insight, too.
Timing matters for a similar reason. Recent reviews help reinforce that your business is delivering value now, not just that it did a good job a few years ago. Consistency matters just as much because one successful review push doesn’t create an ongoing system. The businesses that will be in a stronger position are the ones that treat review generation like a repeatable business habit, not a one-time campaign.
This Isn’t Just an Ecommerce Story
It would be easy to hear all of this and assume it only applies to ecommerce brands because reviews have been a visible part of online shopping for so long. In reality, the broader lesson applies well beyond retail. Consumer brands may feel the shift first because they already depend heavily on reviews to support product comparison and purchase confidence, but that’s only one part of the story.
For local businesses, reviews have long influenced trust and visibility, and that could become even more important as local discovery becomes more conversational. For service businesses, reviews help answer the questions prospects ask before they ever reach out:
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Can this team actually deliver?
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Are they responsive?
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Do they understand businesses like mine?
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What’s it like to work with them once the contract is signed?
For B2B companies, the same principle still holds even if the buying journey is longer and more complex. Buyers want proof that other companies have had a good experience and that expectations were met. Reviews, testimonials, and third-party validation all help build that confidence.
What A Good Review Strategy Looks Like
A strong review strategy starts with timing. The best moment to ask is usually after the customer has seen meaningful value, not simply when the transaction is technically complete. For some businesses, that might happen right after delivery. For others, it might be after onboarding, after a successful milestone, or after a support interaction that solved an important issue. The more closely the request aligns with a positive, memorable moment, the better the feedback tends to be.
It also helps to make the ask more useful. A generic request for a review often produces generic language in return. If you want feedback that will actually help future buyers, it makes sense to guide people toward the details that matter. Ask what problem they were trying to solve, what made them choose your company, what the experience felt like, and what result stood out most. That kind of prompting tends to create reviews that are more specific, more persuasive, and more useful across channels.
Just as important, reviews should be treated as a source of insight, not just a scorecard. Strong reviews can sharpen your messaging because they show how customers describe your value in their own words. They can strengthen ad copy, landing pages, email campaigns, FAQ content, sales enablement, and case study development.
Weaker reviews can be just as valuable because they often point to disconnects between the promise your marketing makes and the experience your business delivers. That’s not just a customer service concern. It’s useful business intelligence that can improve both operations and marketing performance over time.
What Businesses Should Do Right Now
There’s no need to overreact, and there’s no need to rebuild your entire review strategy overnight. But this is a smart moment to take a closer look at the role reviews are playing in your current marketing and customer experience. Start with the basics. Look at where your reviews live, how recent they are, how specific they are, and whether they reflect the kinds of outcomes and differentiators you actually want your business to be known for.
Then ask a more strategic question: if a potential customer researched your company through AI-assisted search today, would the public feedback around your business help you, hurt you, or barely say anything useful at all? That’s the real issue. The businesses that will be in a stronger position are the ones that stop treating reviews as passive proof and start treating them as active signals. They’ll build systems that consistently generate stronger feedback, use that feedback to improve messaging, and pay closer attention to where those signals show up and how they shape perception.
03/17: Why OpenAI’s Ad Platform Matters, Even If It Isn’t Ready Yet
OpenAI testing an Ads Manager for ChatGPT is one of those stories that’s easy to overreact to. On one hand, you’ve got the “this changes everything” crowd. On the other hand, you’ve got people treating it like a novelty that won’t matter unless it looks exactly like Google Ads. The more useful read is somewhere in the middle.
What makes this worth paying attention to isn’t just the idea of ads showing up in ChatGPT. It’s the fact that OpenAI appears to be building the operating layer that turns ad experiments into an actual platform. Search Engine Land reported that OpenAI has started testing an Ads Manager dashboard with a small group of partners. That matters because it signals a shift from “we’re testing ads” to “we’re building the infrastructure that could support a real ad business.”
Ads Are Only Part of the Story
The most important takeaway here isn’t simply that ChatGPT may show more ads. It’s that OpenAI seems to be developing the backend structure needed to support advertising as an actual business line. Once a platform starts building campaign management tools, reporting workflows, and operational systems for advertisers, it stops looking like a one-off experiment and starts looking like a channel in development.
That’s the bigger signal marketers should focus on. ChatGPT is no longer just being shaped as a consumer AI product. It’s also being shaped as a potential media environment. That doesn’t mean it’s ready yet, but it does mean OpenAI appears to be thinking beyond limited ad tests and toward something more formal.
The Tools Still Have A Long Way To Go
The current reporting makes it clear that this still looks like an early-stage product. Some testers are receiving weekly CSV performance reports with metrics like impressions and clicks. That may be enough for a closed beta, but it’s a far cry from the reporting depth and optimization control advertisers are used to on more established platforms.
For most marketers, that’s a meaningful reality check. Mature ad channels don’t just offer inventory, they offer:
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Visibility into performance
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Flexibility in how campaigns are managed
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Enough infrastructure to support real decision-making
Weekly CSV files suggest OpenAI may be serious about the opportunity, but it also proves the operational side is still catching up to the ambition.
ChatGPT Could Become A Real Ad Channel
This is where the story gets more interesting from a long-term marketing perspective. If LLMs keep evolving into ad platforms, the change won’t just be about one more place to buy media. It could reshape how marketers think about advertising in the first place. Instead of building campaigns mainly around direct keyword intent, marketers may need to think more in terms of conversational themes, real customer questions, and the kinds of comparisons people make when they’re still figuring out what they want.
That matters because conversational AI doesn’t behave like traditional search. In paid search, marketers are used to targeting explicit queries and working within a fairly predictable model of intent. In a chatbot environment, the interaction is often broader and less linear. A user might ask for the best option for a certain need, compare two brands head-to-head, ask which product is better for a specific situation, then follow up with questions about reviews, outcomes, or real-world experience. That creates a very different kind of advertising environment, one where brands may need to meet users inside the conversation rather than push them through a more familiar funnel.
It also raises the stakes for differentiation. If users can ask a chatbot which product is better, why one provider stands out, or what other customers seem to think, then simply showing up won’t be enough. Brands will need stronger positioning, clearer proof points, and more credible social proof behind their products or services. In that kind of environment, standing out from competitors may depend less on matching a keyword and more on whether the full picture around your brand holds up when the conversation turns to comparison, trust, and validation.
Performance Will Decide Whether This Actually Matters
Search Engine Land reported that early tests suggest ChatGPT ad click-through rates are trailing Google Search, and Adweek emphasized that point as well. That doesn’t mean the channel is doomed, but it does mean OpenAI has a real adoption problem to solve before most advertisers will treat it as anything more than an interesting experiment.
New ad products often generate curiosity early, but curiosity doesn’t hold budget for long. Marketers need to understand:
If those answers stay fuzzy, the novelty of advertising in ChatGPT won’t be enough to drive serious adoption, especially when strong SEO and organic visibility can already improve how often brands show up naturally in AI-driven results. For most marketers, that makes organic performance the better investment to focus on first while the paid side still takes shape.
High Spend Expectations Raise the Stakes
The reported spending expectations make early adoption even more precarious. Some advertisers have reportedly been asked to commit at least $200,000. That creates even more pressure to prove value quickly. At that level, advertisers aren’t just testing a shiny new format; they’re making a meaningful investment that has to compete with channels that already have stronger reporting and clearer benchmarks.
Early Understanding Matters More Than Early Spend
Most brands won’t need to rush into this. But they also shouldn’t ignore it. There’s a difference between being “early with budget” and being “early with understanding”, and right now the second one matters more. If OpenAI continues building out ad tools and expanding visibility inside ChatGPT, marketers will want a point of view before the channel becomes more mature.
The Smart Move Is to Track the Right Signals
For now, marketers should be watching a few practical things. Is ad inventory becoming more common inside ChatGPT? Are campaign tools getting more robust? Does reporting improve beyond basic exports? Can advertisers eventually target, optimize, and measure performance in ways that make the platform usable at scale?
Those questions matter more than the headline itself. The announcement is interesting, but the next phase is what will determine whether this becomes a meaningful media channel or just another overhyped ad experiment.
03/18: Claude Code and the Shift Toward AI-Supported Development Teams
A lot of AI conversations about development still start in the same place: how can we code faster? That matters, but it is not the most interesting part of what tools like Claude Code are pointing toward. The bigger shift is how AI is starting to show up across the development workflow itself, from understanding an existing codebase to reviewing pull requests to helping teams move from changes to live deployment with less friction. Anthropic’s recent updates around previewing apps, reviewing diffs, monitoring pull requests, and merging work from within Claude Code make that direction pretty clear.
That makes Claude Code worth paying attention to, especially for teams involved in web design and web development. At agencies and in-house teams alike, the work is rarely just about producing code faster. It is about building something that works, supports the intended user experience, reflects the brand, and holds up under real-world use. AI can help accelerate parts of that process, but it still takes experienced people to make sure the final result is successful.
What Claude Code Actually Represents
At a high level, Claude Code is Anthropic’s coding tool built to work in the environments developers already use, including the terminal, IDEs, desktop, web, and Slack. Anthropic positions it as a codebase-aware assistant that can edit files, run commands, review work, and help teams ship more efficiently without forcing them into a completely different process. That is part of what makes it more significant than a standard chatbot that happens to write code snippets.
That distinction matters in web development. Most web teams are not starting from scratch on a blank page. They are working inside existing sites, inherited code, CMS constraints, legacy components, integrations, analytics requirements, accessibility expectations, and stakeholder feedback. A tool that can help navigate and act inside that complexity is much more relevant than one that only performs well in a clean demo.
Why The Team Workflow Angle Matters
One of the clearest signs of where Claude Code is headed is Anthropic’s recent focus on code review and workflow support. In its new Code Review update, Anthropic says Claude Code can dispatch multiple agents on a pull request to surface bugs and leave inline comments. In its desktop workflow update, the company also highlights the ability to preview running apps, inspect changes, monitor PR status, and move closer to merge from one place.
That is a much more interesting story than “AI can draft code.” Web teams already know that code generation is possible. The bigger question is whether AI can reduce friction in the parts of development work that tend to slow teams down:
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reviewing changes
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catching issues before launch
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tracing bugs across files
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making front-end updates without so much context switching
For teams building websites and digital experiences, that could be genuinely useful. Front-end work in particular often involves constant movement between code, browser preview, logs, revisions, and stakeholder feedback. The more those loops tighten, the more efficient the team can become. That does not mean every output is automatically production-ready. It means the path from idea to review to refinement may get shorter.
Where This Connects To Web Design And Development
For web teams, the value of a tool like Claude Code is not in replacing the people doing the work. It’s in helping them move through the work with more support. That can look like faster first passes on technical implementation, or stronger review support on pull requests. It can also look like quicker front-end iteration when developers can preview what changed, inspect errors, and refine the output without bouncing between as many disconnected tools. Anthropic is clearly building toward that kind of workflow.
This is also where the web design side of the conversation starts to matter. In a lot of website work, the challenge is not simply getting functional code written. The challenge is translating design intent into an experience that feels polished, performs well, and supports business goals. That process still depends heavily on people who understand usability, layout, responsiveness, accessibility, content hierarchy, and conversion behavior. AI can help support that work. It does not remove the need for judgment inside it.
Why Human Oversight Still Matters
This is the part that should not get lost in the excitement. Claude Code looks impressive because it pushes AI deeper into real development workflows. Even Anthropic’s own product messaging, though, suggests a support role rather than a replacement role. In the company’s Code Review announcement, for example, it frames the system as a review layer while keeping approval decisions with humans. Anthropic’s recent security preview around Claude Code Security follows a similar logic: the system scans for vulnerabilities and suggests patches for human review.
Human developers still bring the context that makes web work successful. They understand what the client is trying to achieve. They know when a technically valid solution is the wrong business solution. They catch brand inconsistencies, questionable UX choices, accessibility concerns, edge cases, and performance tradeoffs that an AI tool may not fully understand in context. They are also the ones responsible for what goes live.
For an agency like Aztek, that matters even more. Website projects are rarely isolated engineering exercises. They sit inside a larger digital marketing strategy. The site needs to support messaging, lead generation, SEO, user trust, and ongoing content or campaign activity. A tool can assist with implementation and review, but it still takes experienced developers and designers to make sure the finished product works for the business, not just the browser.
What The Future Probably Looks Like
The most realistic view of Claude Code is not that it replaces development teams; It’s that it helps reshape what strong development teams look like.
Recent reporting from WIRED suggests Claude Code is already influencing how work gets done inside Anthropic itself, and not just within engineering. That lines up with the broader direction of the product. The future likely involves more AI inside the workflow, more support during review and debugging, and less patience for slow handoffs or repetitive manual effort.
But that future still leaves a lot of room, and a lot of responsibility, for human expertise. If anything, it may raise the bar. Teams will be expected to move faster, but they will still need to deliver thoughtful, dependable, well-executed work. AI may help raise the floor for speed and assistance, but human developers are still what raise the ceiling for quality.
That is why Claude Code is worth watching. Not because it signals the end of the web team, but because it offers a glimpse of how web teams may work differently going forward. The opportunity is real, and so is the need for people who know how to guide the work, question the output, and shape the final product into something actually worth launching.
03/19: Why Social Search Matters More Than Ever in the Age of AI
For years, search visibility mostly meant one thing: how well your website performed on Google. That’s still important, but search behavior has changed, and AI has sped that change up.
Google’s AI Overviews are designed to give users quick, AI-generated snapshots with links to learn more, and Google says those overviews now appear for more users across more languages and regions than ever before. Google does, however, note that AI responses can include mistakes. That caveat matters because while fast answers are easier to get, the need for confirmation, context, and trust has not gone away.
That is a big reason why businesses can’t ignore social search. When people want a quick summary, they may get it from Google or an AI tool. When they want to see how something works, hear what real people think, compare options, or pressure-test a claim, they often move to platforms like YouTube, TikTok, and Reddit. Social is not just where people scroll anymore. It is where they search.
Social Search Is Already Part of How People Research
This is not a niche trend marketers are forcing into a strategy deck. It reflects how people actually look for information right now.
Adobe reported in February 2026 that 49% of surveyed consumers said they used TikTok as a search engine in 2026, up from 41% in its 2024 survey. HubSpot has also reported that consumers increasingly use social platforms to find answers and discover products, especially as short-form video and creator-led content continue to shape how people research things online.
That does not mean traditional search is dead. It means the path people take to find and evaluate information is more fragmented than it used to be. A person might start with Google. Then they check TikTok to see the product in action. Then they search YouTube for a longer walkthrough. Then they read Reddit threads to see whether real customers had the same experience the brand promised.
AI Is Changing the First Step, Not Eliminating the Rest
There is a temptation to look at AI search and assume it will collapse the whole discovery process into one answer box. In practice, it usually creates a different first step, not a complete final step.
AI Overviews can help users understand a topic quickly, especially when they want a summary from multiple sources. But summaries are not the same thing as confidence. When the stakes are even a little higher, people still want proof. They want examples. They want to hear from other humans. They want to see the thing, not just read a compressed explanation of it. That is where social search becomes more valuable.
In an AI-heavy search environment, brands need content that can do more than rank for a keyword. They need content that helps them show up when people go looking for validation. Social content often does that better than a standard webpage because it feels more immediate, more visual, and maybe most importantly, more human.
Different Platforms Support Different Search Intent
One mistake brands make is talking about social search like it is one single channel. It’s not. Different platforms help answer different kinds of questions.
TikTok Supports Quick Discovery
TikTok is often where people go for quick discovery, visual explanations, trend-driven curiosity, and “show me” content. That makes it powerful for early attention and fast education.
YouTube Supports Deeper Research
YouTube plays a different role. Its own documentation makes clear that YouTube is built around search and discovery, and that relevance, engagement, and quality all influence what surfaces. Titles, descriptions, and content alignment matter. That makes YouTube especially strong for how-to content, product education, deeper explainers, and comparison-driven research.
Reddit Supports Validation and Real-World Perspective
Reddit often fills the trust gap. When people want candid opinions, unfiltered experiences, or honest discussion, they search there. Reddit’s February 2026 earnings release leaned directly into this idea, saying the company is focused on turning its authenticity into more everyday utility. That is a pretty direct signal that human conversation is part of its search value proposition.
What This Means for Brands
The practical takeaway is not that every company suddenly needs to dance on TikTok or try to dominate every platform all at once. It means your visibility strategy has to reflect how people actually research now.
If your brand only shows up in one place, you are easier to ignore. If you show up in multiple search environments with useful, credible content, you are easier to trust.
That can look like:
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answering real customer questions in short videos
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publishing YouTube content built around common search intent
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making your titles, captions, and descriptions match the language people actually use
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creating content that demonstrates expertise instead of just making claims
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paying attention to the conversations customers are already having about your category
The Bigger Shift in Search
The old version of SEO was too easy to define. Rank in Google, get the click, send the traffic to your site. The current version is messier, but also more realistic.
Now, search visibility includes how you appear in AI-generated summaries, whether your brand has useful content on social platforms, whether people can find demonstrations and discussions about you, and whether your expertise shows up in the places people turn when they want a second opinion.
That is why social search matters more than ever in the age of AI. Not because SEO matters less, but because trust-building now happens across more surfaces than it used to.
If your strategy still treats social as promotion and search as SEO, you are probably missing how buyers actually behave. Search has expanded, and visibility has expanded with it. The brands that adapt will be easier to find, and a lot easier to believe.
