Aztek Marketing News Roundup (06/22 - 06/26)

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:

• Should Your Brand Use AI‑Generated Images? Pros, Cons, and Guardrails
• How to Read Google’s New Generative AI Performance Reports in Search Console
• LinkedIn’s New “Suggested Feed”

06/23: Should Your Brand Use AI‑Generated Images? Pros, Cons, and Guardrails

Generative‑AI image tools can crank out polished product shots, or even create entire digital influencers, in seconds and for pennies. That speed and savings sound irresistible, but they arrive with new questions about audience trust and regulatory scrutiny. The real debate isn’t if AI‑generated images belong in your social mix; it’s how to deploy them without torching credibility or running afoul of disclosure rules.

The Upside: Studio Looks Without the Studio Bill

AI tools have already helped mid‑market brands shave well over half of their photography budgets by letting them simulate photoshoot conditions on a laptop. When a marketing team can spin up forty variations of a product shot before lunch, it becomes a lot easier to A/B test angles, lighting, and messaging. In that sense, generative imagery levels the playing field. Suddenly, a brand with a modest budget can match the creative volume once reserved for deep‑pocketed competitors.

The Downside: Audiences Aren’t Buying “Bot Beauty”

Efficiency means nothing if the content feels fake. Consumer studies consistently show that most people can spot, and often distrust, imagery they think was created by a machine. Feed fatigue is already real: after a streak of obviously synthetic visuals, even a well‑targeted ad can come across as cold or uncanny. Some brands are pushing back hard in the opposite direction. Polaroid’s recent “Go Analog” campaign is a reminder that authenticity itself can be a differentiator.

The Compliance Squeeze Is Already Here

The Federal Trade Commission’s updated Endorsement Guides make clear that undisclosed synthetic endorsements can draw five‑figure penalties and headline‑grade enforcement actions. Social platforms are also moving quickly: Instagram now offers an “AI creator” badge and is experimenting with automated image‑labeling. Even if these controls feel optional today, history suggests they will become standard practice once a few enforcement examples hit the news.

Guardrails Before You Hit “Post”

Disclose clearly, every time. Pair #ad with a label like “This image was generated with AI.” Clarity matters more than hashtags.

1. Test for trust impact. Run identical posts: one human‑shot and one AI‑rendered. Watch engagement, saves, and comments. If numbers dip, that is telling you something.
2. Blend, don’t replace. Use AI for concepting, background swaps, or filling product catalog gaps, but keep real faces and moments in the feed to preserve brand warmth.
3. Audit your metadata. Make sure alt text and image data align with platform detection tools so helpful automation does not flag your own content as deceptive.
4. Review legal and brand tone together. Align every visual with the FTC guides and your own values. If it feels off‑brand, it probably is.

So, Should You Use AI‑Generated Images?

AI‑generated images are a powerful tool, not a magic wand. Used thoughtfully, they slash production costs and speed creativity. Used carelessly, they erode the credibility you spent years and real budget building. Start small, label everything, measure the trust impact, and keep a human heartbeat in the mix. Nail that balance and you will bank both the savings and the audience goodwill.

06/24: How to Read Google’s New Generative AI Performance Reports in Search Console

Google recently gave site owners a window into how their pages show up in AI Overviews and AI Mode. On June 3 2026, the Search Central team rolled out a dedicated Generative AI performance report inside Search Console.

If you’ve been wondering whether Google’s AI answers are cannibalizing or amplifying your visibility, this report won’t solve every mystery, but it’s a solid first step.

What Actually Changed

Until now, impressions from AI Overviews and AI Mode were buried inside your standard Web totals. The new Generative AI performance report breaks that slice into its own view for both Search and Discover.

You’ll see familiar dimensions such as pages, countries, devices, and dates, so you can pivot the data the same way you do with traditional results. That lets you answer questions like, “Which blog posts get cited in AI Overviews on mobile in the United States?”

Where to Find It and Why You Might Not

Open Search Console and look under Performance, then Generative AI. If the menu item is missing, Google either hasn’t released the feature to your property yet or you haven’t collected enough AI impressions to qualify.

Tip: The Export button works the same as other reports, so you can pull the numbers into Looker Studio dashboards without extra steps.

The Catch: Data Gaps to Know About

• No clicks or click-through rate yet. The report counts impressions only.
• No query or appearance filters. You can segment by page or device but not by search term.
• Limited API support, so automated pipelines will have to wait.

Keep these limits in mind to avoid drawing conclusions the data can’t support.

Benchmark Your First 30 Days

1. Export a full month of Generative AI impressions.
2. Compare them to total Web impressions for the same period to see what share AI already commands.
3. Flag outlier pages, both high and low performers, for manual review.
4. Track weekly shifts to spot algorithm tweaks or content wins early.

The goal here is to establish a baseline before the numbers start to move.

Optimize Content for AI Answers Without Over-Engineering

Google’s own guidance says the same fundamentals still apply: clear structure, authoritative sourcing, helpful visuals, and content that answers a real question.

Generative systems look for pages they can quote with confidence. In practice:

• Tighten headings so key facts sit in easily cited paragraphs.
• Add unique angles or data that make your piece the obvious source.
• Keep EEAT signals visible, including bylines, citations, and about pages.

Tip: Write for people first, then refine for AI.

Should You Use the Opt Out Toggle

Google is also testing a site-level control that lets you exclude your content from AI Overviews and AI Mode entirely. Opting out protects against miscontextual quotes, but you’ll forfeit any impressions and eventual clicks from generative results. Most brands are better served by monitoring performance first, then deciding if specific sections such as medical or financial advice need tighter control.

Turning Generative AI Data Into Action

The Generative AI performance report is an early-stage feature, but it turns guesswork into data. Add the report to your weekly SEO dashboards, benchmark your visibility now, and refine pages that should be winning AI citations but aren’t. Keep expectations realistic: impressions without click data are a directional signal, not gospel, but ignoring them means flying blind.

06/25: LinkedIn’s New “Suggested Feed”

Last week LinkedIn began testing a LinkedIn suggested feed that surfaces topic-focused posts outside a user’s network. Initial screenshots shared by the platform’s product-marketing team show swipeable options (think “AI at Work” or “Cannes Lions”) sitting beside the main feed. The pilot started with users on-site at Cannes and will expand if engagement stays strong.

This is not LinkedIn’s first alternate feed experiment, but it is the clearest push toward TikTok-style discovery the platform has attempted so far.

Why It Matters for Reach

LinkedIn’s core feed still leans on the connection graph, yet fresh usage data suggests there is more content than the main timeline can handle. The company reported a 14% year-over-year jump in posts and a 30% lift in paid video views in Q1 2026.

For brands, the visibility game is shifting from “How many followers do we have?” to “IS this post relevant for a specific professional topic?” Quality interaction signals (saves, thoughtful comments, and real dwell time) will count more than raw reaction totals.

Practical Next Steps

1. Tighten the hook. Open with a clear point of view or data point a busy professional cannot scroll past.
2. Post natively and context first. Links without commentary rarely earn placement; short narrative or quick slides outperform “Read our blog” blurbs.
3. Map three to five content pillars. If the LinkedIn suggested feed rolls out broadly, the algorithm will reward consistency around recognizable themes.
4. Track the “Other” bucket. In LinkedIn analytics, impressions from suggested surfaces often land in “Other.” Plot that trend line now to spot early lift later.
5. Prompt genuine conversation. Ask for perspectives, not perfunctory “thoughts?” Spammy engagement bait is already on LinkedIn’s naughty list.

The Bigger Shift

LinkedIn is evolving from a professional Rolodex into a discovery engine. The brands that win are the ones creating content worth discovering: useful, opinionated, and aligned to real expertise. If your feed strategy still treats LinkedIn like an email blast, you will miss the bar altogether. Flip the mindset; create for professionals first, algorithms second, and the algorithms are more likely to do the heavy lifting for you.