Amazon Just Gave Advertisers 10 New Brand+ and Performance+ Capabilities. Here Is What Actually Matters.

On July 20, 2026, Amazon released a significant update to Brand+ and Performance+ — ten new capabilities spanning audience data integration, expanded audio formats, streaming TV automation, and AI model improvements. Most will roll out over the coming weeks to Open Beta or General Availability. This is not a minor product refresh. Several of the changes alter the fundamental structure of how these campaign types work — and two of them are active by default on every eligible campaign without any action required. Here is what the update actually contains and what brands running DSP should pay attention to. Your First-Party Data Can Now Feed the AI Directly The most strategically significant change in this update is Audience Signals — available now for both Brand+ Prospecting and Performance+ Customer Acquisition and Unified Consideration, at no additional cost. Until now, first-party audiences in Brand+ and Performance+ functioned as hard targeting constraints. You defined a segment, and the AI optimized within those boundaries. Audience Signals works differently: your audience data enters the campaign as an optimization input, not a targeting fence. The AI uses it to accelerate learning — identifying high-value customer patterns faster — while retaining full freedom to discover users beyond your uploaded segments. Advertisers can bring AMC audiences, DMP segments, or custom-built cohorts connected through Ads Data Manager into Brand+ and Performance+ as optimization inputs in the same way they bring in conversion event data today. The upload path runs through Ads Data Manager — hashed customer lists connected through ADM, then activated as advertiser audiences in Amazon Ads or built as custom segments in AMC. For brands that have been running Brand+ and Performance+ without first-party data integration, this is a meaningful gap to close. The AI optimizes faster when it has signal. Feeding it your actual customer data — purchase history cohorts, high-LTV segments, category buyers — gives it a head start that generic audience models cannot replicate. Audio Is Now General Availability for Brand+ — and Entering Open Beta for Performance+ Audio campaigns drive 43% incremental reach compared to Streaming TV campaigns, and when paired with other Amazon Ads formats, drive 1.8x higher consideration and 2.2x higher purchase intent. When combined with video, Audio improves ROAS efficiency by 6.7–8.7%. Audio for Brand+ moves from Open Beta to General Availability with this update, with expanded third-party exchange open exchange supply now included alongside first-party and third-party Amazon Publisher Direct sources across 16 locales. More importantly, Audio for Performance+ enters Open Beta for the first time — bringing AI-optimized audio to mid- and lower-funnel tactics including Customer Acquisition, Remarketing, Retention, and Consideration. Audio was previously a reach-only format. It is now available as a performance channel. For brands not yet running audio in their Amazon DSP campaigns, the combination of incremental reach and measurable mid-funnel lift makes this worth testing. The format extends into screen-free moments — commutes, workouts, household tasks — that streaming TV and display simply cannot access. Two Model Upgrades Running Automatically in the Background Two of the ten capabilities are enabled by default and require no advertiser action. Both have direct implications for campaign performance. Brand Outcome Optimization predicts user likelihood to take brand-relevant actions — branded search, detail page views, add-to-cart — and optimizes toward those outcomes rather than reach alone. It is active by default on all Brand+ Reach goal line items. Brand advertising has historically been measured on impressions and reach because those were the only metrics available. Brand Outcome Optimization replaces that proxy with actual commerce behavior as the optimization target — a structural improvement in how Brand+ campaigns spend. Prime Video Signal Integration enriches Brand+ ranking with Amazon Prime Video streaming behavior data for improved ad relevancy on Prime Video Ads supply. Amazon's authenticated audience graph now reaches 90% of US households, connecting streaming behavior, shopping activity, and purchase signals across devices. Adding streaming behavior as a ranking signal for Prime Video placements makes those placements more relevant — and more likely to reach shoppers in a receptive context. Both upgrades apply automatically to eligible campaigns. You do not need to activate them. But knowing they are running changes how you should interpret campaign data going forward — performance improvements that appear in reporting over the next few weeks will partly reflect these model changes, not just your own optimization decisions. Automatic Deal Selection for Brand+ STV Brand+ STV line items are now auto-enabled with Automatic Deal Selection — an AI-powered feature that automatically activates and optimizes a group of premium streaming TV deals based on Brand+ audience context and campaign targeting criteria. It expands supply access from a single, pre-configured APD deal to a broader, continuously optimized set of deals across Amazon and the open internet. ADS is enabled by default on new Brand+ STV Prospecting lines and requires no manual activation. The practical impact is expanded reach in premium streaming TV inventory without the manual deal management that previously came with STV campaigns. Amazon's system selects and optimizes deals dynamically based on audience context rather than requiring advertisers to pre-select and monitor individual deals. What This Update Means for How You Run Brand+ and Performance+ Three practical implications stand out. First, if you are running Brand+ or Performance+ without first-party data, the Audience Signals update makes that integration a priority. The feature costs nothing to activate and directly improves AI optimization speed. The longer you wait, the more campaign spend goes toward slower, cold-start learning that your own customer data could have shortcut. Second, audio deserves a place in your DSP plan for Q4. Audio is a full-funnel multiplier, not just an incremental reach play. With Audio for Performance+ now in Open Beta across Customer Acquisition, Remarketing, and Consideration tactics, brands can test audio-driven mid-funnel performance in Q4 — the highest-stakes period of the year — while the format is still early and auction competition is lower than it will be once General Availability arrives. Third, check your Brand+ Reach line items. Brand Outcome Optimization and Prime Video Signal Integration are already active on eligible campaigns. If you are running Brand+ STV, Automatic Deal Selection is also live on new Prospecting lines. Review what is running under the hood before assuming your campaign structure is unchanged. More automation does not remove the need for strategy. It increases the importance of knowing what to feed the system — which audience goal, which creative, which product, which retail destination, and which measurement window. Brand+ and Performance+ are becoming more capable, but they still require the right inputs. The advertisers who get the most out of this update will be the ones who feed the AI better data, not just the ones who turn more features on. If you want to stay updated on Amazon changes, subscribe to our blog. If you need support with PPC, DSP, AMC, analytics, or a long-term growth strategy, contact the ANavigator team at info@anavigator.co  Book a call to get a FREE AUDIT by the link below:     Book a call – FREE AUDIT   Follow my Weekly Newsletter on LinkedIn:  / amazon-digest-for-brands-7232361008185372672   Follow me on LinkedIn:  / ookovalov  Follow ANavigator on social media:  / anavigator    /@anavigator_official  / anavigator7    / @anavigators     LinkedIn page to contact us:   Author: Oleksandr Kovalov Role: Founder & CEO @ ANavigator — The ANavigator Team

Artificial intelligence has transformed the way businesses work. Tasks that once required hours can now be completed in minutes. Content is generated faster, reports are automated, customer data is easier to analyze, and workflows have become more efficient than ever before. At first glance, this should have made hiring easier. Instead, many companies have fallen into a new trap. Rather than using AI to empower specialists, employers have started expecting one person to perform the work of an entire team. Marketing Managers are now expected to design creatives, write copy, edit videos, analyze data, manage advertising campaigns, automate workflows, and master multiple AI tools—all at the same time. Similar expectations are appearing across operations, finance, HR, customer support, and eCommerce management. The result is a hiring paradox: despite having more technology than ever before, finding the right people has become even more difficult. This article explores why the "all-in-one employee" mindset has become one of the biggest hiring challenges in 2026, why AI has unintentionally accelerated this trend, and what successful eCommerce businesses are doing instead.   About This Guide   This guide was prepared by Talents Boutique, an international eCommerce recruitment agency specializing exclusively in hiring for Amazon, Shopify, Walmart Marketplace, TikTok Shop, and DTC brands. The insights shared in this article are based on more than 500 successfully completed recruitment projects, thousands of candidate interviews, employer consultations, salary benchmarking, and ongoing market research across Europe, North America, and Latin America. Because hiring trends evolve rapidly—especially with the widespread adoption of AI—this guide reflects the latest employer expectations, candidate behavior, and recruitment patterns observed throughout 2026. This guide is designed for: - Amazon Sellers - Shopify Brands - TikTok Shop Businesses - Walmart Marketplace Sellers - DTC Brands - Founders - CEOs - COOs - HR Directors - Talent Acquisition Specialists - Hiring Managers - Operations Directors - Agency Owners - Anyone building or scaling an eCommerce team Whether you're hiring your first employee or growing an international team, understanding how AI is changing hiring expectations can help you make better recruitment decisions.   Why Is Everyone Looking for an "All-in-One" Employee?   Just a few years ago, companies typically hired specialists with clearly defined responsibilities. - A Marketing Manager focused on strategy. - A Graphic Designer created visuals. - A Copywriter wrote content. - A PPC Specialist managed advertising. - A Video Editor produced creative assets. Today, many job descriptions combine all of these responsibilities into a single position. It's no longer unusual to see vacancies asking for someone who can: - Build marketing strategies - Launch Meta and Google Ads campaigns - Create graphic designs - Edit videos - Write SEO content - Manage social media - Analyze business performance - Automate workflows with AI - Work with ChatGPT, Midjourney, Canva AI, Notion AI, and other tools On paper, this sounds like an efficient way to reduce hiring costs. In reality, it often creates unrealistic expectations that make recruitment significantly more difficult. According to Talents Boutique Hiring Report 2026, companies searching for highly specialized "all-in-one" professionals often experience longer hiring cycles, smaller candidate pools, and lower offer acceptance rates compared to businesses with clearly defined roles. The challenge isn't that talented professionals don't exist. The challenge is that expecting expertise across too many disciplines usually means compromising on depth, quality, or long-term performance.   AI Didn't Create Generalists — It Raised Employer Expectations One of the biggest misconceptions about AI is that it has created universal specialists. In reality, AI has done something very different. It has expanded what specialists are capable of achieving within their own field. - A Graphic Designer can now create concepts faster. - A Copywriter can research topics more efficiently. - A PPC Specialist can automate reporting. - A Supply Chain Manager can forecast inventory using AI-powered analytics. These professionals have become more productive—not interchangeable. However, many employers interpreted AI differently.  Instead of asking: "How can AI help our specialists work more efficiently?" they started asking: "If AI makes work faster, can't one person simply do everything?" This shift has fundamentally changed hiring expectations. Instead of replacing repetitive tasks, AI has become another required skill added to already overloaded job descriptions. Based on hundreds of candidate interviews conducted by Talents Boutique, experienced professionals rarely reject AI itself. Instead, they express concern about roles where AI is used as justification for combining multiple full-time positions into one. As a result, companies often struggle to attract senior talent because the expectations no longer reflect the realities of modern specialization.   Why Businesses Started Asking for More Skills     Several factors have contributed to this trend. 1. AI Increased Productivity Businesses saw specialists completing tasks faster with AI tools. Naturally, many assumed employees now had spare capacity for additional responsibilities. In reality, increased productivity usually allows professionals to produce higher-quality work, solve more complex problems, and make better strategic decisions—not simply absorb several unrelated jobs. 2. Cost Optimization Became a Priority Economic uncertainty has encouraged companies to maximize efficiency. Rather than hiring separate specialists, businesses often attempt to combine several positions into one role. Although this may reduce payroll costs in the short term, it frequently increases recruitment costs, onboarding time, and employee turnover. At Talents Boutique, recruiters regularly see companies reopening the same "all-in-one" vacancies multiple times because expectations exceed what one person can realistically deliver. 3. AI Created New Responsibilities Ironically, AI hasn't replaced work. It has created entirely new categories of work. Many professionals are now expected to: Evaluate AI-generated outputs Write effective prompts Verify factual accuracy Protect confidential business information Develop AI workflows Train colleagues on AI adoption Select the right AI tools for different business tasks These responsibilities require additional expertise rather than replacing existing responsibilities. 4. Smaller Teams, Bigger Expectations Startups and rapidly growing eCommerce businesses often operate with lean teams. Instead of hiring multiple specialists, founders naturally look for versatile employees. Versatility is valuable. Expecting mastery across several unrelated professions is not. The most successful companies understand the difference between a multi-skilled professional and an unrealistic "all-in-one" employee.   The Hidden Cost of Hiring an "All-in-One" Employee At first, combining several positions into one appears financially efficient. One salary instead of three. One onboarding process. One employee managing multiple business functions. However, the hidden costs usually appear later. According to recruitment observations from Talents Boutique, businesses attempting to hire "all-in-one" professionals often face: - Longer time-to-hire - Smaller pools of qualified candidates - Higher salary expectations - Increased employee burnout - Greater turnover - Lower quality across specialized tasks - Slower business growth due to operational bottlenecks Ironically, the search for the "perfect" employee often becomes more expensive than building a balanced team with clearly defined roles. In today's hiring market, specialization supported by AI consistently outperforms unrealistic versatility. Many candidates describe "all-in-one" vacancies as: - Unclear - Unrealistic - Difficult to prioritize - A sign of unclear business processes - An indicator of potential burnout This perception directly affects hiring outcomes. Highly qualified professionals often decide not to apply at all, while less experienced candidates may apply despite lacking the expertise required to succeed in such a broad role. As a result, employers spend more time interviewing candidates, only to find that few truly match their expectations.   What the Best Companies Do Instead The strongest hiring strategies in 2026 are not focused on finding superheroes. They're focused on building complementary teams. Rather than expecting one employee to master every discipline, successful businesses identify the responsibilities that create the greatest business impact and hire specialists accordingly. For example: Instead of hiring one person to manage content creation, advertising, analytics, design, and automation, they may hire: - A Marketing Manager responsible for strategy - A Performance Marketing Specialist focused on paid advertising - A Graphic Designer supported by AI tools - Freelance specialists for occasional projects - AI solutions to automate repetitive administrative tasks This approach allows every team member to focus on their strengths while AI increases productivity—not workload. At Talents Boutique, recruiters increasingly see employers shifting away from "all-in-one" vacancies toward more focused positions with clearly defined expectations. These companies often experience faster hiring, stronger candidate engagement, and better long-term retention.   AI Should Enhance Expertise—Not Replace It One of the biggest misconceptions surrounding AI is that it replaces expertise. In practice, the opposite is often true. The better someone understands their profession, the more effectively they can use AI. Consider these examples. A Graphic Designer knows which AI-generated concepts are worth developing—and which should be discarded. A PPC Specialist understands which campaign recommendations generated by AI are likely to improve performance and which could waste advertising budget. A Supply Chain Manager can identify forecasting errors that AI might overlook because they understand supplier behavior, seasonal demand, and operational constraints. AI accelerates execution. Expertise ensures quality. Businesses that recognize this distinction tend to achieve better results than those expecting AI to replace professional judgment.   Generalists vs Specialists: Finding the Right Balance This doesn't mean businesses should avoid hiring versatile professionals. Quite the opposite. The most valuable employees often combine deep expertise in one area with a working knowledge of adjacent disciplines. For example: - A Marketing Manager who understands SEO, paid advertising, and analytics. - A Supply Chain Manager familiar with Amazon FBA operations. - A Graphic Designer who can use AI tools to accelerate creative production. - An HR Manager who understands employer branding and recruitment marketing. These professionals remain specialists—but with complementary skills that improve collaboration and decision-making. The difference is important. A versatile specialist strengthens a team. An "all-in-one employee" is often expected to replace one.   Frequently Asked Questions What is an "all-in-one employee"? An "all-in-one employee" is someone expected to perform multiple specialized roles within a single position—for example, combining marketing, design, advertising, analytics, and AI management. Has AI reduced the need for specialists? No. AI has made specialists more productive, but businesses still rely on professional expertise to make strategic decisions, evaluate results, and solve complex problems. Why do companies create "all-in-one" job descriptions? Common reasons include budget optimization, lean teams, rapid business growth, and the misconception that AI can replace specialized expertise. Are generalists valuable? Yes. Professionals with broad business knowledge and deep expertise in one core area are highly valuable. The challenge arises when companies expect mastery across several unrelated professions. How can businesses avoid unrealistic hiring expectations? Start by identifying the responsibilities that have the greatest impact on business performance. Then define clear priorities, separate strategic tasks from operational ones, and use AI to support specialists rather than replace them. What hiring strategy works best in the AI era? The most successful businesses combine skilled specialists, collaborative teams, and AI-powered tools. This approach delivers better results than relying on a single "all-in-one" employee.   Final Thoughts Artificial intelligence has changed how businesses operate—but it hasn't changed the value of expertise. The companies achieving the best hiring outcomes aren't searching for employees who can do everything. They're building teams where specialists use AI to work smarter, collaborate more effectively, and create greater business impact. As AI continues to reshape eCommerce, hiring success will depend less on finding a "universal employee" and more on understanding which skills truly drive growth. For companies navigating these changes, keeping pace with evolving hiring trends is becoming just as important as adopting new technologies.   Looking to Hire or Have Questions? Whether you're building your first eCommerce team or refining your hiring strategy for the AI era, understanding today's talent market can help you make better long-term decisions. If you'd like to discuss your hiring needs or learn more about current recruitment trends, you can book a free consultation with the Talents Boutique team: Book a free consultation.    Author: Lada Khazanov Co-Founder & CEO of Talents Boutique, an international recruitment agency specializing exclusively in eCommerce recruitment.  LinkedIn:  https://www.linkedin.com/in/lada-khazanov-3536b1211/