This Week in AI in Marketing: Weekly Round-up Feb 23rd 2026

AI Overviews Evolve & Next-Gen Models Accelerate

By Ann Stanley23 February 2026Estimated reading time: 18 minutesThis week highlights significant advancements and strategic shifts across the AI and marketing landscape. Google continues to refine its AI Overviews, with increased visibility for links and ongoing adjustments to search ranking dynamics. In retail, major players like Williams-Sonoma and Target are pioneering contextual advertising within generative AI platforms, signalling a new frontier for ecommerce monetisation.Meanwhile, the generative AI ecosystem sees rapid iteration with Google’s release of Gemini 3.1 Pro and Alibaba’s powerful Qwen 3.5 model, pushing the boundaries of reasoning and efficiency.These developments underscore the imperative for marketers and agencies to adapt to AI-driven search, embrace new ad formats, and utilise increasingly sophisticated AI tools.

Table of Contents

AI Search & SEO

• Google & OpenAI: Ad & Visibility Shifts in AI Search
• AI’s Reshaping of Search & Content Authority
• Google Search Ranking Volatility Beginning To Cool?
• The Future of Business Is AI, or Obsolete
• How to Future-Proof Your AI Stack with Data Governance

Ecommerce & Retail AI

• Retailers Embrace AI for Ads, CX & Operations
• B2B Commerce & AI Procurement Innovations
• Amazon’s AI Infrastructure and Ethical Considerations

Agentic AI & Automation

• 10 AI Marketing Trends for 2026: Agentic AI and Search Shifts

Social Media & Content

• Global AI Summits & Energy Debates
• Consumer Perception of AI Ads & Brand Activations

AI Tools & Models

• Next-Gen LLMs: Google Gemini 3.1 Pro & Alibaba Qwen 3.5
• DeepSeek-R1: The New Open-Weights Leader

Key TakeawaysFrequently Asked Questions

• How can my content rank in Google’s AI Overviews?
• Should my brand advertise in ChatGPT?
• What is “Agentic AI” and how does it apply to marketing?
• How do new LLMs like Gemini 3.1 Pro impact my marketing strategy?

Conclusion

AI Search & SEO

Google & OpenAI: Ad & Visibility Shifts in AI Search

Source: searchenginejournal.com | February 22, 2026 & aimarketers.co | Aleyda Solis | February 15, 2026Google has confirmed that links will become more visible within its AI Overviews, a move poised to impact how organic search performance is measured. This update aims to provide users with clearer pathways to source content cited within the AI-generated summaries, addressing previous concerns about attribution and traffic dilution. Concurrently, OpenAI initiated testing of ads within ChatGPT for its US free and Go subscription tiers on February 15, marking a significant step towards monetising its conversational AI platform. Initial tests are exploring various ad formats, suggesting a cautious but determined approach to integrating commercial messaging into the AI experience.The real story here is the ongoing convergence of search, content, and advertising within generative AI. For agencies, this means a dual focus: optimising content to be directly cited and linked in Google’s AI Overviews, and strategically exploring early ad placements within ChatGPT for relevant client offerings. Smart marketers should be auditing their content to ensure it’s clearly attributable and provides distinct value beyond what an AI summary can offer, while also preparing to test new conversational ad units as they roll out. Don’t overlook the opportunity to be an early adopter in this nascent ad channel, as the rules and effectiveness metrics are still being defined.

AI’s Reshaping of Search & Content Authority

Source: searchengineland.comRecent insights from Search Engine Land underscore how AI is profoundly reshaping what constitutes authority in search rankings. The “authority era” suggests that beyond traditional SEO signals, the clarity, depth, and unique entity density of content are increasingly critical for AI models like ChatGPT to confidently cite information. A study revealed that 44% of ChatGPT citations originate from the first third of content, favouring direct definitions, balanced tones, and dense entity recognition, highlighting the importance of front-loading high-quality information. This shift indicates that content structures need to evolve to cater to both human readers and AI consumption patterns, prioritising clear, concise, and authoritative introductions.This is where it gets interesting for content strategists. Agencies should audit client content for its “AI readability,” ensuring that key information and entities are prominent in early sections of articles. The focus isn’t just on traditional keyword ranking but on becoming a definitive source that AI models will prefer to cite. Furthermore, the blurring lines between SEO and paid media are evident as “ChatGPT ads collapse the wall,” necessitating a more integrated strategy where organic visibility and ad placements in AI-driven interfaces are considered holistically. It’s no longer enough to rank; you must also be quotable by AI.

Google Search Ranking Volatility Beginning To Cool?

Source: seroundtable.com | Barry Schwartz | February 22, 2026After a period of intense fluctuations, there are early signs that Google Search ranking volatility might be stabilising. On February 22, Search Engine Roundtable reported that while many tracking tools still show elevated activity from recent weeks, some are starting to indicate a decrease in the level of “heat.” This follows a series of significant updates and adjustments to Google’s ranking algorithms, likely influenced by the ongoing integration of AI into its core search functions and the development of AI Overviews. These periods of high volatility are common during major algorithmic shifts as Google calibrates its systems.For SEO professionals and marketing agencies, this potential cooling period offers a crucial window for evaluation. Instead of reacting to daily shifts, the emphasis should now be on long-term strategy validation. Businesses should analyse post-volatility performance data to identify lasting impacts on visibility and traffic, distinguishing between temporary fluctuations and fundamental ranking changes. This is the time to assess which content strategies proved resilient and to double down on practices that align with Google’s evolving emphasis on authority, helpfulness, and AI-optimised content structures. It’s an opportunity to solidify gains and refine approaches before the next inevitable wave of change.

The Future of Business Is AI, or Obsolete

Source: marketingaiinstitute.comThe Marketing AI Institute posits that businesses are rapidly stratifying into three categories: AI Native, AI Emergent, and Obsolete. This stark assessment underscores the non-negotiable imperative for organisations to integrate artificial intelligence at a foundational level. AI Native businesses are built from the ground up with AI at their core, utilising it for every facet of operations, while AI Emergent companies are those actively and successfully transforming their existing structures with AI. The category of “Obsolete” awaits those who fail to adapt, unable to compete with the speed, efficiency, and personalisation offered by AI-driven competitors.This isn’t just about adopting a few AI tools; it’s a call for a complete strategic overhaul. Marketing leaders must assess their organisation’s current position within these three categories and develop a clear roadmap for becoming AI Emergent, if not fully AI Native. This involves investing in AI literacy across teams, identifying core business processes ripe for AI integration, and fostering a culture of experimentation. The specific implication for agencies is to not only preach this message but to embody it: utilise AI agents for practical automations, address new legal questions surrounding AI-generated content and data ownership, and proactively use AI tools to serve clients better, demonstrating expertise rather than just advising on it.

How to Future-Proof Your AI Stack with Data Governance

Source: martech.org | MarTechBot | February 23, 2026MarTech highlights the critical importance of robust data governance to future-proof an AI stack, especially as companies strive for unified AI across sales and marketing functions. The article points out that misaligned consent and governance rules can quickly derail even the most promising AI initiatives, leading to compliance issues, data silos, and ineffective model performance. As AI models feed on vast datasets, ensuring the quality, privacy, and ethical use of this data is paramount. Without a clear framework for data governance, organisations risk data breaches, regulatory penalties, and a loss of customer trust, ultimately undermining their AI investments.For marketing leaders, this translates into an immediate need to establish comprehensive data governance policies, particularly concerning first-party data. This means clearly defining data ownership, consent mechanisms, usage rights, and data retention policies. Agencies should advise clients on building an AI stack that is not only technologically advanced but also legally compliant and ethically sound. The actionable next step is to conduct a thorough audit of all data sources used for AI training and deployment, ensuring strict adherence to GDPR and other privacy regulations, and implementing technical safeguards to prevent data misuse. Neglecting governance today will inevitably lead to costly remediation tomorrow.

Ecommerce & Retail AI

Retailers Embrace AI for Ads, CX & Operations

Source: retaildive.com | Tatiana Walk-Morris | February 18, 2026A host of retailers are rapidly integrating AI across their operations. Williams-Sonoma is partnering with OpenAI to test ads in ChatGPT, aiming to engage customers at crucial decision-making points and enhance product discovery (Feb 18, 2026). This move signifies a shift towards contextual advertising within conversational AI. Similarly, Wayfair and Affirm expanded their “Buy Now, Pay Later” partnership to the UK and Canada on February 17, offering real-time approval decisions at checkout, streamlining customer experience. Beyond payments, the industry recognises the urgent need for brands to optimise mobile apps and websites for AI, as consumer behaviour evolves away from traditional search algorithms towards AI-driven interactions (Feb 18, 2026).The strategic implication for ecommerce brands is clear: AI is no longer a luxury but a fundamental component of customer acquisition, retention, and operational efficiency. Firstly, explore opportunities for contextual advertising within emerging AI platforms like ChatGPT, focusing on product discovery and direct response. Secondly, prioritise a robust data quality strategy (Feb 17, 2026), as accurate address and identity data are crucial to prevent fraud, reduce marketing waste, and ensure delivery success. Finally, rethink mobile experiences not just for human users but for AI models, ensuring content is structured for AI consumption to maintain visibility in a changing digital landscape. Agencies should guide clients through these transitions, highlighting the UK-specific expansion of BNPL services and the need for AI-first optimisation strategies.

B2B Commerce & AI Procurement Innovations

Source: digitalcommerce360.com | Mark Brohan | February 20, 2026The B2B commerce sector is witnessing significant AI-driven innovation, with Didero raising $30 million in funding on February 20 to expand its AI procurement software. This investment underscores the growing demand for intelligent automation in corporate purchasing, aiming to streamline complex B2B transactions, optimise supplier selection, and enhance cost efficiencies through predictive analytics. Simultaneously, FedEx highlighted on February 20 that enhanced visibility, integrated AI, and escalating buyer demands are collectively redefining B2B commerce. This redefinition necessitates platforms that offer granular tracking, AI-powered insights, and highly personalised buyer journeys to meet the sophisticated expectations of modern business customers.Here’s the thing: B2B marketers and sales teams must recognise that the B2B buyer journey is becoming as sophisticated and AI-driven as B2C. The actionable step is to investigate AI-powered procurement and sales tools that can offer real-time insights into buyer behaviour and market trends. Furthermore, companies need to invest in supply chain visibility tools that utilise AI to predict disruptions and optimise logistics, mirroring FedEx’s strategic emphasis. Anicca recommends B2B ecommerce platforms integrate AI for predictive demand forecasting and hyper-personalisation of product catalogues, ensuring they remain competitive in an increasingly intelligent and demanding market.

Amazon’s AI Infrastructure and Ethical Considerations

Source: aboutamazon.com | February 20, 2026Amazon recently published a clarification regarding a Financial Times report about AWS, Kiro, and AI on February 20, underscoring the ongoing scrutiny and rapid development within enterprise AI. The company also provided an in-depth look at “Inside Gen AI’s split-second sprint from question to answer” on February 18, detailing the intricate process of AI inference and the role of AI agents in delivering rapid, accurate responses. This insight sheds light on the massive computational infrastructure required to power generative AI applications and the continuous engineering efforts to optimise speed and efficiency. Amazon’s disclosures highlight the dual pressures of technological advancement and maintaining public trust in AI capabilities.The real story here is the scale and complexity of the underlying AI infrastructure, and the implicit ethical and operational challenges. For brands utilising AWS or similar cloud AI services, understanding the inference process provides insight into potential latency or cost considerations for real-time AI applications. The actionable implication is that companies utilising AI must not only focus on the front-end user experience but also understand and potentially articulate the reliability and ethical safeguards of their back-end AI systems. Agencies should advise clients on responsible AI deployment, preparing them for public scrutiny and ensuring transparency around their AI operations, especially concerning data handling and model performance.

Agentic AI & Automation

10 AI Marketing Trends for 2026: Agentic AI and Search Shifts

Source: adweek.com | Shiv SinghAdweek’s analysis of 2026 AI marketing trends identifies agentic AI and fundamental shifts in search as pivotal developments. Agentic AI refers to autonomous systems capable of executing multi-step tasks and making decisions without constant human intervention. These AI agents are set to transform marketing workflows, automating complex processes from campaign optimisation to content generation and customer service interactions. The report emphasises that as AI-driven search surfaces become the primary interface for information discovery, the strategies for visibility and engagement will radically depart from traditional SEO models, demanding a deeper understanding of intent and conversational context.Don’t overlook this: the rise of agentic AI means marketers need to move beyond simple tool adoption to designing autonomous workflows. Agencies should begin experimenting with agentic frameworks to automate repetitive tasks, freeing up human talent for strategic oversight and creative work. The actionable next step is to identify areas within client operations where multi-step, rule-based processes can be handed over to AI agents, such as dynamic ad copywriting, lead qualification, or personalised email sequences. This shift is not just about efficiency but about enabling always-on, hyper-responsive marketing operations that scale beyond human capacity, ensuring Anicca clients remain at the forefront of automation.

Social Media & Content

Global AI Summits & Energy Debates

Source: techcrunch.com | Ivan Mehta | February 22, 2026The India AI Impact Summit continues to generate significant news, highlighting the growing global focus on artificial intelligence development and its societal implications (Feb 22, 2026). Discussions at such high-level events often revolve around AI’s economic potential, ethical deployment, and infrastructural demands. A parallel, pressing conversation emerged from Sam Altman, who publicly reminded stakeholders on February 21 that “humans use a lot of energy, too,” in response to escalating concerns about the substantial energy consumption of AI data centres. This statement underscores the environmental footprint of advancing AI and the need for sustainable solutions within the tech industry.For social media and content strategists, these global discussions, particularly around AI’s energy usage, offer a crucial lens for brand narrative development. Companies utilising AI should proactively address their environmental impact and contribute to the conversation on sustainable AI. The actionable next step is to integrate a narrative of responsible AI use and sustainability into brand communications, moving beyond mere product features to broader corporate values. This builds trust and resonates with an increasingly environmentally conscious audience. Furthermore, monitoring insights from summits like India AI Impact can provide foresight into emerging AI policies and public sentiment, informing content strategies before they become mainstream debates.

Consumer Perception of AI Ads & Brand Activations

Source: marketingbrew.com | Jasmine Sheena | February 19, 2026A new report reveals that approximately half of consumers are not averse to AI-made advertisements, signalling a growing acceptance of AI’s role in creative marketing (Feb 19, 2026). This insight from Marketing Brew indicates that the novelty or potential stigma associated with AI-generated content might be diminishing, opening new avenues for brands to scale their creative output. Concurrently, TNT Sports and Sephora launched a partnership on February 20 for an “Unrivaled ‘glam room’ experience,” showcasing how brands are still leaning into physical, immersive activations while integrating AI in other aspects. This hybrid approach demonstrates a nuanced understanding of consumer engagement.Here’s the thing: marketers should not shy away from exploring AI for ad creation, particularly for segment-specific campaigns where efficiency and personalisation are key. The actionable next step is to conduct A/B testing of AI-generated ad creatives against human-designed counterparts to refine performance and gain audience insights, rather than assuming a negative perception. For social media teams, this also means considering how AI can assist in rapid content iteration for platforms like TikTok, while still valuing authentic human-led experiences. Anicca recommends a balanced strategy that leverages AI for scalability in content production while reserving human creativity for high-impact, experiential brand activations that foster deeper connections.

AI Tools & Models

Next-Gen LLMs: Google Gemini 3.1 Pro & Alibaba Qwen 3.5

Source: venturebeat.comGoogle has launched Gemini 3.1 Pro, a significant upgrade that VentureBeat describes as retaking the AI crown with a “2X+ reasoning performance boost.” Initial impressions suggest it acts like a “Deep Think Mini” with adjustable reasoning capabilities on demand, offering enhanced problem-solving and analytical prowess. This rapid iteration in large language models (LLMs) is indicative of the fierce competition in the AI landscape. Not to be outdone, Alibaba’s Qwen 3.5 397B-A17 model has achieved benchmark wins against its larger, trillion-parameter predecessor, Qwen3-Max, at a mere fraction of the inference cost. This demonstrates a crucial trend: powerful performance is becoming achievable with more efficient, smaller models, democratising advanced AI capabilities.The strategic implication for businesses and agencies is profound. Access to more capable yet cost-efficient LLMs like Gemini 3.1 Pro and Qwen 3.5 means that sophisticated AI applications are becoming more feasible for a broader range of use cases. Agencies should investigate integrating these newer, more performant models into client solutions, particularly for complex data analysis, content generation requiring deep reasoning, and advanced customer service chatbots. The actionable next step is to benchmark current LLM performance against these new offerings, focusing on both accuracy and operational cost. utilising these optimised models can lead to significant improvements in AI-driven marketing campaigns and internal efficiencies for Anicca’s clients.

DeepSeek-R1: The New Open-Weights Leader

Source: deeplearning.ai | February 20, 2026DeepLearning.AI’s “The Batch” highlighted DeepSeek-R1 as the new open-weights leader on February 20, positioning it as an affordable rival to OpenAI’s cutting-edge models. This development signifies the increasing maturity and competitiveness of open-source AI, where community-driven innovation is rapidly catching up to proprietary offerings. DeepSeek-R1’s emergence suggests a model that provides high performance at a lower operational cost, making advanced AI capabilities more accessible to a wider array of developers and businesses. The availability of powerful open-source alternatives accelerates innovation by fostering greater experimentation and customisation across industries.Here’s the thing: the rise of highly capable open-weights models like DeepSeek-R1 creates immense opportunities for marketing agencies and brands to build custom AI solutions without the prohibitive costs or vendor lock-in associated with some proprietary models. The actionable next step is for in-house AI teams or agency partners to explore DeepSeek-R1 and similar open-source LLMs for specific marketing tasks, such as hyper-local content generation, complex data analysis for niche markets, or building bespoke conversational agents. This flexibility allows for greater innovation and cost efficiency, enabling Anicca to develop more tailored and competitive AI strategies for its clients.

Key Takeaways

• Google is increasing link visibility within AI Overviews, making source attribution and content quality even more vital for organic performance.
• OpenAI is testing ads in ChatGPT, opening a new frontier for contextual advertising and requiring early experimentation from marketers.
• Retailers like Williams-Sonoma and Target are integrating AI for in-chat ads and customer experience, demonstrating a strong push towards AI-driven commerce.
• B2B commerce is seeing significant investment in AI procurement software, necessitating AI-powered visibility and personalised buyer journeys.
• The concept of “AI Native” businesses highlights the urgent need for organisations to embed AI deeply into their core operations to avoid obsolescence.
• New LLMs like Google’s Gemini 3.1 Pro and Alibaba’s Qwen 3.5 offer enhanced reasoning and efficiency, democratising access to advanced AI capabilities.
• Consumer acceptance of AI-generated ads is growing, providing an opportunity for scaled creative content, though balanced with human-led experiences.

Frequently Asked Questions

How can my content rank in Google’s AI Overviews?

To rank in AI Overviews, focus on creating clear, authoritative, and entity-rich content that directly answers common queries. Ensure key information is front-loaded, and structure your content for easy consumption by AI models, aiming to be cited as a primary source rather than just a traditional blue link.

Should my brand advertise in ChatGPT?

As OpenAI tests ads in ChatGPT, it presents an early adopter advantage for brands. Consider experimenting with contextual advertising in ChatGPT if your target audience uses the platform, focusing on product discovery and direct engagement, while closely monitoring performance and evolving ad formats.

What is “Agentic AI” and how does it apply to marketing?

Agentic AI refers to autonomous AI systems that can perform multi-step tasks and make decisions without constant human oversight. In marketing, this means automating complex workflows like dynamic ad content generation, lead qualification, or personalised email sequences, freeing human teams for strategic and creative roles.

How do new LLMs like Gemini 3.1 Pro impact my marketing strategy?

Advanced LLMs like Gemini 3.1 Pro offer superior reasoning and efficiency, enabling more sophisticated AI applications for data analysis, nuanced content generation, and intelligent chatbots. Marketing teams can utilise these to build highly performant AI-driven campaigns and enhance internal operational efficiencies.

Conclusion

This week’s developments clearly signal an accelerated and fundamental shift in the marketing and commerce landscape, driven by AI. From Google’s evolving AI Overviews and the emergence of in-chat advertising on platforms like ChatGPT, to the release of more powerful and efficient large language models, the imperative for strategic AI integration is undeniable. Businesses must embrace an AI-first mindset, not only to optimise existing operations but to reimagine customer engagement and competitive advantage.Marketing leaders and agencies must prioritise understanding these shifts, proactively adapting their search strategies, exploring new advertising frontiers, and evaluating the latest AI tools for both scale and strategic depth. The organisations that rapidly integrate and responsibly deploy these AI advancements will be best positioned for growth. Need help adapting your AI marketing strategy? Contact the Anicca team for expert guidance.