Tips for AI-Driven Marketing Strategies

AI is only as powerful as the strategy behind it. When I first started using AI in content marketing, I made the same mistake I see a lot of marketers make—I jumped in without a clear plan. I had all these AI tools at my disposal, but without defined objectives, I wasn’t maximizing their potential. That changed when I started treating AI like a strategic partner, not just a tool. Here’s how I approach AI integration in my content marketing workflow: 📍 Set clear marketing goals – Before touching AI, I define the business outcome I want. More traffic? Higher engagement? Improved efficiency? AI needs direction. 🎯 Create SMART AI objectives – Vague goals like "use AI for content" don’t work. Instead, I aim for something measurable: "Increase our blog’s average time on page by 20% in three months using AI-driven headline optimization." 🔗 Align AI with strategy – If AI isn’t helping me scale content, improve quality, or enhance personalization, it’s not the right fit. I focus on AI that amplifies what’s already working. 🤖 Use AI where it makes sense – I let AI handle repetitive tasks like keyword research, content outlines, and SEO recommendations, so I can focus on high-level strategy and creativity. 📊 Measure AI’s impact – AI should drive real results. I track performance metrics, analyze what’s working, and tweak my AI settings accordingly. 🚀 Iterate and improve – AI isn’t set-it-and-forget-it. I review performance regularly and adjust my approach to keep improving. AI works best when it’s guided by strategy. If you’re using AI in content marketing, how do you ensure it’s actually moving the needle? 

It will happen slowly, then all of a sudden. Your customers will shift how they search for information about your products. They will use: 1) Decision engines like Google, designed to help them compare products, confirm product details and make purchases. 2) Information engines like ChatGPT and Google’s AI Overviews that feel more like a conversation with a trusted expert or knowledgable friend. Traditional search engines hand you a research project — many pages to sift through to find the information you seek. Generative AI search engines give you direct answers — with a chance of hallucination and inaccuracies. Here's what marketers need to understand: 🔹 Acknowledge the shift: Your customers are learning how/when to use two different types of search engines. There's the traditional "decision engine" like Google, and the "information engine" like chatGPT. 🔹 Accept that humans are lazy: Humans will choose the most convenient option. It’s human nature. Your customers prefer speed and convenience over absolute precision. 🔹 Information queries are moving to AI: When your customers want to learn about their problems, they’ll have conversations with AI instead of reading your blog posts. If your brand isn't appearing in these AI responses, you're becoming invisible to a growing audience. 🔹 Prepare for reduced website traffic: Expect fewer visits from basic informational queries as AI handles these directly. However, the traffic you do receive will be higher-intent visitors, closer to making a decisions, that should convert better. 🔹 Update your content strategy: Create different content for different search engines — intent-targeted informational content for generative AI search, and conversion-focused content for traditional search. 🔹 Build content AI can't summarize: Create interactive content, like calculators and data-driven content that requires user input. This ensures your brand stays visible even as AI handles informational queries. 🔹 Focus on intent, not keywords: The old approach of targeting high-volume keywords is outdated. Instead, understand and align with your customers' search intentions. The key takeaway? Humans are lazy. Your customers will consistently choose the convenience of direct answers from generative AI, even if those answers are sometimes inaccurate. They want to avoid sifting through pages of search results. As marketers, we need to adapt to this new reality. We must create content that caters to both types of searches: (1) content that helps your brand appear in generative AI responses for informational queries and (2) content that attracts and converts for decision searches on traditional search engines. How are you starting to search differently with generative AI?

Two CEOs asked me the same question this week. My CMO is not embracing AI. Is that an issue? The answer is yes. Forget everything you know about the CMO role. AI just rewrote the job description. What once relied on quarterly reports and linear campaigns now demands real-time insights, adaptive content, and dynamic decision-making. These 13 critical shifts outline exactly how top marketing leaders are recalibrating for the AI era: 1/ Data Velocity ↳ Traditional: Quarterly reports ↳ AI-Era: Real-time insights 💡Pro tip: Set up AI-powered dashboards that flag anomalies instantly. 2/ Campaign Planning ↳ Traditional: Linear campaigns ↳ AI-Era: Dynamic optimization 💡Pro tip: Build flexibility into every campaign for AI-driven pivots. 3/ Customer Segmentation ↳ Traditional: Static personas ↳ AI-Era: Dynamic micro-segments 💡Pro tip: Update segment definitions monthly based on AI behavioral analysis. 4/ Content Creation ↳ Traditional: Planned calendars ↳ AI-Era: Adaptive content streams 💡Pro tip: Use AI to test multiple variations simultaneously. 5/ Budget Allocation ↳ Traditional: Annual budgets ↳ AI-Era: Dynamic resource shifting 💡Pro tip: Set aside 20% for AI-identified opportunities. 6/ Team Structure ↳ Traditional: Siloed specialists ↳ AI-Era: Cross-functional AI teams 💡Pro tip: Rotate team members through AI projects monthly. 7/ Risk Management ↳ Traditional: Avoiding failure ↳ AI-Era: Rapid testing & learning 💡Pro tip: Create an AI experiment budget separate from core marketing. 8/ Customer Journey ↳ Traditional: Linear mapping ↳ AI-Era: Real-time path optimization 💡Pro tip: Review AI journey insights weekly with your team. 9/ Competitive Analysis ↳ Traditional: Quarterly reviews ↳ AI-Era: Continuous monitoring 💡Pro tip: Set up AI alerts for competitor digital footprints. 10/ Skills Development ↳ Traditional: Annual training ↳ AI-Era: Continuous AI upskilling 💡Pro tip: Make AI learning a daily 15-minute team ritual. 11/ Performance Metrics ↳ Traditional: ROI focused ↳ AI-Era: Predictive indicators 💡Pro tip: Build AI models that forecast next quarter's performance. 12/ Brand Management ↳ Traditional: Control & consistency ↳ AI-Era: Adaptive & authentic 💡Pro tip: Use AI to monitor brand sentiment across all channels. 13/ Innovation Approach ↳ Traditional: Project-based ↳ AI-Era: Continuous evolution 💡Pro tip: Create an AI innovation council that meets monthly. Mastering these shifts is the new baseline for leading in the AI-driven marketplace. The CMOs who adapt fastest will define what modern marketing leadership looks like. Which shift are you focusing on first? Share below 👇 — Follow Carolyn Healey for more AI marketing insights. ♻️ Repost if you know a CMO who needs to see this.

The biggest shift in adopting AI is actually a mental one. Humans are creatures of habit! There are so many patterns and behaviors that we're just used to and shifting our thinking and way of operating is hard. Here are 10 mental model shifts that B2B marketers need to make to fully embrace AI: 1️⃣ From ‘1:Many ABM’ to ‘1:1 ABM at Scale’ Traditional ABM was mostly 1:many given the manpower required to do 1:few or 1:1. AI lets you scale hyper-relevant, personalized messaging to each prospect and account, making ABM more about precision 1:1 engagement than just broad targeting. 2️⃣ From ‘One Big Campaign’ to ‘Always-On Micro-Campaigns’ Marketing campaigns often take months to plan and execute. With AI, marketers can continuously test, iterate, and personalize campaigns in real time, creating a network of smaller, targeted engagements based on "see intent, take action". 3️⃣ From ‘Copy as an Art’ to ‘Copy as a System’ The days of writing all copy from scratch are gone. With AI, marketers need to think of copy as modular components that can be generated, tested, and refined dynamically, optimizing for different contexts without manually rewriting everything. 4️⃣ From ‘Content Creation’ to ‘Content Orchestration’ Marketers have long focused on producing content, but AI enables them to shift towards orchestrating it—mixing, remixing, and repurposing existing assets dynamically for different channels, audiences, and formats. 5️⃣ From ‘Buyer Journey Stages’ to ‘Real-Time Adaptive Journeys’ Buyer journeys are no longer a linear funnel with predefined stages. AI allows marketers to personalize and adapt messaging in real-time based on each buyer’s behavior. 6️⃣ From ‘Content as Cost Center’ to ‘Content as Infinite Asset’ Before AI, content production was slow and expensive. Now, a single content asset (e.g., a webinar transcript) can be turned into blogs, social posts, email sequences, and more. AI makes content compounding possible at scale. 7️⃣ From ‘Marketing Team Execution’ to ‘AI-Powered Leverage’ Instead of viewing marketing as a set of tasks executed by a team, AI enables a leverage-based approach where a small team can generate the output of a much larger one. 8️⃣ From ‘SEO-Driven Content’ to ‘AI-Native Content Strategies’ SEO-driven keyword strategies are dying. Companies now have to adapt to creating content that can be ingested, interpreted, and surfaced by LLMs. 9️⃣ From ‘Personalization as Feature’ to ‘Personalization as Default’ Personalization used to be an expensive, resource-heavy add-on. Now, AI enables 1:1 personalization at scale, shifting personalization from a bonus feature to a ubiquitous one. 🔟 From ‘AI as Efficiency Tool’ to ‘AI as Strategic Partner’ Most marketers start using AI to speed up existing workflows, but the real shift happens when AI is a strategic collaborator and thought partner—suggesting campaign ideas, identifying opportunities, and challenging assumptions. What did I miss?

If your brand isn’t showing up in ChatGPT, Perplexity, Gemini, or Bing, you’re already losing the next wave of buyers. The way people research and make decisions has shifted. Buyers are skipping Google. They're skipping websites. They're going straight to AI for answers, comparisons, and recommendations. And if your brand isn’t part of that conversation, it might as well not exist. So what can you do? Here’s a list every marketer should pass directly to their web team (or use as a site audit checklist): 1. Clear, crawlable content structure Use proper headings (H1-H3), paragraphs, and lists. Avoid hiding key content in JavaScript. 2. Schema markup Add structured data (JSON-LD) like Organization, Product, FAQPage, Article, etc. This gives AI more context to pull from. 3. Natural-language content Write how your audience talks. Use conversational headlines and answer real questions your buyers are asking. 4. FAQ-style pages Pages that follow a clear Q&A format are AI gold. These often get pulled into generative search results. 5. Internal knowledge base or help center These create structured, high-context content that AI tools love to surface. 6. Consistent brand identity across the web Make sure your name, logo, and info (what you do, where you’re based, etc.) are consistent on your site and across social/business profiles. 7. External citations and backlinks If other reputable sites don’t mention you, AI models have less reason to trust you. PR and third-party mentions matter more than ever. 8. Optimized About pages Your About, Team, and Company pages should clearly spell out who you are and what makes you different. Add location, founding date, leadership, and mission. 9. Canonical URLs and duplicate content control Make sure you’re signaling the “main” version of your content to avoid confusing bots (and AI) with duplicates. 10. Rich product data (for eCommerce) Use schema to mark up product pages with pricing, availability, specs, and reviews. Keep descriptions clean and scannable. 11. Sitemap and robots.txt setup Ensure all key pages are discoverable and indexable. Don’t let a bad robots.txt file block your content from being seen. 12. Active presence on AI-integrated platforms Places like LinkedIn, YouTube, Reddit, and GitHub feed directly into LLM training and AI search. Stay active where AI is listening. 13. Structured citations (Wikidata, Crunchbase, etc.) If you can get listed in structured databases, AI tools can more easily "understand" your brand and include it confidently. 14. Write content like it’s a prompt Anticipate what people might ask AI: “What’s the best [product] for [use case]?” “Is [your brand] legit?” “Compare [you] vs [competitor]” Then answer that exact question on your site. This is the new game. AI isn’t just summarizing websites, it’s filtering out everything that isn’t clear, structured, or credible. Make it easy for the machines to understand you. And your buyers will follow.

This Shopify AI memo changes everything for marketers. If you're still treating AI as a "nice-to-have" in your marketing stack, Shopify's CEO just sent a wake-up call. A leaked internal memo from Tobias Lütke lays out a clear mandate: AI proficiency is now a core expectation for everyone at Shopify. Not optional. Not a "productivity hack." Required. Looking at this memo, it clearly transforms AI from a futuristic concept into immediate, practical action. Tobi establishes five principles: 1. AI literacy is a fundamental, non-negotiable skill for all employees 2. AI exploration must lead the product prototyping phase 3, AI skill evaluation is being added to performance reviews 4. Employees need to share their AI learning with colleagues 5. Teams must attempt AI solutions before requesting additional headcount I believe this represents the first wave of what will soon become standard across the industry. The companies that adapt fastest will create insurmountable competitive advantages. For brands, this shift means fundamentally rethinking how marketing teams operate. The productivity gap between AI-powered marketers and traditional teams is already widening daily. When competitors can produce and iterate creative assets, analyze data, and optimize campaigns at 10x your speed, catching up becomes nearly impossible. For agencies, the implications are even more profound. Clients will increasingly expect AI capabilities to be baked into your service offerings, not charged as premiums. Agencies that can't demonstrate AI-enhanced efficiency risk pricing themselves out of the market entirely. So how can you take action? Consider these 3 strategies: 1. Create your version of this memo today. Define clear, measurable expectations for AI adoption across your marketing organization. Be specific about tools, training resources, and how performance will be evaluated. Most importantly, lead by example. 2. Restructure your creative workflows around AI prototyping. Start building a library of effective prompts and templates that your team can adapt. Set a goal that 80% of first drafts (whether copy, design concepts, or campaign strategies) should be AI-assisted within 90 days. 3. Build AI skill-sharing into your regular team rhythms. Create dedicated Slack channels for sharing effective prompts. Add 10-minute segments to weekly meetings where team members demonstrate a new AI technique. Document these learnings where everyone can access them. The most revealing line in Lütke's memo might be his final point: teams must demonstrate why they can't use AI before requesting more resources. That's the mindset shift happening right now. And one we're paying close intention to across our entire portfolio of companies as well.

AI in marketing has come a LONG way. Here are 8 ways we use AI in marketing at Avenue Z: ✅ #1 - Market Research & Persona Development ChatGPT is phenomenal at competitor research, review-scraping to uncover reasons why people by specific products, and can develop specific personas/funnel ideas to fuel customer acquisition. ✅ #2 - Creative Our AI-powered research fuels our creative briefs & angles. Best use case for AI-driven creative is background swapping of product images, AI voiceovers & video cutdowns. Still aren’t there yet on HQ/usable GenAI creative from a simple prompt. ✅ #3 - Search Engine STILL the #1 way content is found online. AI Overviews appear above organic search results. We craft on-site content designed to rank not just on org search, but in AI search results too. Showcased an example of this a few months ago, where we consistently appear in AIO for a service of ours. ✅ #4 - UGC Playing around w/ Icon, a platform enabling AI-driven influencer content. If an influencer shoots a 2-min video of your product, you can use their likeness to create endless iterations of the content. Swap the hook, talking points, language or B-roll at scale. Have a winning ad? AI deconstructs the vid & enables its recreation using other influencers. Easy way to test diff demographics or languages w/o having to source specific influencers. Game-changing. ✅ #5 - Ad Copy AI is much better at written content than visual content. Platforms like Meta provide copy iterations that you can test seamlessly. ChatGPT is solid for rewriting content to give a diff tone, look, or feel. Build custom GPTs to train the LLM on brand voice or other examples of copy you like/have performed well. ✅ #6 - Landing Pages We built a custom GPT that generates specific landing page frameworks for any brand/offer/product based on our LP best practices. Eventually we'll see AI create personalized landing pages based on a user’s attributes. Ex: returning customer sees diff website experience than new website visitor. Highlights the importance of collecting ample 1P data in today’s world. ✅ #7 - Measurement For larger brands w/ many different traffic sources & sales channels, attribution can be a futile effort. Multi-touch attribution (MTA) can fall short. This is where marketing mix modeling (MMM) comes in. Prescient AI modernized MMM by powering it with AI/ML to give objective recommendations on budget/allocations based on audience saturation & halo effects your marketing has on other ad platforms & sales channels. ✅ #8 - Targeting/Algorithm Training Ad platform algorithms already use AI in targeting which is why they advocate so heavily for broad targeting. BUT- what’s really cool is the concept of using predictive analytics to make the algorithm/AI smarter. Angler AI doing cool things in this area. Especially effective in niche product categories or brands w/ high AOV. Reminder: you're only as effective as your utilization of the tools around you!

Integrating AI into marketing can significantly enhance efficiency, personalization, and decision-making. When thinking about using AI in your work, please keep the following in mind. ⭐ Define Clear Objectives: Identify specific marketing goals and challenges that AI can help address. Know what you will automate vs. keep human. ⭐ Understand Your Data: Ensure your data is accurate, clean, and well-structured. Establish data governance practices to maintain data quality. ⭐ Choose the Right AI Tools: Select AI tools and platforms that align with your marketing goals. ⭐ Data Privacy and Compliance: Be aware of data privacy regulations like GDPR and CCPA. Ensure that your AI initiatives comply with these regulations and prioritize customer consent and data protection. ⭐ AI Talent and Training: Stay curious and active as AI and tools advance. Follow Marketing AI Institute for updates. ⭐ Test and Iterate: Start with small-scale AI projects or A/B tests to evaluate the impact of AI-driven changes. Continuously monitor results and iterate. ⭐ Personalization and Customer Insights: Leverage AI to gather deep customer insights and deliver personalized content and experiences. AI can help you understand customer behavior, preferences, and buying patterns. ⭐ Content Optimization: Use AI to optimize content creation and distribution. ⭐ Chatbots and Customer Service: Implement AI chatbots for improved customer support and engagement. ⭐ AI-Enhanced Advertising: AI algorithms can optimize ad placement, bidding strategies, and ad creatives to maximize ROI. ⭐ Marketing Automation: Integrate AI-driven marketing automation platforms to streamline repetitive tasks, such as email campaigns, lead nurturing, and social media scheduling. ⭐ Predictive Analytics: Implement AI-driven predictive analytics to forecast customer behavior, sales trends, and market changes. This can inform strategic decision-making. ⭐ Measure ROI and KPIs: Clearly define key performance indicators (KPIs) and regularly measure the ROI of your AI-driven marketing initiatives. Adjust your strategy based on performance data. ⭐ Ethical AI: Be mindful of biases in AI algorithms and ensure ethical AI practices. Regularly audit AI systems to identify and mitigate bias and discrimination. ⭐ Scale Gradually: As you gain confidence in AI applications, scale your efforts gradually. Avoid overextending your resources or making abrupt changes. ⭐ Feedback Loop: Establish a feedback loop with your audience. Listen to customer feedback and see if they are impacted (positively or negatively) by AI and automation plays.

Kayak's AI Strategy Should Wake Up Every Hotel Marketer When Kayak launched Kayak.ai, it wasn’t just a side project. CEO Steve Hafner called it a “test lab for AI-first features,” but it signals something deeper: AI isn’t just assisting search, it’s transforming how travelers shop, decide, and book. In Hafner’s words, metasearch may soon become backend plumbing for tools like ChatGPT or Gemini. Why would AI pull pricing from dozens of sites when it can get structured, bookable data from one place, Kayak? That’s the future Kayak is building for: one where AI agents sit between your potential guest and your hotel. Not browsers. Not visitors. Agents. So here’s the real question for hotel marketers: Are your offers ready for an AI interpreter not a human user? When a guest tells their AI, “Find me a boutique hotel in Chicago with late checkout and no resort fees,” it’s not sifting through your website. It’s going straight to data it understands. Your job is to be in that data. Here’s how to get there: 1. Structure your content for machines. AI doesn’t “read” web pages like people. It parses structured content. Your pricing, packages, and policies need to be machine-readable. 2. Optimize for AI-driven distribution channels. Kayak.ai is just one example. There are more coming. Track where your inventory is showing up. 3. Rethink the funnel. AI agents don’t browse. They book. They won’t nurture leads. They’ll jump from intent to action. Be ready at that exact moment. 4. Train your teams now. Prompting is no longer optional. Knowing how to guide AI internally or in the market will define tomorrow’s marketing success. That’s where I come in. I run an AI Literacy/Mindset Program built specifically for hoteliers. It’s designed to transform your teams not through top-down mandates, but from the bottom-up. It's proven to gain one hour of time per day, per person. We embrace scaling through AI Ascension, a clear path from foundational literacy to real impact: First, we get your team fluent in prompting. Then, we show them where automation fits into their actual workflows. Finally, we introduce AI agents not as hype, but as usable tools for sales, ops, and marketing. This isn’t about chasing buzzwords. It’s about equipping your people to lead AI adoption from the ground floor. Because if they don’t, someone else’s agent will own your booking. Let’s make sure it’s your team that leads.

ChatGPT eCommerce drop: Part 3 (foundational Q&A) Q: Why should eCommerce leaders pay attention to ChatGPT’s shopping assistant? The way consumers discover and decide what to buy is fundamentally shifting, from keyword search to conversation. If your product content isn’t optimized for AI discovery, you're lagging. Q: How is this different from Google search or traditional marketplace discovery? Old-school search engines return a list of links or paid ads. ChatGPT returns curated, context-rich product suggestions with images, pricing, reviews, and direct buy links. Difference is that AI models understand intent, not just keywords. Instead of “best sneakers,” a user may ask, “What’s a comfortable walking shoe for traveling through Europe in the summer?” ChatGPT understands that nuance and recommends accordingly. Q: What powers ChatGPT’s product recommendations? It’s a mix of structured product data and contextual intent signals. Product metadata (titles, descriptions, tags, inventory) Real-world reviews with specific use cases or outcomes Signals of trust (brand credibility, availability, content quality) Integrations with platforms like Shopify and product feed partners The AI model then uses this data to recommend products that match the why, not just the what. Q: So what changes for brands now that AI is in the shopping flow? Discovery is an earned visibility game. You can’t just outbid, you have to out-relevance. Generic content doesn’t work; rich context wins. Volume of reviews matters less; specificity and clarity matter more. The brands showing up in ChatGPT’s results are the ones with deep, well-structured content and high-context product storytelling. Q: What are the key elements brands should focus on to stay visible in AI-driven shopping? Priorities: 1. Structured Data Implement schema markup across product pages. Use tools like Shopify’s native integrations to feed product info cleanly. 2. Contextual Product Descriptions Who is this for? What does it solve? What makes it different? 3. High-Context Reviews Prompt users to share how and why they used a product. 4. Review Accessibility Make reviews public, crawlable, and visible next to your products. 5. Feed Accuracy Keep product data synced: availability, pricing, variants, and descriptions. Outdated info will kill your ranking in AI. AI models favor reviews that mention specific use cases, emotions, and product outcomes. A single thoughtful review like “Perfect for marathon runners with flat feet” now outranks 50 vague 5-star ratings. I’m excited for this AI eCommerce era. More to come from The Other Group #ai #ecommerce #commerce