AI in Content Strategy Development

Here’s the exact AI content strategy I use to take sites from page 5 to page 1 in 2025: 1) Topical Mapping • Start with a root topic. Think “Digital Marketing,” not “How to run Facebook Ads.” • Use ChatGPT to break it into subtopics + FAQs. This is your first-pass topical map. • Validate each subtopic by checking traffic potential via tools like Ahrefs/SEMRush. • Organize content into silos (pillar + clusters). Every piece should fit somewhere. 2) AI Content Workflow (the right way) • Don’t write and publish raw AI. You’ll get nuked. • Use AI for draft generation and outline speed. • Human editor polishes for tone, accuracy, and nuance. (Or use a tool like SurferAI) • Inject real experience, stats, or original examples. That’s how you stand out. • Cap output to ~3–5 articles per day/site. Don’t trip Google’s velocity radar. 3) Entity Optimization (critical in 2025) • Think beyond keywords - identify key entities for your niche. • Use tools like SurferSEO to extract relevant entities from top pages. • Weave entities naturally into headings, body copy, image alt text, etc. • Use internal links to connect related entities and pages. • Use schema markup to help Google understand entity relationships on your site. 4) On-Page Setup for AI Content • Match search intent by checking SERPs and aligning format with top-ranking pages. • Main query in H1. Subtopics covered in H2-H3. • Answer user query as fast as possible. • Add internal links to parent and sibling pages. • Include media (images, video embeds, infographics) to lower bounce rate. • Write naturally. Google's NLP understands natural speech patterns. Explain topics as if you're talking to someone in conversation. 5) Topical Authority Building • Cover each topic fully to position your site as the best resource in that niche. • Avoid shallow posts. Go deep. Expand on how-tos, FAQs, comparisons, pros/cons. • Build out each silo based on topic size and search demand. • Revisit old posts monthly. Merge duplicates. Expand thin content. • Use internal links to connect related articles within the same silo. 6) Link Building That Complements • Don’t build links to garbage AI content. Clean it up first. • Focus on niche-relevant guest posts, citations, and digital PR. • Use branded anchors primarily. Sprinkle in partial matches where it makes sense. • Internal links do 80% of the work early on. Don’t ignore them. 7) Content Maintenance Between Core Updates • Track rankings in GSC or Ahrefs weekly. Flag drops and check affected pages. • Add new internal links when publishing fresh content. • Update old pages with new data, media, and search queries from GSC. • Remove deadweight content that doesn’t rank or convert.

I had an “aha” moment about the future of SEO when using Microsoft 365 Copilot Search* to find a document. My search for “Q4 marketing performance vs budget” surfaced the budget presentation, an email thread about timeline changes, and a document summarizing our quarter’s campaign results. It understood I wanted the complete context behind my query, not just a keyword match. It inferred I was looking for the synthesis between marketing spend and business outcomes. This gives us a direct view into where consumer search is heading. SEO (or AEO, or GEO) strategies already focus on semantic search and user intent, but most teams still optimize individual blog posts and pages rather than building knowledge ecosystems. That approach worked when search engines were sophisticated filing systems. It falls apart when they become reasoning machines. Copilot’s system connects and interprets relationships across organizational content to understand context and deliver comprehensive answers. When Google and Bing’s consumer AI features catch up, your prospect searching “how to reduce customer acquisition costs” might discover your retention strategy content, but only if you’ve built the right conceptual bridges between those ideas. It's long been time for your content strategy to evolve beyond keywords too. Now your content strategy needs to answer, “How do our ideas connect to solve interconnected problems?” Rather than optimizing individual pieces, focus on building comprehensive topic clusters where subtopics link back to a primary expertise area. This positions you as a holistic authority when AI systems look for complete answers. If you have access to M365 Copilot Search, try searching your company’s knowledge base to see which content gets surfaced together. These connections could help reveal how AI systems understand topic relationships, which can provide insights you can apply to your external content strategy. The shift from keyword optimization to intent architecture is happening fast. *Unlike the regular Microsoft Copilot that searches the web, this is the enterprise version that works inside organizations, crawling emails, documents, and internal data. #AIMarketing #AEO #SEO #ContentStrategy

AI from a CMO’s perspective ⭐️ I’ve been using Gen AI in marketing for almost three years. 👩💻 At first, it was basic—summarising reports, copywriting, research and financials (always double-checking for hallucinations). Interestingly, most of AI’s highest-value use cases today are tactical—focused on improving media performance, customer service, and some pricing. I think we are in the Slope of Enlightenment phase, though - so the next stage, where agents become useful, is where it gets more interesting. 🚀 High-Value AI Use Cases ✅ Performance Marketing Optimization – bidding, targeting, and creative testing. ✅ SEO & Content Strategy – keyword clustering and topic ideation. ✅ Email Personalization – Predictive send times, dynamic content, and segmentation. ✅ Predictive Analytics & Customer Insights – churn, LTV, and behaviour forecasting. ✅ Conversational AI & Chatbots – customer service. ✅ Marketing Automation – lead nurturing and campaign optimisation. ✅ Social Listening & Sentiment Analysis – brand perception tracking and competitor insights. ✅ Creative Asset Generation – copy, images, and ad variations. ✅ AI-Powered Media Buying – Autonomous budget allocation based on performance data. ✅ Pricing & Promotion Optimization – dynamic pricing and offer personalization. ⚖️ Medium-Impact AI This is the "in development" zone—where AI is making progress, models aren’t quite there yet, and human expertise is essential. This is where I am learning how to program LLMs more to get the output I want to see from a creative and critical-thinking perspective. 🔸 Brand Strategy Development – insights; strategy is still human-led. 🔸 Creative Concepting & Ideation – AI suggests ideas but lacks true creative intuition. 🔸 Influencer Marketing & Partnerships – AI finds influencers, but human vetting is essential. 🔸 Public Relations & Reputation Management – AI monitors sentiment, but can’t manage crises. 🔸 Customer Journey Mapping – AI identifies patterns, but brands define the experience. 🔸 A/B Testing & Experimentation – AI runs tests, but humans decide what to test and why. 🔸 AI-Powered Video Editing – AI automates some editing, but storytelling remains human. 🔸 Speech & Audio Generation – AI-generated voices are improving, but tone and nuance still need human refinement. 🔸 Basic Copywriting – AI drafts content, but brand voice, depth, and storytelling need a human touch. 🛠 Low-Impact AI These areas are already well-served by existing tools or offer AI alternatives. That said, they still save time—and time is money. ⚡ Meeting Transcription & Summarization – Speeds up workflow ⚡ Stock Image & Background Removal – Convenient, but not a differentiator. ⚡ Automated Social Scheduling – AI optimizes timing, but engagement still requires a strong strategy. ⚡ Simple Graphic Design (Canva, AI Tools) – Good for quick assets, but lacks originality. ⚡ Basic Reporting & Dashboard Creation – AI automates, but interpretation is still key.

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? 

Users aren't clicking anymore: NN/g released a research showing how AI overviews steal 40% of website clicks. People who see AI summaries rarely visit the original source. Your beautifully designed landing pages are starting to be bypassed entirely. Research reveals that: Search habits formed over decades are changing in months AI overviews answer questions without clicks Even AI beginners get hooked after one good experience Traditional search + AI chat work in tandem now Familiarity drives tool choice (ChatGPT, Gemini win) Some teams are already adapting their content architecture – instead of optimizing for clicks, they're optimizing for AI discovery. How to make the shift: Structure content for AI parsing: ⇢ Write in clear question-answer formats ⇢ Use semantic headings (H1, H2, H3) religiously ⇢ Add schema markup for better context ⇢ Create FAQ sections that directly answer user queries ⇢ Break complex concepts into digestible chunks Create conversation-friendly formats: ⇢ Write like you're explaining to a friend ⇢ Use active voice and simple sentences ⇢ Include examples and analogies ⇢ Structure as "If this, then that" logic ⇢ Add comparison tables and step-by-step processes Design for hybrid search behaviors: ⇢ Create content hubs that answer related questions in one place ⇢ Build internal linking that mirrors user thought patterns ⇢ Design for snippet optimization (lists, bullets, numbered steps) ⇢ Add contextual definitions for technical terms ⇢ Create multiple entry points for the same information Advanced moves: ⇢ Test your content in ChatGPT/Claude - does it surface correctly? ⇢ Monitor which snippets get pulled into AI overviews ⇢ Create content specifically for AI training (comprehensive, authoritative) ⇢ Build semantic content clusters around user jobs-to-be-done The companies that figure this out first will dominate discoverability in the AI age. The rest will watch their organic traffic disappear. Your move. P.S. Screenshot this for your next content strategy session

Every month, we publish 100+ content pieces for ourselves and our clients. The most common question I get is: "Do you use AI to create your content?" → Yes and no. We combine AI workflows with humans in the loop. Here's the breakdown: 1. Content Ideation: A human researches multiple sources to find content ideas: - Favikon for LinkedIn top performing posts - Sybill for questions & objections in sales calls  - beehiiv for newsletters in that niche - Sandcastles AI for trending short form content 2. Content Drafting: We don't write posts with AI, but we do use: - Perplexity to find supporting research points - MagicPost to optimize formatting - Claude to fix grammar mistakes 3. Content Development: There are 3 key teams involved: - Content writers that manually write the posts - Designers that create infographics & carousels in Figma - Video editors that edit our videos with Adobe Premiere 4. Content Management: Notion is our second brain. We use it to: - Create content calendars - Keep track of content pipeline - Collaborate with our design & editing team 5. Distribution We publish content on: - LinkedIn: always manually  - Email newsletter: through beehiiv (launching this Friday!) - Blogs: using Webflow's CMS - YouTube: through Buffer (check out Dan's channel) From there, we: - Analyze what performed - Double down on what resonated - Repurpose our best content pieces in different formats If anyone is telling you that AI alone can do your content... I promise you that's not the case. AI + Humans is the only way for the time being.

How I actually use AI for content development (hint: it is not what you think). Most people treat AI as a shortcut. The best results come when you treat it as a collaborator. Here is how you can use it in your own workflow: 1/ Start with real-world proof Open SayWhat and scan what is performing on LinkedIn. Not to copy. To study tone, rhythm, and what sparks engagement. 2/ Anchor to your brand pillars Search by keywords tied to your expertise. This keeps you focused on credibility, not chasing every trend. 3/ Co-create with ChatGPT Build a marketing-specific GPT trained on your voice and past work. Use it to shape ideas into posts that sound like you. 4/ Cross-check with Claude Run the draft through Claude for another perspective. It sharpens the message without replacing your perspective. 5/ Bring the story to life visually Sketch ideas in Illustrator, Canva, or MidJourney. A simple visual can make the message stick. This kind of workflow does not happen overnight. It takes testing, refining, and patience until the process feels natural. AI is not a shortcut. It is your collaborator when you stay in control. ♻️ Share this to inspire stronger client connections 🚀 Follow Michelle Anne Vaira for creative strategy and leadership insights

It’s easy to criticize AI-generated content, saying it’s “not good enough,” but here’s the truth: your audience doesn't care as much as you might think. In fact, most of them are just fine with it. In a recent survey I ran of 250+ B2B buyers, 58% said they like AI-generated content. Only 13% dislike it, and the remaining 29% are neutral. Why the indifference? It could be because 64% of buyers admit they find it hard to tell the difference between AI and human-created content. 62% also say AI content doesn’t affect their trust in a brand. Only 38% lose trust when they know the content is AI-generated. So, what does this mean for your content strategy? 💡 AI-generated content can streamline your process, but the key is balancing efficiency with quality. While buyers may not care HOW content is created, they DO care about its value and relevance. The takeaway? It’s fine to use AI in content creation, but always combine it with human expertise. AI can help with drafting and ideation, but human insights—like survey data, interviews, and expert analysis—are what keep your content authoritative and engaging. And of course, make sure a human refines the final product. This balanced approach allows you to create content efficiently while building trust and credibility with your audience. Want more insights into what B2B buyers are looking for? Check out our report, The 2024-2025 Blueprint for High-Impact Content: https://lnkd.in/gkhaybrq #ContentMarketing #AI #B2BMarketing #ThoughtLeadership #ContentStrategy #AIgeneratedContent

AI is building a mental map of your industry. Your content defines where your brand shows up on that map. Time to update your content strategy for AI search… Imagine AI search engines (e.g. ChatGPT) as a new librarian gets hired to manage the world's largest library. They start their new job with 3 days of training. Their training? Read every book and periodical in the catalog, then help visitors find exactly what they need. Day 1 - Training: Our librarian quickly learned to avoid certain "publishers": Books with big promises but only 3 pages of useful content Authors who contradicted themselves across different publications Promotional pamphlets disguised as educational guides Day 2 - Finding the Good Stuff: Our librarian started gravitating toward publishers who: - Created comprehensive content that demonstrates first-hand experience with the topic - Updated their work regularly while keeping core insights timeless - Backed up every claim with data and expert sources Day 3 - Pattern Recognition: Our librarian started noticing patterns: "Patagonia always appears near 'purpose-driven business'" "Cisco Systems consistently shows up in cybersecurity discussions" "Asana is often mentioned in content about project management” Our librarian wasn't just reading the content of the books and periodicals in the library. They are learning about the concepts and brands mentioned in the content they are consuming. Generative AI search engines are the librarian. They're training on your content right now, deciding which websites, pages and passages they will trust and list as source links. Question: When AI answers your customers' questions, will your content be confidently recommended by the AI librarian? Audit your content strategy. Are you feeding the AI librarian the relevant, comprehensive, timely content they need to see your brand as an authoritative source? What patterns do you want AI to learn about your brand?

I was wrong about AI. I was reluctant at first. I worried it would make content generic. That using it was somehow "cheating." Then I started using AI as a thinking partner, not to replace my work, but to accelerate strategic thinking. It felt a little scary the first time. Like I was outsourcing my job. But then I realized: I wasn't outsourcing my judgment. I was just getting to the strategy faster. Last week, I needed to help cross-functional partners understand the difference between product content and marketing content. I asked AI to help me create a framework. Within seconds, I had a structure with examples and a comparison chart (something that would have taken me hours to build from scratch). Here's the important part: I refined it. Added context specific to our team. Made judgment calls the AI couldn't make. The AI gave me velocity, I brought the strategy. I built a content system, our principles, patterns, and decision frameworks, and saved it where the AI could reference it. Now when I ask for help, it doesn't give me generic marketing fluff. It applies our actual standards. It knows we don't use certain terms in product. It knows when to be concrete vs. aspirational. It knows our voice. The AI became useful when I stopped asking it to think for me and started using it to think with me. The future isn't AI writing content for us. It's AI that understands our content principles well enough to execute them consistently, freeing us to work at a strategic level. That's not replacement. That's elevation. The craft still matters. Finding the exact right words still matters. But now I can spend my time on the strategic work (the systems, the principles, the cross-functional alignment) instead of iterating on individual strings in isolation. How are you using AI in your content design work? I'm curious what's working (and what's not) for others.