During a recent presentation, I was thrilled to learn about Microsoft’s latest tease regarding new AI reporting features in Bing Webmaster Tools. These updates aim to enhance the existing AI performance reports, offering fascinating insights into citation share, query intent grounding, and GEO-focused recommendations.
I stumbled upon shared screenshots from this intriguing presentation delivered by Krishna Madhavan at SEO Week in the bustling city of New York. Azeem Ahmad captured the essence of this moment, highlighting the growing transparency gap between Bing and Google.
Intriguing Details: The presentation shared several slides showcasing these promising new features. One can feel the excitement building within the SEO community as these innovations hint at a more insightful way to track AI interactions.
Bing Webmaster Tools just dropped some VERY COOL stuff at #SEOWeek 2026
Stay Tuned: While these features aren’t live just yet, catching a glimpse of them was very promising. It seems Microsoft is ramping up to offer more ways to navigate AI-driven search results.
Why This Matters: Gaining more transparency on how our content performs in AI search results is invaluable. I eagerly anticipate the day when these tools go live, promising greater clarity and control over AI interactions.
At the moment, details on the exact functionality and release timeline remain vague. I will certainly keep my eyes peeled for further updates to better understand their full potential.
In today’s digital landscape, I’ve noticed that paid search platforms are evolving to prioritize who sees my ads, often without depending solely on my chosen keywords.
This shift means I need to focus on optimization strategies beyond just keywords, such as leveraging audience data, enhancing landing page context, and understanding conversion behaviors. Recognizing this shift is crucial for me to know where to focus my efforts now.
A decade ago, keywords gave me a sense of control. Back then, hypersegmentation and single keyword ad groups were the norm.
We’d meticulously create unique landing pages for each keyword in every ad group, reveling in the manual process, convinced that we controlled the machine.
Times have changed, and the forecast of Google and Microsoft phasing out keywords feels more real than ever.
With tools like Performance Max and emerging AI Max solutions, along with contextual LLM-driven searches such as ChatGPT, I see the industry leaning towards a keywordless future.
Still, keywords remain vital as they reveal user intent and indicate where users stand in their journey:
If these signals are now managed behind a black box, my role as a marketer is evolving. So, what am I optimizing for?
Intent is now inferred from a web of signals, relegating individual keywords to the background. My optimization focus should now be on three main pillars in 2026.
Google now emphasizes customer match and first-party data over mere queries. With Data Manager API integration, it identifies users in auctions matching my key deals.
No longer do I bid on “cloud security.” Instead, I target IT directors (sharing first-party data) investigating SOC 2 compliance, even if they search for something vague like “scaling infrastructure.”
B2B match rates can be challenging, but this is where I must innovate my strategy, broadening one-to-one list matching and collaborating with integration partners.
Clustering individuals by shared pain points and offering on-site experiences help me understand their verified intent before reaching the remarketing list.
My landing page serves as a vital data source. Google’s AI examines it to grasp the nuances of my offerings, making creative assets crucial signals that align with my target themes and keywords.
If my landing page effectively communicates “mid-market manufacturing,” AI identifies relevant users regardless of specific keyword use, transforming my “keyword strategy” into a content strategy.
Opting for a creative approach similar to Meta’s, where Andromeda elevates the creative as a primary targeting signal, is beneficial. These creative inputs define my audience, demanding a balance between creative and technical input.
Journey-aware bidding and value-based bidding mean algorithms now analyze a user’s journey beyond the final click.
Optimization now targets “high-value need states,” feeding the system data about mid-funnel behaviors that result in significant contracts.
The most profound change for digital marketers, including myself, is shifting focus from query-level to user-level intent.
While the previously ignored query “how to manage payroll” might not have targeted enterprise SaaS companies, AI now understands if that user is a financial VP at a large firm, indicating commercial intent.
If it’s the right user, the right signals should prompt AI to act on their purchasing stage.
As AI handles matching, my role shifts towards becoming a data architect.
Data quality determines my success. I must feed AI with valuable leads to optimize for value-based bidding effectively.
Assessing the health of my signal, from landing pages optimized for AI readability to correct technical content, ensures Google accurately targets my audience.
I now focus less on micromanaging search terms and more on managing brand exclusions and negative themes.
The future of search is about being the best solution for the right individual at their evolving need state.
Keywords served as training wheels, but it’s time to see how quickly my data can propel me forward.
I’ve noticed that AI systems are improving in generating Spanish language content, but they’re not quite grasping the nuances of Spanish markets.
In fact, we often see a familiar trend: over 20 Spanish-speaking nations reduced to a single standard. Spain is typically the default, and Mexico might as well be interchangeable with any other country. The rest get simplified into statistical norms.
The root of this problem is structural, involving dialect defaulting, format contamination, and regulatory hallucination. These issues are more pronounced in a generative search setup where one synthesized response replaces several search results.
This misinterpretation acts as a barrier to visibility. Generative AI seeks clarity, and if my content doesn’t specify its market context, it defaults to an average—leading to missed opportunities and misapplication.
To tackle this, I’ve developed a framework that ensures market context is clear across content, technical indicators, and retrieval systems, so AI systems don’t have to assume.
What is Cultural SEO?
Cultural SEO goes beyond mere multilingual support or localization. Its foundation is firm on locale precision—ensuring the market context is clear in retrieval and generation practices so that your Spanish content is associated with the specific country it was intended for.
Here’s a framework that proves effective when working around Spanish and Latin American markets.
You can’t effectively optimize for a market you aren’t serving. Cultural SEO isn’t an afterthought; it’s the backbone of a strategic decision to genuinely operate within a market, encompassing logistics, customer service, compliance, and product-market alignment.
If you ship from Spain to Mexico with unrealistic delivery times or lack local support, even the best hreflang configuration won’t suffice. Users will abandon such experiences, and as AI learns from these interactions, it will deprioritize similar content.
Speaking the market’s language goes beyond spoken words—it’s about conveying trust, ensuring payment and delivery expectations are met, and adhering to regulatory standards.
Assuming you’re committed to these standards, here are the four pillars: segmentation, transcreation, retrieval constraints, and entity reinforcement. Before applying any framework, ensure this commitment.
Pillar 1: Market Segmentation at the Entity Level
International SEO often considers segmentation as a mere folder structure: /es-es/, /es-mx/, /es-ar/, but that’s merely scratching the surface.
In generative search, the challenge is ensuring the AI associates a page with a specific country like Mexico, and accumulates enough market-specific signals to prefer it over a general alternative. If the architecture simplifies differences, visibility diminishes equally.
Pillar 2: Transcreation, Not Just Translation
Translation is about converting words, while transcreation is about interpreting meaning. Given two pages with 95% similar content, the AI merges them into one representation—defaulting to one perceived as standard. Therefore, differentiating with local examples or unique terminologies is essential.
Pillar 3: Retrieval Constraints
In constructing AI experiences like RAG (Retrieval-Augmented Generation), it’s crucial to establish clear boundaries about what content should be sourced for specific markets to avoid defaulting to “Global Spanish.”
Pillar 4: Market Authority Through Entity Reinforcement
AI models learn from both your site’s content and external perceptions. Thus, building location-specific authority through local media presence, partnerships, and consistent regional knowledge graph reinforcement is vital to establish market-specific authority.
Ultimately, Cultural SEO ensures that content not only serves the market but resonates with it. By embracing these pillars, I can ensure my brand isn’t just another “Spanish” entity but a recognized authority in each targeted market.
This journey isn’t about merely adapting your website but architecting systems to reflexively consider the market’s dynamics from the ground up.
When I think about the last time I got hooked on those true crime documentaries, I remember how my streaming app seemed to know exactly what to suggest next. Suddenly, investigative series filled my homepage, and I even got alerts for new releases. The marketing was flawless, and I never saw the behind-the-scenes magic that made it happen—I just dove into the next compelling story.
This is the expectation now. A recent Adobe report reveals that 71% of consumers desire personalized deals and content, with 78% expecting a seamless experience across different channels. Surprisingly, fewer than half of brands meet these expectations consistently.
The root problem lies in the structure of customer data. When it’s scattered across various systems, it becomes difficult for teams to sync insights, timing, and execution effectively. AI cannot magically fix these issues alone. As per the Adobe 2026 report, only a minority of organizations have a data foundation robust enough to support AI at scale.
Starting on the path to modernize and personalize marketing efforts can seem overwhelming. However, by laying a strong foundation for a unified customer experience, progress becomes achievable.
Most brands have ample data, yet it often lacks coherence. If your marketing efforts span across email, web, mobile, paid media, support, and in-person channels, it’s crucial these signals communicate swiftly to shape the next customer interaction.
If alignment isn’t there, the consequences are immediate. Imagine a customer browsing a product online but receiving a different price via email, or having to repeatedly explain their issue to customer support. These inconsistencies slowly erode the trust you’ve built.
Delivering a cohesive customer experience means continuously updating the understanding of the customer and sharing this insight across all teams and touchpoints without delay.
To make this happen, here are a few critical steps:
A unified customer experience begins with a consolidated and dynamic customer profile. Rather than maintaining separate records per channel, build a real-time profile that captures behavior, preferences, and interactions throughout all departments.
With this comprehensive data, customer segmentation becomes more insightful, and messaging more relevant. Customers will no longer face conflicting or redundant communication.
Enhance your data by linking insights directly to actions quickly. For instance, if a customer leaves a cart abandoned, a subtle follow-up can kindle action without delay. Engage with real-time product recommendations and remove offers that no longer resonate.
Real-time relevance is crucial. Our eyes interpret digital ads in under 400 milliseconds, meaning interaction timing is everything. If your systems don’t react swiftly, you miss valuable chances to connect.
AI accelerates these interactions at scale, discerning patterns, predicting intent, and suggesting best actions within milliseconds. Accurate and unified data is essential for AI to function effectively.
In this age of rising privacy standards, protecting customer data is paramount. As more signals are unified and activated in real time, it’s crucial to integrate governance from the ground up.
To maintain a unified experience at scale, companies need a modern cloud foundation to process and activate data effectively, ensuring swift response times, minimal data movement, and stronger security.
Personalization becomes second nature when brands anticipate not just the right message, but the right moment. Unified data, activated in real time with secure infrastructure, elevates personalization from trial-based to operational, making relevance repeatable.
Adobe Experience Platform, powered by AWS, integrates these components, easing execution for your teams. It creates real-time customer profiles that support segmentation and journey orchestration across touchpoints, leveraging AWS’s scalable infrastructure.
Explore our eBook, Capturing Attention in the Age of AI, to discover how Adobe and AWS provide marketers with a complete customer view that optimizes personalization and enhances customer value.
I’m excited to introduce you to the innovative iteration nodes in Profound Agents, designed to revolutionize the way we manage complex workflows.
The beauty of the iteration node lies in its ability to encapsulate a series of steps within your Agent. By setting up these steps just once, I can easily pass in a list of items, and watch as each item seamlessly progresses through the specified sequence, simultaneously.
I’ve recently experienced frustrations with Google Ads as there’s a known issue causing Demand Gen ads to face review delays of over a week. Google acknowledges this problem and assures us that they’re working on a solution.
Some of us advertising on Google have noticed our ads are lingering in review, taking more than seven days—something that deviates from normal review timelines.
What’s happening. Matthew Skelton, a senior PPC specialist I follow, has pointed out a trending issue: Demand Gen campaigns stuck in review for an unexpectedly long time. This delay is noticeable across various accounts and industries, seemingly without any policy breaches causing it.
Interestingly, other campaign types, like Search and Performance Max, aren’t affected and continue processing as usual, which suggests the problem is isolated to Demand Gen ads.
Why we care. For those of us using Demand Gen to test creatives and drive top-of-funnel results, speed is crucial. Long review times hinder our ability to iterate swiftly, delay launches, and make it challenging to respond to seasonal trends or time-sensitive opportunities.
A delay lasting a week can disrupt our pacing and diminish the effectiveness of campaigns relying on rapid optimization.
The response. Ginny Marvin, a Google Ads Liaison, acknowledged this issue specifically impacting Demand Gen image ads, admitting reviews are taking longer than anticipated. She assured us that Google’s team is actively seeking a solution, but no clear timeline has been provided yet.
Bottom line. If you’re experiencing delays with your Demand Gen ads, know that it’s a widespread issue acknowledged by Google rather than something you can directly address.
First seen. This situation was first reported by Matthew Skelton, who shared his insights on LinkedIn.
I’ve been following the shift in Google’s AI Overviews, and it’s exciting to see the organic click-through rate on these searches finally on the rise. After a year-long slump, the CTR is showing promising signs of recovery. But could this mean the end of click losses?
Back in December 2025, the CTR had hit a low of 1.3%, but by February 2026, it had climbed to 2.4%. That’s an impressive 85% jump in just two months, according to the latest data from Seer Interactive.
Understanding CTR Movement. When AI Overviews are part of a search, pages that are cited see a significant increase in clicks compared to pages that aren’t cited, yet they still garner fewer clicks than searches without any AI Overviews.
Here’s a breakdown of the CTR percentages:
No AI Overview: ~3.3% CTR
AI Overview with citation: ~2.1% CTR
AI Overview without citation: ~0.9% CTR
Where are the clicks going?. Interestingly, searches that don’t include AI Overviews are seeing an increase in value. Their CTR rose from 2.8% at the start of 2025 to 3.8% by February 2026.
One factor: AI Overviews are handling quick answers, leaving users with more complex questions to search deeper.
AI Overviews Depend on Query Intent. The presence of AI Overviews varies greatly depending on the type of query:
Informational: ~36% feature AIOs
Transactional: ~5%
Comparison: ~95%
Question: ~86%
A nuanced perspective. It’s important to note that a lower CTR doesn’t always equate to poor results. In instances where clicks remained stable but impressions grew, brands may have appeared more frequently in AI Overviews even as CTR percentages dropped.
The stability of paid search. I noticed that when Google presents an AI Overview, the paid CTR increases slightly from 14.6% to 16.2%. Without AI Overviews, the CTR drops from 26% to 21.8%.
Why this matters. Google’s AI Overviews are not just reducing overall clicks; they’re shifting them. This means you need to aim for your site being cited in AI Overviews and focus on queries where users are more likely to click.
About the Research. Seer analyzed data from 53 brands, 5.47 million queries, and 2.43 billion impressions between January 2025 and February 2026.
I recently sat down with Anuj Srivastava to explore the synergy between engineering and marketing when launching a new franchise.
At First Page Sage, I’ve witnessed countless companies pour millions into fleeting algorithm tricks, only to see them crumble overnight. Genuine authority— the type that withstands every Google update and earns citations from ChatGPT—requires true engineering, not quick hacks.
This belief led me to Scott Hietpas, CEO of Computype, a leader in creating the most resilient labels that adhere to any surface and thrive in any environment. While my team focuses on digital permanence, Scott’s team excels in physical identification systems. We’re both tackling the same challenge: ensuring vital information endures when other solutions fall short.
Scott and his company label blood products across North America’s blood supply chain, and odds are, your car tires are marked with their labels too. Their products can withstand temperatures ranging from -196°C to 204°C. If you’re curious why ‘built to last’ isn’t just a slogan but a powerful competitive advantage, read on.
First Page Sage: Many firms promise durability. Why do cheap labels fail, and what are the hidden costs?
Scott Hietpas: Cheap labels fail because they’re not crafted to endure harsh conditions. Adhesives might not suit cold storage, substrates may crack under high heat, and barcodes can fade and become unreadable. A lab might save a cent per label and feel smart, but then spend $200,000 re-labeling specimens after cold storage failures. Similarly, a pharmaceutical company might lose FDA compliance when commodity labels render codes unreadable, halting production. We engineer labels that adhere to any surface—be it glass, silicone, or textured metals—and perform in diverse environments. The price for failing is always catastrophic. Paying a little more for durability is a small price compared to the colossal cost of failure. We assist our clients in assessing their total expenses and minimizing risks.
First Page Sage: What does it take to engineer for extreme temperatures from -196°C to 204°C?
Hietpas: It involves material science that most labeling companies find too intricate. Cryogenic tasks like biobanking need adhesives that don’t crystallize and substrates that don’t shatter when frozen. High-heat needs in tire manufacturing demand polyimide films that retain integrity under thermal stress. Blood services choose our labels for freeze-thaw cycles and international cold-chain transport. Tire producers rely on us for labels that survive vulcanization at 400°F and stay readable throughout the tire’s lifetime. Standard labels fail under these conditions, and our capability to withstand them is why we confidently say our labels perform universally.
First Page Sage: You dominate the global tire bead and healthcare label market. How did Computype become the go-to for critical industries?
Hietpas: It’s our zero tolerance for failure. If a tire maker’s ID system collapses, defect rates spike and costs soar. If blood labels fail, blood shortages and steep replacement expenses follow. These sectors can’t accept ‘just okay’ solutions. Our labels are engineered for permanence, earning trust through undeniable, long-term performance. Millions of our tire and blood bag labels are scanned during production to ensure functionality before leaving our facility. While competitors sell labels, we offer solutions that outlive the products they identify, solving critical problems.
First Page Sage: How does “stick to any surface” work when dealing with challenging surfaces?
Hietpas: Our labels are tailored for specific uses. Medical silicone needs different bonding agents compared to powder-coated steel. Curved glass requires different flow traits than textured surfaces. Instead of universal adhesives, we create custom solutions for demanding surfaces that don’t respond to generic labels. Our engineering understands adhesive-substrate interactions, optimizing for permanent bonding even under stress. When we claim our labels stick to any surface, it’s because we’ve addressed adhesion issues for difficult materials. Our expertise means we offer ready-to-use or customizable solutions that quickly meet our clients’ challenges.
First Page Sage: How do durable labeling and lasting digital authority align?
Hietpas: Both demand thorough knowledge and application understanding. Inexpensive labels may save costs now but lead to eventual disasters. Similarly, black-hat SEO might provide short-lived success but ultimately ruins your rankings. True durability, both physical and digital, entails designing systems for worst-case scenarios—environmental extremes for us, algorithmic turmoil for you. Companies eyeing short savings or growth hacks often lose to those engineering for durability. For over 50 years, our labels have outlasted the competition. Likewise, First Page Sage excels because your authority strategies outlast algorithm changes. Build lasting solutions, or continually rebuild.
Labels that triumph when all else fails. Explore Computype.com for systems designed for extremes—since in critical applications, there’s no second place.
About a year ago, I found myself walking out of a meeting with engineers focused on enhancing automations for content briefs. Just days after that encounter, someone from the analytics team — who hadn’t even been part of those conversations — surprised me with a tool they’d developed. This tool could generate content briefs using various data pipelines and APIs.
That moment was a revelation for me. Encouraging people to adopt AI isn’t the real challenge; it’s the actual implementation and seamless integration that pose difficulties.
I frequently observe that most SEO teams, including mine, aren’t short on tools. What we struggle with is prioritizing high-impact efforts and achieving alignment within the organization.
In our team, one group might experiment with prompts while another auto-generates briefs, and yet another constructs dashboards no one requested — often resulting in us overlapping each other’s work. Each team contributes something valuable, but duplication tends to dilute the efforts, and everyone races toward execution.
Leadership demands speed; legal teams push for caution; developers need clarity.
The result is often fragmentation, which is not the transformation AI marketing teams require. For AI to have a significant impact on SEO performance, it must be well-structured before scaling; otherwise, this fragmentation only grows.
Through my experience working with large, complex organizations transitioning in this space, I have identified three frameworks that consistently prevent chaos and create momentum. When applied together, they help us align our vision, clarify what we automate, and transition prioritization into execution.
The biggest barrier to adopting AI is coordination. SEO already resides at the crossroads of engineering, content, analytics, products, and branding. With the inclusion of AI and the emergence of social search, we now have to factor in organic social, conversion rate optimization, affiliates, and creativity.
AI spans all these areas, but it’s too extensive for any single person or team. Without a shared mental model, teams tend to drift apart, duplication seeps in, and accountability becomes vague, transforming AI into a race rather than a productivity enhancer.
In leading large teams and collaborating with numerous Fortune 100 executives, I learned how analogies help teams grasp complex ideas quickly. Research supports that analogies improve understanding and the transmission of ideas across different domains. When teams map new concepts onto familiar structures, alignment accelerates.
Introducing: the AI SEO City. Instead of describing AI as a series of tools and experiments, envision your SEO ecosystem as a bustling city.
Think of your website as an SEO house that no longer operates in isolation. Technical SEO creates the foundation. Content hubs define the interior. Off-site SEO offers the curb appeal. User experience provides the staging.
With AI search, this house is now more integrated with a broader city. Platforms like TikTok, Reddit, YouTube, and Amazon shape the responses AI systems deliver.
To thrive in AI search, this city requires a strong planner to advocate for budgets, plan future steps, and maintain effective strategies. Here, the SEO team acts as the planner, while other teams build and manage their respective “buildings.”
The transition from analogy to action centers on ownership. Every major platform becomes a building.
Each of these buildings has a leader, performance indicators linked to business outcomes, AI-enhanced workflows, and a roadmap, making AI projects tangible, accountable, and coordinated.
After aligning our vision, many teams make the mistake of trying to automate everything. This indiscriminate automation creates fragility.
If your go-to person for automation leaves, you risk losing both business processes and valued work. That’s why I use the SOAR framework to navigate smart adoption.
To truly integrate AI, streamlining the basics is crucial. Having robust, standardized processes before incorporating AI can significantly enhance its effectiveness. According to McKinsey’s 2023 State of AI report, organizations that have already digitized and standardized core workflows gain the most from AI.
In my own experience, the easiest and most valuable automations accelerate predefined manual processes. Therefore, my team’s policy has always been to engage in manual tasks before attempting automation.
AI adoption necessitates cross-functional collaboration, making it essential for SEOs to orchestrate teams efficiently across the organization. Revisiting AI SEO City ownership insights can help clarify review processes, QA ownership, and publishing governance.
Establishing regular checkpoints, such as weekly SEO syncs with diverse teams, monthly performance reviews, and quarterly roadmap alignments, encourages consistency and diminishes resistance.
AI has the potential to save people approximately four hours a week, which equates to about 200 hours a year — roughly five weeks.
It’s crucial to utilize AI for tasks like metadata drafting, monthly report insights, FAQ expansion, internal linking suggestions, keyword clustering, and SERP analysis, thus freeing time for executing high-impact tasks.
AI implementation should eventually free up strategists to coordinate across teams, bridge the gap between strategy and business impact, map out enhanced customer search journeys, and anticipate AI search trends.
Google has announced billions of monthly AI Overview users, which has fundamentally altered how queries are presented. Now is not the time to manually write metadata; instead, it’s time to build your AI SEO City.
Even with smart automation and alignment, the chaos resurfaces when prioritization becomes lax. RISE helps pressure-test whether an initiative deserves investment by focusing on reach, intent, scale, and execution.
The RISE framework helps me assess whether an initiative truly warrants resources.
Reach requires you to quantify potential upsides before building anything. You must move beyond gut feelings or trending topics to focus on modeled opportunities based on specific questions.
If positive business impact isn’t numerically clear, it shouldn’t proceed. This approach discourages vanity projects mistakenly labeled as innovative developments and focuses on your leadership and strategic instincts instead of mere tinkering.
Intent drove AI search systems to reward depth over generic content. You need to be able to ask the right questions to ensure each strategy serves the correct purpose.
Scale involves verifying whether an idea can become part of the operating system without repeated effort. In AI-driven SEO, scale is about creating structural efficiencies.
Finally, embedding strategic initiatives into workflows where work actually happens transforms great ideas into real results. Defining acceptance criteria and assigning ownership are crucial steps towards successful execution.
By rigorously applying the RISE framework, the number of AI ideas may decrease, but the quality improves exponentially. Instead of debating which tool is better, the conversation shifts to identifying the right opportunities.
Ultimately, structure matters more than speed when integrating AI into SEO strategies. The winning teams won’t be those generating the most content through AI, but those constructing the strongest systems.
I often find myself overwhelmed by repetitive SEO tasks that eat up valuable time. That’s why I’ve started identifying tasks that can be automated, allowing me to concentrate on strategy, quality assurance, and crucial decision-making.
While tasks like note-taking and setting team reminders are obviously automatable, I’ve discovered that content audits, page outlines, and keyword research can also benefit from automation.
I recommend beginning with basic strategies that help save time on daily repetitive work before diving into more advanced AI tools for automation. It’s essential to conduct a final check personally, as relying solely on AI can sometimes lead to less-than-perfect outcomes.
One way I assess which tasks to automate is by asking myself: Would I assign this task to an intern? Tasks suitable for new employees are often ideal for automation. Whether it’s research or drafting, I let AI handle 70% of it, then I fine-tune the remaining 30% myself.
Some tasks that I’ve found can be automated include data analysis, ensuring best practices are used in updates, creating detailed SEO reports, identifying content gaps, scaling SEO-optimized templates, building editorial calendars, and documenting prompts and standards.
To discover more automation opportunities, I audit existing workflows, review onboarding processes, gather team input on disliked tasks, and explore AI capabilities.
However, automation won’t fix every issue. Core challenges like broken systems, incomplete assets, and a lack of resources still need human intervention.
For instance, I recently automated my team’s content calendar. Using Excel formulas, I quickly identify which content needs updating. By integrating a performance audit with custom AI tools, I can streamline these updates even further.
Similarly, for keyword research, I employ AI to sift through data and generate relevant keywords, saving me valuable time.
For internal linking, tools like Ahrefs can automate the identification of pages that require more links, enhancing site crawling efficiency without manual labor.
By automating outlines and briefs, I ensure consistency and quality across my team’s work, streamlining communication and reducing redundant effort.
On the brand compliance front, custom AI tools help me catch simple errors in high-risk drafts, ensuring they adhere to brand standards before final review.
Manual data validation can be a painstaking process, but with automation, I’m able to swiftly identify and address anomalies in reports, enhancing accuracy.
When it comes to metadata and schema, automating these tasks minimizes errors and ensures that content is optimized for search engines.
Finally, for formatting and shortcoding, I use Excel functions to concatenate code, vastly speeding up what used to be a time-intensive process.
To make automation truly beneficial, it’s critical it complements, rather than complicates, the workflow. Using custom AI solutions allows my team to focus on more impactful, strategic tasks.
