Generative Engine Optimization (GEO) is making organizations scramble — our clients have been asking “Are we ready for the new ways LLMs crawl, index, and return content to users? Does our site support evolving GEO best practices? What can we do to boost results and citations?”  

Large language models (LLMs) and the services that power AI summaries don’t “think” like humans but they do perform similar actions. They seek content, split it into memorable chunks, and rank the chunks for trust and accuracy. If pages use semantic HTML, include facts and cite sources, and include structured metadata, AI crawlers and retrieval systems will find, store, and reproduce content accurately. That improves your chance of being cited correctly in AI overviews.

While GEO has disrupted the way people use search engines, the fundamentals of SEO and digital accessibility continue to be strong indicators of content performance in LLM search results. Making content understandable, usable, and memorable for humans also has benefits for LLMs and GEO.

How LLM systems (and AI-driven overviews) get their facts

Understanding how LLMs crawl, process, and retrieve web content helps us understand why semantic structure and accessibility best practices have a positive effect. When an AI system generates an answer that cites the web, several distinct back-end steps usually happen: 

  1. Crawling — Bots visit URLs and download page content. Some crawlers execute javascript like a browser (Googlebot) while others prefer raw HTML and limit their rendering.
  2. Chunking — Large documents are split into small, logical “chunks” of paragraphs, sections, or other units. These chunks are the pieces that are later retrieved for an answer. How a page’s content is structured with headings, paragraphs, and lists determines the likely chunk boundaries for storage.
  3. Vectorization — Each chunk is then converted into a numeric vector that captures its semantic meaning. These embeddings live in a vector database and enable systems to find chunks quickly. The quality of the vector depends on the clarity of the chunk’s text.
  4. Indexing — Systems will store additional metadata (URL, title, headings, metadata) to filter and rank results. Structured data like schema metadata is especially valuable. 
  5. Retrieval — A user asks a question or performs a search and the system retrieves the most semantically similar chunks via a vector search. It re-ranks those chunks using metadata and other signals and then composes its answer while citing sources (sometimes). 

The Case for Human-Accessible Content

There are many more reasons why digital accessibility is simply the right thing to do. It turns out that in addition to boosting SEO, accessibility best practices help LLMs crawl, chunk, store, and retrieve content more accurately.

During retrieval, small errors like missing text, ambiguous links, or poor heading order can fail to expose the best chunks. Let’s dive into how this can happen and what common accessibility pitfalls contribute to the confusion.

For Content Teams — Authors, Writers, Editors

Illustration of the problem with poor alt text on images, comparing one poor example and one good example

Lack of descriptive “alt” text

While some LLMs can employ machine-vision techniques to “see” images as a human would, descriptive alt text verifies what they are seeing and the context in which the image is relevant. The same best practices for describing images for people will help LLMs accurately understand the content. 

Illustration of poor heading structure, where the poor example shows skipped heading levels while the good example shows consecutive heading levels

Out-of-order heading structures

Similar to semantic HTML, headings provide a clear outline of a page. Machines (and screen readers!) use heading structure to understand hierarchy and context. When a heading level skips from an <h2> to an <h4>, an LLM may fail to determine the proper relationship between content chunks. During retrieval, the model’s understanding is dictated by the flawed structure, not the content’s intrinsic importance. (Source: research thesis PDF, “Investigating Large Language Models ability to evaluate heading-related accessibility barriers”) 

Illustration of poor link text context, where the poor example shows Click Here and Read more links and the good example shows more descriptive and unique text samples

Descriptive and unique links

All of the accessibility barriers surrounding poor link practices affect how LLMs evaluate their importance. Link text is a short textual signal that is vectorized to make proper retrieval possible. Vague link text like “Click here” or “Learn More” does not provide valuable signals. In fact, the same “Learn More” text multiple times on a page can dilute the signals for the URLs they point to.

Using the same link text for more than one destination URLs creates a knowledge conflict. Like people, an LLM is subject to “anchoring bias,” which means it is likely to overweight the first link it processes and underweight or ignore the second, since they both have the same text signal. 

Example of the duplicate link problem: <a href=“[URL-A]”>Duplicate Link Text</a>, and then later in the same article, <a href=“[URL-B]”>Duplicate Link Text</a>. Conversely, when the same URL is used more than once on a page, the same link text should be repeated exactly.

Illustration of plain language with a poor example and a more positive example. The poor example is dense and wordy while the good example if succinct and uses a list to break the text into chunks.

Logical order and readable content

Simple, direct sentences (one fact per sentence) produce cleaner embeddings for LLM retrieval. Human accessibility best practices of plain language and clear structure are the same practices that improve chunking and indexing for LLMs

For Technical Teams — IT, Developers, Engineers

An illustration of poor semantic structure, where the left shows a potential structure made only of HTML div elements, while the good example shows semantic elements used correctly.

Poorly structured semantic HTML

Semantic elements (<article>, <nav>, <main>, <h1>, etc.) add context and suggest relative ranking weight. They make content boundaries explicit, which helps retrieval systems isolate your content from less important elements like ad slots or lists of related articles. 

Illustration of data in written form as one way to parse information, but contrasted with schema markup which can make it easier for robots to collect correct information about a subject.

Lack of schema

This is technical and under the hood of your human-readable content. Machines love additional context and structured schema data is how facts are declared in code — product names, prices, event dates, authors, etc. Search engines have used schema for rich results and LLMs are no different. Right now, server-rendered schema data will guarantee the widest visibility, as not all crawlers execute client-side Javascript completely. 

How to make accessibility even more actionable

The work of digital accessibility is often pushed to the bottom of the priority list. But once again, there are additional ways to frame this work as high value. While this work is beneficial for SEO, our recent research uncovers that it continues to be impactful in the new and evolving world of GEO.

If you need to frame an argument to those that control the investments of time and money, some talking points are: 

Staying steady in the storm

Let’s be clear — this summer was a “generative AI search freak out.” Content teams have scrambled to get smart about LLM-powered search quickly while search providers rolled out new tools and updates weekly. It’s been a tough ride in a rough sea of constant change.

To counter all that, know that the fundamentals are still strong. If your team has been using accessibility as a measure for content effectiveness and SEO discoverability, don’t stop now. If you haven’t yet started, this is one more reason to apply these principles tomorrow. 

If you continue to have questions within this rapidly evolving landscape, talk to us about your questions around SEO, GEO, content strategy, and accessibility conformance. Ask about our training and documentation available for content teams.

Additional Reading

One question we frequently hear from clients, especially those managing web content, is “How can we implement accessibility best practices without breaking the bank or overwhelming our editorial team?”

It’s a valid concern. As a content editor, you’re navigating the daily challenge of maintaining quality while meeting deadlines and managing competing priorities.

When your team decides to prioritize website accessibility, the initial scope can feel daunting. You might wonder “Does this really make a difference?” or “Is remediation worth the effort?” The answer is always a resounding yes.

Whether you’re working on a small site or managing thousands of pages, accessible content improves user experience, ensures legal compliance, boosts SEO performance, and reinforces your brand as inclusive and responsible. As a content editor, you have the power to make steady, meaningful progress with the content you touch every day.

Why Accessibility Creates Business Impact

Accessible content delivers measurable outcomes across multiple business objectives:

Expanded Market Reach: When your content is inaccessible to users with disabilities, you’re limiting your potential audience. Consider that disabilities can be temporary, like a broken arm, and 70% of seniors are now online—a demographic that often benefits from accessible design principles.

Risk Mitigation: Inaccessible websites can lead to legal complaints under the ADA and other regulations, creating both financial and reputational risks.

Enhanced User Experience: Clear structure, descriptive alt text, and keyboard-friendly navigation improve usability for all users while boosting SEO performance.

Brand Differentiation: Demonstrating commitment to accessibility positions your organization as inclusive and socially responsible.

Implementing Accessibility in Your Editorial Workflow

The challenge isn’t whether to implement accessibility—it’s how to do it efficiently without overwhelming your team or budget.

The Fix-It-Forward Approach

Rather than attempting to overhaul your entire site overnight, we recommend a “fix-it-forward” strategy. This approach ensures all new and updated content meets accessibility standards while gradually improving legacy content. The result? Steady progress without resource strain.

Leverage Open Source Tools

Many CMS platforms offer free accessibility tools that integrate directly into your editorial workflow:

Drupal: Editoria11y Accessibility Checker, Accessibility Scanner, CKEditor Accessibility Auditor

WordPress: WP Accessibility, Editoria11y Accessibility Checker, WP ADA Compliance Check Basic

These tools scan your content and flag common WCAG 2.2 AA issues before publication, transforming accessibility checks into routine quality assurance.

Prioritize High-Impact Changes

Focus your efforts on fixes that significantly improve usability for screen reader and keyboard users:

Less critical issues can be addressed during routine content updates, spreading the workload over time.

Manage Legacy Content Strategically

Don’t let your content backlog create paralysis. Prioritize high-traffic pages and those supporting key user journeys. Since refreshing legacy content annually is already an SEO best practice, use these updates as opportunities to implement accessibility improvements.

Build Team Capabilities

Make accessibility part of your content culture through targeted education and resources. Provide internal training, quick reference guides, and trusted resources to keep editors confident and informed.

Recommended Learning Resources:

Track Progress and Celebrate Wins

Measure success by tracking pages published with zero critical accessibility issues. Share achievements in editorial meetings to reinforce your team’s impact and maintain momentum.

Scaling Your Accessibility Program

While regular content checks provide immediate value, sustainable accessibility success requires periodic comprehensive assessments and usability testing. If your team lacks bandwidth for advanced testing, consider adding this to your 1-2 year digital roadmap. Consistent attention over time proves more sustainable and cost-effective than attempting massive one-time remediation.

Start with Free Tools: Google Lighthouse provides immediate insights into accessibility issues and actionable remediation guidance.

Advanced Assessment Options: For teams ready to expand their program, tools like SortSite, SiteImprove, and JAWS screen reader testing offer comprehensive assessments. These advanced tools can uncover complex issues beyond content-level checks, though they may require developer collaboration for implementation.

Quarterly Program Goals:

Consider engaging someone who navigates the web differently than your team does. This perspective will expand your understanding of accessibility’s real-world impact and inform more effective solutions.

Accessibility as Continuous Improvement

Accessibility isn’t a one-time project—it’s an ongoing commitment to inclusive digital experiences.

By integrating accessibility best practices into your publishing workflow, you’ll build a stronger, more inclusive website that protects your brand, empowers your users, and demonstrates digital leadership.

The fix-it-forward approach transforms what seems like an overwhelming challenge into manageable, sustainable progress.

Ready to Accelerate Your Accessibility Journey?

Explore additional insights from our team:

Ready to take action? Contact Oomph to see how we can support your accessibility journey. We start with targeted accessibility audits that identify your highest-impact opportunities, then collaborate with your team to develop a strategic roadmap that aligns with your internal goals while respecting your resources and team size.

When you’re responsible for your organization’s digital presence, it’s natural to focus on what’s visible: the design, the content, the user experience. But beneath every modern website lies a complex ecosystem of technologies, integrations, and workflows that can either accelerate your team’s success or create hidden friction that slows everything down.

That’s where a technical audit becomes invaluable. It’s not just a diagnostic tool—it’s a strategic opportunity to understand the foundation of your platform and make informed decisions about your digital future.

It’s Like a Home Inspection for Your Website

Think about buying a house. You walk through focusing on the big picture—does the kitchen work for your family? Is there enough space? But a good home inspector looks deeper, checking the foundation, examining the electrical system, and spotting that small leak under the bathroom sink that could become a major problem later.

A technical audit takes the same comprehensive approach to your digital platform. We examine not just what’s working today, but what might impact your team’s ability to execute tomorrow. The goal isn’t to find problems for the sake of finding them—it’s to give you the complete picture you need to plan strategically.

Creating Shared Understanding Across Your Entire Team

One of the most powerful outcomes of a technical audit is alignment. Whether you’re managing internal developers, partnering with an agency, or preparing to issue an RFP, having a clear baseline allows everyone to ask better questions and make more accurate decisions.

A strategic technical audit delivers:

Proactive Problem-Solving: Surface technical issues before they become roadblocks to important campaigns or launches.

Performance Optimization: Identify specific improvements that will measurably enhance user experience and conversion rates.

Workflow Enhancement: Reveal friction points that slow down content updates, campaign launches, or day-to-day management tasks.

Vendor Enablement: Provide partners and potential vendors with the context they need to scope work accurately and ask intelligent questions.

Strategic Planning: Create a foundation for long-term digital strategy decisions, from infrastructure investments to editorial tooling.

The organizations we work with often tell us that a technical audit helped them transition from reactive maintenance to proactive digital platform management—a shift that pays dividends across every initiative.

What We Typically Discover

While every platform is unique, certain patterns emerge across industries and organization types. Technical audits frequently reveal:

Security and Maintenance Opportunities: Outdated software, plugins requiring updates, or access configurations that can be strengthened with minimal effort. This often includes ensuring accessibility compliance meets current standards.

Performance Enhancements: Specific optimizations in areas like image compression, caching strategies, or database queries that directly impact user experience. Modern audits also examine search visibility and performance optimization.

Scalability Considerations: Code or architectural decisions that work fine today but could limit growth or flexibility as your needs evolve. This includes evaluating search infrastructure and international expansion capabilities.

Process Improvements: Gaps in version control, deployment workflows, or change management that create unnecessary risk or slow down development cycles.

Editorial Workflow Optimization: Content management processes that feel cumbersome or inconsistent, often because they evolved organically rather than being designed strategically. For global organizations, this includes reviewing translation and localization systems.

Many of these findings aren’t urgent fixes—they’re strategic insights that become incredibly valuable when you’re planning a redesign, launching a major campaign, or evaluating new partnerships.

When a Technical Audit Delivers Maximum Value

You don’t need to wait for problems to emerge. Technical audits are particularly valuable when:

Taking Over Digital Responsibility: You’ve inherited a platform and need a comprehensive understanding of what you’re working with and where the opportunities lie.

Planning Major Initiatives: Before investing in a redesign, platform migration, or significant feature development, understanding your current foundation prevents costly surprises.

Preparing for Vendor Selection: Whether you’re issuing an RFP or evaluating agencies, giving potential partners accurate technical context leads to better proposals and more realistic timelines.

Developing Digital Strategy: When you’re ready to create a roadmap for digital growth, grounding decisions in technical reality rather than assumptions leads to better outcomes. This is especially important when considering AI integration or generative engine optimization strategies.

Our Approach to Technical Audits

We design our audits to build clarity and confidence, not overwhelm you with technical jargon. Rather than simply delivering a report, we walk through findings with your team, prioritize recommendations based on your specific goals, and translate technical insights into actionable business language you can share with stakeholders.

Our methodology goes beyond code analysis. We examine how your platform supports your current workflows, aligns with your organizational objectives, and positions you for future growth. This combination of technical depth and strategic perspective ensures you get insights that drive real business outcomes.

The audit process focuses on partnership, not judgment.

We’re not looking for flaws to criticize—we’re identifying opportunities to help you and your partners make smarter decisions. The result is visibility into the hidden layers of your digital platform and a foundation for more strategic planning, better technology investments, and sustainable long-term success.

Ready to understand what’s really happening under the hood of your digital platform? Let’s talk about how a technical audit could support your goals and strengthen your team’s ability to execute on your digital vision.

If your Drupal site relies on Acquia Search leveraging Solr, you’re likely facing a migration from Acquia Search to SearchStax. We’ve guided numerous organizations through this transition and want to share our proven approach to help you navigate this change successfully.

Before diving into the migration process, this transition presents an excellent opportunity to reassess your search strategy entirely. While Solr remains a powerful and robust solution, the search landscape has evolved significantly with innovative alternatives now available. For organizations considering broader platform transitions, this moment offers strategic value beyond search improvements. Modern React-based solutions can deliver dramatically faster user experiences. Our recent work with ONS demonstrates this potential—by replacing their Solr solution with Algolia Instant Search, we helped them achieve a 40% improvement in search response times while creating a more intuitive experience for their members.

Why the Move to SearchStax?

Acquia announced earlier this year that they’re sunsetting their Acquia Search offering in 2026, positioning SearchStax as the recommended migration path through their new partnership. This transition offers enhanced search capabilities and more direct control over your search environment through SearchStax’s comprehensive dashboard, providing visibility into Solr server performance, data analysis tools, search preview functionality, and advanced configuration options.

The architectural similarity ensures a seamless end-user experience—Solr remains the foundation, requiring no front-end changes for this migration path while delivering improved administrative control.

Our Proven Migration Framework

Through multiple successful migrations, we’ve developed a structured approach that minimizes risk and ensures smooth transitions. Here’s our step-by-step framework:

Phase 1: Foundation Setup

Phase 2: Testing and Validation

Phase 3: Production Implementation

Phase 4: Configuration Management

Phase 5: Transition Management

Addressing Technical Challenges

Our experience across multiple migrations has revealed common technical hurdles that require proactive attention. Configuration issues with Boost by Date Processor settings, Highlighted Fields errors during index rebuilding, and Facet configuration mismatches between environments are frequent challenges. The key to success lies in early identification during lower environment testing and leveraging Acquia support resources to resolve issues before they impact production.

Each migration presents unique challenges based on your specific configuration and content structure. Our approach prioritizes thorough testing and validation to surface these issues early, ensuring smooth production deployment.

Strategic Search Optimization

Successful migration extends beyond technical implementation. Understanding your content architecture, user behavior patterns, and business objectives enables you to optimize search effectiveness during the transition. This migration provides an ideal opportunity to evaluate search performance metrics, refine content indexing strategies, and enhance user experience design.

By following this proven framework and preparing for potential challenges, your organization can successfully transition to SearchStax while improving both administrative capabilities and user search experience. The result is a more robust, manageable search solution that positions your site for future growth and enhanced user engagement.

Our comprehensive migration expertise extends beyond search implementations to complete platform transformations, ensuring your digital infrastructure supports your long-term strategic objectives.

Ready to begin your SearchStax migration? Our team has successfully guided organizations through this transition, delivering improved search performance and streamlined administration. Contact us to discuss your specific migration needs and timeline.

In 2025, the way people discover and engage with digital content has shifted dramatically. Traditional Search Engine Optimization (SEO) is no longer the only strategy that brings people to your website. Meet Generative Engine Optimization (GEO), the emerging frontier for content creators and researchers looking to earn visibility through AI-driven platforms like ChatGPT, Google’s Gemini, and Perplexity.

If your organization hasn’t begun adapting its content strategy for GEO, now is a great opportunity. Here’s everything you need to know about what GEO is, why it matters, and how to start optimizing for it.

What is GEO and How Is It Different From SEO?

While SEO focuses on improving your visibility on traditional search engine results pages (SERPs) by using keywords, backlinks, and technical performance, GEO is about making your content the answer in AI-generated responses.

Rather than presenting users with a list of links as typically experienced with a Google Search, GEO centers on AI tools that synthesize information. These platforms use large language models (LLMs) to provide direct answers to a range of questions. Instead of competing for a top 10 ranking on Google, you’re aiming to be cited, summarized, or linked to by tools like Gemini or ChatGPT.

In short: SEO gets you found, GEO gets you featured.

Why GEO Matters in 2025

AI tools are no longer sidekicks to Google. They’re central players in how people research, compare options, and make decisions. As of May 2025, ChatGPT alone receives over 4.5 billion monthly visits, while Perplexity processes over 500 million searches per month. Google remains the dominant force in online search, with billions of daily visits from users worldwide. But with the direct integration of Gemini into search results, the way people find information is changing. Users can now get answers without ever clicking through to your website (this is called a “zero-click search result”).

Consequently, if your content isn’t showing up in AI answers, you’re missing out on a massive and growing segment of online visibility. Depending on what your website offers, this can be especially important for brand recognition and perception, traffic and lead potential, as well as establishing authority and credibility. In 2025, AI summaries are the new front page of search.

How GEO Works: What AI Tools Are Looking For

Each generative engine has its quirks, but several patterns are emerging across platforms:

1. Structure Matters More Than Ever

AI tools rely on clear, structured content. Use schema markup generously, particularly FAQPage, Organization, Article, and Product types. Structured data helps AI understand your content contextually, making it easier to reference in generated answers.

Tip: Google’s Structured Data Markup Helper is a great place to start reviewing your schema.

2. E-E-A-T Principles Still Rule

Google’s Expertise, Experience, Authoritativeness, and Trustworthiness (E-E-A-T) framework, a core concept for SEO, now extends to AI tools like Gemini. Show credentials, cite data, link to reputable sources, and provide content authored by credible experts.

If you have certifications, awards, partnerships, or original research, feature them clearly. This shows your authority in your area of expertise.

3. Conversation > Keywords

GEO is less about keywords and more about natural language. Write in a conversational tone and frame your content in terms of questions and answers. Think: “What are the best family vacation spots in California?” instead of “California vacation destinations.”

4. Content Freshness is Key

AI platforms (especially Perplexity, which indexes content daily) prioritize content that’s up to date. Refresh evergreen posts annually and use a content calendar to help track when to review content. Be sure to prioritize articles with titles like “Top” or “Best,” as these perform well in answer generation, particularly on ChatGPT.

5. Visuals Are Increasingly Important

Gemini and Perplexity are both investing in multimodal search. Media assets like charts, videos, and well-optimized images can increase the chance of being featured. Also make sure your image alt text, captions, and surrounding content are descriptive.

6. Prioritize Performance & Mobile-Responsiveness 

Don’t ignore performance or the site’s mobile experience. A site that performs well on mobile will load quickly, display clearly on small screens, and typically avoids frustrating interactions (like unclickable buttons or pop-ups). Poor mobile performance (i.e. slow Core Web Vitals) can hurt your rankings, which in turn reduces your visibility to LLMs that rely on search results as part of their input sources.

Tool-Specific GEO Tips

Gemini (Google)

Perplexity

ChatGPT

Tracking GEO Performance

A consequence of AI summaries is that websites may see a drop in clicks and visits within their analytics, particularly a decrease in organic traffic month over month. With users getting the answers they need from AI-generated search responses, they may no longer need to visit your website to get information. However, those users who do click through often stay longer and discover more pages than they did previously.  

Additionally, websites may also see an increase in impressions or referrals from AI assistants. This data is increasingly important to track. 

So even if AI tools don’t always send traffic directly, you can still measure their impact. Here’s how:

Action Items for Digital Teams & Clients

  1. Audit your existing content with these optimization strategies in mind. (Tip: You can even use AI tools like Gemini to identify optimization opportunities for particular pages).
  2. Update schema across all major content types, especially Q&A and organizational pages.
  3. Refresh your high-performing or evergreen content regularly, especially pieces tied to seasons, events, or top lists.
  4. Revise your content strategy to include multimedia assets, structured data, and topic clustering.
  5. Optimize your About page and author bios to strengthen trust signals for LLMs.

Final Thoughts

Optimizing for GEO isn’t just a trend, it’s a fundamental shift in how people find and interact with content online. As AI-generated answers become a dominant part of the discovery experience, your brand’s ability to show up in these spaces could mean the difference between gaining trust or going unnoticed.

By embracing schema, writing conversationally, and refreshing content with purpose, your digital presence can evolve to meet the moment, one where the best answer often wins over the best ranking.

Ready to optimize your content for AI-powered search? Let’s make it happen.

Drupal has long been known for its flexibility, robustness, and scalability. But for many marketers and content creators, that flexibility can come with a steep learning curve — especially when it comes to building layouts and managing design without the help of a developer. That’s about to change in a big way.

Enter Experience Builder, a new initiative in Drupal that promises to radically streamline and simplify how we build and design pages. While still in its early stages, Experience Builder is ready for testing and experimentation — and it’s something marketers should absolutely have on their radar.

What is Experience Builder?

Experience Builder is the evolution of Drupal’s current method of flexible page building called Layout Builder. It takes what we know from layout builder and expands it into a unified, user-friendly tool that allows non-developers to build and theme websites directly in the browser. It’s a huge leap toward making Drupal more accessible for site builders, marketers, and content creators alike.

Unlike other page builders, Experience Builder doesn’t just provide drag-and-drop layout tools. It leverages Drupal’s core strengths — structured data, fine-grained access controls, and reusable components — to ensure consistency across channels and scalability across enterprise-level websites. This makes it uniquely powerful for large organizations managing multiple digital properties.

Dries Buytaert, Drupal’s founder, described it as a response to the fragmented landscape of site-building options in Drupal today. The vision is to consolidate functionality from tools like Paragraphs and Layout Builder into a single, cohesive solution. One that’s intuitive, efficient, and packed with modern capabilities.

Here is a fantastic video demo from DrupalCon Atlanta that was shown by Dries during the keynote address:

Why Now?

The timing couldn’t be better. While Layout Builder was a step in the right direction when it launched in 2018, its limitations became clear as more site builders demanded easier workflows, styling tools, and richer content composition features.

At recent Drupal conventions, the community has rallied around the idea of enhancing user experience across the board. As part of the broader Starshot initiative (now named Drupal CMS), Experience Builder is a key component in bringing Drupal’s usability in line with the expectations of modern content teams.

Why I’m Excited About It

As an engineer who has worked closely with Drupal for years, what excites me most is how Experience Builder can bridge the many gaps in Drupal’s current page-building ecosystem. Today, there are so many ways to structure content — Blocks, Paragraphs, Layout Builder, Panels — that choosing the right one can be overwhelming.

Experience Builder is shaping up to be that “one-stop-shop” we’ve needed. It reduces decision fatigue and gives teams a faster way to get projects off the ground without needing to architect every page structure from scratch.

Even better, it supports creating single-page overrides, component-level editing, and even React-based components right in the editor. That’s something I’ve personally looked forward to for a long time. The ability to build and save reusable components that can be dropped into any page makes this a tool that truly enhances productivity — not just for developers, but for marketers and content creators, too.

My First Look

I had the chance to see Experience Builder in action at DrupalCon Atlanta this year. The live demos were impressive and really opened my eyes to what this tool could do, both for newcomers to Drupal and seasoned site builders. Along with Drupal CMS, and recipes, Experience Builder is easily one of the hottest topics in the Drupal ecosystem right now.

The energy in the room during the sessions was palpable—people are genuinely excited about this. It’s not just another experimental module; it’s a shift in how we think about building on Drupal.

A Game-Changer for Marketers

One of the biggest barriers for marketing teams has always been reliance on developers to make even small layout edits. That’s starting to change.

With Experience Builder, non-developers will be able to build out dynamic, visually engaging pages — without needing to dive into code. That’s a massive win, especially for small teams in government, education, or nonprofit sectors, where resources are limited and time is of the essence.

Being able to make changes quickly, reuse content intelligently, and maintain a consistent brand without touching a template file is something many organizations have wanted for years. Experience Builder delivers on that promise.

Want to Try It Yourself?

If you’re curious to see what the buzz is about, the best way to get started is simple: head to Drupal.org and download the latest version of Drupal CMS. It now comes with an optimized installer that makes getting started faster than ever. Once you’re up and running, you can add the Experience Builder module and start exploring.

Looking Ahead

It’s important to note that this is just the alpha version of the Experience Builder initiative. The team behind it is committed to rapid iteration and community feedback, which means what we’re seeing today is just the beginning.

If this is the foundation, I can only imagine how powerful the tool will become in the next year or two. The Drupal community is known for its collaborative spirit and constant innovation — and Experience Builder is shaping up to be one of the most important steps forward in years.

So if you’re a marketer, content strategist, or anyone who’s ever been frustrated by the limits of page building in Drupal — now’s the time to dive in. Experience Builder is here, and it’s ready to change the game. Contact us for a demo or more information about Experience Builder in Drupal.

Today I learned about a military term that has come into the culture: VUCA, which stands for volatility, uncertainty, complexity, and ambiguity. That certainly describes our current times.

All of this VUCA makes me concentrate on what is stable and slow to change. Its easy to get distracted by that which changes quickly and shines in the light. Its harder to be grateful for what changes slowly. Its harder to see what those things might even be. 

In the face of AI and the way it will transform all industries (if not now, very soon), its important to remember what AI can not yet do well. Maybe it will learn how to create a facsimile of these traits in the future as it becomes more “human” (trained on human data with all its flaws might mean it has embedded within it those traits we find undeniably human). However, these skills seem like the ones that can help us navigate the VUCA that is life today.

Be Curious

AI can ask follow-up questions for clarification, but it does not (yet) ask questions for its own curiosity. It asks when it has been directed to do something. It does not sit idle and wonder what the world is like beyond the walls of the chat window.

Humans and high-order animals have curiosity. We seek information and naturally have questions about our world — why is the sky blue? why does the wind blow? why do waves crash onto the shore?

In our operations, Oomph prides itself on Discovery. This is our chance to ask the big questions — why does your business work the way it does? why are those your goals? who is your audience you have vs. the audience you want? 

In life and work, curiosity is one of our best traits. This means trying new tools, changing our processes and habits for improved outcomes, and exploring something new just to see what it can do. Even with all the VUCA in the world, approaching uncertainty with curiosity keeps us open and engaged with what we can learn next.

Use Judgement

Another important human trait is judgement, and this continues to be invaluable as humans are needed to evaluate AI outputs. 

AI is very good at creating dozens, if not hundreds of outputs. In fact, probabilistic (not deterministic) output is the strength and sometimes weakness of AI — you almost never get the same answer twice.

Our human expertise is needed to curate these outputs. We need to discard what is average and unremarkable to find the outputs that are surprising and valuable. We need to use our judgement and experience to find the ideas that are applicable to the client, the project, and the moment. Given the same 100 outputs, the right ones might be a different selection depending on the problem we want to solve and the industry in which it will be applied.

Exude Empathy

In the world of design and creating software for humans, empathy is what drives the decisions we need to make. In the flow of vibe coding, our judgments will drive technical and architectural decisions while empathy drives interface design and product feature decisions. Humans are still the ones who need to find the problems that are worth solving.

The language on the page, the helpfulness of the tooltip, and the order in which the form elements appear are some examples of how empathy drives interactions. Empathy helps team members identify confusion and redundancy. 

Further, until we are designing for AI Agents and robots as our product’s primary users, we are designing for humans. This means we need to continue to ask humans for feedback, monitor human behavior on our sites and in our apps, and understand why they make the decisions they make. All of this continues to make empathy an important human trait to cultivate.

Make Connections

Mike Bechtel, Chief Futurist at Deloitte Consulting, gave a talk at SXSW this year about how the future favors polymaths instead of specialists. His argument boils down to this: AI is a specialist at almost anything but what humans have shown over time is that the greatest inventions and insights come from disparate teams putting their expertise together or individuals making new connections between disciplines. 

Novel ideas are mash-ups of existing ideas more than brand-new ideas that have never been thought of. And these mash-ups come from curious humans who have broad experience, not deep specialization. They are the ones who can identify and bring the specialists together if need be, but most of all, they can make the connections and see the bigger picture to create new approaches. 

Support Culture

No matter how smart AI gets, it doesn’t “read the room.” It doesn’t build relationships between others, react to group dynamics, or pick up on body language. In an ambiguous human way, it does not sense when something “feels off.”

In group settings, humans command culture. AI won’t directly help you build trust with a client. It won’t read the faces in the room or over Zoom and pause for questions. It won’t sense that people are not engaging and reacting, and therefore you need to change a tactic while speaking. AI is interested in the facts and not the feelings.

Broad team culture and the culture that exists between individuals is built and nurtured by the humans within them. AI might help you craft a good sales pitch, internal memo, or provide ice breaker ideas, but in the end, humans deliver it. Mentoring, supporting culture, collaborating, and building trust continue to be human endeavors.

Break Patterns

AI is very good at replicating patterns and what has already been created. AI is very good at using its vast amount of data to emphasize best practices with patterns that are the most prevalent and potentially the most successful. But it won’t necessarily find ways to break existing patterns to create new and disruptive ones. 

Asking great questions (being curious), applying our experience and judgement, and doing it all with empathy for the humans we support leads to creative, pattern-breaking solutions that AI has not seen before. Best practices don’t stay the best forever. Changes in technology and our interface with it create new best practices. 

The easiest answer (the common denominator that AI may reach for) is not always the best solution. There is a time and a place to repeat common patterns for efficiency, but then there are times when we need to create new patterns. Humans will continue to be the ones who can make that judgement.

Be Human

AI will continue to evolve. It may get better at some of the attributes I mention — or at best, it may get better at looking like it has empathy, supports culture, and mashes existing patterns together to create new ones. But for humans, these traits come more naturally. They don’t have to be trained or prompted to use these traits.

Of all these traits, curiosity may be the most important and impactful one. AI has become our answer-engine, making it less necessary to know it all. But we need to continue to be curious, to wonder about “what if?” AI shouldn’t tell us what to ask, but it should support us in asking deeper questions and finding disparate ideas that could create a new approach.

We no longer need to learn everything. All the answers to what is already known can be provided. It is up to humans to continue with curiosity into what we do not yet know.

Search and SEO are evolving rapidly in the wake of new AI options. Many of our clients are concerned about continuing to receive a return on their SEO investment. They worry about putting effort into the right places. And they worry about how to prepare for a drastic shift in the landscape, should it come. 

The speed of evolution has made these questions difficult to answer with authority. But we conducted research, asked some experts, and have some theories that put these fears into context. Hopefully, they can help your organization navigate these uncharted waters.

Do AI Overviews reduce click-through rates?

In 2024, Google introduced AI-generated answers to queries in its search results. These “AI Overviews” are more likely to appear when a visitor phrases their search query like a question, using “what,” “how,” or “why” language. These overviews provide citations to their sources and a right sidebar (on laptops) with other references. Some are calling the traffic these overviews generate “zero-click” searches.

A screen capture of an example AI Overview as a result from a Google search

While the answer is yes, click-through rates have reduced by as much as 10%, others argue that most websites will be unaffected. For one, Google has scaled back their AI Overviews to only 1.28% of its billions of daily searches. This will likely increase now that AI has become less likely to provide incorrect answers, but the misconception that AI Overviews are everywhere is overblown. 

Further, the same article goes on to assert that 96.5% of all AI Overviews appear for informational keywords — meaning very few overviews are created for transactional, navigational, and local searches. Informational questions are much easier and safer for AI to answer and will likely remain the dominant use case.

Others argue that AI Overviews keep low-performing traffic away from your site. For many years, Google has already been answering queries with information cards. When you Google a business, you are likely to get a card with the business name, phone number, web address, and even a map with their location. Popular businesses might include reviews and specific details like daily open hours. These information cards have already been taking traffic away from your site. But was that the traffic that you wanted? 

These folks argue, if the searcher just wanted to know an answer to a question they had while having a conversation with a friend, they would have come to your site for that information and then left. Their visit would have counted as a bounce and negatively affected your monthly traffic data. Same with the ones that just needed a phone number or wanted to know what time you close. They would have come to your website for that one thing and then left.

Google’s own research says that when people use AI Overviews to start understanding a topic, they end up searching more frequently and express higher satisfaction with the results. Their position is that these overviews scratch the surface and help visitors ask more in-depth follow-up questions. Other recent studies have found that click-through increased for companies featured in AI Overviews, while those without an AI Overview lost traffic.

One thing is for sure: AI Overviews’ prominent position at the top of the results have pushed down organic results and made it harder for high-ranking organic websites to get noticed.

Takeaway:

Mixed. Yes, it is possible that AI Overviews are preventing click-through. It is also possible this traffic was not going to convert. And depending on your product and position in the market, AI Overviews might drive slightly more traffic than organic search. Either way, the result is an even more competitive search landscape than before.

Should I optimize my content for AI Overviews? 

The most obvious next question is “How can my brand rank for AI Overviews?” While this is an important question, remember that AI Overviews often include citations from multiple sources. So while your business may rank for an overview, it is likely not going to be alone. 

The answer to this question is more of the same things you should already know. In order to rank highly, you should: 

Lots of SEO companies want to help your business rank, and AI Overviews is the next frontier. But from all the articles we have reviewed (and there were many), the same best practices apply — there are no shortcuts to great content. 

Takeaway

Yes, optimize your content for AI Overviews, but this does not mean you need to do more than what you are already doing. To be a highly quoted source within your industry has benefits for brand recognition and trust, but just like long-tail keywords, these searches may have low volume. In the end, it is an investment vs. return question. There is a significant overlap between the sources cited in AI Overviews and the top organic search results, therefore, if your site already ranks well, you can’t do much more to get into an AI Overview.

Should I continue investing in SEO for Google?

Some clients worry that Google will be unseated as the dominant search engine now that tools like OpenAI’s ChatGPT have seen an explosion of millions of users. While these tools are indeed experiencing hockey-stick growth, Google completely dominates search volume.

SparkToro charted a 20% growth in search queries for Google in 2024, and crunched the numbers to conclude Google receives 373 times more searches than ChatGPT. 

To put that into context, Google handles 14 billion searches per day. The next closest competitor is Bing search with 613.5 million per day, followed by Yahoo, DuckDuckGo, and then Chat GPT. In other words, your investment would see a larger return if your team optimized content for Bing.com than for ChatGPT. 

These numbers are fresh from March 2025. Things can change, of course, but AI tools are not used only for search, have a relatively small market share, and do not get used daily. They suffer from not being the default tool at hand, which for most people, is a web browser. Google remains synonymous with search for a large percent of the population.

Takeaway

Yes, continue to invest in SEO for Google specifically. Google is still the biggest player in the search market, and their share is gaining, not decreasing (yet).

If we don’t implement structured data, are we losing out on AI crawler traffic?

Structured data is great for all SEO, so actually, you should implement structured data like Schema.org for across-the-board SEO value. 

For those of you using Google Tag Manager (GTM), you might know that you get some structured data for free. When a Googlebot crawls your site, it includes structured JSON data that it creates client-side, which means that Google gets the structured data but it is inaccessible to any other crawler. If the data existed server-side, other bots could access it. 

Most non-Google robot crawlers do not execute Javascript, therefore, they miss out on anything rendered in the browser. These crawlers include Bing, Yahoo, ChatGPT, Claude, and Perplexity. So again, server-side structured data would benefit all the search engine crawlers that are not Google.

But do LLMs really need structured data?

Large Language Models (LLMs) use statistical analysis to predict what word will follow the previous set of words. They do not understand language as much as they can mathematically reproduce its patterns. Therefore, they create structure from unstructured data all the time. 

But while LLMs process and understand unstructured text, providing structured data would significantly help interpret and categorize your content effectively and accurately.

Takeaway

The short answer is no, LLMs do not require structured data to create meaningful connections between content and search intent. But structured data would help them and any other search service to correctly label, tag, and organize your data. The longer answer is an investment in structured metadata would pay off in dividends for all search engines and crawlers.

How can we prepare for SEO’s evolving future?

In mid-2024, when Google first introduced AI Overviews, some in the SEO/SERP industry claimed sites could lose up to 25% of their traffic. That has not come to pass, with some sites reporting as high as 12% and others lows of 8.9% and 2.6% — not insignificant, but lower than expected. And the data is still coming in, with others reporting increases in traffic with specific kinds of intent.

While AI increasingly shapes search results, content strategy will need to shift for sites to remain visible and relevant. High-quality, authoritative, and authentic content that offers depth, accuracy, and unique insights is still valuable currency. AI algorithms are designed to identify and prioritize quality, trustworthy, and well-researched content for inclusion in their summaries. 

Sites should continue to target long-tail and question-based keywords to align content with visitor’s increase in natural language queries. This type of content is often more challenging for AI to fully synthesize and may still necessitate user click-through for a comprehensive understanding. Going deeper to investigate specific intents behind longer conversational queries could also be crucial for attracting relevant traffic. 

Finally, diversifying content formats by incorporating video, infographics, and interactive elements will continue to enhance engagement and provide unique value that text-based AI summaries don’t fully replicate. And optimizing content for featured snippets remains important, as appearing in these snippets increases the likelihood of a website’s content being cited within AI Overviews. 

Takeaway

The fundamentals of great content and best-practice SEO has not changed as dramatically as the tools that crawl your site and serve your content have.

Final Thoughts

Anything in the tech space evolves rapidly, and SEO is no exception. While the methods and the tools we leverage might change, the fundamentals remain strong. Keep doing what you have been doing, keep being curious, and keep asking these important questions of those in your circle whom you trust. We’re all figuring these things out in real time and can benefit from each other’s expertise.

If you have in-depth questions about SEO, content management, and the evolving AI-powered landscape, reach out to our team and we’ll always do our best to answer them thoughtfully and from multiple angles.

AI disclaimer: Google’s Deep Research was used for initial exploration and source gathering. All sources cited in this article were reviewed by the author. ChatGPT was used for follow up questions, as well as AI Overviews for examples of common questions. This article synthesizes these sources and was written by a human.

The tech industry has never been accused of moving slowly. The exponential explosion of AI tools in 2024, though, sets a new standard for fast-moving. The past few months of 2024 rewrote what happened in the past few years. If you have not been actively paying attention to AI, now is the time to start.

I have been intently watching the AI space for over a year. I started from a place of great skepticism, not willing to internalize the hype until I could see real results. I can now say with confidence that when applied to the correct problem with the right expectations, AI can make significant advancements possible no matter the industry.

In 2024, not only did the large language models get more powerful and extensible, but the tools are being created to solve real business problems. Because of this, skepticism about AI has shifted to cautious optimism. Spurred by the Fortune 500’s investments and early impacts, companies of every shape and size are starting to harness the power of AI for efficiency and productivity gains.

Let’s review what happened in Quarter Four of 2024 as a microcosm of the year in AI.

New Foundational Models in the AI Space

A foundational large language model (LLM) is one which other AI tools can be built from. The major foundational LLMs have been Chat GPT, Claude, Llama, and Gemini, operated by OpenAI & Microsoft, Anthropic, Meta, and Google respectively.

In 2024, additional key players entered the space to create their own foundational models. 

Amazon

Amazon has been pumping investments into Anthropic as their operations are huge consumers of AI to drive efficiency. With their own internal foundational LLM, they could remove the need to share their operational data with an external party. Further, like they did with their AWS business, they can monetize their own AI services with their own models. Amazon Nova was launched in early December.

xAI

In May of 2024, X secured funding to start to create and train its own foundational models. Founder Elon Musk was a co-founder of OpenAI. The company announced they would build the world’s largest supercomputer in June and it was operational by December.

Nvidia

In October, AI chip-maker Nvidia announced it own LLM named Nemotron to compete directly with OpenAI and Google — organizations that rely on its chips to train and power their own LLMs. 

Rumors of more to come

Apple Intelligence launched slowly in 2024 and uses OpenAI’s models. Industry insiders think it is natural to expect Apple to create its own LLM and position it as a privacy-first, on-device service. 

Foundational Model Advancements

While some companies are starting to create their own models, the major players have released advanced tools that can use a range of inputs to create a multitude of outputs: 

Multimodal Processing

AI models can now process and understand multiple types of data together, such as images, text, and audio. This allows for more complex interactions with AI tools. 

Google’s NotebookLM was a big hit this year for its ability to use a range of data as sources, from Google Docs to PDFs to web links for text, audio, and video. The tool essentially allows the creation of small, custom RAG databases to query and chat with.

Advanced Reasoning

OpenAI’s 01 reasoning model (pronounced “Oh One”) uses step-by-step “Chain of Thought” to solve complex problems, including math, coding, and scientific tasks. This has led to AI tools that can draw conclusions, make inferences, and form judgments based on information, logic, and experience. The queries take longer but are more accurate and provide more depth.

Google’s Deep Research is a similar product that was released to Gemini users in December.

Enhanced Voice Interaction

More and more AI tools can engage in natural and context-aware voice interactions — think Siri, but way more useful. This includes handling complex queries, understanding different tones and styles, and even mimicking personalities such as Santa Claus.

Vision Capabilities

AI can now “see” and interpret the world through cameras and visual data. This includes the ability to analyze images, identify objects, and understand visual information in real-time. Examples include Meta’s DINOv2, OpenAI’s GPT-4o, and Google’s PaliGemma. 

AI can also interact with screen displays on devices, allowing for a new level of awareness of sensory input. OpenAI’s desktop app for Mac and Windows is contextually aware of what apps are available and in focus. Microsoft’s Co-pilot Vision integrates with the Edge browser to analyze web pages as users browse. Google’s Project Mariner prototype allows Gemini to understand screen context and interact with applications.

While still early and fraught with security and privacy implications, the technology will lead to more advancements for “Agentic AI” which will continue to grow in 2025.

Agentic Capabilities

AI models are moving towards the ability to take actions on behalf of users. No longer confined to chat interfaces alone, these new “Agents” will perform tasks autonomously once trained and set in motion.

Note: Enterprise leader SalesForce launched AgentForce in September 2024. Despite the name, these are not autonomous Agents in the same sense. Custom agents must be trained by humans, given instructions, parameters, prompts, and success criteria. Right now, these agents are more like interns that need management and feedback.

Specialization

2024 also saw an increase in models designed for specific domains and tasks. With reinforcement fine-tuning, companies are creating tools for legal, healthcare, finance, stocks, and sports. 

Examples include Sierra, who offers a specifically trained customer service platform, and LinkedIn agents as hiring assistants.

What this all means for 2025

It’s clear that AI models and tools will continue to advance, and businesses that embrace AI will be in a better position to thrive. To be successful, businesses need an experimental mindset of continuous learning and adaptation: 

While the models will continue to get better into 2025, don’t wait to explore AI. Even if the existing models never improve, they are powerful enough to drive significant gains in business. Now is the time to implement AI in your business. Choose a model that makes sense and is low-friction — if you are an organization that uses Microsoft products, start with a trial of AI add-ons for office tools, for example. Start accumulating experience with the tools at hand, and then expand to include multiple models to evaluate more complex AI options that may have greater business impact. It almost doesn’t matter which you choose, as long as you get started.

Oomph has started to experiment with AI ourselves and Drupal has exciting announcements about integrating AI tools into the authoring experience. If you would like more information, please reach out for a chat.

The U.S. is one of the most linguistically diverse countries in the world. While English may be our official language, the number of people who speak a language other than English at home has actually tripled over the past three decades. 

Statistically speaking, the people you serve are probably among them. 

You might even know they are. Maybe you’ve noticed an uptick in inquiries from non-English speaking people or tracked demographic changes in your analytics. Either way, chances are good that organizations of all kinds will see more, not less, need for translation — especially those in highly regulated and far-reaching industries, like higher education and healthcare.

So, what do you do when translation becomes a top priority for your organization? Here, we explain how to get started.

3 Solutions for Translating Your Website

Many organizations have an a-ha moment when it comes to translations. For our client Lifespan, that moment came during its rebrand to Brown Health University and a growing audience of non-English speaking people. For another client, Visit California, that moment came when developing their marketing strategies for key global audiences. 

Or maybe you’re more like Leica Geosystems, a longtime Oomph client that prioritized translation from the start but needed the right technology to support it. 

Whenever the time comes, you have three main options: 

Manual translation and publishing

When most people think of translating, manual translation comes to mind. In this scenario, someone on your team or someone you hire translates content by hand and uploads the translation as a separate page to the content management system (CMS).

Translating manually will offer you higher quality and more direct control over the content. You’ll also be able to optimize translations for SEO; manual translation is one of the best ways to ensure the right pages are indexed and findable in every language you offer them. Manual translation also has fewer ongoing technical fees and long-term maintenance attached, especially if you use a CMS like Drupal which supports translations by default.

“Drupal comes multi-lingual out of the box, so it’s very easy for editors to publish translations of their site and metadata,” Oomph Senior UX Engineer Kyle Davis says. “Other platforms aren’t going to be as good at that.” 

While manual translation may sound like a winning formula, it can also come at a high cost, pushing it out of reach for smaller organizations or those who can’t allocate a large portion of their budget to translate their website and other materials. 

Integration with a real-time API

Ever seen a website with clickable international flags near the top of the page? That’s a translation API. These machine translation tools can translate content in the blink of an eye, helping users of many different languages access your site in their chosen language. 

“This is different than manual translation, because you aren’t optimizing your content in any way,” Oomph Senior UX Engineer John Cionci says. “You’re simply putting a widget on your page.” 

Despite their plug-and-play reputation, machine translation APIs can actually be fairly curated. Customization and localization options allow you to override certain phrases to make your translations appropriate for a native speaker. This functionality would serve you well if, like Visit California, you have a team to ensure the translation is just right. 

Though APIs are efficient, they also do not take SEO or user experience into account. You’re getting a direct real-time translation of your content, nothing more and nothing less. This might be enough if all you need is a default version of a page in a language other than English; by translating that page, you’re already making it more accessible. 

However, this won’t always cut it if your goal is to create more immersive, branded experiences — experiences your non-English-speaking audience deserves. Some translation API solutions also aren’t as easy to install and configure as they used to be. While the overall cost may be less than manual translation, you’ll also have an upfront development investment and ongoing maintenance to consider. 

Use Case: Visit California

Manual translation doesn’t have to be all or nothing. Visit California has international marketing teams in key markets skilled in their target audiences’ primary languages, enabling them to blend manual and machine translation. 

We worked with Visit California to implement machine translation (think Google Translate) to do the heavy lifting. After a translation is complete, their team comes in to verify that all translated content is accurate and represents their brand. Leveraging the glossary overrides feature of Google Cloud Translate V3, they can tailor the translations to their communication objectives for each region. In addition, their Drupal CMS still allows them to publish manual translations when needed. This hybrid approach has proven to be very effective.

Third-party translation services

The adage “You get what you pay for” rings true for translation services. While third-party translation services cost more than APIs, they also come with higher quality — an investment that can be well worth it for organizations with large non-English-speaking audiences.

Most translation services will provide you with custom code, cutting down on implementation time. While you’ll have little to no technical debt, you will have to keep on top of recurring subscription fees.

What does that get you? If you use a proxy-based solution like MotionPoint, you can expect to have content pulled from your live site, then freshly translated and populated on a unique domain. 

“Because you can serve up content in different languages with unique domains, you get multilingual results indexed on Google and can be discovered,” Oomph Senior Digital Project Manager Julie Elman says. 

Solutions like Ray Enterprise Translation, on the other hand, combine an API with human translation, making it easier to manage, override, moderate, and store translations all within your CMS. 

Use Case: Leica Geosystems

Leica’s Drupal e-commerce store is active in multiple countries and languages, making it difficult to manage ever-changing products, content, and prices. Oomph helped Leica migrate to a single-site model during their migration from Drupal 7 to 8 back in 2019. 

“Oomph has been integral in providing a translation solution that can accommodate content generation in all languages available on our website,” says Jeannie Records Boyle, Leica’s e-Commerce Translation Manager. 

This meant all content had one place to live and could be translated into all supported languages using the Ray Enterprise Translation integration (formerly Lingotek). Authors could then choose which countries the content should be available in, making it easier to author engaging and accurate content that resonates around the world.  

“Whether we spin up a new blog or product page in English or Japanese, for example, we can then translate it to the many other languages we offer, including German, Spanish, Norwegian Bokmål, Dutch, Brazil Portuguese, Italian, and French,” Records Boyle says.

Taking a Strategic Approach to Translation

Translation can be as simple as the click of a button. However, effective translation that supports your business goals is more complex. It requires that you understand who your target audiences are, the languages they speak, and how to structure that content in relation to the English content you already have. 

The other truth about translation is that there is no one-size-fits-all option. The “right” solution depends on your budget, in-house skills, CMS, and myriad other factors — all of which can be tricky to weigh. 

Here at Oomph, we’ve helped many clients make their way through website translation projects big and small. We’re all about facilitating translations that work for your organization, your content admins, and your audience — because we believe in making the Web as accessible as possible for all. 

Want to see a few recent examples or dive deeper into your own website translation project? Let’s talk. 

THE BRIEF

While One Percent for America (OPA) had an admirable goal of helping eligible immigrants become U.S. citizens, the project faced a major stumbling block. Many immigrants had already been misled by various lending institutions, payday loans, or high-interest credit cards. As a result, the OPA platform would need a sense of trustworthiness and authority to shine through.

The platform also had to handle a broad array of tasks through a complex set of workflows, backstops, and software integrations. These tasks included delivering content, signing up users, verifying eligibility, connecting to financial institutions, managing loan data and investment balances, and electronically sending funds to U.S. Citizenship and Immigration Services.

THE APPROACH

Given the challenges, our work began with a month-long discovery process, probing deeper into the audience, competitive landscape, customer journeys, and technological requirements for the platform. Here’s what we learned.

The Borrower Experience

Among those deep in the citizenship process and close to finishing the paperwork, many are simply waiting to have the funds to conclude their journey. For them, we designed as simple a workflow as possible to create an account, pass a security check, and apply for a loan.

Other users who are just starting the process need to understand whether they’re eligible for citizenship and what the process entails. We knew this would require smart, in-depth content to answer their questions and provide guidance — which was also a crucial component in earning their trust. Giving away genuinely helpful information, combined with carefully chosen language and photography, helped lend authenticity to OPA’s stated mission.

The Investor Experience

OPA sought to crowdfund capital from small investors, not institutions, creating a community-led funding source that could scale to meet borrowers’ needs. A key innovation is that funders can choose between two options: making tax-deductible donations or short-term loans.

If an investor makes a loan, at the end of the term they can decide to reinvest for another term, turn the money into a donation, or withdraw the funds. To reinforce the circular nature of the platform, we designed the experience so that borrowers could become investors themselves. The platform makes it easy for borrowers to change their intent and access different tools. Maturity dates are prominently displayed alongside “Lend Again” and “Donate” actions. Testimonials from borrowers on the dashboard reinforce the kinds of people who are helped by an investment.

The Mobile Experience

Our research made it clear the mobile experience had to be best in class, as many users would either prefer using a phone or didn’t have regular access to a tablet or computer. But, that didn’t mean creating a mobile app in addition to a desktop website. Instead, by designing a universal web app, we built a more robust experience — more powerful than most mobile apps — that can be used anywhere, on any device.

However, tasks like signing up for an account or applying for a loan need to be as easy on a mobile device as on a desktop. Key UX elements like step-by-step workflows, large touch targets, generous spacing on form fields, soft colors, and easy-to-read fonts produced a highly user-friendly interface.

THE RESULTS

Together with our technology partners, Craftsman, Motionpoint, and Platform.sh, we built an innovative digital platform that meets its users exactly where they are, from both a technological and cultural standpoint.

This groundbreaking work earned us a Gold Medal from the inaugural 2022 Anthem Awards, in the Innovation in Human and Civil Rights category. The award recognizes new techniques and services that advance communities and boost contributory funds.

In our ongoing partnership with OPA, Oomph will continue working to expand the business model with new features. We’re proud to have helped build this impactful resource to support the community of new Americans.

Oomph has been quiet about our excitement for artificial intelligence (A.I.). While the tech world has exploded with new A.I. products, offerings, and add-ons to existing product suites, we have been formulating an approach to recommend A.I.-related services to our clients. 

One of the biggest reasons why we have been quiet is the complexity and the fast-pace of change in the landscape. Giant companies have been trying A.I. with some loud public failures. The investment and venture capitalist community is hyped on A.I. but has recently become cautious as productivity and profit have not been boosted. It is a familiar boom-then-bust of attention that we have seen before — most recently with AR/VR after the Apple Vision Pro five months ago and previously with the Metaverse, Blockchain/NFTs, and Bitcoin. 

There are many reasons to be optimistic about applications for A.I. in business. And there continue to be many reasons to be cautious as well. Just like any digital tool, A.I. has pros and cons and Oomph has carefully evaluated each. We are sharing our internal thoughts in the hopes that your business can use the same criteria when considering a potential investment in A.I. 

Using A.I.: Not If, but How

Most digital tools now have some kind of A.I. or machine-learning built into them. A.I. has become ubiquitous and embedded in many systems we use every day. Given investor hype for companies that are leveraging A.I., more and more tools are likely to incorporate A.I.

This is not a new phenomenon. Grammarly has been around since 2015 and by many measures, it is an A.I. tool — it is trained on human written language to provide contextual corrections and suggestions for improvements.

Recently, though, embedded A.I. has exploded across markets. Many of the tools Oomph team members use every day have A.I. embedded in them, across sales, design, engineering, and project management — from Google Suite and Zoom to Github and Figma.

The market has already decided that business customers want access to time-saving A.I. tools. Some welcome these options, and others will use them reluctantly.

Either way, the question has very quickly moved from should our business use A.I. to how can our business use A.I. tools responsibly?

The Risks that A.I. Pose

Every technological breakthrough comes with risks. Some pundits (both for and against A.I. advancements) have likened its emergence to the Industrial Revolution of the early 20th century. And a high-level of positive significance is possible, while the cultural, societal, and environmental repercussions could also follow a similar trajectory.

A.I. has its downsides. When evaluating A.I. tools as a solution to our client’s problems, we keep this list of drawbacks and negative effects handy, so that we may review it and think about how to mitigate their negative effects:

We have also found that our company values are a lens through which we can evaluate new technology and any proposed solutions. Oomph has three cultural values that form the center of our approach and our mission, and we add our stated 1% For the Planet commitment to that list as well: 

For each of A.I.’s drawbacks, we use the lens of our cultural values to guide our approach to evaluating and mitigating those potential ill effects. 

A.I. is built upon biased and flawed data

At its core, A.I. is built upon terabytes of data and billions, if not trillions, of individual pieces of content. Training data for Large Language Models (LLMs) like Chat GPT, Llama, and Claude encompass mostly public content as well as special subscriptions through relationships with data providers like the New York Times and Reddit. Image generation tools like Midjourney and Adobe Firefly require billions of images to train them and have skirted similar copyright issues while gobbling up as much free public data as they can find. 

Because LLMs require such a massive amount of data, it is impossible to curate those data sets to only what we may deem as “true” facts or the “perfect” images. Even if we were able to curate these training sets, who makes the determination of what to include or exclude?

The training data would need to be free of bias and free of sarcasm (a very human trait) for it to be reliable and useful. We’ve seen this play out with sometimes hilarious results. Google “A.I. Overviews” have told people to put glue on pizza to prevent the cheese from sliding off or to eat one rock a day for vitamins & minerals. Researchers and journalists traced these suggestions back to the training data from Reddit and The Onion.

Information architects have a saying: “All Data is Dirty.” It means no one creates “perfect” data, where every entry is reviewed, cross-checked for accuracy, and evaluated by a shared set of objective standards. Human bias and accidents always enter the data. Even the simple act of deciding what data to include (and therefore, which data is excluded) is bias. All data is dirty.

Bias & flawed data leads to the perpetuation of stereotypes

Many of the drawbacks of A.I. are interrelated — All data is dirty is related to D.E.I. Gender and racial biases surface in the answers A.I. provides. A.I. will perpetuate the harms that these biases produce as they become easier and easier to use and more and more prevalent. These harms are ones which society is only recently grappling with in a deep and meaningful way, and A.I. could roll back much of our progress.

We’ve seen this start to happen. Early reports from image creation tools discuss a European white male bias inherent in these tools — ask it to generate an image of someone in a specific occupation, and receive many white males in the results, unless that occupation is stereotypically “women’s work.” When AI is used to perform HR tasks, the software often advances those it perceives as males more quickly, and penalizes applications that contain female names and pronouns.

The bias is in the data and very, very difficult to remove. The entirety of digital written language over-indexes privileged white Europeans who can afford the tools to become authors. This comparably small pool of participants is also dominantly male, and the content they have created emphasizes white male perspectives. To curate bias out of the training data and create an equally representative pool is nearly impossible, especially when you consider the exponentially larger and larger sets of data new LLM models require for training.

Further, D.E.I. overflows into environmental impact. Last fall, the Fifth National Climate Assessment outlined the country’s climate status. Not only is the U.S. warming faster than the rest of the world, but they directly linked reductions in greenhouse gas emissions with reducing racial disparities. Climate impacts are felt most heavily in communities of color and low incomes, therefore, climate justice and racial justice are directly related.

Flawed data leads to “Hallucinations” & harms Brands

“Brand Safety” and How A.I. can harm Brands

Brand safety is the practice of protecting a company’s brand and reputation by monitoring online content related to the brand. This includes content the brand is directly responsible for creating about itself as well as the content created by authorized agents (most typically customer service reps, but now AI systems as well).

The data that comes out of A.I. agents will reflect on the brand employing the agent. A real life example is Air Canada. The A.I. chatbot gave a customer an answer that contradicted the information in the URL it provided. The customer chose to believe the A.I. answer, while the company tried to say that it could not be responsible if the customer didn’t follow the URL to the more authoritative information. In court, the customer won and Air Canada lost, resulting in bad publicity for the company.

Brand safety can also be compromised when a 3rd party feeds A.I. tools proprietary client data. Some terms and condition statements for A.I. tools are murky while others are direct. Midjourney’s terms state,

“By using the Services, You grant to Midjourney […] a perpetual, worldwide, non-exclusive, sublicensable no-charge, royalty-free, irrevocable copyright license to reproduce, prepare derivative works of, publicly display, publicly perform, sublicense, and distribute text and image prompts You input into the Services” 

Midjourney’s Terms of Service Statement

That makes it pretty clear that by using Midjourney, you implicitly agree that your data will become part of their system.

The implication that our client’s data might become available to everyone is a huge professional risk that Oomph avoids. Even using ChatGPT to provide content summaries on NDA data can open hidden risks.

What are “Hallucinations” and why do they happen?

It’s important to remember how current A.I. chatbots work. Like a smartphone’s predictive text tool, LLMs form statements by stitching together words, characters, and numbers based on the probability of each unit succeeding the previously generated units. The predictions can be very complex, adhering to grammatical structure and situational context as well as the initial prompt. Given this, they do not truly understand language or context. 

At best, A.I. chatbots are a mirror that reflects how humans sound without a deep understanding of what any of the words mean. 

A.I. systems are trying its best to provide an accurate and truthful answer without a complete understanding of the words it is using. A “hallucination” can occur for a variety of reasons and it is not always possible to trace their origins or reverse-engineer them out of a system. 

As many recent news stories state, hallucinations are a huge problem with A.I. Companies like IBM and McDonald’s can’t get hallucinations under control and have pulled A.I. from their stores because of the headaches they cause. If they can’t make their investments in A.I. pay off, it makes us wonder about the usefulness of A.I. for consumer applications in general. And all of these gaffes hurt consumer’s perception of the brands and the services they provide.

Poor A.I. answers erode Consumer Trust

The aforementioned problems with A.I. are well-known in the tech industry. In the consumer sphere, A.I. has only just started to break into the public consciousness. Consumers are outcome-driven. If A.I. is a tool that can reliably save them time and reduce work, they don’t care how it works, but they do care about its accuracy. 

Consumers are also misinformed or have a very surface level understanding of how A.I. works. In one study, only 30% of people correctly identified six different applications of A.I. People don’t have a complete picture of how pervasive A.I.-powered services already are.

The news media loves a good fail story, and A.I. has been providing plenty of those. With most of the media coverage of A.I. being either fear-mongering (“A.I. will take your job!”) or about hilarious hallucinations (“A.I. suggests you eat rocks!”), consumers will be conditioned to mistrust products and tools labeled “A.I.” 

And for those who have had a first-hand experience with an A.I. tool, a poor A.I. experience makes all A.I. seem poor. 

A.I.’s appetite for electricity is unsustainable

The environmental impact of our digital lives is invisible. Cloud services that store our lifetime of photographs sound like featherly, lightweight repositories that are actually giant, electricity-guzzling warehouses full of heat-producing servers. Cooling these data factories and providing the electricity to run them are a major infrastructure issue cities around the country face. And then A.I. came along.

While difficult to quantify, there are some scientists and journalists studying this issue, and they have found some alarming statistics: 

While the consumption needs are troubling, quickly creating more infrastructure to support these needs is not possible. New energy grids take multiple years and millions if not billions of dollars of investment. Parts of the country are already straining under the weight of our current energy needs and will continue to do so — peak summer demand is projected to grow by 38,000 megawatts nationwide in the next five years. 

While a data center can be built in about a year, it can take five years or longer to connect renewable energy projects to the grid. While most new power projects built in 2024 are clean energy (solar, wind, hydro), they are not being built fast enough. And utilities note that data centers need power 24 hours a day, something most clean sources can’t provide. It should be heartbreaking that carbon-producing fuels like coal and gas are being kept online to support our data needs.

Oomph’s commitment to 1% for the Planet means that we want to design specific uses for A.I. instead of very broad ones. The environmental impact of A.I.’s energy demands is a major factor we consider when deciding how and when to use A.I.

Using our Values to Guide the Evaluation of A.I.

As we previously stated, our company values provide a lens through which we can evaluate A.I. and look to mitigate its negative effects. Many of the solutions cross over and mitigate more than one effect and represent a shared commitment to extracting the best results from any tool in our set

Smart

Driven

Personal

1% for the Planet

In Summary

While this article feels like we are strongly anti-A.I., we still have optimism and excitement about how A.I. systems can be used to augment and support human effort. Tools created with A.I. can make tasks and interactions more efficient, can help non-creatives jumpstart their creativity, and can eventually become agents that assist with complex tasks that are draining and unfulfilling for humans to perform. 

For consumers or our clients to trust A.I., however, we need to provide ethical evaluation criteria. We can not use A.I. as a solve-all tool when it has clearly displayed limitations. We aim to continue to learn from others, experiment ourselves, and evaluate appropriate uses for A.I. with a clear set of criteria that align with our company culture. 

To have a conversation about how your company might want to leverage A.I. responsibly, please contact us anytime.

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