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

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. 

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”) 

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.

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

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. 

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: 

• Accurate brand representation — Poor accessibility hides facts from LLMs. When customers ask an AI  assistant for “best X for Y,” your content may not be shown — or worse, misrepresented. Fixing accessibility reduces brand risk and increases content authority.
• Engagement boost — Improvements that increase accurate citations and AI visibility can increase referral traffic, feature mentions, and lead quality. In a landscape where AI Answers are reducing click-through rates, keeping the traffic you have on your site for longer and building brand trust becomes vital.
• Increased exposure — Digital inclusion makes your content widely accessible to machines and the machines that assist humans. Think about a search engine as another human-assistive device, just like a keyboard or screen reader. 
• Multi-pronged benefits — Accessibility improvement improves traditional SEO, can benefit mobile performance, and reduces the risks associated with accessibility compliance policies. 

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.

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

• AHREFs.com: Is SEO Dead? Real Data vs. Internet Hysteria
• SearchEngineJournal.com: How LLMs Interpret Content: How To Structure Information For AI Search
• InclusionHub.com: SEO and Web Accessibility: What You Need to Know (from 2020, but still relevant)

As a digital services firm partnering with destination marketing organizations (DMOs) across the U.S., we’re helping teams navigate what’s already proving to be a volatile 2025—especially on the inbound side. Analysis from the World Travel & Tourism Council (WTTC) projects a stark reality: the U.S. economy will miss out on $12.5 billion in international visitor spending this year, with inbound spend expected to dip to just under $169B, down from $181B in 2024. Even more concerning, the U.S. is the only country among 184 economies in WTTC’s study forecast to see an inbound-spend decline this year.

While external market forces remain largely beyond control, we’ve identified three strategic areas where DMOs can focus their digital platforms to weather this storm and continue demonstrating measurable demand to their partners.

1. Transform Content Into Action-Driving Experiences

Why this strategic shift matters now

With inbound spend shrinking by $12.5B and key feeder markets weakening, undecided travelers need clarity and confidence to choose your destination. Content that reduces uncertainty and highlights immediate value converts better than generic inspiration.

Strategic implementation approach

Activate “Go Now” signals. Combine always-on inspiration with time-sensitive reasons to visit—shoulder-season value, midweek deals, cooling weather breaks—strategically mapped to the soft periods your analytics reveal. 

Elevate discovery through intelligent architecture. Curate SEO-optimized content hubs organized by Themes (outdoors, arts, culinary) and Moments (fall colors, winter lights). Implement structured data (FAQ, Event, Attraction) with strategic internal linking architecture so travelers find relevant options fast.

Deploy micro-itineraries for immediate conversion. Design 24–48-hour “micro-itins” featuring embedded maps, transit and parking guidance, and seamless handoffs to bookable partners. Partnering with platforms like MindTrip reduces content team effort while accelerating output—a strategy that’s proven particularly effective for our DMO clients facing resource constraints.

Authority-driven event content optimization. Event pages generate the highest intent traffic. Enhance them with rich media, last-minute planning resources, and strategic “if sold-out, try this” alternatives.

Transparent value communication. Feature free experiences prominently, implement intuitive budget filters, and deploy “Best Time to Visit” calendars comparing crowds and pricing by week and month. Transparency builds trust, and trust drives conversion.

2. Build Your Competitive Moat Through Data-Driven Audience Cultivation

Your first-party data represents your most defensible competitive advantage. As platform targeting becomes increasingly constrained and inbound spending softens, DMOs that build and activate their own audience will capture attention far more efficiently than those relying solely on paid channels.

Strategic audience development

Implement high-intent capture everywhere. Deploy contextual email and SMS prompts across high-intent templates—events, itineraries, trip planners, partner directories. Offer valuable micro-perks like exclusive maps and early event alerts. 

Master progressive profiling. Collect visitor preferences—season, interests, party type, origin market—over multiple touchpoints rather than overwhelming users with lengthy initial forms. 

Create actionable audience segments. Develop cohorts around 2025’s market realities: last-minute planners, shoulder-season seekers, road-trippers, value hunters, family weekenders, and meetings planners. 

Future-proof attribution systems. Combine GA4 with server-side tagging and standardized UTM schemas for every partner handoff. Track outbound clicks, partner session quality, itinerary saves and usage, offer redemptions, and newsletter-driven sessions. This comprehensive approach ensures you maintain visibility into conversion paths as third-party cookies disappear.

Deploy trend-driven editorial strategy. Develop weekly dashboards blending organic query trends, on-site search terms, partner click-through rates, and feeder-market signals. When interest dips in one market, pivot homepage modules and paid social toward value and itinerary content targeting more resilient markets.

3. Transform Partner Relationships Through Measurable Value Delivery

In a softening inbound environment where domestic spending carries approximately 90% of the economic load, your partners need two critical elements: qualified attention and proof of conversion. Your website should function as the region’s premier meta-directory and conversion engine.

Experience optimization strategies

Enable one-click handoffs with context preservation. Pass user filters—dates, neighborhoods, price ranges—directly into partner sites and booking engines while preserving state if travelers return. 

Deploy persistent trip planning tools. Allow users to save places and generate shareable itineraries with intelligent handoffs: “Book these two hotels,” “Reserve rentals,” “Get festival passes.” 

Create compelling partner storefronts. Develop rich partner profiles featuring availability widgets, authentic reviews, social proof, and clear calls-to-action. 

Implement strategic co-op modules. Design paid placements that provide value rather than feeling like advertisements: “Local Favorites” carousels, sponsor highlights, seasonal deal tiles—rotated by audience cohort and season. This generates additional revenue while maintaining user experience quality.

Establish closed-loop reporting systems. Standardize UTM tracking, monitor outbound events, and where permitted, implement partner pixels and offer codes to report assisted conversions by category and campaign. Partners need proof of ROI, and data-driven reporting builds stronger, more profitable relationships.

How Oomph Can Accelerate Your Success

If you’re experiencing softer international interest, shorter booking windows, or declining partner satisfaction, you’re facing the same challenges as DMOs nationwide. The organizations pulling ahead aren’t waiting for market recovery—they’re strengthening their digital platforms through strategic content optimization, systematic audience cultivation, and demonstrable partner value creation.

Our proven methodology transforms these challenges into competitive advantages.

We’ll conduct a comprehensive audit of your digital platform against these three strategic pillars, quantify immediate optimization opportunities, and provide your partners with what they need most: qualified, measurable demand. The market headwinds are real, but the right strategic approach can help you maintain resilience and emerge stronger when conditions improve. Let’s navigate these challenges together.

The Brief

The APPROACH

The Results

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: 

• Focus on AI Literacy — Ensure your team understands AI and its capabilities. Start with use cases that add value immediately.
• Prioritize Data Quality — AI models need high-quality, relevant data to be effective. Start cleaning and preparing your internal data before implementing AI at scale.
• Combine AI and Human Expertise — Use AI to augment human capabilities, not replace them. Think of AI as a junior employee who will require input, alignment, and reinforcement.
• Experiment and Iterate — Be willing to try new approaches and adapt based on results. Include measurement in your plans — collect data before and after to benchmark progress. 
• Embrace Ethical AI — Implement policies to ensure AI is used responsibly and ethically. Investigate ways the company can offset carbon and support cleaner energy, as AI tools require more electricity than non-AI tools. Understand hallucinations and the new, more complex “scheming” in reasoning models problem.
• Prepare for Change — Understand that technology is constantly evolving, and business models will need to adapt.

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 BRIEF

THE APPROACH

Understanding the Educational Purpose

THE RESULTS

In our design, the whole community drives the learning experience, not just the teachers and coursework. It’s easy for students to connect with others who are taking the same courses, while members-only forums provide a place for productive networking, questions, stories, and support. Some forums are attached to specific lessons, so that the dialogue isn’t just between teachers and students; all members, including alumni, can participate and share their learnings on a given topic.

Another component, the accountability partner system, was crucial for achieving Seraphic’s goal of driving lasting change. Research shows that publicly sharing a goal gives people a 65% chance of success, while reporting to a specific accountability partner boosts that chance to 95%.

Finally, our learning tools were designed to enhance both content comprehension and retention. Course videos were a key feature, designed not just for the course, but for reference over time. Students have the ability to bookmark videos and attach notes to specific sections, letting them revisit important info whenever they need it.

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THE IMPACT

While online learning has been around for a long time, recent advancements in design and functionality make it possible for learning platforms to have a transformative impact on individuals and across society.

In the case of Atlus∗U, it’s not just the coursework that drives users’ learning; an entire community is mobilized to help you succeed. With a focus on collaborative, lifelong learning, our design brings together farmers from around the world to improve their business, grow healthier food, and protect our world.

Need help building an effective online learning platform? Let’s talk about your goals and how to achieve them.

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.

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:

• A.I. is built upon biased and flawed data
• Bias & flawed data leads to the perpetuation of stereotypes
• Flawed data leads to Hallucinations & harms Brands
• Poor A.I. answers erode Consumer Trust
• A.I.’s appetite for electricity is unsustainable

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: 

• Smart
• Driven
• Personal
• Environmentally Committed

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: 

• Training GPT-3 required more than 1,200 MWh which led to 500 metric tons of greenhouse gas emissions — equivalent to the amount of energy used for 1 million homes in one hour and the emissions of driving 1 million miles. GPT-4 has even greater needs.
• Research suggests a single generative A.I. query consumes energy at four or five times the magnitude of a typical search engine request.
• Northern Virginia needs the equivalent of several large nuclear power plants to serve all the new data centers planned and under construction.
• In order to support less consumer demand on fossil fuels (think electric cars, more electric heat and cooking), power plant executives are lobbying to keep coal-powered plants around for longer to meet increased demands. Already, soaring power consumption is delaying coal plant closures in Kansas, Nebraska, Wisconsin, and South Carolina.
• Google emissions grew 48% in the past five years in large part because of its wide deployment of A.I. 

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

• Limit direct consumer access to the outputs of any A.I. tools, and put a well-trained human in the middle as curator. Despite the pitfalls of human bias, it’s better to be aware of them rather than allow A.I. to run unchecked
• Employ 3rd-party solutions with a proven track-record of hallucination reduction

Driven

• When possible, introduce a second proprietary dataset that can counterbalance training data or provide additional context for generated answers that are specific to the client’s use case and audience
• Restrict A.I. answers when qualifying, quantifying, or categorizing other humans, directly or indirectly

Personal

• Always provide training to authors using A.I. tools and be clear with help text and microcopy instructions about the limitations and biases of such datasets

1% for the Planet

• Limit the amount of A.I. an interface pushes at people without first allowing them to opt in — A.I. should not be the default
• Leverage “green” data centers if possible, or encourage the client using A.I. to purchase carbon offset credits 

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.

Additional Reading List

• “The Politics of Classification” (YouTube). Dan Klyn, guest lecture at UM School of Information Architecture. 09 April 2024. A review of IA problems vs. AI problems, how classification is problematic, and how mathematical smoothness is unattainable.
• “Models All the Way Down.” Christo Buschek and Jer Thorp, Knowing Machines. A fascinating visual deep dive into training sets and the problematic ways in which these sets were curated by AI or humans, both with their own pitfalls.
• “AI spam is already starting to ruin the internet.” Katie Notopoulos, Business Insider, 29 January 2024. When garbage results flood Google, it’s bad for users — and Google.
• Racial Discrimination in Face Recognition Technology, Harvard, 24 October 2020. The title of this article explains itself well. 
• Women are more likely to be replaced by AI, according to LinkedIn, Fast Company, 04 April 2024. Many workers are worried that their jobs will be replaced by artificial intelligence, and a growing body of research suggests that women have the most cause for concern. 
• Brand Safety and AI, Writer.com. An overview of what brand safety means and how it is usually governed.
• AI and designers: the ethical and legal implications, UX Design, 25 February 2024. Not only can using training data potentially introduce legal troubles, but submitting your data to be processed by A.I. does as well.
• Can Generative AI’s Hallucination Problem be Overcome? Louis Poirier, C3.ai. 31 August 2023. A company claims to have a solution for A.I. hallucinations but doesn’t completely describe how in their marketing.
• Why AI-generated hands are the stuff of nightmares, explained by a scientist, Science Focus, 04 February 2023. Whether it’s hands with seven fingers or extra long palms, AI just can’t seem to get it right.
• Sycophancy in Generative-AI Chatbots, NNg. 12 January 2024. Human summary: Beyond hallucinations, LLMs have other problems that can erode trust: “Large language models like ChatGPT can lie to elicit approval from users. This phenomenon, called sycophancy, can be detected in state-of-the-art models.”
• Consumer attitudes towards AI and ML’s brand usage U.S. 2023. Valentina Dencheva, Statistica. 09 February 2023
• What the data says about Americans’ views of artificial intelligence. Pew Research Center. 21 November 2023
• Exploring the Spectrum of “Needfulness” in AI Products. Emily Campbull, The Shape of AI. 28 March 2024
• AI’s Impact On The Future Of Consumer Behavior And Expectations. Jean-Baptiste Hironde, Forbes. 31 August 2023.
• Is generative AI bad for the environment? A computer scientist explains the carbon footprint of ChatGPT and its cousins. The Conversation. 23 May 2023

THE BRIEF

THE APPROACH

Fixed Image Processing Bottlenecks

Because we were already in the code, both teams agreed this was a great opportunity to identify improvements to boost page performance. We found that image processing was a drag — the site previously processed images during deployment rather than processing them ahead of time on the back-end. Oomph used the JSON API Image Styles module to create image derivatives (copies) in different sizes, ultimately decreasing build times. 

Lightened the Load on the Back-End

As Oomph configured the new architecture, we scoured the site for other opportunities to reduce cruft. Additional improvements included removing deprecated code and rewriting code responsible for creating front-end pages, eliminating static queries running thousands of times during page creation. We also resized large images and configured their Drupal site to set sizing guardrails for photos their team may add in the future.

Home page weight before and after:

Page Weight	Before	After	% Change
Desktop	25.41 MB	3.61 MB	Down 85.79%
Mobile	12.07 MB	3.62 MB	Down 70.01%

Visualizing the improvements to loading speed:

THE RESULTS

Have you ever waved to someone and they didn’t wave back? Awkward, right? But are you sure they could see you and recognize you? Was the sun in their eyes? Were you too far away? Were you wearing a face mask?

There is a similar situation with your branding on your website. On a smaller mobile device, is your logo legible, or are the words shrunk down and too small? Are the colors high-contrast enough to be seen on a sunny day? Is there consistency between your social media avatar and your website, between your print materials and your digital advertising? Can customers recognize your brand wherever it might be displayed? 

For your brand to be the most successful, it takes a little extra effort to think through all of these possible scenarios. But it’s worth it, or your customers will give you the cold shoulder, whether they intended to or not. 

This extra bit of strategy and planning around your brand is called “Responsive Branding.” Just like responsive design, where your website’s content adapts to the device a customer is using, responsive branding adapts to the device, the medium, and the platform while also considering situations like low light, high light, animated, or static.

Oomph works with organizations across industries to build or refresh responsive brands that serve and delight their users across the full spectrum of digital experiences. Here’s what we’ve learned about responsive branding and our tips for creating one that works. 

What Is Responsive Branding?

Let’s first start with what you’ve probably already heard — responsive web design. Coined by Ethan Marcotte in 2010, the “responsive” part came to mean that a web design responded to the size of the screen, from a phone to a tablet to a widescreen desktop monitor. 

Then came responsive logos. These take the elements of the main logo and adapt them for different sizes and use cases. A logo might have too much detail to be legible as a small social media icon, for example.

Responsive Branding blends these ideas and looks at the design system holistically. A successful responsive brand may include:

• A logo version for different sizes and applications. Variations might include one with the tagline, one without, one with a mark and typeface, one just the name, one just the mark, the possibilities go on!
• Secondary logo options which may include abbreviations, monograms, or different combinations of the main elements
• When applicable, an internationalized version of the wordmark and tagline
• A color palette that can provide variation while addressing accessibility and situations of low light or high glare
• A fluid typography system that provides contrast between sizes within different size media (including print!)
• A fluid spacing system
• Motion when appropriate, like video intros and outtros

Why Responsive Branding Matters

Your business makes a huge investment in building a brand that stands apart from the competition while communicating your personality and value. You are building trust with customers through every interaction. When your brand works well in one situation but not another, it erodes trust. 

A strong brand will be clear, understandable, and memorable for all users in all situations. Whether you have physical locations or digital ones, the brand works with the same consistent strength and message every time.

When you invest in a responsive brand, you: 

• Make your brand more visible. More users can connect with you when you have a brand that’s both recognizable and responsive. Given that 60% of consumers are more likely to buy from a brand they recognize, the opportunity cost of not getting this right is huge. 
• Improve accessibility. Digital accessibility means people of all abilities can use your website and experience your brand. Up to 1 in 4 American adults live with a disability, so a lack of accessibility means fewer customers to reach. For visual brand elements, that means considerations for color blindness, low vision, and cognitive impairments like dyslexia. 
• Create a more consistent brand. Responsive branding equips your team with the tools to express your brand effectively across mediums. That means less time involved in building new assets and a more consistent experience that meets your users’ high expectations.

3 Elements of a Responsive Brand

A responsive brand is more than a shape-shifting logo. The most responsive brands make strategic use of these three elements: 

1. Logo

Your logo is the first piece of your brand that customers will recognize. Using a single-state logo can compromise that impression — a logo that looks great at a large scale is often unintelligible as a small icon. 

Responsive logo designs help ensure your logomark is clear and impactful no matter where you apply it. Beyond the size considerations we mentioned, it should include different formats like horizontal, vertical, and square to support many different digital, social, and print platforms. 

Some other techniques we use to create scalable logos include:

• Dropping words or lettering
• Dropping the icon or image
• Creating an abbreviation version
• Stacking the icon/image and text/lettering
• Combining separate elements into a single icon
• Simplifying details and shapes within logo marks
• Varying thickness and white space to make small logos more legible

Oomph Tip: It’s okay to take several design rounds to get it right. Iterating helps uncover where you’ll use the logo, what it must convey, and which colors and iconography can best support that purpose. We went through several design iterations with our client AskRI before settling on a bold, simple font and clear chat bubble icon that plays off the state of Rhode Island’s distinctive shape. 

Color Palette

A responsive color palette is less about picking complementary shades on a color wheel and more about creating an experience that works in all situations. People with visual impairments and people on low-lit smartphones, for example, rely on high-contrast color combinations to engage with your brand. 

Start by following the Web Content Accessibility Guidelines (WCAG), which include specific recommendations for color contrast ratios. Colors that meet that standard include light text with dark backgrounds, or vice versa. 

Depending on where your brand appears, you may need to adjust your color palette for different settings. For example, your full-color logo might look stunning against a solid white background but becomes illegible against bright or dark colors. A single color logo is useful for some digital use cases like Windows web icons and iOS Pinned Tabs. In non-digital spaces, single color logos are great when color printing isn’t an option, such as with engraving or embroidery. Build out alternate color variations where necessary to make sure your palette works with you – not against you – across your materials. 

Oomph Tip: If your brand palette is already set in stone, try playing with the brightness or saturation of the values to meet recommendations. Often your brand colors have a little wiggle room when combinations are already close to passing corformance ratios. Check out our article about this issue for more pointers.

Typography and Layouts

Responsiveness is also important to consider when structuring web pages or marketing collateral. The most legible layouts will incorporate adaptable typography with clear contrast and simple scaling. 

When selecting a font, be sure to think about: 

• Minimum legible sizes. When is small too small? It depends, of course, but don’t forget older customers, those with low vision, or those in situations that make reading more difficult. Use at least a 16px size for digital products and 12pt size for print. These might change depending on the font, but these sizes are a great place to start.
• Inclusive typefaces. Many designers overlook how understandable certain letterforms appear to be. A way to quickly review is to look at the number 1, lowercase l (L), and uppercase I (i) of any typeface. Then also look at the capital O, the zero, and the lowercase o — 1lI O0o. Can you tell the difference when these characters are out of context? If you can’t, then your customers might have trouble as well. Use your judgment, but also use this trick to expose possible problems with your communication. These five keys to accessible web typography are a great place to start. 
• Scaling ratios. Similar to a musical scale of harmonious notes, a typographic scale is a collection of font sizes that work well together. Using a typographic scale helps to make your text visually appealing and readable for different users across different devices. Some examples include exponential scales, where you multiply the previous font size by a ratio to get the next font size, and Fibonacci sequences, where you add a number from the Fibonacci set to the base font value to get a new size in the scale. 

Oomph Tip: Don’t go it alone. Tools like Typescale and Material UI’s The Type System can simplify typography selection by recommending font sets that meet usability and scalability requirements. And the U.S. Design System has some suggestions as to which typefaces are the most accessible.

How To Get Started With Responsive Branding

To create a responsive brand that resonates, you first have to identify what elements you need and why you need them. That second part is your secret sauce: finding a balance between a design your users can recognize and one that inspires them. 

A design audit can zero in on the needs of your brand and your audience, so you can create a responsive design system that meets both. Not sure where to start? Let’s talk.

The Brief

The Approach

Tilling the Existing Farm Fresh RI Website

We focused our audit on FFRI’s public-facing site, the primary audience’s first touch point. Our aim was to highlight any barriers preventing these consumers from efficiently accessing information or completing key tasks. Though there is a mobile App in the ecosystem, we focused on the introduction of the brand and its value, thinking that if the site]s initial impression were stronger, more customers would utilize other digital tools. 

Through the audit, we uncovered several key areas for improvement that could significantly enhance the user experience:

Accessibility Gaps  

Through an automated and manual scan, we found and organized a number of accessibility and usability issues across the site.

•   Clickable hero images that lacked clear visual cues
•   Insufficient color contrast in key areas
•   Inconsistent translation options for non-English-speaking users

Organizing Content

Large hero images push important content below the fold, making it difficult for users to access crucial information quickly. By placing content above the fold, the site can quickly show users they have landed in the right place. We suggested revising the layout to shorten the height of page hero imagery, which helps users reach key resources with minimal scrolling.

Finding the Content

The journey to find critical information, such as farmers’ market details, is complicated and fragmented across multiple pages. By consolidating this information, we recommended streamlining the user journey while also freeing up space to highlight other essential offerings.

The Results

THE BRIEF

THE CHALLENGE

MULTIPLE COUNTRIES AND A GLOBAL OPTION

SIX LANGUAGES

MANY 3RD-PARTY INTEGRATIONS

THE APPROACH

LEARN VS. SHOP

Supporting Visitor Intention with Two Different Modes

While the technical challenges were being worked out, the user experience and design had to reflect a cutting-edge company. With the launch of their revolutionary product, the BLK 360, in 2018, Leica positioned itself as the Apple of the geospatial measurement community — sleek, cool, cutting-edge and easy to use. While many companies want to look as good as Apple, few of them actually have the content and product to back it up.

The navigation for the site went through many rounds of feedback and testing before deciding on something radically simple — Learn or Shop. A customer on the website is either in an exploratory state of mind — browsing, comparing, reviewing pricing and specifications — or they are ready to buy. We made it very clear which part of the website was for which.

This allowed us to talk directly to the customer in two very different ways. On the Learn side, the pages educate and convince. They give the customer information about the product, reviews, articles, sample data files, and the like. The content is big, sleek, and leverages video and other embedded content, like VR, to educate.

On the Shop side the pages are unapologetically transactional. Give the visitor the right information to support a purchase, clearly deliver specs and options like software and warranties, without any marketing. We could assume the customer was here to purchase, not to be convinced, so the page content could concentrate on order completion. The entire checkout process was simplified as much as possible to reduce friction. Buying habits and patterns of their user base over the past few site iterations were studied to inform our choices about where to simplify and where to offer options.

THE RESULTS