We’ve all been there: you ask ChatGPT or Claude to write something, and it comes back… fine. Not wrong, not broken. Just… beige. It reads like your first draft as an intern when you were trying really hard not to mess up.

Here’s the truth: the problem isn’t the AI. It’s how you use it.

At Mod Op we work with AI daily, from campaign concepting and brand voice development, to data analysis and technical automation. And the biggest thing we’ve learned? AI is only as good as your ability to use it effectively.

This challenge isn’t just for developers, marketers, or creatives. It’s universal. Whether you’re a developer debugging code, a strategist shaping messaging, or an analyst interpreting data, the ability to communicate clearly with AI determines the quality of what you get back. Prompting is quickly becoming the new professional language, one that transcends disciplines.

To be prompt successfully, you have to skip the “hacks” or secret keywords and think like a creative leader, providing the kind of clarity and direction you’d give your best team when using AI tools.

AI Isn’t Magic. It’s a Mirror.

Think of AI as an eager (but extremely literal) creative partner. It wants to help. It wants to make you happy. But it has no intuition, thus it only reflects what you tell it.

If you say, “Write a social post for our new product,” the AI doesn’t know your audience, your tone, your goal, or what makes your product different. So, it plays it safe. You get something pleasant but forgettable.

The fix? Don’t just tell AI what to do. Tell it what outcome you want. The clearer your goal and context, the more relevant and on-brand the result will be.

Three Ways to Level Up How You Prompt AI

The difference between good and great AI output comes down to how you frame your requests. Here’s how to structure them for better results:

1. Shift your prompt from a command to your intended outcome

Let’s start with a small shift.

Instead of saying: “Fix this code that’s not working.”

Try: “This Python script is supposed to process CSV files and generate a sales report, but it’s failing when files are missing a header. Review the code, identify the issue, and rewrite it with error handling and a summary of missing data.”

The first prompt gives AI a task. The second? It gives it a mission. A mission with context, purpose, and expected outcomes. It transforms the model from a “code fixer” into a tool for problem-solving.

When you describe the goal, the audience, and the tone, you move from basic output to aligned, strategic work.

2. Give AI Professional Context, Not Just General Tasks

AI is flexible, but directionless by default. One simple way to focus it: assign it a role.

Try: “You are the head of brand strategy at a creative agency. Develop a go-to-market plan for a startup entering the sustainability space.”

Suddenly, the AI writes with purpose. The tone shifts, the ideas become more structured, and the recommendations feel like they’re coming from an experienced strategist rather than a neutral machine.

This works in any discipline. Just ask it to “act as a creative director,” “a social strategist,” or “a CMO presenting to investors.” It’s like flipping the right switch in its brain.

3. Replace Vague Requests with Clear Criteria for Success

Want a guaranteed improvement in your AI output? Tell it what success looks like.

For example: “Write a 150-word LinkedIn post announcing our rebrand.

Include:

• A short story about why we changed
• One line about what’s new
• A thank-you to our clients and team
• A friendly call to action at the end

This is the single easiest way to eliminate “almost good” drafts. By outlining your must-haves, you give the AI a finish line to aim for. It’s the same principle as a creative brief; The more specific your parameters, the better the creative execution.

The Power of Better Prompts

Let’s say you’re launching a new eco-friendly product.

Prompt A: “Write a tweet about our new eco-friendly packaging.”

AI Output: “We’re excited to launch our new eco-friendly packaging! Better for you, better for the planet. 🌍”

Now, Prompt B: “You’re the social media manager for a premium skincare brand. Write a tweet announcing our new eco-friendly packaging.”

• Target: Gen Z and millennial shoppers who value sustainability and design • Tone: confident, modern, and witty • Goal: spark engagement and shares • End with a short hashtag

AI Output: “Luxury meets sustainability. ✨ Our new packaging is 100% recyclable – and100% beautiful. Because skincare should look as good as it feels. #GlowResponsibly”

This is the difference between “AI writing” and effective marketing. The results of prompt A aren’t bad – just bland. With prompt B, you give the model context, a role, and a goal. So, it delivers something on-brand and nearly ready to post.

Why This Matters for Marketers and Leaders

AI isn’t replacing creativity. It’s amplifying it. But only if you know how to use it.

For marketing and brand teams, this means:

• Faster ideation: Get 10 campaign angles in the time it takes to write one.
• Smarter analysis: Summarize insights, reports, or audience trends in seconds.
• Better strategy: Pressure-test messaging or positioning through role-based prompts (“Act as a competitor CMO and critique this campaign”).

And for executives, it’s a leadership advantage. You can turn meeting notes into summaries, shape narratives faster, and explore new directions without waiting for a full team sprint.

The key isn’t using AI more. It’s using it smarter.

The Mod Op Approach: Creativity + Clarity

At Mod Op, we use AI to elevate creativity and strategic thinking.

Our teams integrate AI into workflows for research, branding, and campaign development. Not as a replacement for human insight, but as a collaborative tool that helps ideas move faster and sharper.

When you provide AI with the same level of detail and clarity you’d put in any strategic brief, the output quality matches.

That means giving it:

• The mission – what success looks like.
• The context – who it’s for and why it matters.
• The structure – what to include and what to avoid.

Master those three, and you’ll find that AI stops sounding robotic and starts sounding like an extension of your best strategic self. And that’s the essence of vibe coding. Learning to express intent clearly, whether you’re designing campaigns, writing code, or shaping strategy. And sometimes, being reminded of your days as an intern.

While they may be the priority experts in their brand and how it comes to life for the consumer, Marketers don’t always consider themselves as innovation drivers. But with their unique outlook, they can play a critical role in unlocking business innovation.   

At Mod Op, our brand strategy starts with the “irresistible truth.” That’s the nugget crystallized at the heart of the brand. The fundamental and immutable truth that consumers find not only relatable and valuable, but irresistible. And when marketers understand the unforgettable truth of their brand, they start moving down the road to unforgettable innovation. 

The power of being with your customer, understanding their journey, and circling points of tension is huge. Innovative marketers should be focused on developing frameworks that help the business locate points of friction, pull in insights about the causes and potential solutions, and test methods to reduce that friction. Marketers are uniquely positioned to do this because they already understand how the brand comes to life for consumers and where those critical moments of connection—or disconnection—occur. 

If you want to be successful in fulfilling your brand’s vision and mission, you need to lean into innovation and, for marketing teams, that typically begins with accelerating productivity. While marketers aren’t the only professionals using AI tools like ChatGPT, they need to deliver marketplace differentiation. This requires looking beyond simple tools like AI assistants to develop advanced solutions that connect with consumers in entirely new ways. 

Building the Incentive and the Willingness to Innovate 

While AI is useful for improving workflow processes, its most impactful application is unlocking fundamental changes in how marketing teams operate. This clears the path for businesses to explore customer-facing innovations that solve existing consumer problems in completely new ways. 

Let’s consider the famous case of Blockbuster and Netflix and how the incentive and willingness to innovate, or the lack thereof, determined the fate of these businesses. Blockbuster’s vision extended beyond renting videos; they aimed to be the global leader in rentable home entertainment. When Netflix entered the DVD market, Blockbuster was confident they could leverage their store network to compete. They even passed on acquiring Netflix early on because they planned to partner with Enron and launch their own streaming platform. When Enron went bankrupt, Blockbuster was still growing their brick-and-mortar business and never again prioritized building a streaming platform. 

Netflix, however, focused on consumers who found the brick-and-mortar experience inconvenient. Taking a trip to the store, choosing from limited available inventory, returning videos by specific deadlines, and paying late fees created friction points for consumers. Netflix understood that customers would gravitate toward a competitor that met them on their own terms, in their own time, and in their own homes. 

When Delivering Value Means Changing the Business’s Functionality 

Staying true to customer value and recognizing where that value occurs is essential to innovation even when, as with Blockbuster, the business is financially growing, or the innovation itself requires changing or sacrificing core functionality. For example, will Google’s AI-enabled search function reduce search revenue, the company’s primary revenue driver? It remains to be seen, but Google has bet that innovation will pay long-term dividends. 

As AI adoption accelerates, marketers are collaborating more closely with finance, IT, engineering, and other teams across the business. Mod Op’s approach begins with using AI to remove internal friction, then applies it to go-to-market strategy while gathering customer feedback. Throughout this process, internal resistance to change is common. People often view the time required for innovation as a threat to their deadlines and short-term market agility. To address this challenge, Mod Op developed its Audience Lab for internal testing and iteration, enabled by synthetic audiences (digital twinning) for market research and concept testing. 

The bigger vision for the business is not functional, but foundational. Marketers can take this opportunity, and their core expertise, and take an active role in inspiring the business to think of itself not as a maker of product, but as a platform of value. By understanding and responding to friction points along the path to purchase, marketers can deliver value earlier in the funnel.  

When committed to improving the consumer experience, marketers have the power to transform their marketplace experience and access into comprehensive business solutions. 

Just as the SEO Periodic Table became an essential framework for understanding search engine optimization, our team at Mod Op recognized the need for a similar structure for the age of generative AI. As platforms like ChatGPT, Perplexity, Claude, and Google’s AI Overviews transform how people discover and consume information, we created the GEO Periodic Table to help marketers navigate this emerging landscape. Generative Engine Optimization (GEO) is quickly becoming as critical as SEO once was for traditional search, and much like the SEO Periodic Table helped demystify ranking factors, our framework provides a structured way to understand how content gets discovered, surfaced, and recommended in AI-driven platforms. 

Building Visibility in the Age of AI-Powered Search. 

I recently joined Mod Op CTO, Tessa Burg on the Leader Generation podcast to discuss how traditional SEO principles translate to Generative Engine Optimization (GEO) and why a “GEO Periodic Table” is key to making sense of this emerging field.  

Here are 7 questions asked and answered:   

What is the GEO Periodic Table and why do we need it? 

The GEO Periodic Table is a conceptual framework inspired by the original SEO Periodic Table. Just like the original table organized ranking factors into categories like content, links, and user experience, the GEO version maps the most important factors that influence visibility in generative AI search engines. 

GEO introduces completely new essentials that don’t matter as much in traditional SEO. For example, entity strength, contextual authority, structured knowledge, and cross-channel reinforcement. Without a framework, teams are left guessing—unsure which factors matter most and often missing the essential elements needed to optimize content for generative AI discovery. 

How is GEO fundamentally different from traditional SEO?

SEO focuses on ranking factors for traditional search engines where you’re competing for position in a list of blue links. GEO is about discoverability and authority in AI-driven platforms that generate answers instead of listing results. (It’s why GEO is often used in the same conversations as AEO, or answer engine optimization.)  

Success now requires answering two questions: “How do I rank #1?” AND “How do I get cited, referenced, and trusted by AI engines when they’re formulating responses?” 

What are the most critical factors in the GEO Periodic Table? 

Four key elements stand out when considering the most critical factors for GEO:

1. Entity strength – This translates to how well-defined your brand, product, or topic is in the knowledge graph. AI engines need to understand what you represent.  
2. Contextual authority – Are you being cited by trusted third-party sources? It’s not just about having content; it’s about being referenced by others.  
3. Structured knowledge – This corresponds to content formatted for AI training and retrieval. This means clear, well-organized information that machines can easily parse and understand.  
4. Cross-channel reinforcement – using PR, paid media, and branding to support your long-term visibility strategy. 

What GEO factors should we prioritize for immediate impact? 

This is the most critical mindset shift. GEO is long-term infrastructure, not a short-term tactic. With AI engines pulling from multiple sources and constantly learning, you need layered investments. 

Think of it in three phases: short-term accelerants like paid campaigns and PR, mid-term reinforcements through structured content libraries and FAQs, and long-term assets like knowledge graphs and authoritative backlinks. 

GEO amplifies what was already true about search. It’s always been a marathon, but now the stakes are higher and the timeline is longer. 

What does the data tell us about this shift toward AI-powered search? 

AI-powered search reduces organic click-through rates by an average of 34.5%. The data shows this shift is accelerating rapidly with 50% of search traffic expected to shift to AI-driven answer engines by 2028.  

People are getting answers directly from AI instead of clicking through websites. Google’s AI Overviews confirms this — structured, authoritative content now trumps keyword optimization. This isn’t future speculation, it’s happening now. 

How should businesses start preparing for GEO today?

Start with the fundamentals: build structured, authoritative content and optimize for entity recognition. Make sure AI engines can clearly understand what your business does and what expertise you offer. Secure third-party citations through quality PR and relationship building. And, invest in branding and trust signals. In a world where AI engines make decisions about what to surface, trustworthiness is paramount. 

But remember, GEO is meant to work alongside SEO rather than replace it. You need SEO for traditional visibility, while GEO handles AI-first discovery. 

How do you see the GEO Periodic Table evolving?

Similar to the SEO Periodic Table the GEO framework will evolve over time as AI engines become more sophisticated. We’re already seeing changes in how these platforms handle multi-modal content, real-time information, and personalization. 

The framework brings structure to a chaotic landscape, helping teams prioritize, educate stakeholders, and future-proof their strategies for the semantic web. 

It’s not about choosing between SEO and GEO—it’s about integrating both into your strategy as we shift toward a world where context, meaning, and trust matter as much as keywords. The businesses that build strong foundations in both SEO and GEO now will be the ones thriving when this transition is complete. 

If you missed HubSpot’s recent INBOUND conference, here’s the short version: inbound isn’t gone, it’s looping. HubSpot outlined a new operating model for marketers working in an AI-first world. Our team spoke with John Murphy, Senior Partner Development Manager at HubSpot, about HubSpot’s new Loop Marketing playbook and how it changed the marketing game.   

Why the Playbook Had to Change 

Search is no longer the default first step in the customer journey. Buyers increasingly rely on AI systems for answers, and as a result, fewer searches translate into clicks. Recent figures suggest that over half of Google searches end without any site visit. This shift has major implications for marketers, who can no longer depend on search traffic as their main source of inbound leads. 

To adapt, HubSpot is reframing inbound as an ongoing loop rather than a linear funnel. At the same time, it is expanding its platform to help marketers unify their data, personalize at scale, and iterate campaigns more rapidly. As John shared with us, “Our customers really need unified customer data in order to be able to fuel their AI-powered growth.” 

AI Engine Optimization (AEO) 

The Loop Playbook highlights the rise of AI Engine Optimization, where the goal is not simply ranking in search engines but ensuring that brand expertise is included in AI-generated responses. That requires clean, structured data and content that directly answers buyer questions. 

HubSpot’s rebranded Data Hub (formerly Operations Hub) addresses this need by automating data cleanup, merging duplicate records, and enabling both technical and non-technical teams to build unified datasets.  

One of Data Hub’s new features is Data Studio. John describes it as “a spreadsheet-like interface for non-technical users” that can blend first-party and third-party data scattered across tech stacks, build custom datasets with the help of AI, instantly activate that data to segment lists, automate campaigns, and deliver better reporting. 

The Data Hub is designed to ensure that incoming data is clean and reliable. As explained, “it’s going to monitor, it’s going to find those problems, and it’s going to automatically fix those problems like duplicate data, formatting issues, or maybe outdated information”, and it’s all powered by AI. 

Beyond data hygiene, Data Hub empowers marketers to “enhance their segmentation strategy, do personalization in their campaigns at scale, and give them advanced attribution when combined with our Marketing Hub.” The ultimate goal is to provide complete, accurate, and up-to-date information for more effective marketing. 

Exploring the Framework of The Loop 

Loop Marketing is structured as a four-part cycle designed to repeat continuously: 

• Express: Define a brand’s point of view, style, and customer profile, with AI support for analyzing unstructured data like CRM notes and customer interactions. 
• Tailor: Enrich and segment data to deliver intent-driven personalization across websites, landing pages, and campaigns. 
• Amplify: Distribute campaigns across the channels where audiences already spend time—social, video, ads, and email—with integrated scheduling and management. 
• Evolve: Continuously test, measure, and refine campaigns in real time, rather than waiting months for results. 

This loop approach keeps inbound rooted in its original principles of education, value, and relationship-building, but aligns them with faster cycles and AI-assisted execution. The result is several operational shifts that enable: 

Faster Go-to-Market.  One of the most notable changes in the playbook is speed. AI-powered planning, drag-and-drop campaign design, and rapid testing allow teams to move from idea to launch in days rather than months. This compression of timelines makes it possible to stay relevant as buyer behavior shifts more quickly. 

Hyper-Personalization That Feels Human. Forget the days of ‘Hello [Name].’ Personalization has moved beyond inserting a first name in an email. With access to unified CRM data, marketers can now create content that reflects recent events, company updates, or product launches relevant to each contact. At the same time, cross-functional alignment across marketing, sales, and customer success helps ensure personalization feels helpful rather than intrusive. 

Next-Gen Ads and Content at Scale. AI-assisted tools help identify and repurpose content, such as extracting relevant clips from existing videos to create short-form assets for social channels. While full-scale AI video creation isn’t native to HubSpot yet, the playbook supports integrating third-party tools for content production and using HubSpot to distribute assets across channels. 

A Practical Takeaway for Marketers 

Inbound marketing isn’t disappearing; it is adapting to an era defined by AI, fragmented attention, and faster cycles. Success now depends on unifying data, embedding expertise into AI answers, and keeping campaigns in constant motion. 

For marketers, the challenge is no longer whether inbound still works. The challenge is how quickly and effectively they can loop. 

In business, the best approach to the status quo is improving it. But change is hard when that’s all your business knows. It’s human nature to normalize your baseline reality. To start imagining what change could look like, people need to be reminded it’s possible. 

Marketers who are educating themselves about AI right now are already aware that their own status quo could be better. If you have a clear vision, but you know you’re spending a lot of time on manual tasks, you’re probably looking at AI use cases to improve productivity. But adopting an AI pilot is a business initiative, not a transformation in itself. AI transformation demands its own strategy. An AI initiative needs responsible use guardrails – training, upskilling, a dedicated AI academy, a real vision of the future of work.  

As you scale beyond pilot projects, you’ll face resistance. People will worry that AI will eliminate jobs, devalue their expertise, or make more errors than humans. Others will question the costs and technical capabilities required to scale, or struggle to see clear ROI. These concerns aren’t unfounded—upskilling, training, and new technology are significant investments. The productivity gains from AI need to justify these expenses.  

AI Transformation Starts with Vision   

A lot of companies try to rush AI transformation before thinking through the strategy and AI framework. They make an assessment that the business isn’t efficient enough, and then they immediately switch to being dissatisfied with their status quo. They start worrying that their business will fall behind its competitors or even cease to exist in the coming years. But leading with fear creates resistance across the business, because it triggers people’s threat state. That inhibits people’s clear, long-term thinking. 

There’s an old saying about shipbuilding. If you want to build a ship, you don’t drum up the builders, gather the wood, divide the work, and then give orders. Instead, you want to teach the builders to yearn for the vast and endless sea. The desired vision is critical for outweighing business cost – and scaling AI to transform the business comes with real costs.  

Many organizations today approach AI with narrow, cost-focused objectives: “Let’s use AI to reduce cost and increase efficiency.” While these goals aren’t inherently wrong, they often fall short of unlocking AI’s transformative potential and may only deliver short-term gains, if any at all. 

The companies that get AI transformation right start with something much more powerful—a vision that inspires people. Look at how these well-known companies frame their purpose: 

• Microsoft: “To help people and businesses throughout the world realize their full potential.” 
• Nike: “To bring inspiration and innovation to every athlete in the world. If you have a body, you are an athlete.” 
• Habitat for Humanity: “A world where everyone has a decent place to live.” 
• Southwest Airlines: “To become the world’s most loved, most flown, and most profitable airline.”

The vision serves as your company’s north star— shaping mission, strategy, investments, and culture. AI is not a vision in itself; it enables a greater vision. Imagine what each of these companies can now achieve against their aspirational goals because of AI’s capabilities. 

AI is the Catalyst for Imagining Bigger Possibilities 

AI transformation requires you to make change feel irresistible, not just desirable. Employees need to be inspired, and your vision must captivate their attention. This vision also has to sit at the core of what is authentic and truthful about your business, team, and individuals. Cause and conviction are pillars of change for the marketing team. Start with your greatest strengths, your values, and what establishes your authority as a change leader. More importantly, to people across all functions and levels of your business. From there, lean into conviction: identify your bold stances, non-negotiable beliefs, and what you want your business to be known for leading. 

Understanding what needs to be done to improve the experience at those touchpoints will then show us a roadmap for upskilling and training. Once you have an understanding of where you need to go, you can make a clear assessment of the skills you’ll need to get there. With AI, this entails:  

• Learning about responsible use and its importance on an organizational level.  
• Social prompting for audience insights, personalization, and data interpretation at the team level.  
• Exploring AI tools and prompts on the individual level and turning feedback into data experience KPIs instead of social KPIs, as it pertains to your own career path. 

The suggestion of change itself can feel foreign. But to resist AI-driven change is to miss the opportunities AI has given us as professionals. AI clears the way for a marketer with strong vision to lean into that vision, and to draw in colleagues from across the business who want to define and be part of what’s next, and what’s possible. 

Big data has received a lot of attention over the past decade. Industry events, strategy sessions and journal articles have celebrated the promise of massive datasets. However, the real strategic edge may not come from going bigger, but from getting smarter with highly focused, relevant datasets empowered by AI.  

Marketers are increasingly embracing small datasets combined with the power of AI-assisted analysis. A carefully chosen, highly relevant dataset analyzed with assistance from AI delivers faster, more actionable insights than data lakes ever could. 

The Big Advantages of Small Data 

What does small data look like? These datasets are carefully selected, purpose-built collections of information that directly aligns with a specific business question or task. They can be quantitative or qualitative and need to be balanced and intentional in design. For example, 100 lost customer deep dives, 25 post-purchase in-depth interviews, 250 message testing participants, or 45 beta test participants.  

Beyond quicker data collection, small datasets offer the advantages of easier storage and management, and lower costs. Collection timelines are shorter (often days rather than months), and the resulting data is easier for stakeholders to understand and act on. Small data supports agile iteration, putting marketers in a position to quickly course-correct and innovate.  

When coupled with AI-assisted analysis, the advantage of a small dataset expands.  

Faster Insights: AI can quickly group open-ended responses from a small dataset into meaningful patterns and themes. AI can group customers into segments in 15 minutes or less.     

Agile Testing: This approach makes it possible to quickly design, execute and repeat testing, whether evaluating new product messaging, promotional offers or User Experience elements. The principle of “fail fast” becomes reality; marketers can try, learn and optimize faster than ever.        

Decision-making clarity: Focused datasets interpreted with AI empower businesses to make confident, evidence-based decisions without being overwhelmed by the distraction of irrelevant information. 

How to Get Started with Small Data + AI 

Transitioning to small data and AI begins by pinpointing a specific business objective where targeted insights will create measurable value. Once a question has been identified, the next step is to assemble a balanced dataset that is relevant and representative. Marketers can unlock insights by either leveraging existing data from marketing databases or by collecting new data through market research methods such as surveys, interviews or other feedback methods. User-friendly AI platforms can then transform raw information into actionable conclusions. It’s important that all AI-generated analyses should be reviewed by human subject matter experts to ensure meaningful interpretation. Ensure the insights are actionable with clear recommendations. Finally, share successes internally. Championing the role of small data and AI will build momentum for future initiatives.   

The union of small data and AI offers practical applications for marketers, including the following examples. 

Audience Segmentation and Personalization: Rapidly segment data to develop or refine personas and deliver tailored messaging for each segment, driving stronger engagement and conversion rates. 

Automated Reporting and Intelligence: AI-driven dashboards provide organized and updated information from compact datasets. Internal and external stakeholders can monitor results such as campaign performance and sentiment shifts in real time.   

Customer Journey Mapping: Even when working with small samples, AI helps visualize buyer paths, uncover friction points, and highlight Moment of Truth touchpoints. 

The real question isn’t whether to leverage the combination of small data and AI, but whether you can afford to ignore a strategic advantage that’s immediately actionable. In a world where speed, relevance and personalization drive growth, deploying focused datasets coupled with AI is evolving from a competitive advantage to a core expectation for high-performing organizations.  

If you’re like me, you now use AIs like ChatGPT, Midjourney, or Microsoft Copilot every day as part of your work. If you’re really like me, you’re seeing engineers, marketers and other partners use these off-the-shelf AIs as platforms on which to build bespoke tools or solve real business challenges. If you’re basically my long-lost twin, you’re championing this innovation within your organization—because “you + AI” isn’t just a productivity boost, it’s a new ROI narrative.  

And, if you’re thinking like most business leaders would, a pretty logical question starts forming:  
“Why wouldn’t I just use ChatGPT?”  

Beyond the Allure of Off-the-Shelf AI  

The appeal of off-the-shelf AI is obvious: it’s fast, it’s shiny, and, for many teams, it delivers a new baseline of productivity. But when the market is swimming in instant-access tools, “off-the-shelf” is just the beginning—not the answer.  

Why? Because off-the-shelf AI rarely delivers brand differentiation.  

Off-the-shelf AI is built for the median, not the individual brand. These AIs don’t think in terms of your brand’s voice or nuanced campaign objectives, and aren’t aware of historical lessons or performance data. Contextual intelligence remains a work in progress. Inexperienced teams may underestimate the hands-on effort required in off-the-shelf AI, and the “hidden costs” of investing further to shape on-brand, strategic output.  

Pre-packaged AI platforms also aren’t always transparent about where your data ends up. For organizations in regulated spaces, or with sensitive first-party data, this lack of control introduces risks—a non-starter for many sophisticated brands.  

What Bespoke, Agency-Built AI Unlocks  

At Mod Op, we build customized AI solutions. And when the mission is differentiation, customization is everything.  

An agency-guided approach means the AI is trained and tuned according to your market, your tone, your historic learnings, and your key performance drivers. You’re setting a new bar by turning proprietary insight into a lasting edge. 

Bespoke agency solutions, which blend technology with expertise at every stage, require strategists and creative thinkers to be part of the workflow – nearly impossible to replicate with an out-of-the-box platform.  

A one-size-fits-all tool rarely talks natively with the full suite of platforms, analytics tools, and proprietary data pipelines your organization relies on. A custom build allows for interoperability across existing processes, measurement strategies, and channels. Bespoke systems also grant marketers and businesses the power to rapidly iterate and innovate alongside your evolving objectives—even across multiple brands or campaigns.  

Finally, bespoke agency-built AI brings accountability. Off-the-shelf solutions typically provide a black-box experience. Bespoke AI solutions can offer transparency on the “why” behind every insight, action, and algorithmic choice.  

Higher Upfront Costs, Outsized Returns  

Building or integrating bespoke, agency-operated AI is indeed a more significant investment than simply licensing. But through the right lens, that up-front investment is not just justified, it’s transformative.  

Bespoke solutions provide long-term cost efficiencies, reducing the hidden costs we just mentioned. Agency-built AI can evolve with your brand rather than lag behind a vendor’s generic roadmap. This adaptability means you can respond to new market opportunities, regulatory changes, or customer demands quickly.  

When your AI system reflects your proprietary data, brand voice, and business logic, you’re creating IP that can’t simply be copied by a competitor. This means faster time-to-market for novel campaigns and the ability to set trends, not follow them.  

With full transparency on how your AI systems operate and make decisions, it becomes easier to connect the dots between investment and measurable outcomes, refining your model for maximum impact and compliance.  

Making the Case: Why Not Just “Use ChatGPT”?  

The next time you consider “why not just use ChatGPT?”, remember:  

• A bespoke AI is a strategic asset, not just a utility.  
• Custom, agency-operated systems create defensible value through brand- and client-specific data, workflow integration, and proven human expertise.  
• With off-the-shelf, you join the pack. With a bespoke solution, you chart your own course, and can prove the ROI difference at every step. 

Most importantly, every team is on their own journey with AI. But when the goal is true differentiation, ROI, and resilience, building smarter, together, beats buying what’s on the shelf.  

Over the last couple years, marketers and businesses have been enthusiastically finding good use cases for AI technology. Right now, the focus has turned to envisioning the ways individual professionals and entire businesses can transform the way they get work done through innovative use of AI. AI is fostering cross-team collaboration and rewriting marketers’ roles and career paths. But to unlock AI’s full transformational potential, the business needs feedback and vision from all functions and levels of the organization. Forward-looking marketers can seize this opportunity, rooted in their own experiences with using AI at work, to become leaders of change in the business. 

You Need a Strong Foundation Before Building an AI Framework 

Marketers need to build a foundation of trust and transparency to run AI-related initiatives, while concurrently balancing the time constraints all marketers face. That foundation is responsible AI use.  

A recent McKinsey report revealed that only 18% of businesses have formal AI councils – for codifying and guiding responsible AI usage – in place within their org. With AI integrated into core processes at an ever-larger share of businesses, this is concerning. But it also shouldn’t hold business leaders back from adopting useful, transformative AI tools.  

Even without an AI council, business leaders can take a step back, consider their values and the types of compliant data they have available, and establish guiding principles that everyone across the business can reference and follow.  

Understanding Responsible Use Means Understanding and Mitigating Risk 

A responsible use policy provides a framework for the ethical and safe use of AI, including data privacy, transparency, and the responsibility of building trust internally and externally. 

The human professional needs to stay focused on breakthrough moments for the business – connecting different tools at different points in the process, and collaborating with people who work outside of your own skillset. Responsible use makes cross-function collaboration possible.  

The first step is to assess the risk level of AI, which is a factor if you’re using any AI tools in the business. Start by locating points of potential risk. One really good way to do this is by conducting an anonymous, company-wide survey to find which AI apps people are using and gaining value from – and to find out why they’re using these apps. From that informed starting point, move on to thinking about these important types of risks: 

• The likelihood that the business already uses the AI, and the specific use cases, end users, and environments.  
• When it’s necessary to approve an AI app, overall or within a specific use case. Ask yourself and the people using the app: If our org uses and scales this tool, what’s the worst possible outcome? From there, begin ideating what amount and types of controls you need in place to best protect your business, employees, and customers. 
• The measurable value the AI generates for the team and the business.  Consider the metrics that are most important for your business – efficiency, productivity, business growth impact, revenue impact, and any other key metrics. 
• ROI – whether the efficiencies and cost savings of the tool are worth the cost of the tool itself. 

An AI council matters because it empowers the business to continue gaining value from the tools without slowing momentum. The AI council also manages the value process – maximizing the value of tech solutions, and making sure useful tools aren’t siloed away. The AI council is dedicated to exploring all use cases that can deliver value for the business, and to making sure the costs of the tools makes sense for the business.  

When cross-functional collaboration happens, we can really start to see scalable value in the AI tools. The upfront investment of time and strategy pays off in dividends as scale increases.  

AI Strategy and Vision Leads to AI Value 

Marketers should step back, reflect on their professional experiences, and think about their day-to-day and big-picture challenges – not only for themselves internally, but for the customers they serve. Take these steps as you begin imagining AI strategy: 

• Start with listening to internal staff. Understand their priorities and what it means to maximize their strengths in their roles. Ask open-ended questions about people’s personal vision: If you could do anything in this role or company to make a significant impact, what would you love to do? What have you always wanted to do, but haven’t had time, capacity, or resources to do yet? 
• Take fundamental courses on AI in Marketing and Business to unlock your thinking of future potential for the business, employees, and customers you serve. 
• Take a prompting course together  to unlock your creativity and the potential of the tools.  
• Experiment with approved, responsible AI tools within your responsible use guidelines to find other roads for your marketing to explore, and don’t get hung up on any perceived imperfections of these tool (like, ”This is the dumbest they will ever be”). 
• Have conversations with partners and customers to find out what they’re finding value in with AI, and what they expect of products and services like yours to incorporate or what value they expect to be unlocked by AI and new technology.  
• Work with leadership to understand these insights across these items and clearly define what growth and opportunity you are aiming to unlock with AI.  Use the AI council in collaboration to help then define how to get there and what use cases from your insight gathering to start with. 

The more we test and use AI tools for functions such as data science, coding, and content and asset creation, the more marketers will be free to explore their own creativity and evolve in their careers. Along the way, we learn how to view business objectives from the angle of other functions throughout the business. 

Rest assured, when marketers automate as much work as they can – and they should – they won’t lose budget. Instead, their value to the business increases greatly.  

In February of this year, OpenAI cofounder and former Tesla Director of AI Andrej Karpathy made a pronouncement about the future of AI-enabled coding. He gave it an evocative term: “vibe coding.” “You fully give into the vibes, embrace exponentials, and forget that the code exists,” he wrote on X. “I just see stuff, say stuff, run stuff, and copy-paste stuff, and mostly it works.” 

On the surface, this sounds close to one of AI’s most heavily touted use cases: writing code very quickly, and enabling non-technical people to build software. And among experienced developers, AI tools are reshaping coding workflow and software testing. But the AI isn’t good enough yet to allow a person to actually “feel” a software product into existence. Comparably to how gen AI needs some finessing to create digestible written content and images, vibe coding needs specific prompts. If you want the software to have a particular “feel,” you have to explain it to the AI in clear language – and also explain the desired outcome, the end user’s needs, which sources to pull from and which to avoid, and so on.  

Just as ChatGPT doesn’t understand ethics or critical thinking, AI coding tools don’t necessarily understand all rules and product requirements that go into creating software that does what it’s supposed to. The AI may write code that leaves the software vulnerable to malware attacks or data security breaches. It can also overlook key considerations around governance, privacy, accessibility, regional/government regulations and more. Someone still needs to know the project and industry well enough to understand what could go wrong in the code, and to understand the code well enough to fix errors.  

Mod Op’s innovation team is diving into vibe coding, using programs like Cursor and Claude Code. “The vast majority of the lines of code coming out of our team are co-written with AI,” says Aaron Grando, Technology Director at Mod Op. “In some cases, tasks that used to take me a week to code now take about a day.”  

Note that verb, though: “co-written.” Vibe coding, for an experienced developer, can accelerate the engineering side of projects – but success depends on the developer’s expertise. Aaron sees AI as a good way to do regression testing – making sure nothing broke after making a code change – to quickly debug errors and reduce QA overhead. Vibe coding can help write tests to verify whether the software works properly, but an experienced developer brings in their knowledge of the conditions and edge cases that need to be tested.  

For developers, AI can help understand not only whether things work, but how and why. The efficiency gained through effective use of AI also positions developers to grow in their roles, in multiple directions. Now there are more opportunities for developers to explore how to apply their skills to specific areas of the overall business. “I now call myself a product specialist, and see my work through that lens, but I’m still doing engineering work every day,” says Aaron. A potential outcome of vibe coding is that coding will no longer be primarily the domain of developers – and it could also cease being the defining factor of a developer’s role. On the one hand, Aaron says, “There are going to be more coders, but fewer developers.” On the other hand, “I think it puts developers closer to business value than just being the developer that knows how to code.”  

For the beginner coming to vibe coding from a non-technical direction, it’s wise to start small. As far as we can tell, no one is using vibe coding to build a copy of any enterprise software they use at work. A beginning vibe coder should start with a very specific app or software concept. This makes reviewing the code and testing the software easier. It’ll also give you an idea of just how specific your prompts need to be in order to produce even a simple app. For the experienced professional, vibe coding creates opportunities for users to build and test prototypes quickly, and for building highly specific niche applications for their businesses. 

Will the term “vibe coding” stick in the long run? That remains to be seen. But Mod Op believes the process we’re calling vibe coding today will gain steam and inspire real change in how businesses turn ideas into tools, and in who gets to be part of that process.  

What’s your relationship with AI? If you were to ask me that question, I would say AI is like my “boyfriend.” We’re at a stage where I still get the butterflies when “he’s” around. We haven’t decided where exactly we’re going, but we’re excited about doing it together. We’re in this for the long haul. Will we ever get “married?” It’s tough to know what that word would mean for us in the future. But we’re certainly going to move in together. 

The question for marketers today is not whether you’re using artificial intelligence tools to speed up workflow and find fresh inspiration. A better question is whether the AI is improving your work and positioning the marketing team as an invaluable resource to the business. Is AI taking our jobs away? Not yet — but some businesses, including IBM are already making snap judgments that it will, and they’re making marketing staffing decisions accordingly. For marketers, there’s an opportunity to add human experience and skill to advanced tech, and to show why the human element is irreplaceable.  

Innovation requires a broad range of skills and experience 

AI is accelerating a transformation of roles within businesses and marketing teams, and Mod Op is on board with this change. We’re using AI to give marketers the freedom to grow their skillsets and find new solutions and strategies. I believe strongly in learning diverse skills, getting outside of what you might think of as your lane, and following your inspiration. But they need the opportunity – the time and the tools – to explore new things. I began my own career not in marketing but in IT, as a developer. I left an internal IT role to work for a brand agency, and I learned amazing new skills: What does it mean to really interpret the data and come at it from a human-centric approach? What do the patterns in the data mean from that angle – a very different angle than software engineers usually think about? 

At the same time, marketers usually don’t have a connection of the data scientist or engineer’s role.. But they need it now. I would challenge any marketer today to take the initiative and begin learning relevant technical skills. Take basic statistical data science classes, or Python classes. The goal is not to become an engineer, but to demystify what AI is and how it works. Emboldened by new knowledge, marketers can begin identifying new marketing use cases, recommending customizations to the AI tools, and seeing new opportunities for collaboration and continued professional growth. 

Finding valuable new uses for AI isn’t the only reason why marketers should lean into broadening their skill sets right now. A more diverse skill set is a competitive advantage for each individual marketer in the workforce. As long as there’s even a question about whether AI can replace human professionals, businesses will take it upon themselves to answer that question, and to take action accordingly. AI in itself isn’t actually replacing people now, but we’re seeing businesses taking big (if risky) bets and letting go of marketing professionals. A broader skill set is always helpful for hireability and job security. 

Data usage is a competitive differentiator for AI-driven businesses 

Today, any responsible marketer is thinking about using AI tools to their competitive advantage. But the most innovative ones are thinking about the competitive advantages of collecting and processing data ethically. Right now, marketers have access to generally the same types of AI tools and apps. So to stand out, they add their deep understanding of the consumer into the mix. That understanding is essential to training the AI to process only the data necessary to enhance the customer experience. Mod Op is eager to explore AI apps as they come into the marketplace. But at the same time, we’ve set up a compliance and governance program to isolate where those apps are tested and who has access to them, and to make sure we understand the data. Due diligence is an essential process that must be continually ongoing. 

If you want to use AI to add value to your and your team’s marketing work, you need to avoid the common trap of expecting AI to instantly provide magical solutions to complex problems. To continue to do standout work, you need to collaborate, to understand more of the tech teams’ thinking, and to take ownership of your business’s tech stack innovation. The marketer’s grasp and understanding of their audience has never been more important than it is now, with younger consumers increasingly expecting trust and transparency from brands. This is the time for teams to select their data sets and tools wisely. Marketers aren’t being replaced by AI. They’re positioned to guide the whole business to the most useful, ethical, and valuable usage of AI.