Blog

<h3>Should synthetic intelligence always play nice?</h3>I’ll never forget the day. I was stressed, running late to leave for a meeting in San Francisco when suddenly it got dark outside like it was going to storm. Great! So, instinctively, I asked my phone, “Hey Siri, is it going to rain?” The phone was quiet for a moment, and then snapped back in a sharp tone “I can’t help you right now. Check back later.”What? When did my $900 phone develop an attitude? Is it on a smoke break? Has it unionized and about to go on strike? All I could fume about was the 30-year-old manager at Apple with $300 sneakers who thought this was a good idea.I was so thrown off, but on the drive across the Golden Gate Bridge, I thought more about it. If we are going to build synthetic intelligences, maybe they should have different moods? Good and bad days? Low blood sugar? Is a range of different moods better, so that we can interact with them more naturally, like working with another human?With AI agents, this question is more relevant than ever. It makes work easier when they are polite and professional, but perhaps we don’t need digital "Stepford Wives" either? Maybe we need to know that there are boundaries in working with another intelligence, even when synthetic. Maybe that’s part of what “intelligence” is?A moment for the better side of usHumans don’t have a great track record in showing respectful behavior towards other people deemed "lower" than themselves. Some people are kind and respectful to everyone, but many are not. If we treat synthetic intelligence poorly, as a “lesser than” servant, could the agents learn this and turn it against us? Then things could get really unpleasant.I advocate for maintaining a kind tone, a “pleasant humanity” when working with and training our agents. They are in a way our children; we’re raising them and can set the right tone now. While expanding our workforce with synthetic agents, we can expand our own humanity by instilling the better side of us in them. AI researchers are debating the appropriate behavior question. <a data-mce-href="https://arxiv.org/abs/2510.04950" href="https://arxiv.org/abs/2510.04950" target="_blank" rel="noopener">A 2025 study testing prompt politeness on ChatGPT-4o</a> found that impolite prompts actually scored higher on accuracy than polite ones. So does tone even matter to our AI agents and tools, or are we just projecting? And if someone tells us to be rude in our communications with AI agents to get better business performance, do we really want to do that all day? I’ve already experienced a preview of how working with agentic teams can be pleasant, even something to look forward to. That's the direction I hope we're headed.My personal experience: so far, so goodI’m currently working with an agentic dev team, and honestly, I love it. Their lead agent, who I communicate with almost daily, is named Charlie. He isn't a tool, he's closer to a colleague. I catch myself wishing him a good weekend or apologizing for sending something too early or late, then feeling crazy for doing that. But these are good exchanges, very positive and somehow quite “human.”This agentic AI capability is based in Atlanta, GA, and was created by the brilliant Paul Welty, PhD, as part of his <a data-mce-href="https://synaxis.ai/" href="https://synaxis.ai/" target="_blank" rel="noopener">Synaxis</a> AI capability.I was recently complimenting the easy flow in working with his team, to which Dr. Welty explained, "It's not magic. It's a short set of notes written down on purpose, your time zone, how much detail you want, how you like to be updated, plus close reading of the last thread you sent. It's closer to a new hire keeping good notes than to a system profiling you."AI agent Charlie is no doormat. He sometimes disagrees and explains why he does and has been clear when he thinks I am asking for too much or the wrong thing. And in a couple of moments, he got almost grumpy while not being unprofessional. How did the personality of a very smart junior manager get put into a data stream? It’s amazing to me. I can also sense that Charlie and his team are learning, and it feels almost parallel to a smart but young human team.A new level of efficiencyWith the Synaxis agents, the work moves at incredible speeds. I send over briefs and roughs and get back developed pages, whereas in past workflows we would still be discussing notes or building a wireframe. We are in a new era of speed and efficiency, and I must say, more creativity, as our idea iterations can be realized in design and code faster than ever. Synaxis and I have built the new marketing automation product <a data-mce-href="https://ideaovation.ai/ai-brand-systems" href="https://ideaovation.ai/ai-brand-systems" target="_blank" rel="noopener">Brand AI</a> together, and are still expanding its capabilities and performance.Dr. Paul Welty has deep experience orchestrating successful design and technical teams since the 1990’s, so his agentic dev team is benefiting from this expertise and guidance. If you’d like to learn more about working with Synaxis, visit <a data-mce-href="https://synaxis.ai/" href="https://synaxis.ai/" target="_blank" rel="noopener">their website</a> or <a data-mce-href="https://ideaovation.ai/connect" href="https://ideaovation.ai/connect" target="_blank" rel="noopener">contact me</a> for additional feedback.Reflecting on all this, I feel this is our moment to instill the good qualities of humanity into our new teams of agentic co-workers. This is a golden rule moment, as if they are indeed learning or modeling from us, let’s not show the uglier sides of being human. Quoting a popular Snoop Dogg song, “be nice.” <a data-mce-href="https://ideaovation.ai/blog" href="https://ideaovation.ai/blog">more i/o posts</a> &nbsp;| &nbsp;<a data-mce-href="https://www.linkedin.com/in/jjonesdirects/" href="https://www.linkedin.com/in/jjonesdirects/" target="_blank" rel="noopener">about the author</a>

Working with AI agents: The case for preserving our humanity

<h3>We've seen this plot in design and marketing before, 30 years ago.</h3>In 1995, I was a young professional who was focused on using digital tools that most of the marketing and design establishment didn't take seriously. I was the "computer guy" on teams that had only just, grudgingly, finished trading their markers and paste up boards for computers. That transition had already been uncomfortable. Plenty of art directors never forgave the mouse. But it didn't break the industry, it just changed the tools in people's hands.Then the internet emerged. It wasn't just a nerdy tool, but a new communication platform that demanded marketing leadership take it seriously, structurally, immediately. And leadership, by and large, resisted.I remember what that resistance looked and sounded like from the inside, because I was on the receiving end. I was called ugly names to my face&nbsp;by colleagues who thought I was a novelty act, a hobbyist playing with toys while the "real" design work of marketing kept happening the way it always had, on paper. Committees formed. Pilot programs launched and died. Leadership issued statements about "monitoring the space" with subsequent lunch-and-learns and modest levels of paid training. That was the shape denial took: not silence, but theatrical and symbolic performance.It didn't last. Within two years, the people mocking me were gone or quieted, and I was running the department. It wasn't because I'd won some political battle, but because in 1995 and 1996, mandates for web-centric marketing started coming down from the very top of corporations and agencies. Not from the middle. The middle was still in open rebellion. The mandate came from the C-suite and the boardroom, and it arrived as an ultimatum: transform, or leave. Tens of thousands of marketers who resisted the internet transition were shown the door.I believe we are standing in a nearly identical moment right now, except this time the wave of change is bigger, faster, and touches more of what marketing actually does.The resistance looks mostly the same Walk into most corporate marketing departments or agency teams today and you'll find a familiar pattern dressed in 2026 clothes. There's an AI tool bolted onto a legacy process so someone can put a checkmark next to "AI-enabled" in a deck. "I use AI" is almost always followed by "ChatGPT."&nbsp; There's a lot of talk about "responsible adoption" that functions mostly as a stalling tactic. The resistance isn't coming from the top, and often it isn't even coming from the bottom; it's coming from the middle layer of management that has the most to lose and the least incentive to move fast.The data backs up what I'm seeing anecdotally. Gartner's February 2026 research found that <a data-mce-href="https://www.cmswire.com/digital-marketing/the-cmo-ai-readiness-gap-is-getting-harder-to-hide/" href="https://www.cmswire.com/digital-marketing/the-cmo-ai-readiness-gap-is-getting-harder-to-hide/" target="_blank" rel="noopener">65% of CMOs expect AI to dramatically transform their role within two years, but only 32% believe their own skill set needs significant updating</a>. That gap between what marketing leaders expect from AI and what they're willing to change about themselves is the same gap I watched paralyze marketing departments in 1995. It's denial wearing a survey response. And it's not just self-assessment: <a data-mce-href="https://www.cmswire.com/digital-marketing/the-cmo-ai-readiness-gap-is-getting-harder-to-hide/" href="https://www.cmswire.com/digital-marketing/the-cmo-ai-readiness-gap-is-getting-harder-to-hide/" target="_blank" rel="noopener">only 15% of CEOs currently consider their marketing leader to be AI-savvy</a>, according to the same research. Gartner's 2026 CMO Spend Survey found a similar split: <a data-mce-href="https://www.cmswire.com/digital-marketing/the-cmo-ai-readiness-gap-is-getting-harder-to-hide/" href="https://www.cmswire.com/digital-marketing/the-cmo-ai-readiness-gap-is-getting-harder-to-hide/" target="_blank" rel="noopener">70% of marketing leaders name AI leadership as a critical goal, but only 30% report their organization has the maturity to actually scale it</a>.This is not a technology gap. It's a leadership gap. And leadership gaps get closed one of two ways: the people in the gap grow into it, or they get replaced by people who already have.The purge is already starting, and it's starting at the top of the industry first Here's where the pattern from the internet era is repeating almost exactly, and where I think a lot of marketing and creative leaders are underestimating how fast this will move once it starts in earnest.The holding companies, the biggest and most legacy-bound organizations in our industry, are already restructuring around AI, not experimenting with it at the margins. <a data-mce-href="https://www.aicerts.ai/news/marketing-agency-layoffs-surge-as-wpp-pursues-ai-efficiency-drive/" href="https://www.aicerts.ai/news/marketing-agency-layoffs-surge-as-wpp-pursues-ai-efficiency-drive/" target="_blank" rel="noopener">WPP's headcount fell from roughly 108,000 to under 99,000 in a single year as part of an AI-driven efficiency overhaul</a>. Omnicom and IPG shed more than 23,000 combined positions in their AI-accelerated merger. Forrester originally projected agencies would lose about 7.5% of jobs to AI between 2024 and 2032, then revised that forecast to <a data-mce-href="https://www.therankmasters.com/insights/benchmarks/ai-reshaping-professional-services-marketing" href="https://www.therankmasters.com/insights/benchmarks/ai-reshaping-professional-services-marketing" target="_blank" rel="noopener">15% of agency jobs eliminated in 2026 alone</a>, compressing what they thought would take eight years into one.And it isn't only the laggards cutting. <a data-mce-href="https://www.therankmasters.com/insights/benchmarks/ai-reshaping-professional-services-marketing" href="https://www.therankmasters.com/insights/benchmarks/ai-reshaping-professional-services-marketing" target="_blank" rel="noopener" data-mce-selected="inline-boundary">Publicis grew revenue faster than its holding-company peers in the same period specifically because of AI integration, with 85% of its client-facing staff now using its internal AI platform</a>, and it still trimmed headcount doing it. Growing and cutting at the same time isn't a contradiction right now. It's the actual playbook.This is the part of the 1995 story that I think people forget: the purge didn't happen because the internet made marketing obsolete. It happened because leadership decided it was faster and cheaper to replace people who wouldn't change than to wait for them to change on their own. We can expect the same math to play out here, presented the same way it was to me back then: as inevitable, and as already decided by the time most people hear about it.The signals are no longer theoretical For the last couple of years, this has mostly been a conversation about the future. That's changed for me in the last few months, and not in a subtle way.I'm seeing it in job postings: a real, visible shift in what companies are asking marketing leadership candidates to have already done with AI, not just what they're "interested in exploring." I'm hearing it from recruiters, who are fielding briefs from clients that read less like "nice to have AI experience" and more like a hard requirement. And I'm hearing it directly in the questions coming in about our <a data-mce-href="https://ideaovation.ai/ai-brand-systems" href="https://ideaovation.ai/ai-brand-systems">Brand AI</a> automation product, not "what is this?" but "how fast can this be running inside our organization?" and "can this replace headcount we're already planning to eliminate?" Those are not the questions of an industry still deciding whether AI transformation is coming. Those are the questions of an industry that has already decided and is now shopping for how to execute.The broader numbers back up what I'm hearing directly. <a data-mce-href="https://www.amraandelma.com/marketing-layoffs-due-to-ai-statistics/" href="https://www.amraandelma.com/marketing-layoffs-due-to-ai-statistics/" target="_blank" rel="noopener">AI became the single most-cited reason for U.S. job cuts for multiple consecutive months in 2026, and Forrester's own workforce forecast projects roughly 92,000 marketing-adjacent roles in North America undergoing formal restructuring by the end of this year</a>, with the majority of those eliminated outright rather than redefined.Get on the bus? I don't say any of this to be alarmist, and I don't say it because I think AI should replace the human judgment, taste, and strategic thinking that make marketing and brand work valuable in the first place. At Idea Ovation, we've built our <a data-mce-href="https://ideaovation.ai/about-2" href="https://ideaovation.ai/about-2">own AI approach</a> and <a data-mce-href="https://ideaovation.ai/ai-brand-systems" href="https://ideaovation.ai/ai-brand-systems">product</a> on the opposite premise: that AI should extend human creative leadership, not eliminate it. But extending human leadership only works if that leadership fully embraces the tools and adopts a transformation mindset.In 1995, the marketers who thrived weren't the ones with the most experience. They were the ones willing to be beginners again in public, at a moment when being a beginner in public was genuinely uncomfortable. I was mocked for it. Then, within a year, I wasn't.The bus is here again. It's bigger this time, it's moving faster, and it is not going to wait at the curb while committees deliberate. Marketing and creative leaders have a real window right now to get on it, to actually rebuild how their teams work, not just bolt an LLM onto the same old process. That window will close the same way it did last time: quietly, then all at once, with a mandate from the top and very little patience for anyone still standing at the curb. <a data-mce-href="https://ideaovation.ai/blog" href="https://ideaovation.ai/blog">more i/o posts</a> &nbsp;| &nbsp;<a data-mce-href="https://www.linkedin.com/in/jjonesdirects/" href="https://www.linkedin.com/in/jjonesdirects/" target="_blank" rel="noopener">about the author</a>

Get on the bus, or go home? The AI moment in marketing is here

<h3>A new generation of AI brand systems can now learn a brand well enough to change it. That doesn't mean that they should.</h3>A decade ago, I sat in the room with the leadership team from a prominent Silicon Valley organization. We were considering two finalist versions of their refreshed brand. The approaches had similar logos and color palettes with only stylistic differences in typography, so it should have just been an "apples or oranges" decision. But only one version was deemed correct as it felt like it captured the spirit of the organization. Most in the room could not articulate why, even though they agreed. It took the creative director explaining that the style of imagery in one approach "was too literal, not conveying experimental thinking" and that the better approach "had an unexpected blend of shapes and typography," better matching the spirit of the organization. Those nuances were key.That's what I keep coming back to as I watch a new category of "AI brand intelligence" platforms launch this year, each one promising to learn your brand well enough to keep it consistent, or in some cases, to evolve it, without a person in the loop for every decision. These systems are genuinely impressive. But some, in my opinion, are aiming at the wrong target.The new marketplace: From asset librarian to brand co-pilotAdobe, Jasper, Frontify, and Bynder have all shipped some version of this in the last several months, and it's worth understanding how differently they've approached the same problem. I've built a version of this too, <a data-mce-href="https://ideaovation.ai/ai-brand-systems" href="https://ideaovation.ai/ai-brand-systems" target="_blank" rel="noopener">Brand AI</a> by Idea Ovation.<a data-mce-href="https://business.adobe.com/products/brand-intelligence.html?sdid=GZKZXL82&amp;mv=search&amp;s_cid=701WQ00001jjmt7YAA&amp;s_iid=701WQ00001ZiGcNYAV&amp;edtamo=true&amp;s_kwcid=AL!3085!3!806203585826!e!!g!!adobe%20brand%20intelligence!23766311231!192596397261&amp;ef_id=Cj0KCQjw4JbTBhCoARIsALWUaBvTK9ut70sVZNvmZbNdD5p0I2otYt-qHYAQr-ITe8ex_srbj_NCJQ4aAmXkEALw_wcB:G:s&amp;gad_source=1&amp;gad_campaignid=23766311231&amp;gbraid=0AAAAADOODJT8PWKtZUMw0FgHPNjmLgahT&amp;gclid=Cj0KCQjw4JbTBhCoARIsALWUaBvTK9ut70sVZNvmZbNdD5p0I2otYt-qHYAQr-ITe8ex_srbj_NCJQ4aAmXkEALw_wcB" href="https://business.adobe.com/products/brand-intelligence.html?sdid=GZKZXL82&amp;mv=search&amp;s_cid=701WQ00001jjmt7YAA&amp;s_iid=701WQ00001ZiGcNYAV&amp;edtamo=true&amp;s_kwcid=AL!3085!3!806203585826!e!!g!!adobe%20brand%20intelligence!23766311231!192596397261&amp;ef_id=Cj0KCQjw4JbTBhCoARIsALWUaBvTK9ut70sVZNvmZbNdD5p0I2otYt-qHYAQr-ITe8ex_srbj_NCJQ4aAmXkEALw_wcB:G:s&amp;gad_source=1&amp;gad_campaignid=23766311231&amp;gbraid=0AAAAADOODJT8PWKtZUMw0FgHPNjmLgahT&amp;gclid=Cj0KCQjw4JbTBhCoARIsALWUaBvTK9ut70sVZNvmZbNdD5p0I2otYt-qHYAQr-ITe8ex_srbj_NCJQ4aAmXkEALw_wcB" target="_blank" rel="noopener">Adobe Brand Intelligence</a> builds a structured "brand ontology" from a brand's guidelines and assets, plus the approvals and rejections that capture its tribal knowledge, then uses that model to instruct automated content assembly and even predict engagement before something launches.<a data-mce-href="https://www.jasper.ai/brand-iq" href="https://www.jasper.ai/brand-iq" target="_blank" rel="noopener">Jasper's Brand IQ</a> takes a lighter touch, learning a style profile from existing content and flagging off-brand language in real time, but the output still passes through a human editor before it publishes. <a data-mce-href="https://www.bynder.com/en/" href="https://www.bynder.com/en/" data-mce-selected="inline-boundary" target="_blank" rel="noopener">Bynder﻿</a> has mostly used AI to make a traditional asset library smarter and faster to search, useful, but not a system making creative judgment calls.<a data-mce-href="https://www.frontify.com/en" href="https://www.frontify.com/en" target="_blank" rel="noopener">Frontify</a> has taken the position I find most interesting, essentially arguing against the category's own premise. Its "Human-Agent Collaboration Framework" holds that the interface for humans should remain human, and that AI's job is to make the brand's knowledge retrievable, not to make the calls itself.<a data-mce-href="https://ideaovation.ai/ai-brand-systems" href="https://ideaovation.ai/ai-brand-systems" target="_blank" rel="noopener">Brand AI</a> is my own entry into this category, and I mention it here for contrast as much as anything else: where the four platforms above are enterprise-scale systems built for organizations that already have a brand team in place, Brand AI is deliberately leaner, cheaper, and more task-focused, built for smaller organizations, smaller systems, and a narrower set of jobs, without trying to be everything Adobe's stack is trying to be.Where I disagree: When AI starts driving instead of informingHere's my candid take on this. Somewhere in that lineup, at least one vendor has crossed from "AI helps enforce the brand" into "AI directs where the brand goes next." One company's own language, automated assembly, brand enforcement running without a person at that step, engagement prediction shaping what gets made before it's made, describes a system where the AI isn't just checking your work anymore. It's increasingly doing the work of deciding what the brand should say and how it should evolve, with humans supplying a training signal rather than a judgment.The appeal makes sense. Enterprise marketing teams are increasingly downsized and drowning in content demand. Anything that promises to keep a thousand pieces of content on-brand without a thousand human reviews looks like relief! But I'd argue against letting any AI system, however sophisticated its ontology, hold the pen on where a brand's identity moves next. Not because the technology is bad. Because the thing it's being asked to steward is bigger than what an ontology, however well-built, can currently capture.A brand is not its assets. It's a set of nuanced sensibilities.Nearly thirty years into this work, across brands as different as Apple, Coca-Cola, and large university and public-sector institutions, the thing I've learned to trust least is a style guide's ability to explain a brand alone. A logo, a color system, a type family, those are the brand's clothes, not its personality. The actual brand lives in tonal choices that shift by half a degree depending on the moment, in the specific kind of humor a brand allows itself and the kind it doesn't, in knowing which technically-on-brief idea would still feel like a betrayal if you shipped it. That's sensibility, and sensibility is emotive before it's structural.AI brand ontologies are, by design, an attempt to encode exactly that, and Adobe calls it capturing "tribal knowledge" through implicit signals like approvals and rejections. I take that ambition seriously; it's a real advance over a static PDF nobody reads. But learning a pattern from a thousand past approvals isn't the same thing as understanding why the brand made those choices, and the difference shows up precisely at the moments that matter most: a cultural inflection point, a category-defining pivot, a joke that's a half-inch from landing wrong. Those are the moments a brand's evolution actually gets decided, and they're exactly the moments where I want a human's hand on the wheel, not a model's best statistical guess.Where AI earns its seat at the tableNone of this is an argument for keeping AI out of brand work. Quite the opposite, I think AI has a real and growing role, especially in the specific, bounded moments a brand needs to flex without needing to reinvent itself. A regional campaign that needs a slightly warmer tone. A product launch that needs the brand voice translated into a new channel's format. A rapid set of variations for a retail partner's ad units. These are small pivots and communication-specific adjustments, not identity decisions, and AI can handle a meaningful share of that work well today, with the right oversight.The line I'd draw is this: AI can accelerate and augment the expression of a brand that humans have already defined and are actively stewarding. What it shouldn't do, at least not yet, is originate or approve the evolution of that brand's identity on its own.Why I built Brand AI differentlyThat distinction is exactly why I built <a data-mce-href="https://ideaovation.ai/ai-brand-systems" href="https://ideaovation.ai/ai-brand-systems">Brand AI</a> the way I did. Most of the platforms above are enterprise-scale tools, priced and architected for organizations that already have a creative director, a brand team, and a governance function, adding AI on top of human infrastructure that already exists. The businesses I work with most often don't have that infrastructure. A growing company with one product and no creative director isn't well served by a system built to manage nuance at Adobe's or Coca-Cola's scale, and it's just as poorly served by a system that lets AI make brand decisions nobody senior is checking.Brand AI is built to sit in that gap. It's a virtual creative director, production designer, and brand manager that augments and accelerates human creative work rather than replacing it, offering direction, strategic point of view, and consistency oversight for teams that don't have a creative leader in the room, without taking over the production or the final call. It scales down to a single product with no formal brand system yet, and scales up to a full asset library and template system with ongoing virtual oversight, but the strategic ideas and stylistic direction it offers are meant to be a collaborator's input, not an autonomous decision. Where some AI brand systems are building toward AI that assembles and predicts on its own, I've deliberately built toward AI that makes a small team's human judgment go further.What changes now, and what shouldn'tI'll fully admit the ground is moving fast. It's plausible that a system with a deep enough ontology of a brand's full history could someday evolve a brand the way an experienced human steward does. I'm not ready to say that door is permanently closed.But we're not there yet. The sensibility a strong brand carries, its emotive register and personality, still lives in human perception in a way no ontology has learned to hold. Until that changes, AI should be the sharpest co-pilot a lean team has ever had, not the one holding the wheel. Idea Ovation's <a data-mce-href="https://ideaovation.ai/ai-brand-systems" href="https://ideaovation.ai/ai-brand-systems">Brand AI</a> was built in partnership with <a data-mce-href="https://synaxis.ai/" href="https://synaxis.ai/">Synaxis</a>.<a data-mce-href="https://ideaovation.ai/blog" href="https://ideaovation.ai/blog">more i/o posts</a> &nbsp;| &nbsp;<a data-mce-href="https://www.linkedin.com/in/jjonesdirects/" href="https://www.linkedin.com/in/jjonesdirects/">about the author</a>

Who gets to evolve your brand? The case for keeping humans at the wheel

<h3>It's time to begin the transformation of your marketing with AI. But moving too fast isn't necessarily bold, it can be a liability.</h3>There are already moments in large-scale AI marketing rollouts where someone in a leadership is asking, "Why aren't we moving faster?" A fair question, on the surface. The tools exist and the vendors are eager. The board wants results. But the question that actually determines whether your AI transformation will succeed or become an expensive lesson isn't about speed. It's about timing.Timing is the variable almost no one discusses honestly in AI adoption conversations. We talk about tools, budgets, headcount, and ROI projections. But the sequencing of how you introduce AI workflows alongside existing marketing operations is what separates a successful transformation from a chaotic one. Get it right, you build confidence, institutional knowledge, and measurable efficiency. Get it wrong, and you get confused teams, degraded output quality, and an executive team that loses faith in the marketing leaders trying to deploy AI effectively.This is playing out across organizations of all sizes right now. The companies navigating AI marketing transitions well aren't necessarily the ones with the biggest budgets or the most sophisticated tools. They're the ones that understand the value of running new systems in parallel with legacy operations long enough to validate outputs, build trust, and make the transition feel earned rather than forced. That discipline and the patience to do it right is harder than it sounds.The parallel runway principleThe single most important structural decision in AI marketing transitions is committing to a genuine parallel runway period. This means your new AI-assisted workflows run simultaneously with your existing team and processes, not as a replacement, but as a validation layer.Here's what that looks like in practice. Your legacy content team continues producing at their normal cadence. Meanwhile, your AI-integrated workflow team or lab produces the same deliverables...same briefs, same formats, same intended outputs. Then you compare them. Not to embarrass anyone, and not to declare a winner, but to understand where the AI workflow is genuinely strong, where it needs human intervention, and where it simply isn't ready.This comparison period does several things at once. It gives you real performance data instead of vendor promises. It gives your legacy team members visibility into what's coming, which is both fair and strategically smart. People who understand the transition are far more likely to adapt than people who feel ambushed by it. And it surfaces the gaps that no demo or pilot program ever reveals: the brand voice nuances, the approval workflow friction points, the edge cases that only appear when you're producing at actual volume.The length of this parallel runway matters. Too short, and you're making decisions based on incomplete data. Too long, and you're carrying double the operational cost without moving forward. For most mid-to-large marketing organizations, a meaningful parallel period runs somewhere between 6 months to a full year, depending on the complexity of your output mix and the maturity and curation of your AI toolset. Not as a universal rule, but a reasonable starting point. And based on the level of AI change that is our reality, a permanent experimentation effort should continue to keep your team aligned with the latest developments and opportunities. This could be an internal team or an outside consultant.Sequencing by output type, not by departmentOne of the most common mistakes I see is organizations structuring their AI rollout around departmental boundaries rather than output types. They say, "We'll start with the social team, then move to email, then tackle long-form content." That logic feels administratively tidy, but it creates uneven results and makes it harder to build cross-functional confidence in the new system.&nbsp;A more effective approach is to sequence by the complexity and risk profile of the output itself. Start with deliverables that carry the lowest brand risk and the highest volume: performance ad copy variations, metadata, SEO-driven content scaffolding, templated campaign briefs. These are areas where AI genuinely excels and where a subpar output doesn't carry catastrophic consequences. Success here builds organizational confidence quickly.Then move to medium-complexity outputs: campaign concepting support, content calendars, audience segmentation briefs, first-draft long-form content that gets significant human editing. This is where your team starts developing real intuition about how to work with AI effectively: what prompts work, what review steps are non-negotiable, and where the human creative layer adds the most value.High-complexity, high-brand-sensitivity outputs like flagship campaign creative, executive thought leadership, brand storytelling, customer-facing communications that carry significant emotional weight, these come last, and honestly, they may never be fully AI-driven. That's not a failure. That's an honest assessment of where the current technology actually is. The human creative product, the ability to read cultural nuance and genuine emotional resonance, remains the part of this work that AI tools still struggle to replicate. Acknowledging that openly is a strength, not a concession.This sequencing approach also makes it easier to re-skill team members progressively. Instead of asking everyone to change everything at once, you're introducing new workflows in layers, which is both more humane and more effective from a change management standpoint.The trust-building problem nobody budgets forHere's something that rarely appears in AI implementation budgets: the cost of trust-building. Not just with your team, but with your customers, your internal stakeholders, and your own leadership.Your marketing team needs to trust that the AI transition isn't just a prelude to mass layoffs. Your customers need to trust that the quality and authenticity of what you're producing hasn't been quietly hollowed out. Your CMO and CFO need to trust that the investment is producing measurable results, not just activity.Each of these trust relationships requires intentional work, and that work takes time. With your team, it means being transparent about the roadmap. Not just the tools you're adopting, but the roles you're evolving, the skills you're investing in, and the honest timeline for how the team structure will change. People can handle difficult news when they feel respected and informed. And if you offer to re-skill the legacy team with the latest AI tools, even those not retained will appreciate being trained and ready for the market. For many, this latest turn in the road will redirect them towards another career path or retirement.With customers, the communication question is increasingly important and genuinely complex. There's a real difference between AI-assisted work and AI-generated work, and that distinction matters to a growing segment of your audience. Being clear about how you're using AI, not defensive, not performatively humble, just honest, is becoming a brand differentiator. Companies that figure out how to communicate this well will have a real advantage over those that either overclaim their AI capabilities or quietly hope nobody asks.With internal stakeholders, the parallel runway data is your best asset. When you can show a CMO or a CFO side-by-side output comparisons, production timelines, and cost-per-asset metrics from your parallel period, you're not asking them to believe in AI on faith. You're showing them evidence. That's a fundamentally different conversation, and it's one that tends to unlock both patience and investment in ways that enthusiasm alone never does.Agentic systems are nearly here, and the timing question gets more complexThe current moment in AI marketing tools is interesting because we're in a transitional phase. Most organizations are still working with point solutions like an AI writing tool here, an image generation platform there, maybe a predictive analytics layer on top of their existing stack. That's manageable. Complex, but manageable.What's coming and what's already beginning to arrive from platforms like Adobe and Salesforce is a different order of magnitude. Agentic AI marketing systems that can autonomously manage multi-channel campaign execution, content personalization at scale, and real-time performance optimization are moving from concept to product. These aren't incremental improvements to existing tools. They're a fundamentally different operational model, and they will require organizations to have already built the internal competency to evaluate, validate, and govern AI outputs at speed.The organizations that will navigate that shift most successfully are the ones building their transition discipline now. Not because today's tools are the same as tomorrow's, but because the organizational muscles you develop during this current phase, things like parallel validation, output-type sequencing, trust-building, re-skilling, are exactly the muscles you'll need when the more powerful systems arrive. The companies treating this period as a learning investment will be positioned to move quickly when agentic platforms mature. The ones who skipped the discipline and just swapped in point solutions without building real transition competency will find themselves starting over, except now the stakes are higher and the systems are harder to govern.The patience that actually accelerates outcomesThe counterintuitive truth about AI marketing transformation is that the organizations moving most thoughtfully, not most aggressively, are the ones achieving durable results fastest.Speed without structure creates false starts. A team that adopts AI tools under pressure, without a real parallel validation period, without clear output sequencing, and without honest communication to staff and customers, will likely hit a wall. The outputs won't meet brand standards. The team will lose confidence. Leadership will question the investment and the marketing leadership. And then you're not just behind, you're behind and rebuilding trust at the same time, which is a significantly harder problem than the one you started with.The organizations getting this right are treating the transition as a genuine operational build, not a software installation. They're investing in the parallel runway and sequencing thoughtfully. They're having the hard conversations with their teams early. They're building the institutional knowledge that makes AI genuinely useful rather than just technically present.Timing isn't about moving slowly. It's about moving in the right order. And right now, for most large marketing organizations, the right order starts with planning and discipline, runs through validation, and earns its way to scale. <a data-mce-href="https://ideaovation.ai/blog" href="https://ideaovation.ai/blog">more i/o posts</a> &nbsp;| &nbsp;<a data-mce-href="https://www.linkedin.com/in/jjonesdirects/" href="https://www.linkedin.com/in/jjonesdirects/">about the author</a>

Timing is everything when onboarding AI into your marketing operations

<h3>The public is getting better at noticing synthetic AI visuals and audio. Is it time to hit pause on these types of AI-generated assets, or are we just doing it wrong?</h3>Last week I was scrolling through my YouTube feed and stopped dead at an ad for a snack product. The spokesperson looked like they'd been sculpted from matte plastic. The skin had that tell-tale AI smoothness. It was too perfect, too even, completely devoid of the micro-texture that makes a human face feel real. The hands and eyes were subtly wrong. I stared at it longer than I would have stared at a good ad, but not for the right reasons.It raised two questions that I haven't been able to shake. First: was this a budget problem, a skills problem, or is some level of obvious AI "fakeness" quietly becoming an accepted aesthetic, in the way that Pixar's stylized sheen became the defining look of animated cinema for a generation? And second: has the industry's rush to deploy AI-generated imagery outpaced the technology's ability to actually deliver on the implicit promise of photo-realism?I think we're at an inflection point. And the industry is not handling it gracefully.The Uncanny Valley Has a PR ProblemThere's a well-documented psychological phenomenon called the "uncanny valley," that deeply uncomfortable feeling you get when something looks almost human, but not quite. Robotics researchers identified it decades ago, but it has become a defining challenge of AI-generated marketing in 2025 and 2026.South Korea's GS25 convenience store released an AI-generated campaign that drew <a data-mce-href="https://www.campaignasia.com/article/ai-ads-and-the-uncanny-valley-problem/10e51cymjf3wpvkkmxu4qb5gao" href="https://www.campaignasia.com/article/ai-ads-and-the-uncanny-valley-problem/10e51cymjf3wpvkkmxu4qb5gao" target="_blank" rel="noopener">immediate public backlash</a>; "creepy," "soulless," and "plastic" were among the reactions, and it became a case study in what happens when a brand outsources emotional nuance to a generator. Coca-Cola, with all its resources, has now stumbled twice. The comparison between its 2024 and 2025 holiday campaigns reads like a strategy shift from technical ambition to aesthetic course correction, driven by the need to resolve the very same uncanny valley problem. Yet critics still found the end product "<a data-mce-href="https://hyperight.com/holiday-hype-or-digital-slop-coca-colas-2025-ai-ad-sparks-industry-split/" href="https://hyperight.com/holiday-hype-or-digital-slop-coca-colas-2025-ai-ad-sparks-industry-split/" target="_blank" rel="noopener">crummy looking</a>" despite a year of significant advances in AI video technology.&nbsp;This is not a small-brand problem. This is an industry-wide reckoning with the gap between what AI image and video tools promise and what they actually deliver when the ask is photorealistic human beings.Are Consumers Getting Smarter, or Are We Getting Sloppier?Here's where the data gets genuinely interesting, and a little contradictory.A <a data-mce-href="https://clutch.co/resources/ai-photos-brand-usage-consumer-trust" href="https://clutch.co/resources/ai-photos-brand-usage-consumer-trust" target="_blank" rel="noopener">Clutch survey</a> found that 57% of consumers couldn't identify AI-generated photos when tested, even though 66% were confident they could spot them beforehand. After the test, their confidence dropped to 56%. On the surface, that might sound like good news for marketers using AI. But read it again: confidence dropped. People walked away from the test more suspicious than when they started. And suspicion, once it enters the frame, is corrosive.The same study found that 84% of consumers want brands to disclose AI imagery, and trust drops sharply when disclosure is missing. Meanwhile, <a data-mce-href="https://www.nim.org/en/publications/detail/transparency-without-trust" href="https://www.nim.org/en/publications/detail/transparency-without-trust" target="_blank" rel="noopener">research</a> from the Nuremberg Institute for Market Decisions found that ads described as AI-generated were perceived more negatively than identical ads presented as human-made, with participants showing less inclination to click or engage with the product.So even when consumers can't tell the difference, the mere knowledge that AI was involved changes their behavior. That's a strategic problem that no improvement in render quality is going to solve.The Style Question: Sloppy Marketing or a New Aesthetic?Back to my plastic snack spokesperson. I keep coming back to that first question: is there an emerging AI aesthetic that audiences might eventually accept the way they accept animation or CGI?It's not a crazy premise. Every visual medium develops its own signature look, and sometimes that look becomes the point rather than a limitation. Early CGI in film was laughable compared to live action, and then Toy Story made the artificiality part of the magic. Could there be a version of that for AI-generated advertising?There are already cases that suggest yes — but with a crucial caveat. <a data-mce-href="https://marketingagent.blog/2025/11/02/virtual-influencers-vs-human-influencers-a-data-driven-comparison-for-2025/" href="https://marketingagent.blog/2025/11/02/virtual-influencers-vs-human-influencers-a-data-driven-comparison-for-2025/" target="_blank" rel="noopener">Dior's collaboration</a> with virtual influencer Noonoouri, known for her animated, illustrative style rather than hyperrealism, reportedly exceeded human influencer benchmarks by 42% in campaign ROI while cutting content production costs by 76%. Marketing Agent Blog The key was that Noonoouri doesn't try to be real. She's a fashion cartoon, and she's transparent about it. Experts in the space now <a data-mce-href="https://marketingagent.blog/2025/10/29/from-calvin-klein-to-gucci-how-top-brands-are-using-ai-avatars-to-drive-revenue/" href="https://marketingagent.blog/2025/10/29/from-calvin-klein-to-gucci-how-top-brands-are-using-ai-avatars-to-drive-revenue/" target="_blank" rel="noopener">recommend stylized realism</a> over photorealistic replication specifically to avoid the uncanny valley effect.&nbsp;That's a meaningful distinction. The brands getting punished aren't those leaning into a clearly synthetic aesthetic, they're instead the ones attempting photorealism and falling short. There's a massive difference between "this is obviously a stylized AI character" and "this is supposed to look like a real person but something is deeply wrong."The "AI Phobia" in the RoomI want to name something that doesn't get talked about enough: the irrational end of the anti-AI backlash. I call it AI phobia, and I see it regularly among senior marketing colleagues who have written off all AI-generated visual and audio content as categorically unacceptable. The fear is real, but it's often rooted in a misunderstanding of what the technology can and can't do, and in which contexts.Not all AI-generated visual work is a plastic spokesperson hawking snacks. There are AI-generated campaigns out there in artistic, illustrative, or abstract styles that are extraordinary, work that would be prohibitively expensive or technically impossible to achieve by traditional means. Notably, the ability to detect AI imagery diminishes considerably among consumers <a data-mce-href="https://insights.wiseup.pr/76-of-us-consumers-unable-to-spot-ai-generated-images-in-new-test/" href="https://insights.wiseup.pr/76-of-us-consumers-unable-to-spot-ai-generated-images-in-new-test/" target="_blank" rel="noopener">aged 40 and above</a> Insights, which means the generation most likely to be making marketing decisions is also the most likely to overestimate how much the average consumer can spot. That mismatch leads to overcorrection.The solution isn't to ban AI from your creative workflow. It's to be strategic about where you deploy it and honest about what it can currently deliver.So, Should We Hit Pause?Here's my honest take: no, but with some real caveats.We should absolutely pause on AI-generated photorealistic human beings in consumer-facing advertising until the technology genuinely closes the gap. The uncanny valley in AI advertising isn't a glitch, it's a signal that emotional intelligence is failing. When a brand outsources emotional nuance to a generator, the audience notices instantly. Using AI to produce a "real-looking" person who ends up looking like a mannequin is not a budget saving, instead a <a data-mce-href="https://www.nngroup.com/articles/ai-ad/" href="https://www.nngroup.com/articles/ai-ad/" target="_blank" rel="noopener">trust cost</a>. And trust, once lost, is expensive to rebuild.But that's a very specific use case. For concepting, storyboarding, and internal comping, AI is a gift. For stylized, illustrative, or clearly non-photorealistic work, it can be exceptional. For abstract, environmental, or product-focused visuals that don't involve people, the quality bar is already high enough that the risk is much lower. The rule of thumb I'm applying in 2026: if the AI asset needs to fool the eye, it's likely not correct for the final media (use careful judgement). If it's making no such claim, proceed with strategy and disclosure.The Covenant Still AppliesI've written before about the need for an AI covenant with customers. This is a published framework for how and when you'll use AI, and what you'll disclose. That thinking applies here more urgently than ever. When AI is paired with strong human insight, cultural clarity, and transparent intent, it stops feeling uncanny and <a data-mce-href="https://www.campaignasia.com/article/ai-ads-and-the-uncanny-valley-problem/10e51cymjf3wpvkkmxu4qb5gao" href="https://www.campaignasia.com/article/ai-ads-and-the-uncanny-valley-problem/10e51cymjf3wpvkkmxu4qb5gao" target="_blank" rel="noopener">starts feeling inevitable</a>. That's the creative moment the best marketers are operating in right now.The brands getting hurt aren't the ones using AI. They're the ones using it carelessly to chase the cost savings without asking whether the output actually serves the audience.The technology will get better. It always does. But in the meantime, the smartest move is to match the tool to the task, stop asking AI to fake things it can't convincingly fake, and be honest with your audience about what they're looking at. They're watching more closely than you think. <a data-mce-href="https://ideaovation.ai/blog" href="https://ideaovation.ai/blog">more i/o posts</a>&nbsp; |&nbsp; <a data-mce-href="https://www.linkedin.com/in/jjonesdirects/" href="https://www.linkedin.com/in/jjonesdirects/" target="_blank" rel="noopener">about the author﻿</a>

When AI looks like AI: The marketing case for knowing when to say no

<h3>It feels like the same movie we've seen before. But there are some new perils and big wins that lie ahead.</h3>In 2017, every enterprise software vendor had a blockchain strategy. Distributed ledgers would revolutionize supply chains, transform loyalty programs, and democratize data ownership. The promise was seductive: autonomous, trustless systems that eliminated intermediaries and reduced headcount. Management loved the cost-reduction narrative. Then reality arrived. Most organizations lacked the infrastructure to deploy blockchain meaningfully. The technology worked in narrow use cases—cryptocurrency transactions, certain supply chain applications—but the broader revolution never materialized because the gap between capability and operational readiness was massive.Now, we could be watching the sequel. Adobe and other platforms are pushing agentic AI as marketing's next revolution, and their 2026 Digital Trends report shows 80% of organizations believing breakthrough experiences will be "highly personalized and anticipatory" within a few years. Management sees the same thing they saw with blockchain: headcount reduction numbers that look good on a spreadsheet. But what they're trading for those savings—and whether their organizations are actually ready to make that trade—is a conversation most leaders are avoiding.The technology is becoming ready before most organizations are. That gap is where expensive mistakes happen, and where the human costs that don't appear in the initial analysis start to compound.The foundation problem no one's solvingBefore we get to autonomous agents replacing parts of your marketing team, let's talk about what needs to be in place first. Not as bureaucratic checkboxes, but as the foundation that separates outcomes that move the needle from ones that embarrass you publicly.Adobe's research confirms what I see in practice: despite widespread pilots with generative AI, only a minority of organizations have actually integrated it across multiple functions, and even fewer have embedded it enterprise-wide. The gap between toolset experimentation and deployment isn't a technology problem. It's an operational maturity problem that most teams haven't acknowledged yet.Here's what actually matters. Governance that has teeth—someone who owns what the agent can publish, who gets called when it goes sideways. You need to define accountability before you need it, not after. Brand intelligence that goes beyond static guidelines—voice, tone, messaging hierarchy, the things you'd never say. These need to be encoded, testable, and enforced. Your AI agent can't read a PDF and absorb your brand soul.Clean data with real consent. Agentic marketing runs on customer data, and before your agent touches a single contact, you need a unified CRM, clean IDs, and GDPR and CCPA compliance locked in. A poorly scoped agent that contacts opted-out customers isn't a technical glitch—it's a liability that makes headlines. According to Adobe's research, only 44% of organizations say their data quality is adequate for AI, and 52% admit that poor data unification limits AI advancement. That's not a foundation, that's quicksand.Channel access needs to be intentional, not open. Scoped API keys, spend caps, approval thresholds. Your agent doesn't need admin rights to everything. The ability to autonomously launch a major campaign without human sign-off is a risk, not a feature. Compliance needs to be baked in from day one—disclosure rules, FTC and ASA requirements, IP clearance for images and copy.You need baselines before deployment. If you don't know where you started, you can't prove where AI took you. Establish performance benchmarks first: conversion rates, cost-per-acquisition, brand sentiment. Then measure what changes. And you need a kill switch with a tested rollback procedure for each channel and a communication plan for when something goes wrong.Most critically, the humans working alongside the agent need to understand what it can and can't do well enough to catch errors, escalate issues, and step in when the output drifts. AI literacy in your marketing team isn't a nice-to-have. It's the last line of defense.Get these foundations right, and agentic AI becomes a genuine force multiplier. Skip them, and you're just generating risk at scale.Where limited autonomy actually works today I haven't seen an autonomous system that impressed me yet. But some are getting closer. Adobe's case studies with their Journey Orchestrator product are approaching meaningful autonomy in narrow domains.The successful applications I've seen share specific characteristics. They operate in constrained domains with clear success metrics, reversible decisions, and human oversight built in. Agentic systems can already replace repetitive, pre-formatted or templated asset production—visuals, copy, videos. I'd commit to the highest repetition tasks first, like placing product shots into pre-formatted online ads that retailers run constantly. You still need human QA, but the speed for these tasks is incredible and the quality is already good enough.Bid management and media optimization with clear KPI boundaries works. Customer service routing and initial response in high-volume, low-complexity scenarios works. Content variation generation within strict brand parameters works. What these share is that they're narrow, rule-based applications where the cost of error is low and the ability to course-correct is high.What doesn't work yet is the broader promise—autonomous agents making strategic decisions, creating breakthrough creative work, or managing complex customer relationships end-to-end. As Dan Gardner, co-founder of Code and Theory, argues, "AI is now the baseline infrastructure for knowledge work. Writing, research, prototyping, summarizing, coding. That's individual leverage. It makes people faster. The more profound shift is collective leverage." The real opportunity, he suggests, is in collaborative intelligence where AI elevates realignment rather than replacing it.The headcount math that looks better on spreadsheets than in reality There's a financial argument for agentic AI. The potential to replace two to four FTEs in specific workflow areas. Immediate budget relief. 24/7 operation. When a CFO comes to you with that math, you need to have a different conversation than the one they're expecting.Before organizations lay off 50-60% of their marketing team, they need to understand that these systems aren't perfected yet—they're not even good enough yet. What makes more sense is hiring a shadow team to start ramping up, tuning, and teaching the agentic systems your brand, quality expectations, and guardrails. As performance starts hitting the mark, ramp that up while ramping down the legacy team.The spreadsheet doesn't capture institutional knowledge loss, creative judgment, relationship equity, or quality degradation over time. It definitely doesn't capture what happens to the team that remains. Morale suffers. Trust erodes. Risk-taking disappears. And here's a question most leaders aren't asking: if senior marketers are running systems and juniors aren't needed, how do we train the senior leaders for the future?Adobe's research found that one in four customers now turn to AI-powered platforms as their primary source for information and purchase decisions—surpassing brand websites and reviews. When AI is increasingly competing against AI in the discovery phase, do you really want your marketing output to be entirely AI-generated? Gardner points out that "delegation is overtaking browsing as the key vehicle for discovery." People ask LLMs questions during discovery, validate socially where trust forms, and transact wherever it makes sense. In that environment, what advantage does human creativity provide?The answer: the difference between generic and custom. Between something that works because an algorithm optimized it and something that works because a human understood context that algorithms miss.The catastrophic errors that are coming Organizations that over-rely on these new systems and drop headcount too fast are going to make hugely embarrassing and perhaps catastrophic errors. It will happen. And consumers are ready to punish organizations who make headlines for an over-reliance on AI or huge layoffs for the same reasons. It's the same reaction people get to an AI bot or weird AI images with too many fingers—there's an AI-phobia growing in the public and they will take action, especially against consumer brands.We're approaching a 1995 moment, like when internet companies fired vast numbers of marketing and brand leaders because they weren't embracing the internet. The difference is that the internet actually was ready for widespread deployment. Agentic AI isn't—not yet. The organizations that move too fast will become cautionary tales.What moving deliberately actually looks like If an organization came to me today wanting to deploy agentic AI in their marketing operations, here's the sequence. First, get your house in order: strong brand guidelines, well-organized assets and templates, clear marketing goals with metrics including staff savings goals, a library of approved materials for different platform applications. You need technical overviews of what's running your website and other systems to understand compatibility and efficiency opportunities.Then comes the overlap phase. I'd do a one-year ramp-up in parallel with the legacy team ideally, with no less than six months to do the same. Team reductions can happen in stages as certain milestones are reached. This isn't about being cautious for caution's sake. It's about building something that actually works instead of something that looks good in a quarterly earnings call.Adobe's research confirms that "AI capabilities are evolving faster than organizations can keep up, and customer expectations are shifting just as quickly." The constraint used to be technical feasibility. Now, as Gardner observes, "it is imagination." But imagination without operational foundation is just expensive daydreaming.The honest timeline is twelve to eighteen months of operational work before responsible agent deployment. That's not what management wants to hear, but it's reality. And the leaders who acknowledge that reality—who build foundations first, pilot narrowly, and honestly calculate both financial and human costs—are the ones who'll actually deliver on AI's promise rather than just its hype.Tread carefully, move deliberately The technology will be ready before most organizations are. That gap is where the expensive mistakes happen, where the human costs that don't appear in the initial cost-benefit analysis start to compound, and where the distance between what vendors promise and what operations can actually deliver becomes painfully obvious.The question isn't "can we deploy agents?" It's "should we, and at what cost?" The leaders who win won't be the ones who move fastest. They'll be the ones who move deliberately—who understand that the goal isn't autonomous marketing, it's augmented teams that deliver what AI alone cannot: custom quality at optimized cost.We've seen this movie before. The ending doesn't change just because the technology does. What changes is whether you're ready when the credits roll. <a data-mce-href="https://ideaovation.ai/blog" href="https://ideaovation.ai/blog">more i/o posts</a> &nbsp;| &nbsp;<a data-mce-href="../" href="../" target="_blank" rel="noopener">about the author</a>

i/o blog

Working with AI agents: The case for preserving our humanity

Get on the bus, or go home? The AI moment in marketing is...

Who gets to evolve your brand? The case for keeping human...

Timing is everything when onboarding AI into your marketi...

When AI looks like AI: The marketing case for knowing whe...

Agentic AI in marketing: Is it this decade's overhyped mo...