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I remember the meeting in the fall of 2000 vividly. I was sitting at the large meeting table in the glass and steel San Francisco office of a prominent web agency. As the "Experience Lead," I was in these management sessions, and the rest of the leadership team was present. Our usually upbeat Managing Director entered with a serious face and spoke as he walked: "We need to talk."From the front of the room, he turned and delivered the news. "I've learned that some other web agencies are now selling consulting engagements for less than $1 million. And worse, another competitor is offering these engagements with no fees to build up their client list." On that day, he was the messenger of doom, announcing the collapse of a high-dollar phase of web consulting. The .com era was at an end.A different pricing modelBack then, we didn't typically bill by the hour. We used value-based pricing: what did we feel a service was worth in the marketplace? That's what we quoted, and usually billed. We'll come back to this concept.Fast forward to today. Like many marketing consultants, I am onboarding new AI tools at a rapid pace. Last week, I had another (possibly crazy) business concept, and once I had a brand name, I wrote, designed, and launched a new brand and website within 3 hours. I used it as an experiment to build a brand and site fully with AI, and it worked remarkably well. (This is also a message of doom for consumer web CMS products.)With client work, I now write estimates that often end up being too high once the work is actually built. This math issue occurs when projects estimated with an hourly model. Today, work timeframes are collapsing, and that's creating a potential revenue crisis for these types of engagements. I don't want to race to the bottom with fees cut in half by these tools. And then there is the overhead we all now carry: monthly tool subscriptions, API usage fees, token costs. These add up fast and are invisible to clients.The numbers tell the storyHourly billing carries a potential efficiency penalty for any consultant. As consultants become faster and more skilled, they bill fewer hours and earn less money. That's the trap. The same <a data-mce-href="https://lukdigital.com/how-much-does-hiring-a-marketing-consultant-cost/" href="https://lukdigital.com/how-much-does-hiring-a-marketing-consultant-cost/" target="_blank" rel="noopener">analysis of the 2026 consulting market</a> notes that AI has made basic execution cheaper than ever, while digital marketing strategy has gotten more expensive because the complexity has skyrocketed.&nbsp;The broader consulting industry is already restructuring around this reality. The work that used to take a team of six now takes two people with the right martech stack. But the rates clients pay have not moved in step. Of the <a data-mce-href="https://rockstardeveloperuniversity.com/ai-consulting-rates-statistics/" href="https://rockstardeveloperuniversity.com/ai-consulting-rates-statistics/" target="_blank" rel="noopener">consultants who bill hourly</a>, 79% say they want to raise their fees. The tension between those two facts is where most of us are living right now.Back to the early internet yearsValue-based pricing worked well in the .com era, thanks to healthy demand and clients who had no idea how to benchmark what good web work should cost. Can it work again in today's AI reality?I think the answer is yes, but with some modifications. The most significant trend in 2026 is the shift toward value-based pricing tied directly to measurable business outcomes, with fees typically structured as a percentage of cost savings or revenue increases attributable to AI initiatives. According to <a data-mce-href="https://leanware.co/insights/how-much-does-an-ai-consultant-cost" href="https://leanware.co/insights/how-much-does-an-ai-consultant-cost" target="_blank" rel="noopener">recent research</a>, 73% of consulting clients now prefer pricing models tied to measurable business outcomes rather than time spent. LeanwareFor agencies, the consulting formula targets roughly 10 to 20% of the value created, with a 5 to 6x client ROI as the sweet spot, and among $150K+ earners it is the primary model 62% of the time. Flat fees tied to deliverables rather than hours are how the math works when AI has compressed delivery time without reducing the strategic value of the output. <a data-mce-href="https://taskip.net/ai-automation-agency-pricing/" href="https://taskip.net/ai-automation-agency-pricing/" target="_blank" rel="noopener">A Taskip analysis of AI agency pricing</a> put it plainly: "If AI can handle the execution, then execution is no longer where your value sits."&nbsp;Can we demonstrate an edge?Another key question: can we demonstrate higher value not only in speed, but in how we use these tools? There will be a lot of mediocre AI output to rise above, and it's already out there. The difference in our AI-derived products will usually come down to quality, creativity, and judgment, and we will need to demonstrate that clearly to clients.For a full-time in-house person, their edge might be commanding so many tools that they function as a one-person marketing team. For independent contractors, this is a more serious reset. Possibly even a moment to think about different business models, alternative career paths, or other changes.At <a data-mce-href="https://ideaovation.ai/" href="https://ideaovation.ai/" target="_blank" rel="noopener">Idea Ovation</a>, we've responded to this challenge by shifting the value proposition away from execution hours and toward something more durable: AI-powered brand systems and automations that make the client's entire team faster and more consistent. Our <a data-mce-href="https://ideaovation.ai/ai-brand-systems" href="https://ideaovation.ai/ai-brand-systems" target="_blank" rel="noopener">Brand AI Systems</a> approach embeds intelligence into branded templates so that anyone inside an organization can create new assets that stay on-brand, reducing their ongoing dependency on agency hours entirely. That's a value you can price against ROI, not a stopwatch.The joy in all of thisI don't mean to paint a doom-and-gloom story. The creative possibilities for any creative person, and especially marketing professionals, are genuinely extraordinary right now. It's such a joy to easily realize and deploy new ideas like my 3-hour website. The tools are remarkable.But how do we get paid to keep up with and pay for all of these new tools, when the outputs happen at lightning speed? The same way the best .com era agencies survived the 2000 pricing collapse: by doing work that couldn't be easily commoditized, by charging for judgment rather than hours, and by making sure clients understood the difference. The firms that survived that era weren't the fastest or the cheapest. They were the ones who could clearly explain why what they did mattered.That's still the answer. It's just a harder argument to make when anyone with a laptop and a few subscriptions can spin up a brand in an afternoon. <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> Brand AI is built in partnership with <a data-mce-href="https://synaxis.ai/" href="https://synaxis.ai/" target="_blank" rel="noopener">Synaxis</a>

Is AI doing to marketing fees what the internet did in 2000?

<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>

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