Suffering From Marketing Paralysis? Everyone Is Right Now, Here’s The Way Out

A staggering 70% of marketers are actively prioritizing short-term wins over long-term brand growth. For you, that might mean sending or receiving messages like these: Delaying this campaign allows leadership to get in a better position to share a concrete, finalized roadmap. We just don’t know what that is yet, it needs more time. Immediate bandwidth is going to major accounts and the summit. Not sure what we’re going to say. We don’t have a CMO now. And the new products keep changing. We’re pausing new initiatives to allow the leadership transitions to settle and align on next steps. But it’s going to be exciting once we get going. Big things coming! Beyond the 70% in Prof. Christine Moorman ’s Duke University survey - sponsored by Deloitte and the American Marketing Association - Gartner highlights 84% of companies are in the middle of a branding doom loop. It’s not just you. Nearly everyone’s undergoing an AI-driven pivot made more difficult by policy and economic risk. And many of these pivots are starting to spin their wheels for reasons we’ll get in to. Marketers have turned to harvesting existing but similarly shaky demand rather than invest in creating new demand in the dark. Budgets then get pulled from marketing. Marketing leaders are either leaving, or being replaced. But don’t blame leadership or the product team for keeping you in the dark. It’s not their fault, but rather an adjustment to a permanent reality that’s going to require you to see in the new dark. \ The Paralysis of the SaaS-to-AI Pivot For tech firms, the current defensive crouch stems from a massive structural shift: the need to pivot from a traditional, seat-based SaaS product launch paradigm to an AI-based infrastructure model. The old playbook relied on static, linear feature releases, expensive software builds, and cheap distribution curves. The new reality demands a dynamic process built around user cohorts, continuous iteration, and intelligent data layers. Too many companies are failing to recognize and make this transition. The new paradigm can’t be jammed into the shape of the old one. Instead they’re stuck in a devastating AI development doom loop because they keep trying to treat AI like traditional SaaS. Teams waste months trying to build from scratch and maintain custom vector databases, fragmented retrieval-augmented generation (RAG) pipelines or their newer alternatives, and data normalization tools. Then, a single API update from an underlying LLM model collapses the entire custom-coded stack, plunging the engineering team back into chaos. Yes, AI allows you to code faster, but not enough are realizing that, in itself, is changing the problems to be solved and how to solve them. Executive sessions we convened across Silicon Valley in March reveal that the market is no longer waiting for clarity, it is actively pricing its absence. AI’s rapid acceleration is being blamed for layoffs, re-sorting categories, and compressing some execution timelines in real time, while extending the timelines involved with understanding the new markets, determining strategy, reorganizing teams and figuring out what in the world you’re marketing now. Among the heaps of the discarded are CMOs and growth directors being forced out, left idle, or shuffling positions as they wait for product teams to deliver stable products, which aren’t coming… maybe ever. By staying quiet, leadership teams accumulate a brutal strategic tax. In the AI development era, the narrative is no longer downstream of the product, the narrative is the product. \ You’re Not Selling Products Anymore In AI platform or maybe it’s better call them infrastructure markets, delay while you figure out products leaves you drifting out of the market: Narrative Opportunity Cost : Repositioning later with journalists, buyers and influencers – and what’s called the intelligent web - becomes vastly more expensive when the conversation is how experimentation is being done with customers now. That experimentation is the product, and if you’re lucky it will never stand still long enough to create evergreen content. You need to adjust to stories that that have plot twists and focus more on your customers than you. Sales Cycle Drag : Deals crawl to a halt because buyers must be educated on what you are becoming rather than why you matter right now. And then they need to be re-educated as what was true last week is now different. Influencer Misclassification : Markets fall back on legacy labels, branding your platform as "SaaS with AI features" instead of an AI development environment. Because if you don’t move, that’s kind of what you are. Competitive Erosion : Competitors fill the structural void, forcing your team to sell in their shadow. They lead the development and experimentation in public, leaving you to watch from the outside. Internal Talent Tax : Prolonged uncertainty leads to teams spinning their wheels in a panic as foreboding grows. If the decision-making is happening in a room that they’re not in, the longer nothing comes out of that room, the more they fear whatever emerges is going to eat them. \ The Possession Game: Overcoming Buyer Skepticism This industry-wide paralysis ironically creates an unprecedented opportunity for those that follow the logic and see where this is going. And to be clear, if you think you’re going to figure that out on your own, you’re already in trouble. Because you don’t have the answer. The economic model has inverted: software is cheap to start but expensive to operate, driving enterprises to buy infrastructure so they can build internal AI intelligence rather than rent legacy software workflows. Because of this, buyer psychology has fundamentally shifted from "buy to grow" to "build unless proven otherwise". Or at least they’re looking for partners who can enable that. Enterprises now constantly scrutinize whether an application offers durable differentiation or merely bundles features that internal teams can easily replicate with AI. So that answer isn’t with you. The answer is with your customers. And the more you surface that and allow more to gravitate to where the crowd is growing, the more you’ll be at the center. Or to be less philosophical, stop waiting for the product to be ready for your launch cycle. Find a set of existing customers working on something interesting with your team, and make that visible through a cohort announcement. Don’t worry or wait for what they’re working on to turn into a stable product. The stable product is not the destination, the lessons learned and operationalized out of the experimentation that is possible on your infrastructure is now the product. \ Eliminating Launch Fixation: Marketing "Living" Context To break free of paralysis, enterprise marketers must stop waiting for a traditional, static SaaS product launch cycle. Because AI value is fluid and continuous, you cannot pitch a traditional "Version 2.0." Instead, companies must shift marketing’s focus from launching finished software features to launching new context and data primitives. By utilizing managed AI infrastructure directly inside the hosting environment, organizations can turn their existing web footprint into a standardized AI data layer. The story of what to do with that is going to keep evolving among your customers and clients. Treat your digital footprint as a living enterprise brain. You can show how a cohort is utilizing "your GRC dataset and pipes to inform governance processes for AI development." "Our Q1 Alpha Customer Cohort successfully reduced customer support token spend by 55% while hitting a 98% intent resolution accuracy using our server-side context caching." You can show how a select cohort of health insurance carriers are using your infrastructure as a harness to pilot new workflows for call centers. A governance layer is allowing asset managers to more quickly run comparative analysis of past and current fund performance to show how new products compare. These uses may not stabilize into standardized products, and changes to token pricing or harness engineering might supersede them. But how did your team and customers take what they learned and were able to operationalize and re-invest it into getting further and further, and more durably ahead of the curve and turn that into market advantage? Marketing shifts from celebrating a fixed milestone, to promoting an ongoing data asset that external AI models can reliably query. \ The CMO Playbook for Next Week Start now by scheduling a meeting to identify a cohort you can anchor a first marketing and public relations push around this month. A cohort is typically a select group of customers who are brought into your product simultaneously for a specific, potentially limited timeframe. Generally, in a development cycle, instead of opening your software to the general public, which often results in rapid churn when users discover it is an unstable pilot, you position the release as an exclusive, invitation-only working group. In some settings this might be an alpha or a beta group. But you can cut to the chase – and avoid getting sucked into doom loops elsewhere, by seeing what accidental cohorts you already have to start with. By marketing a cohort, you fundamentally shift customer expectations. Instead of thinking, “I am buying a polished, finished software tool,” the mindset becomes, “I am joining an elite group to help co-create the future of this technology.” “I am valued as a customer, and I am superior to my competition because I have been selected for this cohort rather than others.” Media relations around a cohort or design partner strategy should mirror this shift. The focus must move away from product features and toward data, market friction, and human narrative. From a media relations perspective, tech journalists are fatigued by generic “we added AI” press releases. However, they are highly motivated to cover industry-wide failure patterns such as “pilot purgatory,” where companies invest heavily in AI tools that never reach production. When executed properly, a cohort strategy allows marketing to tell a far more compelling story grounded in real-world experimentation and insight. Again, don’t get stuck in a planning cycle, see what you’ve already got happening organically and don’t overthink it. You’re not looking for enduring polish, though you do need a quality work product here. You’re looking for momentum and a repeatable process. And you’re looking for that momentum to be enough for other parts of the organization to see how they can get out of their closed rooms and invite the market into the process, thus accelerating it and building market position. Let’s stick with the media relations part of this, because that’s a secret weapon to getting into a leadership position here. The media narrative should be built around industry opportunities, how your customers are finding better solutions to their customers’ challenges, exclusive access, and collaborative innovation rather than product launches. The first step is to pitch the macro problem your customers are built around. Do not approach reporters with product-centric messaging like, “Our product is in alpha, would you like to interview us?” Instead, position the story around a structural breakdown in the market that leaders in the cohort are working on your infrastructure to better understand help their customers solve. For example, a strong pitch angle would frame the issue as widespread fatigue among B2B buyers from your customers, where companies invest in tools that employees ultimately ignore for instance. The story then becomes about why the current AI rollout model is failing for others, and how a small group of forward-thinking leaders is solving it. The second step is to position the cohort as an industry working group. Rather than presenting it as a test of your product, frame it as a curated, high-level collaboration defining new standards. This elevates the effort from a product trial into something inherently newsworthy. For example, the message might emphasize that a select group of senior operators has been assembled to define the industry specific operational guardrails for how AI will reshape a specific workflow over the next decade. The third step is to offer data and insight exclusivity. Journalists value proprietary insights, especially those grounded in real-world usage. By offering early access to anonymized cohort data for instance, covering performance, adoption challenges, and measurable outcomes, you create a strong incentive for coverage. But you don’t have to have all this data lined up in advance. You can start now by providing access to the experiment, and then, as you have your audience hooked, allow their demand to drive the data process. This whole effort is built on being as efficient and demand-led as possible. \ Okay, Who Else Is Doing This? Leading companies already use variations on this approach successfully. That’s part of how they became leading companies. OpenAI expanded into enterprise markets by working with a small group of major organizations to co-develop custom models, positioning the story around how elite teams were shaping the future of AI. Harvey AI generated significant attention by partnering with a top-tier global law firm and framing the effort as a transformation of legal research. Palantir shifted its narrative entirely by highlighting cohort-based bootcamps, focusing media attention on speed of deployment rather than product features. This approach not only improves media outcomes but also solves a major internal marketing challenge, best illustrated by what can be called the “AI Pivot Doom Loop.” I know, a lot of doom loops. \ Moving Beyond Media Relations Taking practices developed by Crowdstrike, Harvey, Palantir and OpenAI, imagine a SaaS company called TalentFlow. It originally offered a stable applicant tracking system but has spent the past year attempting to build an autonomous AI recruiter. The product is unstable, changes constantly, and frequently produces unreliable outputs. When concrete advances are made, what can be created elsewhere through new harnesses gets out ahead. Marketing is trapped. The legacy product feels outdated, but promoting the new AI leads to immediate churn when users encounter bugs. Customers blame marketing for overhyping an unfinished solution. A cohort strategy breaks this loop. Instead of releasing the AI broadly, the company launches an exclusive cohort program branded as an AI Recruitment Pioneer Cohort. The landing page does not show product screenshots or features. Instead, it focuses entirely on the problem and the exclusivity of participation. The message clearly states that current AI solutions are flawed and invites a limited number of forward-thinking leaders to help build something better. The call to action is not “start a free trial,” but rather an application to join a limited cohort. The application process itself reinforces this positioning. Prospects must demonstrate experience, patience, and willingness to collaborate. Questions focus on prior AI failures and commitment to providing ongoing feedback. Once accepted, participants are explicitly told the product is experimental. Bugs and failures are reframed as valuable insights. Instead of churning, customers engage directly with the product and engineering teams, contributing to improvement. They feel like innovators rather than victims of unfinished software. This strategy protects the marketing team in several ways. It eliminates the need for constant asset redesigns because the core narrative remains stable even as the product evolves. It generates high-value engagement by attracting qualified, patient users who actively contribute to development. It also protects the brand by keeping unstable early-stage experiences out of the public domain. The difference between a traditional launch and a cohort strategy is fundamental. In a traditional launch, the call to action is immediate access, which creates high churn risk. In a cohort strategy, the call to action is an application, which introduces friction but results in higher intent and better-fit participants. Traditional launches set the expectation that the software works flawlessly. Cohort strategies position the product as an evolving environment that requires collaboration. When a product breaks in a traditional model, customers leave and marketing is blamed. In a cohort model, customers report issues and contribute to improvement. Traditional marketing requires constant updates to align with product changes. Cohort-based marketing relies on a stable narrative centered on participation and innovation. You were already testing your product with select customers before launching it. This strategy acknowledges that the new AI destination is not necessarily going to be a new product. Rather, your infrastructure is going to be the environment in which the testing actually results in patterns of usage by customers that are now the product. So now what used to be the testing, is now the product. That doesn’t mean turning the debugging into the product. But rather it means understanding how stable and scalable are evolving as concepts. It also doesn’t mean that you’re not going to launch any more products with traditional SaaS launch processes. Those are likely to continue coming, but you can’t wait for them any longer, and you may need to use the cohort strategy to propel you toward that. The key takeaway is that this approach transforms marketing’s role from following helpless behind, to helping to build the momentum and gather together the strategy and messaging that leadership and product need to help you all move forward together.