For most of my career, bringing a new product to market took time. Two decades ago, it took a year. 10 years ago, it took three to six months. Five years ago, only the companies at the leading edge of continuous innovation might have released meaningful new features every few weeks.
The timeframe was deliberate because it had to be. Teams started with a hypothesis about a market problem they wanted to solve. They conducted discovery, validated the problem with customers, translated those findings into a storyboard, tested the storyboard with prospects, and then decided what to build for a prototype. Then the expensive production software development process began after all of that.
Marketing ran in parallel. While product teams were building, marketers had months to understand the audience, develop segmentation and targeting, establish positioning, create messaging, and build the assets required for a successful launch.
Today that model has to be completely rethought, and it’s because of AI, and increasingly agentic AI. AI is compressing the timeframe between an idea and a working product, and between a product and its market. What once took months can now happen in days, hours, or even minutes.
The implication isn't simply that companies will build software faster. The entire go-to-market model is changing because the product-development cycle and the marketing cycle are now moving at different speeds than they did even a few years ago. This will require the entire business to change.
The most obvious change is on the product side. AI-assisted development can now take an idea and rapidly turn it into a functional prototype or user experience. Storyboarding and early software development, which once consumed weeks or months, can increasingly be compressed into hours or minutes and directly into a full functioning prototype, which is a major change.
Cloud computing, microservices, continuous integration and continuous deployment, automated testing, and coding copilots have been moving software development in this direction for years. Although each innovation has made the development process faster, none of them have been the catalyst for the lightning-fast impact of today’s AI models. AI represents an exponential lever on top of that foundation.
We are moving from a world where a product idea could take a year to reach the market to one where an idea can become a functional prototype in an afternoon. But there is an important distinction marketers, product leaders, and executives need to keep in mind. A prototype is not an enterprise product. AI can accelerate the creation of a workflow or user experience. It doesn't eliminate the need for discovery, customer feedback, or human judgment. It doesn't automatically produce software that can operate securely at internet scale in today’s enterprise clouds. Security, reliability, governance, testing, and hardening still matter.
The hype around AI can obscure that distinction. The companies that benefit most will not be the ones that confuse speed with validation. They'll be the ones that use speed to increase the number of ideas they can test, learn from, and improve, and the rest of the business will need to take the same journey.
Marketing faces the same timeframe compression. A product launch that once required a year of preparation became a six-month process, and then three months. Today, it’s about a month. That creates a new problem.
The foundational work marketing traditionally did over several months, such as market research, positioning, segmentation, messaging, content development, competitive analysis, campaign design, and launch planning, now needs to happen in a fraction of the time.
Fortunately, AI is making that possible. AI-assisted positioning, strategy, segmentation, analytics, design, and content creation are compressing marketing workflows just as AI-assisted development is compressing product workflows. In the past year alone, we've moved from AI-generated marketing material that could be laughable to outputs that can be genuinely publishable. That doesn't mean marketers become less important or marketing becomes easier. It means the job changes to meet today’s reality.
The marketer's advantage increasingly comes from knowing what to ask, what to validate, what to reject, and what signals matter rather than simply producing the artifact. The same source material that can be used to rapidly prototype a product can, and should, become the foundational material for marketing. Product assumptions, customer problems, use cases, and workflows can inform positioning, content, segmentation, and launch strategy almost immediately. This means product and marketing can no longer afford to operate as sequential functions. They must work in parallel more than ever before.
In some ways, marketing has been ahead of this transformation. Over the past several years, the way buyers discover companies and products has changed dramatically. Google search is no longer the only, or always the primary, path to discovery. Buyers increasingly encounter information through AI-generated answers, communities, social channels, third-party content, and other digital environments. That has forced marketers to rethink search, content, discovery optimization, and demand generation.
We went from SEO to a much broader world of GEO and AEO, where the objective isn't simply to rank a webpage. It's to make a company's expertise, products, and perspectives discoverable and understandable to AI systems that increasingly influence what buyers see. The change is bigger than search. Marketing visibility itself has become harder to measure. Cookie restrictions, changing privacy policies, and the migration of discovery into environments marketers don't control have reduced the visibility companies once had into the buyer journey.
The answer isn't to pretend the old measurement model still works. It's to find the signal where the buyer has moved. That means marketers need to understand how content is consumed by humans, traditional search engines, AI crawlers, and real-time retrieval systems (RAG) inside large language models. It means understanding the influence of third-party communities and peer-to-peer environments on these results. It means measuring content consumption and business impact across a much more fragmented and ultimately closed ecosystem.
Marketing has to meet the buyer where the buyer actually is. This changes how I think about every product launch. Historically, an audience success plan might identify enterprise buyers, commercial buyers, practitioners, partners, sales teams, and employees. Today, AI is another audience to add to that list. We need to think about an AI system almost the way we think about a buyer. Can an LLM understand what our product does? Can it accurately describe our capabilities? Can it answer the questions a potential customer is asking? Can it identify our company as a credible solution to a particular problem?
Something as simple as adding a comprehensive FAQ to a blog post illustrates the shift. A human reader may never scroll to the bottom of the page to read the FAQ. An AI system may find that structured information extremely useful and citable. That means the content experience has to serve both audiences. And the definition of "audience" may soon become even more literal.
Agent-to-agent commerce is beginning to introduce a world in which an AI agent can act on behalf of a customer to research, compare, and purchase products. In that environment, a company isn't only marketing to a human decision-maker. It is also providing information to the agent making or influencing the decision. That creates an entirely new marketing discipline.
If an agent is going to recommend or purchase a product, it needs to understand who you are, what your product does, what it costs, how it compares to alternatives, and whether it solves the problem its user has asked it to solve. AI isn't just another channel. It can become part of the buying process itself. We’re seeing this already today with solutions like Meta Muse and Walmart's Agent To Agent ECommerce.
And with that comes another consequence of this transformation. AI is changing the technology stack itself. For years, companies accumulated expensive marketing technology to solve increasingly specific problems. Now, a growth leader can use AI-assisted development to build an analytics dashboard or workflow tailored to the company's exact needs in a fraction of the time it once took to evaluate vendors. I've seen organizations build their own CRM functionality, outreach tools, and social marketing systems rather than buying every capability as a $50,000-a-year SaaS package in their martech stacks.
That's both good news and bad news. The good news is that marketing technology can become dramatically more efficient and less expensive. The bad news is that when marketing starts building software, marketing also inherits some of the responsibilities of software ownership. The systems require maintenance, updates, testing, security, monitoring, documentation, and quality control, most of which are new components to manage as a marketer.
We are somewhat drunk on the novelty of "vibe coding" right now. It's incredibly rewarding to solve a problem over a weekend rather than spend months evaluating 14 vendors. But software doesn't stop existing when the weekend ends. AI can get things wrong. Internally built systems need to be maintained. The technical load doesn't disappear. It moves. Marketing leaders need to account for that as they redesign their organizations and technology stacks.
The shift we're experiencing isn't really about AI replacing a particular function. It's about AI removing the time it takes to complete them. The product team doesn't have to wait months to show marketing what it's building. Marketing doesn't have to wait for a finished product to begin learning about its market. Customers can interact with prototypes earlier. AI systems can become discovery channels. And in some cases, AI agents may become customers in their own right.
The upside of getting this right is enormous. AI isn’t just another channel through which consumers discover products. It is becoming a consumer and customer in its own right. For businesses, that represents an entirely new layer of demand. It is a second customer interface, where AI systems and agents discover, evaluate, recommend, and ultimately purchase on behalf of people. The companies that build products for how AI discovers and evaluates them, while simultaneously using AI to make their own marketing and business dramatically more intelligent and personalized, will have the opportunity to unlock entirely new revenue lines with a much lower cost of sale.