Anju Visen-Singh is a guest contributor to Digital Journal. She is the founder and principal of Throughline, an operations consulting practice that helps scaling and technology companies build the connection between product, marketing and go-to-market. The views expressed are her own.
In January 2026, Anthropic launched Cowork, an AI computing agent that automates complex multi-step knowledge work. A small team built it in roughly 10 days, with most of the code written by Claude Code, the AI development tool Anthropic had released months earlier. Since then, Anthropic has shipped a major new release roughly every two weeks.
This is what the product development clock on the leading edge looks like now, and what it will look like everywhere else within the next few months and years.
Your product and engineering teams can move so quickly. The real question is whether the trading system around them can. If the product can be shipped in 10 days and the rest of the organization takes 10 weeks to absorb, position and sell what is being shipped, you have accelerated your dysfunction instead of your growth.
High-growth technology companies have often struggled with a fine line between product, marketing and market penetration. The product team builds something that the market needs, but marketing is not informed enough about. Or marketing develops a narrative that sales isn’t trained to use. Or sales close deals that produce data that the product team never sees.
Or all of the above. Functions are likely to be capable. The connections between them are not.
Before AI, this manifested itself as a slow growth drag: inconsistent messaging, longer lift times, uneven pipeline quality, missed opportunities for expansion. The cost was real, but often sunk.
HE has removed that buffer.
The same inconsistency that once cost efficiency points is now compounded by structural risk. The system is being asked to operate at a speed it was never designed to handle.
This compression is happening on both sides of the market. The observation that buyers complete most of their research before committing to a sale is well established. What has changed is the speed and depth at which this research now occurs.
A buyer with an AI assistant can synthesize analyst perspectives, compare competitors, match capabilities to their specific use case, and pressure test positioning in minutes. By the time they engage a sales team, they are validating a decision rather than gathering information.
In multiple growth-stage environments, a consistent behavior is emerging: sometimes buyers are arriving with a clearer understanding of the product than the teams responsible for selling it.
This is a system design issue.
Asymmetry is tricky
AI is not being uniformly adopted across the trading system. It is being adopted function after function, in different depths, for different purposes. And the function closest to product and engineering is often further ahead. Products that would have taken months to build are taking days. Engineering and, to an increasing extent, product are being restructured around it.
Marketing is on the move, but mostly at the tactical layer. Social Media Examiner AI Marketing Industry Report 2025 found that 90% of AI use by marketers was focused on text-based tasks: generating ideas, creating drafts, writing headlines.
Positioning, market analysis and competitive intelligence were largely left out. Sales were explicitly named as other functions left over from Bain Technology Report 2025despite having the greatest upside potential.
Both reports are already a year behind the current rate of change and the numbers will have moved. But the behavior they describe, high adoption within functions, shallow strategic depth, and an asymmetry between product, engineering, and commercial teams, has not been resolved. If anything, the division has had another year to widen.
Customer success is using AI to handle volume, not generate intelligence. It’s closing tickets faster without constantly converting customer feedback into upstream product or go-to-market intelligence.
Each function works faster within its own walls, at different levels and speeds. And the connections between them are remaining afterwards.
The line is getting faster at every junction and slower at every intersection.
This is not an abstract operational problem. Consider how it looks in practice. A product team provides a significant capability. Three weeks later, sales are still using a deck that doesn’t reflect that. A shopper who researched the product that morning knows more about the new feature than the rep they’re talking to. The deal stalls on a question the rep can’t answer. What came out of that conversation never made it to the product team.
Nothing about this failure is visible on a dashboard. It shows up later, in profit rates that don’t move despite genuine product progress, in a pipeline that feels healthy until it shuts down, in expansion revenue that’s left on the table because the new value was never shown to the already paying customer.
AI is boosting production. It is also reinforcing leaks.
Reconstruction of the line
There is a solution and it requires real change.
- Maturity Parity Solution. A high-speed system cannot function with mismatched components. If engineering and product work with deep AI integration and other functions do not, information sharing is expanded. Every function must achieve a strategic knowledge base of AI, not just usage. This allows the truth of the product to move at the same speed that it was created.
- Replace handouts with continuous intelligence. The linear sequence of sales from construction to market does not hold at current speeds. Information should move continuously, not episodically. This requires a common layer of context where product changes, market feedback and customer input are captured and translated in near real-time. The goal is to remove the translation tax that occurs whenever information crosses a functional boundary.
- Expand the base unit. The traditional product trio is becoming too narrow for today’s environment. The entity responsible for the build must also be responsible for how what is built is understood and sold. Bringing marketing and product enablement directly into the development cycle keeps positioning, narrative and internal readiness evolving alongside the product itself. Consider expanding this to a commercial group that also includes marketing, sales and customer success for appropriate initiatives.
- Redefine activation as a real-time system. Activation can no longer function as a downstream training event. It should function as a real-time service layer, moving from static knowledge transfer to dynamic knowledge retrieval. When a sales conversation is taking place, the system should provide the most current and relevant product information at that moment. Access speed becomes the differentiator.
Here AI ceases to be a function-level tool and becomes an in-line capability. An agent that monitors product development in real-time, drafts updated positioning for human review, directs briefs to marketing, sales, and customer success immediately, and feeds customer friction to product as an ongoing, prioritized input rather than a monthly or weekly report isn’t far off. The components exist today. What most organizations have not yet built is the architecture that connects them. The agent speeds up work between functions. Judgment within them remains human.
There will be two types of companies as we go through this cycle. The first will improve each function independently. They will deploy better AI tools in engineering, product, marketing, sales and customer success. Each team will move faster while the system remains fragmented. They will become faster at doing the wrong job.
The second will treat the general line as a system to improve. They will understand that in a market defined by endless, AI-generated production, the sustainable advantage is cohesion. Product intelligence will continuously reach the market. Customer data will influence product decisions in real time. The organization will function as a linked system rather than a delivery chain.
The divide between these two models is already forming. The general line was a limitation before AI. Now that’s a complicated risk.
Building it and continuously developing it is the only way to turn AI speed into market scale.
