The AI product paradox – Digital Journal

Anju Visen-Singh is a guest contributor to Digital Journal. She’s the founder and principal of Throughline, an operating advisory practice that helps scaleups 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, a desktop AI agent that automates complex, multi-step knowledge work. A small team built it in approximately 10 days, with the majority 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 now looks like at the leading edge, and what it will look like everywhere else within the next few months and years.

Your product and engineering teams can likely move that fast. The real question is whether the commercial system around them can. If the product can ship in 10 days and the rest of the organization takes 10 weeks to absorb, position, and sell what shipped, you have accelerated your dysfunction instead of your growth.

High-growth technology companies have often struggled with a broken throughline between product, marketing, and go-to-market. The product team builds something the market needs, but marketing has not been briefed on it well enough. Or marketing develops a narrative that sales has not been trained to use. Or sales closes deals that produce input the product team never sees. 

Or all of the above. The functions are likely capable. The connections between them are not.

Before AI, this showed up as a slow-moving drag on growth: inconsistent messaging, longer ramp times, uneven pipeline quality, missed expansion opportunities. The cost was real, but often absorbed.

AI has removed that buffer.

The same misalignment that once cost points of efficiency now compounds into 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 the majority of their research before engaging with sales is well established. What has changed is the speed and depth at which that research now happens.

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 behaviour is emerging: buyers are sometimes arriving with a clearer understanding of the product than the teams responsible for selling it.

This is a system design issue.

The asymmetry is compounding

AI is not being adopted uniformly across the commercial system. It is being adopted function by function, at different depths, for different purposes. And the function closest to product and engineering is often running furthest ahead. Products that would have taken months to build are taking days. Engineering and, to a growing extent, the product are restructuring around it.

Marketing is moving, but primarily at the tactical layer. Social Media Examiner’s 2025 AI Marketing Industry Report found that 90% of marketers’ AI use was concentrated in text-based tasks: idea generation, draft creation, headline writing. 

Positioning, market analysis, and competitive intelligence were largely left out. Sales was named explicitly as lagging other functions by Bain’s 2025 Technology Report, despite having the greatest potential upside.

Both reports are already a year behind the current pace of change, and the numbers will have moved. But the behaviour they describe, high adoption within functions, shallow strategic depth, and an asymmetry between product, engineering and the commercial teams, has not been resolved. If anything, the divide has had another year to widen.

Customer success is using AI to handle volume, not to generate intelligence. It is closing tickets faster, without consistently converting customer feedback into upstream product or go-to-market intelligence.

Every function is running faster inside its own walls, at different levels and different speeds. And the connections between them are falling further behind.

The throughline is getting faster at every node and slower at every junction.

This is not an abstract operating problem. Consider what it looks like in practice. A product team ships a meaningful capability. Three weeks later, sales is still using a deck that does not reflect it. A buyer who researched the product that morning knows more about the new feature than the rep they are speaking to. The deal stalls on a question the rep cannot answer. What came up in that conversation never reached the product team.

Nothing about this failure is visible on a dashboard. It shows up later, in win rates that do not move despite genuine product progress, in a pipeline that feels healthy until it does not close, in expansion revenue that is left on the table because new value was never shown to the customer who already paid for it.

AI is amplifying output. It is also amplifying leakage.

Rebuilding the throughline

There is a solution, and it requires making real changes.

1. Solve for maturity parity. A high-speed system cannot operate with mismatched components. If engineering and product are operating with deep AI integration and other functions are not, the information divide widens. Every function has to reach a baseline of strategic AI literacy, not just usage. This allows product truth to move at the same speed it is created.
2. Replace handoffs with continuous intelligence. The linear sequence of build-to-market to sell does not hold at current speeds. Information has to move continuously, not episodically. This requires a shared context layer 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 every time information crosses a functional boundary.
3. Expand the core unit. The traditional product trio is becoming too narrow for the current environment. The unit responsible for building also has to be responsible for how what is built is understood and sold. Bringing product marketing and enablement directly into the development cycle keeps positioning, narrative, and internal readiness evolving alongside the product itself. Consider expanding this to a commercial pod that also includes marketing, sales, and customer success for appropriate initiatives.
4. Redefine enablement as a real-time system. Enablement can no longer function as a downstream training event. It needs to operate as a real-time service layer, moving from static knowledge transfer to dynamic knowledge retrieval. When a sales conversation is happening, the system should deliver the most current, relevant product insight at that moment. The speed of access becomes the differentiator.

This is where AI stops being a function-level tool and becomes a throughline capability. An agent that monitors product development in real time, drafts updated positioning for human review, routes briefs to marketing, sales, and customer success at once, and feeds customer friction back to product as a continuous, prioritized input rather than a monthly or weekly report is not far off. The components exist today. What most organizations have not yet built is the architecture that connects them. The agent speeds up the work between functions. The judgment within them stays human.

There will be two types of companies as we go through this cycle. The first will upgrade each function independently. They will deploy better AI tools across engineering, product, marketing, sales, and customer success. Each team will move faster while the system stays fragmented. They will get faster at doing misaligned work.

The second will treat the throughline as the system to upgrade. They will recognize that in a market defined by infinite, AI-generated output, the lasting advantage is cohesion. Product intelligence will reach go-to-market continuously. Customer input will influence product decisions in near real time. The organization will operate as a connected system instead of a series of handoffs.

The divide between these two models is already forming. The throughline was a constraint before AI. It is a compounding risk now.

Building it and continuously evolving it is the only way to convert AI speed into market scale.