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Design, then engineering is the most expensive way to arrange the sequence.A prototype approved by everyone in the room ...
04/09/2026

Design, then engineering is the most expensive way to arrange the sequence.

A prototype approved by everyone in the room can still rest on an integration nobody has tested, a data model that does not exist yet or a third-party service whose rate limit breaks the intended flow.

None of that is a design failure. It is what a design review is not looking at, which is why the question surfaces during development as rework instead of during design as a decision. Our article covers what changes when the engineer is in the room from week one. Link in comments.

Search for "product design" and the results will mix two very different businesses.One builds websites and brand systems...
03/09/2026

Search for "product design" and the results will mix two very different businesses.

One builds websites and brand systems, and describes the interface work as product design.
The other runs a structured phase before development, where a product manager, a designer and an engineer decide together what to build, what it will cost and whether the technology meets the requirements.

Both use the phrase in good faith. A buyer comparing the two is not comparing like with like.
The distinction matters most when budget is about to be committed. UI/UX ends at the screens. A product design phase ends at a scope your development team can build, estimate and start on, plus the artefacts they need to do it: feature breakdown with numbers, non-functional requirements, release plan and a recommended team shape.

The quick test: does the design work come back with an engineering estimate produced by someone who sat in the design sessions? If yes, feasibility was a live constraint. If no, it becomes a later verdict, delivered by people who were not in the room when the decisions were made.

Swipe through for how the two services compare, which one fits which situation and how to tell before you sign. Link in comments.

Third-party involvement in data breaches went from 15% of all breaches in 2024, to 30% in 2025, to 48% in 2026. Three co...
28/08/2026

Third-party involvement in data breaches went from 15% of all breaches in 2024, to 30% in 2025, to 48% in 2026. Three consecutive years. Three consecutive jumps.

Whatever companies are currently doing to govern their vendors is not keeping pace with how much work those vendors are doing.

The five categories of vendor risk product leaders manage today (visibility, alignment, cost, security and lock-in) are broadly the same as they were five years ago, and the playbook for each is contract boilerplate by now. The sixth is not.

Your vendor's engineers now write code with AI assistance, and a vendor claiming otherwise is either behind or not being straight with you. Where the source code goes, who owns the output, which licences you inherit and what the review gate has to catch have all moved.

The 2026 Ncontracts survey found 72% of institutions are only partially aware of which of their vendors use AI. Not one respondent described themselves as extremely confident in managing AI vendor risk.

Swipe through for the six categories, the questions to add to your diligence and the compliance dates your contract now has to anticipate. Link in comments.

The trap that catches more corporate pilots than the AI Act does is mundane. Using production customer data for an inter...
27/08/2026

The trap that catches more corporate pilots than the AI Act does is mundane. Using production customer data for an internal experiment is a new purpose under GDPR, requiring its own lawful basis and a compatibility assessment.

Discovering this at review is what turns a six week build into a five month project. Our article covers which dates already apply, which were deferred, and which one catches teams by surprise. Link in comments.

Most corporate pilots are planned around one workstream. The other four decide the launch date.The single most useful sc...
26/08/2026

Most corporate pilots are planned around one workstream. The other four decide the launch date.

The single most useful scheduling decision a corporate product lead makes is starting security, data protection, integration and AI classification in the same week as the build, not after. Our article covers what each queue actually involves. Link in comments.

Cheap building did not kill the MVP. It sharpened what the MVP was ever for. The cost of being wrong did not fall. Only ...
25/08/2026

Cheap building did not kill the MVP. It sharpened what the MVP was ever for. The cost of being wrong did not fall. Only the cost of finding out did.

Which means the case for deliberate validation is stronger now than it was two years ago, not weaker. Our article covers what a corporate MVP is actually for in 2026 and how the framing shifts when the build stops being the constraint. Link in comments.

The gap between running an AI pilot and running a product built on it is where most corporate innovation programmes now ...
24/08/2026

The gap between running an AI pilot and running a product built on it is where most corporate innovation programmes now live.

The difference is not the model or the engineering team. It is whether the pilot was scoped to produce a decision, or to produce a demo. One is easy to fund, harder to unwind. The other tends to be the opposite. Our article covers how to make the distinction visible before the pilot starts. Link in comments.

The whole point of an MVP is that it becomes something else if it works. Which is a bigger problem than it sounds, when ...
23/08/2026

The whole point of an MVP is that it becomes something else if it works. Which is a bigger problem than it sounds, when the underlying code is harder to extend than any codebase produced in the last four years.

The trade being made is short-term speed for medium-term flexibility, and most teams are not tracking it. Our article covers what the shift means for corporate pilots specifically. Link in comments.

The average engineering team has AI generating more code than the pipeline can absorb. That has a specific consequence f...
22/08/2026

The average engineering team has AI generating more code than the pipeline can absorb. That has a specific consequence for anyone planning a corporate MVP.

The time between a working version and a shippable one is longer now, not shorter. Budgeting fewer weeks for review, integration and hardening because "AI made it faster" is the exact planning error the data flags. Our article covers what to plan for instead. Link in comments.

The engineering was rarely the problem to begin with. It just used to be the biggest line on the invoice. That has chang...
21/08/2026

The engineering was rarely the problem to begin with. It just used to be the biggest line on the invoice. That has changed, and the invoice now hides the actual constraint.

Approvals, integrations and compliance queues absorb a quarter of calendar time by default. Product leaders who plan the build first and the reviews second lose that quarter every time. Our article walks through what to plan for instead. Link in comments.

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