Insights tagged "Featured"

  • What a product design phase needs from you before it starts
    Ionut Lomer - 23 Sep 2026
    A product design phase is a four to six week engagement in which a product manager, a UX designer and a software development lead turn a product idea into a scope that can be estimated and built. Those three roles run the work, but several decisions that shape the outcome belong to the client and are made before it starts: who holds decision authority, how much time domain and technical experts can give, how much of the technical role survives price negotiation and what "complete" is taken to mean. This article sets out what each role does, what each needs from the client and how to check readiness before signing.
  • AI coding agents: measuring ROI before you scale them
    Tudor Iordache - 16 Sep 2026
    Gartner's 2026 market guide for enterprise AI coding agents reports that 90% of engineering leaders see improvements from the tools, against a net average productivity gain of 19.3%. A separate Gartner analysis of technology adoption puts the share of software engineering leaders reporting significant ROI from AI across the development lifecycle at 35%. The distance between those figures is not a tooling failure. It is what happens when a team adopts agents without a pre-adoption baseline, budgets for them as seats while vendors move to usage-based pricing and expects acceleration in the parts of delivery that agents do not touch.
  • Mihai Bâlea - 28 Aug 2026
    AI has entered product design at both ends of the process and barely touched the middle. Discovery now runs through AI research synthesis and synthetic participants, generation runs through prompt-to-prototype tools, and the design phase between them is the only part of the sequence that produces output that somebody can be held to. That distinction matters because generated design output becomes usable only where a system enforces constraints rather than documenting them, and enforcement is the part that most teams have not built. This article uses a three-zone model, discovery, design, and generation, to locate where AI-assisted product design breaks down inside a funded team.
  • Alex Marciuc - 21 Aug 2026
    Product design and UI/UX design are not the same service. UI/UX design is the craft of the interface: information architecture, user flows, visual direction, design system foundations. Product design in software development is the phase that runs before a build and decides what gets built, whether it is technically feasible and what it will cost. It is delivered by a trio of product manager, UX designer and software development lead, and it ends in a validated, estimated, buildable scope rather than a set of screens.
  • 7 steps to validate a product idea before you build
    Paula Cristea - 7 Aug 2026
    Product validation is the work of gathering evidence that a specific problem is worth solving for a specific audience, before any production code is written. A practical sequence runs through seven steps: define the segment, write the problem hypothesis, run discovery interviews, map existing substitutes, spike the riskiest technical assumption, prototype, then score the evidence. The output is not a feeling about the idea. It is a written argument that can survive a co-founder or an investor asking why.
  • How to choose a product design agency in 2026
    Stefan Sarbu - 3 Aug 2026
    Choosing a product design agency comes down to three things that are hard to read from a proposal: how the team thinks about problems, who actually does the work and whether the scope matches what the budget can carry. Price ranges are useful for spotting an outlier, but they rarely separate a good fit from a bad one. The more reliable signal is whether an agency asks difficult questions before it shows you screens.
  • Why a product design phase comes before the build budget
    Alex Marciuc - 30 Jul 2026
    A product design phase is four to six weeks of structured work sitting between an approved idea and a committed build budget. It produces a validated prototype, a prioritised feature breakdown with ranged estimates, a technical architecture with integration risk resolved, and a release plan with cost scenarios attached. The output is not a set of design artifacts. It is a narrower cone of uncertainty, and a scope that can be cut without breaking. Teams that skip the phase rarely save the money. They move it into the build, where it gets spent on rework nobody planned for.
  • Six principles for AI systems that work in production
    Tudor Iordache - 2 Jul 2026
    The AI projects that work in production share a small number of structural decisions. None of them is about the model. All of them are about the system around the model: the threshold defined before the build begins, the constraint treated as architecture rather than an obstacle, the surface designed as the actual product, the MVP scoped as the first instance of the platform, the human kept in the loop as a permanent feature rather than a transitional one. The discipline of building AI well is the discipline of getting these decisions right early enough for the model to do its job, and of refusing to defer them until the cost of deferral becomes unavoidable.
  • Five MVP decisions that turn an AI MVP into an enterprise AI platform
    Stefan Sarbu - 29 Jun 2026
    The MVP-to-platform transition fails not because the technical architecture is wrong, but because most teams treat it as a scaling problem when it is actually a redesign problem. The MVP gets you to one customer. The platform requires you to be honest about what was specific to that customer and what is genuinely transferable. The teams that get this right do not generalize reactively. They build the MVP knowing which decisions will need to be revisited at the platform stage, and they revisit them deliberately.
  • Human-in-the-loop AI systems where judgement stays in the workflow
    Tudor Iordache - 24 Jun 2026
    Human-in-the-loop is not the version of AI you build while waiting for the model to get good enough to replace the human. It is the version of AI that actually works in domains where being wrong is expensive. The teams treating it as a transitional design are building systems that will eventually be replaced. The teams treating it as the destination are building systems that compound trust and improve over time.

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