Author: Alessandro Zulberti

Clarity in High-Season Sales

The Company approached its holiday campaigns with a clear ambition: convert seasonal traffic into meaningful engagement and confident purchasing decisions.

Earlier campaigns assumed that high holiday intent meant users already understood the product value and needed only visual appeal and refreshed layouts to convert. In reality, users required reassurance, guidance, and clearer differentiation, especially under time pressure.

I led UX research and usability testing across successive holiday campaigns, guiding their evolution from surface-level optimisation toward behaviourally informed experiences.

The initial goal was direct: understand why a visually refined landing page failed to convert during high-stakes holiday moments.

Usability testing revealed that users were not lost; they were unconvinced. Under time pressure and emotional load, behaviour changed markedly. Users became more risk-averse, scanned less, and relied on quick heuristics.

These hesitation points reappeared across successive holiday seasons, particularly around uncertainty about product suitability, shipping timing, and gift appropriateness.

Quantitative data confirmed limited engagement beyond the first scroll. Qualitative sessions explained why: unmet expectations and delayed reassurance.

User Journey Analysis – Desktop vs Mobile

Primary Methods

– Remote usability testing, including five-second tests
– Targeted user interviews
– Scroll-depth and click data analysis
– Prototype evaluation of new layout variations

Diagnosing Misalignment Through Usability Testing

The same pattern emerged repeatedly: users were not confused enough to stop, but not confident enough to convert.

One participant captured the tension clearly: after filtering, they felt mostly confident they had found the right product, but still worried there might be a better option they had missed.

With baseline issues identified, the focus shifted from diagnosis to intentional redesign.

One design direction was deliberately deprioritised: a visually dominant, hero-first layout that pushed explanatory content further down the page. Testing showed that users needed clearer value framing and decision aids earlier, not more visual emphasis.

Using behavioural principles, the journey was reshaped to reduce cognitive load, strengthen salience, and align content order with how decisions are made under pressure.

The landing page evolved from a static presentation into a sequence of deliberate decision moments, separating inspiration from commerce, elevating the Gift Finder, clarifying support pathways, and rewriting copy for rapid scanning.

Primary Methods

– Scenario-based usability testing
– GA4 behavioural data review
– ContentSquare heatmaps
– A/B test planning
– Device-specific observations
– Feedback analysis from live prototypes

Real-Time Research During Campaign Rollout

Before launch, we conducted pre-live validation to refine nuance rather than uncover major flaws.

At one point, analytics suggested improved click-through on promotional elements. However, usability sessions revealed that many of these clicks were driven by uncertainty rather than true engagement.

The tension was resolved by restructuring the hierarchy so promotional elements supported, rather than replaced, decision-critical content.

A small change produced a disproportionate effect: introducing concise, high-salience value statements such as warranty, gifting suitability, and delivery certainty directly above the product grid. This reduced early-journey hesitation and restored confidence.

This project was not about redesigning a landing page. It was about learning how people decide when attention is fragmented and stakes are high.

Clarity, in these moments, is not aesthetic. It is functional.

Research embedded into planning, prioritisation, and platform strategy across a growing digital ecosystem.
A reusable model for comparing UX impact across initiatives without inflating results.

May 24, 2026
Accessibility as a Strategic Foundation in the D2C Ecosystem

The Company was evolving rapidly: new eCommerce platforms, a shift to composable architecture, a global design system rollout, and increasing regulatory pressure across markets.

Yet accessibility quality remained inconsistent. Issues accumulated across templates, UI components, and content practices, creating friction not only for users with accessibility needs but for anyone navigating the site under real-world constraints.

Teams were committed to delivering high-quality experiences, but with competing priorities and fragmented ownership, accessibility was often treated as a corrective task: reviewed late, fixed locally, and rarely scaled.

As Senior UX Researcher and accessibility lead, I established a structured, organisation-wide approach that positioned accessibility as a driver of product quality, risk reduction, and user trust rather than a compliance checkbox.

The accessibility challenge was structural, not symptomatic.

Legacy SAP Commerce Cloud pages coexisted with new headless components, creating inconsistent patterns and uneven accessibility quality. The design system lacked WCAG-aligned guidance and governance, and responsibility for accessibility was unclear across design, development, content, and QA.

Teams shipped quickly. Reviews happened late, accessibility debt accumulated, and fixes became increasingly costly.

The core challenge was not identifying accessibility issues, but building the structures, processes, and shared understanding required to prevent them.

The accessibility program was built on four parallel pillars designed to embed accessibility into everyday decisions rather than post-release correction.

Accessibility Governance and Foundations

I authored and introduced an accessibility roadmap aligned with WCAG 2.2 AA and tailored to the Company’s platforms and component libraries. It defined responsibility boundaries, acceptance criteria, mandatory checkpoints, and escalation paths.

Embedding Accessibility in the Design System

Working with design and engineering leads, I integrated accessibility requirements directly into component specifications, including semantic structure, contrast constraints, keyboard behaviour, error handling, and inclusive copy guidance.

Continuous Audits and Behavioural Validation

I established an audit program combining automated scanning, manual WCAG reviews, and user-centred observation using screen reader workflows.

Cross-Functional Enablement

I supported teams with training and lightweight tooling, including accessibility office hours, pattern libraries with dos and don’ts, sprint-level checklists, and pre-release smoke tests.

Transforming the Checkout Experience

Checkout was prioritised due to its complexity and revenue sensitivity. I supported the redesign by mapping the screen reader journey, standardising form structures, aligning validation logic, and ensuring dynamic steps were announced and focus-managed.

The most confusing interaction before standardisation was the transition between checkout steps. Users, especially those using screen readers, received no announcement of context change and often did not realise the page had advanced. Resolving this significantly reduced ambiguity and cognitive load for all users.

Component-Level Accessibility Fixes

Several components required systemic intervention, including accordions, tabs, filters, product carousels, and heading structures.

Addressing these issues at the design-system level ensured fixes scaled consistently across global markets rather than being reintroduced through local variations.

Accessibility Monitoring and Dashboards

To support prioritisation, I introduced a structured approach to tracking automated scan trends, issue recurrence, component regressions, and accessibility debt over time.

Recurring regressions in design-system components proved most effective in shifting stakeholder prioritisation. They demonstrated that the cost of inaction multiplied across markets and releases, prompting leadership to prioritise systemic fixes over page-level patches.

Redefining Content Practices

Accessibility extended beyond UI components. I worked with content and marketing teams to evolve alt-text practices, heading usage, link naming, and inclusive copywriting standards.

These changes improved clarity for assistive technologies and reduced cognitive load more broadly, reinforcing that structural clarity, not content reduction, was the primary driver of improved readability.

The accessibility program reshaped how the organisation approached digital quality.

Strategic Impact

Accessibility became part of the standard definition of done. Platform migrations launched with stronger foundations, fewer regressions, and a reduced backlog of accessibility debt.

User Impact

Screen reader journeys became more predictable. Form-heavy flows such as checkout, account creation, and repairs showed reduced friction and clearer progression.

Organisational Impact

Design system teams adopted accessibility-first component governance. Development teams integrated checks into CI/CD workflows. Content and marketing teams embedded inclusive writing practices as standard.

The clearest signal of change was the integration of accessibility criteria directly into the design system’s component acceptance process: no new component or update could ship without meeting accessibility requirements.

Accessibility moved from specialist review to shared expectation.

Accessibility is not a sprint deliverable. It is an organisational capability.

This work demonstrated that sustainable improvement depends on shared ownership, clear standards, early intervention, continuous validation, and a unified source of truth.

It also reinforced a core NN/g principle: accessible design improves usability for everyone.

The foundation established here supports every future platform, every market rollout, and every digital experience the Company will launch.

A reusable model for comparing UX impact credibly across initiatives, journeys, and exposure levels.
Platform design shaped by governance, participation design, and long-term usability.

April 24, 2026
Agentic AI Reading Instrument

This experiment helps inspect short ideas about agentic AI through fixed critical lenses. Rather than simulating an assistant, it reads where delegation compresses context, assumes capability, and leaves hidden recovery work behind.

Editorial experiment

Agentic AI reading instrument

Paste a short scenario about an AI agent. The page reads it through fixed lenses so you can see where delegation compresses context, hides repair work, or should stop.

Not a chatbot. Not a feasibility checker. A compact diagnostic.

Load an example

Scenario An AI agent books and rearranges my travel automatically.
Enter one short scenario only. This version reads one agentic situation at a time, not a list, comparison, or instruction prompt.

April 18, 2026
Scalable ROI Framework Matrix for UX Measurement

As UX work expanded across multiple journeys and markets, the organisation faced a growing disconnect between behavioural insight and business decision-making.

Teams were improving checkout flows, refining product listings, adjusting navigation, and iterating on templates. Each initiative showed signs of behavioural change, yet there was no shared way to compare their value or prioritise investment across the portfolio.

The problem was not a lack of data. It was the absence of a common financial language for UX impact.

This case study documents how a scalable ROI framework was designed to translate UX behaviour into credible, comparable business signals.

UX initiatives were evaluated in isolation.

Checkout changes affected a small proportion of users but carried high intent. Product page listing and navigation changes reached more users but produced subtler behavioural shifts. Template updates varied by market and maturity.

Without a shared framework, UX prioritisation stalled across initiatives and discussions defaulted to subjective judgement rather than evidence.

The core challenge was comparability and credibility, not measurement volume.

The strategy was to design a single, reusable ROI framework that could be applied consistently to any UX change, regardless of journey depth or market size.

The framework needed to:

Core Requirements

– Connect behavioural metrics to business impact
– Normalise performance across different exposure levels
– Support forecasting before launch and accountability after release
– Prevent inflated ROI claims in deep-funnel contexts
– Produce clear, trusted ROI tiers for decision-making

Scalability was a deliberate design goal, not an afterthought.

A Consistent Measurement Logic

The framework translates UX behaviour into business impact using a single principle: impact is a function of behavioural change and exposure.

Rather than relying on relative uplift or raw analytics, the model:

– Measures conversion change in percentage points
– Weights impact by the proportion of users actually exposed
– Applies consistent time normalisation across initiatives
– Evaluates performance over multiple post-launch windows

This ensured that improvements were neither overstated nor dismissed.

Time-Based Validation

To avoid premature conclusions:

– Early post-launch windows captured adoption effects
– Later checkpoints confirmed behavioural stabilisation

This approach allowed the team to detect short-term volatility, long-term consistency, and false positives driven by novelty or traffic noise.

Portfolio-Level Visibility

Each UX change was documented in a dedicated update view and rolled into an overview layer showing journey step, relative exposure, direction and stability of impact, ROI tier classification, and confidence notes.

This shifted conversations from “Is this UX change good?” to “Where should we invest next for the strongest return?”

Discipline Through Rejection

Several commonly used ROI approaches were explicitly rejected:

– Relative uplift percentages that exaggerated deep-funnel impact
– Applying changes to total site traffic regardless of exposure
– Blind use of industry benchmarks without contextual adjustment
– Volatile revenue-per-session models

The final framework prioritised realism over persuasion.

The framework was first validated through a checkout optimisation initiative, then adopted as the standard evaluation model for UX changes.

Key outcomes included:

– A shared, auditable ROI language across teams
– Increased trust in UX impact reporting
– Faster, evidence-based prioritisation decisions
– More disciplined allocation of engineering effort

Importantly, the framework was also used to deprioritise initiatives with limited exposure and low strategic leverage. It proved capable of constraining investment, not just justifying it.

UX shifted from a cost discussion to a decision-support function.

This work changed one foundational assumption: conversion change has no meaning without exposure context.

Before the framework, impact discussions focused on the size of behavioural shifts. Afterwards, they focused on how many users those shifts actually affected.

That shift reframed UX ROI from advocacy to accountability.

The framework does not replace qualitative research, brand thinking, or accessibility judgement. It complements them by providing a clear validation layer where financial decisions require evidence.

In doing so, it raised the maturity of UX conversations not by inflating impact, but by making it comparable, bounded, and trustworthy.

How accessibility moved from late-stage remediation to a governed, organisation-wide quality practice.
Research-led design for a health platform shaped by trust, cognitive load, and long-term engagement.

March 24, 2026
Signal-Driven Discovery
Working Framework

What it is

A method for handling weak, continuous evidence — deciding which day-to-day signals deserve interpretation, which need probing, and which should not harden into confident stories.

The problem it addresses

Teams say they want continuous discovery, but most of the evidence arriving day to day is fragmentary, delayed, and easy to overread. By the time a conversion dip, a strange search term, or a support pattern gets noticed, the pressure is already to explain it fast and act faster. The real problem isn’t a lack of data — it’s the absence of a disciplined way to decide which weak signals carry meaning and which are noise dressed as insight.

Signal-Driven Discovery exists to slow that reflex at the right moment. It treats a signal as a disturbance to be tested, not a finding to be explained — and builds in, at every step, the specific way that step tends to be misread under delivery pressure.

When to use it
- When behaviour shifts and no single release, campaign, or seasonal factor explains it cleanly.
- When interviews are too slow, expensive, or operationally heavy to trigger every time something moves.
- When several weak signals cluster around the same part of the journey but the problem is still blurry.
- When the next step needs to be a concrete probe or decision, not another round of speculative discussion.
- When quiet absences need tracking alongside the loud anomalies teams already notice.
How it works: the six steps

Each step carries the mistake it invites. The misread is the point — the method’s value is in naming, in advance, how each stage goes wrong when a team is in a hurry to be right.

01 · Signal — Notice a shift, absence, or recurring trace that refuses to stay incidental. Name the disturbance without pretending it already explains itself. What gets misread: a single anomaly is treated as insight before its shape, context, or persistence has been checked.

02 · Triage — Check whether the signal survives basic context: timing, segment, instrumentation, recent releases, operational noise. Decide whether it deserves attention now, later, or not at all. What gets misread: triage becomes explanation, and the team smuggles a favourite cause in before the evidence has narrowed.

03 · Interpretation — Read across sources until the pattern is legible enough to frame a working explanation. Analytics, search, recordings, verbatims, and support should tighten the same question, not perform agreement. What gets misread: cross-source repetition is mistaken for certainty, even when each source is echoing the same blind spot.

04 · Probe — Push the interpretation hard enough to expose where it fails. A probe can be a fast analysis cut, a counter-question, a lightweight experiment, or a small piece of qualitative follow-up. What gets misread: any probe that confirms the first hunch is taken as validation, while disconfirming evidence is treated as noise.

05 · Decision — Translate the strongest remaining reading into a concrete move: test, content change, design change, escalation, or deliberate non-action. If there’s no decision pathway, the framework stops being useful. What gets misread: decision is reduced to shipping something, even when the right move is to escalate, wait, or gather a different kind of evidence.

06 · Loop — Carry the result into the next round: what changed, what stayed absent, what now deserves quieter ongoing listening. The loop keeps anomalies and ambient signals in conversation instead of dying as isolated tickets. What gets misread: the loop is treated as closure, so the team records an outcome but never adjusts what it watches next.

A worked example

Signal. Mobile conversion on a category page drops over three weeks. No release, campaign, or seasonal factor explains it. Alongside it, an unusual internal search term starts recurring, and support sees a small rise in “can’t find” contacts on the same category.

Triage. The dip survives context: it isn’t a tracking change, isn’t confined to one campaign segment, and predates the most recent release. Three weak signals — conversion, search, support — cluster on the same part of the journey. It deserves attention now. The discipline here is resisting the ready explanation (“the new filter broke it”) before the evidence narrows.

Interpretation. Read across the three sources. They tighten the same question — users on this category can’t locate a subset of products — rather than three separate problems. The risk at this step is treating the agreement as proof; all three could be echoing the same instrumentation gap, so the reading stays a working explanation, not a conclusion.

Probe. A fast session-replay cut on the search term, plus a lightweight check of zero-result queries. This is designed to disconfirm — if replays show users finding products by another route, the interpretation fails. They don’t: users search the recurring term, get no useful result, and abandon.

Decision. The strongest reading — a findability gap for a product subset, not a checkout problem — points to a concrete move: fix the search mapping and category tagging for that subset, and instrument the zero-result path so the absence stays visible. Shipping a broad category redesign would be the misread here; the evidence supports a narrow fix, not a rebuild.

Loop. After the fix, check what changed (conversion recovery on that category), what stayed absent (whether the search term still returns nothing), and what now deserves ongoing listening (zero-result queries as a standing signal, not a one-off investigation).

Where it breaks
- Weak instrumentation turns noise into false signals, or hides the signals that matter.
- No decision pathway leaves the team able to describe a pattern but unable to act on it.
- Overinterpretation makes correlation sound like understanding, especially under delivery pressure.
- Organisational constraints block escalation, so the method keeps surfacing issues it has no permission to move.
- Signal that reflects the wrong users or the wrong question. The most dangerous failure isn’t thin signal — it’s abundant, well-instrumented signal produced by the wrong population or by a moment that isn’t the one the decision is about. Traffic from existing users can’t tell you why non-users never arrive; support contacts capture people who complained, not those who left silently. Before a signal is trusted, confirm it’s produced by the users you’re deciding for, encountering the problem you’re actually deciding about. If it isn’t, the method needs a scheduled study, not more signal.
March 19, 2026
EU Consumer Law and UX: The Consumer as Ecosystem

EU consumer law has moved past disclosure. Four regulations — right of withdrawal, legal guarantee, right to repair, age verification — now place active obligations on ecommerce interfaces. Each one lands in a different ecosystem state. Each one is currently met at the lowest possible interface weight. This series maps the gap between legal obligation and interaction design, using the user-ecosystem framework.

Applying the user-ecosystem framework — Youngblood and Chesluk, Rethinking Users (BIS Publishers, 2020) · NN/g, 2025.

The Regulatory Landscape

01. Right of withdrawal (Dir. 2011/83/EU · 2023/2673)
02. Legal guarantee & warranty (Dir. 2019/771 · ECGT 2024/825)
03. Right to repair (Dir. 2024/1799)
04. Age verification (DSA · EU Digital Identity)

01 · Right of withdrawal: The right to undo a purchase

Dir. 2011/83/EU · amended 2023/2673 · in force 19 June 2026

The consumer has 14 days to cancel any online purchase without giving a reason. The amended directive now requires an active withdrawal function, not just a policy link, in the post-purchase interface. Most interfaces do not provide it.

Stage 1: Browse

Acquisition mode — legal node absent

The intentional browser (Cognitive): Scanning options, building preference.
The aspirational self (Emotional): Projecting desire onto the product.
The market participant (Commercial): Responding to price, promotion, scarcity.
The rights-holder (Absent): Withdrawal right exists (absent).

Tension: No legal archetypes are active here, and this is appropriate. The ecosystem is correctly configured for browsing. The absence of the legal node at this stage reveals where and how it eventually surfaces.

Note: Nothing to redesign at this stage. The gap is downstream.

Stage 2: Product page

High intent — disclosed but not received

The evaluating agent (Cognitive): Processing product info, reviews, fit.
The committed self (Emotional): Investment building toward purchase.
The conversion target (Commercial): Responding to interface optimised for sale.
The informed consumer (Legal): 14-day right in footer link or small print.
The deadline-holder (Absent): 14-day window not yet relevant (absent).

Tension: The legal archetype is present but weightless. The cognitive archetype is directed at the product. Disclosure is occurring; comprehension is not.

Note: The right is disclosed at the moment of highest purchase intent, the state least receptive to legal information.

Stage 3: Checkout

Completion mode — disclosure met, function absent

The overloaded agent (Cognitive): Managing payment, address, delivery.
The completion-seeker (Emotional): Strong drive to finish the transaction.
The converting customer (Commercial): Interface minimises friction toward payment.
The acknowledged rights-holder (Legal): Right referenced; disclosure legally met.
The future returner (Absent): 14-day window does not yet exist (absent).

Tension: Disclosure is met. The cognitive archetype is at maximum load. The legal information lands in a hostile ecosystem state and is processed by no active archetype.

Note: Disclosure does not equal function. Checkout satisfies the information requirement. The withdrawal function belongs in the post-purchase ecosystem.

Stage 4: Post-purchase

Where the law places its obligation (Active Legal Stage)

The evaluating owner (Cognitive): Assessing product against expectation.
The uncertain or disappointed self (Emotional): Post-purchase dissonance; desire to correct.
The deadline-holder (Temporal): 14-day clock running; deadline not shown.
The active rights-holder (Absent): Withdrawal function required here, absent in most interfaces (absent).

Tension: The ecosystem has completely changed. The clock is running. The consumer is evaluating a product they own. The withdrawal function the directive requires to be here is absent.

Note: Dir. 2023/2673 is explicit: the withdrawal function must be in the account area or on relevant pages, not a footer link. The temporal archetype must also be activated: the consumer needs to see not just that they can withdraw, but when that right expires.

Stage 5: Withdrawal

The symmetry test (Active Legal Stage)

The problem-solver under pressure (Cognitive): Navigating an unfamiliar flow under deadline.
The frustrated consumer (Emotional): Friction is experienced as injustice here.
The deadline-holder (Temporal): Urgency is high; hours or days remaining.
The rights-exerciser (Legal): Attempting to exercise a right the interface resists.
The asymmetric interface (Absent): Withdrawal harder than purchase by design (absent).

Tension: The ecosystem is now the inverse of purchase. The law requires withdrawal to be as easy as purchase. The footer link fails this test on every dimension.

Note: The symmetry principle: if purchase took two clicks and a primary button, withdrawal must take the same. The interface structurally opposed to this is not merely poor UX. It is non-compliant.

Active Artifact Comparison: Footer Link vs. Withdrawal Function

In ecosystem terms, the withdrawal button is an active artifact—a designed object that performs the consumer’s right. Its absence from the order view is not a UX omission. It is the ecosystem refusing to activate a node the law requires to be present.

Current State (Fails the Standard): The node is present, but its weight is near zero. A footer link signals administrative content. The user who wants to withdraw must know to look there, navigate past unrelated links, and work through a policy page. The symmetry test is not met.
Required State (Directive Standard): The active artifact is doing its work. The withdrawal function is contextual, in the order view, with a live deadline. The button carries the same action register as the purchase button. Symmetry of effort.

Active Artifact: The withdrawal button is a legal actor. When absent, the right cannot be exercised. When present with a deadline counter, it performs the law’s symmetry requirement on behalf of the consumer, making the safe action the natural one.

“The trader shall ensure that the consumer can exercise the right of withdrawal by means of a clearly labelled withdrawal function placed in the consumer’s account area or on any other relevant page.”
— Directive 2023/2673 · Amendment to Article 11

02 · Legal guarantee & warranty: Two rights, one confusion

Dir. 2019/771 · ECGT Dir. 2024/825 · in force 27 Sept 2026

Every product sold in the EU carries a mandatory 2-year legal guarantee. Most interfaces promote the commercial warranty instead—a voluntary manufacturer’s offer. The ECGT directive now requires these to be clearly distinguished. They are not currently.

Stage 1: Browse

Acquisition mode — guarantee invisible

The intentional browser (Cognitive): Building product preference, comparing options.
The aspirational self (Emotional): Desire-led engagement with products.
The market participant (Commercial): Responding to pricing and brand signals.
The guarantee-holder (Absent): 2-year legal guarantee exists (absent).

Tension: No legal archetypes are active at browsing. The legal guarantee exists in law, but has no presence in the browsing ecosystem. The commercial warranty, by contrast, is often promoted actively through badge design and product imagery.

Note: The asymmetry begins here: the mandatory right is invisible, the voluntary offer is prominent.

Stage 2: Product page

Where the law requires clear distinction (Active Legal Stage)

The evaluating agent (Cognitive): Reading specs, reviews, warranty claims.
The confidence-seeker (Emotional): Warranty information increases purchase confidence.
The promoted warranty (Commercial): Commercial offer, prominently placed.
The legal guarantee (Absent): Mandatory 2-year right, absent or buried (absent).
The breakdown-holder (Absent): Guarantee becomes relevant only when product fails (absent).

Tension: The ECGT directive requires both to be present and distinct on the product page. Currently the commercial warranty dominates because it is a marketing asset. The legal guarantee, which is stronger and mandatory, is either absent or indistinguishable from the commercial offer.

Note: ECGT 2024/825 creates two separate legal objects: the statutory guarantee label (mandatory and seller-owned) and the commercial durability guarantee label (voluntary and manufacturer-owned). Most product pages currently show one undifferentiated badge.

Stage 3: Checkout

Purchase confirmed — two clocks now running

The completing agent (Cognitive): Finishing the transaction; bandwidth minimal.
The dual-clock holder (Temporal): Legal guarantee and commercial warranty both activated at purchase.
The guarantee-holder (Legal): Legal guarantee begins; consumer is often unaware.
The warranty-holder (Commercial): Commercial warranty confirmed; consumer may notice this one.

Tension: Two legally distinct timers start at the moment of purchase. The consumer is aware of neither. The checkout confirmation page typically shows order summary and delivery estimate, not the start of their consumer rights.

Note: A confirmation message that says “Your 2-year guarantee starts today” would activate the temporal archetype at the correct moment. Most interfaces do not do this.

Stage 4: Breakdown

The ecosystem the law was written for (Active Legal Stage)

The problem-solver (Cognitive): Trying to get a defective product repaired or replaced.
The frustrated owner (Emotional): Stress, urgency, and sense of loss.
The deadline-holder (Temporal): Is the product still within two years? The consumer often does not know.
The rights-exerciser (Legal): Legal guarantee entitles free repair or replacement.
The commercial warranty path (Absent): Interface redirects to paid support or upsell (absent).

Tension: The legal archetype is now maximally relevant. The consumer has a right to free repair or replacement. If the guarantee was never clearly communicated, the interface directs them toward paid support, an upsell, or manufacturer channels that obscure the mandatory right.

Note: The ecosystem at breakdown is the one the law was designed for. But the information the consumer needs was disclosed at a completely different ecosystem state (high purchase intent) and was not retained. The active artifact that could bridge these states is a guarantee card or account record surfaced at the moment of breakdown.

Active Artifact Comparison: Vocabulary Confusion vs. Clear Distinction

The guarantee label is an active artifact. Currently, it amplifies the commercial warranty and renders the legal guarantee invisible. Under ECGT 2024/825, it must do the opposite: make the mandatory right legible and the voluntary offer secondary.

Current State (Fails the Standard): The commercial warranty dominates the interface. The legal guarantee, the stronger and mandatory right, is absent or indistinguishable. When the product fails, the consumer does not know which protection applies or how to invoke it.
Required State (Directive Standard): Two distinct labels, two distinct rights. The mandatory legal guarantee is primary. The commercial warranty is secondary and clearly voluntary. Both can link to a claim process, but the consumer can tell immediately which right is theirs by default.

Active Artifact: The guarantee label on a product page is a legal actor. When it says “2-year warranty” without distinguishing legal from commercial, it performs the seller’s interest, not the consumer’s right. The ECGT directive requires it to perform both, separately, clearly, and in that order.

“Traders shall provide consumers with clear information on the statutory guarantee of conformity and on the distinction between the statutory guarantee and any commercial guarantee offered.”
— ECGT Directive 2024/825 · Article 6b

03 · Right to repair: The product page after purchase

Dir. 2024/1799 · member states apply from 31 July 2026

The product page has always been a sales endpoint. The Right to Repair makes it the entry point to a legally mandated post-purchase infrastructure: repairability scores, spare parts availability, and repair pricing. None of these currently exist as active interface nodes.

Stage 1: Browse

Acquisition mode — repairability invisible

The intentional browser (Cognitive): Evaluating products on price, brand, and features.
The aspirational self (Emotional): Desire-led engagement.
The market participant (Commercial): Responding to commercial signals.
The repair-rights holder (Absent): Right to repair and spare parts access (absent).

Tension: The repairability of a product is not a visible attribute in the browsing ecosystem. The consumer has no interface node to evaluate it against. The commercial ecosystem is optimised for replacement, not repair.

Note: The ecosystem at browsing reflects the commercial incentive: sell new products. The Right to Repair introduces a counter-incentive that currently has no interface home.

Stage 2: Product page

The product page must now carry lifecycle information (Active Legal Stage)

The evaluating agent (Cognitive): Reading specs, comparing models.
The conversion target (Commercial): Interface optimised toward purchase completion.
The repairability-aware buyer (Absent): Repairability score required on product page, absent in most interfaces (absent).
The long-term owner (Absent): Spare parts availability over product lifetime is not shown (absent).

Tension: Dir. 2024/1799 requires repairability information on the product page. Currently, the product page is a pure sales surface. Repairability scores, spare parts availability, and repair cost indicators have no visual language, no established placement, and no interface precedent.

Note: This is the most structurally disruptive regulation in the series. It requires the product page to carry information that is actively against the commercial interest: the long-term cost of ownership, at the moment of purchase.

Stage 3: Ownership

The post-purchase ecosystem — repair need building

The maintaining owner (Cognitive): Caring for product, noticing wear or faults.
The invested owner (Emotional): Attachment to product; preference for repair over replacement.
The replacement-nudged consumer (Commercial): Interface surfaces new products; repair path is not offered.
The repair-rights holder (Absent): Right to spare parts and repair information, no interface home (absent).

Tension: The consumer is in ownership mode. A fault develops. The current ecosystem offers no repair pathway. The interface was not designed to support post-purchase repair decisions. The path of least resistance is replacement.

Note: The Right to Repair creates an obligation during the ownership phase that has no current interface expression. The consumer’s repair rights are invisible to the ecosystem.

Stage 4: Repair decision

A choice the interface must now support (Active Legal Stage)

The repair-or-replace decision-maker (Cognitive): Weighing repair cost against replacement cost.
The cost-conscious owner (Emotional): Financial and environmental consideration.
The guarantee-extender (Temporal): Repair under guarantee extends legal protection by one year.
The rights-exerciser (Legal): Spare parts must be available at reasonable price; repair cannot be blocked.
The independent repairer (Absent): Third-party repairers now have legal access, not yet integrated into ecommerce flows (absent).

Tension: The directive creates a new decision point the interface must support. The repair-or-replace choice is currently invisible. The commercial incentive is replacement. The legal obligation is to make repair the accessible option.

Note: A product repaired under warranty gains an additional year of legal guarantee. This changes the repair calculus, but only if the consumer knows it exists. The interface that surfaces this information at the repair decision moment is performing the directive’s intent.

Active Artifact Comparison: Product Page as Sales Endpoint vs. Repair Gateway

The repairability score is a legally mandated active artifact. Currently, it does not exist as an interface node. When it does, it changes the nature of the product page from a sales-only surface to a lifecycle interface that must support both acquisition and long-term ownership.

Current State (Fails the Standard): The product page is a sales endpoint. No repairability information, no spare parts access, and no repair pathway. The ecosystem is optimised for purchase. The Right to Repair has no active artifact here.
Required State (Directive Standard): The product page now carries lifecycle information. Repairability score, spare parts availability, and repair pathway are visible at point of purchase. The consumer can evaluate the long-term cost of ownership before buying.

Active Artifact: The repairability score is a legal actor before purchase and after. It changes the product decision at point of sale, and it anchors the repair infrastructure that must remain accessible for the product’s lifetime. The commercial incentive is replacement. The legal obligation is repair.

“Manufacturers shall provide information concerning spare parts and repair on their website, make them available at a reasonable price, and shall not use hardware or software techniques that impede repair.”
— Directive 2024/1799 · Article 5

04 · Age verification: The fragmented gate

DSA 2022/2065 · EU Digital Identity Wallet · national law variations

Age-restricted products online are governed by a patchwork of national laws, platform rules, and product-category regulations. There is no single EU standard. The result is a fragmented legal node that arrives at the moment of highest purchase intent and currently resolves into either a privacy violation or a dark pattern.

Stage 1: Browse

Pre-restriction — ecosystem unaware

The intentional browser (Cognitive): Scanning products, building intent.
The aspirational self (Emotional): Desire-led engagement.
The market participant (Commercial): Responding to commercial signals.
The age-restricted buyer (Absent): Product category triggers verification requirement (absent).

Tension: The consumer is browsing without awareness that an age restriction will interrupt the journey. The legal node does not yet exist in the ecosystem. It will arrive at the worst possible moment.

Note: The design question begins here: when should the restriction become visible? Surfacing it early reduces checkout friction, but also introduces a gate before the consumer has committed.

Stage 2: Cart / Checkout

The legal node arrives at maximum purchase intent (Active Legal Stage)

The completing agent (Cognitive): Focused entirely on transaction completion.
The completion-seeker (Emotional): Friction is acutely felt; abandonment risk high.
The converting customer (Commercial): Interface optimised to reach payment confirmation.
The age-verifier (Legal): Verification required, but method is undefined by any single EU standard.
The privacy-holder (Absent): Consumer wary of data collection during verification (absent).

Tension: The legal node arrives at the moment of highest purchase intent. Cognitive archetype: completion-focused. Emotional archetype: friction-averse. Any method that introduces steps, requests documents, or requires account creation will generate abandonment. The commercial and legal archetypes are in direct opposition.

Note: No single EU standard governs this moment. National laws vary by product category. The interface must resolve a legally fragmented requirement with a coherent user experience.

Stage 3: Verification

The verification method determines everything (Active Legal Stage)

The interrupted agent (Cognitive): Task switched from purchase to identity; cognitive cost is high.
The surveilled self (Emotional): Verification often reads as data collection, not protection.
The friction interface (Commercial): Document upload, date of birth entry, account creation, all increase abandonment.
The identity-holder (Legal): Must prove age; method varies wildly by platform and market.
The autonomic user (Absent): EU Digital Identity Wallet: age confirmed without data shared, not yet available everywhere (absent).

Tension: The verification method is the design. A document upload harvests data and introduces maximum friction. A date-of-birth field is bypassable and legally inadequate. The EU Digital Identity Wallet offers a third path: cryptographic age confirmation with no data transfer. But this infrastructure is not yet uniformly available.

Note: The autonomic user archetype is active here. When the Digital Identity Wallet verifies age automatically, the consumer and the verification system become indistinguishable, a single node within the ecosystem.

Stage 4: Purchase confirmed

Verification resolved — ecosystem resumes

The completing agent (Cognitive): Transaction resumes; verification step complete.
The relieved consumer (Emotional): Friction resolved; purchase intent recovers.
The converted customer (Commercial): Purchase complete, if abandonment did not occur.
The verified buyer (Legal): Age confirmed; legal obligation met for this transaction.

Tension: If verification was smooth and privacy-preserving, the ecosystem recovers. If it required document upload or account creation, a significant share of consumers abandoned at the previous stage and never reach here.

Note: The design outcome is measured at this stage. The method that minimises the distance between the legal requirement and purchase completion, in effort, time, and privacy cost, is the ecosystem-aware solution.

Active Artifact Comparison: Friction Gate vs. Privacy-Preserving Signal

The age verification mechanism is an active artifact with two possible natures. Currently, it is either a data-harvesting gate or a bypassable checkbox. The EU Digital Identity Wallet proposes a third state: a privacy-preserving signal that confirms age without revealing it.

Current State (Fails the Standard): The current dominant pattern is either document upload or a date-of-birth field. The first harvests personal data and introduces maximum friction. The second is trivially bypassable and legally inadequate. Both fail on privacy, friction, or legal certainty.
Required State (Directive Standard): EU Digital Identity Wallet: confirm age, share nothing. A cryptographic proof that the consumer is over 18, without revealing date of birth, name, or other personal data. Low friction, low privacy cost, and high legal certainty.

Autonomic User: Youngblood and Chesluk’s concept of the autonomic user, where technology and user become a single whole, is most visible here. When the EU Digital Identity Wallet verifies age automatically and privately, the user does not perform verification. The ecosystem performs it.

“The EU age verification initiative aims to allow EU users to prove they are old enough to access age-restricted content without sharing any other personal information, privacy-preserving and interoperable with EU Digital Identity Wallets.”
— European Commission · Age Verification Blueprint, 2025

March 15, 2026
Rethinking Users as Ecosystems: My take

Scenario Under Analysis

A driver holds a phone to their ear while navigating traffic, despite the car having Bluetooth audio, steering wheel controls, and a speakerphone. The technology to keep hands free already exists. Why doesn’t the ecosystem use it?
Mike Youngblood and Ben Chesluk’s framework challenges the assumption that a user is a single, coherent agent with unified goals. Instead, they propose treating the user as an ecosystem: a dynamic network of competing roles, contexts, habits, social pressures, and devices that interact in real time.

This reframing is especially illuminating in the driving-while-calling scenario, where the user is simultaneously a driver, a conversational participant, a social being, and an operator of multiple overlapping technologies, each making competing demands on attention and behavior.

The Attentive Driver

Navigating, anticipating hazards, reading signs, and making split-second decisions. This role demands near-full cognitive bandwidth. Youngblood and Chesluk would flag this as a node under extreme load, one whose demands are not being respected by the ecosystem’s other nodes.

The Caller

Engaged in a conversation with social stakes: a work call, a family check-in, a negotiation. This role carries norms of presence and attentiveness. The physical act of holding the phone signals social engagement, even when technically unnecessary. The ecosystem enacts intimacy through posture.

The Handheld Phone

A device designed for palm-and-ear use. Its form factor trains users toward a particular posture of engagement. Even when alternatives exist, the phone’s physical affordances reassert themselves as defaults. In ecosystem terms, this node has strong pull. It recruits behaviour through shape and habit.

Car Bluetooth / Speaker

Available, capable, and hands-free, yet often unused. This node represents latent infrastructure that the ecosystem fails to activate. In Youngblood and Chesluk’s model, a node that exists but is not recruited is a design failure: the ecosystem has not built a pathway that makes this the path of least resistance.

Steering Wheel Controls

Buttons for answer, end, and volume, placed precisely to keep eyes on road and hands on wheel. A thoughtful design intervention, but in the ecosystem this node is often never learned, or is overridden by habitual phone-reaching. Its potential is blocked by onboarding gaps and habitual inertia.

Law and Social Norms

In the UK and many jurisdictions, holding a phone while driving is illegal. Yet enforcement is inconsistent, and social norms around just a quick call persist. This node exerts pressure on the ecosystem but competes with convenience and social expectation. The ecosystem absorbs legal norms as one input among many, not necessarily the dominant one.

Ecosystem Tensions

Embodied Habit versus Designed Alternatives

Phone-to-ear is a deeply trained motor habit. The body knows what to do when a call arrives. Bluetooth and wheel controls require conscious re-routing of that habit through unfamiliar inputs. Youngblood and Chesluk would note that ecosystems favour low-friction paths, and habit is the lowest friction of all. Design must work with the body’s memory, not against it.

Social Presence versus Physical Safety

Holding the phone enacts a posture of relational presence. I am here, with you. Speaker mode or Bluetooth routes the voice to the car, but the caller’s voice becomes environmental, less intimate. The ecosystem is performing a social relationship through a physical gesture, even at the cost of safety. The design question becomes: how do you preserve the social quality without the dangerous posture?

Device Autonomy versus Contextual Awareness

The phone does not know it is in a moving vehicle. It just rings and waits to be answered. The car’s system may detect motion and prompt routing, but these systems are often siloed, not integrated. The ecosystem’s nodes do not communicate. A connected ecosystem would share context: the phone knows it is paired, the car knows it is moving, and together they could redirect the call automatically.

User Agency versus Protective Friction

I know what I am doing. Drivers resist systems that feel paternalistic or override their choice. Automatic rerouting to speaker or Bluetooth could be lifesaving, but users may disable it, defeating the design. An ecosystem-aware design must calibrate between preserving user agency and providing guardrails, nudging rather than forcing, and making the safe path feel like the natural one.

Ecosystem-Aware Design Opportunities

Opportunity 1: Contextual Auto-Routing

When the phone detects pairing with a moving vehicle’s Bluetooth, incoming calls automatically route to car audio with a brief haptic confirmation. The path of least resistance becomes the safe path.

Opportunity 2: Steering Wheel Onboarding Ritual

On first Bluetooth pairing, the car’s display walks the driver through wheel controls with a 30-second simulation, building the motor memory before the first real call. The node is activated through rehearsal, not just availability.

Opportunity 3: Social Presence Signalling Without Holding

A small in-car camera or presence indicator could signal attentiveness to the caller without requiring the physical phone posture. The social signal becomes decoupled from the dangerous gesture.
Opportunity 4: Graceful Decline with Auto-Reply
If a call comes in and no hands-free mode is active, the ecosystem offers a one-tap driving, will call back message, with a reminder to call back on arrival. This reduces the temptation to reach for the phone entirely.

Opportunity 5: Cross-Node Ecosystem Pairing

Phone, car, and wearable share a unified context layer. The watch knows the car is moving, nudges the wrist, and gives a subtle route to car prompt on the face. One tap confirms. The ecosystem nodes finally talk to each other.
The user is not a single point of interaction. They are a living system, shaped by context, habit, social role, and competing devices. Design that ignores this complexity does not serve users. It simply adds one more node to an already overwhelmed ecosystem.

Paraphrase of Youngblood and Chesluk, Rethinking Users as Ecosystems

March 13, 2026
Creating a Seamless Omnichannel Service

Excess Baggage operated luggage storage, insurance, retail, and travel accessory services across 15 international airports and 18 rail stations.

Airport Luggage Storage and Concierge Area

As customer numbers and spend increased, the organisation committed to digitising its services to support a more coherent omnichannel experience.

The challenge was not introducing digital touchpoints, but ensuring that online services reduced friction in high-pressure, time-critical airport contexts while integrating cleanly with physical locations and operational systems.

Several services relied on fragmented booking and operational flows. The online booking process for left luggage and storage, in particular, required unnecessary steps and fields, increasing interaction cost at moments when travellers were already under stress.

In parallel, new services such as remote baggage check-in, confiscated item return, and excess baggage handling introduced additional complexity across channels.

Airport Bag Wrapping Service Area

The core challenge was to reduce interaction cost for customers while maintaining operational clarity for staff across locations.

Optimising the Booking Journey

The left luggage and storage booking flow was streamlined to reduce unnecessary steps and form fields, with a clear focus on speed, clarity, and predictability.

Key improvements included:

– Clearer progression through booking steps
– Reduced cognitive load during data entry
– Early visibility of pricing and storage duration
– Support for post-booking actions such as charge tracking and retrieval reminders

The goal was not only to improve usability, but to lower interaction cost at scale across multiple locations.

Collection Times and Customer Details Form

Delivery Details Form – Excess Baggage Company

Shipping Restrictions and Protection Details

Service Blueprinting for Omnichannel Alignment

To support consistency across digital and physical touchpoints, I created service blueprints mapping frontstage user interactions, backstage staff actions, and supporting systems and dependencies.

In a security-constrained, time-critical environment like airports, service blueprints were essential to align customer-facing flows with staffing, logistics, and physical space constraints that could not be resolved at interface level alone.

This work exposed misalignments between customer expectations and operational reality, helping teams coordinate changes across departments rather than solving issues in isolation.

Solutions were typically piloted in a limited number of locations, validated against real operational constraints, and only then scaled across airports and stations.

Self-Service Luggage Weighing Kiosks

Problem to Solve

Passengers often face stress and inconvenience at the airport due to uncertainty about luggage weight, leading to unexpected fees and delays.

Benefit

– Save time and avoid unexpected fees
– Enhance the user experience through an intuitive, multilingual interface
– Offer a cost-effective service model for airports and airlines

Feature Set

– Precise weight measurement
– Multilingual support in 15+ languages
– Flexible payment options including NFC and coin payments
– Cross-selling opportunities for additional airport services

Luggage Weight Check Kiosks

Luggage Weighing Process Flow Diagram

Luggage Weight Result and Allowance Feedback

Post & Fly

Problem to Solve

Travellers rushing through airport security with prohibited or restricted items faced a difficult choice: dispose of the items or find a quick, reliable solution.

Desired Outcome

Post & Fly was designed to offer a seamless retrieval service through:

– A streamlined online portal
– Timely collection and processing by staff
– A transparent 30-day retrieval timeframe

Service Selection and Pricing Options

Delivery Details and Order Summary Form

UX Methods

– Empathy mapping
– User interviews
– Mental-model analysis

Unified Staff Dashboards

Problem to Solve

In a fast-paced airport environment, fragmented dashboard experiences can lead to confusion, frustration, and decreased efficiency for both employees and customers.

Desired Outcome

– Unify the user experience across services
– Enhance employee productivity through clearer workflows
– Optimise for a fast-paced, time-sensitive environment

Platform design where structure, permissions, and participation patterns mattered as much as interface decisions.
A decision-support framework for comparing UX impact across initiatives with credibility and exposure context.

February 24, 2026
Interpreting Intent: When Agents Decide for Users

In planning meetings, it now comes up almost casually. Someone reports that a task is done, the agent took care of it, and the conversation moves on.

Later, when the decision is questioned, there is a pause. No one remembers why that option was chosen. There is no error to point to, no rule that was broken, just an outcome that arrived already settled.

Traditional UX research assumed a stable sequence: intent forms in the user, interaction expresses it, systems execute, and behaviour becomes evidence. That assumption held as long as systems waited to be instructed.

Agentic systems do not wait.

What enters the system is rarely a complete instruction. It is partial, sometimes contradictory, often shaped by convenience. The system interprets it, fills in what is missing, resolves conflicts it was never told about, then acts. By the time an outcome appears, the decision has already been made somewhere else.

Intent becomes legible inside the system, not at the interface, and that is where the shift happens.

This matters because interpretation is not execution. Tools carry out instructions when the path is explicit. Agents reconstruct the path by inferring goals, ranking constraints, and deciding what matters more, all before anything visible occurs. These choices feel smooth because they are meant to, but they are still choices.

You see this in ordinary product moments. A travel agent defaults to the cheapest flight rather than the fastest one. A scheduling agent compresses meetings without surfacing what was sacrificed. When someone asks why, the answer is brief and unsatisfying. “It made sense.” The explanation closes the discussion without explaining the decision.

Fluency does that. It compresses complexity until it looks resolved.

UX measurement starts to slip here because it still treats behaviour as a stand-in for intent. The task completed. The user did not undo it. The log looks clean. In agent-mediated systems, those signals no longer mean what they used to.

Acceptance often reflects effort rather than agreement. Undoing a decision takes time. Challenging the system requires confidence. In busy contexts, silence is efficient, not affirmative.

When we treat agent outputs as user behaviour, authorship is quietly reassigned. We analyse the system’s decisions and attribute them to the user, producing data that appears robust while masking where agency actually moved.

This is why task success stops being a reliable indicator. An agent can succeed while intent drifts, and the failure mode does not look like error. It looks like progress.

In research sessions, the signal usually appears after the fact. Ask participants how the outcome was reached and they describe the result, not the path. Ask whether this is what they would have done themselves, or whether the system led them there, and the answer takes longer.

That hesitation matters more than the answer.

The question does not measure efficiency. It surfaces authorship, and it reveals where decision-making shifted without friction, discussion, or explicit consent.

Once interpretation happens inside the system, responsibility should move with it. Often it does not. The system decides, the user carries the consequence, and there is no clear boundary where ownership can be contested or reclaimed.

At that point, this stops being only a UX problem. It becomes a governance failure, one where authority moves upstream while liability remains downstream.

Labels and disclosures do little here. What matters are boundaries: which assumptions were made, where decisions were resolved, and when interpretation became action. Those are governance questions, not interface refinements.

This tension is not new. Susan Sontag warned that interpretation makes meaning manageable by stripping away what resists clarity. Agents do the same to intent because they have to act, and action demands resolution.

What disappears is not noise. It is the unresolved part that signalled something was at stake.

In UX, ambiguity was long treated as a usability flaw. In agentic systems, ambiguity is often the signal that should slow things down rather than be compressed away.

Agentic systems force separations that UX once collapsed. Expression is not interpretation. Interpretation is not action. Action is not acceptance. Research that fails to keep these apart will continue to report confidence where none exists.

The shift is not about adding features or refining prompts. It is about what we treat as evidence when decisions are no longer authored in one place.

Outcomes explain what happened.
Authorship explains how it happened.

If that distinction stays implicit, behaviour will keep being misread, alignment overstated, and the result will look convincing.

January 24, 2026
From Chat to Control: Why AI Interfaces Need Symbols, Not Sentences

I was reading a short post by Jakob Nielsen when something clicked uncomfortably into place.

His argument was clean. As AI agents mature, traditional user interfaces dissolve. Users stop navigating. They instruct. Screens become temporary. In some cases, they disappear.

That claim is directionally correct. But it leaves a gap that matters in practice.

If the interface recedes, control does not vanish with it. It relocates. And right now, that control is being pushed almost entirely onto conversational language.

I started noticing the cost of that shift in small moments. Planning meetings where prompts kept getting longer. Reviews where nobody could explain why an answer felt wrong, only that it did. Research summaries that sounded confident until someone asked where a claim came from.

Language was doing too much work.

The Roman Numeral Phase of AI

Natural language is powerful. It is also inefficient when used as a control surface.

We are already compensating. Prompts expand, the same constraints reappear in request after request, and tone gets negotiated instead of enforced. When the system hesitates, users explain themselves again, usually in longer and more careful ways, hoping precision will emerge from volume.

This is the Roman Numeral phase of AI.

Roman numerals were fine for labelling. They failed at calculation. The system broke not because people lacked intelligence, but because the notation could not express state, absence, or transformation. What changed mathematics was not fluency. It was the introduction of zero and positional logic.

Zero mattered because it altered what the system could do, not how politely it described itself.

That distinction matters here.

What we are missing in AI interaction is not better wording. It is a symbolic layer that compresses intent into something the system can execute reliably, without requiring the user to restate rules every time.

Not a new language. Not “AI-speak”. Something closer to operators.

Symbols as Control, Not Style

I started sketching this out informally while working. Nothing formal. Just marks I kept wishing I could add without explanation.

Take a simple task.

Old way:

“Hey, can you help me summarise this article? Please don’t be too wordy, make sure you cite sources accurately, avoid your usual intro, and if there’s controversy, show both sides.”

It works. Sometimes. It also relies on interpretation, memory, and goodwill.

New way:

Summarise this article [-][#][~]

Those symbols are not shorthand. They change behaviour.

[-] strips conversational padding. No greetings. No framing. Output starts with content.

[#] enforces attribution. Claims must be grounded or marked as uncertain.

[~] allows synthesis without forcing convergence. Nuance stays visible.

Read left to right, they function as constraints. Remove one, and the output shifts. Combine them, and you get something closer to an instrument than a conversation.

This is not about efficiency theatre. It is about where errors surface.

Without explicit constraints, problems appear late. During review. During decision-making. Sometimes after shipping. With them, failure shows up earlier, where it is cheaper to deal with.

That is the practical difference.

When Friction Disappears Too Cleanly

Someone commented on my post a few days later, her framing widened the picture.

She described adaptive UI as a bridge. A messy middle where voice, agents, and screens overlap. Hybrid systems that mostly disappoint, but still teach teams where things break. She is right about that phase. Anyone working in this space has seen it.

She also described hardware “kits”. Rings, glasses, watches. Personal ecosystems shaped by context and profession.

I like the vision. I share the concern.

Jaron Lanier’s You Are Not a Gadget keeps coming back to me here. Users rarely choose what is best. They choose what is bundled, frictionless, or already there. Hardware kits look like choice. In practice, they tend to collapse around defaults.

Once that happens, control becomes harder to recover.

The same risk applies to personal agents. The agent that “knows you best” may simply be the one that has collected the most data across the widest surface. That does not automatically make it the one that serves you best.

Continuity feels empowering until it becomes enclosing.

Without a portable grammar of intent, something you can carry across systems, you lose the ability to break the glass. You inherit behaviour you did not explicitly choose. Correction becomes verbose again, because it has to fight accumulated assumptions.

That is where symbolic control starts to matter. Not as elegance. As friction you can apply deliberately.

The Humanisation Problem

Caleb Sponheim’s article arrived later and closed the loop for me.

His argument is blunt. Humanising AI is a trap. Personality modes, conversational fluff, emotional language. All of it increases engagement. Much of it reduces reliability.

I have seen this play out in practice. A summary opens with “Love this brief!” and nobody questions the substance. A system says it is “thinking”, and users wait patiently for something that is not cognition at all, just computation wrapped in metaphor.

Human language invites human mental models. Those models expect judgement, consistency, accountability. LLMs offer none of those things.

Caleb cites evidence showing that warmth correlates with higher error rates and lower trust. Even without the studies, the pattern is familiar. When the interface feels like a person, people forgive it like one. That is rarely what organisations want from a tool.

Symbols cut through that. They do not pretend to care. They do not reassure. They specify.

That is their advantage.

Control Does Not Disappear

Nielsen is right about one thing that is easy to miss. UX is not dying. It is moving.

When UI recedes, control does not disappear. It relocates into language, defaults, policies, and unseen execution paths. If designers do not shape those layers, they still exist. They just harden without scrutiny.

Right now, conversational interfaces are carrying too much of that load. They are being asked to express intent, enforce boundaries, convey confidence, and negotiate tone, all at once. That is why prompts grow. That is why constraints repeat. That is where systems begin to break.

Symbolic grammar is not a solution in itself. It will fail in places. It will be misused. Some teams will treat it as style rather than control. Others will resist the friction entirely.

That tension is real and unresolved.

But the direction is clear enough to name. As interfaces fade, grammar becomes infrastructure. Not expressive grammar. Operational grammar. The kind that decides what the system is allowed to do before it decides how friendly it sounds.

When that layer is missing, language fills the gap. And language, on its own, is a fragile place to put control.

January 24, 2026