Diego Mas González

Stakeholders who help shape a study are more likely to trust it; findings need to be translated into what each audience already cares about; and a project needs internal champions to push it forward. Adoption is part of the research job, not something that happens after the report is delivered.

In one post, they describe bringing product managers, designers, and engineers in early, giving a designer usability detail and a product manager the link to revenue, and finding colleagues who will amplify user feedback. Without those allies, they write, the work becomes “a research deck that lives in a folder.”

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Customer experience research follows journeys across channels such as support calls or bill payment, while UX research examines how people navigate, struggle, or succeed inside a digital product. Combining them gives a fuller view of how customers perceive a product and whether that perception holds up in real interactions.

One post gives a worked example: customer experience research finds that customers drop off after contacting support, and UX research then finds usability flaws in the self-service portal that make support articles hard to find.

• The combined insight changes both the support process and the digital tool behind it.
• Elsewhere, they recommend that market-research-led teams observe behavior in context, use semi-structured interviews instead of surface-level focus groups, map journeys rather than rely only on demographics, and triangulate qualitative and quantitative data.

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After drafting and editing discussion guides or survey questions themselves, they use an agent to tighten wording, check the flow and logic of questions, and suggest anything missing.

• They review each suggested question to confirm it is open-ended and not leading.
• Synthesis, the final report, and delivery stay with the researcher.
• They say AI use should be transparent and human expertise should remain at the centre.

They cite a college professor’s old comment — stop looking at the trees and work out what you are trying to say in the forest — to explain how AI can help with overlong writing. The post starts with a marketer who lost a client because the work felt “too AI-generated,” and uses that experience to draw the line.

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Their advice is to test a role against a deliberate mix: roughly 60% skills you know deeply, 25% you have some experience in, and 15% you want to grow into.

• In interviews, they suggest asking what a typical week looks like, which tools and methods will actually be used, and whether you can speak with someone currently in the role.
• Unclear expectations during hiring are a signal, and walking away is a legitimate option.

The clearest case in the posts comes after four interview rounds: a job advertised as UX Researcher turned out to involve spreadsheets, survey data, and web analytics rather than study plans, customer discussions, and usability testing. In a separate post, design, strategy, and consulting work appears under titles like Business Engagement, while research sprints can sit under Product Management.

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The post clearly explains how they distinguish the two disciplines and then bring them together.

The post draws a working line between customer experience research, which follows journeys across channels such as support calls and bill payment, and UX research, which studies product-level behavior: how people navigate, struggle, or complete a task in a digital experience.

• The two disciplines are distinct on their own but more useful together, showing how a customer perceives a product and how that perception plays out in real interactions.
• The example makes the argument concrete: CX research finds that customers drop off after contacting support; UX research then uncovers usability flaws in the self-service portal that make support articles hard to reach.
• Together, the findings point to changes in both the support process and the digital tool behind it.

For brands planning a campaign

For a brief centered on research integration, this is the piece to cite. It supports a campaign angle about unifying research functions rather than adding another survey, and offers a copy-ready example of two research streams producing one actionable finding.

Readers interested in their approach to adoption and stakeholder relationships, beyond methods alone, should start here.

The central claim: good research does not carry itself; people do.

• The post lays out three practices: involve product managers, designers, and engineers early so they can help shape scope and trust the results; translate findings into what each audience cares about, since a designer tracks usability problems while a product manager wants to know how they connect to revenue; and identify internal champions who will amplify the work.
• Findings nobody listens to stay in a deck in a folder, the post argues, making research impact an organizational problem, not a reporting problem.

For brands planning a campaign

Useful for a brief about getting research adopted inside a large engineering or product organization. The post frames the work as coalition-building, a fit for messaging aimed at research leaders who need to influence teams they do not manage.

It’s a natural second read after the UX/CX post and explains the vocabulary they use elsewhere.

The argument comes down to one contrast: market research uncovers what people do; UX research explains why they do it.

• The post offers four moves for market-research-led teams: observe behavior in context rather than relying on self-reported survey data; replace surface-level focus-group questions with semi-structured interviews that get at motivations; use journey mapping to find friction rather than stopping at demographics; and triangulate qualitative and quantitative findings for a fuller picture.
• These are additions to market research, not a replacement for it.

For brands planning a campaign

A campaign aimed at market research or insights teams can use this framing to make the case for adopting UX methods without abandoning existing programs. The post also shows how they write for a non-UX audience.

It explains what they want from a research role and how they judge whether a position is genuinely a research job—useful for anyone assessing them for a senior research hire.

Diego Mas González describes taking a job advertised as UX Researcher after four interview rounds, only to find that the work consisted of spreadsheets, survey data, and web analytics rather than study plans, customer discussions, surveys, and asset testing.

• They are clear that their strengths lie in qualitative research and product marketing.
• The mismatch was apparent early on, though the frustration built gradually.
• Their practical advice: ask in interviews what a typical week looks like, which tools and methods the role actually uses, and whether you can speak with the current postholder; treat unclear expectations as a signal; and be willing to walk away.
• They also list the criteria they now apply: whether a role uses qualitative research, project management, and product marketing; whether it is mid-to-senior level, with scope to shape the project, execute, and mentor; and whether the team works across stakeholders rather than staying siloed.

For brands planning a campaign

A hiring-focused campaign can use this post to show how they evaluate roles and what they expect from a research function. It also offers a candidate-facing angle on mismatched job titles and interview questions, a topic that resonates with practitioner audiences.

It’s also a readable sample of their voice.

The post opens with a story about a marketer losing a client because the work felt AI-generated, then describes a specific division of labor.

• The researcher drafts, edits, and re-edits discussion guides and survey questions from scratch; only afterward does an agent act as a second editor to tighten wording, improve question flow, or suggest additions.
• They review any suggested questions for leading phrasing, openness, and fit with the guide before using them.
• Insight synthesis, final reporting, and delivery remain with the researcher.
• Their position: AI belongs in the toolkit if its use is transparent and human expertise stays central.
• A college professor’s remark—to look through the forest, past the trees—explains why overlong writing is the specific problem AI helps solve.

For brands planning a campaign

A strong fit for campaigns about responsible AI adoption in research teams. The post takes an opinionated, non-utopian position and offers a concrete workflow, making it relevant to vendors or training providers addressing methodological and quality concerns rather than tool hype.

Someone who has read only the UX/CX post would miss this side of their work.

The post argues that titles do not define range and that UX professionals tend to chase a small set of them.

• Diego Mas González suggests weighting a search toward skills held deeply, skills backed by some experience, and skills worth growing into.
• They point to roles where the work appears under unexpected titles, such as business engagement, product management, or product marketing.
• Research sprints, strategy, and consulting, they add, can all sit outside the label “design.”
• The advice: treat a skill mix as a Venn diagram, not a box.

For brands planning a campaign

Use this for a campaign about reskilling, internal mobility, or hiring for range rather than a fixed title. The post supports an angle about research skills transferring to adjacent functions, a useful hook for learning and development audiences.

The post lays out three practices: involve product managers, designers, and engineers early so they can shape the scope and are more likely to trust the results; tailor findings to each audience, since designers want usability detail while product managers want the link to business revenue; and find colleagues who will champion user feedback and push the work forward. Without those allies, research becomes a deck that lives in a folder.

Three numbered practices, each with a short rationale, followed by a question inviting readers to share their experience.

For brands planning a campaign

A fit for brands selling research platforms, collaboration tools, or research operations services, with a message focused on getting findings acted on, not just collected.

Examples: Source 1

Customer experience research examines product journeys across channels, such as support calls or paying a bill; UX research examines how people navigate, struggle, or succeed inside a digital experience.

• In the example, CX research finds customers dropping off after contacting support, and UX research uncovers usability flaws in the self-service portal that make support articles hard to find.
• Together, the disciplines show how customers perceive a product and how that perception holds up in practice.

Definitions of each discipline, followed by a two-step example showing how one finding leads to the other.

For brands planning a campaign

Suited to CX platforms, support and contact-centre tooling, and journey analytics vendors seeking a researcher fluent in both the journey and product perspectives.

Examples: Source 1

For organisations built around market research, four changes follow: observe behaviour in context rather than relying on self-reported survey answers, use semi-structured interviews to move past surface-level focus groups, map customer journeys instead of leaning on demographics and personas alone, and triangulate qualitative and quantitative data for a fuller picture. Diego frames this as making market research more human-centred.

Four labelled recommendations, each naming a method and the limitation it addresses.

For brands planning a campaign

Relevant to market research platforms, survey and insight vendors, and agencies making the case for qualitative depth alongside quantitative offerings.

Examples: Source 1

Any suggested question is reviewed to confirm it is not leading and is open-ended.

• Synthesis, the final report, and delivery stay with Diego.
• The stated condition is transparency about AI use with human expertise kept at the centre.

A personal anecdote opens the post, followed by a step-by-step account of where AI enters the process and where it stops.

The post endorses a specific role for AI in research, with a clear boundary.

For brands planning a campaign

A fit for AI research and survey tools seeking a practitioner to demonstrate a defensible, bounded process rather than make broad automation claims.

Examples: Source 1

The post turns that into advice for candidates: ask what a typical week looks like, which tools and methods the role actually uses, and whether you can speak with someone currently in the job. The stated lesson is that unclear expectations during hiring are a signal worth acting on.

A first-person mismatch story, a checklist of interview questions, and a closing question for readers.

For brands planning a campaign

Useful for recruiting, hiring, and career-development brands speaking to research job seekers.

Examples: Source 1

The suggested approach is to look for roles that use roughly 60% of skills you know deeply, 25% you have some experience in, and 15% you want to grow into, searching through networks, mentors, job boards, and AI. Diego notes that design, strategy, and consulting work can sit under titles like Business Engagement, or research sprints under Product Management.

A simple percentage model for assessing a role, with examples of unexpected pairings between titles and work.

For brands planning a campaign

Fits professional development, learning, and job-search products, as well as brands addressing career changers in design and research.

Examples: Source 1

Richard Best maps role changes, such as product designers moving toward full-stack work and UX researchers toward research automation leads. Diego Mas González explores the same territory by asking what a role actually involves and how much of it is research.

Richard Best argues from the tool and industry side, weighing a specific design platform against a dozen AI-native challengers and warning that slow tools cost users their careers. Diego Mas González focuses on practice and career fit: how studies gain traction, how research disciplines combine, and when to draw the line on an ill-matched role.

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Christophe Jammet describes enterprise pilots collapsing because nobody studied how people would actually make decisions. Diego Mas González makes the same case for research: insights need champions and stakeholder involvement to change anything.

Christophe Jammet works at the programme and content level, writing about paid amplification and publishing a white paper on human-centred design in AI and automation. Diego Mas González focuses on individual studies, describing interview guides, survey questions, usability testing, and the people inside a company who carry findings into decisions.

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Daryn Edelman offers ready-made questions about why a role exists, what the first 90 days require, and how the team is structured. After landing a role that did not match its title, Diego Mas González recommends asking what a typical week looks like and which tools and methods the job actually uses.

Daryn Edelman covers interviews and job searches broadly, including questions for recruiters and managers and about company vision.

• Diego Mas González draws on one research role to offer a specific test of skill mix: about 60% known, 25% familiar, and 15% to grow into.
• That makes the advice especially relevant to research and design careers.

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Richard’s two posts make concrete claims a campaign can use: a six-year wait for a variant-bloat fix, AI prototyping capped around 60 prompts a month, an MCP server that strips semantic tokens into raw hex values, a five-person team paying $1,500 to $2,000 a year before AI credits, and a named field of challengers including Pencil.dev, Paper, Google Stitch, Penpot, Subframe, Framer, v0, and Lovable.

• The career post forecasts role shifts such as product designer to full-stack product designer and UX researcher to UX research automation lead.
• If the buyer task is arguing that design and research teams must adapt to AI-native tooling, Richard offers a sharper industry-change thesis than Diego’s research-practice posts.

The tradeoff is losing the practical research-operations case.

• Two written LinkedIn posts cover a worked support-drop-off example and a three-part adoption plan, but do not show study design, discussion-guide review, stakeholder adoption, or UX/CX integration.
• For a campaign about getting research acted on, ask for examples of study design and how research reached stakeholders.
• No audience demographics, engagement data, sponsorship format, rate, or inquiry route is present.

Examples: Source 1 · Source 2

Christophe’s white-paper post states that enterprise AI pilots collapse not because of technical limits but because teams skipped the UX research that would show how people actually navigate decisions.

• It names two implementation patterns: privacy automation that scaled by preserving human expertise at critical decision points, and platform migrations that succeeded by mapping mental models before moving workflows.
• The content-marketing post adds a distribution argument about targeted paid amplification and underused budget for the right audience.
• If the buyer task is AI adoption strategy or enterprise transformation, Christophe is a stronger fit than Diego’s narrower research-practice posts.

The tradeoff is less evidence of hands-on qualitative research.

• Christophe’s material is a white-paper promotion plus a content-marketing/paid-media argument, not a full research-process example.
• It does not match Diego’s guide-writing and AI-editing boundary or the CX/UX support example.
• A campaign would need to decide whether it is buying AI-and-human-centered-design thought leadership or research-operations depth.
• The basis is two written posts; no audience, engagement, sponsorship terms, rates, or inquiry route are present.

Examples: Source 1 · Source 2

Daryn’s two posts give a ready question bank for candidates interviewing companies: recruiter questions, job questions, team insights, manager perspectives, and company vision. For a tech company building a talent-brand or research-career campaign alongside the research-lead brief, these posts supply practical interview prompts for a clear job-seeker audience.

The tradeoff is that this does not carry a campaign aimed at research leads.

• Daryn’s posts do not show UX/CX research integration, research operations, AI-in-research boundaries, or stakeholder adoption.
• Choosing Daryn would trade research-lead credibility for hiring and career content.
• The two posts are substantially the same interview framework, so range is limited.
• No audience, engagement, sponsorship terms, rates, or inquiry route is present.

Examples: Source 1 · Source 2