{{ s.num }}
Crawl
{{ s.name }}
{{ s.crawl }}
Human layer
{{ s.watch }}
{{ x.t }}
{{ x.d }}
Level 01 — Crawl
AI augments the individual.
Off-the-shelf LLMs draft, summarise, cluster, and package across the workflow, and serve as a thinking partner.
The work isn’t systematised, and gains are individual and largely unseen at the organisational level. But a significant shift is already under way: steps that used to happen sequentially are being compressed into a single step, or “conversation,” with an AI.
Researcher
at the centre
LLM
Transcript
Research plan
Summary
Cluster
Report
Method
Insight
At Crawl, AI behaves like a toolbox, not an autonomous system. Ten tasks, ten accelerators, one researcher.
Level 02 — Walk
AI becomes a system.
Walk is about building purpose-centred agents, skills, and Retrieval-Augmented Generation (RAG) systems that extend off-the-shelf AI. The focus shifts from individual efficiency to team-level systems, with evaluations, or “evals,” emerging as a discipline in their own right.
Crawl asks
“How can AI help me do this task?”
Walk asks
“How can we build an environment where research work is continuously supported?”
Level 03 — Run
Research arrives before anyone asks.
Run changes the shape of a research practice entirely. The focus is production-grade research systems designed to anticipate organisational needs and push insights to stakeholders, rather than respond to a brief.
Multi-agent pipelines, automated workflows, and analytics integrations condense the standard workflow into far fewer steps. Because the reasoning behind AI outputs is often invisible, evals become a key operational requirement.
Proactive meeting interventions
The research comes to the meeting.
Agents “listen in” to stakeholder meetings and chime in with existing insights or suggest new research initiatives. If a search finds nothing in the library, the agent reroutes the person to request original research.
Product sync · meeting chat · illustrative
PMWhy do trial users drop off on day four?
DesignerWe should really do some research on that.
Research agentThis was studied in February. Three main reasons are in the linked report. There’s a gap on enterprise trials. Request a new study?Open reportRequest study
AI system
Output
Evaluation
Human
Approve · correct · investigate
Back to the system
{{ hitlTitle }}
{{ hitlMsg }}
Human-in-the-loop
Where judgement stays essential.
A person must review, approve, or correct AI outputs at particular points before they’re finalised or acted upon. That puts accountability back into the system, especially for high-risk decisions.
Judge.Editor.Researcher.Strategist.Guardian of evidence.
Step into the loop. Decide what happens to the finding.
{{ b.label }}
Risk and quality
Seven risks to manage as you scale AI.
Teams at the cutting edge aren’t anti-AI. They’re building “AI antidotes” into their systems, because skilled humans must remain in the loop.
More automationSpeed. Scale. Efficiency.
=
More operational responsibilityQuality. Consent. Privacy. Bias. Traceability. Verification.
{{ r.d }}
Antidote: {{ r.a }}
The researcher’s role
The role is changing. It isn’t disappearing.
CrawlResearcher using AI toolsOff-the-shelf tools augment individual tasks. The workflow stays largely intact.
WalkResearcher designing AI-supported systemsAgents, skills, and RAG built for the team, with evals as a discipline in their own right.
RunResearcher designing, evaluating and governing agentic systemsFrom moderator to agentic system designer. ResearchOps can no longer work from the sidelines: it needs a clear understanding of research craft.
Threads that run through every level
The emergent make phase
Insight→Prototype→Change→Measure→New signal↺
Researchers vibe-code functional prototypes, co-create with participants, fix low-risk UI issues, and share insights in the language of designers and PMs.
Democratisation
ResearchDesignerPMEngineerMarketingLeadership
Agents, repositories and self-serve tools widen access to research. Governance, evidence and research craft still decide what’s trustworthy.
ResearchOps: the operating layer
Governance · data · repositories · templates · agents · evaluation · metadata · privacy · workflow · automation
The map has no separate ResearchOps section, because AI makes the whole workflow systematisable. It’s all operations.
The full map
Crawl, walk, run: one control.
Drag across the maturity levels and watch ten stages connect, merge, and collapse into an agentic system. Select a stage to compare what changes at each level.
Crawl · toolsWalk · systemsRun · ecosystem
{{ s.short }}
Analysis · Synthesis · Packaging
Agentic systemHITL · evals · grounding
Stage {{ cur.num }} · viewing {{ lvlName }}
{{ cur.name }}
{{ e.t }}
Watch for{{ cur.watch }}
Practices at {{ lvlName }}
{{ l.n }}
{{ l.text }}
Glossary
The words that decide what you can trust.
Read the glossary as a set of warning labels: almost every entry comes with a “but.” Select a term to see what it connects to.
Where it appears · {{ gsel.where }}
{{ gsel.t }}
{{ gsel.def }}
Why it matters{{ gsel.why }}
Connected to
Source: How to AI UXR, produced by Kate Towsey for The ResearchOps Review with the support of Strella. 562 data points gathered January–May 2026. © The ResearchOps Review, 2026.
This is an interactive reading of the map. The original map remains the source of truth. Scenario examples marked “illustrative” were written to demonstrate practices described in it.