Chapter 50: DesignOps & ResearchOps

1. Executive Summary

DesignOps and ResearchOps transform design and research from isolated craft activities into scalable, strategic capabilities for B2B organizations. DesignOps focuses on the systems, tools, and processes that enable design teams to work efficiently—managing design systems, component libraries, collaboration tools, and workflow standards. ResearchOps builds the infrastructure for continuous customer learning through participant recruitment pipelines, insight repositories, ethical frameworks, and democratized access to research findings. Together, they reduce operational friction, accelerate time-to-insight, improve cross-functional collaboration, and ensure design and research scale with organizational growth. For B2B IT services companies managing complex enterprise products across multiple stakeholder groups, mature DesignOps and ResearchOps practices are essential for maintaining consistency, velocity, and customer-centricity at scale.

2. Definitions & Scope

DesignOps

DesignOps encompasses the operational practices, tools, infrastructure, and governance that enable design teams to deliver high-quality work efficiently and at scale. In B2B contexts, this includes:

• Design System Management: Governance, versioning, contribution models, and adoption tracking for design systems and component libraries
• Tool Stack Optimization: Standardizing and integrating design tools (Figma, Sketch, Adobe XD), handoff tools, prototyping platforms, and collaboration software
• Workflow & Process Design: Establishing design sprints, critique sessions, review cycles, approval workflows, and quality gates
• Resource & Capacity Planning: Forecasting design needs, allocating designers to initiatives, managing workload distribution
• Knowledge Management: Documentation standards, pattern libraries, design decision records, onboarding materials
• Cross-Functional Integration: Defining how design interfaces with product, engineering, research, marketing, and sales

ResearchOps

ResearchOps establishes the systems and processes that enable research teams to generate, manage, and democratize customer insights continuously. For B2B organizations, this encompasses:

• Participant Recruitment Infrastructure: Building and maintaining panels of enterprise users across roles, industries, and company sizes; screening protocols; incentive management
• Research Tools & Platforms: Standardizing on tools for moderated research (UserTesting, Lookback), surveys (Qualtrics), analysis (Dovetail, Aurelius), and repository management
• Insight Management: Creating centralized repositories, tagging taxonomies, synthesis frameworks, and searchable knowledge bases
• Ethical & Legal Compliance: Managing consent forms, NDAs, data privacy (GDPR, CCPA), incentive tax reporting, recording policies
• Democratization Programs: Training non-researchers to conduct studies, self-service insight access, research socialization rituals
• Quality & Standards: Research methodology guidelines, reporting templates, evidence hierarchies, validation protocols

B2B Specific Considerations

B2B environments introduce unique complexities:

• Multi-Stakeholder Design: Design systems must accommodate diverse personas from executives to end-users
• Enterprise Recruitment Challenges: Accessing senior stakeholders, navigating procurement gatekeepers, coordinating across time zones
• Security & Compliance: Tool approval processes, data residency requirements, air-gapped environment considerations
• Long Sales Cycles: Research insights must remain relevant across 6-18 month buying journeys
• Account-Based Insights: Organizing research by account, not just user type, for enterprise sales alignment

3. Customer Jobs & Pain Map

4. Framework / Model: DesignOps & ResearchOps Maturity

Maturity Levels

Level 1: Ad-Hoc (Individual Practice)

• DesignOps: Designers use personal tools; no shared libraries; inconsistent processes; tribal knowledge
• ResearchOps: Researchers recruit individually; insights in personal folders; no standardized methods; siloed findings
• Indicators: High rework; slow onboarding; duplicated efforts; limited cross-team awareness

Level 2: Emerging (Team Standards)

• DesignOps: Team-level design libraries; basic Figma organization; informal critique rhythms; some documentation
• ResearchOps: Shared participant lists; team insight folder; basic repository (Google Drive); informal sharing
• Indicators: Team consistency improving; still varies across teams; limited discoverability

Level 3: Defined (Organizational Systems)

• DesignOps: Enterprise design system; centralized component library; formalized workflows; tool standardization; dedicated DesignOps role
• ResearchOps: Managed participant panel; insight repository platform (Dovetail); research practice guide; dedicated ResearchOps coordinator
• Indicators: Cross-team consistency; scalable processes; measurable efficiency gains; growing adoption

Level 4: Managed (Optimized & Integrated)

• DesignOps: Design system with contribution model; automated design QA; integrated design-dev workflow; capacity modeling; multi-product federation
• ResearchOps: Continuous research programs; democratized insight access; research analytics; ethics board; participant CRM with lifecycle management
• Indicators: Design/research as competitive advantage; high velocity; data-driven optimization; strategic influence

Level 5: Strategic (Innovation Engine)

• DesignOps: AI-assisted design workflow; predictive capacity planning; design system ROI quantified; cross-company design platform
• ResearchOps: Predictive insight engines; automated synthesis; real-time insight activation; research marketplace
• Indicators: Industry leadership; measurable business impact attribution; continuous innovation

Assessment Dimensions

DesignOps:

1. Design System Maturity (documentation, components, patterns, governance)
2. Tool Infrastructure (standardization, integration, automation)
3. Workflow Effectiveness (processes, reviews, handoffs)
4. Knowledge Management (documentation, searchability, onboarding)
5. Resource Optimization (capacity planning, allocation, utilization)

ResearchOps:

1. Recruitment Infrastructure (panel size, diversity, activation speed)
2. Insight Management (repository, tagging, searchability, synthesis quality)
3. Democratization (self-service access, training, research literacy)
4. Compliance & Ethics (policies, reviews, data governance)
5. Tool Ecosystem (research platforms, integration, automation)

5. Implementation Playbook

Phase 1: Foundation (Days 0-30)

Week 1: Assessment & Alignment

• Conduct DesignOps/ResearchOps maturity assessment across teams
• Interview designers, researchers, PMs, and engineers about pain points
• Audit current tools, processes, and repositories
• Define success metrics aligned to business objectives
• Secure executive sponsorship and initial budget
• Form cross-functional working group (design, research, product, eng)

Week 2: Quick Wins & Tooling

• Standardize on primary design tool (Figma recommended for collaboration)
• Set up basic design file organization structure
• Implement shared insight repository (start with Dovetail or Notion)
• Create intake forms for design requests and research studies
• Establish weekly DesignOps/ResearchOps office hours
• Document current-state workflows (as-is mapping)

Week 3: Design System & Research Standards Kickoff

• Audit existing UI components across products; identify duplication
• Establish design system working group with designers and frontend engineers
• Document 5-10 most-used components in Figma with specifications
• Create research practice guide covering basic methodologies
• Develop participant consent and NDA templates
• Set up basic participant tracking spreadsheet or Airtable

Week 4: Governance & Processes

• Define design system contribution model (centralized vs federated)
• Create design review process with defined stages and DRIs
• Establish research ethics review process and checklist
• Document design-to-engineering handoff workflow
• Create research synthesis template and tagging taxonomy
• Schedule recurring governance meetings (design system council, research review)

Phase 2: Scale & Adoption (Days 30-90)

Month 2: Infrastructure Build-Out

• Expand design system to 50+ components with usage guidelines
• Implement design token system for theming and consistency
• Build participant recruitment panel (target: 50-100 qualified contacts)
• Integrate research tools with product development workflow
• Create design file templates for common deliverables
• Establish design QA process and checklist

Month 2: Democratization Programs

• Train PMs and engineers on design system usage
• Launch "Research 101" training for non-researchers
• Create insight dashboards accessible to all product teams
• Implement design critique rotation schedule
• Establish research "show & tell" monthly ritual
• Build self-service research request portal

Month 3: Optimization & Measurement

• Track design system adoption metrics (component usage, contribution rate)
• Measure research velocity (time-to-recruit, study completion rate)
• Survey designers and researchers on process satisfaction
• Identify bottlenecks through value stream mapping
• Automate repetitive tasks (screener distribution, consent management)
• Create DesignOps/ResearchOps dashboard with key metrics

Month 3: Integration & Expansion

• Integrate design system with engineering component library (Storybook)
• Connect participant panel to CRM for account-based research
• Establish cross-product design system federation model
• Create research insight tagging aligned to product roadmap themes
• Build design capacity planning model
• Pilot AI-assisted tools (automated tagging, transcription)

6. Design & Engineering Guidance

Design System Technical Architecture

Component Library Structure:

• Atomic Design Hierarchy: Atoms (buttons, inputs) → Molecules (search bar) → Organisms (navigation) → Templates → Pages
• Multi-Platform Support: Web (React), Mobile (React Native/Flutter), Email templates
• Versioning Strategy: Semantic versioning for component library; changelog with migration guides
• Documentation Platform: Storybook for component playground; Zeroheight for usage guidelines

Design-Dev Workflow Integration:

1. Design Phase: Work in Figma using design system library; annotate with Redlines or Zeplin
2. Review Gate: Design review against system guidelines; accessibility audit (WCAG 2.1 AA)
3. Handoff: Figma Dev Mode or Zeplin for specs; design QA checklist completion
4. Development: Engineer implements using component library; references Storybook documentation
5. Design QA: Designer reviews in staging environment; logs discrepancies in Jira
6. Iteration: Bug fixes and refinements tracked; design system updates proposed if pattern emerges

Tool Stack Recommendations

Design Tools:

• Primary Design Tool: Figma (collaborative, cloud-based, dev-friendly)
• Prototyping: Figma for high-fidelity; Whimsical for workflows; ProtoPie for advanced interactions
• Handoff: Figma Dev Mode (native) or Zeplin (for legacy workflows)
• Design System Documentation: Zeroheight, Supernova, or custom-built in Storybook

Research Tools:

• Moderated Research: UserTesting, Lookback.io, or Zoom with recording
• Surveys: Qualtrics (enterprise), Typeform (lightweight), or Sprig (in-product)
• Analysis & Repository: Dovetail (insights), Aurelius (for synthesis), or Notion (budget option)
• Participant Management: Respondent.io, User Interviews, or custom Airtable/HubSpot integration
• Unmoderated Testing: UserTesting, Maze, or Lyssna (UsabilityHub)

Integration Requirements:

• Single sign-on (SSO) for enterprise tools
• API connections between research tools and product analytics (Amplitude, Mixpanel)
• Design file version control integrated with development Git workflow
• Insight repository searchable from Slack, product roadmap tools (Productboard)

7. Back-Office & Ops Integration

Internal Tool Design Operations

Admin Portal DesignOps:

• Extend design system to back-office tools (customer success dashboards, admin panels, reporting interfaces)
• Create specialized components for data-heavy interfaces (tables, filters, bulk actions)
• Establish faster iteration cycles for internal tools while maintaining brand consistency
• Consider lower fidelity acceptable for internal-only interfaces (prioritize function over polish)

Operational Efficiency Research:

• Include internal users (CS agents, support staff, ops teams) in research participant panels
• Conduct workflow studies on back-office tools to reduce operational friction
• Establish feedback loops from customer-facing teams to product/design
• Track operational metrics (ticket resolution time, data entry errors) as research outcomes

Cross-Functional Enablement

Sales & Marketing Alignment:

• Provide design assets (screenshots, demo videos, UI mockups) for sales enablement through centralized library
• Conduct research on sales objections and buying committee concerns; synthesize for product positioning
• Create branded templates for customer presentations using design system principles
• Enable marketing to access product UI assets while maintaining version control

Customer Success Integration:

• Share research insights on user pain points with CS teams for proactive outreach
• Use CS ticket data to inform research prioritization and design improvements
• Involve CS team in participant recruitment for post-sales research
• Create design templates for customer onboarding, training materials, and help documentation

Finance & Legal Considerations:

• Manage research incentive budgets and tax reporting (1099 thresholds)
• Ensure design tool contracts support user growth projections
• Maintain compliance with data privacy regulations in research operations
• Track design system ROI for budget justification (time saved, consistency gains)

8. Metrics That Matter

Measurement Cadence

• Weekly: Design throughput, research study progress, tool usage
• Monthly: Adoption metrics, democratization indicators, quality scores
• Quarterly: Business impact, ROI calculation, maturity assessment, strategy review

9. AI Considerations

AI-Augmented DesignOps

Design System Intelligence:

• Automated Component Suggestions: AI analyzes design files to suggest existing components instead of custom creation; flags inconsistencies
• Design QA Automation: AI-powered visual regression testing; accessibility issue detection; brand guideline compliance checking
• Smart Documentation: Auto-generate component usage guidelines from design files and code; maintain sync between Figma and Storybook
• Predictive Capacity Planning: ML models forecast design demand based on roadmap; optimize designer allocation

Implementation Approach:

• Pilot tools like Microsoft's Design Intelligence, Figma AI features, or custom GPT-4 Vision integrations
• Start with low-risk use cases (documentation, suggestion) before automated decision-making
• Maintain human oversight for brand-critical decisions
• Measure AI impact on velocity without sacrificing quality

AI-Powered ResearchOps

Insight Synthesis Automation:

• Automated Transcription & Tagging: AI transcription (Otter.ai, Dovetail AI) with auto-tagging to taxonomy; sentiment analysis
• Pattern Recognition: AI identifies themes across studies; surfaces contradictions; highlights strong signals
• Insight Summarization: GPT-4 generates research summaries, key findings, recommendations from raw data
• Participant Screening: AI-assisted screener analysis; qualification scoring; demographic gap identification

Continuous Research at Scale:

• Use AI chatbots for initial user interviews at scale; human researchers for depth
• Automated survey analysis with statistical significance testing
• AI-moderated unmoderated tests with follow-up question branching
• Real-time insight alerts when new data matches existing themes

Ethical Guardrails:

• Transparent disclosure when AI is involved in research process
• Human review of AI-generated insights before sharing to stakeholders
• Bias detection in AI recommendations (overrepresentation of vocal segments)
• Data privacy compliance when using third-party AI tools (SOC 2, GDPR)

Emerging Capabilities (12-24 months)

• Generative Design Exploration: AI generates design variations based on design system; designers curate and refine
• Predictive Research: AI recommends research questions based on product roadmap and past learnings
• Real-Time Insight Activation: AI monitors product usage and auto-surfaces relevant research insights to teams
• Cross-Study Meta-Analysis: AI synthesizes insights across years of research to answer strategic questions

10. Risk & Anti-Patterns

Top 5 Risks and Mitigations

1. Design System Becomes a Bottleneck

• Risk: Centralized design system team can't keep pace with product team needs; contribution process too rigid; innovation stifled
• Mitigation: Implement federated contribution model with clear guidelines; establish "fast-track" for product-specific components; create innovation tokens for experimentation outside system; measure cycle time and adjust governance

2. Insight Repository Becomes a Graveyard

• Risk: Research accumulated but not accessed; outdated insights treated as current; no curation or synthesis; search yields irrelevant results
• Mitigation: Assign insight curation responsibility; implement expiration dates and refresh cycles; create curated playlists for key topics; use AI to surface relevant insights contextually; measure usage and retire unused content

3. Tool Proliferation and Fragmentation

• Risk: Teams adopt different tools creating silos; integration nightmares; license waste; knowledge fragmentation; onboarding complexity
• Mitigation: Establish tool approval process with architecture review; negotiate enterprise agreements for standardization; build integration layer; sunset redundant tools with migration plans; maintain approved tool registry

4. Research Ethics and Compliance Failures

• Risk: Participant consent gaps; data privacy violations (GDPR, CCPA); incentive tax reporting errors; recording without permission; bias in recruitment
• Mitigation: Implement mandatory ethics training; create compliance checklist for every study; legal review of templates; participant data governance policy; diversity monitoring in panels; incident response plan

5. Ops Overhead Exceeds Value Creation

• Risk: DesignOps/ResearchOps processes become bureaucratic; excessive governance slows teams; process for process sake; teams work around systems
• Mitigation: Continuously measure process efficiency; implement "stop doing" reviews quarterly; automate repetitive tasks; maintain 80/20 rule (focus on high-value ops); gather team feedback and iterate rapidly

Anti-Patterns to Avoid

• "Build it and they will come": Don't create ops infrastructure without active adoption strategy and stakeholder buy-in
• Ops team isolation: ResearchOps/DesignOps must be embedded with practitioners, not separate service organizations
• Premature standardization: Don't enforce rigid standards before understanding team needs and workflow variations
• Insight hoarding: Avoid treating research as proprietary to research team; democratization is the goal
• Tool-first thinking: Don't select tools before defining processes and requirements; process before platforms
• Neglecting internal users: Back-office tools and internal stakeholder research deserve ops support too

11. Case Snapshot: Enterprise SaaS Platform Transformation

Company: MidMarket B2B SaaS (HR Tech), 200 employees, 8-person design team, 3 researchers

Challenge: The design team struggled with inconsistency across 4 product lines, slow handoffs to engineering (averaging 2 weeks), and duplicated research efforts. Insights were trapped in individual folders, designers rebuilt similar components repeatedly, and new hires took 6+ weeks to become productive. Engineering complained about incomplete specifications and accessibility gaps.

DesignOps Implementation (6 months):

• Months 1-2: Conducted component audit revealing 37 unique button variations across products; standardized on Figma; created core design system with 40 foundational components; established design review process with accessibility checklist
• Months 3-4: Built Storybook integration for engineering; implemented Figma libraries with automated sync; created design QA workflow with staging environment reviews; launched weekly design system office hours
• Months 5-6: Achieved 72% component reuse rate; reduced design-to-dev handoff to 3 days; decreased accessibility defects by 85%; new designer productivity in 2 weeks

ResearchOps Implementation (6 months):

• Months 1-2: Built participant panel of 120 HR professionals and IT admins through incentivized recruitment; implemented Dovetail for insight management; created research methodology guide
• Months 3-4: Launched "Research 101" training for PMs; established participant screening automation; tagged 18 months of historical research into repository; created insight newsletter
• Months 5-6: Reduced recruitment time from 3 weeks to 4 days; increased insight access from 15% to 68% of product team; completed 40% more studies with same team size

Business Impact:

• Time-to-market for new features reduced 35%
• Customer satisfaction (CSAT) with UI improved from 6.8 to 8.2/10
• Engineering rework reduced 60% due to better specs and design QA
• Design system ROI calculated at 12:1 (time saved vs investment)
• Research insights directly attributed to $2.3M in retained ARR through churn-reducing improvements

12. Checklist & Templates

DesignOps Implementation Checklist

Foundation:

• Conduct design maturity assessment across teams
• Audit current tool stack and identify redundancies
• Define design system vision, scope, and governance model
• Secure executive sponsorship and budget allocation
• Standardize on primary design tool (Figma recommended)
• Create design file organization structure and naming conventions
• Establish design request intake process
• Document current-state design workflow

Design System Build:

• Conduct UI component audit across all products
• Define design token architecture (colors, typography, spacing, elevation)
• Create 20+ foundational components with specifications
• Build Figma component library with variants and auto-layout
• Document usage guidelines and accessibility requirements (WCAG 2.1 AA)
• Establish contribution model (centralized/federated/hybrid)
• Integrate with engineering component library (Storybook/custom)
• Create design system roadmap aligned to product needs

Workflow & Process:

• Define design review stages with clear DRIs and exit criteria
• Create design-to-engineering handoff checklist and workflow
• Implement design QA process with staging environment reviews
• Establish design critique schedule and facilitation guidelines
• Document accessibility review process and tools
• Create onboarding curriculum for new designers
• Set up capacity planning and resource allocation model

Measurement:

• Define design system adoption metrics and targets
• Implement component usage tracking (Figma analytics or custom)
• Create design velocity dashboard (request intake to delivery)
• Establish design quality metrics (QA defects, accessibility compliance)
• Calculate design system ROI (time saved, consistency gains)

ResearchOps Implementation Checklist

Foundation:

• Assess current research maturity and pain points
• Define ResearchOps mission, scope, and success metrics
• Select and implement insight repository platform (Dovetail/Aurelius/Notion)
• Create research methodology guide covering key methods
• Develop participant consent templates (standard, NDA, recording)
• Establish ethics review process and compliance checklist
• Document research request intake workflow
• Set up research tool stack (moderated, surveys, analysis)

Participant Recruitment Infrastructure:

• Build participant database with 50-100 qualified contacts
• Create screening questionnaire templates by persona
• Implement incentive management process (payment, tax reporting)
• Define recruitment SLAs (time-to-recruit targets by study type)
• Establish participant CRM integration (HubSpot/Salesforce)
• Create diversity and inclusion guidelines for panel balance
• Set up automated screener distribution and scheduling

Insight Management:

• Define insight tagging taxonomy aligned to product themes
• Create research synthesis templates (study types: usability, discovery, validation)
• Migrate historical research into centralized repository
• Establish insight quality standards and peer review process
• Build searchable insight database with self-service access
• Create insight visualization dashboard for stakeholders
• Implement insight expiration and refresh policies

Democratization:

• Launch "Research 101" training for non-researchers
• Create self-service research toolkit (templates, how-to guides)
• Establish research socialization rituals (show & tell, newsletter)
• Enable cross-functional insight access (Slack integration, product tools)
• Define when to engage research team vs. self-service
• Measure insight usage and decision influence

Templates

1. Design System Contribution Proposal Template

# Component Proposal: [Component Name]

## Business Need
- What problem does this solve?
- Which products/features need this?
- User impact if not addressed:

## Design Exploration
- [Figma link to explorations]
- Variants considered: [list]
- Accessibility considerations: [WCAG compliance notes]

## Technical Requirements
- Responsive behavior:
- Interaction states: [default, hover, focus, active, disabled]
- Dependencies: [other components, tokens]

## Contribution Checklist
- [ ] Design specs complete with all states
- [ ] Accessibility audit passed (WCAG 2.1 AA)
- [ ] Engineering feasibility review completed
- [ ] Usage guidelines documented
- [ ] Design system council approval

## Rollout Plan
- Timeline:
- Communication strategy:
- Training needs:

2. Research Study Brief Template

# Research Study Brief: [Study Name]

## Background & Objectives
- Business question:
- Research objectives (3-5):
- Success criteria:

## Methodology
- Method: [usability testing, interviews, surveys, etc.]
- Participant criteria:
  - Roles: [e.g., IT admins, HR managers]
  - Company size: [e.g., 100-1000 employees]
  - Industry: [if relevant]
  - Exclusions:
- Sample size & rationale:

## Logistics
- Timeline: [recruitment, fielding, analysis, readout]
- Budget: [incentives, tools, other]
- DRI: [lead researcher]
- Stakeholders: [who needs insights]

## Ethics & Compliance
- [ ] Consent form prepared
- [ ] NDA required (if yes, template attached)
- [ ] Recording permissions obtained
- [ ] Data privacy review completed (GDPR/CCPA)
- [ ] Incentive tax reporting planned (if >$600/person)

## Output & Activation
- Deliverables: [report, presentation, insight cards]
- Readout audience:
- Insight repository tags:
- Decision dependencies:

3. Design Review Checklist Template

# Design Review Checklist

## Functional Requirements
- [ ] User flows complete for all paths (happy path, edge cases, errors)
- [ ] All user stories addressed
- [ ] Content strategy defined (copy, empty states, error messages)
- [ ] Data requirements specified

## Design System Compliance
- [ ] Uses design system components (or documents new component need)
- [ ] Design tokens applied correctly (colors, typography, spacing)
- [ ] Interaction patterns consistent with established guidelines
- [ ] Responsive design for all breakpoints (mobile, tablet, desktop)

## Accessibility (WCAG 2.1 AA)
- [ ] Color contrast ratios meet requirements (4.5:1 text, 3:1 UI)
- [ ] Keyboard navigation defined for all interactions
- [ ] Screen reader labels and ARIA attributes documented
- [ ] Focus states visible and logical
- [ ] No content solely conveyed by color

## Engineering Handoff
- [ ] Interactive prototype or detailed specs provided
- [ ] All states documented (default, hover, focus, active, disabled, loading, error)
- [ ] Edge cases and error scenarios designed
- [ ] Animation/transition specs included (if applicable)
- [ ] Asset export complete (icons, images at required resolutions)

## Stakeholder Sign-Off
- [ ] Product Manager approval
- [ ] Engineering feasibility review
- [ ] Legal/compliance review (if required)
- [ ] Accessibility specialist review

13. Call to Action

Next 5 Days: Establish Your Ops Foundation

Action 1: Assess Your Current State (Day 1-2) Conduct a rapid DesignOps and ResearchOps maturity assessment. Interview 5-8 designers, researchers, PMs, and engineers about their biggest operational pain points. Audit your current tools, processes, and knowledge management systems. Identify the top 3 bottlenecks preventing your team from scaling effectively. Document findings in a one-page brief with specific examples and quantified impact (time wasted, opportunities missed).

Action 2: Secure Quick Win Commitment (Day 3-4) Choose ONE high-impact, low-effort operational improvement to implement immediately. Options: (1) Centralize research insights into a shared Notion or Dovetail repository with basic tagging, (2) Create a Figma component library with your 10 most-used UI elements, or (3) Establish a participant recruitment tracker with 20-30 qualified contacts. Secure stakeholder buy-in, allocate 10-15 hours of focused time, and complete implementation within 2 weeks. Measure before/after impact.

Action 3: Build Your Ops Roadmap (Day 5) Based on your assessment and quick win, draft a 90-day DesignOps/ResearchOps roadmap. Define 3-5 key initiatives with clear owners, timelines, success metrics, and resource requirements. Prioritize based on pain severity and strategic value. Include both infrastructure builds (design system, research repository) and enablement programs (training, democratization). Present to leadership with business case showing projected efficiency gains and capacity unlocked. Request dedicated DesignOps/ResearchOps ownership—even at 25-50% allocation to start.

The Bottom Line: DesignOps and ResearchOps are not overhead—they are force multipliers. Every hour invested in operational excellence returns 5-10 hours in team productivity, insight velocity, and customer impact. Start small, measure relentlessly, and scale what works. Your design and research teams—and your customers—will thank you.