Disruption Analysis: How We Identified High-Impact AI Opportunities¶
Last Updated: January 26, 2026
Analysis Period: February 2024 - September 2025
TL;DR¶
In February 2024, Seer conducted a comprehensive disruption analysis of all marketing workflows to identify which tasks AI could transform. We scored 180+ workflows using a 4-factor framework, validated estimates with division leaders, and selected "Horizon 1" targets for agentic automation.
Result: 85+ hours/month time savings potential across all divisions.
The 4-Factor Framework¶
Overview¶
Every workflow was evaluated on 4 dimensions that determine AI suitability. Each factor scored 1-5 (max 20 points total):
| Factor | Key Question | High Score Indicators | AI Capability |
|---|---|---|---|
| 📊 Data-Driven | Does this task rely on data analysis? | Reporting, performance analysis, trend identification | AI excels at pattern recognition and data synthesis |
| 🔁 Repetitive | Is it structured and follows the same steps? | Email templates, standardized processes, form filling | AI excels at automating consistent workflows |
| 🔮 Prediction-Based | Does it involve forecasting or pattern recognition? | Lead scoring, churn prediction, budget forecasting | AI excels at probabilistic modeling |
| ✨ Generative | Does it create content, images, or code? | Blog posts, ad copy, product descriptions | AI excels at content generation |
Scoring Examples¶
High Disruptability (Score: 19/20)
Task: Content Audit
- Data-Driven: 5 (heavy analytics from BigQuery, DataForSEO)
- Repetitive: 5 (same structure each time)
- Prediction: 4 (CTR forecasting, keyword opportunities)
- Generative: 5 (audit report, recommendations)
= 19/20 → Prime candidate for automation
Low Disruptability (Score: 10/20)
Task: Client Onboarding
- Data-Driven: 2 (mostly relationship building)
- Repetitive: 4 (some standard steps)
- Prediction: 1 (minimal forecasting)
- Generative: 3 (some documentation)
= 10/20 → Better suited for human handling
The Full Journey (Feb 2024 - Jan 2026)¶
Phase 1: Data Collection (Feb-Mar 2024)¶
What we did:
- Extracted all deliverables and workflows from Wrike (our PM tool with time tracking)
- Captured actual hours spent on each task type (2024 YTD data)
- Cataloged existing templates, examples, and practitioner feedback
Data source: Wrike time tracking data + division interviews
Output: Disruption Analysis CSV with 180+ workflows scored
Phase 2: Scoring & Prioritization (Apr-May 2024)¶
What we did:
- Scored each workflow on 4-factor framework (1-5 per factor)
- Overlaid business impact metrics:
- Revenue generated (2023-2024)
- Frequency (# of times sold)
- Client feedback & pain points
- Generated "Disruptability Priority" ranking
Formula:
Disruptability Score = (Data-Driven + Repetitive + Prediction + Generative) / 4
Priority Rank = Disruptability Score × Business Impact Weight
Output: Ranked list of 180+ workflows by automation potential
Phase 3: Division Leader Validation (Sept 2025)¶
What we did:
- Presented top-ranked workflows to division leaders
- Leaders added "thumb on scale" for strategic priorities:
- High-revenue deliverables
- High-frequency pain points
- Client-requested capabilities
- Validated time estimates based on practitioner experience
- Created Division Review Booklets with:
- User stories (practitioner workflow)
- Sales stories (client value proposition)
- Example deliverables (templates, samples)
- Success metrics (KPIs, timeframes)
Key Review Booklets:
Output: 9 SEO workflows, 10 Analytics workflows, 8 Creative workflows, 8 CS workflows approved for Horizon 1
Phase 4: Horizon 1 Selection (Oct 2025)¶
Selection Criteria:
| Criterion | Why It Matters |
|---|---|
| Immediate impact | High frequency, clear ROI, practitioner buy-in |
| Feasibility | Available data, existing examples, known patterns |
| Strategic value | Client-facing, revenue-driving, competitive advantage |
Horizon 1 Definition: The first workflows to build as agentic automation (production-ready by Q1 2026).
Total Selected: 35 workflows across 4 divisions
Phase 5: Implementation (Nov 2025 - Ongoing)¶
Implementation Paths:
- Seer Signals Agents (Early Testing)
- Quick prototypes for select clients
- Validated approach, identified gaps
-
Limited by platform constraints
-
Seer Agent Engine (This Project - Production)
- Production-grade plugin system
- MCP integrations (BigQuery, DataForSEO, Wrike)
- Command-based workflows with skill-driven behavior
- Full test coverage, version control
Status (as of Jan 2026):
- ✅ SEO Division: 5 workflows in production
- ✅ Analytics Division: 4 workflows in production
- ✅ Paid Media Division: 2 workflows in production
- ✅ Operations Division: 1 workflow in production
- 🚧 Creative Division: In development
Time Savings Data¶
IMPORTANT DISCLAIMERS¶
Read Before Using These Estimates
- Time estimates are TARGETS based on division leader validation (Sept 2025)
- "Agent Time" = Strategic review phase only (final edits, QA, client customization)
- Full workflow includes: Data collection + analysis (automated) + review (human)
- Actual savings vary by:
- Client complexity (data quality, scope)
- Practitioner experience (learning curve)
- Data availability (API access, historical data)
- These are Horizon 1 baseline estimates subject to refinement with real usage data
- Not validated with actual production usage yet - these are leader-validated targets
SEO Division¶
Source: SEO Review Booklet
| Workflow | Manual (Baseline) | Agent (Review Only) | Time Savings | Priority | Source |
|---|---|---|---|---|---|
| Content Audit | 15-20 hrs/month | 1-2 hrs review/month | 13-18 hrs/month | 9.2 | Booklet §1 |
| Competitive Analysis | 10-15 hours | 2-3 hrs review | 7-13 hours | 8.6 | Booklet §2 |
| Search Landscape | 8-12 hours | 2-3 hrs review | 5-10 hours | 8.2 | Booklet §4 |
| Keyword Mapping | 6-10 hours | 1-2 hrs review | 4-9 hours | 8.0 | Booklet §5 |
| SEO Monthly Reports | 4-6 hours | 30 minutes | 3.5-5.5 hours | 8.0 | Booklet §6 |
| Quick Wins Audit | 2 weeks | 2 hours | ~78 hours | 8.5 | Booklet §7 |
| Technical SEO Audit | 20-30 hours | 4 hours | 16-26 hours | 8.0 | Booklet §8 |
| Content Gap Analysis | 15-20 hrs/quarter | 2 hours | 13-18 hrs/quarter | 7.5 | Booklet §9 |
Total SEO Savings: 65-110 hours/month (varies by client mix)
Analytics Division¶
Source: Analytics Review Booklet
| Workflow | Manual (Baseline) | Agent (Review Only) | Time Savings | Priority | Source |
|---|---|---|---|---|---|
| Analytics Strategy | 50 hours | 8 hours | 42 hours | 11 | Booklet - Analytics Compass |
| Funnel Analysis | 8-10 hours | 1 hour | 7-9 hours | — | Booklet - Funnel Analysis |
| Event Monitoring | 2-4 hrs/client/month | 15 min/client/month | 1.75-3.75 hrs/client | — | Booklet - Event Tracking Health |
| Reporting Buddy | 8 hours | 30 minutes | 7.5 hours | — | Booklet - Reporting Buddy |
| Ad Hoc Analysis | 4 hours | 45 minutes | 3.25 hours | — | Booklet - Ad Hoc Performance Analysis |
Total Analytics Savings: 61-65 hours per major project + ongoing monthly savings
Creative Division¶
Source: Creative Review Booklet
| Workflow | Manual (Baseline) | Agent (Review Only) | Time Savings | Priority | Source |
|---|---|---|---|---|---|
| SEO Content Creation | 10 hours/piece | 2 hours/piece | 8 hours/piece | 11 | Booklet - SCAI |
| Brand Content | 20 hours/campaign | 4 hours/campaign | 16 hours/campaign | 9 | Booklet - Brand Content |
| Asset Production | 15 hours/campaign | 2 hours/campaign | 13 hours/campaign | 9 | Booklet - Paid Media Assets |
| Creative Playbook | 25 hours | 6 hours | 19 hours | 25 | Booklet - Creative Media Playbook |
| UX/UI Audit | 50 hours | 10 hours | 40 hours | 26 | Booklet - UX/UI Audit |
| Audience Research | 80 hours | 12 hours | 68 hours | 38 | Booklet - Foundational Audience Analysis |
Total Creative Savings: Varies widely by engagement type (10-68 hours per project)
Client Services Division¶
Source: CS Review Booklet
| Workflow | Manual (Baseline) | Agent (Review Only) | Time Savings | Priority | Source |
|---|---|---|---|---|---|
| Deliverable Management | 12 hours/client/month | 1 hour/month | 11 hours/month | — | Booklet - Deliverable Management |
| Client Health Monitoring | 20 hours/month | 3 hours/month | 17 hours/month | — | Booklet - Health Monitoring |
| Burn Reporting | 1 hour/client/month | 15 min/month | 45 min/month | — | Booklet - Burn Reports |
| Status Updates | 3-6 hours/client/month | 1 hour/month | 2-5 hours/month | — | Booklet - Status Sheets |
| QBR Preparation | 12 hours/client/quarter | 2 hours/quarter | 10 hours/quarter | — | Booklet - QBR |
Total CS Savings: 30-44 hours/client/month (operational efficiency)
Advanced Framework: AI Exposure Levels (E0-E9)¶
The AI Readiness Workshop uses a more granular AI Exposure framework based on the same 4-factor principles.
Understanding E0-E9¶
The 4-factor framework tells you WHY a task is disruptable.
The E0-E9 framework tells you WHAT TYPE OF AI can help and HOW MUCH time you'll save.
Framework Mapping¶
| E-Score | Name | Time Savings | Primary 4-Factor Alignment | Use Cases | Tool Examples |
|---|---|---|---|---|---|
| E0 | Manual Only | 0% | None (human judgment required) | In-person meetings, relationship building | — |
| E1 | AI Writing | 40% | Generative (high) + Repetitive (medium) | Blog posts, email drafts, social content | ChatGPT, Claude, Jasper |
| E2 | AI-Enhanced Tools | 50% | Data-Driven (high) + Repetitive (high) | SEMrush AI, HubSpot AI, GA Intelligence | SEMrush, Ahrefs, GA4 |
| E7 | AI Analysis | 30% | Data-Driven (high) + Prediction (high) | Lead scoring, trend analysis, forecasting | Tableau Pulse, Looker AI |
| E9 | AI Automation | 60% | Repetitive (very high) + Prediction (medium) | Auto-scheduling, triggered emails | Zapier, Make, n8n |
How Disruptability Score Maps to E-Score¶
From code analysis (stage2-disruption-analysis.html):
Disruptability Score = (Data-Driven + Repetitive + Prediction + Generative) / 4
Range: 1-10
E-Score Assignment Logic:
- E9 (60%): Disruptability ≥ 8.0 AND Repetitive ≥ 9
- E7 (30%): Disruptability ≥ 7.0 AND (Data-Driven ≥ 8 OR Prediction ≥ 8)
- E2 (50%): Disruptability ≥ 6.0 AND (Data-Driven ≥ 7 OR Repetitive ≥ 7)
- E1 (40%): Disruptability ≥ 5.0 AND Generative ≥ 7
- E0 (0%): Disruptability < 5.0
Example Task Mapping¶
Task: Monthly Sales Performance Reporting
- Data-Driven: 10, Repetitive: 9, Prediction: 8, Generative: 4
- Disruptability: (10+9+8+4)/4 = 7.75
- Assigned E-Score: E2 (AI-Enhanced Tools, 50% time savings)
- Recommended Tools: Looker Studio AI, Tableau Pulse, Power BI Copilot
Task: Blog & Thought Leadership Content
- Data-Driven: 5, Repetitive: 7, Prediction: 5, Generative: 10
- Disruptability: (5+7+5+10)/4 = 6.75
- Assigned E-Score: E1 (AI Writing, 40% time savings)
- Recommended Tools: ChatGPT, Claude, Jasper, Copy.ai
Interactive Tool: AI Readiness Workshop¶
Launch the tool: https://ai-readiness-workshop.jsdemo.workers.dev/
The workshop guides you through three stages:
Stage 1: AI Readiness Assessment (~10 min)¶
Evaluate organizational preparedness across 5 dimensions:
- Strategy & Vision
- Data Infrastructure
- Technology Stack
- Culture & Skills
- Governance & Ethics
Output: Radar chart with readiness score (0-100) and maturity level
Stage 2: AI Policy Builder (~15 min)¶
Create governance guardrails through 13-question wizard:
- Approved AI tools and use cases
- Data handling requirements
- Human oversight rules
- Quality review processes
Output: Complete, editable policy document with industry-specific language
Stage 3: AI Disruption Analysis (~20 min)¶
Identify high-impact automation opportunities:
- Select industry and marketing function(s)
- Review pre-populated task templates (8 per function)
- Adjust hours spent and AI factors per task
- View prioritized opportunities with exposure scoring
Output: Priority matrix, task-by-task exposure scores, 90-day pilot roadmap
How to Use This Data¶
For Practitioners¶
Expectations:
- ✅ First workflow may take longer (learning curve)
- ✅ Complex clients may exceed baseline times
- ✅ Simpler clients may finish faster
- ⚠️ Review time ≠ total time (data collection is automated but still takes time)
Tips:
- Start with simpler clients to build familiarity
- Track your actual time vs estimates (help us refine!)
- Focus on strategic value-add during review phase
For Leaders¶
Use this data for:
| Use Case | How to Use |
|---|---|
| Capacity planning | "How many audits can a team handle with AI assistance?" |
| ROI projection | "What's the value of saved practitioner hours?" (Hourly rate × hours saved) |
| Prioritization | "Which workflows to automate first?" (Use Priority column) |
| Hiring decisions | "Can we defer hiring if we automate X workflows?" |
| Client pricing | "Can we offer lower pricing due to efficiency gains?" |
Important: These are targets, not guarantees. Real savings emerge over 3-6 months as team adapts.
For Clients¶
What these estimates mean for you:
- Timeline variability: Your specific timeline depends on:
- Data availability and quality
- Scope complexity
-
Review/approval cycles
-
Quality expectation: Agent-assisted work maintains or exceeds manual quality standards because:
- Seer methodology baked into skills
- More time for strategic thinking (not data collection)
-
Consistent application of best practices
-
Cost structure: Time savings may translate to:
- Lower project costs
- More deliverables within same budget
- Faster turnaround times
Questions?¶
- FAQ: Frequently Asked Questions
- Contact: Reach out to your division leader
- Interactive Tool: AI Readiness Workshop
- Source Data: Disruption Analysis CSV (internal)
Last Updated: January 26, 2026
Methodology Source: Marketing AI Institute (Paul Roetzer) - 4-Factor Framework & E0-E9 Exposure Levels