Discover What to Automate
How to identify high-impact automation opportunities before investing in tools.
1. Why “What to Automate” Comes Before “How”
Automation can transform productivity — but only when you start with the right problems.
Too many organisations jump straight to tools, bypassing the deeper question: what should we automate first?
A 2024 McKinsey Global Survey found that while 75% of organisations have piloted automation, fewer than half have scaled those efforts successfully. The reason isn’t technology — it’s clarity. Leaders automate tasks before understanding value, readiness, and interdependencies.
That’s why the most successful automation initiatives begin with strategic discovery: mapping your workflows, measuring friction, and ranking opportunities by their impact vs effort ratio.
2. The New Reality: AI Automation in 2025
AI has changed the game. Modern automation goes beyond simple rule-based scripts — it can read, write, summarise, extract, and decide. But that power also raises the stakes for clarity and governance.
According to Deloitte’s 2025 Intelligent Automation Report, organisations that defined automation strategy upfront achieved 40% higher ROI than those that jumped straight to implementation.
In other words, the discovery phase is the differentiator.
3. Signs You’re Ready to Automate
Before deciding what to automate, confirm if your business is ready. The following indicators suggest strong potential:
Manual repetition — Tasks performed frequently with predictable steps.
Fragmented tools — Teams copying data between platforms.
Bottlenecks — Work queues building up around approvals or data entry.
Error-prone processes — Mistakes that cost time or reputation.
Low employee satisfaction — Team frustration with tedious admin.
Data latency — Delays between insight and action.
If three or more apply, you’re likely sitting on untapped efficiency.
4. The 80/20 Rule of Automation
Not every task is worth automating. The goal is impactful efficiency, not perfection.
Think of automation opportunities in three categories:
| Priority | Characteristics | Examples |
|---|---|---|
| Tier 1 (High ROI) | Repetitive, time-consuming, low-risk | Report generation, invoice matching, lead qualification |
| Tier 2 (Moderate ROI) | Requires judgment, moderate complexity | Customer service triage, proposal drafts, document summaries |
| Tier 3 (Low ROI) | Rare or strategic tasks, creative-heavy | Strategy meetings, one-off analysis, human-sensitive conversations |
Start with Tier 1 — where measurable impact is clear and adoption is easy.
5. The 4-Step Framework: How to Identify What to Automate
This simple framework helps you pinpoint automation opportunities before buying tools.
Step 1 — Map your workflows
List every process within a department or project: from lead capture to delivery.
Note who’s involved, which tools they use, and what data moves between them.
Output: A visual map (e.g., using Miro or Lucidchart) showing dependencies and bottlenecks.
Step 2 — Measure friction
For each step, record:
Frequency per week/month
Average time taken
Error rate
Frustration score (1–5)
This exposes the tasks that consume the most time with the least strategic value.
Step 3 — Rank by ROI potential
Plot your findings on an impact vs effort matrix.
Tasks in the top-left quadrant (high impact, low effort) are your “quick wins”.
Formula:
ROI potential = (Time saved × Error reduction × Frequency) ÷ Complexity
Step 4 — Assess readiness
Consider:
Is data clean and accessible?
Are steps rule-based or judgment-based?
Do stakeholders support change?
Are there compliance constraints?
This step often saves weeks of false starts — and thousands in wasted spend.
6. The Common Pitfalls of Automation Planning
Avoid these classic traps seen across mid-market implementations:
Tool chasing: Adopting tech for novelty rather than need.
Under-scoping: Focusing on a single team without considering dependencies.
Over-engineering: Automating every edge case instead of focusing on 80% coverage.
Neglecting change management: Assuming staff will simply adopt new systems.
Lack of governance: No version control, monitoring, or human oversight.
According to PwC’s 2024 AI Adoption Index, 43% of automation projects fail due to “poor process understanding before implementation.”
The fix? Spend as much time mapping the process as selecting the tool.
7. Case Example: From Manual Ops to Intelligent Workflow
Company: UK-based professional services firm (45 employees)
Problem: Repetitive reporting, inconsistent data entry, and low team morale.
Approach:
Conducted workflow analysis across departments.
Identified 27 repetitive tasks; prioritised top 6 for automation.
Implemented lightweight integrations using Make (Integromat) and Google Workspace APIs.
Result:12+ hours saved weekly
37% reduction in manual errors
ROI realised within 6 weeks
Staff redeployed to higher-value work
The insight: success came from process clarity, not complexity.
8. How to Involve Your Team
Automation succeeds when people feel ownership.
Involve employees early by asking three questions:
Which parts of your work feel repetitive or frustrating?
What information do you often wait on?
What would you love to stop doing manually?
This not only uncovers prime candidates for automation but also builds emotional buy-in — essential for adoption success.
9. Measuring What Matters
Once you’ve identified targets, define measurable success criteria before starting.
Track:
Time saved per task
Error reduction
Turnaround time
Employee satisfaction
Cost per transaction
Compliance accuracy
These KPIs ensure automation remains a performance tool, not just a technology project.
10. Tools vs Systems Thinking
Automation tools are enablers — not solutions in themselves.
The difference between a tactical and strategic automation approach lies in systems thinking: connecting processes, data, and people into one coherent flow.
Ask:
Does this automation integrate with upstream/downstream processes?
Is there visibility across functions?
How will we maintain it?
Who owns it after deployment?
Without these answers, even the smartest tool becomes tomorrow’s abandoned workflow.
11. Linking Discovery to ROI
Every automation project should link directly to measurable outcomes.
Examples include:
| Goal | Metric | Automation Example |
|---|---|---|
| Operational efficiency | Time per task | Auto-populate reports from CRM data |
| Revenue growth | Lead conversion rate | Automated lead scoring |
| Risk reduction | Error rate | Compliance checks or policy reminders |
| Employee engagement | eNPS | Reduce admin load on teams |
Tie each automation to one KPI your leadership team already tracks — this aligns technical wins with business value.
12. When (and When Not) to Automate
You should not automate:
Tasks requiring empathy, nuanced judgment, or deep creativity
Processes that change weekly or lack stable inputs
Systems with poor data quality or inconsistent structure
Start small. Automate one clear, rule-based process, then iterate.
13. Governance and Responsible Automation
Responsible automation is about transparency, control, and accountability.
Each implementation should document:
Who owns each automation
What data it touches
How decisions are made or overridden
When it was last reviewed
How errors are handled
Research from MIT Sloan Management Review (2024) highlights that organisations with strong AI governance frameworks experience 35% fewer compliance issues during automation adoption.
Governance isn’t bureaucracy — it’s business continuity.
14. Quick Wins: High-Impact Automations for SMEs
| Department | Task | Tools (example stack) | Value |
|---|---|---|---|
| Marketing | Lead capture & scoring | HubSpot + Zapier | Improved lead response |
| Finance | Invoice data extraction | Google Drive + Make | Reduced admin load |
| Operations | Meeting notes → tasks | Notion + AI summariser | Faster follow-up |
| HR | Candidate screening | Airtable + GPT agent | Time saved in filtering |
| Customer Support | Email triage | Gmail + AI classifier | Faster replies |
| Admin | File naming & routing | Drive API + Script | Reduced clutter |
15. Turning Insight into Action
Once you’ve mapped and ranked your automation opportunities, your next step is validation — testing feasibility and business alignment.
At Brand Automation AI, our process follows three stages:
Workflow Analysis — Map and measure.
Tool Selection & Strategy — Choose wisely.
Adoption Guidance — Implement responsibly.
Each stage is modular — so you can start where you are.
16. Summary: The Data Behind Discovery
Automation success starts not with code, but with clarity.
By understanding where your business spends time, what causes friction, and how processes interlink, you set the foundation for scalable, trustworthy AI-driven systems.
Discover what to automate — and the technology will take care of itself.
17. References
McKinsey & Co. (2024). The State of AI in 2024: From Pilots to Profit.
Deloitte (2025). Intelligent Automation Benchmark Report.
PwC (2024). AI Adoption Index: Lessons from Implementation Failures.
MIT Sloan Management Review (2024). Building Responsible AI Governance.
Gartner (2025). AI Transformation Playbook: From Projects to Platforms.