AI Implementation Roadmap
6 concepts / ~38 min / Advanced
From discovery to scale: audit AI readiness, identify high-ROI use cases, design safe implementations, and measure impact. Practical AI, not hype.
What This Playbook Covers
- ✓Audit your organization's AI readiness across processes, data, and team capability
- ✓Map potential AI use cases and score them by impact, feasibility, and risk
- ✓Design a pilot implementation with clear success metrics and guardrails
- ✓Build QA and feedback loops so AI improves over time instead of degrading
- ✓Create a roadmap to scale AI across the organization without losing control
Process Automation
Operations
💡 The Concept
Process automation replaces manual, repetitive tasks with technology-driven workflows. Every hour spent on automatable tasks costs 3-5x more than the automation itself over 12 months. Companies that automate key processes see 30-50% efficiency gains within the first year. McKinsey estimates 60% of all occupations have at least 30% automatable activities — the question isn't IF you'll automate, but WHEN.
⚠️ The Trap
The biggest mistake is automating bad processes. If your process is flawed, automating it just means you produce bad outcomes faster. Also, trying to automate everything at once leads to 'automation fatigue' — teams lose trust when automated systems produce errors, and the cleanup work exceeds the original manual effort.
🎯 The Action
Start with a 'Process Audit': list every recurring task your team does weekly. Score each on time spent (hours/week), error rate, and automation feasibility. Automate the top 3 tasks that score highest on all three dimensions. Use a framework like: if it takes > 2 hours/week AND has < 5% decision-making involved, automate it. Measure ROI after 30 days.
Knowledge Check
Challenge coming soon for this concept.
Use the playbook as context, then scope the live work.
If this playbook maps directly to an operating bottleneck, KnowMBA can help turn it into a diagnostic, sprint, or managed implementation path.