Designing for AI Readiness: A Framework for Product Leaders
Artificial intelligence is no longer a question of “if” but “how.” As design and product leaders, we’re constantly asked: Should this feature be AI-powered? Is it ready? How do we know when to invest?
At Sense, where we reimagined the recruiter experience with AI & Automation, these questions came up daily. Instead of treating AI as a magic dust to sprinkle over features, we built a framework to evaluate readiness — for both the task at hand and the phase of AI maturity.
1. The Task Lens: Is This Work AI-Suitable?
Not every workflow benefits from AI. To decide, we classify tasks into three categories:
Repetitive / Rule-Based → Automate
If the work is repeatable and deterministic, it’s a strong candidate for automation.
Recruiting example: scheduling interviews, sending reminders, updating job postings.
Analytical / Pattern-Based → Augment with AI
If the work requires identifying patterns, summarizing information, or making recommendations, AI can provide leverage — but human oversight is still needed.
Recruiting example: résumé parsing, candidate-job matching, sentiment analysis of interviews.
Creative / Strategic → Support, Don’t Replace
If the work requires human empathy, creativity, or high-stakes judgment, AI should act as a copilot, not a driver.
Recruiting example: crafting personalized outreach, negotiating offers, building client relationships.
👉 The design principle: AI should remove friction from low-value work, support humans in analytical tasks, and inspire creativity without taking agency away.
2. The Maturity Lens: What Phase of AI Belongs Here?
Beyond the task, we also consider the phase of AI maturity we want to design for. In recruiting, we framed it in three stages:
Phase 1 – Intelligent Automation
Goal: Reduce busy work.
Automates repetitive, rule-based workflows (scheduling, surveys, reminders).
Metrics: recruiter hours saved, candidate engagement touchpoints automated.
Design role: make automation flexible, visible, and easy to adjust.
Phase 2 – Generative Copilots
Goal: Accelerate productivity and personalization.
AI generates content or suggestions recruiters can edit (JDs, screening questions, outreach campaigns).
Metrics: time saved per task, increase in personalized engagement, adoption/usage of AI features.
Design role: ensure transparency (“AI suggested this”), inline editing, and fast iteration.
Phase 3 – Autonomous Agents
Goal: Transform staffing economics.
Agents orchestrate end-to-end flows (candidate matching, outreach, screening, submissions).
Metrics: time-to-hire, cost-per-hire, predictive sourcing accuracy, client satisfaction.
Design role: shift UX from execution → orchestration. Recruiters oversee flows, review scorecards, and focus on relationships.
👉 The design principle: earlier phases build trust and adoption, laying the foundation for autonomous systems. You can’t jump to Phase 3 without delivering wins in Phase 1 and 2.
3. Putting It Together: The AI Readiness Framework
When evaluating a feature for AI:
Map the Task – Is it repetitive, analytical, or strategic?
Choose the AI Phase – Should it be automated, copiloted, or agentic?
Design for Trust – Always give users transparency, control, and feedback loops.
Why This Matters for Design Leaders
AI is not just a technical capability. It’s a trust capability. Users adopt AI when:
It saves them real time.
It respects their agency.
It scales with their workflows.