Quest Diagnostics Employee Skill Gap Closure:
AI-Enabled Kaizen Integration

Repeatable AI leverages the Harada Method – a goal-setting and self-reliance framework – reimagined in digital form ("HaradaFlow"). The Harada Method, developed by Takashi Harada, is known for mapping out ambitious goals and the skills, habits, and actions needed to achieve them.

In today's fast-paced and competitive world, achieving sustainable growth and high performance requires more than just setting goals. It requires a systematic approach that not only clarifies objectives but also refines skills, improves processes, and fosters continuous improvement. The Harada Method is an effective personal development system that emphasizes self-reliance, skill-building, and disciplined action. When combined with frameworks like Kaizen, Lean Thinking, and SMART goals, the Harada Method can be leveraged to form a powerful, integrated approach for comprehensive growth and achievement.

Traditionally, practitioners create an 8x8 matrix of personal goals and tasks. Repeatable AI expands this into role-specific "deliverables matrices" for Quest. For each key role, an AI-generated Harada-style matrix lays out the core responsibilities (columns) and tangible deliverables (rows) of that role. In parallel, a performance matrix maps the top KPIs for the role against "problems to be solved" under each KPI – complete with the type of expert or strategy needed for each problem.

This two-matrix system instantly highlights skill and performance gaps. For example, if a Continuous Improvement Specialist role has a deliverable "facilitate Kaizen event report-out" and the individual struggles with that deliverable, the matrix will pinpoint it and even suggest an expert (or AI resource) that can help. HaradaFlow provides a data-driven, persistent skill diagnostic, ensuring that no competency gap stays hidden.

By asking each employee (and their manager) to validate and adjust these AI-generated matrices, Quest creates a shared, concrete understanding of the skills and outputs required in every position. This approach turns abstract competencies into a clear development roadmap, linking personal growth to organizational goals.

A core part of Repeatable AI is an AI "deliverable evaluator" that can review work products and give instant expert feedback. In practice, this means when a team member produces a Lean deliverable – whether it's a value stream map, an A3 report, a standard work document, or a new process SOP – an AI agent analyzes it against best-practice patterns and Quest's knowledge base.

The AI checks for completeness, clarity, and alignment with Lean principles, offering suggestions much like a seasoned coach would. For instance, during a Kaizen event, the team might draft a fishbone diagram for root-cause analysis. The AI evaluator could immediately point out if one cause category seems under-explored or if data doesn't support a listed cause, prompting the team to investigate further on the spot.

This real-time deliverable-centered feedback closes the Check-Act loop within minutes instead of days or weeks. Quest has already observed the power of such rapid feedback: in a pilot, AI analysis of five years of past Kaizen reports revealed a pattern of failed initiatives that humans hadn't identified for three years. By catching issues early, the AI helps the team "get better at solving problems, not just solve one problem" – echoing the Toyota Kata ethos.

To truly accelerate Kaizen cycles, Quest will deploy AI voice agents as on-demand coaches and participants in improvement events. These intelligent assistants can listen to team discussions, answer questions, and even role-play scenarios.

For example, during a week-long Kaizen blitz, a voice-activated AI agent (available via team headsets or a conference speaker) can be asked, "What was our average specimen throughput last quarter?" and instantly retrieve that metric, saving time digging through reports. It can proactively chime in with data: "Yesterday's cycle time was 10% above target, related to machine downtime at 3pm." This keeps teams armed with facts in real time.

Moreover, AI voice role-play adds a new dimension to training: team members can practice a difficult conversation or a report-out presentation with the AI acting as a skeptical executive or a concerned client. This kind of simulation, already used in sales training, builds confidence and competence by immersive learning.

In effect, the AI voice agent is like having a veteran Lean sensei and a data analyst in the room 24/7. It provides just-in-time knowledge and coaching, dramatically compressing learning cycles.

Repeatable AI's integration leads to an evolved continuous improvement cycle often dubbed "AI-Kaizen". In addition to the classic Plan-Do-Check-Act, Quest's teams will leverage a cycle of Plan – Do – Check – Act – Learn – Predict – Optimize.

Concretely, this means after implementing an improvement (Act), the AI immediately helps the team Learn by analyzing outcomes (e.g., did error rates drop?). It then Predicts future performance or risks using machine learning (e.g., projecting that "if we continue this new process, turnaround time will drop by another 20% next quarter"). Finally, it suggests how to Optimize the solution further or how to standardize it.

This continuous loop is largely automated in terms of data crunching, allowing the human team to focus on creative problem-solving and implementation. The result is a self-reinforcing system: every Kaizen event not only fixes a local issue but also trains the AI to provide better insights next time and trains the people through immediate feedback.

Meta-improvement capability (improving the process of improvement itself) is achieved. As one Quest supervisor remarked during the initial trials, "It's like having a consultant who never sleeps" – the AI monitors processes at all hours, ensuring opportunities for improvement are flagged even in off shifts.