Define, Measure, Analyze, Improve, Control — automated at the data layer, accountable at the human layer. Run projects in weeks, not quarters, without giving up rigor or audit trail.
For each phase: what the agent does, what the human still does, and the calendar time difference we consistently see in the field.
10–15 days → 1–2 days
Agent does
Sweep complaint logs, NPS comments, escalation tickets, and SLA breach data. Surface candidate projects ranked by financial impact, customer impact, and feasibility. Draft a SIPOC and a problem statement from the evidence.
Human still owns
Charter the project, secure sponsor sign-off, ratify the problem statement and CTQ tree, set the win condition.
20–40 days → 2–5 days
Agent does
Pull event logs from ERP, CRM, core banking, MES, or whatever system holds the truth. Build the baseline cycle-time distribution, defect rate, Cp/Cpk, and DPMO. Run Gauge R&R on the measurement system itself. Output a baseline pack a master black belt would accept.
Human still owns
Validate the measurement system, approve the baseline, sign off on data quality.
15–30 days → 2–4 days
Agent does
Run hypothesis tests at scale (t-tests, ANOVA, chi-square, regression) across every plausible factor. Build a fishbone from observed data, not whiteboard speculation. Rank root causes by p-value and effect size. Flag interaction effects humans typically miss.
Human still owns
Choose which hypotheses are worth pursuing, judge the credibility of the analysis, interpret the why.
30–60 days → 2–4 weeks
Agent does
Simulate candidate interventions against the historical event log. Project sigma uplift and dollar impact for each. Generate the pilot test plan, sample size, and stop conditions. Track the pilot in flight and pause it if a guardrail metric breaks.
Human still owns
Approve the intervention. Run change management. Negotiate with the line, the union, the regulator. Own the outcome.
Forever (manual) → Forever (continuous)
Agent does
Run SPC charts in real time. Detect Western Electric and Nelson rule violations. Auto-generate a CAPA when control limits breach. Maintain a complete audit trail with input data, model version, and reasoning for every decision.
Human still owns
Decide what to do when the agent flags a special cause. Sponsor the corrective action. Sign the audit log.
Statistical credibility, audit traceability, and human accountability are non-negotiable — especially in regulated industries.
Agents use established methods — t-tests, ANOVA, regression, time-series decomposition. The math is auditable, the same as a black belt would do by hand.
Agents propose. Humans dispose. Define, Measure, Analyze, and Improve all require explicit sign-off before the next phase begins.
Every agent decision logs the input data hash, model version, prompt, and reasoning. Replayable for SOX, GxP, FFIEC, ISO 9001, and customer audits.
Agents have their own SPC. If the agent's recommendation accuracy or processing time drifts, the platform flags it before it affects a project.
DMAIC is the five-phase improvement framework at the core of Six Sigma — Define, Measure, Analyze, Improve, Control. AI agents augment each phase by handling the data-heavy work: pulling event logs, building baselines, running hypothesis tests, simulating interventions, and monitoring control charts. Humans keep the judgment, the change management, and the accountability.
Across our engagements, end-to-end project cycle time drops from 90–180 days to 30–60 days. The biggest compressions are in Measure (20–40 days down to 2–5) and Analyze (15–30 days down to 2–4). Improve takes about the same calendar time because change management is the bottleneck. Control becomes continuous instead of periodic.
If implemented carelessly, yes. PipeIQ's framework runs only established statistical methods — t-tests, ANOVA, chi-square, regression, time-series tests — with documented assumption checking. Every test produces the same output a trained black belt would: p-value, effect size, confidence interval, and a written assumption-validity note. The output is auditable line by line.
Three guardrails: (1) effect size has to clear a practical-significance threshold, not just statistical significance — agents won't flag a cause that's real but tiny; (2) the agent runs a holdout validation against an unseen slice of the event log before recommending; (3) every root cause goes to a human for review before it enters the Improve phase. Spurious correlations get filtered before they reach the steering committee.
Read access to the event-source systems (ERP, CRM, MES, core banking, ticketing, etc.) and a target data store the agents can write to — typically a Lakehouse, but we also work with existing data warehouses. The first project usually goes live in 6–10 weeks. We don't require new tooling for the statistical work; agents output to Minitab, JMP, or Power BI if your team already uses those.
Yes, and this is often the biggest unlock. Manual control charts get reviewed weekly or monthly. Agent-monitored charts run continuously and flag the moment a Nelson rule fires. We've seen drift detected in hours that would have taken a month to surface manually, and intermittent special-cause patterns (only happens on Thursday third shift) that human review almost never catches.
The broader methodology view: how agents fit into Lean and Six Sigma practice.
DMAIC in regulated FS: AML, KYC, model risk, loan ops, and back-office Six Sigma at scale.
The parent service: PipeIQ's full process-improvement agent platform.
Bring a candidate DMAIC project to a 30-minute call. We'll map the agent role for each phase and give you a realistic timeline.
