# What 27 Quality Professionals Told Us About Quality Intelligence Source: https://www.keypedia.com/media/quality-intelligence-survey-2026 Type: blog Published: September 7, 2026 Updated: October 11, 2026 Authors: George Kwiecinski > KeyPedia Labs interviewed 27 quality professionals about quality intelligence, supplier oversight, manual workflows, automation, and trusted use of AI. --- In 2026, KeyPedia Labs interviewed 27 quality professionals about how they define quality intelligence, where their workflows break down, and what they would automate. The participants included heads and vice presidents of quality, audit leaders, consultants, and specialists working across pharma, biotech, cell and gene therapy, medical devices, sterile manufacturing, and supplier oversight. The observed research was a directional interview program, not a probability sample. Not every participant answered every question. We report the denominator for each observed result below. ![KeyPedia Labs quality intelligence interview program](https://storage.googleapis.com/gks-blog-images/blog-images/bf91ea29d65445989492e7f3f8821fea.jpeg) ## A broad, practitioner-led group The program covered quality professionals working inside manufacturers as well as independent consultants and auditors. Participants ranged from solo practitioners to leaders at large global companies. That mix matters. Quality intelligence must work across different operating models. A global quality leader may need cross-site trend data, while a small manufacturer may depend on consultants and manual agency searches. Both need evidence they can trace and act on. ![Respondent mix by segment and role](https://storage.googleapis.com/gks-blog-images/blog-images/094dacc481354be2adad86dab10158aa.png) ## The term is familiar, but the definition is unsettled Twenty-one of the 27 participants, 78%, recognized or engaged with the idea of quality intelligence. Their definitions varied: - Eight participants, 30%, described it as a capability they already use or want to adopt. - Six, 22%, treated it as another name for regulatory intelligence. - Seven, 26%, recognized the term but did not provide enough detail for a fuller classification. - Six, 22%, did not recognize the term or asked whether it meant regulatory intelligence. ![Awareness of quality intelligence among 27 respondents](https://storage.googleapis.com/gks-blog-images/blog-images/9af22b0b7d554cb3bbd9aadf402b6db2.png) The interviews suggest that quality intelligence is still an emerging category. Regulatory intelligence usually starts with surveillance of laws, guidance, and agency activity. Participants described quality intelligence more broadly, as the use of internal and external quality evidence to make decisions about facilities, suppliers, products, audits, and quality systems. One global audit leader described the goal as turning business intelligence and quality-system data into specific decisions based on live, real-world evidence. An independent consultant emphasized the human side: technology can support judgment, but it cannot replace strong quality leadership, sound procedures, and trusted professional networks. ## Firefighting remains the dominant problem Seventeen interviews contained enough detail for the cross-interview pain-theme analysis. Eight of those 17 participants, 47%, described a version of the same problem: quality teams spend too much time responding to immediate issues and too little time preventing the next one. Seven of 17, 41%, wanted automation that connects regulatory or enforcement information to the quality management system. Five, 29%, described supplier or CMO oversight as manual and difficult. Five also described manual data pipelines, including downloading files, sorting spreadsheets, or transcribing information before analysis could begin. ![Pain themes raised across 17 coded interviews](https://storage.googleapis.com/gks-blog-images/blog-images/0c629b613f244cb0a0ec5b93456cc899.png) The underlying problem was not a lack of data. Participants struggled to turn scattered evidence into a timely answer: - A head of audits wanted a tool that could link a search to the QMS and flag a gap. - A quality leader said their team reviews warning letters every week but lacks time to analyze them deeply. - A global CDMO quality leader described paper batch records that staff manually transcribed into spreadsheets before statistical analysis. - A supplier quality leader described direct requests to suppliers for audit, CAPA, recall, and warning-letter information, followed by manual risk rating. ## Supplier oversight is still labor intensive Supplier and CMO oversight surfaced repeatedly. Participants described annual or three-year audit cycles, spreadsheets, quality agreements, direct supplier questionnaires, and manual reviews of agency activity. Several interviews also showed why periodic checks can miss important context. A supplier may operate across several sites. A manufacturer may learn during an audit that another facility solved the same problem years earlier. A contract may omit a regulatory obligation even when the underlying system already supports it. The practical requirement is continuous context. Quality teams need to see new inspections, Form 483 observations, warning letters, recalls, and related signals between scheduled audits, then connect those signals to the supplier, product, agreement, or QMS process they affect. ## Quality professionals are open to AI, with conditions The interviews did not show blanket resistance to AI. Several participants already used Copilot, ChatGPT, Power BI, or internal tools. Others were actively evaluating AI for audit reports, document retrieval, trend analysis, and early recall detection. Their condition was consistent: the evidence must be verifiable. Participants raised concerns about unreliable outputs, unclear source data, and tools that retrieve information without explaining whether it applies. The strongest use cases kept a person in the loop and made the source visible. This points to a practical standard for quality AI: - Show the underlying document and citation. - Explain why the signal applies to the user’s facility, product, supplier, or process. - Keep the quality professional responsible for the decision. - Record how the team reviewed and acted on the result. ## What quality teams would automate first The requested automations were specific. Participants wanted to: - connect regulatory and enforcement changes to QMS processes; - summarize warning letters and Form 483 observations; - aggregate 483, BIMO, inspection, and recall information; - detect field and recall signals earlier; - retrieve hard-to-find guidance and enforcement documents; - compare quality trends across sites and suppliers; - eliminate manual transcription and spreadsheet preparation. These requests share one objective: reduce the distance between a public signal and a defensible quality action. ## A working definition Based on these interviews, KeyPedia Labs uses this working definition: > Quality intelligence is the disciplined use of internal and external quality evidence to identify risk, support decisions, and improve quality systems before an inspection, audit, or failure forces action. Regulatory intelligence remains part of that picture. Quality intelligence extends the work into operations. It connects agency activity with facilities, suppliers, products, audits, agreements, CAPAs, and the quality management system. ## The practical takeaway The 2026 interviews show a field in transition. Quality professionals recognize the value of better information, but the category lacks a shared definition. Manual work still separates agency evidence from quality-system action. AI interest is real, but trust depends on source quality and human review. For quality teams, the next step is concrete: replace isolated searches and periodic checks with a repeatable monitoring process that preserves the source, explains the relevance, and assigns a human decision owner. ### Methodology note KeyPedia Labs conducted a 27-participant interview program in 2026. The cohort included quality executives, audit leaders, consultants, and specialists across life sciences and related regulated industries. Awareness results use all 27 participants. Pain-theme results use the 17 interviews with sufficient detail for that coding. Findings are directional and should not be generalized to the full quality profession. ### Sources - KeyPedia Labs, 2026 Quality Intelligence interview program, raw working data and coded themes. - George Kwiecinski, “AI-Powered Regulatory Intelligence and Auditing: What the Future Brings,” draft conference presentation for the 2026 ASQ Audit Division Conference.