You Don't Need to Fix Everything Before Deploying AI in Quality. Here's Why.

In our work helping medical device companies prepare for FDA inspections, close 483 observations, and build defensible quality systems, we hear a version of the same story constantly:

“We know we need to modernize our quality operations. We're working on it. We just need to get our systems integrated first. Then we can think about AI.”

We understand why quality leaders think this way. The regulatory environment is unforgiving. The pressure to maintain compliance while transforming digital infrastructure at the same time is real. And the volume of industry advice pointing toward “fix everything first” makes it easy to conclude that AI has to wait until the digital foundation is solid.

New survey data puts numbers to what we've been seeing on the ground. A March 2026 MasterControl survey of 300 life sciences professionals in quality and manufacturing found that between 55% and 64% of organizations are running their most critical processes in a hybrid, partly manual and partly digital state. More striking: none of the processes surveyed had reached a fully intelligent operational state.

The survey frames this as a connectivity crisis, and it is. Nearly 60% of respondents said integrated systems are essential infrastructure before deploying AI. The dominant prescription is a three-to-five-year infrastructure journey before AI deployment is viable.

In our experience advising regulated companies, that prescription leaves organizations exposed.

The cost of paralysis by analysis

Here's the tension we see with clients in the middle of digital transformation programs: while the infrastructure work proceeds, quality risk doesn't pause.

CAPAs accumulate. Audit observations go untracked across product lines. Complaint signals that might point to a systemic issue sit in one system while the related production records sit in another. The people who know where the risk lives are the same ones manually extracting, reconciling, and reporting data, which leaves less time for the judgment-heavy work that actually reduces risk.

The “fix everything first” mindset treats AI as a reward for finishing your digital transformation. For quality and audit work specifically, it can speed the transformation up instead.

The four obstacles the survey identifies (compliance complexity, data visibility gaps, system fragmentation, and operational inefficiency) are exactly the conditions where audit intelligence does the most good. Not after those conditions are solved. During them.

The intelligence layer vs. the infrastructure question

MB&A separates two kinds of AI decisions, and the difference matters for how quality leaders spend their transformation budgets and time.

Infrastructure AI, the kind that needs a fully unified, connected digital foundation, really is a multi-year journey. If you're replacing your QMS, unifying your manufacturing execution system, and building a connected data architecture from the ground up, plan accordingly. That work is real, it's complex, and it needs the phased approach the survey describes.

Audit intelligence is different. It works from the documents and records your quality system already holds, so it doesn't wait on a platform migration. That is why Monica Burt founded Qualera in 2025. MB&A uses it on audits to give the auditor a first read of a company's documents and records, and an MB&A lead auditor decides every finding.

For MB&A clients, this means the quality intelligence conversation doesn't have to wait for the infrastructure conversation to finish. The two can run in parallel. Often, they should.

What regulators are already expecting

We want to be direct about something the survey doesn't address: FDA, notified bodies under EU MDR, and their international counterparts are not slowing their inspections while the industry works through its connectivity problems.

FDA 483 observations on CAPA effectiveness, complaint handling, and corrective action timeliness continue to be among the most frequently cited findings in medical device inspections. Companies that walk into an inspection with structured, traceable evidence, with the patterns already found, are in a very different position from those presenting reports compiled by hand from disconnected systems.

Audit intelligence doesn't just speed up digital transformation. In a regulated environment, it changes the quality of evidence you have when it matters most.

Five things quality leaders should prioritize now

1. Don't tie your intelligence timeline to your infrastructure timeline. A three-to-five-year platform migration plan doesn't have to mean three to five years without audit intelligence. Evaluate the two separately.
2. Map your existing quality data before your next transformation decision. Your QMS, CAPA, complaint, and vigilance data almost certainly holds more than you're getting out of it today. Before you pay to replace a platform, understand what you can learn from what you already have.
3. Bring regulatory readiness into the AI conversation. The right question isn't “will this integrate with our future architecture?” It's “can this give my quality team defensible evidence before the next inspection?”
4. Address cultural resistance directly. In our experience, the resistance often comes from quality professionals who doubt that AI understands the regulatory nuance of their work. Tools built for medical device quality, with an experienced auditor deciding every finding, answer that doubt with results rather than promises.
5. Start now. Every audit cycle you run with audit intelligence gives you a clearer picture of where your risk lives. Companies that start today build that advantage while others wait for perfect infrastructure.

MB&A's perspective

The life sciences instinct for caution serves an important purpose. We advise clients on regulatory risk for a living, and we understand why quality leaders want certainty before they put new technology into compliance-critical work.

But in the AI conversation, caution has turned into paralysis. The industry expects AI to change quality operations within a year or two, and almost no one has reached a fully intelligent quality state. That gap will close, but it won't close while quality leaders wait for a clean infrastructure that may be years away.

MB&A helps medical device companies put audit intelligence to work without waiting for a complete digital overhaul. Talk with us about preparing for your next inspection with the kind of evidence that changes outcomes.

Source: MasterControl, “Are You Ready for AI Deployment in Manufacturing?” (survey of 300 life sciences professionals in quality and manufacturing, 2026).