The News
A PR pitch circulating on behalf of Michael Privat, an engineering leader overseeing 500-plus engineers at a healthcare IT company that processes data for more than half of all U.S. health insurance claims, argues that enterprise AI adoption has produced a paradox: individual productivity has increased sharply, but organizational returns have not followed. Privat’s central claim is that organizations layered AI on top of inherited processes without redesigning those processes, so efficiency gains were absorbed by doing more of the same work rather than producing measurable financial returns. The pitch cites a Writer survey finding that individual AI users are delivering 5x productivity gains, yet only 29% of their organizations are seeing significant ROI.
Analyst Take
The productivity paradox has a structural cause
The argument Privat is making is not new, but his vantage point gives it weight. Processing data for a majority of U.S. health insurance claims means operating in an environment where process failures have regulatory and financial consequences that dwarf a missed sprint deadline. When he says that headcount remains the primary lever and that approvals still assume changes are slow and expensive, he is describing a governance architecture that was never designed to benefit from acceleration. AI arrived and accelerated the inputs. The outputs stayed the same because the decision-making infrastructure governing those outputs was never touched.
This is the distinction that most enterprise AI ROI conversations miss entirely. The question is not whether AI makes individual contributors faster. It does. The question is whether the organization’s operating model was redesigned to translate that speed into something a CFO can measure. In most cases, the honest answer is no.
What the engineering time data reveals
ECI Research’s 2026 Application Development survey found that 65.2% of respondents selected “0–20” when asked what percentage of engineering time is spent on net-new innovation. That number is striking in the context of Privat’s argument. If nearly two-thirds of engineering organizations are devoting at most a fifth of their time to genuinely new work, AI-driven productivity gains are overwhelmingly being absorbed by maintenance, compliance overhead, and inherited process debt. The efficiency is real. It is just going to the wrong places.
For ITDMs, this is a capital allocation problem. If you cannot redirect the hours AI frees up toward innovation or structural cost reduction, you are effectively paying for two things: the AI tooling and the same operational baseline you had before. For developers, the experience is more immediate. More throughput without process change typically means more tickets, more reviews, and more deployment ceremonies, not more time building things that matter.
The security risk dimension compounds the problem
There is a second layer to this that Privat’s pitch does not address directly but that ECI Research data makes visible. According to ECI Research’s 2026 Application Development: DevSecOps & AppSec survey, 45.3% of respondents said AI-assisted development has “increased risk moderately,” and an additional 17.2% said it has “increased risk significantly.” That means roughly six in ten organizations using AI-assisted development are absorbing net-new security risk alongside their productivity gains. In a healthcare IT environment governed by HIPAA and processing claims at national scale, that risk profile is not a background concern. It is a potential compliance event.
The combination is corrosive. AI accelerates code output. Security review processes, which were already a leading barrier to CI/CD maturity in ECI Research’s survey data, are now receiving more volume without proportional increases in capacity. The efficiency gain at the individual level creates a bottleneck at the governance layer. ROI evaporates in that gap.
Who should be paying attention
Privat’s argument has the most direct relevance for engineering leaders in regulated industries, but the structural problem he is describing is not sector-specific. Any organization that deployed AI coding tools, AI-assisted workflows, or AI-driven automation without simultaneously redesigning the approval, staffing, and measurement models around those tools is probably in the same position: faster inputs, unchanged outputs, and a CFO who is running out of patience.
The 29% ROI figure from the Writer survey is consistent with what ECI Research observes across its enterprise respondent base. Organizations that are seeing returns have typically done two things: changed how they measure engineering productivity (moving away from headcount-normalized metrics) and restructured at least one high-volume process to take advantage of lower cycle times. Neither is a technology decision. Both are management decisions.
Looking Ahead
The next 12–18 months will separate organizations that treat AI as a productivity layer from those that treat it as a process redesign catalyst. The former group will continue to report impressive individual-level metrics and disappointing balance-sheet outcomes. The latter will start to show up in operating margin comparisons in ways that are hard to explain away. The healthcare IT sector, where Privat operates, is likely to be a leading indicator: the combination of high process complexity, heavy compliance overhead, and enormous data volumes means the delta between a well-redesigned operating model and a poorly redesigned one will be unusually large.
For enterprise technology vendors, this dynamic creates a clear product and go-to-market implication. The current generation of AI tools is largely optimized for individual-level productivity. The next competitive battleground is workflow-level transformation: tools that do not just make engineers faster but that change what the organization decides to do with that speed. Vendors that can demonstrate measurable process redesign outcomes, rather than seat-level adoption metrics, will be the ones winning enterprise renewals in 2026 and beyond.
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