The High-Risk AI Compliance Delay, Explained for SaaS Founders in Europe

Direct answer: The compliance deadline for high-risk AI systems used in recruitment, credit scoring and education has been pushed back to December 2027, giving European SaaS founders more runway before conformity assessments, technical documentation and human-oversight requirements become mandatory. That extra time is useful only if you spend it building the right architecture now rather than treating the delay as permission to ignore the requirements until 2027 arrives.

According to an EU AI Act timeline update reported in August 2026, the European Commission has moved the compliance deadline for high-risk AI system categories — specifically recruitment, credit scoring and education — to December 2027. This is a meaningful shift from the original staged rollout, and it directly affects any SaaS company building or embedding AI features into products that touch hiring, lending, or learning platforms sold into European markets. For founders who have been racing to retrofit compliance into products already in market, the delay changes the near-term pressure without changing the underlying obligation. The requirements themselves — risk management systems, data governance, technical documentation, human oversight, and conformity assessments — are not being softened, only rescheduled. A precise breakdown of which sub-categories within recruitment, credit scoring and education fall under the "high-risk" classification versus lighter-touch tiers is not fully detailed in the available reporting, so this post reasons from the general pattern of how the Act classifies systems that affect access to employment, credit, or education rather than asserting specifics not yet public.

What Actually Changed, and Why It's Real

The EU AI Act was always designed as a staged rollout rather than a single cutover date. Prohibited practices came into force first, general-purpose AI model obligations followed, and the high-risk system requirements were slated for a later phase precisely because they require the most preparation — both from vendors and from the regulators building out the conformity assessment infrastructure to actually enforce them. The August 2026 timeline update pushing high-risk deadlines for recruitment, credit scoring and education to December 2027 fits that pattern: regulators have acknowledged that the certification bodies, standards, and guidance documents needed to assess these systems fairly are not yet mature enough to support enforcement on the original schedule.

This is a real, structural delay, not a signal that the requirements are being watered down. If anything, delays like this tend to precede more rigorous enforcement once the deadline does land, because regulators use the extra time to finalize harmonized standards and build assessment capacity. SaaS founders who read this as "we have more time to worry about it later" are misreading the situation. The founders who benefit are the ones who read it as "we now have a realistic window to build compliance into the architecture properly instead of bolting it on under deadline pressure."

Why the Three Categories Matter Together

Recruitment, credit scoring, and education are grouped together in this delay because they share a common thread: each is a system that materially affects a person's access to opportunity — a job, a loan, or a qualification. That's precisely why the AI Act treats them as high-risk in the first place, and why any SaaS product with AI-driven candidate screening, automated credit decisioning, or adaptive learning and assessment tools needs to treat this category seriously regardless of which exact vertical it serves.

It's also worth noting what this delay is not. It is not a broad reprieve for every AI system that touches these three sectors — a scheduling tool used by a recruitment team, for instance, is a very different thing from a system that screens or ranks candidates. The Act draws its lines around decision influence, not around the industry label attached to the product. That distinction matters enormously for SaaS founders trying to figure out where they actually stand, because it means the classification exercise has to happen at the feature level, not the product-category level. Two companies selling into the same vertical can have very different exposure depending on how much decision-making authority their specific features actually carry.

How Staged Rollouts Like This Usually Play Out

Large regulatory frameworks with staged enforcement dates rarely move in one clean jump from "no obligation" to "full obligation." What typically happens instead is a lengthening runway during which guidance documents, harmonized standards, and accredited assessment bodies gradually come online, followed by a period where early movers who built toward the requirements ahead of time have a real operational advantage over those who waited. The pattern seen with this specific delay — regulators buying time to build out the infrastructure needed to assess high-risk systems fairly — is consistent with how similar frameworks have unfolded elsewhere. Founders who have lived through other compliance transitions, whether around data protection, financial services licensing, or accessibility standards, will recognize the shape of this one.

Why This Specifically Matters to SaaS Founders in Europe

If you're building or selling SaaS in Europe — whether you're headquartered there or simply serving European customers — this delay changes your planning horizon but not your obligation. A few reasons this lands differently for SaaS founders than for larger, better-resourced incumbents:

You're shipping features faster than your compliance posture can keep up. Most early- and growth-stage SaaS teams add AI-assisted screening, scoring, or recommendation features incrementally, often without a formal AI governance review at each release. The extra runway to December 2027 gives you time to build governance into your release process rather than discovering, mid-audit, that three product cycles' worth of features need retrofitting.

Your customers will ask before regulators do. Enterprise buyers in HR tech, fintech, and edtech are already asking vendors for AI risk documentation as part of procurement, well ahead of the legal deadline. A SaaS company that can show a clear technical file, a risk management process, and human-oversight controls has a real sales advantage over one that says "we're waiting until 2027." Compliance readiness is becoming a competitive differentiator in enterprise sales cycles, not just a legal requirement.

Rework is expensive when it happens under deadline pressure. The founders who wait until late 2027 to start will be competing for the same compliance consultants, the same conformity assessment slots, and the same engineering time as every other company doing the same thing at once. Building the technical documentation, logging, and human-oversight hooks into your architecture now, while there's no deadline crunch, is materially cheaper than doing it as an emergency retrofit later.

Your investors and acquirers will ask about this before 2027. Due diligence for Series B and later rounds, and certainly for acquisitions, increasingly includes an AI governance review for companies operating in regulated verticals. Having a credible answer — architecture, documentation, and a roadmap — before the deadline forces your hand is worth more than the answer itself once everyone else has one too.

Small teams feel architectural debt more acutely than large ones. A ten-person SaaS team that added AI-assisted screening as a feature eighteen months ago, without a dedicated compliance function reviewing it, is far more likely to have decision logic scattered across services with no single place to trace a decision back to its inputs. Larger, better-resourced companies often have platform teams whose job is exactly this kind of cross-cutting concern; smaller teams usually don't, which means the gap between "what we have" and "what December 2027 requires" tends to be wider precisely where the budget to close it is tighter. That's an argument for starting the scoping work early, when the fix is still a matter of adding structure rather than untangling years of accumulated shortcuts.

Multi-market SaaS products add another layer of complexity. A founder selling the same product into the UK, the EU, and further afield needs to be clear about which regulatory regime applies to which customer, since the AI Act's obligations attach to where a system is used or where its outputs affect people, not simply where the vendor is based. That means a single codebase may need to support different oversight and documentation postures depending on which market a given customer sits in, which is exactly the kind of requirement that's far easier to design for up front than to add after the fact.

What Changes in Practice for Your Product

The practical shift for SaaS founders is architectural, not just documentational. A few concrete changes worth planning for:

Data and Decision Logging

If your product makes or materially influences a decision about a candidate, a credit applicant, or a student, you need structured logging of what data went into that decision, what the system output was, and what human review (if any) occurred. This isn't a compliance form you fill out after the fact — it needs to be built into the data model and the request/response flow of the feature itself. Retrofitting this into a product that wasn't designed for it usually means touching the core decision pipeline, not just adding a logging wrapper.

Human Oversight Hooks

High-risk systems under the Act require a meaningful human-in-the-loop capability — not a rubber-stamp "approve" button, but a genuine ability for a human reviewer to see the basis for a decision and override it. Building this well means your product needs a reviewer-facing interface that surfaces the relevant inputs and model outputs clearly, which is a real product surface to design and build, not an afterthought.

Technical Documentation as a Living Artifact

The conformity assessment process expects technical documentation describing the system's purpose, design, data sources, and risk mitigations, kept current as the system changes. Treating this as a one-time document written for a deadline is a mistake — it needs to be maintained the way you'd maintain any other piece of product documentation, updated with each meaningful release.

Vendor and Model Dependency Mapping

If your product relies on third-party models or embedded AI components — increasingly common in SaaS — you need clarity on what those upstream providers can and cannot attest to regarding their own compliance posture. This is worth resolving now, while you have leverage and time to switch providers if needed, rather than after you've built two more years of product on top of a dependency that can't support your compliance file.

Release Process Changes

Perhaps the least obvious practical shift is what this does to your release cadence for high-risk features. A feature that materially changes how a candidate is scored, how a credit decision is weighted, or how a student is assessed can no longer ship as a routine, low-scrutiny update once these rules are in force. It needs a review step that checks whether the change affects the technical documentation, the risk assessment, or the human-oversight interface. Building that checkpoint into your existing release process now — even informally — means you won't need to invent a new governance process from scratch under deadline pressure in 2027. Teams that already run staged rollouts or feature-flagged releases have a natural place to slot this in; teams that ship continuously to all users at once will need to think harder about how a compliance checkpoint fits without undermining their release velocity elsewhere.

What SaaS Founders Should Actually Do With the Extra Time

The temptation is to treat December 2027 as far away. It isn't, in product-development terms — most of the changes above touch core architecture, not surface-level settings, and that kind of work takes real planning and engineering time to do without disrupting a live product. A practical sequence:

1. Classify your features now. Identify which parts of your product plausibly fall into the high-risk categories — recruitment, credit scoring, education — and which don't. This scoping exercise alone often reveals more exposure than founders expect, especially in features added incrementally over time.
2. Build the logging and oversight layer as a platform capability, not a one-off feature, so every current and future high-risk feature can plug into it rather than each team building its own version.
3. Bake documentation into your release process so the technical file updates itself as a byproduct of normal engineering work, rather than becoming a scramble before an assessment.
4. Revisit your architecture with an outside team if your current codebase wasn't built with this in mind. A lot of SaaS products accumulate AI features additively, and the resulting architecture often isn't well-suited to the kind of clean data lineage and decision traceability this requires. This is exactly the kind of structural work that benefits from Custom Software Development done deliberately, with governance and traceability designed in from the start rather than layered on top of an existing system under pressure.

Founders building in adjacent regulated spaces have faced similar structural retrofits before — the same pattern of "regulation defines the shape a product must take" shows up well beyond AI Act categories. Teams building InsurTech products have had to design for regulatory traceability and auditability from day one because the underlying product is inherently compliance-sensitive, and that discipline is instructive for any SaaS founder now facing a similar requirement in recruitment, credit, or education. It's also worth remembering that regulatory timelines can move in either direction — the pattern of announced deadlines slipping or tightening isn't unique to AI; 2026's corporate net-zero rollback shows how quickly compliance expectations can shift in response to political and economic pressure, which is one more reason to build flexible, well-documented systems rather than betting everything on today's exact deadline holding. And if your SaaS product serves customers well beyond Europe, it's worth reviewing how your compliance posture travels — a team building a software development company presence in the UAE faces a very different regulatory backdrop, and knowing where your architecture needs to flex by region avoids costly rebuilds later.

Pricing Context: What This Kind of Work Typically Falls Under

Compliance-driven architecture work varies a lot by scope, but here's how it typically maps to Scult's service tiers for SaaS founders assessing this kind of project:

Scope of work	Typical tier	What's usually included
Feature classification and a lightweight logging layer added to an existing product	Essential ($1,000)	Scoping the high-risk surface area, basic decision logging, initial documentation structure
Building human-oversight interfaces and structured technical documentation into an active product	Growth ($2,000)	Reviewer-facing UI, structured audit trails, documentation process integrated into release cadence
Full architectural rework for a product with multiple high-risk features across recruitment, credit, or education workflows	Enterprise ($4,000+)	End-to-end data lineage redesign, vendor dependency mapping, ongoing compliance-ready release architecture

These are starting-point frames, not fixed quotes — the right tier depends on how much of your existing product needs to be touched versus built fresh. A product with a single, well-isolated screening feature is a very different project from a platform with credit decisioning logic woven through several services, and an honest scoping conversation up front is what determines which tier actually fits.

How to Sequence This Work Realistically

None of this needs to happen in a single sprint, and trying to do it that way is usually counterproductive — it produces a compliance layer built in a rush, disconnected from how the rest of the product actually works. A more realistic sequence spreads the work across quarters rather than weeks: one quarter to classify features and agree on scope, one or two quarters to build the shared logging and oversight platform capability, and then an ongoing cadence of folding each existing high-risk feature into that platform as capacity allows, alongside normal product work. Treating it as a standing engineering priority rather than a one-off project is what keeps the technical documentation current without needing a separate scramble every time the product changes. Founders who plan this way tend to arrive at the 2027 deadline with a system that was built calmly, tested against real usage, and already trusted by the teams who use it — which is a materially different position than arriving with a compliance package assembled in the final months before enforcement begins.

Key Takeaways

• The December 2027 deadline for high-risk recruitment, credit scoring, and education AI systems is a real delay, not a loosening of the underlying requirements — treat the extra time as planning runway, not a reason to deprioritize.
• Enterprise buyers and investors are already asking about AI governance ahead of the legal deadline, making early readiness a sales and fundraising advantage, not just a compliance checkbox.
• Decision logging, human-oversight hooks, and living technical documentation are architectural changes, not paperwork — they need engineering time built into your roadmap now.
• Map your dependency on third-party models and embedded AI components early, while you still have time to switch if a vendor can't support your compliance needs.
• Products built additively over multiple release cycles often need a structural review to retrofit clean data lineage and traceability — this is a deliberate engineering project, not a quick patch.
• Regulatory timelines shift; design for flexibility rather than betting your architecture on today's exact date holding.

If you're trying to figure out whether your product's current architecture can support what's coming in 2027, or where to start scoping the work, book a meeting with our team and we'll walk through it with you.

Frequently Asked Questions

What is the EU AI Act's high-risk classification, in plain terms?

It's a category the Act uses for AI systems that materially affect a person's access to opportunities like employment, credit, or education. Systems in this category face the strictest obligations under the Act, including risk management, documentation, and human oversight requirements.

Why were the deadlines for recruitment, credit scoring, and education pushed to December 2027?

Based on the EU AI Act timeline update reported in August 2026, the delay reflects the time regulators and standards bodies need to finalize the conformity assessment infrastructure required to enforce these rules fairly. It is a scheduling adjustment, not a change to the substance of the requirements.

Does this delay apply to all AI features in a SaaS product?

No. It specifically applies to systems classified as high-risk within the recruitment, credit scoring, and education categories. Other AI features in your product, such as general chat assistants or internal analytics, fall under different parts of the Act with different timelines.

How do I know if my SaaS product falls into a high-risk category?

Start by mapping which features make or materially influence a decision about a candidate's employment prospects, a person's access to credit, or a student's educational outcomes. If a feature does that, it's worth treating as potentially high-risk and scoping it against the Act's criteria in detail.

What happens if I ignore this until closer to 2027?

You'll likely face a compressed timeline competing with other companies for the same compliance consultants, conformity assessment capacity, and engineering resources. Rework done under deadline pressure is typically more expensive and riskier than the same work planned early.

Is this delay likely to be extended again?

There's no way to know that with certainty, and it would be irresponsible to plan around another extension happening. The safer approach is to treat December 2027 as the real date and build toward it steadily.

What counts as "human oversight" under the Act?

It generally means a human reviewer has meaningful visibility into the basis for an AI-driven decision and a genuine ability to override it, not just a formal approval button with no real information to act on. Building this well requires a proper reviewer interface, not a checkbox.

Do early-stage SaaS startups need to worry about this, or just larger companies?

Any company selling AI-driven recruitment, credit, or education features into European markets is in scope regardless of size. Early-stage companies actually benefit most from starting early, since retrofitting compliance into a young, still-flexible codebase is far cheaper than doing it later.

What is "technical documentation" supposed to contain?

It typically describes the system's purpose, design, data sources, and the risk mitigations built into it, and is expected to stay current as the system evolves. Treating it as a one-time document instead of a living artifact tends to create problems at assessment time.

How does this affect fundraising for European SaaS companies?

Investors doing diligence on companies in regulated verticals are increasingly asking about AI governance readiness well ahead of legal deadlines. Having a clear architecture and roadmap for compliance can materially smooth a raise, especially at Series B and beyond.

Can I just buy a compliance tool to handle this instead of changing my architecture?

Off-the-shelf tools can help with parts of documentation and monitoring, but the core requirements around decision logging and human oversight usually need to be built into your product's actual data flow. A tool bolted on top rarely captures the full picture a conformity assessment expects.

What's the difference between recruitment AI and general HR software under the Act?

The distinction generally comes down to whether the AI materially influences employment decisions, such as screening or ranking candidates, versus simply supporting administrative HR tasks. Features that influence who gets hired or advanced are the ones likely to draw high-risk scrutiny.

How does credit scoring AI get classified under this delay?

Credit scoring systems that determine or materially influence whether someone gets a loan or credit line are the kind of system this category targets. The exact boundary between "high-risk" and lighter-touch credit-adjacent tools depends on specifics not fully detailed in current public reporting, so it's worth scoping carefully rather than assuming.

What does "education" cover in this context?

It generally covers AI systems involved in decisions about access to education, assessment, or grading that materially affect a student's outcomes or opportunities. Adaptive learning tools that merely personalize content pacing are a different risk tier than tools that determine grades or admissions outcomes.

How long does it typically take to build a compliant logging and oversight layer?

It depends heavily on how your product's data model is currently structured, but treating it as a platform-level capability rather than a per-feature patch tends to take a few months of focused engineering work rather than weeks. Starting well before 2027 avoids that timeline colliding with your deadline.

What's the cost range for this kind of compliance-driven engineering work?

It varies by scope, but SaaS founders typically see work in this space fall into ranges like Essential ($1,000) for scoping and lightweight logging, Growth ($2,000) for oversight interfaces and structured documentation, and Enterprise ($4,000+) for full architectural rework across multiple high-risk features.

Should I hire in-house or work with an outside team for this?

That depends on your team's current bandwidth and expertise in compliance-driven architecture. Many SaaS founders bring in outside engineering support for the initial architectural rework and then maintain it in-house once the patterns are established.

What's the risk of doing nothing until the deadline arrives?

Beyond legal exposure, you risk losing enterprise deals to competitors who can already show compliance readiness, and you risk a costly, rushed retrofit competing for scarce assessment and consulting capacity right before the deadline. Both risks compound the longer you wait.

Does this affect SaaS companies outside the EU that sell into Europe?

Yes. The Act generally applies based on where the system is used or where its outputs affect people, not just where the company is headquartered. A SaaS company outside Europe selling recruitment, credit, or education AI features to European customers is very likely in scope.

How does this interact with GDPR requirements I already comply with?

GDPR and the AI Act overlap in areas like data governance and individual rights but aren't the same framework — GDPR compliance doesn't automatically satisfy AI Act high-risk requirements. It's worth reviewing both together rather than assuming one covers the other.

What's a "conformity assessment" and do I need one?

It's the formal process by which a high-risk AI system is evaluated against the Act's requirements before or during deployment. If your product's relevant features fall into the high-risk categories, you'll need to go through this process before the December 2027 deadline.

Can I use this extra time to add more AI features instead of focusing on compliance?

You can, but every high-risk feature you add without governance built in adds to the eventual retrofit cost. It's more efficient to build the governance layer first, then add features on top of it, than to keep adding features and compound the eventual rework.

What happens to features I've already shipped that might be high-risk?

Existing features need to go through the same classification and retrofit process as new ones — the deadline doesn't distinguish between old and new functionality. Auditing your current feature set now is often more revealing than founders expect.

How do I map dependency on third-party AI models for compliance purposes?

Start by listing every embedded model or AI vendor your high-risk features rely on, then ask each vendor directly what documentation and attestations they can provide about their own compliance posture. Gaps here are worth resolving early, while switching providers is still relatively low-cost.

Will European regulators actually enforce this once December 2027 arrives?

The extra time granted by this delay is generally read as regulators preparing to enforce more rigorously once the assessment infrastructure is ready, not as a sign that enforcement will be lax. Planning as though enforcement will be real is the safer assumption.

What's the single biggest architectural mistake SaaS founders make here?

Treating decision logging and human oversight as bolt-on features added just before an assessment, rather than building them into the core data flow of the product from the start. That mismatch is usually what makes retrofits expensive and risky.

How does this affect our sales cycles with European enterprise customers?

Enterprise buyers in regulated verticals are increasingly asking for AI risk documentation during procurement, ahead of the legal deadline. Being able to answer those questions credibly is becoming a differentiator in competitive sales situations.

Is there a way to test whether our current architecture supports these requirements?

A structural review of your data lineage — tracing how inputs flow into a decision and how that decision is logged and reviewable — is the most direct way to find out. This kind of review is exactly the starting point for the kind of Custom Software Development work needed to close any gaps.

Do smaller feature teams need separate compliance processes, or one shared one?

A shared, platform-level compliance layer that every team's high-risk features plug into is generally more efficient and consistent than each team building its own version. It also makes ongoing documentation easier to maintain.

What documentation should we start keeping today, even before building new architecture?

Start documenting what data each relevant feature uses, what decision it produces, and what human review (if any) currently exists. This baseline makes the eventual formal technical documentation process much faster.

How does this compare to how other regulated industries have handled similar shifts?

Products in inherently regulated spaces, like insurance technology, have long had to build traceability and auditability into their core architecture rather than adding it later, and that discipline offers a useful model for SaaS founders now facing a similar requirement.

Could this deadline move again given how other 2026 regulatory rollbacks have gone?

Regulatory timelines across different domains have shown they can shift under political and economic pressure, as seen in 2026's broader retreat from some ESG commitments. That history is a reason to build flexible systems, not a reason to assume this specific deadline will move.

What's the first practical step a SaaS founder should take this month?

Run a feature-by-feature classification exercise to identify which parts of your product plausibly touch recruitment, credit scoring, or education decisions. That scoping work is inexpensive and reveals your actual exposure before you commit to any bigger architectural project.

How does this affect our engineering roadmap for the next year?

It suggests carving out dedicated time for a platform-level logging and oversight layer rather than treating compliance as an occasional side task. Founders who build this into their normal roadmap planning avoid the scramble later.

Should this affect how we structure new product features going forward?

Yes — any new feature that could plausibly influence a hiring, credit, or education decision should be designed with decision logging and human-oversight hooks from the start. Designing for this from day one is far cheaper than retrofitting it.

What's the risk of over-engineering our compliance response?

Building an overly heavy compliance layer for features that aren't actually high-risk wastes engineering time that could go toward product development. Accurate classification up front avoids both under- and over-building.

How does this delay affect our pricing or contract terms with European customers?

Some enterprise contracts already include clauses about AI governance and documentation ahead of the legal deadline. It's worth reviewing your current customer contracts to see if any commitments already reference AI Act compliance timelines.

Do we need a dedicated compliance hire for this?

Not necessarily at early stage — a lot of the initial architectural work can be handled by existing engineering teams or outside partners, with a dedicated compliance function added as the company scales and the number of high-risk features grows.

What's the relationship between this delay and general-purpose AI model obligations under the Act?

Those are separate tracks within the same law, targeting the underlying AI models themselves rather than the specific high-risk applications built on top of them. A SaaS company should track both, since a compliant model doesn't automatically make every application built on it compliant.

How should we communicate this timeline internally to our team?

Frame it as a planning deadline for the engineering roadmap, not just a legal date on a compliance calendar, so the relevant architectural work actually gets scheduled and resourced rather than deprioritized. Treating it as an engineering milestone tends to get better follow-through than treating it as a legal footnote.

What's a realistic timeline to start seeing results from a compliance-focused architecture project?

Initial classification and scoping can often be done in a matter of weeks, while the deeper architectural work — logging, oversight interfaces, documentation integration — typically unfolds over a few months depending on the size of the affected feature set.

Does this delay reduce the urgency of getting started?

It reduces the immediate legal pressure but not the practical urgency, since the underlying engineering work still takes real time and is cheaper to do without deadline pressure. Founders who treat the delay as reduced urgency tend to end up doing the same work later, at higher cost.

How does this affect our data retention practices?

Decision logging for high-risk features generally implies retaining structured records of inputs, outputs, and any human review long enough to support an audit or assessment. This should be reviewed alongside your existing data retention and privacy policies to avoid conflicts.

What if our AI features are provided entirely by a third-party vendor rather than built in-house?

You still need clarity on whether your vendor's system meets the relevant requirements, since responsibility for compliance can extend to how you deploy and use a system, not just who built it. This is exactly why mapping vendor dependencies early matters.

How does this apply to AI features still in beta or pilot with European customers?

Features in active use with real customers, even in beta, can still fall under the relevant classification if they materially influence recruitment, credit, or education decisions. It's worth including beta features in your classification exercise rather than assuming they're exempt.

What's the best way to keep technical documentation current without slowing down releases?

Integrating documentation updates into your existing release process, so they happen as a natural byproduct of shipping a feature change, avoids the alternative of a documentation backlog that has to be caught up under deadline pressure.

Are there specific European countries with additional requirements beyond the EU-wide Act?

National regulators may issue additional guidance within the framework of the Act, so it's worth checking whether the specific European markets you serve have any supplementary requirements alongside the EU-wide rules.

How should we prioritize this against other engineering work competing for the same resources?

Weigh it against the cost of a compressed retrofit closer to the deadline and the sales and fundraising advantage of early readiness — for most SaaS companies with genuine high-risk exposure, that combination justifies starting sooner rather than later.

What does "success" look like for a SaaS founder navigating this delay well?

By the time December 2027 arrives, your high-risk features should already have structured decision logging, functioning human-oversight interfaces, and current technical documentation built into your normal release process, rather than a rushed project started in late 2027.

Where should a founder start if they're not sure how big this problem is for their product?

A structural review of your current architecture against the classification and logging requirements is the clearest way to size the problem, and it's a natural starting point for a focused engineering engagement rather than guessing at scope.