AI Interoperability Rules: What Gov Digitalization Needs

In December 2025, Europe’s biggest regulatory fight about AI isn’t happening inside a ministry—it’s happening around a messaging app. The European Commission has opened a formal investigation into whether Meta unfairly blocked third‑party AI providers from using WhatsApp’s business tools. On paper, it’s a debate about competition and interoperability. In practice, it’s a preview of a problem every government faces when it tries to digitize services with AI: how “open” should the system be, and what do we sacrifice to get there?

This matters directly to our series on አርቲፊሻል ኢንተሊጀንስ በመንግስታዊ አገልግሎቶች ዲጂታላይዜሽን. When governments push to reduce bureaucracy, shorten queues, and deliver better digital public services, they quickly run into the same design tension the private sector is arguing about: managed ecosystems vs. open interoperability. Get it wrong, and you don’t “empower users.” You create spam, outages, security gaps, and procurement chaos.

What follows is a practical, government-focused way to think about interoperability and AI—without repeating the platform wars, and without building public services that break the moment they become popular.

Interoperability isn’t “good” by default—governance is

Interoperability is valuable when it’s targeted, testable, and governed. It’s harmful when it’s treated as a moral requirement that overrides reliability and security.

The RSS article argues that certain interoperability mandates can force a “managed ecosystem” (where a platform controls access to keep quality high) to behave like an “open pipe” system (where many third parties plug in freely). That can erase differentiation in consumer tech. In government digitalization, the stakes are even higher because the “product” is often an essential service: identity, benefits, tax, health appointments, licensing.

Here’s the reality I’ve seen across digital transformation programs: “Open” without strong rules becomes fragmentation. Different vendors interpret standards differently. APIs drift. Service levels vary. And when something fails, citizens don’t blame Vendor A vs Vendor B—they blame the agency.

So the right question for public service leaders isn’t “Are we interoperable?” It’s:

• Interoperable for what specific user journey? (e.g., business registration, permit renewal, benefits eligibility)
• Interoperable at what layer? (data exchange, identity, messaging, payments)
• Interoperable under whose accountability? (who carries risk when something goes wrong?)

The WhatsApp–AI dispute is a warning sign for public-sector AI

If you mandate broad interoperability into high-trust systems, you increase attack surface and operational load. That’s true for messaging platforms, and it’s true for government.

The core dispute described in the source: Meta changed policy to restrict third-party AI vendors from using WhatsApp Business Solution if AI is their primary service. Regulators suspect this could crowd out competitors. The counterargument is straightforward: opening the door widely to automated agents can flood a network designed for human communication—raising risks like spam, fraud, and degraded reliability.

Now translate that into government digital public services:

• A benefits chatbot connects to case-management APIs.
• A third-party “AI assistant” plugs into appointment booking.
• Automated agents begin generating traffic at machine speed.

If the interoperability mandate is too blunt, agencies can end up forced into a choice that looks familiar:

1. Allow broad access and accept increased fraud/spam load, degraded performance, and complicated incident response.
2. Restrict access and risk claims of unfairness, lock-in, or non-compliance.

Public institutions should not copy-paste consumer-tech rules. Government has different obligations: due process, privacy, equity, and continuity of service.

Managed ecosystems can be the ethical choice

A managed ecosystem sounds “closed,” but in government it can be the most citizen-protective design when it’s done right.

A managed ecosystem means:

• Strong onboarding for vendors (security review, red teaming, privacy assessment)
• Clear rate limits and abuse controls
• Consistent UX patterns (citizens don’t relearn the service every time)
• One accountable operator (the agency can answer for what happens)

Openness still matters—but it should come through published standards, fair procurement, and modular architecture, not by forcing every sensitive system to accept any connector at any time.

What “open vs managed” really means in government AI systems

Government should be open in standards and outcomes, but managed in execution for high-risk services.

The article compares Apple-style curation vs Android-style openness. In public service modernization, that same split exists, but we often pretend it doesn’t.

Where openness helps most

Use open interoperability aggressively when the risks are manageable and the upside is large:

• Data standards for non-sensitive exchange (e.g., geospatial, transport schedules)
• Open APIs for business enablement (e.g., company registry lookups, permit status tracking)
• Portability of citizen-provided data (citizen controls what they share)
• Auditability and transparency (open documentation, model cards, decision logs)

This is where interoperability reduces bureaucracy fast. A citizen shouldn’t submit the same document to three agencies because systems can’t talk.

Where managed access is non-negotiable

For sensitive or high-impact services, managed access isn’t “anti-innovation.” It’s risk control:

• Digital ID, authentication, and authorization
• Benefits eligibility, taxation, and enforcement workflows
• Health records and clinical scheduling
• Justice, policing, and child protection systems

In these areas, forcing plug-and-play interoperability can create exactly the “bridge risk” problem raised in the RSS content: when you connect systems with different security assumptions, the weakest link sets the real security level.

A simple rule: the more harm a mistake can cause, the more “managed” your ecosystem should be.

Interoperability mandates can backfire: a public-sector lens

Bad interoperability policy doesn’t create competition; it creates compliance-driven engineering that crowds out delivery.

The RSS article points to a “two-tier” outcome where features are withheld in certain markets due to regulatory requirements. Governments can accidentally do a similar thing internally: build services that technically comply with interoperability checklists but deliver a worse citizen experience.

Three common failure patterns

1. Checklist interoperability

   • Agencies publish APIs but don’t guarantee uptime, versioning discipline, or support.
   • Vendors integrate once, then break six months later.

2. Unfunded security externalities

   • Interoperability expands access but budgets don’t expand for monitoring, abuse prevention, and incident response.
   • Result: more fraud investigations, more downtime, and slower service.

3. Procurement fragmentation disguised as openness

   • Each agency buys a different chatbot, workflow engine, and document AI tool.
   • Citizens get inconsistent answers and incompatible experiences.

This is why “AI in government services” can’t be treated as a tools rollout. It’s an operating model.

A practical framework: “Interoperable by design, gated by risk”

The best approach for AI-driven government digital transformation is modular architecture with risk-based gates.

Here’s a framework you can apply whether you’re modernizing a single agency portal or building a whole-of-government platform.

1) Define interoperability layers (don’t mix them)

Interoperability isn’t one thing. Separate it into layers:

• Identity layer: digital ID, authentication, roles
• Data layer: schemas, data quality, consent
• Process layer: workflow handoffs, case status
• Interface layer: portals, chat, messaging, call center tools
• AI layer: model access, prompts, evaluation, guardrails

When governments blur these, they end up opening the wrong layer. Example: opening sensitive case APIs just to enable a nicer chatbot UI.

2) Gate third-party AI access like you gate financial systems

If third-party AI agents can trigger actions (submit applications, change records, schedule appointments), treat them like financial integrators:

• Mandatory vendor verification and security testing
• Strict scopes and permissions (read vs write access)
• Rate limiting per organization and per user
• Continuous monitoring and anomaly detection
• Clear liability and incident procedures

This is how you keep interoperability from becoming “anyone can automate anything.”

3) Require measurable service quality, not vague openness

For public services, reliability is part of fairness. A system that fails under load discriminates against people who can’t try again later.

So define service-level requirements that apply to any interoperating party:

• Uptime targets for critical APIs
• Maximum response time for citizen-facing flows
• Versioning and deprecation policies
• Accessibility requirements
• Logging and audit trail completeness

Openness without service quality is just another bureaucratic burden—this time for developers.

4) Build “safe interoperability sandboxes” for innovation

If you want innovation without risking production services:

• Provide synthetic datasets and realistic test environments
• Offer a staging API gateway with the same controls as production
• Run time-boxed pilot programs for new AI capabilities
• Publish evaluation benchmarks (accuracy, bias checks, refusal behavior)

This is how you support local startups and integrators while protecting citizens.

People also ask: the questions agencies should settle early

Should government platforms be open like Android or curated like Apple?

For high-risk services, curation wins because it concentrates accountability and reduces security gaps. For low-risk data and informational services, openness wins because it improves reuse and speeds delivery.

Does interoperability reduce vendor lock-in?

Sometimes. But the real antidote to lock-in is good architecture and contracts: modular components, clear data portability, escrow where needed, and the ability to swap layers without rewriting everything.

Will AI increase bureaucracy if it’s regulated too tightly?

Yes—if regulation is blunt. The goal is risk-based controls: stricter for decisioning and record changes, lighter for information and guidance tools.

What to do next (if you’re leading AI in public services)

The Meta interoperability dispute under the DMA is a useful signal: when rules treat openness as the only virtue, systems get worse at the job they were built to do. Government can avoid that trap by being explicit about which parts of public service delivery must remain managed, and which parts should be interoperable.

If you’re working on AI መፍትሄዎች ለመንግስታዊ አገልግሎቶች ዲጂታላይዜሽን, take a hard stance: design for citizens first. Reliability, privacy, and security aren’t “trade-offs” you accept later—they’re the foundation that makes automation trustworthy.

The next step is practical: map one high-volume citizen journey (like licensing renewal or benefits application), identify where interoperability removes repetitive paperwork, and then put strong gates where AI could cause harm. Open where it helps. Managed where it protects.

What would change in your agency if interoperability was measured by faster, safer service delivery—not by how many systems you connected?