AI Systems Under Pressure: The Next Wave of Deployment Challenges

Lee Akay

A China–U.S. Perspective on Enterprise-Scale AI

As AI continues its rapid transition from prototypes to production systems, we are entering a new and defining phase of the technology lifecycle: the operational reckoning. At the Innovation Discovery Center (IDC), where we help build and evaluate advanced AI platforms on both sides of the Pacific, we’re seeing the same trend repeated—regardless of industry or geography:

This post, the first in our AI That Works series, expands on the themes introduced in our series introduction, where we emphasized the widening gap between what’s technically possible and what’s operationally sound. This article takes a deeper look at eight critical deployment bottlenecks based on our collaborations with leading research teams, hospital systems and platform developers in both China and the United States.

And we’re not stopping here: this blog also sets the stage for a forthcoming white paper authored by IDC researchers and international experts, exploring how scalable, verifiable, and memory-capable AI can be implemented across diverse regulatory and infrastructure contexts.

Why These Challenges Matter

These aren't theoretical concerns—they're real obstacles facing organizations trying to deliver responsible, high-performance AI at scale.

Whether you're deploying clinical copilots, customer-facing agents, compliance automation, or infrastructure intelligence, the problems outlined here appear repeatedly in our field work and consultations.

And yet, these challenges are solvable

What we’ve learned at IDC is that effective solutions depend heavily on the specific context—data systems, user workflows, regulations, and even organizational culture. A technique that works for a tertiary hospital in Hangzhou, China might fail in a rural U.S. hospital network, and vice versa.

In this series we’ll examine architecture-level solutions. We’ll also explore when, why, and how to apply them. Misapplying even good ideas can lead to wasted investment, internal fatigue, or even the abandonment of an AI initiative altogether.

1. Fragmented Architectures and Tool Chain Chaos

“We have too many disconnected models, tools, and data sources.”

Most organizational AI systems are cobbled together from a variety of components: fine-tuned LLMs, RAG pipelines, agents, vector databases, APIs, and orchestration layers. But these often operate in silos, creating friction, latency, and governance blind spots.

Emerging solutions like LangGraph, DSPy, and CrewAI attempt to bridge these tools but without standardized orchestration and memory, complexity remains high.

What Can Be Done:

The key isn’t finding one platform to rule them all. It is building modular, orchestrated systems that can evolve. Future posts will explore how to design flexible “backbone architectures” that allow new models and tools to plug in with minimal disruption.

2. Lack of Long-Term Memory and Context Awareness

“Our AI doesn’t remember what happened yesterday.”

Today’s production AI mostly interacts in a stateless way. In healthcare, law, or customer support, this leads to frustrating gaps in reasoning and repetition.

Memory-enhanced systems like LangGraph’s persistent agents, ERNIE’s memory layers (Baidu), and graph-based memory modules (Huawei Pangu)—are promising but not widely adopted.

What Can Be Done:

We’ll explore how memory can be implemented without overwhelming complexity, including prompt engineering patterns and lightweight memory controllers. But just bolting memory on doesn’t work, it requires intentional design and evaluation strategies.

3. Hallucination, Verifiability, and Trust

“We can’t trust what the model outputs—even if it sounds right.”

AI hallucination isn’t just a technical quirk—it’s a trust crisis. Whether generating clinical summaries or legal opinions, unverifiable outputs can be costly or even dangerous.

Research teams at THUNLP (Tsinghua) and others are developing verifier agents, self-critic subsystems, and retrieval-grounded evaluators. Most AI platforms today still ship with “blind confidence.”

What Can Be Done:

You don’t need to wait for perfect AI to gain trust—you need verifiability layers. We’ll share frameworks that allow outputs to be grounded, cross-checked, or human-reviewed—while preserving speed and usability.

4. The Specialization vs Generalization Dilemma

“Do we fine-tune for our domain, or stay general for flexibility?”

Fine-tuned models like MedLM or LawLLM offer domain expertise but lose general flexibility. Meanwhile, base models like GPT-4o or DeepSeek-VL are versatile but shallow in key areas.

Chinese developers increasingly use MoE architectures and LoRA adapters to balance performance and adaptability but routing and scaling remain complex.

What Can Be Done:

This doesn’t have to be a binary choice. We’ll show how to apply dynamic model routing, adapter layers, and hybrid approaches that allow both flexibility and focus, without having to rewrite your stack.

5. Inference Costs and Latency Pressure

“Our AI works—until usage spikes or budgets tighten.”

Large language models require a lot of resources. Under agentic workflows, inference costs can skyrocket.

Edge-ready models like MiniCPM, Mistral, and quantized variants offer some relief. But truly cost sensitive, real-time systems require more than just smaller models, they require smarter pipelines.

What Can Be Done:

Optimizing inference requires full-stack thinking: model selection, caching, routing strategies, and deployment scheduling. We'll explore how enterprises are managing cost without compromising capability.

6. Evaluation Without Real-World Benchmarks

“We don’t know how to measure success.”

Academic metrics (e.g., MMLU, GSM8K) are poorly aligned with enterprise use cases.

What organizations need are task-grounded evaluation frameworks: How well does an AI care-plan assistant actually improve outcomes?

What Can Be Done:We’ll walk you through how to build custom evaluation pipelines grounded in domain-specific tasks, measuring ROI, safety, and user impact in production, not just the lab.

7. Static Systems Without Feedback Loops

“Our system doesn’t learn from its mistakes or users.”

Many AI deployments lack feedback loops. When things go wrong, there’s no memory of it. No learning occurs unless a full retraining cycle is initiated.

The emerging solution? Lightweight, incremental refinement loops, user feedback signals, auto-labeling, and synthetic A/B testing.

What Can Be Done:

We'll share designs that allow systems to adapt over time, without expensive retraining cycles, by using small data, prompt updates, and behavior shaping.

8. Governance and Cross-Border Compliance

“Can we deploy globally without violating sovereignty or safety?”

Generative AI governance is evolving fast. In China, red-teaming and licensing are required. In the EU and US, regulatory frameworks like the AI Act, HIPAA, and GDPR create overlapping constraints.

What Can Be Done:

We'll examine deployable governance models, from privacy focused inference to federated learning setups that organizations are using to navigate regulatory complexity.

A Thoughtful, Context Based Path Forward

If there’s one idea we want to leave you with, it’s this:

There is no universal playbook for AI deployment—but there are universal principles.

Systems must be designed for modularity, memory, transparency, and trust, and those principles must be adapted—case by case—to your specific environment.