The legal tech space has never been more saturated with AI solutions. 

In the last two years, general-purpose Large Language Models (LLMs) and AI “Copilots” (like ChatGPT, Gemini, or Microsoft Copilot) have created enormous excitement. But one thing is becoming increasingly clear: legal teams cannot rely on generic AI tooling for high-stakes, regulated work.

Sure, general-purpose AI can help with day-to-day tasks like summarizing emails or transcribing meetings. But these broad systems aren’t built to handle the precision, context, and risk management required within many legal and compliance workflows.

Where This Problem Shows Up Most Clearly

While many legal workflows expose the limitations of generic AI, the gaps are especially obvious in marketing compliance - a high-volume, high-risk domain where teams must interpret regulations precisely, apply organization-specific rules, and document decisions consistently.

This article uses marketing compliance as the representative use case because it highlights the shortcomings of generic AI most clearly. The patterns described apply to any regulated workflow that depends on institutional knowledge, repeatable logic, and auditable decision-making.

Generic AI Falls Short for Legal

General-purpose AI platforms are powerful - but they are designed to be broad knowledge engines, not tools that understand the nuance of regulated legal work.

When applied to complex workflows like marketing compliance review, they often create more problems than they solve - for example:

1. Misinterpreting Risk

A core task for legal and compliance teams is to identify risk based on a firm's unique risk appetite, internal policies, and the specific regulatory frameworks they operate in. 

General LLMs cannot do this. Trained on undifferentiated public data, they lack the fine-tuning required to interpret policy meaningfully. As a result, they often:

• Generate False Positives: Flagging perfectly acceptable phrases (which fall within your risk tolerance) that sound vaguely risky to a generalized model. This forces human reviewers to spend time dismissing irrelevant alerts - creating more manual review instead of reducing it.

• Miss Critical Negatives: Crucially, generic AI models do not understand your organization’s rules, an ACCC update from last quarter, or a specific M&A disclosure standard. They simply do not recognize subtle but material compliance issues.

In law, where error tolerance is near zero, generic models cannot meet this standard. They simply haven’t been trained to reason in the language of risk.

2. A State of Continual Proof of Concept

Effective legal and compliance review relies on institutional knowledge and repeatable logic that provides better output over time. In generic AI models, every interaction is a clean slate - they don’t retain:

• past approvals

• recurring decisions

• tone or phrasing preferences

• internal exceptions

• audit feedback

Imagine a General Counsel (GC) who approves a specific phrasing for a disclaimer. The next day, the marketing team submits similar content - but the generic model starts from scratch, forcing the GC to re-explain the same rules again and again.

This inability to establish durable, shared memory means generic AI never escapes proof-of-concept mode. It cannot deliver consistency, scale, or auditability.

In high-volume areas like marketing compliance, this becomes a major blocker to operational efficiency.

3. Fragmented Workflows and Fading Efficiencies

Because generic copilots typically sit outside established approval or review systems, the workflow isn’t as efficient as it could be. It typically looks like this: 

1. Copy content from the marketing or CMS platform

2. Paste it into the general AI tool

3. Copy the suggested edits

4. Paste them back into the compliance or legal system.

This copy-and-paste loop introduces:

• workflow friction

• audit gaps

• version inconsistencies

• extra manual review

• fragmented communication.

Ultimately, this makes the generic AI workflow more convoluted than it needs to be, slowing legal and compliance teams down.

The Real Value-Drivers: Purpose-Built AI Agents

The goal of AI in legal teams is not marginal improvement. It is a step-change in efficiency, accuracy, and risk mitigation. 

This requires moving from Generic AI to Domain-specific AI agents - like Haast - which are purpose-built to solve a defined legal problem end-to-end.

True Risk Understanding

Purpose-built tools, like Haast, learn your:

• Organizational rules

• Regulatory frameworks (e.g. FCA, ASIC, SEC, FINRA)

• Tone and style guides

• Appetite for risk

• Product-specific nuances

This dramatically reduces false positives and enables the system to flag genuinely material issues.

Deep Workflow Integration

Purpose-built tools integrate directly into existing approval, workflow, and documentation systems - eliminating manual handoffs and creating a clear, audit-ready chain of compliance.

Haast, for example, integrates with key content creation programs, such as Microsoft Word, Monday.com, and Figma.

Continuous Improvement Over Time 

Unlike generic tools, purpose-built tools can retain decisions, preferences, and patterns over time. Meaning the system continuously improves and becomes more consistent and accurate - compounding its value over time. 

And because decisions and content versions are retained, legal teams have complete audit trails and clarity over how and why a decision was made.

Ability to Handle Complex Content

Specialized AI can process formats that foundational LLMs struggle with, including:

• long-form documents

• video mp4 formats 

• podcasts and audio transcription

• slides and multimedia assets

The Bottom Line

Regulated legal workflows - especially high-volume areas like marketing compliance - are too nuanced, contextual, and high-stakes for generic AI solutions.

To see real transformation - Legal teams must move beyond the allure of the generalist copilot and invest in specialized AI agents that can:

• understand their rules

• reflect their decisions

• integrate into their workflows

• reduce real risk

• and scale with their organization

Generic AI gives you automation. Domain-specific AI gives you accuracy, trust, and scalability.