GPT-4 Enterprise Use Cases

With GPT-4’s improved reasoning and longer context, enterprise use cases that were previously impractical become viable. Here are the high-value applications I’m seeing emerge.

1. Contract Analysis and Legal Review

GPT-4’s 32K context can process entire contracts:

class ContractAnalyzer:
    """Analyze legal contracts with GPT-4."""

    def __init__(self, client):
        self.client = client

    async def full_contract_review(self, contract_text: str) -> dict:
        """Comprehensive contract analysis."""

        prompt = """Analyze this contract comprehensively.

Provide analysis in these sections:

1. **Executive Summary**: 2-3 sentence overview
2. **Key Terms**:
   - Parties involved
   - Effective dates
   - Termination conditions
   - Payment terms
3. **Obligations**:
   - Your obligations (as the signing party)
   - Counterparty obligations
4. **Risk Assessment**:
   - High risk clauses (indemnification, liability)
   - Unusual or non-standard terms
   - Missing standard protections
5. **Recommendations**:
   - Clauses to negotiate
   - Questions to clarify
   - Suggested modifications

Contract:
{contract}"""

        response = await self.client.chat_completion(
            model="gpt-4-32k",
            messages=[
                {"role": "system", "content": "You are a corporate attorney reviewing contracts. Be thorough and flag any concerns."},
                {"role": "user", "content": prompt.format(contract=contract_text)}
            ],
            temperature=0.2
        )

        return {
            "analysis": response.content,
            "tokens_used": response.usage.total_tokens,
            "model": "gpt-4-32k"
        }

    async def compare_to_template(
        self,
        contract: str,
        template: str
    ) -> dict:
        """Compare contract against standard template."""

        prompt = """Compare this contract against our standard template.

Identify:
1. Missing clauses from our template
2. Additional clauses not in template
3. Modifications to standard language
4. Deviations in key terms (pricing, liability caps, etc.)

Standard Template:
{template}

Contract Under Review:
{contract}"""

        response = await self.client.chat_completion(
            model="gpt-4-32k",
            messages=[
                {"role": "user", "content": prompt.format(
                    template=template,
                    contract=contract
                )}
            ]
        )

        return response.content

2. Code Review and Architecture Analysis

class CodeReviewer:
    """Comprehensive code review with GPT-4."""

    async def review_pull_request(
        self,
        diff: str,
        context_files: list[str] = None
    ) -> dict:
        """Review a pull request."""

        context = ""
        if context_files:
            context = "\n\nRelated files for context:\n" + "\n---\n".join(context_files)

        prompt = f"""Review this pull request diff.

Provide feedback on:
1. **Correctness**: Logic errors, edge cases, potential bugs
2. **Security**: Vulnerabilities, injection risks, auth issues
3. **Performance**: Inefficiencies, N+1 queries, memory issues
4. **Maintainability**: Code clarity, naming, documentation
5. **Testing**: Missing test cases, edge cases to cover

For each issue, specify:
- Severity (Critical/High/Medium/Low)
- File and line number
- Description
- Suggested fix

Diff:

{diff}

{context}"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are a senior software engineer conducting a thorough code review."},
                {"role": "user", "content": prompt}
            ],
            temperature=0.1
        )

        return self._parse_review(response.content)

    def _parse_review(self, content: str) -> dict:
        """Parse review into structured format."""
        # Implementation to extract issues, severity, etc.
        return {"raw": content, "parsed": True}

    async def analyze_architecture(
        self,
        codebase_summary: str,
        specific_concern: str = None
    ) -> dict:
        """Analyze codebase architecture."""

        prompt = f"""Analyze this codebase architecture.

{f'Specific concern to address: {specific_concern}' if specific_concern else ''}

Codebase Summary:
{codebase_summary}

Provide analysis on:
1. Overall architecture pattern (monolith, microservices, etc.)
2. Separation of concerns
3. Dependency management
4. Scalability considerations
5. Testing architecture
6. Potential improvements"""

        response = await self.client.chat_completion(
            model="gpt-4-32k",
            messages=[{"role": "user", "content": prompt}]
        )

        return response.content

3. Financial Analysis and Reporting

class FinancialAnalyzer:
    """Financial document analysis with GPT-4."""

    async def analyze_financial_statements(
        self,
        statements: str,
        period: str = "Q4 2022"
    ) -> dict:
        """Analyze financial statements."""

        prompt = f"""Analyze these financial statements for {period}.

Financial Statements:
{statements}

Provide:

1. **Key Metrics Summary**:
   - Revenue and growth rate
   - Gross margin
   - Operating margin
   - Net income
   - Cash flow from operations

2. **Trend Analysis**:
   - QoQ and YoY comparisons
   - Concerning trends
   - Positive developments

3. **Health Indicators**:
   - Liquidity ratios
   - Debt levels
   - Working capital

4. **Risk Factors**:
   - Identified concerns
   - Areas needing investigation

5. **Outlook**:
   - Based on current trends
   - Key factors to watch"""

        response = await self.client.chat_completion(
            model="gpt-4-32k",
            messages=[
                {"role": "system", "content": "You are a financial analyst. Provide thorough, accurate analysis."},
                {"role": "user", "content": prompt}
            ],
            temperature=0.2
        )

        return response.content

    async def earnings_call_summary(
        self,
        transcript: str
    ) -> dict:
        """Summarize earnings call transcript."""

        prompt = f"""Summarize this earnings call transcript.

Transcript:
{transcript}

Provide:
1. **Key Announcements**: Major news and updates
2. **Financial Highlights**: Numbers mentioned
3. **Guidance**: Forward-looking statements
4. **Management Tone**: Optimistic/cautious/concerned
5. **Analyst Concerns**: Key questions and answers
6. **Red Flags**: Any concerning statements"""

        response = await self.client.chat_completion(
            model="gpt-4-32k",
            messages=[{"role": "user", "content": prompt}]
        )

        return response.content

4. Customer Support Escalation Analysis

class SupportAnalyzer:
    """Analyze support escalations with GPT-4."""

    async def analyze_escalation(
        self,
        ticket_history: str,
        customer_profile: str = None
    ) -> dict:
        """Analyze support escalation for resolution."""

        context = f"\nCustomer Profile:\n{customer_profile}" if customer_profile else ""

        prompt = f"""Analyze this support escalation.

Ticket History:
{ticket_history}
{context}

Provide:

1. **Issue Summary**: What is the core problem?

2. **Root Cause Analysis**:
   - Technical cause (if identifiable)
   - Process failure (if applicable)
   - Communication gaps

3. **Customer Sentiment**:
   - Frustration level (1-10)
   - Key pain points
   - Expectations

4. **Resolution Recommendation**:
   - Immediate actions
   - Long-term fix
   - Compensation recommendation (if warranted)

5. **Prevention**:
   - How to prevent similar escalations
   - Process improvements needed"""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are a customer success manager analyzing escalations to find optimal resolutions."},
                {"role": "user", "content": prompt}
            ]
        )

        return response.content

    async def batch_analyze_tickets(
        self,
        tickets: list[dict]
    ) -> dict:
        """Analyze patterns across multiple tickets."""

        tickets_text = "\n---\n".join([
            f"Ticket {t['id']}: {t['summary']}\nCategory: {t['category']}\nResolution: {t['resolution']}"
            for t in tickets
        ])

        prompt = f"""Analyze these support tickets for patterns.

Tickets:
{tickets_text}

Identify:
1. Common issues and their frequency
2. Categories needing attention
3. Resolution effectiveness
4. Systemic problems
5. Recommendations for improvement"""

        response = await self.client.chat_completion(
            model="gpt-4-32k",
            messages=[{"role": "user", "content": prompt}]
        )

        return response.content

5. Technical Documentation Generation

class DocumentationGenerator:
    """Generate technical documentation with GPT-4."""

    async def generate_api_docs(
        self,
        openapi_spec: str
    ) -> str:
        """Generate human-readable API documentation."""

        prompt = f"""Generate comprehensive API documentation from this OpenAPI spec.

OpenAPI Spec:
{openapi_spec}

Generate documentation including:
1. Overview and authentication
2. Each endpoint with:
   - Description
   - Parameters
   - Request examples
   - Response examples
   - Error codes
3. Common use cases
4. Rate limits and best practices

Format in Markdown."""

        response = await self.client.chat_completion(
            model="gpt-4-32k",
            messages=[{"role": "user", "content": prompt}]
        )

        return response.content

    async def generate_runbook(
        self,
        system_description: str,
        incident_types: list[str]
    ) -> str:
        """Generate operational runbook."""

        incidents = "\n".join(f"- {i}" for i in incident_types)

        prompt = f"""Generate an operational runbook for this system.

System Description:
{system_description}

Incident Types to Cover:
{incidents}

For each incident type, include:
1. Detection criteria
2. Impact assessment steps
3. Step-by-step resolution
4. Escalation criteria
5. Post-incident actions

Format in Markdown with clear sections."""

        response = await self.client.chat_completion(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}]
        )

        return response.content

ROI Considerations

Use Case	Manual Time	GPT-4 Time	Cost Savings
Contract review	4 hours	10 min	~$400/contract
Code review	1 hour	5 min	~$100/PR
Financial analysis	2 hours	15 min	~$200/report
Support escalation	30 min	2 min	~$50/ticket

GPT-4’s cost (~$0.10 per analysis) is negligible compared to time savings.

Implementation Strategy

1. Start with high-value, low-risk: Documentation, analysis
2. Add human review: AI assists, humans verify
3. Measure quality: Track accuracy, corrections needed
4. Expand gradually: Build confidence before automation

GPT-4 enables automation of knowledge work that was previously too complex for AI. The enterprise applications are substantial.