AI Agents in the Enterprise: Business Applications & Use Cases

# Customer service agent implementation
class CustomerServiceAgent:
    def __init__(self):
        self.triage = TriageAgent()
        self.kb = KnowledgeBaseAgent()
        self.technical = TechnicalAgent()
        self.escalation = HumanHandoffAgent()
    
    async def handle_ticket(self, ticket: SupportTicket) -> Response:
        # Step 1: Understand the issue
        understanding = await self.triage.analyze(ticket)
        
        # Step 2: Search knowledge base
        if understanding.can_resolve:
            solution = await self.kb.find_solution(understanding)
            
            if solution.confidence > 0.9:
                return await self.provide_solution(ticket, solution)
        
        # Step 3: Try automated resolution
        if understanding.issue_type == "technical":
            solution = await self.technical.diagnose(understanding)
            
            if solution:
                return await self.guided_resolution(ticket, solution)
        
        # Step 4: Escalate to human
        return await self.escalation.escalate(ticket, understanding)


# Real-world results
CASE_STUDY = {
    "company": "Telus",
    "implementation": "AI agent for customer support",
    "results": {
        "automation_rate": "40%",
        "resolution_time": "-35%",
        "customer_satisfaction": "+12%",
        "cost_savings": "$15M/year"
    }
}

class SalesDevelopmentAgent:
    def __init__(self):
        self.research = LeadResearchAgent()
        self.outreach = OutreachAgent()
        self.qualify = QualificationAgent()
    
    async def process_lead(self, lead: Lead) -> LeadStatus:
        # Research the lead
        context = await self.research.gather(lead.company)
        
        # Personalize outreach
        outreach = await self.outreach.generate(lead, context)
        
        # Send via preferred channel
        await self.outreach.send(lead.email, outreach)
        
        # Qualify responses
        response = await self.wait_for_response(lead)
        
        if response:
            qualification = await self.qualify.assess(response)
            
            return LeadStatus(
                qualified=qualification.score > 0.7,
                score=qualification.score,
                next_action=qualification.recommendation
            )
        
        return LeadStatus(qualified=False, score=0)


CASE_STUDY = {
    "company": "Gong",
    "implementation": "AI sales coach agent",
    "results": {
        "rep_productivity": "+25%",
        "pipeline_visibility": "100%",
        "win_rate": "+15%"
    }
}

class RecruitmentAgent:
    def __init__(self):
        self.screener = ResumeScreener()
        self.scheduler = InterviewScheduler()
        self.coordinator = InterviewCoordinator()
    
    async def process_application(self, application: Application) -> Decision:
        # Screen resume
        screening = await self.screener.evaluate(application.resume)
        
        if screening.score < 0.5:
            return Decision(reject=True, reason=screening.reason)
        
        # Schedule interview
        if screening.score > 0.8:
            slots = await self.scheduler.get_available_slots(
                application.role,
                interviewers=application.interviewers
            )
            
            # Auto-schedule
            scheduled = await self.scheduler.schedule(
                application,
                preferred=slots[0]
            )
            
            return Decision(
                accept=True,
                next_step="interview_scheduled",
                details=scheduled
            )
        
        return Decision(accept=True, next_step="human_review")


CASE_STUDY = {
    "company": "Eightfold AI",
    "implementation": "AI recruitment agent",
    "results": {
        "time_to_hire": "-40%",
        "screening_time": "-60%",
        "candidate_quality": "+25%"
    }
}

class EmployeeServiceAgent:
    def __init__(self):
        self.policy = PolicyAgent()
        self.ticket = TicketAgent()
        self.benefits = BenefitsAgent()
    
    async def handle_request(self, request: EmployeeRequest) -> Response:
        # Understand intent
        intent = await self.classify(request)
        
        if intent.type == "policy":
            answer = await self.policy.answer(intent.question)
            return Response(type="answer", content=answer)
        
        elif intent.type == "ticket":
            ticket = await self.ticket.create(intent)
            return Response(type="ticket", ticket_id=ticket.id)
        
        elif intent.type == "benefits":
            info = await self.benefits.lookup(intent)
            return Response(type="info", content=info)
        
        elif intent.type == "action":
            # Execute action
            result = await self.execute_action(intent)
            return Response(type="action", result=result)


CASE_STUDY = {
    "company": "Workday",
    "implementation": "AI employee service agent",
    "results": {
        "hr_ticket_volume": "-50%",
        "answer_accuracy": "95%",
        "employee_satisfaction": "+20%"
    }
}

class AccountsPayableAgent:
    def __init__(self):
        self.extractor = InvoiceExtractor()
        self.validator = InvoiceValidator()
        self.approver = ApprovalAgent()
        self.processor = PaymentProcessor()
    
    async def process_invoice(self, invoice: Invoice) -> ProcessingResult:
        # Extract data
        extracted = await self.extractor.extract(invoice.pdf)
        
        # Validate
        validation = await self.validator.validate(extracted)
        
        if not validation.valid:
            return ProcessingResult(
                status="rejected",
                reason=validation.issues
            )
        
        # Route for approval
        approval = await self.approver.route(extracted)
        
        if approval.required:
            return ProcessingResult(
                status="pending_approval",
                approver=approval.approver,
                details=approval.details
            )
        
        # Process payment
        payment = await self.processor.execute(extracted)
        
        return ProcessingResult(
            status="completed",
            payment_id=payment.id
        )


CASE_STUDY = {
    "company": "Coupa",
    "implementation": "AI accounts payable",
    "results": {
        "processing_time": "-80%",
        "error_rate": "-95%",
        "cost_per_invoice": "-70%"
    }
}

class FinancialReportingAgent:
    def __init__(self):
        self.collector = DataCollector()
        self.analyzer = AnalysisAgent()
        self.writer = ReportWriter()
        self.reviewer = ComplianceReviewer()
    
    async def generate_report(self, report_type: str, period: str) -> Report:
        # Collect data
        data = await self.collector.gather(period)
        
        # Analyze
        analysis = await self.analyzer.analyze(data, report_type)
        
        # Write report
        draft = await self.writer.generate(analysis, report_type)
        
        # Review compliance
        review = await self.reviewer.check(draft, report_type)
        
        if review.issues:
            # Fix issues
            draft = await self.writer.revise(draft, review.issues)
        
        return Report(
            content=draft,
            compliance_status=review.status,
            analysis=analysis
        )


CASE_STUDY = {
    "company": "Anaplan",
    "implementation": "AI financial planning",
    "results": {
        "planning_time": "-60%",
        "forecast_accuracy": "+30%",
        "analyst_time_on_analysis": "+40%"
    }
}

class SupplyChainAgent:
    def __init__(self):
        self.demand = DemandForecaster()
        self.inventory = InventoryOptimizer()
        self.logistics = LogisticsAgent()
        self.alert = AlertAgent()
    
    async def manage_supply_chain(self) -> SupplyChainStatus:
        # Forecast demand
        forecast = await self.demand.forecast(horizon="30d")
        
        # Optimize inventory
        recommendations = await self.inventory.optimize(forecast)
        
        # Execute recommendations
        for rec in recommendations:
            if rec.action == "reorder":
                await self.logistics.create_purchase_order(rec)
            elif rec.action == "move":
                await self.logistics.rebalance(rec)
        
        # Monitor for issues
        alerts = await self.alert.check_supply_chain()
        
        return SupplyChainStatus(
            forecast=forecast,
            actions_taken=recommendations,
            alerts=alerts
        )


CASE_STUDY = {
    "company": "IBM Sterling",
    "implementation": "AI supply chain agent",
    "results": {
        "inventory_costs": "-20%",
        "stockouts": "-50%",
        "demand_accuracy": "+25%"
    }
}

class ProcessAutomationAgent:
    def __init__(self):
        self.discover = ProcessDiscoverer()
        self.automate = AutomationAgent()
        self.monitor = ProcessMonitor()
    
    async def automate_process(self, process: BusinessProcess) -> AutomationResult:
        # Discover current process
        current = await self.discover.map(process)
        
        # Identify automation opportunities
        opportunities = await self.discover.find_automation_points(current)
        
        # Build automation
        automated = await self.automate.build(opportunities)
        
        # Deploy and monitor
        deployed = await self.deploy(automated)
        
        # Ongoing monitoring
        metrics = await self.monitor.track(deployed)
        
        return AutomationResult(
            process=process,
            automation_level=metrics.automation_percentage,
            time_savings=metrics.hours_saved,
            error_reduction=metrics.error_reduction
        )


CASE_STUDY = {
    "company": "UiPath",
    "implementation": "AI process automation",
    "results": {
        "processes_automated": "500+",
        "hours_saved": "2M+",
        "roi": "300%"
    }
}

# Process assessment for agent implementation
class ImplementationAssessment:
    def assess(self, process: BusinessProcess) -> AssessmentResult:
        # Score dimensions
        scores = {
            "volume": self.score_volume(process),
            "complexity": self.score_complexity(process),
            "repeatability": self.score_repeatability(process),
            "value": self.score_value(process),
            "feasibility": self.score_feasibility(process)
        }
        
        # Calculate overall
        overall = sum(scores.values()) / len(scores)
        
        return AssessmentResult(
            overall_score=overall,
            dimension_scores=scores,
            recommendation=self.get_recommendation(overall, scores)
        )
    
    def get_recommendation(self, overall, scores) -> str:
        if overall > 0.8:
            return "High priority for agent automation"
        elif overall > 0.6:
            return "Good candidate, proceed with pilot"
        elif overall > 0.4:
            return "Consider in phase 2"
        else:
            return "Not recommended for automation"

# Pilot implementation framework
PILOT_FRAMEWORK = {
    "phase_1": {
        "duration": "4-6 weeks",
        "scope": "Single process, limited scale",
        "success_criteria": {
            "accuracy": ">90%",
            "handling_time": "-40%",
            "escalation_rate": "<20%"
        }
    },
    
    "phase_2": {
        "duration": "8-12 weeks",
        "scope": "Multiple processes, expanded scope",
        "success_criteria": {
            "accuracy": ">95%",
            "handling_time": "-60%",
            "escalation_rate": "<10%"
        }
    },
    
    "phase_3": {
        "duration": "12-16 weeks",
        "scope": "Full deployment, continuous improvement",
        "success_criteria": {
            "accuracy": ">98%",
            "handling_time": "-80%",
            "escalation_rate": "<5%"
        }
    }
}

# Prioritization framework
def prioritize_use_cases(use_cases: list) -> list:
    scored = []
    
    for uc in use_cases:
        score = (
            uc.volume * 0.3 +
            uc.value * 0.3 +
            uc.feasibility * 0.2 +
            uc.data_availability * 0.2
        )
        scored.append((uc, score))
    
    # Return sorted by score
    return sorted(scored, key=lambda x: x[1], reverse=True)

# Bad: Automating complex, low-value processes
automate_everything = True  # Don't do this!

# Good: Focus on high-impact, feasible processes
# - High volume
# - Clear rules
# - Good data
# - High value

# Design for human oversight
class HumanInTheLoop:
    def __init__(self):
        self.confidence_threshold = 0.85
    
    async def process(self, request):
        result = await self.agent.execute(request)
        
        if result.confidence < self.confidence_threshold:
            # Escalate to human
            return await self.escalate(request, result)
        
        if result.requires_approval:
            # Get human approval
            return await self.request_approval(request, result)
        
        return result