Case Study
How Let’s Play Sports Uncovered $100K in Annual AI Savings - in a 2-Week AWS AI Assessment
Let’s Play Sports used a two-week AWS AI Assessment to prioritize four agentic AI use cases, building a board-ready roadmap projecting $100k in annual savings against a $50K build investment and a 400% two-year ROI.
Industry: Sports & Recreation | AWS AI Assessment · Sprint 1: Modernization to Agents | July 2026
The challenge
Manual operations, fragmented financials, and an ESOP deadline
Nearly every core workflow at Let’s Play Sports depended heavily on manual effort. Around 10 FTE were dedicated to consolidating financial data from 19 separate QuickBooks accounts into company-wide P&L reports. League scheduling also required repeated manual corrections, contributing to potential revenue losses. More than 8,100 job applications were reviewed manually each year, while customers had no way to book practices online and after-hours inquiries often went to voicemail or competitors.
These challenges became more urgent as LPS worked toward ESOP readiness by March 31, 2027, requiring cleaner and more auditable financial processes. With 90% of customer interactions happening through text-based channels, slow response times also created customer experience and revenue risks. At the same time, competitive pressure from AI-enabled rivals and a planned migration from SQL Server 2014 to AWS RDS created the right opportunity for LPS to modernize its technology, automate key workflows, and improve operational scalability.
The AllCode solution
A quantified, board-ready AI roadmap in two weeks
AllCode applied AWS’s published four-phase assessment methodology — Use Case Discovery, AI Readiness, AWS Fit Validation, and ROI Business Case — in a two-week engagement.
Four agentic use cases were evaluated end-to-end and ranked by business impact, agentic fit, technical feasibility, and AWS platform fit. Each was delivered with a scoped 90-day POC, success criteria, guardrails, and a per-use-case ROI model. Existing ad-hoc ChatGPT workflows were translated into designs for governed, auditable agents with human-in-the-loop gates.
The first POC candidate, applicant screening and interview coordination, was scoped for a 6–8 week path to live, with a four-quarter roadmap covering all four agents.
Applicant screening & interview coordination
Financial reporting & P&L consolidation
Multi-facility scheduling & resource optimization
Self-service practice booking
Architecture
From ad-hoc AI to governed, auditable AWS agents
The assessment validated an AWS-native architecture for governed agentic AI while aligning with LPS’s planned database modernization. Existing ad-hoc ChatGPT workflows were redesigned around secure, auditable controls, human-in-the-loop gates, PII handling, and long-term observability.
AWS services in the target architecture: Amazon Bedrock AgentCore (Runtime, Memory, Identity, Policy, Gateway, Observability, Evaluations), Amazon Quick Suite (Quick Index, Quick Flows, Quick Sight), Amazon Bedrock Guardrails, Amazon RDS, Amazon S3, Amazon QuickSight, Amazon SNS/SES, Amazon Cognito, IAM Identity Center, and AWS Secrets Manager.
The roadmap also supports the planned migration from SQL Server 2014 colocation to AWS RDS, helping create a centralized foundation for future AI-driven operations and ESOP/SOX-oriented auditability.
Results
Before and After
| Metric | Before | After / Projected |
|---|---|---|
| Month-end close | 10 days | 3 days |
| Scheduling effort | 20 hours per week per facility | 4 hours per week per facility, an 80% reduction |
| Resume review time | 15 minutes per application | 2 minutes per application, an 87% reduction |
| Time to first interview | 8 days | 3 days |
| Self-service booking | 0% self-service booking | 35% adoption target with 24/7 booking in under 3 minutes |
| Schedule satisfaction | 6.2 out of 10 | 8.5 out of 10 target |
| Scheduling revenue impact | Up to 75,000 per year in revenue at risk | $100,000 per year projected value from scheduling optimisation |
| AI governance | Ad-hoc ChatGPT use with no formal guardrails | Governed agents with guardrails, HITL gates, PII redaction and audit logs |
| Total annual impact | Fragmented manual workflows across 19 facilities | $100,000 projected annual savings across a 4-agent portfolio |
Business impact
Projected impact across a four-agent portfolio
dollars in projected annual savings across 4 agents
dollars annual value from scheduling optimization
Month projected payback period
%
Reduction in manual scheduling labor
Partnering with AllCode allowed us to transform fragmented operational processes into a strategic asset. By modernizing our workflows with generative AI, we’re building the auditability and scalability required to meet our ESOP readiness goals with confidence and saving costs.