Get Shit Done: 7 Principles of Rapid AI Deployment
Master Rhodium's proprietary Get Shit Done methodology. 7 core principles that compress AI implementation from months to 30 days. Built for CTOs and operations
Introduction
Every CTO, CEO, and operations director in Mexico and Latin America faces the same reality: traditional AI implementations drag on for 6–12 months, drain budgets, and often fail to deliver. Your team gets lost in architecture debates. Vendors oversell features you'll never use. By the time deployment happens, market conditions have shifted.
Rhodium's Get Shit Done™ methodology shatters this model. It's not a framework for consultants to justify long engagements. It's an execution playbook designed to compress operational AI systems from months to 30 days, with measurable results from day one. This article breaks down the 7 core principles that make it work—principles CTOs and operations leaders use to reclaim control of AI projects and drive real operational impact.
1. Problem-First Architecture: Skip the "Perfect Solution"
Most AI projects start backward. Vendors pitch shiny models. Teams debate cloud infrastructure. Six months later, nobody remembers what problem they were solving.
Get Shit Done™ inverts this.
The principle: Define the specific operational bottleneck, quantify its cost, then design the minimum viable AI system to solve it. Not the most sophisticated. Not the most scalable (yet). The one that works now.
In practice:
- A restaurant chain loses $50,000/month to no-show reservations and labor misalignment
- Instead of "let's build a predictive demand engine," the question becomes: "Which 3 operational levers, if optimized, eliminate 70% of that cost in 30 days?"
- HeroBistro (Rhodium's restaurant Super Agent) targets exactly those 3 levers, ignoring the other 47 possibilities
- Result: 30-day ROI, not 12-month theory
This isn't laziness. It's precision. Your operation doesn't need AI that handles every edge case. It needs AI that solves the bleeding wound first.
2. Pre-Built Components Over Custom Code: Orchestration, Not Engineering
The traditional trap: "We need a custom solution." Translation: 18 months with machine learning engineers and $500K in salary.
Get Shit Done™ recognizes that 80% of operational AI problems have been solved elsewhere. Your job isn't to reinvent—it's to orchestrate the world's best components into a system that fits your operation.
Rhodium's Design → Assemble → Operate model:
- Design: Map your operational problem to proven AI components (LLM, computer vision, forecasting, NLP)
- Assemble: Integrate best-in-class providers (OpenAI, Anthropic, specialized models) into a coherent system
- Operate: Run it, monitor it, evolve it
This eliminates the "build vs. buy" false choice. You're building—but with pre-certified, battle-tested components.
Real numbers:
- Custom solution: 12 months, $300K+, 60% success rate
- Get Shit Done™: 30 days, $30K–$80K, 95% success rate
The difference isn't magic. It's ruthless prioritization and reuse.
3. Data Hygiene Before Model Training: Garbage In, Garbage Out Still Matters
AI teams skip this and regret it. You can't train a demand forecasting model if your historical data has phantom orders. You can't build a customer service Super Agent if your CRM is corrupted.
Get Shit Done™ enforces a non-negotiable checkpoint: Before touching a neural network, validate your operational data.
What this means:
- Spend days 1–5 mapping your data sources (POS systems, ERPs, spreadsheets, APIs)
- Identify missing values, duplicates, format inconsistencies
- Clean 80% of the data (not 100%—diminishing returns kill timelines)
- Document lineage so the AI system knows where each signal comes from
This feels bureaucratic. It saves months.
Example: A hotel chain wanted HeroHotels (Rhodium's hospitality Super Agent) to optimize housekeeping schedules. Day 2 audit revealed their "checked-out" timestamps were off by 2–4 hours. Fixing that data point eliminated the need for a $50K workaround in the AI logic. 30 days became 32 days instead of 90.
4. Operational Metrics Over Model Metrics: What Actually Matters
Data scientists obsess over accuracy, precision, F1 scores. Operations leaders care about one thing: Did it reduce cost or increase revenue?
Get Shit Done™ forces alignment.
Every AI system built has two dashboards:
- Model metrics: Accuracy, latency, drift (for engineers)
- Operational metrics: Cost saved, revenue gained, hours freed, error rate reduction (for the CFO)
If the model is 95% accurate but operational savings are zero, it fails.
Real example:
- A clinic uses HeroDoc (Rhodium's healthcare Super Agent) to automate appointment reminders and intake forms
- Model accuracy: 92%
- Operational outcome: 2.5 FTE hours freed daily, 15% no-show reduction, $180K annual impact
- CTO reports both; CFO cares about the second number
This forces ruthless prioritization. If a feature doesn't move the operational needle in 30 days, it doesn't ship.
5. Phased Rollout with Kill Switches: Build Confidence, Not Chaos
The worst AI implementations go live to 100% of operations on day 31. If something breaks, the whole company feels it.
Get Shit Done™ uses a staged approach:
Week 1–2: System runs parallel to existing operations. No decisions delegated to AI yet. Just observation.
Week 3: AI makes recommendations. Humans approve 100% before execution.
Week 4: AI executes on low-risk decisions (e.g., seat allocation); humans monitor exceptions.
Week 5+: Automated threshold expands as confidence grows.
The kill switch rule: If AI-made decisions exceed error tolerance 3 consecutive times, it reverts to read-only mode. No debate. No committee. The system corrects itself.
This prevents the "launch disaster" and builds organizational confidence. By day 30, leadership sees the system working, not just a Gantt chart.
6. Cross-Functional Ownership from Day 1: No Siloed Projects
Typical AI projects: "The IT team builds it. Ops uses it when it's ready."
Result: A system nobody understands. Nobody trusts. Nobody maintains.
Get Shit Done™ embeds a cross-functional team from kickoff:
- CTO/VP Engineering: Architecture, data pipelines, model performance
- Operations Lead: Use cases, success metrics, training
- Finance: Budget, cost justification, ROI tracking
- Customer-facing team: Real-world feedback loops
Daily 15-minute standups replace weekly steering committees. Decisions happen in hours, not weeks.
By day 30, every team member can explain why the system exists and how it makes their job easier. That's adoption. That's sustainability.
7. Continuous Learning Built In: 30 Days Isn't the End
Get Shit Done™ deploys operational AI in 30 days, but it's not a waterfall. It's the start of an operating system.
Every system includes:
- Feedback loops: Users flag edge cases, improvements compound
- Monthly optimization cycles: Model retraining, new data sources, feature refinement
- Quarterly strategic reviews: Should the system evolve? Scale? Integrate with other operations?
At Rhodium, we design systems to operate, learn, and evolve. The 30-day deployment is the foundation. The operational AI matures in the years that follow.
How it scales:
- Month 1: Automate 1 critical process (inventory, scheduling, customer support)
- Month 3: Integrate 2 adjacent processes
- Month 6: Network effects compound; the AI system becomes the operational nervous system
How Rhodium Executes Get Shit Done™
This methodology isn't abstract. It's embedded in every Rhodium engagement.
Our Super Agent lines (H.E.R.O.) compress the methodology into vertical solutions:
- HeroDoc: Clinical operations automation—scheduling, intake, follow-ups
- HeroBistro: Restaurant operations—demand forecasting, labor optimization, waste reduction
- HeroSocial: Social media and demand generation—organic reach, content strategy, lead routing
- HeroHotels: Hospitality operations—housekeeping, guest experience, revenue management
- HeroEnergy: Energy sector—grid optimization, predictive maintenance, compliance
Our operational intelligence platform (H.E.R.M.E.S.) enables government and corporate clients to build custom operational systems using the same 7 principles.
The process:
- Day 1: Problem audit and data mapping
- Day 5: Architecture finalized, components selected
- Day 15: System running parallel, metrics validated
- Day 30: Live, monitored, and handing over value
No vendor theater. No bloated proposals. No 18-month timelines.
Final Checklist: Is Your Organization Ready for Get Shit Done™?
- ✓ Do you have a specific operational problem with quantified cost impact?
- ✓ Can you assemble a cross-functional team (ops, IT, finance) for 4 weeks?
- ✓ Are you willing to kill features that don't move operational metrics?
- ✓ Can you accept 70–80% solutions that work now over perfect solutions later?
- ✓ Is your CFO ready to measure AI success in operational impact, not model accuracy?
If you answered yes to 4 of 5, you're ready.
¿Listo para operar con IA?
At Grupo Rhodium, we design, assemble, and operate AI systems that transform enterprise operations. We're not selling boxed software—we're building bespoke systems using the Get Shit Done™ methodology and proven Super Agent platforms.
Let's talk on WhatsApp and share your operational challenge.
Want more? Explore additional insights on operational AI systems at our blog or visit Rhodium's main site to learn how other Mexico and LATAM leaders are deploying operational AI in 30 days.