Industry Insights

The Urgency Behind Rapid AI Deployment

Today’s data leaders face relentless pressure to deliver AI solutions that don’t just demo well—but scale across the enterprise. Yet many organizations still start from zero: fragmented data sources, no data lake, and limited governance.

The good news? You can go from zero to AI in less than seven months.
This article unpacks how one organization built a robust data foundation in four months and deployed production-ready AI agents within three more—unlocking measurable ROI and operational efficiency.

In this real-world use case, there were no data lakes or warehouses. Descriptive analytics needs were fulfilled through Tableau dashboards and data flows, but there was no single source of truth. 

Prioritizing projects and effectively deploying resources is crucial. Even as we focus on building this foundation, daily "Keep the Lights On" (KTLO) requests do not cease. It is essential to engage with all stakeholders to ensure alignment with the broader vision and the future, more efficient deployment of analytics. Only the most critical requests should redirect the team from laying this foundation. 

The success of an organization relies on a well-structured and motivated team. The journey is challenging, and the team's commitment to pushing through foundational work with urgency and excellence is vital to success. Leadership must empower the team to take ownership of the solution and drive progress, especially for global teams. Challenges will arise, but rapid iteration and learning from failures are the only ways to move forward effectively.

Phase 1: Building the 4-Month Data Foundation That Makes AI Possible

Before AI can transform your business, you need data that you can trust.
Without a unified data foundation, your AI strategy is like building on sand.

The first four months focused on four indispensable pillars:

1. Data Quality & Accuracy

Clean, complete, and consistent data ensures your AI models don’t inherit bias or inaccuracies. “Garbage in, garbage out” applies rigorously here.

2. Data Governance & Compliance

Clear data ownership, privacy policies, and access controls are the backbone of trustworthy AI. Governance isn’t bureaucracy—it’s scalability.

3. Data Integration & Accessibility

Silos kill speed. Centralizing data from across the enterprise created a single source of truth, powering analytics and AI alike.

4. Scalability & Timeliness

Real-time, high-volume data streaming from over 12,000 IoT meters was migrated using Databricks’ Lakehouse and Unity Catalog. The result? 20% cost reduction and a unified data model ready for AI workloads.

Platform Choice:
The team selected Databricks’ Medallion Architecture for its ability to integrate data engineering and AI in one environment. Workspaces for Dev, QA, and Prod ensured CI/CD, while Unity Catalog streamlined governance and role-based access.

Leadership Tip:
Success in this phase hinges on stakeholder alignment. “Keep the Lights On” (KTLO) tasks never stop—but leaders must prioritize long-term foundation work and empower global teams to “own the solution.”

Phase 2: Deploying and Scaling AI Agents (Months 4–7)

With data governance and quality now established, the organization moved rapidly into AI deployment. Phase 2 proved that once the foundation is in place, AI acceleration is no longer a multi-year initiative—it’s a managed rollout.

Key Steps to Scale AI Responsibly

1. AI Model Development & Training: Use high-quality, labeled data to train robust, unbiased models that map directly to your business needs.
2. Deployment & Integration: Seamlessly embed AI outputs by integrating AI solutions into existing workflows and systems to enable faster, more informed decision-making.
3.  Monitoring & Optimization: Continuously evaluate model drift, performance, and ROI to ensure sustained value delivery.
4. Expansion: Identifying new use cases and strategically expanding AI capabilities to maximize impact and ROI.
   
   

Four Agentic AI use cases were deployed during this timeframe, with the first being an agent to support a field engineering team. The impact was immediate and quantifiable: research time on dead IoT devices was reduced from over 20 minutes to just 2 minutes, providing an instant return on investment (ROI).

Measurable Impact

The first AI agent supported a field engineering team, reducing diagnostic research on inactive IoT devices from 20 minutes to 2 minutes.

The success of the first agent paved the way for further enhancements, including adding capabilities like automatically summarizing support tickets and integrating device reboot prompts. This iterative approach allows for the solution to move toward deploying autonomous agents for frequent issues. Ultimately, by prioritizing a meticulous data foundation first, the organization ensures it is deploying smarter, more reliable, and more impactful AI.

This success created momentum—Each iteration demonstrated a principle that enterprise AI leaders know well: velocity is nothing without veracity.

Leadership Lessons: From Data Chaos to Controlled Acceleration

This transformation underscores that rapid AI deployment is a leadership challenge, not just a technical one.

 Leaders who:

• Align stakeholders around long-term data investments,
  
  
• Build governance and integration from day one, and
  
  
• Empower cross-functional teams to iterate fast and “fail forward”—
  
  

…will see the fastest path from proof of concept to scalable enterprise impact.

Key Takeaways for Data and AI Leaders

• Prioritize data trustworthiness before AI deployment. The strongest models start with clean, governed data.
  
  
• Adopt a two-phase roadmap. Build your data foundation (months 1–4), then scale AI (months 4–7).
  
  
• Empower your team. Decentralized ownership drives faster delivery and higher morale.
  
  
• Measure ROI early. Demonstrate quantifiable wins—like time savings or cost reduction—to fuel executive support for future investments.
  
  

Ready to Build Smarter, Scalable AI?

Rapid AI deployment is achievable—with the right foundation, leadership, talent and roadmap.
Burtch Works partners with data and technology leaders to build high-performing analytics and AI teams that deliver measurable enterprise impact. 

Accurate compensation data and market intelligence are often scarce but essential to winning the talent race. Discover current salary benchmarks and market trends in Burtch Work’s latest  2025 Data Science and AI professionals compensation report for current market trends and expectations of job seekers and hiring managers.

To learn how we can help you accelerate your next AI initiative. Contact our team today.

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Author: Stephen Tiepel
Product-focused data and engineering leader with over 27 years of experience building and scaling global teams.

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