Case Study Deep Dive: How We Launched an AI Product in 90 Days

Speed is everything in today’s AI-driven world. Enterprises that can move quickly from idea to impact hold a decisive advantage. At We Think AI (WTA), we’ve developed a repeatable framework that allows us to deliver enterprise-grade AI products in record time. This case study takes a deep dive into how we successfully launched a fully functional AI product in just 90 days—from concept to deployment—using our AI-Accelerated Development Model. We’ll explore the challenges we faced, the structured methodology we applied, the detailed execution timeline, and the critical lessons that other organizations can adopt to accelerate their AI initiatives.

The Challenge

Our client, a Fortune 500 enterprise, wanted to embed AI capabilities into their core operations. They faced three significant challenges:

1. Tight Timelines: The product needed to be live within one quarter, aligned with their fiscal planning cycle, and delays would have meant missed opportunities and lost competitive ground.
2. Complex Integration: The solution had to work seamlessly with multiple legacy systems, ERP platforms, and compliance-heavy environments across geographies.
3. Scalability: The product needed to handle thousands of concurrent users across regions and process millions of transactions while maintaining stability and low latency.

Traditional development timelines of 9–12 months would have placed them behind competitors already experimenting with AI-driven efficiencies. The critical question was straightforward: how do we accelerate delivery without sacrificing quality, governance, or security?

The WTA SPEED Framework

We applied our proprietary SPEED framework, a 5-stage methodology explicitly designed for AI delivery at scale:

1. Strategy Alignment – Defined objectives, mapped success metrics, and secured executive sponsorship to guarantee resources and alignment.
2. Platform & Data Foundation – Built robust cloud infrastructure, implemented secure APIs, created scalable data pipelines, and integrated governance mechanisms.
3. Experimentation & Prototyping – Developed rapid proof-of-concepts to validate technical feasibility, measure ROI potential, and identify early adoption barriers.
4. Enterprise Integration – Connected the AI solution to ERP, CRM, HRM, and compliance systems, ensuring seamless interoperability and low-friction workflows.
5. Delivery & Scale – Deployed the product with comprehensive monitoring, governance frameworks, and structured adoption programs to ensure long-term success.

The SPEED framework enabled us to compress months of work into weeks without cutting corners, keeping both business and technology needs fully aligned.

The Execution Timeline

Day 1–30: Foundation & Prototyping

• Conducted deep-dive workshops with stakeholders to define business use cases, ranked by feasibility and potential impact.
• Designed an extensible system architecture to support scalability, security, and compliance.
• Established high-quality data pipelines, validated sensor and transaction data, and implemented automated integrity checks.
• Built multiple prototypes to showcase early functionality, sharing them with stakeholders for rapid iteration and buy-in.

Day 31–60: Integration & Iteration

• Deployed validated AI models into controlled staging environments for rigorous testing.
• Integrated APIs with enterprise systems, focusing on ERP connectors, CRM workflows, and authentication services.
• Conducted detailed compliance audits addressing GDPR, HIPAA, SOC 2, and industry-specific regulatory requirements.
• Piloted the product with select user groups, collected feedback, and refined both technical workflows and the user interface.
• Introduced human-in-the-loop processes, enabling analysts to validate AI decisions and improve trust in outputs.

Day 61–90: Launch & Scale

• Executed a phased rollout plan across the enterprise, starting with priority teams before extending globally.
• Conducted role-specific training sessions and created adoption playbooks for end-users, managers, and technical staff.
• Implemented real-time monitoring dashboards for accuracy, latency, fairness, compliance adherence, and ROI measurement.
• Established long-term product governance councils and created a roadmap for scaling with additional features, such as predictive dashboards and intelligent assistants.

Key Success Factors

• Executive Sponsorship: Top-level support secured budget, visibility, and organizational commitment to push the project forward.
• Cross-Functional Collaboration: Data scientists, engineers, compliance officers, product managers, and domain experts worked together in parallel streams.
• Human-in-the-Loop Design: Analysts validated AI outputs, ensuring accountability and building trust across departments.
• AI-Accelerated Toolchain: Leveraged pre-trained models, automation frameworks, and reusable templates to dramatically shorten delivery cycles.
• Agile Iteration: Weekly sprints, continuous integration, and feedback loops kept the project aligned with evolving business requirements.
• Change Management: A structured approach to training and communication minimized resistance and ensured smooth adoption.

Outcomes

• On-Time Delivery: The AI product was launched within the 90-day target, meeting the client’s strategic deadlines.
• Business Impact: Reduced manual workloads by 40%, improved decision-making speed, and boosted operational efficiency by 25% within the first quarter of use.
• Scalability: Designed for global adoption, with the ability to onboard thousands of users with minimal reconfiguration.
• Compliance Confidence: The product successfully passed all regulatory audits, backed by clear documentation and explainable AI practices.
• Future Roadmap: New modules, including predictive analytics dashboards and customer-facing AI assistants, were added to the development pipeline using the SPEED framework.

Lessons Learned

1. Precise alignment with business strategy is critical to avoid scope creep and maintain stakeholder confidence.
2. Early prototyping accelerates buy-in, uncovers challenges, and reduces risks downstream.
3. Governance and compliance must be built into the design from day one—retrofitting later introduces significant cost and complexity.
4. Adoption and change management are as important as technical success. Training, communication, and building trust drive real business impact.
5. Agile iteration and flexible planning enable organizations to adapt quickly without losing momentum.
6. Success requires a cultural shift—teams must embrace AI not as a tool but as a co-pilot for business transformation.

Conclusion

Launching an AI product in 90 days is not just a bold claim—it's a repeatable process when supported by the proper framework, tools, and mindset. By applying the WTA SPEED methodology, leveraging AI-accelerated development practices, and embedding governance from the start, we helped our client achieve outcomes in three months that would typically take nearly a year.

This case study proves that speed and rigor can coexist. When combined, they produce transformative results that deliver measurable business impact while setting the foundation for long-term scalability and innovation.

Want to bring your AI ideas to life in 90 days or less? Discover how WTA’s SPEED framework can accelerate your AI journey, reduce risks, and deliver sustainable, measurable business outcomes faster than you thought possible.