How To Get Started With GenAI: Project Phases, Quick Wins And Success Factors

From Pilot to Scale: How to Successfully Launch GenAI in Your Business

Many companies face the challenge of introducing generative AI (GenAI) without becoming overwhelmed. A structured, iterative approach with clear goals, lean pilot projects, and realistic success metrics is essential. This article explains how companies can initiate, test, evaluate, and scale GenAI projects.

1. Starting Point: Motivations and Use Cases for GenAI

Before launching any project, the following questions should be answered:

• What pressing problem needs to be solved (e.g., customer advice, data quality, content generation)?
• Are there already internal goals related to customer experience or efficiency gains?
• What systems and data are available?

Typical starting points include high bounce rates for complex products, the manual maintenance of extensive product data, or recurring support inquiries on standard topics. These challenges are well suited for GenAI, as they can be addressed with manageable effort.

2. Project Phases at a Glance: From MVP to Go-Live

A proven rollout typically includes four phases:

Phase 1: Discovery & Goal Definition
Identify relevant stakeholders (e.g., e-commerce, IT, marketing, legal), define measurable goals (e.g., +30% conversion rate), and assess feasibility (system landscape, data availability).

Phase 2: Setup & Pilot (MVP)
Select a concrete use case (e.g., conversational advisor for a specific product category), prepare integration (API, data sources, UI embedding), and test and optimize content and prompts (including editorial guidelines and data privacy checks).

Phase 3: Live Test & Optimization
Conduct A/B tests with a control group, monitor relevant KPIs (engagement, uplift, bounce rate, user feedback), and iteratively adjust UX, prompt templates, and data enrichment.

Phase 4: Rollout & Scaling
Expand to other categories, markets, or use cases; integrate with marketing automation, PIM, or CRM systems; train and empower teams (training, guidelines, quality assurance processes).

3. Quick Wins: Fast Success with GenAI

The following use cases typically show measurable results in a short time:

• Conversational Advisor (Frontend): Improved guidance and higher conversion rates.
• Product Data Management (Backend): Reduced manual work and enhanced user experience.
• Content Generation (Marketing): Faster time-to-market and SEO optimization.

These use cases can be tested individually or in combination, depending on available resources and technical maturity.

4. Success Factors for Sustainable GenAI Implementation

Successful GenAI projects typically include:

• Top-management support (top-down sponsorship)
• Clearly defined goals and KPIs (a solid business case)
• Focus on team enablement via user-friendly tools and training
• Iterative approach with small steps, frequent testing, and fast learning cycles
• Close collaboration with the GenAI vendor (strong partnership)

5. Lessons Learned from Frontnow Projects

Experience from Frontnow projects shows that the greatest potential often lies not in the frontend, but in improved data structures. GenAI does not replace strategy, but it can accelerate execution significantly.

Companies that involve CX, IT, and content stakeholders early see faster scalability. Well-crafted prompt guidelines are also highly valuable, as they steer both quality and brand consistency.

Conclusion: Entering the GenAI Era Is a Thoughtful First Step

GenAI does not require a complete transformation, but rather a smart and measurable beginning. Companies that select use cases with direct business value, test them systematically, and optimize them step by step will see quick results—and earn trust both internally and externally.