GenAI Strategy and ROI

Ali Arsanjani
4 min readApr 13, 2024

In this article I will outline the key factors and steps in creating a robust strategy and roadmap for GenAI with a strong focus on ROI. You can tailor it to the specific needs and capabilities of your organization. Here are some key steps to developing and implementing a successful GenAI strategy.

1. Define Clear Objectives and KPIs for ROI

Focus on Aligned Business Goals. Clearly define what you hope to achieve with GenAI. Whether it’s enhancing customer support through automated interactions or accelerating R&D by predicting experimental outcomes, your goals should directly address key challenges or opportunities.

Single-mindedly focus on defining ROI for your company and the internally available metrics that can be used to measure it. Develop specific, measurable KPIs such as

reduced operational costs,
increased sales conversions,
higher customer satisfaction scores, or
faster time to market for new products

These should provide a clear linkage to financial returns.

Some Example ROI Focus Areas

Customer Support. Implement GenAI-powered chatbots to handle routine inquiries. This could significantly reduce operational costs and improve customer satisfaction through faster response times.

Content Creation. Deploy GenAI tools to generate creative content, thereby reducing the resources and time required to get an initial draft or even a decent marketing copy ready for review. This helps increase content assimilation and production without compromising quality.

The ROI — focused GenAI Roadmap to Production

Cosnider and scope lower risk domains , initially within the company for R&D, Experimentation or POC development areas. Initially augment existing predictive AI modeling with GenAI. Focus on how to shorten development cycles and reduce R&D costs.

Start with the definition of the use-cases that are horizontal across your internal company functions. Develop POCs and then gradually move them into test and production level deployments; internally focused.

Then, venture into a parallel thread that is relevent to your industry domain. You have now experienced a POC, developed skills, tech stack familiarity and generally have a better organizational notion as a company of how to develop these POCs in lower risk areas.

Rank the use cases by ROI and maintain focus on the development of those use cases to achieve the ROI metrics you have set up to achieve and measure them along the development process.

2. Assess Current Landscape

Internal Capabilities. Review your current technological infrastructure, data availability, talent pool, and existing AI capabilities. Understanding these will help determine how ready your organization is to adopt GenAI solutions.

External Factors. Examine market trends, such as the adoption of GenAI in your industry, potential regulatory changes, and ethical considerations. Also, analyze your competitors’ use of AI to identify your relative strengths and areas for improvement.

3. Identify High-Impact Use Cases

Prioritize. Identify and prioritize use cases based on their potential impact and strategic alignment. For example, a telecom company might focus on customer service automation to reduce call center load.

Explore degrees of Feasibility. Evaluate the technical feasibility and data requirements for each use case. Consider the necessary changes to existing processes and systems. Have a candid evaluation of the technical expertise of your teams.

4. Develop a Phased Roadmap

Set Goals of Short-Term Wins. Implement pilot projects that can quickly show value and help gain organizational buy-in, such as introducing AI to automate repetitive tasks in customer service.

Align short term wins as stepping stones for Long-Term Vision. Plan for broader integration, considering necessary upgrades to infrastructure, potential scaling issues, and future-proofing against technological changes.

5. Build or Buy Decision

Start with In-House Development. Decide if developing solutions internally is viable based on your team’s expertise and the uniqueness of your needs.

Vendor Selection. If buying, assess and choose vendors based not only on current capabilities but also on support, scalability, and alignment with your strategic goals. Make sure you focus not only on the market hyped capabilities like pre trained models but also on their solution completeness: can they enable you to grow and achieve higher levels of sophistication in your gen Ai journey…

6. Implementation and Integration

Data Preparation. Ensure you have robust, clean, and ethically sourced data that you have legal access to, for training or more likely , tuning and customizing your models. Start with adaptor tuning and other parameters efficient fine tuning capabilities to fail fast for just extorting to see what datasets work best in which of your business domains.

Model Tuning and Deployment: Choose the right frameworks and platforms for custom tuning, development and deployment, considering factors like scalability, security, and ease of integration. SKIF you go with a managed solution like vertex Ai or a do it yourself gke implementation? Experiment with model tuning for your domain then scale with more elaborate fine tuning.

Enterprise Integration. Consider integrating GenAI solutions into existing workflows without disrupting them unduly.

7. Continuous Monitoring and Improvement

Performance Tracking. Regularly review the performance of GenAI applications against the established KPIs. Adjust strategies as needed.

Feedback Loop. Implement mechanisms for collecting and incorporating feedback from users and stakeholders.

Ethical Considerations. Continuously assess and mitigate ethical risks like bias in AI models and ensure transparency in AI-driven decisions.

8. Foster Collaboration and Upskilling

Cross-Functional Teams. Promote collaboration across various departments to integrate diverse insights and expertise in GenAI initiatives.

Training and Education. Invest in training programs to enhance your team’s GenAI capabilities, ensuring they stay abreast of the latest AI trends and technologies.



Ali Arsanjani

Director Google, AI/ML & GenAI| EX: WW Tech Leader, Chief Principal AI/ML Solution Architect, AWS | IBM Distinguished Engineer and CTO Analytics & ML