Generative artificial intelligence (gen AI) is transforming the business world by creating new opportunities for innovation, productivity and efficiency. This guide offers a clear roadmap for businesses to begin their gen AI journey. It provides practical insights accessible to all levels of technical expertise, while also outlining the roles of key stakeholders throughout the AI adoption process.

1. Establish generative AI goals for your business

Establishing clear objectives is crucial for the success of your gen AI initiative.

Identify specific business challenges that gen AI could address

When establishing Generative AI goals, start by examining your organization’s overarching strategic objectives. Whether it’s improving customer experience, increasing operational efficiency, or driving innovation, your AI initiatives should directly support these broader business aims.

Identify transformative opportunities

Look beyond incremental improvements and focus on how Generative AI can fundamentally transform your business processes or offerings. This might involve reimagining product development cycles, creating new revenue streams, or revolutionizing decision-making processes. For example, a media company might set a goal to use Generative AI to create personalized content at scale, potentially opening up new markets or audience segments.

Involve business leaders to outline expected outcomes and success metrics

Establish clear, quantifiable metrics to gauge the success of your Generative AI initiatives. These could include financial indicators like revenue growth or cost savings, operational metrics such as productivity improvements or time saved, or customer-centric measures like satisfaction scores or engagement rates.

2. Define your gen AI use case

With a clear picture of the business problem and desired outcomes, it’s necessary to delve into the details to boil down the business problem into a use case.

Technical feasibility assessment

Conduct a technical feasibility assessment to evaluate the complexity of integrating generative AI into existing systems. This includes determining whether custom model development is necessary or if pre-trained models can be utilized, and considering the computational requirements for different use cases.

Prioritize the right use case

Develop a scoring matrix to weigh factors such as potential revenue impact, cost reduction opportunities, improvement in key business metrics, technical complexity, resource requirements, and time to implementation.

Design a proof of concept (PoC)

Once a use case is chosen, outline a technical proof of concept that includes data preprocessing requirements, model selection criteria, integration points with existing systems, and performance metrics and evaluation criteria.

3. Involve stakeholders early

Early engagement of key stakeholders is vital for aligning your gen AI initiative with organizational needs and ensuring broad support. Most teams should include at least four types of team members.

• Business Manager: Involve experts from the business units that will be impacted by the selected use cases. They will help align the pilot with their strategic goals and identify any change management and process reengineering required to successfully run the pilot.
• AI Developer / Software engineers: Provide user-interface, front-end application and scalability support.  Organizations in which AI developers or software engineers are involved in the stage of developing AI use cases are much more likely to reach mature levels of AI implementation.
• Data Scientists and AI experts:  Historically we have seen Data Scientists build and choose traditional ML models for their use cases. We now see their role evolving into developing foundation models for gen AI.  Data Scientists will typically help with training, validating, and maintaining foundation models that are optimized for data tasks.
• Data Engineer:  A data engineer sets the foundation of building any generating AI app by preparing, cleaning and validating data required to train and deploy AI models. They design data pipelines that integrate different datasets to ensure the quality, reliability, and scalability needed for AI applications.

4. Assess your data landscape

A thorough evaluation of your data assets is essential for successful gen AI implementation.

Take inventory and evaluate existing data sources relevant to your gen AI goals

Data is indeed the foundation of generative AI, and a comprehensive inventory is crucial. Start by identifying all potential data sources across your organization, including structured databases. Assess each source for its relevance to your specific gen AI goals. For example, if you’re developing a customer service chatbot, you’ll want to focus on customer interaction logs, product information databases, and FAQs

Use IBM® watsonx.data™ to centralize and prepare your data for gen AI workloads

Tools such as IBM watsonx.data can be invaluable in centralizing and preparing your data for gen AI workloads. For instance, watsonx.data offers a single point of entry to access all your data across cloud and on-premises environments. This unified access simplifies data management and integration tasks. By using this centralized approach, watsonx.data streamlines the process of preparing and validating data for AI models. As a result of this, your gen AI initiatives are built on a solid foundation of trusted, governed data.

Bring in data engineers to assess data quality and set up data preparation processes

This is when your data engineers use their expertise to evaluate data quality and establish robust data preparation processes. Remember, the quality of your data directly impacts the performance of your gen AI models.

5. Select foundation model for your use case

Choosing the right AI model is a critical decision that shapes your project’s success.

Choose the appropriate model type for your use case

Data scientists play a crucial role in selecting the right foundation model for your specific use case. They evaluate factors like model performance, size, and specialization to find the best fit. IBM watsonx.ai offers a foundation model library that simplifies this process, providing a range of pre-trained models optimized for different tasks. This library allows data scientists to quickly experiment with various models, accelerating the selection process and ensuring the chosen model aligns with the project’s requirements.

Evaluate pretrained models in watsonx.ai, such as IBM Granite

These models are trained on trusted enterprise data from sources such as the internet, academia, code, legal and finance, making them ideal for a wide range of business applications. Consider the tradeoffs between pretrained models, such as IBM Granite available in platforms such as watsonx.ai and custom-built options.

Involve developers to plan model integration into existing systems and workflows

Engage your AI developers early to plan how the chosen model integrates with your existing systems and workflows, helping to ensure a smooth adoption process.

6. Train and validate the model

Training and validation are crucial steps in refining your gen AI model’s performance.

Monitor training progress, adjust parameters and evaluate model performance

Use platforms such as watsonx.ai for efficient training of your model. Throughout the process, closely monitor progress and adjust parameters to optimize performance.

Conduct thorough testing to assess model behavior and compliance

Rigorous testing is crucial. Governance toolkits such as watsonx.governance can help assess your model’s behavior and help ensure compliance with relevant regulations and ethical guidelines.

Use watsonx.ai to train the model on your prepared data set

This step is iterative, often requiring multiple rounds of refinement to achieve the wanted results.

7. Deploy the model

Deploying your gen AI model marks the transition from development to real-world application.

Integrate the trained model into your production environment with IT and developers

Developers take the lead in integrating models into existing business applications. They focus on creating APIs or interfaces that allow seamless communication between the foundation model and the application. Developers also handle aspects like data preprocessing, output formatting, and scalability; ensuring the model’s responses align with business logic and user experience requirements.

Establish feedback loops with users and your technical team for continuous improvement

It is essential to establish clear feedback loops with users and your technical team. This ongoing communication is vital for identifying issues, gathering insights and driving continuous improvement of your gen AI solution.

8. Scale and evolve

As your gen AI project matures, it’s time to expand its impact and capabilities.

Expand successful AI workloads to other areas of your business

As your initial gen AI project proves its value, look for opportunities to apply it across your organization.

Explore advanced features in watsonx.ai for more complex use cases

This might involve adapting the model for similar use cases or exploring more advanced features in platforms such as watsonx.ai to tackle complex challenges.

Maintain strong governance practices as you scale gen AI capabilities

As you scale, it’s crucial to maintain strong governance practices. Tools such as watsonx.governance can help ensure that your expanding gen AI capabilities remain ethical, compliant and aligned with your business objectives.

Embark on your gen AI transformation

Adopting generative AI is more than just implementing new technology, it’s a transformative journey that can reshape your business landscape. This guide has laid the foundation for using gen AI to drive innovation and secure competitive advantages. As you take your next steps, remember to:

• Prioritize ethical practices in AI development and deployment
• Foster a culture of continuous innovation and learning
• Stay adaptable as gen AI technologies and best practices evolve

By embracing these principles, you’ll be well positioned to unlock the full potential of generative AI in your business.

Unleash the power of gen AI in your business today

Discover how the IBM watsonx platform can accelerate your gen AI goals. From data preparation with watsonx.data to model development with watsonx.ai and responsible AI practices with watsonx.governance, we have the tools to support your journey every step of the way.