Generative AI Readiness: What Does Good Look Like?

To make the most of GenAI, focus on these six dimensions.

Organizations of all sizes in virtually all industries want to infuse the power of generative AI (GenAI) into their operations. How does an organization prepare to take full advantage of generative AI across functions, departments and business units? What are the most important capabilities to build up or acquire?

To Achieve High GenAI Readiness, You Need a Framework

To help you be intentional about your generative AI readiness, we’ve defined a framework that covers six dimensions of readiness:

    1. Strategy and Governance
    2. Data Management
    3. AI Models
    4. Platform Technology and Operations
    5. People, Skills and Organization
    6. Adoption and Adaptation

The following are some highlights of what higher levels of readiness look like for each of these dimensions. Note that these are descriptions of future states for these dimensions, snapshots of your GenAI destination, so to speak.

Most organizations will implement many GenAI projects at the same time they are progressing along these dimensions, and the lessons learned from those early projects will help inform the readiness improvement efforts.

Drive GenAI Strategy with Business Requirements, Use Cases and Clear Governance

In an organization with a high degree of GenAI readiness, business and IT leaders collaborate to set clear objectives aligned to business priorities and actively manage a GenAI project pipeline.

Given the exceptional opportunities for innovation and optimization GenAI brings, it is more important than ever for organizations to achieve consensus in their transformation strategy. Starting with a focused set of strategy workshops, including all stakeholders who will be involved in this transformation, ensures all voices are heard, facilitates the path to agreement and gives everyone a solid vision of the future state of the organization and how to get there.

It’s vital to gain a clear view of the use cases that are most important for the business. Organizations often struggle with prioritization, as potential GenAI use cases extend into every corner of the enterprise. As part of our Professional Services for Generative AI, Dell Technologies has created a use case prioritization tool so business, IT and finance professionals can identify, analyze and prioritize use cases according to business value and technical feasibility.

With new use cases comes potential risk, making it especially important for organizations to have effective oversight of all GenAI projects. This ensures compliance with regulations, risk management guidelines and evolving ethical considerations.

Get Your Data House in Order

Many organizations start their generative AI journey using pre-trained models, which require access to an organization’s data to provide the context needed for successful implementation of GenAI use cases. Whether that data is provided via model tuning or augmentation (e.g., Retrieval Augmented Generation or RAG), delivering good data to the model in a timely manner becomes key to GenAI success.

As such, a high-readiness organization prioritizes scalable data management as a key enabler for GenAI, coordinating discovery, acquisition and curation of data. Business analysts and stakeholders should have access to an easy-to-use catalog of enterprise data resources.

With data management now in focus, organizations can ensure data is clean prior to use, reducing errors and bias and preventing exposure of proprietary information. A good indication of maturity is the use of data models to support both structured and unstructured data, simple integrations, automated transformations and pipelines.

Match the Model to the Use Case and Continuously Monitor Performance

Given the costly, time-consuming and expertise-intensive nature of training a model, many organizations will choose to use techniques such as RAG, prompt-engineering or fine-tuning of a pre-trained model to quickly realize value from GenAI.

The number of choices available to customers when selecting pre-trained models is growing daily, which presents new challenges and new opportunities. Key factors in model selection should include user experience, operations, fairness and privacy, and security.

Selecting the right model is just the start. A high-readiness organization establishes processes for evaluating the performance of its chosen generative AI models, regularly tuning model parameters to optimize effectiveness. Organizations should frequently assess models for safety, fairness, accuracy and compliance.

Build a Solid Technology and Operational Foundation

Once an organization selects use cases and models, they need a trusted platform to implement and run them. The mature organization will utilize a GenAI technology stack appropriate to their use cases, security and data constraints, and ensure these technologies are standardized across the organization and priority use cases. AI data is seamlessly integrated with multiple data sources.

Scalable data management is key to GenAI success, so highly mature organizations will have a GenAI-ready data management architecture such as Dell’s data lakehouse for analytics, with advanced analytics tools.

Level Up Skills and Organization

People with AI skills are well positioned to embrace GenAI. However, there are new skills needed beyond those required for traditional AI. A high-readiness GenAI organization provides training for specialists on platforms and tools, architecture, data engineering and the like. End users learn data analytics principles and how to construct effective prompts. This is supplemented with new support and operations teams dedicated to generative AI.

Manage Adoption and Adaptation

An organization at a high level of GenAI readiness has a clear understanding of where and how generative AI can add value. The initial strategy sessions help create that early view, but this is not a static space. Business and IT must continue to work together to integrate GenAI into new initiatives.

Continuous improvement within GenAI should be standard practice for organizations and can be achieved in a number of ways. Teams can capture human and automated feedback from model outputs and incorporate lessons learned into model training, guardrails and information retrieval.

These organizations integrate automated compliance with corporate policy, data privacy and government regulations into development and deployment processes.

Embark on Short-term GenAI Opportunities and Advance GenAI Readiness

As an organization moves to higher readiness levels, the opportunities for leveraging the benefits of generative AI increase in number and business impact.

But don’t think you need to wait until the readiness dimensions reach a certain level to begin applying GenAI to key use cases. You can and should begin with shorter-term, tactical projects that can provide efficiencies and financial benefits today.

If you’re looking to apply GenAI best practices, Dell Consulting Services can help in many ways. A great place to start is a Generative AI Accelerator Workshop, a half-day interactive strategic session with business and IT leaders to assess your organization’s GenAI readiness.

Learn more about Dell’s full range of GenAI services here.

About the Author: Bethan Williams

Bethan Williams has over 20 years of experience in the IT industry and 10 years within the Dell Technologies family. Beth started her IT career as a software developer and then moved into consulting and leadership roles in order to realize her passion for helping teams embrace new ideas and approaches. Beth is experienced in transforming teams to use innovative and lean techniques such as Agile, DevOps and IaC approaches, building and scaling highly skilled and cohesive teams, strategy development, enablement and leading application transformation projects at enterprise scale. Prior to Dell, Beth has been a key contributor and leader in professional service organisations across the Dell Technologies family, including SpringSource, VMware, Pivotal, EMC and now Dell. Beth is currently Dell’s Global Portfolio Lead for Applications and Data consulting.