Building GenAI competence for business growth

To embark on the GenAI technology adoption journey for business success, organizations require foundational activities related to GenAI investment, guidance in prioritizing use cases, and identification of key stakeholders essential for building and implementing successful initiatives, according to IDC.

Essential key activities

Establish a responsible AI policy: This must include defined principles around fairness, transparency, protections, and accountability relating to the data used to train models, as well as how the results are used. A responsible AI policy should also provide transparency on the roles and responsibilities of developers, users, and other stakeholders, while addressing legal and compliance issues.

Build an AI strategy and road map: A set of defined, measurable, and prioritized GenAI use cases is required to align the organization on the key areas that will deliver the maximum business impact in the short, medium, and long term.

Design an intelligence architecture: Managing the life cycle and governance of data, models, and business context for every use case is critical. The architecture should also include protocols for data privacy, security, and intellectual property protection.

Reskill and train staff: New competencies will be required to build and use GenAI models, such as ‘prompt engineers’ to write and test prompts for GenAI systems. Every organization must create a new skills map for core AI technologies and business capabilities to deploy GenAI at scale across the organization. Organizations should also build personalized training program for key roles.

Once the key activities are in place, organizations must develop a clear understanding of the core GenAI technologies, as well as their foundation models and capabilities. At the center of any GenAI system is a generative foundation model, including the well-known large language models (LLMs). The game changer in the AI market is the ability for these models to be trained on extraordinarily large amounts of semi-structured and unstructured content and generate new content based on simple prompt requests.

Assessing three GenAI use cases

The next step in defining the path to GenAI impact is prioritizing an identified set of use cases. IDC defines a use case as a business-funded initiative enabled by technology that delivers a measurable outcome.

• Industry: These involve more custom work and, in some cases, may require organizations to build their own generative AI models. Examples include generative drug discovery in life sciences and generative material design for manufacturing. Specialized use cases tend to be built around specific models and model providers, with custom integration architectures designed for individual clients.
• Business function: These use cases typically involve integrating a model (or multiple models) with corporate data for use by specific departments or business functions, such as Marketing, Sales, and Procurement. Many organizations are already testing these types of use cases but are concerned about intellectual property leakage and data governance.
• Productivity: These use cases are aligned with work tasks, such as summarizing reports, creating job descriptions, or generating Java code. GenAI functionality for productivity improvement is being infused into existing applications, such as Microsoft 360 Copilot or Duet AI for Google. For many of these use cases, business value can be delivered through the content and data that the underlying foundation models have been pretrained on.

GenAI framework adoption

Ultimately, GenAI will be widely adopted only if the data, models, and applications that use them are trusted by end users and customers. To achieve this, organizations need to establish a well-orchestrated trust and oversight program to ensure that GenAI technologies can be deployed in a sustainable manner. Organizations and AI vendors must understand the benefits and limitations associated with GenAI use and be prepared to remediate issues while complying with regional data privacy regulations.

Finally, IDC recommends adopting a “three horizons” framework to help organizations transform their business models using GenAI. Horizon 1 focuses on near-term, incremental innovation, followed by disruptive innovation in the medium-term Horizon 2 and long-term business model transformation in Horizon 3. The framework drives alignment across all business domains and helps prioritize key initiatives.

“As the industry moves forward with this fundamental transition to AI embedded into every business and technology function in the enterprise, IDC believes that every CEO will need to have an AI strategy — and generative AI is the trigger,” said Phil Carter, group VP, Thought Leadership research at IDC. “It is best to get started quickly. We are hopeful that this framework will help every organization develop their own path to impact.”