Intelligent Information Retrieval (IIR) is a sophisticated technology that facilitates the extraction, evaluation, and presentation of necessary information in a nuanced manner. IIR deploys methods from artificial intelligence (AI) to streamline information handling, providing contextualized results for users' queries rather than mere keyword-based data.

The IIR framework incorporates several novel elements that enhance its capacity for effective data retrieval:

• Application of AI: Employing AI algorithms to retrieve the required information renders IIR technologically superior to basic, keyword-driven processes. It can understand context, analyze patterns, and deliver personalized results.
• Contextual Data Retrieval: As opposed to only identifying search terms, IIR examines the context of users' queries. This processing results in highly relevant, situational, and individualized search results.
• Adaptive Learning: Intelligent information retrieval systems can adapt and learn from user behaviors and preferences, improving their ability to provide tailored search results over time.
• Data Structuring: IIR can efficiently handle and organize unstructured data, enabling more precise information identification and retrieval.

These system characteristics contribute significantly to IIR’s broad acceptance across varying industries.

A well-executed IIR implementation process starts with a detailed understanding of an organization’s information needs, followed by the careful selection of an appropriate IIR system. It's also essential to consider the return on investment, as well as the technical support and services provided by the software vendor.

Successful IIR implementation also necessitates a training phase for staff to become familiar with the software's intricacies. With stringent planning, regular monitoring, and flexibility to adapt, IIR can prove instrumental in handling large volumes of information with precision, relevance, and effectiveness, providing a transformative advantage to organizations.

The IIR framework endows users with several extremely beneficial features:

• Advanced Search Results: Utilizing AI algorithms, IIR delivers superior, contextually appropriate results rather than merely keyword-based outcomes. This functionality reduces the time and effort users spend on sifting through irrelevant data.
• Adaptability: As IIR learns from specific user behaviors and preferences, it adapts to fine-tune results, enhancing its pertinence and utility over time.
• Improved Handling of Unstructured Data: IIR’s ability to manage, sort and retrieve from unstructured data presents a significant advancement over traditional systems.
• Automated Processes: IIR can automate many processes, ultimately elevating operational efficiency.

However, despite its many benefits, challenges with IIR exist as well.

Effective implementation of IIR has some potential setbacks:

• Dependence on AI: Complete reliance on AI for accurate results may become problematic if glitches or unanticipated problems arise within AI systems.
• Data Privacy Concerns: A system capable of learning user behaviors, needs, and preferences invariably raises data privacy issues.
• Complex Set Up: Deploying an IIR method may require a complex setup process, demanding technical proficiency, training, and time.
• Investment: Implementing an IIR method can involve significant investments, potentially deterring smaller organizations or budget-conscious ones.

In conclusion, Intelligent Information Retrieval, with its ability to learn, adapt, and provide contextually accurate results, is evolving into an indispensable tool. However, organizations must thoroughly consider its potential drawbacks and develop strategies to address these challenges effectively in the implementation process. The ultimate goal should be to leverage the full potential of IIR while ensuring data security and efficiency.