Information Retrieval (IR) when supplemented with Artificial Intelligence (AI) presents a promising intersection of technologies that immensely benefit business domains. Simply put, information retrieval is the activity of obtaining resource information, relevant to an information need, from a collection of data sources. This can be substantially enhanced by leveraging the capabilities of AI, providing a transformative approach to data interpretation and decision-making across diverse fields.

Information retrieval with AI possesses key attributes:

• Topical Relevance: AI-enabled IR systems excel in finding resources that are contextually and topically relevant to user query. This is crucial in extensive databases where manually searching for pertinent information proves challenging and time-consuming.
• AI Algorithms: Employing AI algorithms, such as machine learning and natural language processing, enhances the performance of the IR system, offering a more sophisticated relevance between query and resultant data.
• Robustness: IR with AI resists noise and inconsistencies in data due to its inherent resilience and adaptability, thereby retrieving useful, accurate and reliable information.
• Scalability: AI-enhanced IR systems are scalable to comprehend and manage large and complex data sets.
• Semantic Understanding: Advanced AI capabilities enable the system to understand the semantic context of the user query, thereby improving the relevance of search results.
• Automated Indexing: The AI-component of the system can automatically generate index terms from the document text, facilitating efficient and more accurate search results.
• Pain-Free Integration: These systems often seamlessly integrate into an organization’s pre-existing technological framework, reducing integration challenges.

AI technologies bring a host of benefits to information retrieval systems such as:

• Enhanced Precision: AI increases the precision of search outcomes by understanding the context behind user queries and delivering an accurately matched result.
• Quick Decision Making: AI-led information retrieval offers quick and real-time access to data which accelerates business decision-making processes.
• Reduced Human Error: By automating the data extraction and analysis processes, AI reduces the possibility of human errors and inconsistencies, thereby enhancing overall efficiency and accuracy.
• Improved UX: Intuitive and intelligent systems significantly improve user experience, offering personalized recommendations based on user behavior and preferences.
• Resourceful insights: Through pattern recognition and predictive analytics, AI can provide valuable insights for effective decision-making and strategy planning.

Despite the merits, AI in information retrieval does have potential hurdles:

• Algorithm Bias: Since AI models learn from data, they might unintentionally inherit biases existing in the training data which might affect the system's fairness and neutrality in information retrieval.
• Complex Implementation: Designing and implementing an AI-based IR system can be complex, and requires significant expertise in artificial intelligence and related technologies.
• High Cost: The cost of developing and maintaining AI-based systems could be a financial deterrent for small and medium businesses.
• Regular system updates: As AI technology rapidly evolves, constant updates and system maintenance are required to leverage its fullest potential.
• Security Concerns: AI systems demand a robust cybersecurity framework to safeguard sensitive information against potential threats and breaches, which can be a challenging task for organizations.

Closing the gap between potential drawbacks and realizing the full potential of AI-driven information retrieval systems necessitates strategic planning, thorough understanding of the technology, and careful evaluation.

A well-strategized implementation of AI in information retrieval requires a detailed understanding of business needs and judicious selection of AI techniques. It is of paramount importance to prepare a comprehensive plan evaluating the benefits against potential challenges and making sure that there is alignment with the organization’s requirements and objectives for successful deployment. Ensuring the system's scalability and adaptability to accommodate business growth and varied user needs should form part of an efficacious implementation strategy. Hence, both the execution of the system and its real-time monitoring is crucial for reaping the full benefits of integrating AI with Information Retrieval.