Text Generation Algorithms, also known as language generation models, are innovative machine learning models designed for creating written text. Their application is broad, ranging from automated content creation, generating product descriptions, customer engagement through chatbots, and numerous others.

• Broad Utility: They are used extensively across various sectors due to their ability to synthesize human-like text, responding to a wide variety of inputs, making them highly versatile.

• Complex Programming: These algorithms utilize intricate processes, often involving Natural Language Processing (NLP) and deep learning models, to generate appropriate and coherent human language.

• Customizable Outputs: Despite the standard programming, there is room for customization in terms of language style, tone, context, and relevancy, thus catering to varied needs.

• State-of-the-Art Technology: Text Generation Algorithms incorporate the most recent advancements in machine learning and artificial intelligence, making them increasingly sophisticated.

Just like COTS software, implementing Text Generation Algorithms calls for strategic planning and careful consideration of organizational needs. Understanding the strengths and limitations of these systems is essential in determining whether they are the right fit for the organization or not. A cost-benefit analysis is required to appreciate the economic dimensions of the decision to adopt these algorithms.

The selection of the right algorithm for one’s specific needs can be a complex task, given the widespread availability of these models. A careful examination of the algorithm’s design, its methodology, and its track record in generating high-quality content is vital. It is also beneficial to explore the type of user support provided by the algorithm’s developers.

There are numerous inherent advantages to using Text Generation Algorithms, which includes:

• Efficiency and Speed: These models have an edge over human capabilities in terms of speed, thus capable of generating vast amounts of text within a relatively short time, increasing overall efficiency.

• Cost-effectiveness: Implementing Text Generation Algorithms can be seen as a one-time investment, which subsequently reduces the recurring expenses of human resources usually required for content generation.

• Intelligent and Contextual Outputs: By leveraging deep learning models, these systems generate intelligent and contextual text that closely mimics human-generated content.

• Scalability: They offer significant scalability potential, and can easily adapt to increased demand without significant additional development work.

Despite their numerous benefits, there are a few challenges that organizations may face when implementing Text Generation Algorithms:

• Limited Creativity: As algorithms, they may fall short in terms of ingenuity and creativity often displayed by human writers. They're subject to their training and rules, which might limit novelty.

• Context Sensitivity: The algorithms may not fully comprehend the context or subtext of the desired content, leading to messages being misinterpreted or missing out on crucial details.

• Ethical Considerations: There may be concerns over the authenticity of the content generated by these algorithms, and using machine-generated text without proper disclosure may lead to ethical implications.

• Potential Misuses: These algorithms could be used to create misleading information or propaganda, hence it's crucial to monitor their application closely.

In conclusion, Text Generation Algorithms hold immense potential in our increasingly digital world. While they do have their challenges, the benefits they offer could make them an invaluable tool for businesses looking to reduce costs, increase efficiency, and embrace the latest advancements in AI and machine learning. By understanding the nuances of these algorithms, businesses can maximally harness their potential while simultaneously mitigating potential drawbacks.