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As the biopharmaceutical industry explores innovative and novel ways to handle the complexities of drug formulation, artificial intelligence (AI) technology is experiencing a marked rise in incorporation. Processor-intensive tasks such as antibody engineering, which were once hindered by time and resource constraints, are now becoming quicker and more efficient thanks to AI.

AI antibody engineering employs machine learning algorithms and predictive analytics to streamline the process of formulating therapeutic monoclonal antibodies. These highly specialized antibodies, customized to target specific antigens within the body, have proven extremely useful in the treatment of a wide range of conditions, most noticeably oncological, autoimmune, and infectious diseases. However, the conventional methodology is often laborious, involving substantial trial and error to identify the optimal antibody for a particular ailment. AI aids in pioneering precise solutions, transforming the face of this complex process.

Essential to this transformation is the development of AI technology platform. They operate as the primary tool facilitating AI-driven discovery of therapeutic antibodies. The computational power of these platforms enables them to analyze massive datasets relating to protein structures, epitope-antibody interactions, and the responses of different antibodies to diverse antigens. They can recognize patterns and draw conclusions from this analysis, predicting which antibodies will have the highest affinity for specific antigens and which are most likely to be therapeutically effective.

Moreover, these platforms empower researchers to optimize the properties of medicinal monoclonal antibodies as per specific criteria, such as stability, expression capacity, and low immunogenicity. They can effectively and efficiently modify the characteristics of these antibodies at a molecular level, enhancing their overall therapeutic potential.

AI-based antibody screening is another crucial application in this domain. It has made a significant impact by drastically reducing the timeline and resources required to identify potential monoclonal antibodies that can be developed into drugs. Traditional screening methods typically analyze one antibody candidate at a time; however, AI can screen multiple candidates simultaneously, thus speeding up the process immensely. Furthermore, machine learning algorithms can 'learn' the traits of successful antibody candidates over time and apply that knowledge to predict future success rates of untested antibodies, boosting the efficiency of the screening process.

Already, several pharmaceutical firms have successfully adopted AI platforms to enhance their antibody engineering efforts. AI has proven valuable in managing the complexities of the process, reducing the timelines and costs associated with antibody drug development, and enabling the discovery of novel therapeutic antibodies.

Moreover, amid the COVID-19 crisis, AI's role was instrumental in this field. AI helped scientists rapidly design antibodies to neutralize the virus, highlighting the potential of AI in responding quickly to emerging global health threats.

However, while the possibilities of AI in antibody engineering are immense, it is not a replacement for human input. Its role, as with any technology, should be to augment human capabilities, not replace them. The future lies in combining the strengths of human and machine intelligence in a synergistic manner to accelerate the discovery and development of new antibody drugs.

AI in antibody engineering is undoubtedly a promising field, merging the immense potentials of both healthcare and technology. When harnessed correctly, it can advance drug discovery, contribute significantly to advanced personalized healthcare.