Personalized medicine (PM) aims individualized approach to prevention, diagnosis, and treatment. Precision Medicine applies the paradigm of PM by defining groups of individuals with akin characteristics. Often the two terms have been used interchangeably. The quest for PM has been advancing for centuries as traditional nosology classification defines groups of clinical conditions with relatively similar prognoses and treatment options. However, any individual is characterized by a unique set of multiple characteristics and therefore the achievement of PM implies the determination of myriad demographic, epidemiological, clinical, laboratory, and imaging parameters. The accelerated identification of numerous biological variables associated with diverse health conditions contributes to the fulfillment of one of the pre-requisites for PM. The advent of multiplex analytical platforms contributes to the determination of thousands of biological parameters using minute amounts of serum or other biological matrixes. Finally, big data analysis and machine learning contribute to the processing and integration of the multiplexed data at the individual level, allowing for the personalized definition of susceptibility, diagnosis, prognosis, prevention, and treatment. Autoantibodies are traditional biomarkers for autoimmune diseases and can contribute to PM in many aspects, including identification of individuals at risk, early diagnosis, disease sub-phenotyping, definition of prognosis, and treatment, as well as monitoring disease activity. Herein we address how autoantibodies can promote PM in autoimmune diseases using the examples of systemic lupus erythematosus, antiphospholipid syndrome, rheumatoid arthritis, Sjögren syndrome, systemic sclerosis, idiopathic inflammatory myopathies, autoimmune hepatitis, primary biliary cholangitis, and autoimmune neurologic diseases.