Today’s indispensable bound between Information Technology (IT) and Business bears heavy expectations on IT to enable firms achieve their strategic business goals and drive competitiveness [1] and revenue growth via offered Technology Solutions and Services. To assess achievement level associated with such expectations, mechanisms must exist for determining the relationship between Organization’s Technology investments and services provided by IT, enabling quantification of effectiveness. In spite of many methods and tools on the market for measuring the Return On Investment (ROI) [2], Net Present Values (NPV), etc., and various studies that have been conducted toward measuring IT’s Business effectiveness, the result has been mostly qualitative, speculative and hypothetical. A mechanism does not seem to exist for measuring [3] quantitatively the effectiveness of incurred technology investment in an organization by leveraging such concepts as Neural Nets or Fuzzy Logic. While Neural Network has been providing possibilities for solving problems in various fields such as Medicine, Engineering, Finance, Economics, etc., [4] its capabilities do not appear to have been explored adequately in the field of Information Technology. Hence, a research is being conducted to develop a Neural Nets/Expert Systems model that can identify within a firm the correlation between IT cost factors, IT services, percentage of utilized services by Business Functions and their associative technology costs inline with the percentage of contributions made by each Function toward achieving Business Objectives. Once developed, the model can calculate Yielded Unit Costs of IT Services and Business Objectives for comparison with their respective optimized unit costs to determine effectiveness and impact that Technology investment has caused on achieving Objectives during a given fiscal period. Neural Network’s modeling is used for developing patterns and quantifying correlations between various layers based on past experiences. Additionally, the model can more accurately forecast required Technology investment for an upcoming fiscal period.