There are over 50 different CPQ systems available today, the majority of which rely on CRM applications as their primary system of record. That’s great for manufacturers who produce products that have little if any variation in them. Assemble-To-Order (ATO) and Make-To-Stock (MTS) manufacturers whose product strategies and business models don’t include product variation can use any CPQ system available today. CPQ vendors know that ATO and MTS manufacturers won’t challenge their application technologies and constraints. As a result, CPQ vendors are slowly becoming complacent, not pushing themselves as hard as if they had to scale their applications to support customers’ entirely new approaches to selling customizable products. There are those CPQ vendors who are embracing intelligence, machine learning and Artificial Intelligence (AI) which is the future of CPQ. These forward-thinking CPQ providers reflect the future, yet the majority are clinging to outdated beliefs of what manufacturers’ customers want. Configuration Lifecycle Management (CLM) is designed to provide manufacturers with greater scale, speed and a simplified framework for developing and launching configurable products. In 2017 and beyond, the success of product configuration strategies for all manufacturers will be based on more open, scalable architectures, not tightly controlled, closed applications like CPQ alone. CPQ strategies are powerful yet slow to react to market needs, takes years to develop and require full regression testing especially at the model and author levels, Data architectures were built to scale CRM data as the system of record. The future of CLM is an open one. Capitalizing on a single product database as the system of record, CLM scales across the enterprise, past constraints that limit CPQ systems’ value.The CLM landscape today and into 2018 and beyond is going to reflect the graphic below. The future of successful product selling strategies and new business models is going to be driven by CLM’s more open, scalable architecture. Transforming product knowledge into revenue growth is possible when all there’s a scalable system of record of all product configuration data that can flex to customers’ changing product needs.