The buzz around Artificial Intelligence (AI) continues to grow, dominating conversations across industries and functions around digital transformation. The realm of Configure, Price, Quote (CPQ) software is no different. In fact, CPQ is beginning to emerge as a compelling example of how AI and machine learning can fundamentally redefine the way products and services are designed and executed. AI tools are augmenting existing CPQ applications by provisioning “intelligent agents” that can successfully complete quotes, as well as manage contracts, incentives and discounts across channels.
By automating these business processes, organizations now can boost cross-selling and up-selling, and also optimize prices based on the insights derived from exhaustive predictive analytics. As compared to spreadsheets, self-learning machines deliver a significant improvement in processing power as they can effectively–and quickly–mine huge volumes of data aggregated from varied sources. The result? Better, actionable insights that enable informed decision making for enhanced operating efficiency and business agility.
We expect 2018 to be the year when machines begin to reg-imagine CPQ by taking on greater workloads. Many CPQ vendors are already devising ways to integrate intelligent agents into their core applications, with select modules attracting special attention. Beside sales automation applications that merge the traditional organizational silos, the focus areas include real-time integration of AI and voice-activated CPQ workflows for efficient business management.
Apttus, the Quote-to-Cash software specialist, recently highlighted its ‘Maxcould’ intelligent agent that can receive voice commands in real time, and alter quotes and contracts accordingly. This use case, if implemented successfully on scale, could potentially eliminate issues such as pricing and contract delays that are among the most common pain points for sales worldwide. Over the next couple of years, I foresee CPQ vendors developing prototypes of self-configurable selling modules that will perfectly suit distribution-based business models.
Looking at a broader level over the long term, AI could influence the leadership facets of sales cycles as well. The insights generated by machines will drive up CPQ adoption rates substantially, boosting inter-department collaboration. Salesforce’s Einstein and other AI agents from the stable of Apttus, Infor, Oracle and SAP offer a glimpse into how this sphere of expertise is evolving. However, much remains to be done here as most of these agents are currently primarily functional for providing discount guidance. With time, they will gradually get involved more deeply in the automation of Special Pricing Requests (SPRs), a process that usually costs sales operation teams thousands of hours annually.
Another interesting dimension to explore in the context of AI reshaping CPQ is the role of cloud computing. Cloud-based CPQ providers must enable easy integration between in-house ERP and CRM systems. This will be the foundation for the next generation of intelligent selling. Legacy CPQ applications have struggled to foster this integration, often breaking down when tasked with linking ERP, CRM and pricing systems. With AI in the mix though, real-time integration is not only possible, but profitable too. It can let sales teams speed up sales cycles, and close deals faster.
Mobility is also coming to the fore as the CPQ–AI interplay gains momentum. Dedicated mobile apps are beginning to penetrate the CPQ ecosystem. Armed with smart AI that reduces quote times, and increases win rates, on-field sales teams can now get work done on the fly. By leveraging intelligent agents and mobile devices in tandem, field representatives can cross departmental and geographical boundaries, thus significantly improving customer responsiveness.
Overall, AI-powered CPQ, if designed and built thoughtfully, could empower organizations to synchronize multi-channel and omni-channel sales. The key here would lie in delivering intuitive, compelling user experiences while expanding sales channels across the board. And, AI-driven intelligent agents promise to facilitate the same through machine learning and real-time integration. Not only would this lead to dynamic tracking of customer responses, but it would also reduce information latency by providing customized updates for each unique situation. Indeed, the world of CPQ, as we have known it for many years, could undergo a sea change in the coming years, transforming sales processes in turn.
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