The consumer products (CP) industry is at a pivotal juncture, facing myriad challenges that threaten to disrupt traditional business models. From rising operational costs and shifting consumer preferences to increased competition and supply chain complexities, CP companies must adapt to survive. This makes now the right time to focus on technologies like artificial intelligence (AI), particularly in global business services (GBS), where it can drive improvements in cost, quality and competitiveness. CP leaders can deliver cost savings, quality improvements, operational efficiencies, and multiply the impact of each employee in both the back and front office operations by reinventing how GBS services are delivered using agentic AI. As illustrated in our first article in a series on agentic AI in CP, leveraging agentic AI can streamline operations, enhance customer experiences and ultimately drive profitability. In this second article, we explore the key topics surrounding the implementation of an agentic AI–first strategy in GBS and offer actionable recommendations for industry leaders.
Reinventing the operating model
While agentic AI and generative AI promise to make dramatic improvements to productivity and workflows for knowledge workers and back-office functions like invoicing, payroll and benefits management, purely iterative approaches to individual workflows can only take companies so far. Organizations often make enormous efforts to understand the current state of a business process and then re-engineering a people-centric approach to adjusting the workflow, optimizing the process and tackling one-off opportunities for improvement. This approach moves the ball forward and yields short-term gains but often creates a patchwork of new solutions that further entrench both data and workflow silos. What’s needed is an approach that both accelerates cost takeout and maximizes workforce investments.
Instead, CP companies have an opportunity to leap forward with a unified, AI-first model where agents handle requests, collaborate with other agents, and humans shift into roles as orchestrators and strategic decision-makers. This isn’t about removing humans but rather elevating them. Human involvement becomes purposeful, arriving at the right moment with the right context to drive the impact.
Take for example a typical invoicing process for a large CP firm. Manual processes such as invoice capture, validation, exception handling and functions handled by Enterprise Resource Planning (ERP) platforms could each be improved individually through automation and by providing workers with new tools to improve the workflow and reduce human errors.
Building a new operating model with an enterprise AI system, Retrieval-augmented Generation (RAG) pipeline and large language model can shift most of this work from humans to AI agents. Beyond efficiencies and the reduction of human errors, this approach can have profound implications on labor cost structures, data insights and the speed of execution.
Benefits and impacts of an AI-first model in GBS
By moving to a fully automated agentic model, CP companies can optimize costs, reimagine their value chains and improve productivity, all while maintaining the flexibility to scale operations in response to market demands.
