Gen AI’s Impact in Manufacturing and CPG Industry

Introduction

Since the launch of the first widely adapted generative AI in 2022 i.e. ChatGPT, all the global industries are going through a paradigm shift. This includes both behavioral and mental shift challenging status quo which was prevalent from past decades. Seems like after the invention of internet in 1990’s, generative AI might claim as one of the disturbing innovations.

The journey of generative AI from traditional AI came a long way with advancement in how we generate contents at scale with all possible outcomes like text, videos, images, etc. This advancement in technology is a true reflection of technological growth and impact on existing and future generations. This impact is visible in all industries and specifically in CPG and manufacturing sector.

Generative AI in a Consumer Product Goods industry

Until last year, CPG industry had a reputation of following traditional and time-tested practices from product development, sourcing, supply chain, distribution, and customer engagement. With evident of generative AI, traditional business operating model have been challenged for next level innovation for growth and survival in highly competitive global environment. Below diagram depicts some of the core functional areas in a CPG industry.

Generative AI in a Consumer Product Goods industry

Generative AI Use Case in a Consumer Product Goods Industry

With advancement in Gen-AI technologies, there are several applications which leverage benefits of generative AI. Depending on business needs and priorities, either a strategic linear approach of transformation can be adapted or can pick and choose relevant use cases on a need basis. Below are some of the most common required transformations use cases in North America based CPG industry.

Generative AI Use Case in a Consumer Product Goods Industry

Gen-AI Adaption Challenges:

Once business goals are defined, below are some of the core Gen-AI implementation challenges CPG organizations are experiencing:

Create an organization wide Gen-AI policy which should be fluid enough to provide short term and long-term deliverables
Basic awareness training to key stakeholders and employees
Manage data security and cybersecurity risk which might surface due to new stream of generative contents capabilities
Technical architecture design to accommodate both in-house build and buy i.e. third-party solution provider integrations
Federated group or committee which help navigate with upfront decision about technologies and re-use across organization
Create solution which can also be adapted in international markets and languages
Prioritize Gen-AI use cases with Opex budget constraints.
The generation of AI-generated content raises ethical concerns, such as deepfake creation and copyright issues. Businesses must implement ethical guidelines and safeguards.

Skills Challenges:

Upskill existing technical resources to have understanding about LLM, Deep Learning, Integration tools, Available Gen-AI offerings like Chat-GPT, Google Gemini, Microsoft Co-Pilot, AWS-AI, Meta Llama, etc.
On-board appropriate consulting partners to help establish basic standards like AI-Ops, AI project management, etc.

Summary:

In general, Generative AI opens several transformation opportunities in this sector. Just like any new technological advancements, it comes with some risk that should be addressed upfront. Given the right environment and organization culture, leaders can leverage Gen-AI transformations for innovation, cost benefits, scalability, and competitive advantage for the organization. In this exciting journey, those who adopt will certainly hold the advantage to thrive!!