Generative AI (gen AI) promises to transform business processes.1 The value added from gen AI could reach $ 4.4 trillion per year, according to McKinsey analysis. Historically, the packaging industry has lagged behind other industries in adopting new digital technologies—including traditional AI and machine learning.2 However, even in this industry, excitement is building around generative AI.
We conducted a survey of more than 200 paper and packaging executives across substrates and geographies to understand the current state of gen AI adoption in the industry, identify areas where gen AI can create the most value, and pinpoint key challenges with gen AI adoption at scale.
The survey finds that executives see plenty of potential for gen AI to drive business value in their organizations and are willing to invest in it. Although only about one in four respondents report that their companies are actively engaged in gen AI efforts, most of those respondents said the impact of gen AI is already meeting or exceeding their expectations.
In this article, we draw on results from the survey to identify opportunities for packaging companies to capture value from adopting gen AI.
Leaders see potential for gen AI in packaging
The intention to use gen AI is there—and some actions are already under way. Approximately 95 percent of survey respondents believe their companies should invest in gen AI, and approximately 77 percent say their companies have moderate to strong intentions to use gen AI in the near future (Exhibit 1).
One thing that might be standing in the way of immediate investment and deployment is a lack of deep expertise: only 30 percent say their company leaders have a high degree of knowledge and understanding of gen AI’s potential. Indeed, only 24 percent of survey respondents report that they either have launched (13 percent) or are developing (11 percent) gen AI tools or solutions in their area of work. Most of these early efforts have paid off: 62 percent of those who have adopted gen AI describe the level of impact postimplementation to be at or above what they anticipated (Exhibit 2).
Respondents see the potential for gen AI to have a substantial impact on both growth and productivity. A majority of respondents expect gen AI to enable more than 8 percent revenue growth and more than 6 percent cost savings (Exhibit 3).
Gen AI opportunities exist across the packaging and paper value chain
There are multiple areas in which gen AI can drive value for paper and packaging companies—many more than can benefit from traditional AI, mainly because of gen AI’s ability to work with messy data and to generate new content, and because gen AI’s accessible user interface makes adoption easier. Respondents believe that gen AI can be applied across all key functions in the industry, from accelerating the R&D pipeline to improving back-office functions (table).
Generative AI offers value-creating opportunities across functions
| R&D | Commercial | Supply chain | Manufacturing | Procurement | Corporate functions |
|---|---|---|---|---|---|
| Intellectual property/patent-competitive analysis | Real-time adviser for sales reps | Warehouse design using digital simulation | Maintenance scheduling and work order management | Category market insight report | Self-serve and automated HR functions |
| Rapid idea to visualization | Optimization marketing elements through automated A/B testing | Order shipment optimization | Schedule enhancement | Fraud pattern recognition | Generative AI meeting support |
| Rapid consumer test and product iterations | Leads identification and prioritization | Automation of warehouse operations through real-time analytics | Issue identification using image/video processing | Contract analytics and term optimization | Summarize financial announcements |
| Prompt design to optimize for parameters, eg, strength, materials, manufacturability | Automate the creation of omnichannel marketing workflows | Route enhancement | Production plan optimization | Purchase order status chatbot | Financial planning dashboard |
| Voice-of-the-customer analysis | Upselling and cross-selling recommendations | Risk management support, with data aggregation and synthesis | Reach golden-batch conditions | AI contract repository analyzer | Automate accounting with data classification |
Innovation and R&D are key drivers for growth in the paper and packaging industry, especially given the global focus on sustainability and a transition to green materials. Respondents believe that gen AI can boost R&D productivity by assisting each step of the R&D life cycle: helping companies generate new ideas through intellectual property (IP) and patent analytics, providing customer analysis, generating customization options, accelerating the idea-to-visualization process, and streamlining field-testing feedback. As an example of how it’s already being used, one large plastics packaging company is integrating gen AI into its design studio to create user-centric packaging designs that comply with sustainability standards.
In the paper and packaging industry, as in other sectors, the commercial function is where the enthusiasm for the impact of gen AI is highest. Respondents expect gen AI to affect every commercial process, from improving demand generation (for example, by optimizing marketing spend and providing data-driven lead generation and prioritization) to increasing the productivity of sales teams through tools such as real-time sales advisers, semiautomated omnichannel workflows, and personalized customer outreach content. Gen AI is also expected to improve pricing capabilities by giving companies greater visibility into their costs and combining this information with external data such as market insights and customers’ willingness to pay—thereby enabling companies to maximize their end-to-end margins.
Overall, gen AI is expected to increase efficiency across all major cost drivers: respondents believe it will help streamline the supply chain and manufacturing processes, facilitate the procurement of raw materials, and automate support functions. Companies are already experimenting with a variety of gen AI use cases. For example, a large paper products manufacturer is using a gen AI tool to automate its order management process and personalize order processing to each customer’s expectations. Another company is using gen AI solutions to enhance visual inspection systems for waste, aiming to improve the quality of recycled paper and cardboard. Many players have started using gen-AI-enabled customer support chatbots to personalize responses while reducing costs.
Companies face challenges in adopting gen AI
While there is excitement about the potential of gen AI in the paper and packaging industry, respondents cite several challenges that hinder quick and seamless adoption of this technology (Exhibit 4). The biggest challenges executives report are limited access to data and to a modern tech stack, as well as concerns about how gen AI adoption could raise privacy and IP problems. Respondents also report having a limited understanding of specific use cases that could drive value from adopting gen AI and express concerns about the associated costs.
How to get started
The cumulative potential of gen AI means that paper and packaging companies that deploy these technologies—and do so ahead of the competition—can unlock considerable business value and achieve a competitive advantage.
According to our research and experience, successful gen AI transformations require building capabilities in six areas3:
- developing a gen AI strategy in alignment with the overall technology strategy to achieve a competitive advantage
- setting up a scalable tech stack and infrastructure to support multiple gen AI solutions
- building a robust data foundation to scale gen AI across the organization
- defining an operating model that brings business, operations, and technology together
- identifying and retaining the right talent and skills required to advance gen AI
- ensuring adoption at scale while also managing risk and responsible use
As companies get started, it’s important to balance quick impact with transformative opportunities to maintain momentum. We suggest companies launch two use cases from any part of the value chain that can deliver quick impact and build excitement, along with two additional use cases that may take longer to implement but can bring about large-scale improvements across the entire business.
Successful adoption of gen AI won’t happen without a focus on change management. And, crucially, gen AI solutions should be developed and deployed in a safe, trustworthy, and ethical manner, with a careful eye toward preventing bias and discrimination and ensuring transparency and explainability. Managing risk requires building a governance structure that provides oversight, supports rapid decision making, and includes training for users.
Generative AI is still rapidly evolving; the opportunities to harness its power throughout the paper and packaging value chain will only grow. Companies would do well to develop a gen AI strategy now and start to build the capabilities that will allow them to incorporate gen AI into their business processes. By doing so, they are likely to gain a competitive edge as leaders in the forthcoming industry transformation.
