Breaking down the benefits and pitfalls of AI implementation in B2B manufacturing
Last year, no topic at the intersection of business and technology generated more discussion than artificial intelligence (AI) and its impacts. As we look ahead to 2024, it’s helpful to narrow the focus and examine how manufacturers specifically are likely to implement AI and unpack how those implementations will enhance operations and the overall customer experience.
AI trends to look out for
Some of the more visible areas where manufacturers will implement AI are in low-stakes, high-effort areas, such as the development of product brochures, technical manuals and other documentation. Not only do these areas require a significant amount of effort to maintain and keep current, but they are also lower risk to wade into leveraging AI.
As products continually change over time, process manuals, sales brochures and other content need consistent updating. B2B manufacturers can use AI to help with this by feeding their previous sales brochures into generative AI, that can then rapidly create an accurate, updated brochure. Manufacturers who have not already implemented or are planning to implement AI to improve efficiency in content generation in these technical and marketing-oriented areas are already falling behind.
Manufacturers can also expect to see AI implemented to enhance the customer feedback loop. AI can significantly speed up the flow of feedback from customers to companies, which will ultimately improve the customer experience.
In fact, many companies currently receive more customer feedback than they can effectively digest and analyze. AI can expedite the process of mining that data and identifying opportunities for responding to customer needs. B2B manufacturers can even begin recording conversations with customers and product ambassadors at industry events and later use AI to aggregate and analyze those conversations for insights.
Today, it’s not unusual for companies to generate consumer insights using six-month customer research panels. AI’s ability to consume and assess vast amounts of information in very short timeframes will give manufacturers a way to identify insights and improve processes much faster, allowing some to become industry leaders in anticipating and responding to marketplace changes.
The benefits of implementing AI in B2B manufacturing
Aside from the trends that B2B manufacturers will start to see with AI implementation this year, it’s important for executives and investors to understand how they will benefit from these investments.
- Process automation. Considering that manufacturing companies are often already at the cutting-edge of process efficiency, it’s likely that companies are already using AI to further enhance and augment new and existing automation efforts.
More specifically, in robotic process automation, AI will help identify areas that may have been overlooked for automation in the past but would benefit from it. This would contribute to additional efficiency improvement, even for companies that have already launched efficiency enhancement efforts.
- Demand forecasting. Many manufacturers already use sophisticated tools for demand forecasting and inventing management. However, AI gives them the ability to quickly combine intelligence from multiple business areas and leverage it to generate enhanced predictive forecasting.
- Quality control and safety. Leveraging AI-enabled imaging and other sensor data can help improve quality control during the production process. Early detection of defects and anomalies can prevent downstream disasters in the form of large-scale recalls (and worse).
In its current iteration, AI is much more user-friendly than in the past, and companies that can democratize AI implementations across their organizations have the potential to reap AI’s benefits more broadly. However, implementation is not always easy – but with proper training and a few guidelines, manufacturers can avoid some common pitfalls.
Beware of pitfalls
Despite its benefits, it’s important to keep in mind a few issues associated with AI implementations. First, manufacturers should never implement AI into their business simply because others are doing it or because they believe they’re supposed to. Companies must identify specific benefits that AI can deliver and how it can enhance operations or improve the experience for their customers.
It’s also critical to address the significant legal, ethical and privacy constraints before implementing AI. For example, food and beverage manufacturers that make products for diabetic customers or those with dietary restrictions need to be aware of the issues around using AI to generate content related to those health challenges.
Additionally, it’s extremely important that manufacturers also implement proper training and guidelines for any employees that will interact with it. AI doesn’t ease the burdens associated with legal, ethical and compliance issues. The decision to implement AI demands that manufacturers focus more intently on these issues and ensure that their employees are as equally well-versed.
Being aware of these pitfalls will ensure that a company’s AI implementations deliver the operational and customer experience benefits expected.
Overall, 2024 offers a unique opportunity for B2B manufacturers to begin investing in AI to create more efficient and effective business operations and customer relations. While AI can be overwhelming to navigate, it also offers great opportunity to streamline common processes and elevate experiences. With the right partner with deep expertise in data, insights and AI, businesses can begin to become AI-ready and create a roadmap that makes sense for their unique business needs.
Arun Kumar, is EVP, Data and Insights – Global Practice Leader at Hero Digital. Arun believes organizations need to combine technology at scale with the power of human insight and empathy to develop meaningful, relevant, and experience-based relationships with constituents. He has led teams for some of the world’s top agencies including Wunderman Thompson and Publicis Sapient. Arun has helped build multi-channel touchpoints and direct-to-consumer strategies for brands like The American Red Cross, Bose, Carnival, Newell Brands, and TD Bank.