Donna McGuckin in Consumer Goods Technology: Making the Most of Generative AI

An article written by Digital Wave Technology’s VP, Customer Advisory, Donna McGuckin. Find the original article published by Consumer Goods Technology here.

Making the Most of Generative AI Even With Limited Technical Teams

Have you ever tried to implement a new technology that promised fantastic return on investment, but never saw the results? If so, you’re not alone. Forty-nine percent of retail businesses regretted a software purchase made between Q2 2022 and Q3 2023, according to Gartner.

At the same time, the Josh Bersin Company found that CPGs often struggle to hire for their software, data, and product marketing teams, and only 0.1% of the global talent pool of data scientists work for CPGs.

Unfortunately, the talent deficit compounded by the risk associated with making a software purchase may falsely deter CPGs from implementing otherwise game-changing technologies, like generative AI. However, the beauty of generative AI is that it can serve as a co-pilot, maximizing ROI without large data teams or expensive technical resources.

Let’s examine how CPGs optimize the implementation of generative AI with existing resources.

Setting the Data Up for Success

Data is the foundation of digital innovation. Without complete and thorough data and security protocols, CPGs can’t grow or compete. Fortunately, generative AI can help business users clean data, fill in gaps, and validate data. Serving as a co-pilot, generative AI can automatically improve the data, while a few employees oversee the process and reviews for quality control. The best strategy is to integrate data with analytics and generative AI, incorporating science to the data to bring value.

For instance, product data can often be lacking, creating confusion for shoppers. Product data should be customer focused, with generative AI-driven attributes accounting for preferences in how consumers shop whether it be occasion, lifestyle, dietary preferences, nutritional information and more. Generative AI can analyze consumer reviews and provide automatic recommendations for how to improve product descriptions, product detail pages, product-specific marketing and even the product itself.

The more complete a CPG’s data is, the better its digital transformation will be. Generative AI can take the heavy lifting out of data management, opening doors for future innovation and efficiencies.

Starting Small and Building With Confidence

With an evolving technology like generative AI, it can be easy to become overwhelmed. Fortunately, the best implementations are managed through educated, incremental growth, propelling innovation without opening the brand to unnecessary risk.

First, technology leaders must identify where to begin their AI journey by evaluating their business needs. AI initiatives should be driven by purpose and strategy, not just a desire to experiment with the latest technology. These initial goals are also critical when it comes to determining success after implementation.

Once the use case is identified, CPGs must build confidence in the new tools through casual conversations and activities that encourage team members to experiment with the technology. Then, these conversations can shift towards showcasing how the technology will make jobs easier, serving as a co-pilot to daily tasks.

It is important for technology leaders to know what to measure to be successful. Don’t try to boil the ocean. Identify and focus on success metrics that really matter like revenue, margins, conversions, productivity, and efficiency. Once success has been established, CPGs can build incremental value by increasing generative AI use cases, developing new ways for the technology to improve the business and build adoption.

Choosing Tools That Scale

Implementing new technology can be costly and time consuming for CPGs so it’s important that brands invest in tools that will grow alongside them. For generative AI, there are use cases to support the business from every angle. It’s okay to start small, it’s a journey to build upon.

Today, CPGs often use generative AI to power personalization and direct-to-consumer opportunities. One way they do this is through product copy enhancements. If consumers value sustainability, brands may want to incorporate eco-friendly language into the product copy to build stronger relationships. For example, DTC brands can emphasize sustainable packaging on the product detail page or at checkout.

Generative AI can power stronger, more agile advertising campaigns. If a marketer creates an advertisement with global appeal, generative AI provides the ability to create variations translating to regional differences, including languages at scale in seconds. The co-pilot technology can also develop an advertisement that gives the design team a starting point.

Ultimately, generative AI gives CPGs the power to take the enterprise processes to the next level by increasing efficiencies at scale.

Establishing Generative AI as a Co-Pilot

Innovation is constant in the CPG industry so it’s pivotal that brands correctly implement technology that can take their businesses to the next level. By starting small and deliberately introducing generative AI as a co-pilot, CPGs can optimize the technology without requiring additional in-house resources, while simultaneously enabling their teams to lean into more strategic interactions.

—Donna McGuckin, VP of Customer Advisory, Digital Wave Technology

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