How To Scale an AI and ML into Your Business (CPGs) - III

Updated: Mar 25


Scaling up AI and advanced analytics ­applications and building related capabilities is typically a two- to three-year process. (See Exhibit 3.) Before they get started, brands need to take a number of steps to avoid common roadblocks and ensure quick ­implementation.

Narrow Down the Choices

CPGs need to focus on just a handful of applications. Launching 10 to 15 initiatives ­simultaneously makes it more likely that those initiatives will get stuck at the PoC stage—in part because the attention of the senior managers overseeing them will be spread too thin. Companies that focus on a handful of opportunities (those with the greatest feasibility and potential impact) have a better chance of delivering them at scale. For example, a global fashion brand selected two areas to focus on: personalized consumer engagement and store assortment. This allowed the brand to make the necessary investments in data science and IT resources. It also ensured that there would be enough senior-level engagement to make the initiatives succeed.

Get High-Level Buy-In

Before launching any application, companies need to ensure that it will actually get traction. Top executives (typically country or brand general managers or above) should be willing to sponsor and pilot an application in their division or geography, and a senior business leader should be able to dedicate 20% to 30% of his or her time to steering its development. Brands should also work to ensure that there is significant interest among the local team members who will help launch the applications. Delivering an end-to-end AI and advanced analytics solution will add to their workload—and in the process will end up significantly challenging existing habits throughout the organization.

Assess Build-Versus-Buy Choices

Multiple vendors offer ways to address specific issues with off-the-shelf (or semi-­customizable) AI and advanced analytics software. While an off-the-shelf solution offers the advantage of speed, it comes at the expense of intellectual property ownership. It will also lack the functionality of a custom-­built solution, as well as any internal understanding that would come from building it in-house. CPGs should determine early on the areas best served by existing software and those for which it would be best to build their own solutions (potentially with partners, as long as they own the IP). These decisions should be based on the criticality of the process addressed as well as the company’s data advantage over its vendors. For example, a beauty company chose to build a custom solution for predicting market trends that was specific to its categories and hosted in its own environment. This allowed the brand to maintain complete control over a tool that could one day become a competitive advantage.

Address Market-Specific Needs

Consumer behaviors, digital ecosystems, and access to data assets have evolved in significantly different ways across major markets. Any market-specific constraints or requirements need to be tackled early on, or the organization risks encountering showstoppers late in the design or implementation phase.

Prepare for Impact

Deploying AI and advanced analytics solutions at scale typically requires building new technology environments (to feed, host, and maintain algorithms), adapting to the current ecosystem (for example, by replacing existing expert systems in some key functions, such as operations), and standardizing efforts around data structure and taxonomy. Such actions don’t need to be taken during the prototype phase, but they should be prepared for if a company wants to avoid having to scrap everything and begin anew after a first pilot has proved successful. For example, while it’s not necessary to clean and structure a global SKU database before prototyping a demand-­forecasting solution, it is important to create a first draft of a common taxonomy to describe related products.

Manage The Change

Introducing AI and advanced analytics solutions systematically challenges any existing decision-making processes and, in some cases, drastically reduces or even eliminates the time it takes to complete certain tasks. For example, a luxury goods company found that an effective demand forecasting engine led to a 60% to 80% reduction in the time its supply chain department spent on daily demand planning. To avoid organizational resistance, companies need to effectively communicate to their staff the impact of AI and advanced analytics ahead of time and give teams perspective on how these applications will affect their jobs and ways of working.

Deploying select AI and advanced analytics applications can yield significant benefits in the short term, but the broader opportunity for CPG companies lies in how these applications will help put the consumer at the center of their operating model. CPG companies can use AI and advanced analytics to translate consumer-­related data into insights and then disperse those insights throughout the organization—from product design to supply chain to marketing and sales.

To achieve that goal, investments in AI and advanced analytics will not be enough. It will require significant changes to ways of working throughout the organization, from board-level decision making to shop floor operations. It’s a long journey, but CPG companies can start small by focusing on a few applications and relentlessly pushing them from end to end.


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