Updated: Mar 25
AI and Advanced Data Analytics - II
While determining which AI and advanced analytics applications would be most effective is relatively simple, deploying them across the organization is a task that still eludes most CPGs. Of the 30 CPGs in our study, all had started working on at least one AI and advanced analytics application and half of them on four or more. (See Exhibit 2.) But none had taken a single application and administered it throughout the organization.
BARRIERS TO SCALING UP
Most of the CPG executives we interviewed for our study said scaling up AI and advanced analytics—and making sure they’re adopted—is now a key topic of discussion among senior leaders. But they also cited a number of challenges. Scaling up even one application is difficult, with significant investment and managerial efforts required across the organization.
It is typically necessary to build a small proof of concept (PoC) for a specific application on a specific brand and in a specific country. But deploying that PoC at scale will often require work on multiple fronts: on the AI and advanced analytics themselves, so that they’re robust enough to be deployed globally across the entire organization; on the data, to solidify its quality and standardize its taxonomy across countries and brands; and on existing IT systems, which may be made redundant by new AI and advanced analytics applications or require significant changes before they can either provide or receive data from the applications.
Other areas that need attention include existing business processes, managerial routines, and job descriptions, because AI and advanced analytics will alter any current decision-making processes, automate manual tasks and calculations, and in the process, change the day-to-day duties of a large number of employees and managers. And finally, talent and skills, because building and maintaining applications requires a talent pool (data scientists, engineers, and analysts) that CPGs will need to at least partially develop to avoid relying solely on third-party vendors.
In considering these difficulties, we identified six distinct roadblocks to effectively scaling up AI and advanced analytics at CPG organizations:
Lack of Vision. The organization makes only a limited effort to evaluate the impact of AI and advanced analytics, along with the associated size of the prize, and to educate senior management accordingly, which limits the willingness to invest.
Insufficient Prioritization. This leads to a “PoC explosion,” which dilutes efforts. The organization launches multiple small tests with various vendors but performs no follow-through. It also fails to put the necessary effort into industrializing, scaling up, and rolling out the application.
Talent Gap. Difficulty identifying, recruiting, and retaining the right talent (data scientists, engineers, analysts) leads to an overreliance on outside vendors, which makes it hard to control the execution. Meanwhile, the organization makes multiple attempts to develop local expertise, but it often lacks critical mass.
Limited Data Governance. The organization has no processes in place for data management, quality, or ownership, nor does it have common (cross-division, cross-country) data taxonomies to facilitate scaling up.
Underestimated Impact. The organization misjudges the level of investment required in change management and in shoring up any related skills. It fails to fully anticipate the impact that AI and advanced analytics will have on existing business processes, decision-making processes, and managerial routines, as well as on employees’ daily jobs and required skills.
Inadequate Market Specificity. The organization doesn’t consider how digital ecosystems, data availability, channel dynamics, and vendor capabilities vary across markets. It also fails to recognize the differences in requirements, priorities, and constraints among different markets.
Dr.Ella Burcu Keskin