Developing and Building Meaningful Artificial Intelligence

Artificial intelligence (AI) has quickly evolved from buzzword to vital business capability. The technology is being embedded into every key business function throughout the enterprise, providing forward-thinking consumer goods companies with unparalleled insight, control and performance. The fact that AI is providing CGs with unprecedented analytic firepower is not particularly newsworthy ― the technology has become table stakes in vital business functions like demand planning and supply chain execution. But what is of interest is how leading CGs are developing these game-changing enhancements to their tech stack. Are they doing the work themselves? Working in conjunction with a technology partner? Or turning the whole exercise over to a third party? In this Targeted Research report we examine how leading CGs are tackling the AI development conundrum and where they are on their AI investment roadmap. Are you keeping up with CG's pacesetters? Read on to find out! You would be hard-pressed to find a consumer goods technology executive who does not wholeheartedly believe in the power of artificial intelligence. This technology is a vital tool today and will only become more powerful in the years to come as it is refined and the use cases become more plentiful. While having clear and tangible benefits does not necessarily mean a technology has organizational support, that is thankfully not the case with AI. In fact, an overwhelming majority (70%) of respondents report that their organization’s leadership is committed to leveraging AI in business design and execution and implementing the technology in the field. In addition, 60% of surveyed CGs report that their organization is willing to invest the time and resources required to validate new AI initiatives through proof of results and timely feedback. However, that same statistic can be viewed from a glass-half-empty perspective, with 40% of CGs unwilling to invest the time to validate new AI initiatives ― which may leave many playing catch up in the years to come. The majority of CG organizations are committed to developing and deploying AI now and into the future, but they might not have the proper culture and capacity in place to fully leverage these next-gen developments. Sixty-three percent of respondents report that their organization does not have the change management maturity and cross-functional leadership to take ownership of next-gen AI initiatives. With the majority of CGs reporting they lack the change management and internal leadership to take ownership of AI, it is no surprise that many are turning to outside sources for help. Twenty-seven percent of CGs report that their AI development approach is powered completely by an outside vendor, and 61% work hand-in-hand with outside vendors to develop new AI use cases. Just 12% of CGs report that they have the AI development firepower in-house to cultivate new initiatives without third-party assistance. When it comes to scaling a new initiative from a lab environment to full-scale production, the numbers reveal once again that CGs are dependent on outside sources ― 62% report partner support is required. Visibility into consumer purchase rates and overall store inventory in near-real time is critical for effective supply chain planning, especially when CGs are leveraging AI capabilities. However, 59% of CG organizations report that they are unable to drive meaningful AI initiatives due to a lack of this vital visibility. Developing a successful AI roadmap requires more than just committing to its development; it requires pinpointing the areas where the technology can make a meaningful contribution to the organization and the bottom line. We asked CGs what areas could best benefit from an AI-based analytic approach, and demand planning topped the list with 63%. Uncovering demand shifts before they occur has always been and will remain a cornerstone of success for CG manufacturers. Joining demand planning as top priorities are revenue growth management (50%), promotion execution (37%) and supply chain execution (17%). Embedding meaningful AI into these business functions would greatly increase their capabilities, but many CGs lack the internal resources to build and deploy these competences. We asked survey respondents if they are interested in leveraging the native AI capabilities of the public cloud, and 38% report they are interested in building a use case; 24% say they are interested in using the full stack; and 7% say they are already leveraging the power of public cloud AI. It is one thing to pinpoint the need for artificial intelligence in key areas throughout the organization; it is quite another to allocate investment. We asked CGs for their timeline for building AI capabilities into key business functions. Over the next three to six months 21% will be leveraging AI to improve promotion execution, and 10% have planned upgrades for revenue growth management, supply chain execution and suggested ordering. When the timeline is stretched to six to 12 months, product or partner data harmonization (21%) and suggested ordering (20%) rise to the top of the list. Looking even further out, CGs will be embedding AI in demand planning (29%), revenue growth management (28%) and supply chain execution (21%) in the next 12 to 24 months. With all this planned investment in critical business functions, it is important to explore how leading CGs evaluate the success of a proof of concept. Forty-five percent report that a project can be considered successfully if it can be clearly explained to key stakeholders, 34% rely on a successful field test, 14% examine the total cost of ownership, and 7% weigh the solutions ability to provide benefits to other key business areas. And what is the expected ROI timeline for these projects? According to the survey respondents, they are looking for fast return on AI investment, with 69% reporting they expect a return within 12 months. Artificial intelligence development and deployment requires a specialized toolset and a highly skilled workforce to executive effectively and economically. When asked if they have the talent pool to pull this off, 47% of CGs answered flat-out no. Those who do have the in-house talent rely on data scientists (37%), technical architects (33%), subject matter experts (30%), data engineers (20%) and compliance and model governance experts (13%) to get the job done. And once this tech has been developed, tested and deployed, who owns the change management initiatives needed to ensure success? In 52% of CGs, that is handled by individual business units, while 27% rely on IT and 17% on a data organization. Methodology This research was conducted in June and July of this year, targeting high-ranking headquarters staff at major consumer goods manufacturers. Forty-five percent of survey takers work at companies with annual revenue of $1 billion or more, and nearly a third (31%) are employed at companies with revenue north of $5 billion. Not surprisingly, consumer packaged goods manufacturers are far and away the most represented vertical in this research (60%), but a significant number of survey takers report they work for specialty (23%) and apparel/footwear and accessories (17%) manufacturers as well. Seventy-three percent of respondents hold titles of director level or higher, with 30% having designations of VP or higher. In terms of key business functions, respondents are greatly varied, but IT/technology (27%), marketing (20%) and insights/analytics (17%) are the most greatly represented. Conclusion Artificial intelligence has the potential to supercharge every major consumer goods business function, and savvy CGs are hard at work developing, field testing and deploying game-changing AI-powered technology that will set them apart from the competition. But they cannot do it alone. Constructing an internal team of AI experts is costly and difficult, and even the most advanced organizations rely on third-party partners to augment their internal resources, with some even turning AI development over entirely to a technology partner. AI is being leveraged throughout the consumer goods industry in various forms, but only through continued investment and commitment to its development will CGs reap the potential massive rewards. Key AI Development Takeaways: An overwhelming majority (70%) of CGs report that their organization’s leadership is committed to leveraging AI in business design and execution and implementing the technology in the field. Just 12% of CGs report that they have the AI development firepower in-house to develop new initiatives without third-party assistance. 59% of CG’s don’t believe they have comprehensive data sets to executive on a next-gen AI initiative. 63% of CGs named demand planning as the area that could best benefit from an AI-based analytic approach. In the next three to six months 21% of CGs will be leveraging AI to improve promotion execution, and 10% have planned upgrades for revenue growth management, supply chain execution and suggested ordering. CGs demand fast ROI for their AI investments ― 69% expect a return within 12 months. 47% of CGs do not have the internal talent pool to effectively manage their AI development needs.

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