In this article, learn about:
How AI is reshaping trade spend management for CPG companies
Practical applications of AI
Risks, limitations, and best practices for integrating AI tools
AI is taking the supply chain industry by storm—and trade spend management is no exception. Because trade spend is inherently complex, many CPGs struggle not only to achieve strong ROI but to measure it accurately in the first place. Between manual planning, fragmented data, and unpredictable shopper behavior, it’s easy for promotional budgets to underperform.
In this guide, we’ll explore how CPGs can leverage AI to optimize trade promotions, reduce inefficiencies, and better measure performance.
Using AI for Smarter Trade Spend
Historically, trade spend management has been a fairly manual process, relying on historical data and human intuition to make decisions. In addition, trade spend optimization has typically been a reactive process, with CPG companies responding to the success or failure of past promotions, rather than optimizing in real time.
The complex nature of trade spend management lends itself to several hurdles, such as:
Lack of visibility across the business: Sales, finance, and marketing often use different systems, making it difficult to get a single, accurate view of total spend and promotional performance.
Manual data entry and tracking: Many organizations still rely heavily on spreadsheets or disconnected tools, slowing down planning cycles and increasing the risk of human error.
Limited predictive insight: Without advanced analytics, teams struggle to use historical data to forecast outcomes, leaving planning reactive instead of strategic.
AI has the ability to address these hurdles and impact all phases of trade spend management, enabling CPG companies to adopt data-backed strategies at every stage—from planning and forecasting to real-time execution and post-event analysis.
Potential Uses
There are many ways AI can be integrated into trade spend workflows, whether through dedicated AI-powered trade management software or by using LLMs (Large Language Models) like ChatGPT to support analysis and decision-making. Here are some areas that AI can be used to improve trade spend management:
Task Automation
AI tools are useful for taking on tedious or time-consuming tasks, such as data entry, creating reports, or combining and analyzing data. This can free up employee time to focus on the creativity, strategy, and nuance of trade spend management.
Optimizing Product Placement
AI can be used to improve product placement decisions within a store. AI models can take in large amounts of data, such as market trends, consumer behavior data, and historical sales and then recommend product placement strategies.
Respond Faster to Market Shifts
AI-powered tools can also be used to help CPGs react more quickly to market changes. Instead of relying simply on analyzing historical performance, AI can continually scan performance data to identify patterns and trends, often before it becomes obvious in traditional reporting.
Another use of AI is for scenario planning. AI can be given plans and then model multiple potential outcomes. This allows teams planning the trade promotions to test for possible good and bad outcomes in advance, which can give the team a better understanding of potential risks and rewards, as well as plans that may need adjustments.
Analyzing Large Quantities of Data
AI makes it possible to analyze vast amounts of data. By using AI to examine data such as market trends, consumer behavior, and historical sales, businesses can more quickly plan future strategies, pinpoint weak spots, and detect market shifts. Even if a company cannot afford a third-party analytics provider, LLMs can still help with this type of analysis. These models can be trained on a company’s data to provide tailored insights and optimize trade spend, forecast demand, and make data-driven decisions.
However, it’s important to note that before providing any LLM or software company with your data, you should thoroughly understand the data privacy policies and ownership rights. Ensuring that sensitive business and customer information is protected is critical, and you should confirm how the data will be stored, used, and potentially shared and whether or not your business allows the use of a such tools.
Some businesses will generate AI agents of their own specifically for sensitive data use to avoid these risks.
Disadvantages
AI offers significant advantages, but it’s not without limitations. As companies adopt AI tools, it’s important to understand potential risks and challenges so they can be proactively managed. Specifically, having a company-wide policy on how to engage and utilize AI is an important step in protecting sensitive information.
Data Privacy and Consumer Trust
Most CPG companies won’t have the resources to build their own AI model, meaning they must rely on a third-party AI provider. This choice requires careful consideration of how the third-party maintains and uses the data necessary to manage trade promotions, such as POS data, retailer agreements, historical performance, and more. It’s important to ensure that any chosen third-party software or platforms have clear policies in place to govern data storage, security and encryption, and limits on how or when data can be accessed.
Relying on AI Alone
Even with AI innovations, human judgment is still a crucial part of trade spend management. Managing trade spend requires nuance, context, reasoning, and interpretation that AI alone cannot provide.
AI is a powerful tool to optimize and empower teams managing trade spend, not replace them. Human expertise is necessary to:
Check and validate AI recommendations
Provide context and company expertise
Interpret data
Make final decisions
Bias and Errors
AI algorithms are only as good as the data they train on. If the historical data has errors, inconsistencies, gaps, or embedded biases, this can skew the results produced and even lead to flawed recommendations. Regular auditing, developing clean data practices, and having human oversight of projects can help mitigate these risks.
Efficient Integration
Changing workflows is never easy, and even teams eager to implement AI tools may struggle to use them effectively. A study from MIT suggests that as many as 95% of GenAI pilots fail, highlighting the need to design AI systems for friction rather than avoid it. Examples include integrating AI into existing workflows, learning from user feedback, and evolving AI systems in line with the organization’s needs.
Summary
AI offers the opportunity to take trade spend management and optimization far beyond what was previously possible. Companies that thoughtfully integrate AI into their existing workflows (while also safeguarding against potential risks) can benefit from increased efficiency, faster response time and decision making, and a more well-rounded, data-driven approach to trade spend.
