AI in retail: Use cases and prompts for CPG professionals
Key takeaways
Precision at scale: AI moves CPG professionals from generic "best guess" promotions to data-backed, SKU-level precision.
Hyper-personalization: Utilizing generative AI prompts can reduce content creation time by 80% while increasing engagement through behavioral segmentation.
Proactive execution: AI-driven retail execution tools identify planogram gaps and out-of-stocks in real-time, protecting promotional ROI.
Predictive innovation: Predictive models can forecast demand shifts and consumer sentiment up to six months in advance.
Executive summary
In the competitive landscape of 2026, TELUS Agriculture & Consumer Goods is empowering CPG professionals to bridge the gap between data and action. The integration of Artificial Intelligence (AI) has shifted from a "nice-to-have" innovation to the core of modern Trade Promotion Management. This blog explores how specific AI prompts can transform daily workflows, from spotting emerging market trends to automating the reconciliation of trade deductions, allowing teams to focus on strategic growth rather than manual data entry.
Understanding AI in the CPG retail industry
What is AI in a retail context?
In the retail and CPG sectors, AI refers to the application of machine learning (ML), computer vision, and generative models to harmonize massive datasets, such as POS, social sentiment, and supply chain logs, into actionable insights. For CPG professionals, this means having a digital "copilot" capable of predicting the outcome of a promotion before it even launches.
Why is AI relevant for CPG professionals today?
CPG companies that lead in AI adoption are seeing three times greater total shareholder returns compared to their peers, according to McKinsey. Forbes reports 90% of the retail industry is planning to increase AI budgets in 2026. Meaning? The technology is essential for managing narrowing margins and the increasing complexity of omnichannel retail.
The new CPG tech stack: predictive vs. generative tools
The modern tech stack is divided into two functional areas:
Predictive AI: The engine that handles demand forecasting, inventory rebalancing, and price optimization.
Generative AI: The interface that allows users to query data using natural language prompts, creating everything from marketing copy to vendor negotiation scripts.
Use Case 1: AI for promotional strategies
What are the benefits of AI-driven promotional strategy?
Inefficient trade spend can erode margins significantly. AI-powered Trade Promotion Management ensures that every dollar reduces inefficiencies, prioritizing spend toward incremental lift. By 2035, AI is expected to automate over 50% of current brand marketing activities, primarily in performance monitoring and strategy adjustment.
How can prompting improve promotional precision?
Professionals can use specific prompts to move beyond static reports.
Tactical optimization: "Analyze our [Season] promotional data for [Product Category] across [Retailer Name]. Identify which specific tactics (e.g., BOGO vs. 20% off) resulted in the highest incremental volume without cannibalizing our premium SKUs."
Predictive planning: "Based on our historical promotional data and upcoming [Market Event/Seasonality], predict the expected volume uplift and profit margin for a [Percentage] price reduction on [Product] over a two-week period."
Cannibalization alert: "Review our proposed promotional calendar for Q3 and identify any overlapping promotions across our brand portfolio that might lead to product cannibalization or trade fund misallocation."
Use Case 2: AI for customer segmentation
What is the role of AI in customer segmentation?
AI leverages diverse data points to create comprehensive consumer insights, grouping individuals based on evolving patterns and preferences.
What are effective prompts for personalized engagement?
Persona creation: "Based on our RFM (Recency, Frequency, Monetary) analysis, create three distinct customer personas for our new [Product]. For each persona, draft a personalized value propositions such as 'sustainability' or 'convenience'."
Journey-based engagement: "Segment our [Region] customer database by their current stage in the purchase journey. Generate a set of targeted discount codes designed specifically to convert 'first-time browsers' into 'repeat buyers'."
Churn mitigation: "Identify a segment of customers who previously purchased [Product] monthly but have not placed an order in the last 60 days. Suggest a 'win-back' promotional offer tailored to their past flavor preferences."
Use Case 3: AI for marketing research
How does AI spot trends before a macro-shift?
By analyzing social chatter and product reviews, AI can shorten concept-to-launch timelines by up to 80%. It detects early signals of demand, such as a sudden interest in specific ingredients or flavor profiles.
What prompts help win the innovation pipeline?
Competitive pain point analysis: "Summarize the top three complaints found in 1-star reviews of our top three competitors in the [Product Category] over the last six months. Suggest how we can position our upcoming product launch to address these specific pain points."
Emerging trend detection: "Analyze recent social media discussions and online forums related to [Product Category]. Identify emerging flavor profiles or ingredient preferences that have increased in mention volume by more than 20% over the last quarter."
Brand perception audit: "Evaluate the sentiment of our latest [Marketing Campaign] based on consumer comments across YouTube and Instagram. Contrast this with the sentiment of [Competitor Name]'s most recent campaign to identify our unique market strengths."
Use Case 4: AI for retail execution
How does AI bridge the gap between strategy and the shelf?
Retail Execution solutions use computer vision to digitize in-store conditions. Field reps can capture a photo of a shelf, and AI instantly detects missing SKUs or misplaced promotional signage.
What prompts optimize field team performance?
Field Team prioritization: "Rank our top 50 retail locations in [Region] by 'Promotional Compliance Score.' For the bottom 10 locations, identify the specific SKUs most frequently out-of-stock and suggest a revised route plan for our field reps to prioritize these stores tomorrow morning."
Compliance gap analysis: "Using the image data from our last 100 store audits, identify which retailers are consistently failing to implement our [Promotion Name] end-cap displays and suggest a tailored email script for our account managers to address this with those specific buyers."
Share-of-shelf tracking: "Analyze our current share-of-shelf versus [Competitor Name] in the [Category] aisle across [National Retailer]. Highlight specific stores where our shelf facing has decreased by more than 10% in the last month."
Digital transformation in CPG
Why is digital transformation necessary now?
Digital transformation is the foundation for AI. Without unified data—where shipment, POS, and trade spend data "speak" to each other—AI cannot provide accurate predictions. According to a CPG in Retail Focus report, 54% of leaders rank faster decision-making as the top expected outcome from their AI investments. AI can help synthesize internal data to prove the ROI of a new platform like TELUS Revenue Growth Management (RGM) Analytics.
Conclusion: the future of AI in retail
The future of CPG lies in artificial intelligence, both in predictive AI systems that handle forecasting and price optimization, and generative AI that allows users for data query and creative output using natural language prompts.
By mastering the art of the prompt, CPG professionals can unlock unprecedented levels of efficiency and growth.
From AI prompts to performance: powering promotions with TELUS
TELUS Agriculture & Consumer Goods offers a unified suite of tools designed for the modern professional, including:
Trade Promotion Management with AI Assistant, our award-winning FueliX generative AI built into our TPM software for natural language queries.
Retail Execution with AI-powered Rapid Image Recognition that turns shelf photos into sales insights in minutes.
FAQ
How do AI prompts help optimize promotional strategy in CPG?
AI prompts help optimize promotional strategy in CPG by allowing users to query large datasets in natural language to identify high-ROI tactics, SKU-level performance gaps, and specific opportunities for incremental lift.
How can AI improve trade promotion management?
AI can improve trade promotion management by automating manual deduction reconciliation, enforcing fund allocation policies, and providing predictive analytics to forecast the success of future promotional calendars.
Can AI integrate with Trade Promotion Management (TPM) software?
AI can integrate with Trade Promotion Management (TPM) software through embedded agents that streamline workflows, provide real-time reporting, and offer proactive recommendations to sales and finance teams.
Why is unified data important for AI in trade promotion management?
Unified data is important for AI in trade promotion management because AI models require a single "source of truth"—combining disparate data from POS, shipments, and retailers—to generate accurate forecasts and actionable insights.
