Welcome to the third part of our comprehensive 5-part best practice guide, where we will explore how Edge AI is revolutionizing sales strategies, bridging the gap between boardroom decisions and field execution in Consumer Goods companies.
CPG businesses develop their sales strategies at headquarters, but sales volume is driven in the field. The role of artificial intelligence is to stay as close as possible to the frontlines of sales, empowering your team to become more skilled, sophisticated, and self-sufficient in driving more sales. This is the core mission of Edge AI in CPG Retail Execution.
What is Edge AI?
The Edge AI concept gained traction in the 2010s alongside the development of the Internet of Things (IoT). An increasing number of edge devices were collecting data that required immediate analysis and response on-site. This raised a crucial question: Why transmit data to a server for processing and then back to the edge device when AI algorithms could run directly on the device itself?
Certainly, machine learning models require large datasets and significant computational power for training, which typically occurs in the cloud. However, once trained, these models can be deployed on local devices for real-time inference. This approach offers several advantages:
- Real-time analysis and intelligent decision-making
- Improved customer experience via AI-embedded product feedback
- Lower connectivity costs through reduced edge-to-cloud data traffic
- Continuous functionality, independent of network availability
- Minimized storage needs, with only essential data sent to core systems
- Enhanced data privacy at the device level
For field teams, Edge AI can operate on their mobile devices, enhancing their autonomy within established guidelines. This empowers them to make decisions and execute tasks independently, reducing the need to consult supervisors or managers when encountering anomalies.
Challenges of Edge AI
Despite its potential, Edge AI faces several obstacles that slow its adoption. The technology isn't suitable for all AI tasks, particularly those involving large and complex models, which remain better suited to cloud computing. Where AI can be deployed at the edge, it requires careful optimization for resource-constrained devices, balancing reliable performance with energy efficiency and processing power.
These complexities have extended the technology's maturation period. To address these challenges, a hybrid approach shows promise: simpler processing tasks can be performed on edge devices, while more complex and comprehensive operations are offloaded to cloud resources. This strategy leverages the strengths of both edge and cloud computing, potentially accelerating the widespread implementation of Edge AI.
Evolution of technology
Gartner first included Edge AI in its Emerging Technology Hype Cycle in 2018, projecting it would reach peak maturity - the "Plateau of Productivity" - within 5-10 years. As of 2024, six years later, it remains at the Peak of Inflated Expectations, though with an optimistic forecast to reach the "plateau" in less than two years. This timeline suggests companies should seriously consider adopting this technology now to capitalize on the advantages of being early adopters.
Several other Gartner predictions reinforce the imminent widespread applicability of the technology:
- By 2025, 75% of enterprise-generated data will be processed at the edge, outside traditional centralized data centers or the cloud.
- Machine learning will be involved in at least 50% of edge computing deployments by 2026, up from 5% in 2022.
- By 2027, two-thirds of all Tier 1 multichannel retailers will have implemented edge computing in their stores.
Regarding generative artificial intelligence (Gen AI), current training and public inference models are cloud-based. However, as smaller, domain-specific inference models develop, Gen AI will transition to edge deployment. Edge GenAI promises more efficient, secure, and contextualized models on local devices, enabling faster functionality, improved response times, and real-time applications. Nevertheless, Gartner estimates this technology is in its early stages, with full maturity expected in 5-10 years.
Some Edge Computing technologies have already matured and see widespread use. Computer Vision and Image Recognition lead this category. In the CPG sector, automatic shelf recognition technology exemplifies this, operating directly on devices without server connections. This approach ensures quicker results and functions independently of internet connectivity, which may be unreliable or unavailable in some stores.
Importance of EDGE AI for CPG Sales
AI is currently the most buzzworthy topic in CPG, yet alongside the hype, a certain skepticism is emerging within the industry. Demonstrating AI's business value often remains a challenge for CIOs, with existing investments continuing to yield returns that fall short of CEO expectations. This may partly stem from many AI use cases being tied to strategic issues such as cost reduction, supply chain optimization, or pricing policy. These applications, typically utilized at headquarters, often remain somewhat theoretical in nature.
A potential solution lies in focusing on more practical, tactical applications of AI, bringing it closer to the sales frontline. Here, AI-driven recommendations can be immediately implemented by field teams, with results that are clearly measurable.
Edge IA Retail Execution Use Case: Store-level Strategies & Playbooks
Sales strategies are often developed at the regional or channel level, offering broad direction but missing store-specific solutions. AI solves this by benchmarking customers, identifying opportunities and recommending targeted actions for each store. Edge AI goes further by tailoring store-level strategies and providing real-time playbooks to sales reps during store visits. It can recommend focusing on increasing orders for out-of-stock SKUs or launching local promotions for product A, which has performed well in similar locations – equipping reps with strong, data-backed arguments for store negotiations.
These Edge AI benefits – real-time, situation-based analytics and recommendations – are core to the functionality of Coach AI from Spring Global. This first-in-class AI-powered solution transforms field representatives into top-performing, autonomous professionals. Using Edge AI technology, Coach AI operates directly on the mobile devices of your field teams, guiding them to extreme performance in sales, retail execution, skills development, and product promotion. The uplift in effectiveness from Coach AI can be precisely measured, demonstrating significant performance gains across your field operations.
Addressing industry skepticism about AI, Coach AI provides a practical, measurable solution for CPG field execution. Edge AI technology helps align field reps more closely with corporate objectives, enhancing overall customer experience and corporate image by empowering representatives with the information they need to be consultative and bring added value to your customers.
