AI in Agriculture to Enhance Agri Customer Experience

Sourav Sengupta

What is the first thing you read on product labels before buying any packaged food item—the source of food, the price, or the nutritional information? Our guess is the nutritional information. According to the Charleston Orwig study on food labeling and consumer behavior, 71% of consumers expect nutritional information to be easy to find. Yet, only 7% of all manufacturers provide essential information on their labels. With price, nutritional facts, and ingredients becoming crucial in the product buying decision, agribusiness organizations must meet the end-user demands of providing comprehensive information on food ingredients and nutritional values for products in the expanding packaged foods segment.

Providing nutritional information on food packaging labels is a way for companies to communicate the authenticity of their products to their customers. Such information also apprises the end consumer of the source of the product. To offer this information, food companies are now familiarizing themselves with farming operations and are adopting technologies such as blockchain to enhance customer experience (CX).

To illustrate, Dutch supermarket major Albert Heijin has deployed blockchain in its orange juice product to ensure it can be traced across the supply chain. According to FoodNavigator, a news publication for the food industry, consumers can scan QR codes on orange juice packs at Albert Heijin and see the entire journey the orange juice has taken from farm to supermarket.

With such visibility into the supply chain, agribusiness companies can negotiate lucrative deals for the farming community while also creating innovative food products with consumer product goods (CPG) companies, thus enabling CX transformation. In addition, as consumers expect products to be available through the year, on demand, agribusiness companies need to expand their supply network to improve sourcing, distribution, and enhance end-product variety. At the same time, to meet evolving consumer demands on health, safety, and traceability, these firms must also adhere to stringent regulatory requirements on packaging and labeling.

Building Resilience with a Neural CX Network

Agribusinesses can better engage with their customers and create exponential business value by adopting neural traits into their operations and business processes. These traits are derived from the Neural ManufacturingTM framework, where the value chains, including those in agribusinesses, are connected, cognitive, and collaborative.

To enable neural traits, agribusinesses need to understand where customers want to engage with them, what needs must be fulfilled, and how they want to engage with a brand. By creating neural CX networks, organizations can build systems that provide sufficient early warnings to sense shifts in demand, address logistical challenges, and provide a personalized CX. By deriving actionable insights through improved market signals, an organization can handle any external shocks, drive customer retention and support, ensure greater customer responsiveness, and improve brand engagement.

An example of how agribusinesses can create neural experience networks for their customers is through personalized experiences. A leading agri input company used augmented and virtual reality to build a digital portal for its customers. This boosted brand experience across the firm’s branches in 58 countries and improved new brand launches by 66%. Content production increased by 90%, thereby improving customer engagement across product lines.

In another case, an agri-retailer saw its customer orders increase by 300% during the agricultural peak season thanks to its adoption of adaptive, intelligent, and cognitive operations. This change made the firm’s business processes resilient and led to zero outages, leading to a 20% rise in branch revenue and improved customer satisfaction.

Neural Networks for Innovative Customer Engagement

In today’s volatile, uncertain, complex, and ambiguous (VUCA) world, rising market complexities, increasing external ecosystem factors, and inconsistencies in supply and demand make it difficult for the existing technology landscape to provide real-time actionable insights. Most organizations have invested heavily in enterprise resource planning (ERP), customer relationship management (CRM), and other IT systems. But they still struggle to be proactive in addressing customer engagement issues and mitigating risks. Bringing customer and market intelligence and cognition into sales and service processes and systems are crucial to having a proactive and predictive business process. Technology is the lynchpin in such cases where machine learning (ML) and artificial intelligence (AI) can lend neural traits to marketing, sales, and the service business function. Neural networks can provide organizations with the right tools to predict potential problems and, in some cases, take preventive steps. A deep learning neural network can be effective in many areas using different predictive algorithms.

3A (assess, analyze, advice) framework for AI maturity in retail business processes

An infographic explaining the 3A (assess, analyze, advice) framework for AI maturity in retail business processes. It is a knowledge-based recommendation engine that helps retailers improve AI maturity and reap the benefits of digital transformation. The framework consists of steps that range from assessing the current state of AI maturity to full-blown AI democratization. The first step is to discover any organizational issues that may be hindering data analytics, such as manual processes or high technology debt. Next, analytics are examined to reveal any silos in data or on-premise structured data. Consolidating data warehouses and implementing predictive analytics can help to localize analytics. The second step is to organize, which involves improving organizational awareness, investing in value proofing Al models, and reducing tech debt. The third step is to enable organizational alignment and focus on building Al products. Laying the AI foundation requires the enablement of cloud or on-premise big data. Data harmonization and minimal viable product on Al use cases are also necessary. The next step is to disrupt the current business strategy with Al-driven decisions, Al governance, and an established Al office. With this 3A framework, retailers can upgrade their AI maturity and fully digitalize their knowledge management framework.

A Design Thinking Approach to Agribusinesses

As agribusiness models become increasingly sensitive to changing consumer demand, industry players need to analyze the implications of systemic disruptions on operational activities and prepare accordingly. Design thinking can address end-user requirements by understanding what they need from a product or service. Design thinking elements also prioritize digital technologies to make CX processes truly scalable and immune from disruptions.

About the author

Sourav Sengupta

Sourav Sengupta is an industry advisor with TCS’ Manufacturing business unit. He has over 24 years of experience across the agribusiness, food, paper, packaging, chemical, and process industries. He is an industry practitioner, providing advisory services for process industry firms in Asia Pacific, Europe, and the North America. His areas of expertise are customer experience, manufacturing, supply chain, sustainability, and digital transformation. Sourav holds a Bachelor’s degree in mechanical engineering and a Master’s in business administration from the University of Mumbai, India.

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Reimagining Customer Experience in Agribusiness the Neural Way

About the author

Sourav Sengupta

TCS is here to make a difference through technology.

We’re in it for good, driving positive change for the benefit of all.

Extraordinary expertise leads to remarkable results.

Want to be a global change-maker? Join our team.

Find the latest news about TCS in our Newsroom

Recent Press releases

Recent News

Recent recognitions

TCS works hand in hand with world-leading investors.

About the author

Sourav Sengupta

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