FutureCentral > AI in Agribusiness: From Predictive Models to Generative and Agentic AI Across the Agri-Value Chain
AI in Agribusiness book cover
Agribusiness & Food Systems · MBA / Postgraduate

AI in Agribusiness

From Predictive Models to Generative and Agentic AI Across the Agri-Value Chain

Austin PM · FutureCentral Press

Connect predictive models, generative AI and agentic workflows with decisions across inputs, farm services, supply chains, finance, markets and agribusiness governance.

Manuscript chapters available29 chaptersCases and architecture exercises
Read the samplesRequest academic copyView contents
EditionFirst edition · in preparation
Structure29 chapters · 9 parts
AudienceMBA and agribusiness managers
AboutRead samplesFrameworksContentsResourcesAcademic enquiries

AI decisions across the agri-value chain

The book connects AI fundamentals and data infrastructure with agribusiness operations. It examines generative and agentic workflows, input industries, farm services, commodity trading, supply chains, logistics, finance and consumer platforms.

Frameworks, Indian and international cases, applied exercises and discussion questions ask readers to examine a deployment’s architecture, its operating commitments and its contribution to the firm’s strategy.

What readers will learn

  • Connect AI capabilities with agribusiness data and operating needs.
  • Evaluate generative and agentic workflows in agricultural services and operations.
  • Assess supply-chain visibility, demand response and cross-functional integration.
  • Examine AI applications in markets, finance and consumer platforms.
  • Connect deployment choices with sustainability, governance and responsible AI.
  • Design capability-building plans around a firm’s strategic priorities.

Work through the architecture behind the decision

The chapter exercises ask readers to name a firm, examine its operating context and specify the commitments a deployment requires. The representative sample develops a supply-chain architecture and links it with procurement, trading, customer engagement and compliance.

Read before you decide

Explore the book and its agribusiness exercises

Read a representative chapter, then examine its architecture exercise online.

Book sample · Chapter 15

AI in Agri-Supply Chain Management

The Visibility Stack, the Demand-Sense-and-Respond Architecture, and the Cross-Functional Integration Across the Agri-Value Chain

The chapter includes two frameworks, case illustrations, an architecture exercise, discussion questions and references.

Read sample chapter →
Practical work sample

Agribusiness Architecture Exercise Sample

AI in Agribusiness: From Predictive Models to Generative and Agentic AI Across the Agri-Value Chain

Design the visibility, demand-response and integration commitments for a named Indian agribusiness firm.

Read architecture exercise →
Two frameworks from Chapter 15

Frameworks from the book

The Supply Chain Visibility Stack

Examine five layers: multi-source data integration, real-time visibility, predictive analytics, agentic execution and cross-functional integration. The stack supports a layer-by-layer assessment of the deployment’s capability.

The Demand-Sense-and-Respond Architecture

Connect demand-signal integration and short-horizon forecasting with inventory and routing decisions, agentic execution and cross-functional coordination.

Read Chapter 15 for the frameworks and their application →

Table of contents

View all 29 chapters

Part I: FOUNDATIONS

  1. Agribusiness in the Age of AI
    The Three Waves and the Five Drivers That Are Remaking the Agri-Value Chain
  2. How AI Creates Value in Agribusiness
    The Five Drivers, the Cases That Prove Them, and the Deployment Architecture That Compounds Them
  3. Predictive ML for Agribusiness Managers
    Model Classes, Feature Engineering, and the Governance That Keeps Predictions Honest
  4. Agribusiness Data and the AI Infrastructure
    The Data Layers, the Infrastructure Stack, and the Governance That Holds Them Together
  5. The Strategic Logic of AI Adoption in Agribusiness
    Capability Maturity, Organizational Design, and the Investment Architecture That Compounds

Part II: GENERATIVE AI IN AGRIBUSINESS

  1. Generative AI Foundations for Agribusiness
    Foundation Models, Fine-Tuning, Retrieval-Augmented Generation, and the Evaluation Discipline That Makes Generative Deployments Operational
  2. Vernacular Advisory and Customer-Facing GenAI
    The Farmer-Facing Deployment, the Advisory Quality Discipline, and the Brand and Trust Architecture
  3. GenAI for Compliance, ESG, and Document Workflows
    The Drafting Architecture, the Audit Trail Discipline, and the Compounding Returns from the Document-Heavy Workflows

Part III: AGENTIC AI IN AGRIBUSINESS

  1. Agentic AI Foundations for Agribusiness
    Agent Architecture, Tool Use, Policy Envelopes, and the Governance That Makes Agents Trustworthy
  2. Agentic AI in Agribusiness Operations
    Procurement, Supply Chain, Customer Engagement, and the Operational Integration That Compounds Across the Working Firm
  3. Agentic AI in Compliance, Traceability, and ESG
    The Structurally Most Important Agentic Deployment Category in Agribusiness in 2026

Part IV: PRIMARY PRODUCTION AND INPUT INDUSTRIES

  1. AI in Agri-Input Industries
    Seeds, Fertilizers, Crop Protection, and Machinery: The Industry-Specific Deployment Patterns
  2. AI in Farm Services, FPOs, and Agri-Advisory
    The Aggregator Architecture, the Service-Delivery Stack, and the Compounding with the Indian Digital Public Infrastructure
  3. AI in Commodity Trading and Price Intelligence
    The Trading-Floor Architecture, the Price-Intelligence Stack, and the Agentic Capability That Operates at the Speed and Scale Commodity Markets Require

Part V: SUPPLY CHAIN AND LOGISTICS

  1. AI in Agri-Supply Chain Management
    The Visibility Stack, the Demand-Sense-and-Respond Architecture, and the Cross-Functional Integration Across the Agri-Value Chain
  2. AI in Cold Chain and Perishables Logistics
    The Spoilage-Prediction Architecture, the Routing-Under-Constraint Stack, and the Operational Discipline the Perishable Categories Require
  3. AI for Food Traceability, Safety, and Blockchain
    The Trace-Forward and Trace-Back Architecture, the Blockchain Integration Question, and the Regulatory-and-Buyer-Driven Compounding

Part VI: AGRIBUSINESS FINANCE AND MARKETS

  1. AI in Agri-Fintech
    The Smallholder Credit Architecture, the Insurance Innovation, and the Compounding with the Indian Digital Public Infrastructure
  2. AI in Commodity Markets and Price Risk Management
    The Hedging Architecture, the Derivatives Strategy Stack, and the Cross-Functional Integration With the Trading and Procurement Deployments
  3. AI in Agri-Investment and the Startup Ecosystem
    The Deal-Flow Architecture, the Due-Diligence Stack, and the AI-Augmented Investment Decisions

Part VII: RETAIL, CONSUMER, AND PLATFORM BUSINESSES

  1. AI in Food Retail and FMCG
    The Retail-and-Consumer Architecture, the Demand-Sensing Stack, and the Cross-Functional Integration With the Broader Agribusiness Value Chain
  2. Agri-Platform Businesses and Digital Marketplaces
    The Platform Business-Model Architecture, the Network-Effects-and-AI Stack, and the Strategic Position of the Indian Agritech Platforms in the Global Smallholder-Inclusion Agenda
  3. Quick Commerce, D2C, and the New Food Consumer Journey
    The Hyperlocal Architecture, the Direct-to-Consumer Stack, and the Compounding Across the Modern Consumer Engagement

Part VIII: SUSTAINABILITY, ESG, AND GOVERNANCE

  1. AI for Sustainable Agribusiness
    The Emissions-and-Water Architecture, the Soil-and-Biodiversity Stack, and the Cross-Functional Integration With the Broader Sustainability Agenda
  2. ESG, Green Finance, and Sustainability Reporting in Agribusiness
    The Reporting Architecture, the Green-Finance Stack, and the Compounding With the Agentic Compliance Architecture
  3. Governance, Ethics, and Responsible AI in Agribusiness
    The Governance Architecture, the Ethics Stack, and the Cross-Functional Integration Across the Agribusiness AI Capability

Part IX: STRATEGY AND THE FUTURE

  1. Building AI Capability in Agribusiness Organizations
    The Talent Architecture, the Organizational-Design Stack, and the Multi-Year Capability Roadmap
  2. India’s Agribusiness AI Transformation
    The Country-Case Synthesis, the Digital Public Infrastructure Story, and the Global Reference for the Smallholder-Anchored AI Model
  3. The Future of Agribusiness — Capstone
    The Architectural Synthesis, the 2030 Horizon, and the Strategic Position of the AI-Native Agribusiness Firm
Learning and teaching support

Teaching and study resources

Applied agribusiness exercises

Exercises ask readers to specify a firm’s context and design its deployment commitments.

Discussion questions

Chapter questions support examination of the argument and alternative deployment choices.

Frameworks and decision tools

Named frameworks organize assessments of capability, architecture and strategy.

Case illustrations

Indian and international examples connect chapter concepts with agribusiness applications.

Key terms and further reading

Chapter terms, references and reading suggestions support further study.

Academic evaluation

Faculty can enquire about inspection copies and available materials for their intended use.

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