Chapter 7: Implementing AI in Your Business
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Published:2026
Rahul Pratap Singh Kaurav, Surabhi Koul, 2026. "Implementing AI in Your Business", AI and Business: Mapping the Present for Harnessing the Future, Rahul Pratap Singh Kaurav, Surabhi Koul
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AI is no longer a futuristic aspiration – it has become a present-day necessity. However, despite widespread interest, many businesses struggle with the practicalities of implementation. This chapter serves as a pragmatic guide to implementing AI in your business – one decision, one process and one system at a time. We move beyond conceptual discussion and into the nuts and bolts of integrating AI within your operations. From aligning AI initiatives with strategic business goals to selecting tools, preparing data infrastructure and scaling successful pilots, this chapter offers a structured, step-by-step framework supported by real-world examples.
AI holds immense promise, but unlocking its value depends entirely on execution. Across industries, AI is revolutionizing how businesses operate, make decisions, serve customers and innovate. From personalized customer experiences to predictive maintenance, AI applications have matured from experimental technologies to mission-critical capabilities. However, a gap remains. Many businesses recognize AI’s potential but stumble when trying to move from aspiration to action.
| Phase | Action Taken | Impact |
|---|---|---|
| Cloud migration | Migrated 157B+ data points and 61K+ pipelines to AWS Cloud | Enabled scalable, real-time analytics and AI model training |
| Model deployment | 2,000+ AI models making 55M+ daily decisions | Automated fraud detection, personalization, operations |
| Engagement engine | Partnered with H2O.ai to deliver tailored, real-time interactions | Improved customer satisfaction, proactive financial advice |
| Fraud detection | Real-time scam alerts, behavioural insights | Significant reduction in financial fraud |
| Cultural shift | Embedded AI governance and cross-functional AI squads | Fostered trust, ethical AI, and fast organizational learning |
| Phase | Action Taken | Impact |
|---|---|---|
| Cloud migration | Migrated 157B+ data points and 61K+ pipelines to AWS Cloud | Enabled scalable, real-time analytics and AI model training |
| Model deployment | 2,000+ AI models making 55M+ daily decisions | Automated fraud detection, personalization, operations |
| Engagement engine | Partnered with H2O.ai to deliver tailored, real-time interactions | Improved customer satisfaction, proactive financial advice |
| Fraud detection | Real-time scam alerts, behavioural insights | Significant reduction in financial fraud |
| Cultural shift | Embedded AI governance and cross-functional AI squads | Fostered trust, ethical AI, and fast organizational learning |
