The promise of Artificial Intelligence lies in its ability to amplify human capital, transform industries, and solve complex global societal challenges at unprecedented speed and scale. The promise of Artificial Intelligence for enterprises is the transformation of core business performance through intelligent automation, enhanced decision-making, and innovation at scale.

1. Operational Efficiency & Cost Reduction
   • Automates repetitive tasks (e.g., invoice processing, HR queries, IT ticketing)
   • Optimizes supply chains, manufacturing, and logistics with predictive analytics
   • Reduces error rates, rework, and downtime

An Opportunity: AI-powered RPA (Robotic Process Automation) in finance reduces processing time by 60–80%.

2. Smarter, Data-Driven Decision Making

• AI transforms raw data into real-time insights for executives and frontline teams
• Predictive and prescriptive analytics guide business forecasting, risk mitigation, and resource allocation

An Opportunity: AI models in retail forecast demand down to the SKU/store level, improving inventory accuracy and reducing waste.

3. Enhanced Customer Experience

• Personalizes interactions through chatbots, recommendation engines, and real-time support
• Enables 24/7 customer service with intelligent assistants and natural language processing

An Opportunity: Banks use conversational AI to answer 80% of customer questions without human agents.

4. Product and Service Innovation

• Powers new offerings like AI-generated content, autonomous systems, and intelligent platforms
• Accelerates R&D cycles via AI-driven simulations, digital twins, or rapid prototyping

An Opportunity: Pharma companies use AI to reduce drug discovery timelines from years to months.

5. Workforce Augmentation, Not Just Automation

• Enhances productivity of knowledge workers with tools like copilots, generative AI, and intelligent search
• Enables HR and operations to make better talent, culture, and workforce decisions

An Opportunity: Legal firms use AI to summarize cases, draft contracts, and support compliance reviews.

6. Enterprise Agility & Competitive Advantage

• Accelerates time-to-market with faster experimentation and decision cycles
• Helps pivot faster to meet customer needs and adapt to market disruption

An Opportunity: AI-enabled scenario planning helps firms adapt to economic shocks, supply constraints, or geopolitical risks.

1. Lack of Clear Strategy or Use-Case Prioritization

• Many enterprises jump into AI without defining why or where it creates value.
• AI projects often stall when not aligned with measurable business outcomes.

The Challenge: “Shiny object syndrome”—pursuing AI for innovation theater, not impact.

2. Data Fragmentation & Poor Data Quality

• AI depends on high-quality, integrated, labeled data—but most enterprises suffer from siloed, inconsistent, or legacy data systems.
• Privacy, compliance (GDPR, HIPAA), and access controls further complicate data readiness.

The Challenge: 80% of AI effort is often spent on data preparation.

3. Legacy Infrastructure & Integration Barriers

• Old IT systems weren’t built for AI-scale processing or interoperability.
• Enterprises often face delays modernizing infrastructure (cloud migration, data lakes, APIs).

The Challenge: MLOps and data pipelines are often immature or underfunded.

4. Talent Shortage & Organizational Readiness

• AI success requires rare skill combinations: data science, ML engineering, domain expertise, and change leadership.
• Even with tech talent, the business-side understanding of AI is often weak.

The Challenge: Many projects fail due to “AI stuck in the lab”—not embedded into the business.

5. Low Trust and Resistance to Change

• Employees may fear job displacement, leading to low adoption.
• Business leaders often hesitate to rely on “black box” algorithms, especially in regulated industries.

The Challenge: Change management and stakeholder education are crucial to overcome fear and friction.

6. Ethics, Bias, and Regulatory Risk

• AI can amplify biases in training data and decisions (e.g., in hiring, lending, policing).
• Regulators (e.g., EU AI Act) are introducing strict governance requirements.

The Challenge: Enterprises need strong AI governance frameworks—explainability, fairness, human oversight.

7. Scaling from Pilots to Production

• Many enterprises succeed in pilot use cases but struggle with repeatability, maintenance, or scaling across geographies and business units.

The Challenge: unclear ownership, lack of ROI tracking, model drift, and security risks.

8. Undefined ROI & Business Case Uncertainty

• AI projects may take months or years to yield ROI, making it hard to sustain investment.
• Benefits are often indirect (e.g., productivity, insights), not easily quantified.

The Challenge: Without a strong value realization framework, leadership support fades.

9. Market Demand & Timeliness

• Enterprise AI adoption is accelerating, but implementation remains complex and misunderstood.
• Executives and managers are actively seeking credible technical and business process guidance beyond AI hype — especially on ROI, technology and process tools, governance, and integration.

10. Practical Knowledge Gap

Most enterprises are grappling with answers to key questions such as:

• How do we scale (technologically) AI beyond pilots?
• How do we govern data and models?
• What org structure is needed?
• How do we holistically de-risk enterprise risks associated with AI?
• How do we capture sustainable and consistent ROI?

A Practitioners’ Blueprint to Designing, Building, Governing, and Scaling AI in the Enterprise

What Makes It Valuable?

To resonate with readers, our book will focus on:

• Battle-tested playbook
• Detailed implementation methodology, and program governance
• Vertical industry specific implementation templates
• Detailed roadmaps for risk, regulation, talent, and alignment with business units in specific vertical markets (such as healthcare, et al)
• Clear technology and process frameworks for scaling AI responsibly
• Updated content on regular basis for registered readers (available online)
• Exclusive online forum to educate, engage and collaborate with subject matter experts

Draft Table of Contents

Part I: Understanding the Opportunity & Risk

1. The Promise and Peril of AI in the Enterprise

• AI beyond proof-of-concept
• Hype vs. sustainable value

2. The Enterprise Context

• Complexity, risk, and legacy infrastructure
• Regulatory and reputational constraints

Part II: Designing the AI Strategy

3. AI and the Business Model

• Where AI fits (cost, growth, customer experience)
• Build vs. buy vs. partner

4. Aligning with Corporate Strategy

• Executive sponsorship
• Governance alignment

5. AI Use Case Prioritization

• ROI frameworks
• Risk scoring
• Quick wins vs. transformational bets

Part III: Building Operating Model & Infrastructure

6. The AI Operating Model

• Centralized vs. federated models
• AI Centers of Excellence

7. Data Foundations

• Data readiness, privacy, and lineage
• What real data governance looks like

8. Tooling, Platforms, and MLOps

• Open source vs. commercial stacks
• Model deployment, monitoring, and retraining

Part IV: Talent, Change, and Governance

9. Hiring and Organizing for AI

• What roles you need (and don’t)
• AI product teams and cross-functional collaboration

10. Culture, Change, and Education

• AI literacy for business leaders
• Managing fear, resistance, and upskilling

11. Risk, Ethics, and Governance

• Model auditability
• Bias, transparency, and regulatory readiness

Part V: Execution and Scaling

12. From Pilot to Production

• Why most pilots fail
• Playbook for scaling successful use cases

13. Measuring Success

• KPIs that matter
• Linking AI to business outcomes

14. Evolving the Enterprise

• Continuous learning loop
• AI as part of the corporate DNA

Appendices

• Case Studies: Manufacturing, Finance, Healthcare, Retail, Travel
• Templates: AI Use Case Scorecard, Implementation Model, Risk Checklist, ROI Calculator
• Glossary: Enterprise AI Terms

Led by Anil Chintapalli, our project collaborators include current and/or former executives from firms such as Google, Meta, Amazon, Apple, Microsoft, OpenAI, Anthropic, Nvidia, GE, Kyndryl, Verizon, Telstra, Woolworths, Expedia – to name a few.

Our development team behind this project has brought more than 2,100 traditionally published books to market and produced 350+ national bestsellers—including nineteen #1 New York Times bestsellers and over 300 Million books sold.