New Book Challenges Organizations to Rethink How AI Creates Business Value
NEW YORK, June 2026 — Artificial Intelligence has moved from research labs to boardroom agendas at remarkable speed. Enterprises across every sector are launching initiatives centered on machine learning, predictive analytics, Generative AI, and autonomous systems. Yet for all the excitement surrounding AI, a critical question remains unanswered:
Why do so many AI projects fail to deliver lasting business impact?
According to enterprise technology leaders Ankit Anand, Dr. Scott Burk, and Kinshuk Dutta, the answer often has little to do with the quality of the models themselves.
Their newly released book, The Deployed Data Scientist: MLOps and Analytics in Practice, argues that organizations have spent years mastering experimentation while giving far less attention to the operational systems required to sustain AI in production.
The result is a growing divide between AI potential and AI performance.
The Cost of Staying in Pilot Mode
Across industries, organizations are reporting successful proofs of concept, promising prototypes, and innovative demonstrations. However, many of these initiatives never mature into enterprise-wide capabilities.
What begins as a technical achievement frequently encounters obstacles once it enters a live business environment.
Among the most common challenges are:
- Inconsistent data quality
- Weak governance structures
- Limited monitoring capabilities
- Unclear operational ownership
- Deployment bottlenecks
- Regulatory and compliance concerns
- Performance degradation over time
These issues often emerge after deployment, when organizations must manage AI systems under real-world conditions rather than controlled testing environments.
The authors believe this is where the next phase of AI maturity will be defined.
A Shift from Building Models to Building Systems
The central message of The Deployed Data Scientist is straightforward: organizations do not create value through models alone. They create value through systems that can reliably support those models over time.
This requires a broader perspective that extends beyond data science into engineering, governance, operations, and business accountability.
“Organizations often view deployment as the finish line. In reality, deployment is where the most important work begins. Sustainable AI depends on the ability to monitor, govern, maintain, and continuously improve intelligent systems.”
— Dr. Scott Burk
“The conversation around AI has largely centered on innovation. The next challenge is operationalization. Enterprises need repeatable frameworks that allow AI systems to perform consistently in dynamic business environments.”
— Ankit Anand
“AI initiatives succeed when technology, governance, trusted data, and operational processes work together. The organizations that understand this will be positioned to generate long-term value from their AI investments.”
— Kinshuk Dutta
Industry Perspectives on the Next Wave of AI Adoption
Business leaders are increasingly recognizing that operational readiness is becoming a competitive advantage.
“The future belongs to organizations that can move beyond experimentation and build dependable AI capabilities. Reliability, transparency, governance, and operational discipline will separate leaders from followers. This book provides practical guidance for achieving that transformation.”
— Partha Ghosh, CEO, MaiTY (NeurCG GmbH)
Inside the Framework
Rather than focusing exclusively on technical model development, the book provides a practical roadmap for organizations seeking to establish production-grade AI operations.
Readers will find guidance on:
- Designing resilient data pipelines
- Establishing data contracts and stewardship practices
- Implementing MLOps methodologies
- Automating model deployment workflows
- Building cloud-native AI environments
- Creating observability and monitoring frameworks
- Managing model drift and performance degradation
- Incorporating human oversight into AI workflows
- Operationalizing Generative AI and LLM-based systems
- Strengthening governance and accountability
The book presents these capabilities as essential components of a modern AI operating model rather than optional enhancements.
The Growing Complexity of Modern AI
The rapid evolution of AI technologies has introduced opportunities that were difficult to imagine only a few years ago. Large Language Models, intelligent agents, and autonomous decision-support systems are creating new possibilities for innovation and productivity.
At the same time, they are introducing new risks and responsibilities.
Organizations must now address issues such as:
- Responsible AI governance
- Prompt and context management
- AI output validation
- Explainability requirements
- Compliance and regulatory oversight
- Monitoring autonomous behaviors
- Risk management for AI-generated outcomes
The authors argue that these challenges cannot be solved solely through better models. They require operational structures that balance innovation with control.
Connecting the Foundations of Enterprise AI
The release of The Deployed Data Scientist reflects a broader vision shared by its authors: successful AI adoption begins with trusted data and matures through disciplined operations.
Their collective work explores the full lifecycle of enterprise intelligence, including:
- Data readiness and quality management
- Enterprise governance strategies
- MLOps and analytics operationalization
- Agentic AI and intelligent automation
- Responsible AI architecture and oversight
Together, these disciplines form the backbone of scalable and trustworthy AI ecosystems.
Questions Every AI Leader Should Be Asking
As organizations increase their investments in AI, several strategic questions continue to surface:
- Why do successful pilots often fail to scale?
- What distinguishes production AI from experimental AI?
- How should enterprises structure governance around machine learning systems?
- What role does observability play in reliability and trust?
- How can Generative AI be implemented responsibly?
- Which controls are necessary to support AI at scale?
- How should MLOps capabilities evolve within the enterprise?
- What does long-term AI sustainability actually look like?
The book provides practical perspectives on each of these questions, drawing from real-world enterprise experience.
Expertise Shaped by Practice
The authors collectively bring more than 75 years of experience across data management, artificial intelligence, analytics, cloud platforms, governance, statistics, digital transformation, education, and technology leadership.
Their careers span academia, Fortune 500 enterprises, software organizations, consulting firms, and research initiatives, providing a multidisciplinary view of how AI succeeds—or fails—in large organizations.
About the Authors
Ankit Anand
Ankit Anand is a Data Management Architect, inventor, AI researcher, and technology leader whose work focuses on enterprise data management, governance, analytics modernization, machine learning platforms, and AI infrastructure. He has led initiatives aimed at strengthening data quality, improving governance, and enabling enterprise-scale AI adoption.
Dr. Scott Burk
Dr. Scott Burk is an author, educator, statistician, and analytics practitioner with more than 30 years of experience in artificial intelligence, machine learning, analytics, and enterprise data strategy. He serves as an Adjunct Professor in the Master of Science in Data Science program at the CUNY School of Professional Studies.
His published works include The Executive Guide to AI and Analytics, Data for AI, AI Agents at Work, the It’s All Analytics series, and The Deployed Data Scientist. He is also the creator of The Data Linguist, an educational platform dedicated to analytics and AI education.
Kinshuk Dutta
Kinshuk Dutta is a technology executive, IEEE Senior Member, speaker, and published author specializing in Master Data Management, Data Governance, Agentic AI, MLOps, and enterprise-scale AI systems. He is co-author of Data for AI, AI Agents at Work, and The Deployed Data Scientist. He currently serves as Head of Go-To-Market, Americas, for ON EBX, a Cloud Software Group business unit.
Final Reflections
As AI adoption accelerates worldwide, the conversation is shifting from what AI can do to how organizations can make it work consistently, responsibly, and at scale.
The Deployed Data Scientist contributes to that discussion by offering a practical perspective on one of the industry’s most pressing challenges: transforming AI from a promising experiment into a dependable business capability.
