Anil Chintapalli: Managing Partner at Human Capital Development

Can you tell us about your new book and who is publishing it?

Our book is titled ABC: AI, Blockchain, and Cybersecurity for Finance, and it will be published by Routledge this year. We consider it a groundbreaking work as these three technologies must be on every enterprise’s radar. 

Who are the three of you who authored the book, and what are your professional backgrounds?

We are Anil Chintapalli, Dr. David Metcalf, and Dr. Max Hooper. Anil serves as Chairman of People360, Managing Partner at Human Capital Development, Strategic Advisor to McKinsey & Company, and is a member of the Forbes Business Council and the Fast Company Executive Board. David is General Partner and Director at Global Blockchain Ventures, and Max is a General Partner and Managing Director at Global Blockchain Ventures.

Why did you focus on the three technologies you mentioned?

Our book focuses on artificial intelligence, blockchain, and cybersecurity — which we collectively refer to as “ABC.” We chose these three because they are poised to radically transform the enterprise landscape of every single industry, and we believe they must be embedded in every enterprise’s operating model to survive and thrive in this new era.

Why do you believe these ABC technologies are so significant right now?

We see tremendous promise in their ability to amplify human capital, transform industries, and solve complex global socio-economic challenges at unprecedented speed and scale. These are not incremental improvements — they represent a fundamental shift in how enterprises operate.

What kind of practical guidance can readers expect from the book?

As enterprises face these rapidly evolving technologies, we wanted to offer essential insights to help businesses stay ahead of the curve. We provide guidance on how organizations can leverage these technologies across the full journey — identifying opportunities and risks, designing enterprise strategy, building operating models and resilient infrastructure, addressing talent concerns including the emerging agentic workforce, managing change, establishing governance, planning for execution and scaling, and ultimately achieving return on investment.

How would you describe the overall approach you took in writing the book?

We set out to articulate an operating, functional, and pragmatic blueprint on these technologies. Our goal was to show how ABC will shape and define the future of business, operations, and the technology landscape — not just theoretically, but in a way that leaders can act on.

You identify 10 critical AI challenges for enterprises. What is the first one?

The first is a Lack of Clear Strategy or Use-Case Prioritization. We see many enterprises jumping into AI without defining why or where it creates value. This leads to what we call “shiny object syndrome” — pursuing AI for innovation theater rather than for real impact or definitive business outcomes.

What about data — is that a major obstacle?

Absolutely. Data Fragmentation and Poor Data Quality is our second challenge. In our experience, 80% of AI effort is often spent on data preparation to address data quality issues and fragmentation challenges. It is one of the most underestimated barriers to success.

What infrastructure barriers do you see enterprises struggling with?

Legacy Infrastructure and Integration Barriers are a significant issue — that is our third challenge. Machine learning operations and data pipelines are often immature and underfunded, which creates substantive barriers to execution. You simply cannot build modern AI capabilities on a crumbling foundation.

How does the talent gap affect AI implementation?

It is a serious problem, and it is our fourth challenge: Talent Shortage and Organizational Readiness. Too many projects fail because AI gets stuck in the lab — it never gets embedded into the business. This happens when organizations lack the specific multi-functional and domain-related talent needed to bridge that gap.

What role does organizational culture play in AI adoption?

It plays a huge role. Our fifth challenge is Low Trust and Resistance to Change. Change management and stakeholder education are absolutely crucial to overcoming the fear and friction that naturally arise when introducing transformative technologies. Without addressing the human side, even the best AI initiative will stall.

How should enterprises handle the ethical and regulatory risks that come with AI?

We dedicate our sixth challenge to exactly this: Ethics, Bias, and Regulatory Risk. Enterprises need strong AI governance frameworks that prioritize explainability, fairness, and human oversight. Getting this wrong is not just a reputational risk — it can have serious legal and societal consequences.

Many companies can run a successful AI pilot but struggle to scale it. Why is that?

That is our seventh challenge — Scaling from Pilots to Production — and it is one of the most frustrating patterns we see. Unclear ownership, lack of ROI tracking, model drift, and security risks all combine to undermine a project’s ability to make that leap. The pilot-to-production gap is real, and it requires deliberate planning to close.

What happens when enterprises cannot demonstrate clear ROI from AI?

Leadership support fades — it is that simple. Our eighth challenge is Undefined ROI and Business Case Uncertainty. Without a strong value realization framework in place from the start, AI initiatives lose their champions and funding. Demonstrating tangible business value is not optional; it is essential for survival.

You also mention market demand and timeliness as a challenge. Can you explain that?

Our ninth challenge is Market Demand and Timeliness. Without a proven implementation methodology, practical outcomes simply do not align with the theoretical promise of AI. Organizations can fall behind not because the technology failed them, but because they lacked the right structure and timing to deliver results when the market needed them.

What is the tenth challenge, and why does it matter?

The tenth is the Practical Knowledge Gap. Without extensive and upfront investment in addressing the knowledge gap — the why, what, when, and how of AI — project outcomes are compromised from the outset. We cannot overstate how important it is for organizations to build this foundational understanding before diving into execution.

Do you offer solutions to these challenges in the book, or just identify them?

We absolutely go beyond identifying them. We outline pragmatic solutions to address each of these challenges, all holistically supported by pertinent case studies. Our intention was always to give readers something they can act on, not just a list of problems to worry about.

You also speak about a post-quantum future. What do you mean by that?

ABC — AI, blockchain, and cybersecurity — will shape and define not only the current technology landscape but also the post-quantum future. As quantum computing advances, the entire security and computational paradigm will shift, and organizations that have embedded these three technologies into their operating model will be far better positioned to adapt.

Who is this book written for?

While it is titled for finance, we believe ABC: AI, Blockchain, and Cybersecurity for Finance is an essential resource for anyone who wants to understand and prepare for the future of technology. The challenges and frameworks we present are broadly applicable across enterprise contexts.

Have the three of you published books before?

Yes, we all bring prior publishing experience to this project. Anil authored a technology book on SAP published by John Wiley, while David and Max have authored multiple publications on topics such as, but not limited to, Blockchain. Beyond our books, we each bring substantive individual experience spanning technology, operations, and investments relevant to the AI, blockchain, and cybersecurity sectors.