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Next Digital Book Review: How to Win the AI Era With Cloud, AI/ML, and Generative AI (Lessons From Gartner-Recognized Korean Case Studies)

ByInnoVirtuoso

What if your next digital transformation didn’t stall after a few pilots—but actually scaled across the enterprise? And what if the playbook came from a CIO/CDO who built two programs so effective they became the first Korean success stories recognized by Gartner for global digital transformation excellence?

That’s the promise of “Next Digital: Surviving in the Age of AI: Key Digital Transformation Technologies.” It’s a practical, field-tested guide from an executive who has spent nearly three decades inside a Fortune Global 500 organization, leading transformation in IT, smart factories, AI, cloud, and blockchain. The author’s flagship platforms—HWADAP (a cloud-based data analytics platform) and AIDA (a generative AI chatbot)—earned Gartner recognition in 2023 for their business impact and technical achievement. That’s rare air.

In this review, I’ll break down what the book covers, why it stands out, and how to apply its lessons—whether you’re a CIO, business leader, data scientist, or founder trying to navigate the AI wave without drowning in hype. I’ll also share the most actionable takeaways you can use immediately.

Let’s get into it.

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What This Book Is Really About

At its core, this book is a pragmatic blueprint for digital transformation in the AI era. It strips away buzzwords and focuses on what actually moves the needle: three core technologies—Cloud, AI/ML, and Generative AI—plus a product-led operating model that converts technology into measurable business value.

Here’s what you’ll find:

  • A crisp framework for building a scalable digital foundation on cloud.
  • A no-nonsense approach to operationalizing AI/ML with MLOps and governance.
  • A detailed look at enterprise-grade Generative AI that is safe, useful, and trusted.
  • Real-world case studies: HWADAP and AIDA, both recognized by Gartner as global exemplars.
  • Lessons learned in the field: what to prioritize, how to structure teams, how to manage risk, and how to measure value.

If you want a practical “how-to” for the AI era—from someone who has actually done it at scale—this book delivers.

Why This Book Matters Now

We’re at an inflection point. Cloud is no longer optional. Data is a strategic asset. And Generative AI has unlocked a new interface for work.

Yet most organizations still struggle to move beyond pilots. Many are stuck in “pilot purgatory”—testing, testing, testing, and never scaling to production. If that sounds familiar, you’re not alone. Even global studies show how difficult it is to derisk AI and deliver value at scale across the enterprise (see insights from McKinsey).

That’s why this book is timely. It shows how to engineer “boring reliability” in the foundation, and “bold outcomes” at the edge—through use cases that matter. It also demonstrates how to govern AI in a way that earns trust.

Here’s why that matters: trust is the new moat. If your employees and customers trust your AI, and it actually helps them do their jobs better, you win. This book keeps that front and center.

Meet the Author: A Practiced Change Leader

The author brings real weight to the topic:

The tone of the book reflects that experience. It’s not theoretical. It’s operational, value-driven, and grounded in how to actually make transformation work—inside a complex, regulated, large-scale environment.

The Three Core Technologies: Cloud, AI/ML, Generative AI

The book narrows its focus to the three technologies that matter most right now. Not because others don’t matter—but because these three form the backbone of competitive advantage in the AI era.

Cloud: The Scalable Foundation

Cloud is the substrate. It gives you elastic compute, resilient infrastructure, and faster time to market. The book aligns with broad standards like the NIST definition of cloud computing, but it’s pragmatic about what you need to implement:

  • A secure landing zone, with guardrails baked in.
  • A modern data platform (think lakehouse) for analytics and AI.
  • Automated pipelines for ingestion, quality, cataloging, and lineage.
  • FinOps discipline to manage spend and value (FinOps Foundation).

Why it matters: Without a robust cloud foundation, every AI initiative will suffer. You’ll hit scaling issues. Costs will spike. Security will lag. Cloud is your “operating system for innovation.”

AI/ML: From Models to Business Value

AI/ML is where you turn data into predictions, recommendations, and automation. The book emphasizes MLOps and production-readiness. That means:

  • Clear model objectives tied to business KPIs.
  • Reproducible training pipelines and feature stores.
  • Model monitoring for drift, bias, and performance.
  • Cross-functional teams with product managers, data scientists, and engineers.

If you need a primer on robust AI delivery, this MLOps guide is a helpful reference. The message is consistent: reliable ML requires engineering rigor and business alignment.

Generative AI: A New Interface for the Enterprise

Generative AI changes how people work. It lets users query knowledge, draft content, and automate tasks via natural language. But it must be safe, grounded, and governed. The book focuses on:

  • Retrieval-Augmented Generation (RAG) to ground answers in your data (Microsoft’s RAG overview).
  • Role-based access controls and data redaction.
  • Prompt patterns, templates, and quality controls.
  • Human-in-the-loop workflows for sensitive decisions.
  • Ongoing education in “prompt literacy.”

For broader perspective on the impacts and opportunities, see Stanford HAI. Bottom line: Generative AI is a force multiplier—if you design for reliability and trust from day one.

Inside the Gartner-Recognized Cases: HWADAP and AIDA

Two initiatives define the book’s credibility: HWADAP and AIDA. Each demonstrates what “good” looks like when technology meets business outcomes.

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HWADAP: The Cloud-Based Data Analytics Platform

HWADAP is the enterprise data backbone. It brings data together across business units and functions, enabling analytics, AI/ML, and self-service BI. Think of it as the circulatory system for information.

Key ingredients:

  • A lakehouse architecture for scalable storage and compute.
  • Stream/batch ingestion with quality checks at the edge.
  • A governed data catalog and lineage for trust and traceability.
  • Data products owned by domain teams, curated for reuse.
  • Self-service analytics with guardrails, not gatekeepers.

The business impact shows up in cycle-time reduction, better forecasts, higher yield in manufacturing, and faster product iterations. If you’re into advanced manufacturing, these outcomes align with global exemplars like the WEF Global Lighthouse Network.

Operating model highlights:

  • Product-centric teams. Data isn’t “projects”; it’s a platform with SLAs.
  • Data stewards and domain owners. Governance embedded, not bolted on.
  • FinOps and chargeback models for shared services.
  • Clear service tiers for ingestion, storage, compute, and analytics.

Why it works: HWADAP doesn’t try to be everything to everyone. It sets standards and reusable components. Then it empowers teams to build on top—safely and quickly.

AIDA: The Enterprise-Grade Generative AI Chatbot

AIDA is the enterprise’s natural language interface. Employees ask questions like “What’s our latest policy on X?” or “Summarize customer feedback for product Y.” AIDA retrieves information from trusted sources and synthesizes answers.

Under the hood:

  • RAG to connect LLMs with enterprise knowledge bases.
  • Connectors to documents, wikis, ERP/CRM, and data products.
  • Metadata-driven access control, so answers respect permissions.
  • Safety filters for PII, toxicity, and off-topic prompts.
  • Feedback loops and human oversight to improve quality.

Change management was a big part of AIDA’s rollout:

  • Clear use cases for each department.
  • “Champion” networks to embed adoption and gather feedback.
  • Training on prompts, privacy, and best practices.
  • Transparent metrics: usage, satisfaction, answer quality, and time saved.

Risk management: The team designed for safety from day one—reducing hallucinations with grounding, restricting sensitive data, and establishing a review process for critical outputs. This aligns with frameworks like the NIST AI Risk Management Framework.

Why Gartner’s Recognition Matters

Gartner recognition signals global excellence across two fronts: demonstrable business value and technical execution. Many enterprises declare “AI at scale.” Few prove it.

This recognition validates that:

  • The cloud foundation was strong enough to support enterprise-wide AI.
  • The architecture choices were standard enough to be sustainable, not fragile.
  • The outcomes were measurable, repeatable, and trusted beyond a single team.

It also shows that transformation can succeed outside of Silicon Valley, at massive scale, in complex industries. That’s encouraging for leaders everywhere.

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A Practical Playbook You Can Use

One of the book’s biggest strengths is its playbook. Here’s a distilled version you can adapt.

1) Start With Value, Not Tools
– Define 3–5 must-win outcomes. Tie each to revenue, cost, risk, or experience.
– Map constraints early: data access, compliance, skills, and budget.
– Choose use cases you can ship in 90 days, then scale.

2) Build the Foundation Incrementally
– Set up a secure cloud landing zone.
– Stand up a minimal viable data platform with governance.
– Standardize ingestion, quality checks, and metadata capture.

3) Treat Data and AI as Products
– Assign product managers, not just project managers.
– Define SLAs for data freshness, completeness, and usability.
– Publish data contracts and a common catalog.

4) Make MLOps and AI Governance Non-Negotiable
– Implement versioned pipelines, model registries, and monitoring.
– Track performance, drift, and fairness.
– Establish review boards for high-risk use cases.
Helpful references: Google’s MLOps guide, NIST AI RMF.

5) Roll Out GenAI Safely
– Start with RAG to ground outputs in your facts.
– Use role-based access and data redaction.
– Build feedback loops and human checks for sensitive tasks.
– Upskill teams on prompt patterns (see DeepLearning.AI’s prompt course).

6) Invest in FinOps and Cost Transparency
– Set budgets by product.
– Use usage dashboards and savings plans.
– Tie cost to value delivered via shared KPIs.
See the FinOps Foundation for practices.

7) Design for Adoption From Day One
– Communicate in plain language. Explain “what’s in it for me.”
– Train champions inside each function.
– Instrument usage and satisfaction. Iterate monthly.

8) Secure by Design
– Shift-left security in pipelines and infrastructure.
– Automate compliance checks and audit trails.
– Limit data access by default and log everything.

9) Measure and Narrate Impact
– Decide your “North Star” metrics early.
– Publish quarterly impact reports to executives and teams.
– Celebrate wins and codify playbooks so others can repeat them.

10) Keep Humans in the Loop
– AI should augment, not replace, critical judgment.
– Pair automation with oversight, especially in regulated areas.

Let me be clear: this is not about perfection on day one. It’s about building muscle. Ship small, learn fast, scale what works.

Who Should Read This Book

  • CIOs and CDOs who must turn AI strategy into enterprise-wide results.
  • CTOs and engineering leaders building platforms and guardrails.
  • Business unit leaders who want outcomes, not buzzwords.
  • Data leaders and product managers driving data/AI products.
  • Founders and operators building AI-native organizations.

If you need to align executives, IT, and business teams around a practical roadmap, this book earns a spot on your desk.

Where the Book Shines—and Where It’s Lighter

Strengths:

  • Pragmatic clarity. It focuses on what works in the real world.
  • Proven cases. HWADAP and AIDA move beyond pilot hype to scaled value.
  • Balanced view. It addresses technology, operating model, change, and risk.
  • Transferable lessons. Although rooted in Hanwha’s experience, the guidance is broadly applicable.

What it’s lighter on:

  • Deep model internals. This isn’t a math-heavy ML textbook.
  • Niche edge/OT topics. It touches smart factories but doesn’t dive deep into all industrial protocols.
  • Startup constraints. The playbook is enterprise-friendly; early-stage startups may adapt it more selectively.

Still, that’s by design. This is a strategy-and-execution field guide, not a theory text.

How to Apply the Lessons: A 30-60-90 Day Plan

Want to put this book to work? Here’s a quick-start plan you can tailor.

First 30 days:

  • Run a value-and-constraint workshop with business leaders.
  • Shortlist three high-impact use cases with 90-day horizons.
  • Define shared KPIs and guardrails (security, privacy, compliance).
  • Stand up a cloud landing zone with basic IAM and network controls.

Days 31–60:

  • Launch a minimal data platform (ingest, quality, catalog, lineage).
  • Start a GenAI pilot with RAG for one department (e.g., HR policies or customer support).
  • Establish a MLOps path for one predictive use case.
  • Recruit champions and kick off training on data literacy and prompt literacy.

Days 61–90:

  • Ship the first production workloads with dashboards and SLAs.
  • Implement model monitoring and incident playbooks.
  • Publish a business impact report with time saved, cost reduced, or revenue uplift.
  • Plan the next two quarters based on lessons learned and adoption data.

Pro tip: keep a weekly “show the work” demo. Momentum builds trust.

Memorable Ideas You’ll Take With You

  • Cloud is your innovation OS. Standardize it.
  • Data products beat data projects. Own outcomes, not outputs.
  • MLOps is table stakes. Reliability earns trust.
  • RAG is how GenAI speaks your company’s language.
  • Value > Volume. Fewer, better use cases—shipped to production—win.
  • Culture eats tooling. Champions and training drive adoption.
  • Governance is an enabler, not a blocker, when it’s designed into the pipeline.

Final Verdict

“Next Digital” is a standout playbook for leaders who want to turn AI from theory into durable advantage. It blends strategy with execution, architecture with adoption, and speed with safety. The Gartner-recognized cases—HWADAP and AIDA—aren’t just trophy stories; they’re templates you can adapt.

If you read one book this quarter to guide your AI-era transformation, make it this one.

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Helpful Resources Cited

FAQ: Next Digital and AI-Era Transformation

Q: Is this book technical or business-focused?
A: It’s a business-first, technology-fluent guide. You’ll get enough architecture and operating model detail to execute, without diving into heavy math.

Q: What is HWADAP?
A: HWADAP is a cloud-based enterprise data analytics platform. It unifies data, enforces governance, and enables analytics and AI across functions. It’s the backbone for scalable digital and AI initiatives.

Q: What is AIDA?
A: AIDA is an enterprise-grade generative AI chatbot. It uses RAG to answer questions from authoritative internal sources, with strong access controls and safeguards to reduce hallucinations.

Q: How is this different from typical “AI strategy” books?
A: It’s grounded in Gartner-recognized, at-scale deployments. You get concrete operating models, guardrails, and adoption tactics—not just high-level frameworks.

Q: Do small and mid-sized businesses benefit from this book?
A: Yes. The principles—value-first use cases, cloud foundations, MLOps, RAG for GenAI, and strong governance—apply at any scale. SMBs can start lighter and grow.

Q: How do we measure ROI for AI and digital transformation?
A: Tie each initiative to a business KPI: cycle time, yield, forecast accuracy, cost-to-serve, NPS, or revenue uplift. Track adoption, time saved, and quality. Publish quarterly impact reports.

Q: Is cloud mandatory for AI transformation?
A: For most organizations, yes. Cloud provides elasticity, managed services, and speed you can’t match on-prem at the same cost or pace. See the NIST cloud definition for fundamentals.

Q: How do we start GenAI safely?
A: Begin with RAG in low-risk domains (policies, knowledge bases). Enforce role-based access, mask sensitive data, and establish human reviews for critical outputs. Use a framework like NIST AI RMF.

Q: What skills do teams need in the AI era?
A: Product management for data/AI, MLOps engineering, data stewardship, domain analytics, and prompt literacy. Upskill business users to become AI co-pilots, not just consumers.

Q: How do we avoid “pilot purgatory”?
A: Start with clear KPIs, build on a reusable platform, and move pilots to production with SLAs. Limit the number of pilots. Prioritize a few high-value use cases and scale them.

Clear takeaway: The AI era rewards organizations that build a solid cloud and data foundation, operationalize AI with discipline, and deploy GenAI that employees trust and use. “Next Digital” shows how to do exactly that—backed by proof, not promises.

If you found this review useful, stick around for more deep dives on AI strategy, data platforms, and real-world transformation. Subscribe to get the next guide in your inbox.

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InnoVirtuoso

Hello there! I'm passionate content writer navigating the digital landscape. With a deep love for words and an insatiable curiosity about technology.

With 10 years of experience in IT field, I write mostly about emerging tech, sharing news, informational articles and covering other topics I find interesting.

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