Architectures for the Intelligent AI-Ready Enterprise E-Book

Boris Bialek

• Herausgeber: Packt Publishing
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 642

Veröffentlichungsjahr: 2025

Beschreibung

AI is reshaping industries, yet most organizations struggle to scale beyond pilots. Architectures for the Intelligent AI-Ready Enterprise bridges this gap with practical frameworks for building AI-ready architectures that deliver lasting business value.
The book helps you explore System of Action databases and see why they're revolutionizing real-time decision-making. Through real-world applications across industries, from manufacturing and healthcare to financial services and retail, you'll discover how leading organizations transform their operations. You'll learn semantic data protection techniques that enable AI in regulated industries, as well as master advanced patterns including agentic AI and multi-agent orchestration.
Written by MongoDB and industry practitioners, this book combines strategy with technical depth and proven business value. You’ll modernize by enabling AI innovation while preserving existing investments, implement trustworthy AI with governance frameworks, and build scalable solutions using a unified data platform like MongoDB that delivers measurable ROI and transformation.
Whether you're architecting next-generation systems or modernizing legacy infrastructure, this book provides the patterns, case studies, and expert guidance to build enterprises that’ll thrive in an intelligent future.

Leseprobe

Architectures for the Intelligent AI-Ready Enterprise

Building real-world solutions with MongoDB

Boris Bialek, Sebastian Rojas Arbulu, Taylor Hedgecock

Architectures for the Intelligent AI-Ready Enterprise

Copyright © 2025 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

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Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

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First published: September 2025

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Published by Packt Publishing Ltd.

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ISBN 978-1-80611-715-4

www.packtpub.com

Foreword

If you had told me a few years ago that I’d be writing the foreword to a book on AI, I might’ve raised an eyebrow. And yet, here we are, right in the middle of one of the fastest-moving technological shifts in modern history.

Unlike prior technological shifts, what is different this time is the pace. Change is not unfolding over years; it is happening in months, weeks, sometimes even days.

The question facing every organization is not whether AI will reshape their business; it is how fast they can adapt and whether they will lead or fall behind.

Throughout my career, I have seen what separates the companies that thrive during moments like these from those that fall behind. It is rarely just the technology. It is the mindset—the willingness to rethink how you operate, how you deliver value, and how you use data to drive better outcomes. AI, especially generative and agentic systems, demands exactly that kind of rethink.

At MongoDB, we have worked closely with thousands of organizations across various industries, including financial services, healthcare, insurance, retail, and manufacturing, as they navigate their AI journeys. And the pattern is clear: the companies that succeed do not start with algorithms or models. They start with the foundation. That foundation is the data.

Consider a major financial institution that transitioned from traditional batch-based fraud detection to real-time monitoring using MongoDB Atlas, reducing rollout times from weeks to minutes and saving millions annually. Or consider a global pharmaceutical company that used MongoDB and generative AI to cut clinical report generation from twelve weeks to just ten minutes. These AI-enabled systems were transformational for both businesses and were made possible by modern, flexible infrastructure built to support intelligent workloads at scale.

The lesson is clear: if you want to implement AI successfully, your data foundation has to come first.

That is why this book matters.

It doesn’t just talk about what is possible with AI; it shows you how to make it a reality. You will find hard-earned lessons from those already deploying AI in production, plus architectural blueprints and implementation strategies that you can apply immediately across any industry.

You will also learn why document-based data models are quickly becoming central to AI applications, how vector search unlocks meaning from unstructured data, and why blending operational and analytical capabilities creates real architectural leverage.

Just as important, this book addresses the often-overlooked realities of trust, governance, security, and scale. Building powerful AI is one thing. Making it responsible, resilient, and production-ready is something else entirely.

Whether you are a technology leader, architect, engineer, data scientist, or anyone responsible for implementing AI in your organization, this book offers a clear, practical path forward.

I am excited to see what you will build next.

Jim Scharf

Chief Technology Officer,

MongoDB, Inc.

Note from the author

The advent of large language models (LLMs), predicated on transformer-based architectures, began in 2018. At the same time, advancements in GPU technology facilitated greater parallel computational capabilities, culminating in the Nvidia V100 and catalyzing the generative AI (GenAI) domain. Natural language processing moved beyond reliance on rule-based systems and narrow subject area training, benefiting from comprehensive internet data and yielding significant advancements. The 2022 public beta release of ChatGPT, along with the introduction of competing LLMs, most notably Google Gemini and Anthropic Claude (utilized in AWS Bedrock), precipitated a shift in software development paradigms. Software design, coding, and business use cases expanded in unprecedented ways.

My preliminary investigations into vector utilization for semantic search and enhancements to MongoDB’s existing text search functionality predated these developments. The domains of embeddings, models, and nearest neighbors converged with the emerging field of LLMs. This convergence enabled the rapid development of a preliminary retrieval-augmented generation (RAG) solution, which facilitated the interpretation of PDF documents and the generation of responses based on natural language queries and vectorized document data. The MongoDB Industry Solutions team recognized the innovative potential and expedited implementation through the MongoDB Atlas platform. The advantages of an integrated platform over multi-component systems were substantial. However, the proliferation of components and permutations created challenges, particularly the overreliance on singular, nascent components. Consequently, the focus shifted toward solution design with specific business outcomes, referred to as the art of the possible. Client requests increasingly emphasized implementation details (how) rather than conceptualization (what).

During 2023 and 2024, reference architectures and established designs were developed, warranting broader dissemination. Solutions originating in specific use cases demonstrated cross-industry applicability. The concept of the consolidated data store was refined and required further documentation.

In the summer of 2025, a directive was issued to the team to compile industry-specific solution designs, based on current agentic AI patterns and templates, into a comprehensive publication. Foundational chapters were subsequently incorporated to accommodate varied experience levels.

Collaboration with strategic business partners has provided diverse perspectives, enriching the content. The insights of the CXO Advisory team, focused on application modernization and the use of generative AI tools for legacy system enhancement, have also been incorporated.

It is anticipated that this publication will serve as a valuable resource for those interested in industry solutions leveraging GenAI.

Boris Bialek

Vice President and Field CTO,

MongoDB, Inc.

Acknowledgements

Every book has its origin story, and ours began with a simple yet overwhelming challenge. As MongoDB’s Industry Solutions team, we specialize in presenting MongoDB as a solution for specific industries. We speak our customers’ language and understand their industry needs, roadblocks, market trends, and competitors.

Over the years, we have spent countless hours documenting industry solutions across our blogs, the MongoDB solution library, and numerous articles and presentations that we have passionately created to help developers and organizations solve their most challenging data problems. But when clients asked us to help them navigate this wealth of knowledge, we faced an uncomfortable truth: having thousands of scattered links, no matter how valuable each one might be, had become overwhelming rather than helpful.

It was Raghu Viswanathan, our remarkable leader in education and documentation, who identified the opportunity we hadn’t yet seen. In a conversation that began as a brainstorm about how to better serve our community, he suggested something that felt both obvious and audacious: “Why not turn all these insights into a book?” Without his clarity and persistent encouragement, this project would have remained nothing more than a collection of good intentions instead of becoming something real.

Writing a book in the technology space is never a solo endeavor. This is especially true when discussing real-world architectures and solutions. We are deeply grateful to the teams at Iguazio (acquired by QuantumBlack, AI by McKinsey), Fireworks AI, Dataworkz, Encore, Cognigy, and RegData, whose real-world implementations and feedback helped us understand what actually works in practice. Equally important are our technology partners such as Amazon Web Services, Microsoft Azure, Google Cloud, Confluent, Capgemini, and others. Their platforms and expertise make the solutions we discuss possible.

We would also like to thank our publishing partners at Packt, who proved that the best collaborations happen when expertise meets passion. Through countless revision cycles, they pushed us to think about our readers at every step. They asked the hard questions that helped transform our technical expertise into something both authoritative and accessible.

And to our colleagues across Industry Solutions, Product, and other internal teams, you know who you are. Your fingerprints are on every chapter.

This book is for every builder, architect, and strategist working to solve what comes next with AI. It is for you.

Contributors

About the authors

Boris Bialek has worked in the IT industry since the 1990s and was one of the initial drivers of Linux in Europe, delivering the first SAP port to Linux, conducting the first benchmarks, and securing the first clients. Since then, he has led product and development teams across IBM and FIS, driving innovation for both the end product and development productivity. Boris Bialek joined MongoDB in 2019, igniting a focus on industry solutions based on MongoDB’s document model. Promoted to global field CTO and VP of industries, he drives technical design. He works directly with numerous clients, helping them gain the benefits of the MongoDB Atlas data platform. Boris holds a master’s in computer science from the Karlsruhe Institute of Technology.

Sebastian Rojas Arbulu is an industry solutions specialist at MongoDB, where he collaborates with numerous stakeholders across diverse industries to help customers realize the transformative value of MongoDB through tailored, data-driven solutions, particularly for AI integration. Sebastian also leads his team’s content strategy, including numerous additions such as blogs, white papers, magazines, and other thought leadership pieces. With a background in IT consulting, marketing, and digital transformation, among other areas, he has extensive experience in identifying customer needs and developing innovative solutions that prepare data for intelligent applications and unlock new possibilities. He holds a bachelor of business administration degree.

Taylor Hedgecock is a strategic program leader and transformation partner who turns vision into velocity. With a career spanning startups to multinationals, she brings a mix of operational rigor, narrative clarity, and cross-functional orchestration. At MongoDB, she has led high-impact programs across AI, partner ecosystems, and services modernization, often serving as the connective tissue between vision and execution. Her work has guided C-level priorities, enabled go-to-market readiness, and driven large-scale change, establishing her as a trusted leader in aligning stakeholders, translating strategy into story, and driving outcomes that last. Taylor currently serves as senior program manager on the industry solutions team, partnering with ISVs and AI innovators to bring next-generation solutions to market. Previously, she was chief of staff for professional services leadership, where she helped launch new offerings and guided modernization strategy, shaping MongoDB’s vision for applying AI to its hardest problems.

Benjamin Lorenz has been a key contributor to MongoDB since 2016, driving growth across the Central European sales region. With deep expertise in strategic customer initiatives, he partners with decision-makers to align tailored solutions with business goals—leveraging the power of MongoDB’s developer data platform. As industry solutions principal for telco & media, Benjamin guides global clients through digital transformation, helping them unlock innovative, data-driven revenue streams.

Francesc Mateu is a principal of industry solutions at MongoDB, with 20+ years in B2B SaaS and IT innovation, including 15 years in digital health. As a startup founder and product leader, he possesses an entrepreneurial mindset aimed at helping healthcare organizations modernize their data architectures. His work includes designing digital platforms that support patient-centered care and value-based models, including telemedicine solutions for capturing patient-reported outcomes. At MongoDB, he works globally with sales, partners, and product teams to help healthcare systems adopt AI-ready, standards-based architectures—leveraging technologies such as FHIR and openEHR—to create tailored solutions to meet each organization’s unique needs.

Genevieve Broadhead is the global lead for retail solutions at MongoDB, based in Barcelona. She helps global retailers and retail software companies modernize data architectures for real-time personalization, omnichannel experiences, and AI-powered operations. With a computer engineering degree from Trinity College Dublin and a decade of experience in system design, Genevieve has extensive experience bridging business needs with cutting-edge technology. A recognized thought leader, she speaks regularly on cloud-native data pipelines, AI adoption, and composable commerce. She also serves on the MACH Alliance tech council, shaping standards for modern retail architectures.

Dr. Humza Akhtar is the smart manufacturing and automotive expert at MongoDB. Prior to joining MongoDB, he worked at Ernst & Young Canada in its digital operations consultancy practice. After completing his education in Singapore, he worked in the Singapore manufacturing industry for many years on Industry 4.0 research and implementation. He has spent his entire career enabling connected factories and connected cars for global manufacturing and automotive clients. He is a published author on Industry 4.0, and these days, his interest lies in enabling the use of generative AI within the automotive sector. Humza holds a master’s degree in embedded systems and a doctorate in computer science from Nanyang Technological University, Singapore.

Jeff Needham is MongoDB’s insurance industry expert, with nearly 30 years of experience in software delivery. As former senior director of architecture at Travelers, he led one of the industry’s most successful MongoDB adoptions. His career spans leadership at major software companies and healthcare giants such as Aetna/CVS. Jeff’s technical expertise and strategic insight drive exceptional outcomes for MongoDB’s complex enterprise engagements, helping organizations navigate digital transformation. He holds a master’s in political strategy from George Washington University.

Ken Wiebke has been working in the software development industry for over 30 years, with a career spanning development, architecture, and leadership. Throughout his career, Ken has driven change in organizations ranging from small to Fortune 500, including the shift from waterfall to agile. Serving in leadership roles for over 15 years, Ken’s focus has been on driving efficiencies and building high-performance teams that consistently deliver on time and within budget. Ken joined MongoDB in December 2024 as a CxO advisor to help organizations leverage the power of MongoDB and transform their legacy software to modern tech stacks.

Luis Pazmino Diaz holds over two decades of experience in the technology sector, particularly within banking and finance. Previously, he served as global strategy architect for Backbase, director of innovation at Temenos, and solutions advisor at major enterprise software firms such as SAP and Oracle. As MongoDB’s industry principal for financial services, he delivers strategic guidance to clients and solution partners across Europe, the Middle East, and Latin America. Based in Madrid, Spain, Luis has been widely recognized as a financial innovation expert.

Peyman Parsi began his career in financial services software engineering at SS&C, focusing on building wealth management software for the banking industry. In 2001, he joined the Toronto Stock Exchange (TSX), leading the development of capital markets solutions. Over 18 years at TSX, Peyman delivered several large-scale transformations and held the position of chief technology delivery officer. In 2020, he embarked on a new journey in FinTech, serving as CTO at Blanc Labs, with a primary focus on banking and digital lending solutions. Peyman is a member of the advisory board of the CIO Association of Canada and joined MongoDB in 2024 as senior principal of financial services industry solutions for the Americas.

Prashant Juttokonda is an expert in enterprise data architecture and modernization at MongoDB. Previously, he held leadership roles at EPAM, TCS, and IBM, advising global clients on cloud adoption, data transformation, and AI-ready architectures. With over 30 years of experience across banking, retail, and energy sectors, he has led large-scale modernization programs and driven the adoption of frameworks such as Data Mesh and Data Fabric. He is a frequent speaker and published thought leader in data strategy. His focus is on enabling generative AI and resilient data platforms. He holds a B.Sc. in mathematics and numerous certifications in cybersecurity and cloud technologies.

Raphael Schor is a mechanical engineer with 20+ years of experience in mechanical development, plant engineering, and industrial maintenance. He has served as CTO in the automotive and packaging industries, leading R&D and digital transformation. Since 2023, Raphael has served as principal for manufacturing and motion at MongoDB. In this role, he bridges the gap between industrial engineering challenges and modern data architecture. His focus includes digital twins, smart factories, and generative AI. Raphael holds a bachelor’s in mechanical engineering and a Master of Advanced Studies in Management, Technology, and Economics from ETH Zurich.

Rodrigo Leal, with over 20 years in the technology industry, is the principal retail industry solutions for Latin America at MongoDB. Prior to MongoDB, Rodrigo served as a senior principal solution specialist at Qualtrics, enhancing employee, customer, brand, and product experiences. Earlier, he was part of NCR’s Walmart Global Team, playing a role in launching self-checkout technology in Mexico and Central America, and was recognized with two NCR President’s Club awards. He previously worked with Oracle and MicroStrategy as an account manager and sales engineer. Known for strengths in consultative multi-product selling, he is dedicated to uncovering customer needs and crafting solutions that often reveal previously unseen opportunities.

Thorsten Walther boasts over 25 years in tech leadership, blending enterprise expertise, entrepreneurial drive, and deep technical acumen to drive digital transformation. He founded and led INSPIFY, an AI-powered SaaS platform for luxury retail. His career spans leadership roles at Credit Suisse and SOFGEN Services, as well as extensive advisory work in finance, retail, pharmacy, and enterprise software. Currently, as managing director, CXO advisory for Asia at MongoDB, he guides senior executives on digital transformation. Uniquely, Thorsten was a professional footballer in Germany’s Bundesliga and France’s Ligue 1 and 2. He holds an MBA from the University of Liverpool.

Wei You Pan is the global director of financial services industry solutions at MongoDB. With over 25 years spanning fintech, data architecture, and financial services, he empowers institutions to overcome complex data challenges and drive innovation. His expertise includes trading, loan origination, risk management, and sustainability, supported by credentials in enterprise architecture (SCEA), financial risk management (FRM), and climate risk (SCR). His cross-disciplinary background enables him to uniquely bridge technology and business, helping organizations realize the full value of their data.

About the reviewers

Coral Parmar serves as lead product manager on MongoDB’s search portfolio, bringing more than 20 years of technology and systems experience to help developers navigate modern data challenges across diverse verticals. His career spans leadership roles in MongoDB technical services, AdTech companies, and data development at UPS, providing deep expertise in scaling customer solutions for complex data systems across supply chain, logistics, and advertising technologies. Throughout his career, he has maintained a passion for solving complex problems and empowering teams to build effective, scalable solutions in rapidly evolving technical landscapes. He holds a master’s in information systems from New Jersey Institute of Technology.

James Osgood is a staff solutions architect at MongoDB with over 30 years of experience spanning software development and financial services. He began his career in the audio and video industry before moving into financial services, where he specialized in low-latency trading and market surveillance systems. Since joining MongoDB in 2017, James has partnered with major customers across London and Europe. Today, his focus is on helping global enterprises transform mission-critical financial systems and modernize broader application estates with purpose-built AI-driven modernization solutions.

Jim Blackhurst is a distinguished solutions architect at MongoDB, with more than 20 years of experience designing and delivering distributed data systems. He currently works with MongoDB’s application modernization team, helping some of the world’s largest organizations modernize their estates through purpose-built AI modernization tools, liberating them from the grip of legacy technologies. Before joining MongoDB, Jim spent his career in the video game industry, architecting and operating backend systems for some of the most iconic global gaming brands, working with data at scale before “scale” became a thing. Based in London, Jim continues to focus on pushing the boundaries of distributed systems design and helping enterprises unlock new possibilities with data.

Julia Pak is a product manager at MongoDB on the enterprise initiatives and tools team. She currently focuses on developing internal products that enhance organizational productivity through data centralization and AI-powered features. Prior to joining MongoDB, Julia worked in the insurance and advertising technology industries, building external products from the ground up. She earned her bachelor of arts degree in history from Princeton University.

Shash Thakor is a senior product manager at MongoDB, primarily responsible for Atlas networking. He has 15+ years of product and engineering leadership experience in developing highly distributed, scalable, and secure software systems. Before moving into product management, he was a software developer with Cisco Systems and Juniper Networks, responsible for developing switching and routing software systems deployed in many data centers across the world and used by hyperscalers, big enterprises, and small businesses. He has multiple patents in distributed systems, security, and zero-touch provisioning. He holds a master’s in computer science from the University of Maryland, College Park (UMCP).

Contents

Note from the author

Acknowledgements

Preface

How this book will help you

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Part 1: AI and Key Concepts

AI Modernization to Innovation

Understanding innovation: Creating new value

Strategic inflection points: Andy Grove’s theory applied to AI

Navigating the AI inflection point

Understanding modernization: The often-overlooked prerequisite

Common modernization strategies

Where innovation meets modernization: The AI intersection

The AI implementation pitfall: When innovation lacks foundation

Modern data platforms: The backbone of AI-ready transformation

Why modern data platforms are necessary

Enabling innovation through agility and speed

Simplifying modernization without starting over

Powering AI at scale

Summary

References

What Sets GenAI, RAG, and Agentic AI Apart

How AI evolved: From theory to ChatGPT

A small walk into history

AlphaGo and the turning point in AI

The emergence of LLMs

GenAI: Creating new content from patterns

How GenAI works

Limitations and challenges of GenAI

From data to vectors

The embedding models and “embedders”

Vector databases and their importance

Chunking strategies for AI applications

Semantic search: Putting vectors to work

Beyond keyword matching

Multimodal applications of semantic search

RAG: Enhancing LLMs with contextual data

How RAG works

Beyond RAG: Hybrid search approaches

Reranking: Refining search results

Agentic AI: Automating decision-making and reasoning

Agentic AI foundation

What is an agent?

Digital experts or multi-agent systems: Collaborative problem-solving

How agentic AI works

Summary

References

The System of Action

Building an AI-ready data foundation

What is a system of action?

Unified data access architecture

Ensuring data quality and consistency

Real-time context and RAG

Scalability, availability, and performance

Governance, security, and compliance

Model training and fine-tuning

Practical considerations for AI data design

A good data structure is critical

Data flow

Operationalizing a system of action database

Deployment patterns

Performance monitoring and optimization

Cost management and resource allocation

Maintenance workflows and data lifecycle management

Migration strategies from legacy systems

Team training and adoption considerations

Summary

References

Trustworthy AI, Compliance, and Data Governance

Why ethical AI matters

The rising stakes of AI implementation

Defining the core concepts

Ethical frameworks: From principles to practice

Bridging principles and implementation

Bias audits

Ethical review boards

Transparent documentation

Stakeholder engagement

Navigating the regulatory landscape

Healthcare

Financial services

Building trustworthy and responsible AI

Safeguarding data

Protection and privacy requirements

Building robust AI data governance

Managing risk: assessment and mitigation strategies

Risk assessment

Practical risk management approaches

Transparency in action: Explainability mechanisms

AI transparency

AI explainability

The business case for explainable AI

Operationalizing trustworthy AI through governance

The road ahead: Emerging trends and future directions

Evolution of AI governance

Persistent challenges and opportunities

Summary

References

Modernization Using AI

The modernization challenge

Motivations for modernization

Business imperatives: Competitive pressure and innovation

Technical limitations: The growing burden of legacy architecture

Why AI alone isn’t the answer

Unlocking innovation with AI-powered modernization

Start with the right data foundation

Automating the modernization factory process

Orchestration: how the factory is automated

Where AI accelerates the process

Analysis

Test generation

Code transformation and testing

Deploying and migrating

Establishing a repeatable modernization process

Summary

References

Part 2: Real-World Case Studies and Implementations

Practical Applications of Agentic and GenAI in Manufacturing – Part 1

The path to success in manufacturing AI

GenAI-powered supply chain optimization

Multi-level planning approaches

Inventory classification and optimization approaches

ABC analysis and its limitations

MCIC and the need for GenAI

AI and MongoDB for inventory optimization

GenAI-powered inventory classification

Methodology for implementing GenAI-powered inventory classification

Atlas: Unified AI infrastructure

GenAI inventory classification demo: A visual walkthrough

Step 1: Starting with basic classification

Step 2: Generating new AI-powered criteria

Step 3: Integrating new criteria into classification

Step 4: Weighting and running analysis

Raw material management via agentic AI

Demand forecasting and inventory optimization

Benefits of MongoDB for inventory management

Reimagining inventory management for Industry 5.0

Summary

References

Practical Applications of Agentic and GenAI in Manufacturing – Part 2

Predictive maintenance and multi-agent collaboration

Optimal maintenance strategy

Current state and challenges

How AI and MongoDB help

Stage 1: Machine prioritization

Stage 2: Failure prediction

Stage 3: Repair plan generators

Stage 4: Maintenance guidance generation

Multi-agent collaboration system

Optimizing a production environment

Knowledge management and preservation

The challenge of institutional knowledge and AI-powered solutions

Real-time knowledge application

Hyper-personalized in-cabin experiences

Challenges and AI-powered solutions for in-car voice assistants

GenAI: transforming in-car assistants

Solution architecture: MongoDB Atlas and Google Cloud integration

Advanced agentic architecture: MongoDB Atlas and Google Cloud integration

RAG implementation challenges for vehicle manuals

Google Cloud and MongoDB: Better together

Strategic advantages of AI-integrated in-cabin systems

Fleet management and optimization

Scheduler agent for fleet operations

Logical and physical architecture

MongoDB for fleet scheduler

Agent profile and instructions

Short-term and long-term memory

Connected fleet incident advisor

Incident advisor architecture

Data types and storage

Advantages of MongoDB for fleet management

The expanding role of AI in manufacturing

Summary

References

AI-Driven Strategies for Media and Telecommunication Industries

Evolving landscape of media and telecommunication

Content discovery and personalization

Content suggestions and personalization platform

Content suggestions and personalization

Content summarization and reformatting

Keyword and entity extraction

Automatic creation of insights and summaries

Search generative experiences (SGEs)

Smart conversational interfaces

Gamified learning experiences

Service assurance

Agentic AIOps for network management

Building AI-powered network systems for telecommunications

The next era of AI-powered operations

Fraud detection and prevention

The expanding role of AI in media and telecommunication

Differential pricing

Video search and clipping

Summary

References

Cognigy’s Voice and Chatbots in the Time of Agentic AI

The evolution from rule-based to goal-oriented AI

Case study: How a Tier-1 airline responded to crisis

The limitations that held us back

The agentic AI breakthrough

Why data is the lifeblood of agentic AI

The scope of modern data requirements

MongoDB’s role in enabling real-time intelligence

Real-world application: transforming retail customer experience

The technical foundation for seamless integration

Real-time performance in critical moments

When systems are pushed to their limits

The complexity behind simple requests

Scaling excellence, not mistakes

The mathematics of transformation

Demonstrated results across industries

The foundation for sustainable growth

Personalization isn’t magic, it’s data mastery

The architecture of intelligent personalization

The technical foundation for personalization excellence

The stakes of accuracy

The comprehensive requirements for AI excellence

Governance and compliance framework

Summary

References

Harnessing AI to Transform the Retail Industry

Semantic search powered by vector search

Transforming retail search

Building a unified customer view

Evolving from reactive to proactive

Personalized marketing and content generation

Meeting the content demands of modern retail with GenAI

Accelerating personalized content with GenAI and LLMs

Leveraging modern databases for scalable, AI-driven marketing

How agentic AI is revolutionizing adaptive marketing in retail

Demand forecasting and predictive analytics

AI-driven demand forecasting for smarter inventory and supply chain management

How GenAI is reshaping predictive analytics in retail

Transforming predictive analytics with agentic AI in retail

Digitizing in-store interactions with intelligence

From paper to insight: Digital receipts as a data catalyst

Building a real-time omnichannel customer profile

Personalization at the point of sale

Agentic AI: From insights to intelligent action

Conversational and agentic chatbots

How GenAI chatbots are revolutionizing retail engagement

Powering intelligent conversations with search and AI

From scripted to smart: Transforming retail chatbots with agentic AI

The expanding role of AI in retail

Proactive loss prevention

AI-driven merchandising execution

Self-healing store operations

Dynamic workforce orchestration

Real-time sustainability optimization

Summary

References

Financial Services and the Next Wave of AI

The evolution of AI in finance

The power of finance-specific embeddings

Transforming credit applications with AI

Building a smarter credit system with MongoDB

Revolutionizing enterprise knowledge management in banking with GenAI

Challenges of traditional EKM systems in banking

How GenAI is transforming EKM systems in banking

Use cases of GenAI for internal EKM systems in banks

Architectural considerations for GenAI-powered EKM systems

The impact of GenAI on EKM systems

Better digital banking experiences through AI‑driven interactions

Elevating customer experience with GenAI

AI-powered digital banking data foundations

Reference solution architecture for AI-powered customer support

AI-enhanced financial crime mitigation and compliance

Strengthening financial crime mitigation with AI

Emerging trends redefining the future of AI in compliance

AI for regulatory intelligence and policy automation

MongoDB’s role in KYC and AML

Strategic business benefits

Multimodal and AI-driven ESG analysis

MongoDB’s role in ESG data management

AI-driven ESG policy and regulatory compliance

Straight-through payments processing powered by AI

Business outlook

The role of GenAI

The road ahead

Capital markets

Reimagining investment portfolio management with agentic AI

Intelligent investment portfolio management

How MongoDB unlocks AI-powered portfolio management

Intelligent investment portfolio management with AI agents

The expanding role of AI in financial services

Summary

References

RegData, MongoDB, and Voyage AI: Semantic Data Protection in FSI

The data protection dilemma in financial AI

Understanding the MongoDB, RegData, and Voyage AI approach to semantic data protection

What is semantic data protection?

Key techniques in semantic data protection

Format-preserving tokenization

Contextual semantic protection

Semantic partitioning with token classes

Deterministic tokens

Building a comprehensive semantic protection architecture

MongoDB as the foundation with RegData’s data security platform

API gateway with MongoDB and RegData’s prompt decoration

Protected vector search with MongoDB Atlas and Voyage AI

Secure output rendering

Domain-specific intelligence for enhanced security and performance

Leveraging financial-specific embeddings for enhanced protection

Real-world innovation: interactive banking

Building the future with advanced techniques and emerging standards

MCP

Hybrid protection strategies

Compliance and regulatory considerations

Regulatory framework alignment

Auditability and explainability

Summary

References

Driving Client Success in Banking with GenAI Copilots

The catch-22 of wealth relationship management

Scaling a successful relationship management GenAI copilot

How the relationship management GenAI copilot works under the hood

GenAI factory: Powering copilots, agents, and GenAI apps

How the AI factory addresses FSI engineering needs

Leading FSI use cases where GenAI brings real value

Case study: A GenAI-driven smart call center analysis application

What now? How enterprises can succeed with GenAI

Summary

Delivering Business Value with AI in Insurance

The evolution of data architectures

Claim handling as an example

The spectrum of AI in insurance

Traditional machine learning

GenAI and LLMs

Agentic AI systems

Agentic workflows in insurance

Architecting for applications

The converged datastore

Managing operational structured and unstructured data

Architecture features for agentic systems

Root domain entity and domain schema

Unified search across all data types

Event-based architecture for autonomous actions

AI-driven improvements in claim handling for better business outcomes

AI maturity and implementation strategy

The three layers of GenAI

Domain-driven AI implementation

Working together: applications, data, and AI

Modernization and AI-forward architecture

AI-forward architecture

Underwriting and risk management

Advanced analytics

Claim processing

Customer experience

Real-world examples of domain-specific AI

Practical AI use cases in insurance

Claim management using LLMs and Vector Search for RAG

AI-enhanced claim adjustment for auto insurance

PDF search application with Vector Search and LLMs

The future of AI in insurance

Predictive analytics for customer engagement

Crop insurance and precision farming

Predictive maintenance for property insurance

Usage-based insurance (UBI) for commercial fleets

Summary

References

Automating Insurance Underwriting with Fireworks AI and MongoDB

Understanding the importance of speed

The broken workflow

The vision

Setting up the core technical components

Document architecture

The 10-step AI pipeline: From email to quote

The RAG advantage

Using MongoDB Atlas for modern database infrastructure

Inference layer implementation: Fireworks AI

Exploring the results

Quantitative impact

Qualitative transformation

Diving deep into the technical innovation

Production-grade RAG implementation

Real-world application: A quote request journey

Transforming daily operations

Industry impact and implications

Broader technology adoption

Regulatory considerations

Summary

References

AI-Powered Transformation of Healthcare and Life Sciences

Understanding the AI revolution in healthcare

Why traditional solutions fall short

Demystifying the AI terminology

The transformative opportunity of GenAI

Building the right architecture for healthcare AI

The challenge of healthcare data architectures

Preparing for the healthcare data needs of the future

A document-based, AI-ready approach

Enhancing flexibility and interoperability with the facade model

The new stack for healthcare AI

Implementation architecture patterns

AI-powered care coordination

Orchestrating specialized agent roles in clinical workflows

Natural language clinical intelligence

Clinical context understanding

Semantic search capabilities

Using GenAI for visual diagnostics

Medical visual question-answering

Applying vector embeddings

Extending intelligence to life sciences

Revolutionizing CSRs with GenAI and MongoDB

Looking ahead to intelligent healthcare

Summary

References

Part 3: The Future of Intelligent Enterprise

Enterprise Document Management with MongoDB and AI

The digital filing cabinet era

The hidden costs of legacy systems

Legacy architectural constraints

When digital promises failed to deliver

The unstructured data challenge

Redefining document management with Encore

Claims processing

Call center support

Compliance and audit readiness

Building a platform for new EDM with MongoDB and Encore

The urgent case for EDM modernization

Summary

References

Democratizing Agentic AI for Enterprise with Dataworkz and MongoDB

Tailoring AI for every organization

Case study 1: Client Insight Engine – agentic AI for financial advisors

Case study 2: DevOps Efficiency Agent – supercharging developer productivity

Case study 3: Brand Messaging Agent – ensuring on-brand communications at scale

Implementing effective AI solutions with Dataworkz and MongoDB

Turning your AI strategy into action

Future directions for enterprise agentic AI

Summary

Outlook: Beyond Today’s AI

From tools to context: The rise of intelligent architectures

MCP: Building blocks for contextual intelligence

Causal AI: Beyond prediction, toward impact

Memory architectures: Persistent context for intelligent agents

Constitutional AI: Governing intelligence with principles

Multi-agent systems: From solo models to cooperative intelligence

Looking back to look forward: Patterns in the field

Foundational architecture: From theory to practice

Industry applications: Validation through diversity

Partner ecosystem: Specialized excellence on unified foundations

Universal patterns across domains

Final thought: Architecture is the intelligence

References

Afterword

What we learned

Architectural insights

Beyond case studies

What’s next

Index

Other Books You May Enjoy

Landmarks

Cover

Index

Preface

This book is about how organizations can move beyond surface-level AI adoption and implement AI as a true driver of business transformation. It explains the strategic importance of distinguishing between modernization and innovation, and how both are essential for successful AI deployment. Through real-world implementations, success stories, and practical frameworks, it provides a roadmap for navigating the AI inflection point, aligning data infrastructure with AI goals, and building trustworthy, scalable, and context-aware AI systems.

The book is organized into three parts. The first part lays the foundation, covering core AI concepts, system architectures, governance, and modernization approaches that prepare organizations for large-scale adoption. The second part explores industry applications, showing how agentic and generative AI (GenAI) can reshape sectors such as manufacturing, media, retail, financial services, insurance, and healthcare. The final part looks ahead, presenting advanced implementation patterns, governance models, and emerging technologies such as Model Context Protocol (MCP) and causal AI, equipping readers with strategies to sustain innovation and adapt to the next wave of intelligent systems.

How this book will help you

Inside, you will learn the core patterns for building intelligent architectures, with a focus on GenAI, retrieval-augmented generation (RAG), and agentic systems powered by AI agents. You will see how these capabilities are applied across industries, supported by mapped reference architectures and detailed implementation guidance. The book also explores emerging directions such as causal intelligence, MCP, and advanced multi-agent design patterns. Whether you are modernizing legacy infrastructure or creating new platforms, it equips you with the vocabulary, frameworks, and practical context to move faster, reduce guesswork, and build reliable, scalable, and context-aware AI systems.

Who this book is for

This book is for:

IT decision-makers exploring where to place strategic AI betsEnterprise and solution architects rethinking their data and application stackTechnical ears and curious builders who want to understand how intelligent systems are structured and deployedBusiness strategists and domain owners seeking to translate AI hype into domain-specific outcomes

You don’t need deep AI experience to get value from this book, but you should feel comfortable thinking in terms of data systems, application layers, and business architecture. If you’re already experienced with AI concepts covered here, feel free to skip the early chapters and jump into the real-world case studies and future-focused content.

What this book covers

Chapter 1, AI Modernization to Innovation, outlines the difference between modernization and true innovation and how to structure teams, data, and processes to turn AI experiments into business outcomes.

Chapter 2, What Sets GenAI, RAG, and Agentic AI Apart, defines GenAI, RAG, and agentic systems, and explains when to use each approach.

Chapter 3, The System of Action, describes the document-oriented system of action and why unified, low-latency access to multimodal data and embeddings is critical for AI workloads.

Chapter 4, Trustworthy AI, Compliance, and Data Governance, summarizes governance, privacy, explainability, and risk management practices required for production AI.

Chapter 5, Modernization Using AI, gives practical patterns for using AI to accelerate legacy modernization while preserving correctness and governance.

Chapter 6, Practical Applications of Agentic and GenAI in Manufacturing – Part 1, focuses on supply-chain and inventory use cases, including embedding-driven classification and autonomous procurement helpers.

Chapter 7, Practical Applications of Agentic and GenAI in Manufacturing – Part II, focuses on factory-floor operations, including predictive maintenance, quality inspection, and multi-agent production orchestration.

Chapter 8, AI-Driven Strategies for Media and Telecommunication Industries, covers personalization, search experiences, AI operations, and fraud detection tailored to media and telecom sectors.

Chapter 9, Cognigy’s Voice and Chatbots in the Time of Agentic AI, examines voice and chat systems for high-throughput, goal-oriented customer interactions.

Chapter 10, Harnessing AI to Transform the Retail Industry, explains personalization, demand forecasting, inventory optimization, and real-time decision-making in retail.

Chapter 11, Financial Services and the Next Wave of AI, outlines the sector’s next AI transformation, from customer insight and compliance automation to AI-enhanced risk management and service models.

Chapter 12, RegData, MongoDB, and Voyage AI: Semantic Data Protection in FSI, describes semantic protection and audit approaches that enable compliant use of large language models (LLMs) in finance.

Chapter 13, Driving Client Success in Banking with GenAI Copilots, shows how banking copilots can automate advisor tasks, surface research, and support compliant client communications.

Chapter 14, Delivering Business Value with AI in Insurance, outlines converged datastores and AI patterns to improve underwriting, claims, and customer outcomes.

Chapter 15, Automating Insurance Underwriting with Fireworks AI and MongoDB, details an end-to-end underwriting pipeline using retrieval-grounded AI for faster, more accurate policy intake and quoting.

Chapter 16, AI-Powered Transformation of Healthcare and Life Sciences, addresses clinician overload with FHIR facade patterns, clinical RAG, and multi-agent care coordination to achieve better patient outcomes.

Chapter 17, Enterprise Document Management with Encore and MongoDB, demonstrates how to turn unstructured enterprise dark data into enriched, searchable knowledge for operational and AI use.

Chapter 18, Democratizing Agentic AI for Enterprise with Dataworkz and MongoDB, provides architectural guidance, governance practices, and real-world cases for deploying safe, observable, and effective agentic AI.

Chapter 19, Outlook: Beyond Today’s AI, looks ahead to MCP, memory-driven agents, and causal AI as drivers of the next wave of intelligent systems.

To get the most out of this book

No specific tooling expertise is required, though a working understanding of enterprise systems and data architecture will help you engage more deeply with the material. Readers interested in implementation details, can explore:

MongoDB Solutions Library: https://www.mongodb.com/docs/atlas/architecture/current/solutions-library/MongoDB for Artificial Intelligence: https://www.mongodb.com/solutions/use-cases/artificial-intelligence

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We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://packt.link/gbp/9781806117154.

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Part 1:

AI and Key Concepts

The following set of chapters lays the conceptual and architectural foundation for building intelligent systems with generative and agentic AI. It introduces the shift from modernization to innovation. It also explains the importance of real-time, document-based data models, and it describes the new architectural thinking required to implement AI at scale in a secure and responsible way.

This part of the book includes the following chapters:

Chapter 1, AI Modernization to InnovationChapter 2, What Sets GenAI, RAG, and Agentic AI ApartChapter 3, The System of ActionChapter 4, Trustworthy AI, Compliance, and Data GovernanceChapter 5, Modernization Using AI

1

AI Modernization to Innovation

Many readers are at different stages of their AI journey, from initial exploration to active implementation planning. Some are leading teams tasked with doing something with AI. Others are watching competitors announce AI initiatives while wondering what their next move should be. Still others have tried AI pilots that showed promise in demos but somehow never made it to production. If any of this sounds familiar, you’ve found the right resource.

While countless publications theorize about AI’s promise, this book takes a different approach: it’s a field guide built by practitioners for practitioners, designed to help you navigate past the endless testing phase and move toward real-world AI implementation at scale.

Across industries, we’ve gathered hard-won lessons from teams that have actually done the work. These aren’t abstract frameworks or vendor pitches; they’re playbooks forged in the fire of enterprise constraints, integration realities, and performance expectations. We also look beyond the buzzwords. Though we cover generative AI (GenAI) and AI agents, we also discuss the data architectures, governance models, and design patterns that make this technology actually work. Because in the end, AI transformation isn’t just about adopting new tools; it’s about rethinking how your systems, teams, and business strategy fit together.

This practical focus matters now more than ever. While the AI revolution promises unprecedented productivity growth potential, the reality is that most organizations remain trapped in a cycle of modernization without innovation. Nearly all companies are investing in AI technologies, but few have integrated them deeply enough to deliver game-changing results. The difference lies in their approach [1].

How do you close this gap? It starts with distinguishing between two concepts that often get conflated: modernization and innovation. In this chapter, we’ll untangle those definitions and show why understanding the difference is strategic. You’ll learn how modernization sets the stage for AI success, how innovation extends its reach, and how both become essential when navigating a moment of rapid disruption.

At the end of this chapter, you will walk away with a sharper grasp of the core dynamics shaping successful AI adoption, including:

The fundamental differences between innovation and modernization, and why both matter in AI deployments How Andy Grove’s theory of strategic inflection points applies to today’s AI revolution Why modernizing legacy systems and data infrastructure is essential for successful AI implementationHow to avoid common pitfalls organizations face when pursuing AI initiatives without proper foundationsHow to implement practical approaches to balancing innovation and modernization in your AI strategy

Understanding innovation: Creating new value

Before you can effectively harness AI to modernize or innovate, you need a practical understanding of what these terms actually mean and how they differ in both execution and impact.

Innovation, at its core, is the process of creating and implementing new ideas, methods, products, or services that add value or improve upon existing ones. It involves turning creative concepts into practical solutions that meet real-world needs or solve problems more effectively.

Innovation can take many forms, including the following:

Product innovation: Developing new or significantly improved goods or services. Think of the move from paper maps to GPS apps.Process innovation: Introducing new ways of producing or delivering products. Robotic process automation (RPA) has revolutionized standardized processes and decision-making, leading to innovations such as autonomous underwriting or real-time claims management for insurance companies. Business model innovation: Redefining how a company creates and captures value. Probably the most famous of all innovations was Netflix’s multi-level innovation, first shipping DVDs to people’s homes, rather than picking them up at Blockbuster, and then delivering content via streaming to make it even easier than mail. Social innovation: Using new technologies to address environmental and societal needs. AI is accelerating environmental, social,andgovernance (ESG) progress. This includes everything from smart energy systems to automated carbon tracking. In places such as Brazil, platforms such as PicPay are expanding access to financial tools and social programs for underserved communities.

Each of these examples started with an idea, but became an innovation only once it was applied to solve a problem at scale. In this sense, innovation requires more than invention. It requires execution.

Strategic inflection points: Andy Grove’s theory applied to AI

Few frameworks better explain what’s at stake in today’s AI race than Andy Grove’s theory of strategic inflection points, introduced in his book Only the Paranoid Survive. Grove describes these moments as times when the fundamentals of a business (or an entire industry) undergo dramatic, irreversible change. These shifts can be triggered by new technology, regulatory upheaval, competitive pressure, or all three at once.

Grove’s central insight? You don’t see an inflection point clearly until you’re in it. And by then, it’s often too late to catch up. Companies that adapt early can leap ahead. Those that hesitate, resist, or cling to old models often don’t survive.

Strategic inflection points require leadership to do more than just optimize; they demand reinvention. Grove famously said: “Only the paranoid survive.” In his view, success requires constant vigilance, relentless questioning of assumptions, and the courage to bet on transformation before the path is proven.

The best-known example? Intel.

In the 1980s, Intel dominated the memory chip business. But competitors from Japan began producing faster, cheaper, and higher-quality alternatives. Grove and then-CEO Gordon Moore made a bold call: they abandoned their legacy business and pivoted entirely to microprocessors. At the time, this market was small and uncertain. But the gamble paid off. Intel’s chips became the backbone of the personal computing revolution, transforming the company into one of the most important tech players of the modern era.

Today, AI presents a similar inflection point. The introduction of large language models (LLMs), retrieval-augmented generation (RAG), and agentic systems may prove to be as foundational to the AI era as the first microprocessors were to the PC era. These technologies are not just new features; they’re new substrates for how business is done.

Unlike the microprocessor revolution, which unfolded over decades, AI is evolving at unprecedented speed. Expectations are rising faster than infrastructure can keep up. And many organizations haven’t even started modernizing their foundations.

If you’re feeling the urgency, you should be. This is what an inflection point feels like.

Navigating the AI inflection point

Organizations that recognize this moment as a true inflection point can prepare their infrastructure and capabilities for the AI-driven future. Beyond new tools, success demands a fundamental rethink of how data, technology, and business processes work together.

At the core of this transformation are five critical capabilities:

Flexible, future-ready data infrastructure: Legacy systems face significant challenges with AI requirements. Rigid database schemas and monolithic architectures often cannot support dynamic AI applications. Organizations need infrastructure that can adapt to rapidly evolving AI capabilities without requiring complete system overhauls. This means adopting platforms that support schema flexibility, can handle diverse data types from structured databases to unstructured documents and multimedia content, and can scale both vertically and horizontally as AI workloads grow. The infrastructure must also support real-time data processing and streaming, as many AI applications require immediate access to the most current information.Fluency in vector embeddings and semantic search technologies: These technologies form the backbone of many modern AI applications. Vector embeddings allow AI systems to understand and process human language, images, and other complex data types by converting them into mathematical representations that machines can work with. While many AI implementations can succeed without deep technical knowledge of embeddings, organizations pursuing more sophisticated or differentiated AI solutions will benefit from teams that understand how to generate, store, and query these embeddings effectively. This includes knowledge of different embedding models, understanding when to use pre-trained versus custom embeddings, and expertise in vector databases and similarity search algorithms. This expertise becomes particularly valuable when building AI applications that can truly understand and reason about complex, domain-specific data.Architectures that bridge AI and operations: Too often, AI initiatives are built separately from the data sources and business systems they’re meant to enhance. That leads to data silos, synchronization problems, and AI applications that work with stale or incomplete information. Successful organizations design architectures where AI capabilities are deeply integrated with operational systems, allowing for real-time insights and automated decision-making based on current business data. This integration requires careful consideration of data flow, API design, and event-driven architectures that can propagate changes across both traditional and AI systems.Strategies for maintaining data consistency between systems: This becomes critical when AI applications need to work alongside legacy systems during transition periods, though this remains one of the most challenging aspects of AI implementation. Organizations cannot typically replace all their systems overnight, so they need approaches for managing data synchronization across multiple platforms, even when perfect consistency may not be achievable. This includes implementing change data capture mechanisms, designing data validation processes, and establishing clear data governance policies. The strategy must also account for the fact that AI systems may process and transform data differently than traditional applications, requiring new approaches to data lineage and quality management. Organizations should expect this to be an ongoing challenge rather than a problem with straightforward solutions. Guardrails for responsible AI: As pressure builds to move fast, the temptation to cut corners grows. But without robust governance frameworks, organizations risk deploying systems that are biased, brittle, or out of compliance. Practical AI governance means codifying policies for data privacy and security, algorithmic bias and fairness, model explainability and transparency, and regulatory compliance across different jurisdictions. The governance framework must be enforceable, providing clear guidelines for AI development teams while not slowing down innovation unnecessarily. Done well, governance becomes a catalyst, not a constraint.

These approaches address a critical challenge at the AI inflection point: the need to bridge operational data and AI capabilities without creating new data silos or overly complex architectures. Organizations that successfully navigate this inflection point will find themselves with significant competitive advantages, while those that fail to adapt risk being left behind as AI transforms their industries. The key is to start building these capabilities now, before the competitive pressure becomes overwhelming.

Understanding modernization: The often-overlooked prerequisite

In addition to the high impact potential of innovation, there’s another critical lever in the transformation toolkit: modernization. While modernization itself is a broader term often used to describe physical infrastructure, technical modernization refers to the upgrading or replacement of outdated technologies, systems, or processes with newer, more efficient, and more advanced ones to improve performance, productivity, and competitiveness. Key elements of technical modernization include the following:

Digitalization using technologies such as automation, AI, cloud computing, or Internet of Things (IoT)Digitization of analog processes, for instance, replacing paper records with digital systemsSystem upgrades, such as modernizing legacy IT infrastructure or software (commonly referred to as refactoring)Integration of modern tools into existing workflows to increase efficiency and reduce costsCybersecurity improvements that address evolving threats and meet industry standards

The goal of modernization is to enhance capabilities, reduce operational risks, and remain competitive in a fast-changing technological landscape.

In the context of this book, modernization comes in two distinct forms. The first form involves modernization through the adoption of advanced technologies to address what is arguably the most significant challenge facing many enterprises today: legacy systems. Here, the term legacy system is used in a broad sense. While these systems often undergo physical upgrades every three to five years (a cycle deeply familiar in the mainframe world and certainly impacting IBM results), the software running on these systems often remains outdated. This leads to situations where companies are running decades-old business logic and software, despite having upgraded to newer hardware. The underlying code and business processes can be as old as half a century, creating significant challenges for modernization efforts.

The second approach, which is often the best way to go in scenarios such as this, is a complete system replacement built from scratch. But, as is often the case in business, the direct approach may not be available as the reasons for not touching a system as such are plenty: implicit business know-how, regulatory compliance, connectivity to existing machines, and more.

Common modernization strategies

Modernizing legacy applications isn’t one size fits all. According to industry research and best practices [2], there are several common modernization strategies that organizations can employ:

Refactoring: When a company refactors, developers update the code base, improving the code structure, performance, and maintainability. This can involve enhancing existing code without changing functionality, or migrating from older languages and frameworks to more modern alternatives. For example, an application built in Ruby might be refactored into Rust, or a system using an old version of Java with Spring might be refactored to modern Java with Quarkus. This action allows for independent scaling, easier maintenance, and access to current development tools and practices.Replatforming