FPL Architect Maps Blueprint to Ground Agentic AI in Corporate Memory

Building the Thinking Enterprise: From Enterprise Architecture to Enterprise Intelligence — Ai4

Ai4

Autonomous enterprise agents fail without structured organizational context, requiring architects to convert siloed system records into persistent context and memory layers to automate cross-functional decisions.

AI-assisted summary of Building the Thinking Enterprise: From Enterprise Architecture to Enterprise Intelligence — original source. Check the original for full context.

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Detailed notes

## Background Published on YouTube by the channel Ai4 on August 21, 2026, this presentation features Chalamayya Batchu Veera, Principal Enterprise Architect for AI & Data at Florida Power & Light. The talk outlines how traditional Enterprise Architecture must transition from documenting technology landscapes into an active Enterprise Intelligence Architecture that connects capabilities, data, policies, and institutional knowledge to power AI-native organizations.

## TL;DR Enterprise AI initiatives frequently stumble because organizations lack connected context, turning straightforward decisions like application decommissioning into months-long cross-silo investigations. To overcome this, Enterprise Architecture must evolve into Enterprise Intelligence Architecture by layering explicit Enterprise Context, institutional Enterprise Memory, and reasoning-driven Enterprise Intelligence atop existing enterprise assets. This blueprint transforms organizations into 'Thinking Enterprises' that adapt continuously and compress multi-week evaluations into days. Ultimately, this centralized intelligence layer grounds domain-specific autonomous agents while upholding a human-centered approach designed to amplify human expertise rather than replace it.

## Atmosphere & style The presentation carries a pragmatic, authoritative, and architectural tone grounded in years of hard-won enterprise systems experience. It begins with an instantly recognizable corporate frustration—the deceptively simple question of retiring an application—before methodically building toward a structured blueprint for the agentic AI era. The pacing is deliberate and reassuring, stripping away generic AI hype to focus on the unglamorous yet vital discipline of data governance, institutional memory, and collaborative human-AI decision-making.

## Key takeaways - [00:04](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=4s) Routine IT decisions, like retiring an application, routinely take weeks or months because knowledge of impacts, integrations, data, and compliance is fragmented across organizational silos. - [00:04](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=4s) Enterprise Architecture's historic mission is not creating new knowledge from scratch, but organizing, connecting, and exposing fragmented context into a shared understanding of how the enterprise operates. - [02:11](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=131s) Traditional EA artifacts were crafted for human consumers who intuitively bridge semantic gaps, whereas AI models cannot infer unstated context on their own. - [02:11](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=131s) To support AI adoption, Enterprise Architecture must evolve into Enterprise Intelligence Architecture, layering Context, Memory, and Intelligence over existing enterprise assets. - [03:56](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=236s) Enterprise Context adds semantic meaning to disconnected catalogs, defining relationships and mapping upstream and downstream dependencies across systems. - [03:56](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=236s) Enterprise Memory functions as a 'Context++' layer, preserving not just current-state snapshots but historical decisions, rationale, events, and lessons learned. - [03:56](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=236s) Enterprise Intelligence acts as a reasoning engine to evaluate scenarios and propose next-best actions while eliminating hallucinations by grounding reasoning in context and memory. - [03:56](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=236s) The ultimate objective of the thinking enterprise is augmenting and amplifying human decision-making rather than replacing human expertise. - [07:49](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=469s) A Thinking Enterprise continuously adapts and learns, reducing complex cross-functional assessments from months to days through queryable institutional memory. - [07:49](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=469s) Transforming into an intelligence-driven architecture requires long-term commitment and architectural discipline; it cannot be resolved in a rapid two-to-three-month sprint. - [11:09](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=669s) Enterprise intelligence must follow core guiding principles: business outcomes first, context and memory enabling intelligence, trust by design, and human-centered collaboration. - [11:09](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=669s) In the emerging agentic era, autonomous domain and strategic agents require a centralized Enterprise Intelligence Services layer to access shared context and institutional memory across silos.

## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=4s) The Disconnected Enterprise and the Real Role of Enterprise Architecture

Many IT professionals who have spent years building large-scale platforms—such as ERPs, CRMs, cloud modernization, and complex migrations—frequently encounter a deceptively simple question in meetings: *"Can we retire an application X?"* While the question sounds straightforward on the surface, organizations routinely struggle for weeks or months to produce a definitive answer. The reason for this delay is not a lack of data, but rather the extreme fragmentation of ownership across the enterprise.

```mermaid flowchart TD accTitle: Fragmented Knowledge in Application Retirement Decisions subgraph Silos["Fragmented Organizational Silos"] direction TB B["Business Unit<br/><i>Understands business impact</i>"] T["Technology Team<br/><i>Knows upstream & downstream integrations</i>"] D["Data Team<br/><i>Tracks generated information & pipelines</i>"] G["Governance & Risk<br/><i>Monitors compliance & security risk</i>"] end

Q["Question: 'Can we retire Application X?'"] --> Silos Silos --> F["Missing Link: Enterprise Context & Connected Knowledge"] ```

When dissecting the retirement of an application, different organizational units hold disparate pieces of the puzzle: - **Business Teams** understand the direct operational and functional impact. - **Technology Teams** track the technical architecture, upstream feeds, and downstream system dependencies. - **Data Teams** know what specific data sets and information streams are generated and consumed by the application. - **Governance & Risk Teams** understand the compliance mandates, regulatory requirements, and risk exposure tied to the system.

While everyone owns a piece of the answer, no single group owns the complete picture. Enterprise Architecture (EA) has spent decades addressing this exact dilemma. Historically, EA began by producing documentation, establishing standards, enforcing governance, and building roadmaps. Over time, it evolved into uncovering the connections and context between data, systems, and processes. EA never created enterprise knowledge from scratch; its core mission has always been to organize, connect, and expose that knowledge to create a shared understanding of how the enterprise operates.

---

[02:11](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=131s) The Shift from Human Consumers to AI: Introducing Enterprise Intelligence Architecture

A fundamental shift has disrupted enterprise architecture. Historically, EA artifacts and shared context were designed exclusively for human consumption—architects, engineering leaders, C-suite executives, and developers. Humans possess an innate ability to infer missing context and bridge semantic gaps intuitively. AI systems, however, cannot infer unstated organizational context on their own; they require structured, explicit context to generate accurate reasoning and actionable decisions.

To bridge this gap, Enterprise Architecture must evolve into **Enterprise Intelligence Architecture**. Traditional EA established the foundation layer—the baseline structures, ARB (Architecture Review Board) governance artifacts, and operating models. The modern evolution requires building three complementary layers on top of that enterprise foundation: **Context**, **Memory**, and **Intelligence**. Together, these capabilities turn a conventional organization into an adaptive, thinking enterprise.

```mermaid flowchart TD accTitle: Layers of Enterprise Intelligence Architecture subgraph EIArchitecture["Enterprise Intelligence Architecture"] direction TB L4["Enterprise Intelligence<br/><i>Reasoning engine, next-best actions, alternatives</i>"] L3["Enterprise Memory<br/><i>Trusted knowledge store, context++, decision rationale</i>"] L2["Enterprise Context<br/><i>Semantic meaning, relationships, dependency resolution</i>"] L1["Enterprise Foundation & Intelligence Core<br/><i>Existing assets, ARBs, applications, policies, data</i>"] end

L1 --> L2 L2 --> L3 L3 --> L4 ```

---

[03:56](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=236s) The Three Capabilities: Enterprise Context, Memory, and Intelligence

Enterprise Context provides semantic meaning to raw organizational assets. Architects typically maintain inventories of applications, data outputs, access policies, and past Architecture Review Board (ARB) decisions, but these assets exist in disconnected silos. The Context capability weaves these assets together by: - Defining explicit relationships and mapping surrounding ecosystems. - Resolving complex upstream and downstream dependencies to assess systemic impact. - Delivering end-to-end visibility across business capabilities, software assets, and governance constraints.

Without this layer, organizational choices remain trapped in silos. Building context takes considerable time and organizational effort, but it serves as the essential bedrock for higher-level intelligence.

Once context is established, it must be persisted within **Enterprise Memory**. Enterprise Memory serves as the trusted grounding layer for intelligent systems. Rather than merely storing current-state snapshots, Enterprise Memory functions as a "Context++" repository—capturing not just *what* the enterprise knows, but *why* specific decisions were made, what events generated those outcomes, and what institutional lessons were learned along the way.

Sitting at the apex is **Enterprise Intelligence**, acting as the reasoning brain. Powered by grounded context and trusted memory, the intelligence layer evaluates scenarios, constructs alternative paths, and recommends optimal next actions while eliminating common failure modes like model hallucinations. This architecture is designed specifically to amplify human decision-making rather than displace human expertise:

> "Whatever we are doing to build the thinking enterprise, it's all about providing the context, meaning, and intelligence just to amplify our thinking process, but not to replace us."

---

[07:49](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=469s) Blueprint and Execution of the Thinking Enterprise

A **Thinking Enterprise** operates as a continuous learning system that adapts, reasons, and formulates superior operational decisions dynamically. In such an enterprise, questions that previously required three weeks to two months of cross-functional investigation—such as decommissioning an application—can be evaluated in days because context and institutional memory are continuously updated and queryable.

```mermaid flowchart LR accTitle: Logical Blueprint from Enterprise Assets to Services subgraph Assets["1. Enterprise Assets"] A1["ARB Records"] A2["Business & Licensing Docs"] A3["App Inventories"] end

subgraph Core["2. Intelligence Core"] C1["Digitized EA<br/>Shared Understanding"] end

subgraph Capabilities["3. Context & Memory"] K1["Enterprise Context<br/><i>Semantic Meaning</i>"] K2["Enterprise Memory<br/><i>Knowledge & Rationale</i>"] end

subgraph Services["4. Intelligence Services"] S1["Enterprise Intelligence<br/>Services"] end

Assets --> Core Core --> K1 K1 --> K2 K2 --> Services ```

The logical blueprint for this transformation progresses across interconnected tiers: 1. **Existing Enterprise Assets:** Capitalizes on existing artifacts, including procurement records, licensing agreements, application catalogs, business strategy documents, and historical ARB reviews. 2. **Enterprise Intelligence Core:** Digitizes the traditional shared understanding cultivated by EA into an accessible, structured knowledge base. 3. **Enterprise Context & Enterprise Memory:** Synthesizes raw records into connected meaning and preserves long-term institutional rationale. 4. **Enterprise Intelligence Services:** Serves as the queryable interface answering complex strategic and technical questions across the enterprise.

This journey cannot be accomplished in a trivial two-to-three-month sprint. Seasoned architects with ten to fifteen years of organizational tenure still require time to untangle complex system dependencies. Building this automated knowledge and memory layer requires deliberate investment in people, data structures, and architectural discipline.

---

[11:09](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=669s) Core Principles, Technology Mapping, and the Agentic Era

Any durable enterprise intelligence transformation must be governed by foundational architectural principles: - **Business Comes First:** Technology must never drive intelligence for its own sake; business objectives and tangible organizational outcomes must steer adoption. - **Context Enables Intelligence:** AI and advanced analytics cannot generate reliable insights in an organizational vacuum without explicit context. - **Memory Enables Intelligence:** Systems must continuously learn, retain historical lessons, and update their knowledge bases just like a human brain. - **Trust by Design:** Governance, explainability, compliance, and security cannot be retrofitted as an afterthought; they must be baked directly into the core architectural methodology from day one. - **Human-Centered Intelligence:** Systems must operate as a collaborative partnership—"Human + AI"—where technology amplifies human judgment rather than eliminating human oversight.

```mermaid flowchart TD accTitle: Agentic Architecture Leveraging Centralized Enterprise Intelligence subgraph TopLevel["Enterprise Agentic Layer"] EA1["Enterprise Strategy Agent"] EA2["Governance & Compliance Agent"] end

subgraph CentralServices["Centralized Intelligence Services"] EIS["Enterprise Intelligence Services<br/><i>Enterprise Context & Memory Core</i>"] end

subgraph DomainAgents["Domain & Capability Agents"] DA1["Finance Domain Agent"] DA2["HR Domain Agent"] DA3["IT / Cloud Domain Agent"] end

EA1 --> EIS EA2 --> EIS EIS --> DA1 EIS --> DA2 EIS --> DA3 ```

In the emerging agentic landscape, autonomous agents will operate across specific domains, verticals, and departmental capabilities. However, enterprise-wide agents must rely on a centralized layer of **Enterprise Intelligence Services** to infer holistic context, retrieve enterprise memory, and provide cohesive guidance across disparate business functions. Rather than re-inventing existing technology investments, organizations must orchestrate their existing data, application, and governance components into an integrated intelligence layer that powers the next generation of enterprise AI.

## Notable quotes > Enterprise AI is only as intelligent as the enterprise knowledge, context, and relationships it can access, but most organizations have not architected this foundation for AI. — Chalamayya Batchu Veera

> [00:04](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=4s) Can we retire an application X? — Chalamayya Batchu Veera

> [00:04](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=4s) The reason for this delay is not a lack of data, but rather the extreme fragmentation of ownership across the enterprise. — Chalamayya Batchu Veera

> [00:04](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=4s) While everyone owns a piece of the answer, no single group owns the complete picture. — Chalamayya Batchu Veera

> [00:04](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=4s) EA never created enterprise knowledge from scratch; its core mission has always been to organize, connect, and expose that knowledge to create a shared understanding of how the enterprise operates. — Chalamayya Batchu Veera

> [02:11](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=131s) Humans possess an innate ability to infer missing context and bridge semantic gaps intuitively. AI systems, however, cannot infer unstated organizational context on their own; they require structured, explicit context to generate accurate reasoning and actionable decisions. — Chalamayya Batchu Veera

> [03:56](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=236s) Whatever we are doing to build the thinking enterprise, it's all about providing the context, meaning, and intelligence just to amplify our thinking process, but not to replace us. — Chalamayya Batchu Veera

> [03:56](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=236s) Enterprise Memory serves as the trusted grounding layer for intelligent systems. — Chalamayya Batchu Veera

> [07:49](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=469s) A Thinking Enterprise operates as a continuous learning system that adapts, reasons, and formulates superior operational decisions dynamically. — Chalamayya Batchu Veera

> [11:09](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=669s) Technology must never drive intelligence for its own sake; business objectives and tangible organizational outcomes must steer adoption. — Chalamayya Batchu Veera

> [11:09](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=669s) Governance, explainability, compliance, and security cannot be retrofitted as an afterthought; they must be baked directly into the core architectural methodology from day one. — Chalamayya Batchu Veera

## Mentioned Chalamayya Batchu Veera, Florida Power & Light, Ai4, Enterprise Architecture (EA), Enterprise Intelligence Architecture, Architecture Review Board (ARB), ERP, CRM

## Video description Chalamayya Batchu Veera - Principal Enterprise Architect - AI & Data, Florida Power & Light

Enterprise AI is only as intelligent as the enterprise knowledge, context, and relationships it can access, but most organizations have not architected this foundation for AI. In this talk, I will explain how Enterprise Architecture must evolve from documenting systems and technology landscapes into an intelligence layer that enables trusted, scalable, and governed AI adoption. I will introduce the concept of Enterprise Intelligence Architecture, a blueprint that connects business capabilities, data, applications, knowledge, policies, and decisions to support AI-native organizations. Attendees will learn how Enterprise Architects can help bridge the gap between AI capabilities and enterprise context while creating a foundation for responsible AI transformation. Join me to explore how the future of Enterprise Architecture is shifting from managing technology complexity to enabling enterprise intelligence.

[Source](https://www.youtube.com/watch?v=j_SgY7rXpM8)

中文简报

FPL 架构师绘制蓝图：以企业记忆夯实智能体 AI 自主企业智能体在缺乏结构化组织上下文时往往会失效，这要求架构师将孤立的系统记录转化为持久的上下文与记忆层，以实现跨职能决策的自动化。 企业 AI 项目屡屡受挫，是因为组织缺乏互联的上下文，导致像应用程序退役这样简单的决策演变成长达数月的跨孤岛调查。为克服这一难题，企业架构必须演变为企业智能架构，在现有企业资产之上叠加显性的企业上下文、制度化的企业记忆以及推理驱动的企业智能。该蓝图将组织转变为能够持续适应并将数周评估时间压缩至数天的“思考型企业”。最终，这一集中的智能层不仅为特定领域的自主智能体提供了落脚点，同时坚守了以人为本的理念，旨在增强而非取代人类的专业能力。 像退役应用程序这类常规 IT 决策通常需要耗费数周或数月，因为关于影响、集成、数据和合规的知识支离破碎地散落在各个组织孤岛中。 (0:04) 企业架构历来的使命并非从零创造新知识，而是组织、连接并呈现零散的上下文，形成对企业运作方式的共同理解。 (0:04) 传统的 EA 产物是为人类受众设计的，人类能凭直觉弥合语义鸿沟，而 AI 模型无法自行推断未明确表述的上下文。 (2:11) 为了支持 AI 落地，企业架构必须演进为企业智能架构，在现有企业资产之上叠加上下文、记忆和智能层。 (2:11) 企业上下文为原本割裂的资产目录增添语义，定义各类关系并梳理跨系统的上下游依赖。 (3:56)

Reading summary

The presentation argues that enterprise architecture must evolve into an 'Enterprise Intelligence Architecture' that translates fragmented organizational knowledge into structured context and memory for AI systems. This transformation is intended to accelerate complex cross-functional decisions while augmenting rather than replacing human expertise. Organizational Silos and Architecture's Traditional Role (0:04) Deceptively straightforward tasks—such as deciding whether an application can be decommissioned—often stall for weeks or months in large organizations. According to the speaker, this delay is caused by extreme fragmentation rather than a lack of information: business units track functional impact, engineering teams understand system integrations, data teams monitor data pipelines, and governance teams oversee compliance. Because ownership is fractured, no single group holds the complete operational context. Enterprise architecture historically addressed this disconnect not by creating institutional knowledge from scratch, but by uncovering, connecting, and standardizing relationships between systems, processes, and policies to build a shared baseline of understanding across the enterprise. From Human Intuition to Structured Enterprise Memory (2:11) Traditional architectural documentation was produced for human stakeholders who instinctively infer unstated context and bridge semantic nuances. AI models cannot infer these missing details independently and require explicit, structured context to avoid hallucinations and make reliable recommendations. To bridge this divide, the speaker outlines an evolution adding three layers above existing foundations: Enterprise Context, which explicitly maps dependencies and systemic relationships; Enterprise Memory, a trusted 'Context++' repository storing historical decisions, rationales, and organizational lessons; and Enterprise Intelligence, a top-level reasoning engine that evaluates operational alternatives and suggests next-best actions. Execution Blueprint and Decision Velocity (7:49) In a fully realized 'Thinking Enterprise,' raw assets—including licensing records, application inventories, and Architecture Review Board (ARB) logs—feed into an intelligence core that maintains dynamic context and memory. Exposing this through queryable enterprise intelligence services allows organizations to compress cross-functional investigations that once took up to two months down to mere days. However, the presentation cautions that this capability cannot be built in a standard two-to-three-month project sprint. Untangling historical system dependencies requires deliberate long-term investment in data structures, architecture discipline, and the deep institutional knowledge of experienced practitioners. Core Principles and Centralized Agentic Integration (11:09) The architecture is governed by core principles: prioritizing business objectives over technology adoption, treating context and memory as prerequisites for machine intelligence, embedding governance and trust into the system design, and maintaining a human-centered partnership where AI amplifies human judgment. As organizations deploy autonomous AI agents across specialized domains such as HR, finance, and IT, the speaker stresses that domain agents cannot operate successfully in isolation. They must connect to a centralized layer of enterprise intelligence services to retrieve holistic corporate context and ensure strategic coherence across disparate business functions. The framework frames enterprise intelligence as an evolutionary discipline designed to operationalize existing institutional assets, though its success depends heavily on the long-term, non-trivial organizational effort required to codify and maintain clean context across complex legacy environments.

精炼阅读摘要

Ai4研讨指出，随着企业系统日益复杂，传统面向人类理解的企业架构已无法直接适配AI，必须演进为包含上下文、记忆与智能的三层企业智能架构，以支撑智能化决策并放大人类专家的思考能力。 企业孤岛与传统架构认知的断层 (0:04) 在大型企业推进ERP、CRM、云现代化及系统迁移过程中，评估“能否停用某款应用程序”这类看似基础的问题，往往需要跨职能部门耗费数周乃至数月才能得出结论。这种低效的根源并非缺乏数据，而是企业所有权与知识的严重碎片化。业务部门掌握运营与功能影响，技术团队了解架构及上下游系统依赖，数据团队追踪管道与生成信息，治理与风控团队则关注合规要求与安全风险。各部门仅掌握局部信息，无人统揽全局。 演讲者指出，传统企业架构（EA）数十年来一直致力于缓解该问题。早期EA侧重于标准制定、合规管理与路线图编制，随后逐步转向梳理数据、系统与流程之间的关联与背景。然而，传统架构资产从根本上是面向人类消费者设计的；人类能够凭直觉填补语义空白，而AI系统缺乏这种推断能力，必须依赖结构化、显性化的上下文才能进行准确推理与决策。 上下文、记忆与智能三层能力模型 (3:56) 为弥合人机理解差异，企业架构需演进为包含三大核心能力的“企业智能架构”。第一层是企业上下文（Enterprise Context），旨在为分散的应用程序目录、数据资产与治理评审记录赋予显性语义关系，理清复杂的上下游依赖并评估系统性影响；构建这一层需要投入大量组织精力，但它是高级智能的基础。 在上下文之上是企业记忆（Enterprise Memory）与企业智能（Enterprise Intelligence）。企业记忆超越了静态快照，充当记录“决策原因、触发事件与历史经验教训”的信任知识库（Context++）。最顶层的企业智能则作为推理中枢，依托可信记忆与上下文评估方案并推荐最佳行动，从而消除模型幻觉。演讲者明确强调，该架构的核心目标是提供上下文与智能以放大人类决策过程，而非取代人类专业知识。 思考型企业的实施蓝图与建设周期 (7:49) 在设想的“思考型企业”（Thinking Enterprise）中，组织表现为一个持续学习的动态自适应系统，原本需要三至八周跨部门调研的应用停用评估，可缩短至数天内完成。其逻辑架构蓝图由下至上分为四个层级：底层为历史评审记录与应用清单等企业既有资产，向上依次为数字化的智能核心层、沉淀语义与长期决策理由的上下文与记忆层，以及最终提供复杂战略与技术问答查询的智能服务层。 演讲者提醒，构建该体系绝非两到三个月的敏捷冲刺所能完成。即使拥有十至十五年经验的资深架构师，理清错综复杂的系统依赖关系仍需耗费大量时间。实现自动化的知识与记忆流转，必须在人才、数据结构与架构规约上进行持久且有规划的战略投入。 智能体时代的设计原则与集中赋能 (11:09) 构建企业智能架构必须遵循五大核心原则：业务优先（避免纯技术驱动）、上下文支撑智能（脱离上下文AI无法产生可靠见解）、记忆赋能智能（持续学习与更新经验）、设计即信任（治理、可解释性与安全从第一天起内嵌）以及以人为中心的智能（人机协同共生）。 针对正在兴起的智能体（Agentic）生态，各业务垂直领域将广泛部署财务、人力、IT等专业智能体。然而，跨领域的顶层战略与治理智能体必须依赖集中的“企业智能服务”来获取全景上下文与机构记忆。企业无需推倒重来重建系统，关键在于将现有的数据、应用与治理资产有机编排为一体化智能层，为下一代企业AI提供支撑。 该报告主张企业向智能化演进的关键在于将分散的组织知识转化为可被机器消费的上下文与记忆，但其落地依赖长期的架构沉淀与治理内嵌，如何在不颠覆既有技术投资的前提下维持高质量显性上下文，仍是组织面临的长期工程挑战。

Detailed notes — zh

## Background 本视频由 Ai4 频道于 2026 年 8 月 21 日发布在 YouTube 平台，主讲嘉宾为佛罗里达电力与照明公司（Florida Power & Light）的人工智能与数据首席企业架构师 Chalamayya Batchu Veera。演讲围绕企业架构（EA）的范式转移展开，阐述了如何将传统的系统与技术景观文档记录演化为企业智能架构，为 AI 原生组织打通上下文、知识、策略与决策的底座支撑。 ## TL;DR 演讲从企业内部看似简单却因跨部门信息割裂而耗时数月的“能否下线某款应用”这一痛点切入，指出企业缺失的并非数据而是上下文连接机制。面对无法像人类一样自主脑补隐性语境的 AI，企业架构必须向前迈进，构建由企业上下文（Context）、企业记忆（Memory）和企业智能（Intelligence）组成的三层核心架构。通过践行业务优先、内生信任与以人为本等五大原则，企业可将沉淀的历史决策与资产转化为集中的企业智能服务，在智能体（Agent）时代系统化赋能跨域决策，其核心宗旨在于放大和增强人类思考而非取而代之。 ## Atmosphere & style 分享洋溢着资深从业者特有的务实与从容，讲者以行业普遍存在的架构痛点幽默开场，迅速引发参会同行的强烈共鸣。作为全天峰会收官场次的演讲，整体节奏张弛有度、层层递进，既避开了浮夸的 AI 概念炒作，又以严密的工程逻辑展现出对下一代思考型企业蓝图的从容构想，交流氛围专注、诚恳且兼顾人文关怀。 ## Key takeaways - [00:04](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=4s) 企业难以快速回答应用下线等架构决策，根源不在于缺乏数据，而在于业务、技术、数据与合规权责割裂且缺失统一的上下文连接机制。 - [01:43](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=103s) 人类专家擅长自行补齐语境，但 AI 必须依赖精准、结构化的企业上下文才能进行可靠推理，促使传统企业架构必须升级为企业智能架构。 - [03:56](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=236s) 构建思考型企业（Thinking Enterprise）依赖三大递进核心支柱：企业上下文（Context）、企业记忆（Memory）与企业智能（Intelligence）。 - [03:56](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=236s) 企业记忆不仅存储已知的事实，更记录得出结论的原因、触发因素及决策全貌（Context++），是企业沉淀认知与持续学习的基础载体。 - [03:56](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=236s) 企业智能通过将模型推理严格锚定在企业私有的上下文与记忆中来遏制 AI 幻觉，其核心价值在于放大与增强人类思考，而非取代人类。 - [07:57](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=477s) 在思考型企业闭环中，依托集中沉淀的上下文与动态更新的记忆，原本耗时数周甚至数月的跨领域调研决策可大幅缩短至数天内完成。 - [07:57](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=477s) 落地企业智能无需推翻现有 IT 积累，而是将 ARB 评审记录、采购规范及软件许可等存量资产数字化汇入智能核心，属于长期系统性工程。 - [11:09](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=669s) 架构转型必须遵循五大核心原则：业务优先、上下文赋能智能、记忆驱动智能、设计即内生信任（Trust by Design）以及以人为中心的智能。 - [11:09](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=669s) 在面向智能体的 Agentic 架构中，企业应集中构建全局统一的企业智能服务（Enterprise Intelligence Services），供各领域 Agent 协同调用与推理。 ## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=4s) 企业架构的痛点：看似简单却难以回答的应用下线问题 许多具有 IT 背景的从业者都曾构建过复杂的系统，涵盖 CRM、云端现代化改造、系统迁移等重大项目。然而，在日常工作中，架构师和技术管理者往往会在会议中被问到一个看似极简的问题：“我们是否可以下线（retire）某款应用 X？”虽然台下或线上参会的企业架构师（EA）和解决方案架构师人数众多，但大家对这一场景深有共鸣。这个问题表面上清晰直接，但企业往往需要耗费数周乃至数月才能给出一个确切的答复。 之所以难以快速回答，根源在于企业信息与权责的割裂： - **业务部门**：清楚该应用停用后带来的业务影响与流程变动； - **技术团队**：掌握该应用的上下游集成关系、技术依赖及关联系统； - **数据团队**：了解该应用产生、流转及消费的核心业务数据； - **合规治理团队**：明确该应用涉及的风险等级、审计要求与合规标准。 每个人都只掌握了答案的一个碎片，却没有任何一个人或单一团队能给出完整的全局解答。这并不是因为企业缺乏数据，事实上企业拥有所必需的一切数据，真正缺失的是将这些孤立数据点关联起来的“上下文与连接机制”。 --- ### [01:43](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=103s) 企业架构的演进：从服务人类专家到面向 AI 的智能底座 回顾企业架构过去几十年的发展历程，EA 最早从梳理文档、建立技术标准、制定治理规范和规划技术路线图起步。随着时间推移，EA 逐步演化为理解系统连接、业务上下文、关联数据与应用生态的核心实践。企业架构本身并不凭空创造企业知识，而是负责将分散的知识连接并组织起来，构建起支撑整个组织运转的“共享认知模型（shared understanding）”。 然而，近期的技术变革彻底打破了原有的受众格局。过去，EA 产出的架构图谱和上下文主要是给企业架构师、技术主管和工程师等人类专家查阅的。人类天生具备强大的语境推理能力，能够自然地脑补缺失的背景信息；但 AI 则完全不同，它无法凭空推断未明示的语境，必须被喂入高度结构化、精准的企业上下文，才能做出可靠的推断与决策。因此，传统的企业架构实践必须向前演进，升级为“企业智能架构（Enterprise Intelligence Architecture）”。 ```mermaid flowchart TD accTitle: 传统企业架构向企业智能架构的演化路径 subgraph Legacy["传统企业架构层 (基础层)"] A["业务与技术文档"] B["ARB 架构评审记录"] C["系统与数据孤岛"] end subgraph Intelligence["企业智能架构 (核心三层)"] D["企业上下文 (Context)"] E["企业记忆 (Memory)"] F["企业智能 (Intelligence)"] end Legacy -->|"数字化连接与结构化"| D D -->|"赋予业务含义并建立关联"| E E -->|"持久化存储与持续演进"| F F -->|"驱动推理与辅助决策"| G["思考型企业 (Thinking Enterprise)"] ``` --- ### [03:56](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=236s) 思考型企业的三大核心支柱：上下文、记忆与智能 为了从基础的企业架构迈向具备自主思考能力的“思考型企业（Thinking Enterprise）”，必须在原有的碎片化基础之上，构建三层递进的核心能力：企业上下文（Context）、企业记忆（Memory）与企业智能（Intelligence）。 1. **企业上下文（Enterprise Context）**： 企业架构师通常了解各业务线运行的应用、产生的知识、流转的数据、架构评审委员会（ARB）的历史决策以及安全访问策略。但这些要素长期处于碎片化状态。上下文能力通过赋予业务含义、明确相互关系、解析系统依赖，提供对整个周边生态的端到端全局视野。如果缺乏这一层，系统依然孤立，决策依然受困于部门墙。 2. **企业记忆（Enterprise Memory）**： 在构建出关联上下文后，必须有可信的载体来存储和维护这些知识。企业记忆不仅保存“我们已知的事实（what we know）”，更记录下“我们为何得出此结论（why we know it）”、是由什么触发生成的，以及围绕决策展开的全部背景（Context++）。它是连接认知与学习的沉淀层。 3. **企业智能（Enterprise Intelligence）**： 企业智能作为整个架构的“大脑”，依托可信的记忆与上下文进行逻辑推理，提供备选方案，并推荐最佳后续行动（Next Best Action）。所有输出都严格锚定（grounded）在企业特有的记忆和上下文之中，从而从根本上遏制 AI 生成中的“幻觉（Hallucination）”问题。 演讲者特别强调，构建思考型企业的初衷绝非用 AI 取代架构师或技术人员。AI 模型无论输入了多少上下文，都需要持续演进与学习；这一系列架构变革的根本目的在于“放大与增强人类的思考能力，而非取而代之（amplify our thinking process, not to replace us）”。 --- ### [07:57](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=477s) 落地思考型企业：逻辑蓝图与建设路径 所谓“思考型企业”，本质上是一个能够持续自我学习、动态适应、展开严谨推理并提供更优决策的闭环系统。回到最初那个“能否下线应用 X”的问题：在传统模式下，调研业务影响、上下游链路及合规风险往往需要三到四周甚至几个月；而在上下文集中沉淀、记忆动态更新的思考型企业架构中，该问题可以在几天甚至更短的时间内得到精准解答。 ```mermaid flowchart LR accTitle: 思考型企业逻辑蓝图与服务流转 subgraph Assets["企业现有资产"] A1["ARB 评审记录"] A2["业务与采购文档"] A3["软件许可与应用目录"] end subgraph Core["企业智能核心"] B1["数字化共享认知"] B2["企业上下文层"] B3["企业记忆层"] end subgraph Services["智能服务与交付"] C1["推理与决策推荐"] C2["智能体协同 (Agents)"] end Assets --> Core B1 --> B2 --> B3 Core --> Services ``` 在落地构建时，企业并不需要推翻既有的技术积累重起炉灶，而是将现有的 ARB 记录、业务规范、采购合同、软件许可等资产，数字化汇入企业智能核心（Enterprise Intelligence Core），依序构建上下文能力、记忆存储以及对上暴露的智能服务。这绝非耗时两三个月即可速成的轻量级项目；即便是在企业工作了 10 至 15 年的资深专家，梳理全面信息也需要时间。因此，构建持久的企业记忆是一项需要长期投入但回报巨大的系统工程。 --- ### [11:09](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=669s) 转型五大核心原则与面向 Agent 的智能体系 任何成功的架构转型都必须遵循扎实的设计原则，避免落入单纯追求技术的陷阱： - **业务优先（Business Comes First）**：坚决反对由技术盲目驱动智能，必须由业务目标与业务成果来定义和驱动智能需求； - **上下文赋能智能（Context Enables Intelligence）**：上下文是所有数据与 AI 逻辑成立的基石，脱离上下文的智能毫无价值； - **记忆驱动智能（Memory Enables Intelligence）**：系统必须模拟人类大脑的认知机制，具备持续吸纳新知识并沉淀记忆的能力； - **设计即内生信任（Trust by Design）**：治理、可解释性与安全性必须在架构设计之初就融入底层，绝不能在系统建成后再作为事后补丁添加； - **以人为中心的智能（Human-Centered Intelligence）**：坚持“人类 + AI”协同决策的范式，由 AI 赋能人类专家做最终把控。 在迈向智能体（Agentic）时代的趋势下，架构形态需要进一步清晰分工：底层 Foundation、Context、Memory 到 Intelligence 的通用组件无论规模大小在各企业中均已部分存在，关键在于如何系统化组织。针对 Agentic 架构，建议在企业全局层面集中构建统一的“企业智能服务（Enterprise Intelligence Services）”，让各业务领域（Domain）的上层 Agent 调用该核心服务来推断知识、解答复杂疑问并提供方案指导；各垂直业务线内部可拥有局部的特定 Agent，但全局维度的智能协同与决策推理，应当稳固架设在统一的企业智能底座之上。 ### [13:49](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=829s) 总结与致谢：迈向企业智能服务 在对企业智能服务（Enterprise Intelligence Services）以及从传统企业架构向企业智能演进的核心框架进行系统阐述后，讲者正式结束了当天的分享内容。他轻松地向在座观众表示，正如自己在分享开始时所承诺的那样，会提前几分钟结束演讲，为大家留出宝贵的休息与交流时间（"give you some time early"）。 在演讲的最后，讲者向全场参会者表达了诚挚的谢意，特别感谢大家在整个会议日程的最后一场分论坛（"the last session"）中依然选择留下来认真倾听与探讨企业智能化的未来路径。 - **核心收尾要点**：完成了关于 Enterprise Intelligence Services 赋能 AI 原生企业的体系化阐述。 - **现场互动与致谢**：提前结束演讲为听众留出时间，并感谢参会人员坚持听到全天最后一场分享。 ## Notable quotes > [00:04](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=4s) 我们是否可以下线（retire）某款应用 X？ — Chalamayya Batchu Veera > [01:43](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=103s) 共享认知模型（shared understanding） — Chalamayya Batchu Veera > [01:43](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=103s) 企业智能架构（Enterprise Intelligence Architecture） — Chalamayya Batchu Veera > [03:56](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=236s) 我们已知的事实（what we know） — Chalamayya Batchu Veera > [03:56](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=236s) 我们为何得出此结论（why we know it） — Chalamayya Batchu Veera > [03:56](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=236s) 最佳后续行动（Next Best Action） — Chalamayya Batchu Veera > [03:56](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=236s) 放大与增强人类的思考能力，而非取而代之（amplify our thinking process, not to replace us） — Chalamayya Batchu Veera > [11:09](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=669s) 业务优先（Business Comes First） — Chalamayya Batchu Veera > [11:09](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=669s) 上下文赋能智能（Context Enables Intelligence） — Chalamayya Batchu Veera > [11:09](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=669s) 记忆驱动智能（Memory Enables Intelligence） — Chalamayya Batchu Veera > [11:09](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=669s) 设计即内生信任（Trust by Design） — Chalamayya Batchu Veera > [11:09](https://www.youtube.com/watch?v=j_SgY7rXpM8&t=669s) 以人为中心的智能（Human-Centered Intelligence） — Chalamayya Batchu Veera ## Mentioned Chalamayya Batchu Veera, Florida Power & Light, Ai4, CRM, ARB (架构评审委员会), 企业架构 (Enterprise Architecture), 企业智能架构 (Enterprise Intelligence Architecture), 思考型企业 (Thinking Enterprise), 企业上下文 (Enterprise Context), 企业记忆 (Enterprise Memory), 企业智能 (Enterprise Intelligence), 企业智能服务 (Enterprise Intelligence Services) ## Video description Chalamayya Batchu Veera - Principal Enterprise Architect - AI & Data, Florida Power & Light Enterprise AI is only as intelligent as the enterprise knowledge, context, and relationships it can access, but most organizations have not architected this foundation for AI. In this talk, I will explain how Enterprise Architecture must evolve from documenting systems and technology landscapes into an intelligence layer that enables trusted, scalable, and governed AI adoption. I will introduce the concept of Enterprise Intelligence Architecture, a blueprint that connects business capabilities, data, applications, knowledge, policies, and decisions to support AI-native organizations. Attendees will learn how Enterprise Architects can help bridge the gap between AI capabilities and enterprise context while creating a foundation for responsible AI transformation. Join me to explore how the future of Enterprise Architecture is shifting from managing technology complexity to enabling enterprise intelligence. [Source](https://www.youtube.com/watch?v=j_SgY7rXpM8)

Source quotes

[0:04] Chalamayya Batchu Veera: 我们是否可以下线（retire）某款应用 X？ [1:43] Chalamayya Batchu Veera: 共享认知模型（shared understanding） [1:43] Chalamayya Batchu Veera: 企业智能架构（Enterprise Intelligence Architecture） [3:56] Chalamayya Batchu Veera: 我们已知的事实（what we know） [3:56] Chalamayya Batchu Veera: 我们为何得出此结论（why we know it） [3:56] Chalamayya Batchu Veera: 最佳后续行动（Next Best Action） [3:56] Chalamayya Batchu Veera: 放大与增强人类的思考能力，而非取而代之（amplify our thinking process, not to replace us） [11:09] Chalamayya Batchu Veera: 业务优先（Business Comes First） [11:09] Chalamayya Batchu Veera: 上下文赋能智能（Context Enables Intelligence） [11:09] Chalamayya Batchu Veera: 记忆驱动智能（Memory Enables Intelligence） [11:09] Chalamayya Batchu Veera: 设计即内生信任（Trust by Design） [11:09] Chalamayya Batchu Veera: 以人为中心的智能（Human-Centered Intelligence）

Source quotes

Chalamayya Batchu Veera: Enterprise AI is only as intelligent as the enterprise knowledge, context, and relationships it can access, but most organizations have not architected this foundation for AI. [0:04] Chalamayya Batchu Veera: Can we retire an application X? [0:04] Chalamayya Batchu Veera: The reason for this delay is not a lack of data, but rather the extreme fragmentation of ownership across the enterprise. [0:04] Chalamayya Batchu Veera: While everyone owns a piece of the answer, no single group owns the complete picture. [0:04] Chalamayya Batchu Veera: EA never created enterprise knowledge from scratch; its core mission has always been to organize, connect, and expose that knowledge to create a shared understanding of how the enterprise operates. [2:11] Chalamayya Batchu Veera: Humans possess an innate ability to infer missing context and bridge semantic gaps intuitively. AI systems, however, cannot infer unstated organizational context on their own; they require structured, explicit context to generate accurate reasoning and actionable decisions. [3:56] Chalamayya Batchu Veera: Whatever we are doing to build the thinking enterprise, it's all about providing the context, meaning, and intelligence just to amplify our thinking process, but not to replace us. [3:56] Chalamayya Batchu Veera: Enterprise Memory serves as the trusted grounding layer for intelligent systems. [7:49] Chalamayya Batchu Veera: A Thinking Enterprise operates as a continuous learning system that adapts, reasons, and formulates superior operational decisions dynamically. [11:09] Chalamayya Batchu Veera: Technology must never drive intelligence for its own sake; business objectives and tangible organizational outcomes must steer adoption. [11:09] Chalamayya Batchu Veera: Governance, explainability, compliance, and security cannot be retrofitted as an afterthought; they must be baked directly into the core architectural methodology from day one.

Original transcript

[0:04] Most of us coming from the IT background  have built great systems beps, CRM, cloud [0:11] modernization, migration and whatnot. However, [0:15] we always end up in meetings like  this asked a very simple question. [0:24] Can we retire an application X? How many I  know we don't have large cur but how many [0:29] have heard this question architects EAS or  solution architects yeah the question seems [0:36] to be very straightforward right okay that's  not a complex question however it takes time [0:42] to answer this and why is that one business  knows the impact two technology knows the [0:52] upstream and downstream integrations and the  applications Three, the data team knows what [1:00] is the information generated via that  application. Last but not the least, [1:05] governance which knows what is the risk  and compliance of that application. [1:12] So everyone owns part of that answer but no one  owns the complete answer. Are we missing some [1:21] data? Absolutely not. We have all the data that  we need. What we are missing is the connecting [1:29] between all these data points and that's what  enterprise architecture has already been doing [1:35] many years. So let's take some few seconds  to recognize what EI has done over the years. [1:43] Enterprise architecture begins with  documentation, establish standards, [1:48] governance and build road maps. Over the decades,  it evolved into understanding the connections, [1:56] the context, the associated data, applications  and whatnot. Enterprise architecture did not [2:03] create enterprise knowledge. They connected it and  they organized it and made available for overall [2:10] shared understanding how an enterprise operates.  But something fundamentally changed recently. Over [2:22] the years all the enterprise architecture  was helping people like architects, leaders, [2:28] engineers and whatnot. Now we have a new audience  to get same understanding which is AI. It's very [2:36] normal for any human to infer context which comes  naturally. It's not same for any AI. the context [2:48] has to be fed into I to get the right decisions.  So what must enterprise architecture practice [2:53] evolve into and that's where we're talking  about the enterprise intelligence architecture [3:00] and enterprise architecture has already been doing  the foundation layer which was the basic structure [3:06] on the documents how the business operates the  decisions taken the ARBs which you've seen in [3:13] the previous session and also they've shared  understanding of how these components connect [3:19] but again it's fragment mented not all together.  So the next evolution is building the top three [3:27] layers which would go from enterprise foundation  to thinking enterprise. So those things are [3:35] context, memory and intelligence. And these three  layers are not very new. They're pretty old like [3:40] in any every AI or data project you see all these  three layers. But what is the thing that changes [3:46] with respect to EI practice? So let's begin  with our new capability enterprise context. [3:56] So most of the enterprise architects would have  already known what are the applications used by [4:02] each business, what knowledge are they generating,  what data is coming out, what decisions did we [4:08] take to bring that application, right? We go  through ARBs, we go through a lot of analysis [4:12] and last but not the least the policies around the  access for the application. As I said, these are [4:18] all fragmented and how do I bring them together is  putting a context layer or a context capability to [4:25] bring all these individual components together.  What is context? So the context adds the meaning [4:34] defines what relationships are there resolves  dependencies and understands the dependencies [4:40] to understand the impact and also provides the  entire end to-end layer of all the surrounding [4:48] ecosystem components. So enterprise context  gives the enterprise knowledge to start with [4:58] what are things around it. Now what would happen  without enterprise context? Still the systems are [5:06] fragmented, decisions taken in silos. Building  enterprise context is not so easy. It takes time. [5:14] It takes effort. But it's definitely worth moving  all the context towards an enterprise level. Now [5:21] with having context, where do I store it? Because  okay, I got the context. I know the relationships. [5:28] So we go to the next capability which is the  enterprise memory. Once you define the context you [5:36] need to store it somewhere and enterprise memory  becomes the capability which is the trusted layer [5:44] for any intelligence layers. So we have defined  the context we need to store it we defined the [5:50] relationships and we've also got so many decisions  taken in the process. Now this is all what we [6:00] know. Now the enterprise memory is not only used  to store what we know. It also stores why do we [6:07] know it, what generated it and all things around  the context++. So we got to enterprise context [6:16] giving a meaning. Now we are storing that meaning  and also the learning from that meaning. Now let's [6:22] proceed to the next layer where it creates  the foundation for enterprise intelligence. [6:31] Enterprise intelligence is the brain leveraging  the trusted memory and the trusted context to [6:42] provide and understand the context around it.  give alternatives by providing reasoning and [6:49] also recommend next best actions and all these  things have to be grounded in the context and [6:56] in the memory that you've already created. Many  of you would have heard the words hallucination. [7:02] It's not giving me what I want. So all things  related to enterprise come to this enterprise [7:10] intelligence once you have grounded information  in the context and in the memory again in the [7:17] previous session it was like u how are we going  to be replaced definitely not how muchever context [7:25] is given to a AI model it has to keep learning it  has to evolve so whatever we are doing to build to [7:35] thinking enterprise it's all about providing the  context meaning intelligence just to amplify our [7:42] thinking process but not to replace us I just  want to be very careful in doing that now with [7:49] the intelligence layer the context layer and the  memory layer we get to the thinking enterprise [7:57] so what what exactly is a thinking enterprise we  have all these layers so a thinking enterprise is [8:02] a continuous learning system which could adapt,  which could reason, which could provide better [8:10] decisions than what we have done before. Let's go  back to the question that I asked before. Can we [8:18] replace an application X? We definitely get an  answer in 3 weeks, four weeks or maybe couple [8:23] of months. But if you have all the context stored  in one place and the context keeps learning, the [8:30] memory keeps updated. We get this answer in days  versus weeks and months. Once you have all these [8:39] layer figured out, the initial question would be  answered in less time. Now that we have learned [8:46] different capabilities of a thinking enterprise,  now how do we architect this? Right? Again, [8:52] I'm just providing a logical blueprint on what  it takes to build a thinking enterprise. It all [9:00] starts with what we have discussed so far. Now  the existing enterprise assets where we have ES [9:07] creating the ARBs, the business documents, the  procurement documents, the license documents, [9:13] what are all the applications, where are  they used, all these become the existing [9:18] enterprise assets which are fed into the  enterprise intelligence core which is [9:23] basically the shared understanding that EA has  already created but in a more digitized form. [9:28] Once we have the enterprise intelligence  core built, it's up to enterprise context [9:34] to create meaning and then enterprise memory to  store it. Once we have the lawyer layers built, [9:40] the enterprise intelligence services serves all  the questions for any enterprise. Now this seems [9:51] to be pretty easy, right? There is like no a lot  of technologies involved but believe me there is [9:56] a lot of technology stack that is being involved  but again we are not going to replace or reinvest [10:03] what we have done we are just going to augment  what we're already doing and what it takes for [10:08] us to build this what is the typical journey so we  start with today's enterprise right basically the [10:17] enterprise has all the assets that we do the first  layer that we have to build or the capability that [10:23] we build is the context give meaning and move  to the memory on storing the context and then [10:31] the intelligent services however you want to  build. Last but not the least is it quick 2 [10:38] month 3 month period or does it take time? It does  takes [clears throat] time. any architect or any [10:47] resource who have been in the company even for 10  years 15 years still they they take time to answer [10:52] any simple question now to build this kind of  information knowledge and memory it takes time and [10:59] people however building this enables to provide  better answers and better solutions going forward [11:09] every successful transformation follows basic  principles and these principles are business comes [11:17] first. Uh I do I would not encourage anyone  where technology drives intelligence. It's [11:24] a business driving intelligence. The outcomes  drives intelligence. And then context enables [11:29] intelligence as we were discussing all throughout  the slides. Without the context, it's not easy to [11:33] build any intelligence. Be it AI, be it data,  be it anything. uh memory enables intelligence [11:39] because the systems have to continuously learn  and store just like a human brain. They have to [11:44] learn the new stuff and store the stuff. Trust by  design. I kind of didn't emphasize this more on [11:50] the previous slides. The trust should be baked  into the architecture or the methodology. It's [11:57] not aftermath where you created a solution and now  you want to govern it. the governance processes, [12:02] the explanability, the security have to be built  into the architecture. Last but not the least, the [12:10] human centered intelligence. It's human plus AI  taking the decision based on the capabilities that [12:17] we've already built and not the other way around.  So again, this technology mapping I would like [12:23] to call illustrative. I'm not uh supporting any  technology any vendor any software but what each [12:30] of these layers would definitely have in terms of  technology mapping from foundation to context to [12:35] memory intelligence and the trust and governance  right these are foundational components and I'm [12:43] not asking to restart thinking about it these  components are used by every enterprise be it [12:50] a small enterprise medium enterprise or a larger  enterprise these components are already being used [12:56] but it's about how you organize them, use them and  build the thinking layer on top of an enterprise. [13:05] So how does this ch thing changes in  agentic world right? So in my view the [13:15] enterprise intelligence services once it  becomes centralized at an enterprise level [13:21] I would recommend the agent take services or  the agents to leverage those services to infer [13:27] information knowledge provide answer to the  questions asked or provide some guidance to [13:33] the solutions that you need going forward.  Every layer can be agentic definitely within [13:39] those particular domains or verticals  or the capabilities but if there are any [13:44] agentic capabilities at an enterprise level  I would leave it on the top leveraging the [13:49] enterprise intelligence services with  that I finish my topic as I said I'll [13:54] give you some time early thank you so much  for staying back on the last session of the