KT's AI-Driven Document Modernization with Microsoft 365 Copilot

Korea Telecom’s shift from a fragmented file sprawl to a unified, AI-augmented knowledge layer shows how Microsoft 365 Copilot can be the hinge between legacy habits and modern information workflows, delivering faster discovery, clearer context, and measurable productivity gains across a large enterprise.

Background / Overview​

For decades, enterprises have wrestled with the same document problem: steady daily file creation combined with decentralized storage habits leads to duplicated content, lost knowledge, and wasted time. Korea Telecom (KT), one of South Korea’s largest telecom operators, faced precisely this reality. Employees routinely stored files locally on PCs, in departmental folders, or in disparate cloud locations. Attempts to centralize on OneDrive and SharePoint produced mixed adoption; technical workarounds such as DRM-encrypted files limited the usability of documents inside new collaboration and AI tooling. The result was a knowledge base that existed in pieces rather than as an integrated, company-wide asset.
KT’s response was to take a pragmatic, ecosystem-aligned approach: keep the familiarity of the Microsoft 365 family while adding an AI layer that could index, summarize, and surface documents where users already work. The chosen linchpin was Microsoft 365 Copilot, combined with OneDrive, SharePoint, and information protection controls, to create a single logical index of corporate content without forcing a radical change to day-to-day workflows.
This article examines how KT implemented that strategy, what technical and organizational choices mattered, what outcomes were reported, and what risks and practical lessons other enterprises should weigh when adopting Copilot-driven document management and retrieval.

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Why KT needed more than simple migration​

The core problem: fragmented document sprawl​

KT’s document challenge mirrors that of many large organizations:

• Documents created daily across teams, business units, and projects
• Files saved in many places—local drives, shared folders, legacy repositories
• Duplication of effort and repeated re-creation of documents
• Difficulty discovering the most current version or the “right” document for a task
• Security and compliance friction when encrypted/DRM files can’t be easily indexed or consumed by newer tools

These symptoms reduce employee productivity and create friction for knowledge workers. KT’s leadership recognized that moving files into OneDrive/SharePoint was necessary but not sufficient: they needed useful discoverability and incentives for employees to actually centralize their work.

The organizational barrier: adoption, not just technology​

KT’s initial push for centralization ran into behavior and culture limits. Many employees had established personal or team-based storage patterns and perceived migration as extra overhead. That’s the classic adoption chasm: technical capability exists, but adoption lags because the change requires users to alter habits without gaining immediate benefit.
KT therefore framed the project not as a forced migration but as an experience improvement: keep the familiar apps (Word, Excel, Teams), add search and summarization capabilities that reduce time to value, and show quick wins through an early access pilot. This “practical persuasion” approach leverages the existing ecosystem rather than replacing it.

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The solution stack KT deployed​

Core components​

• Microsoft 365 Copilot integrated into Word, Excel, Teams, and OneDrive
• OneDrive for Business and SharePoint Online as the document store and collaboration layer
• Azure Information Protection (AIP) and related controls to preserve security and compliance
• Internal Center of Excellence (CoE) and citizen developer groups to prototype workflows and drive adoption
• Integration work to improve PDF compatibility and indexing across protected content

What Copilot provided in this context​

Copilot was used as the intelligent access layer that sits on top of OneDrive and SharePoint. In KT’s rollout, Copilot functions served three critical roles:

• Indexing and search: Copilot aggregates signals from indexed documents so employees can ask natural-language questions and receive precise answers or pointed document references.
• Summarization and referencing: Long reports or collections of files can be distilled into concise summaries, saving time for managers and project leads who need quick context before meetings.
• Workflow nudge: By surfacing the immediate benefit of centralization (e.g., “I can summarize the latest product deck for you”), Copilot encouraged employees to place files where Copilot can find them, smoothing the path to adoption.

These features let KT preserve day-to-day tools while introducing an AI-driven reason to centralize documents.

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Implementation journey: pilot, integration, and scaling​

Pilot design and early wins​

KT began with a targeted early access program, rolling Copilot out to a manageable cohort of early adopters and citizen developers. This allowed the company to:

• Validate technical integration points (indexing, metadata, PDF handling).
• Collect user stories and measurable productivity improvements to champion internally.
• Iterate governance and information protection settings before scaling.

Early adopters became internal advocates. When colleagues saw Copilot quickly summarize dossiers or extract key facts from large reports, the value proposition moved from theoretical to practical. That momentum helped overcome the “we don’t want to change our storage habits” resistance.

Technical integration and remediation​

Practical implementation required several technical fixes and policy alignments:

• Ensuring documents protected by DRM/AIP could be scanned, indexed, and referenced by Copilot while preserving access controls.
• Improving PDF compatibility so Copilot could parse and summarize commonly used PDF formats.
• Establishing organizational metadata and retention policies in SharePoint to improve indexing relevance.
• Configuring admin controls so Copilot’s access footprint matched KT’s security posture and compliance obligations.

These steps are typical for enterprises migrating from legacy file patterns: the platform integration is often straightforward, but edge cases (encrypted PDFs, proprietary formats) require focused remediation.

Change management and the CoE​

KT leaned on a formal AX (AI transformation) CoE and its culture of citizen developers to accelerate adoption. Small teams prototyped use cases—meeting-brief generation, contract summarization, and project handoffs—then shared those wins company-wide. Training and skilling were staged to scale across the organization, aligning with KT’s broader AI and cloud partnership strategy.

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Reported outcomes: productivity, adoption, and engagement​

KT reported perceptible improvements after Copilot integration. The most important outcomes:

• Faster document retrieval: Employees spent less time hunting for the right files because Copilot could find and summarize the most relevant documents.
• Higher OneDrive/SharePoint adoption: When employees realized that files stored centrally could be indexed and used by Copilot, they were more willing to upload and centralize content.
• Better team collaboration: Summaries and quick insights made handoffs smoother; team leads could get to the heart of long reports without reading everything.
• Time savings at scale: KT characterized the efficiency gains in terms of thousands of hours saved annually across the workforce—translating those hours into operational savings became part of the internal business case.

These outcomes highlight an important pattern: when AI demonstrably reduces friction for the user, behavior change follows. The combination of familiarity (same apps), capability (AI search and summarization), and clear pain relief (less searching) is what drove adoption.

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Technical specifics and constraints to be aware of​

Enterprises must pay attention to the platform’s documented limitations and admin controls when planning a rollout.

• File types and limits: The AI layer supports most text-based formats (documents, presentations, spreadsheets, PDFs), though some formats—images, meeting recordings, and OneNote—may have limited or delayed support depending on the vendor’s rollout schedule.
• File-size and batching constraints: Current operational limits can restrict how many files can be processed in a single summarization action and the maximum file size Copilot will ingest from OneDrive/SharePoint.
• Licensing and availability: Copilot functionality and capabilities vary by license type and tenant configuration; some capabilities are gated behind Copilot licensing or phased rollouts.
• Privacy and access controls: Indexing does not bypass existing access permissions, but administrators should understand how Copilot sources content and what metadata it exposes in summaries and search results.
• DRM and encryption: Documents protected by DRM or legacy encryption can inhibit AI indexing if not remediated; integration with information protection tools is often necessary.

These constraints are important to validate during proof-of-concept work. Planning for remediation of edge cases—large PDFs, encrypted archives, or proprietary content types—avoids surprises in full-scale deployment.

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Security, compliance, and governance: the non-negotiables​

For a telecom operator handling customer data and regulated information, security and compliance are core concerns. KT’s approach emphasized:

• Preservation of existing access controls: Copilot’s indexing respects OneDrive/SharePoint permissions so that responsive summaries and search results do not expose content to unauthorized users.
• Integration with Azure Information Protection: Protecting sensitive documents while enabling legitimate search and summarization required coordination between information protection policies and the Copilot indexing process.
• Policy-driven enabling: Admins must make deliberate choices about which document libraries and tenants are part of the Copilot index and which remain restricted.
• Responsible-AI and model governance: As KT builds deeper AI products, it must align model use with internal responsible AI guidelines—ensuring explainability, guarding against hallucinations, and maintaining auditability of AI-generated outputs.

These governance decisions are less about tool features and more about organizational risk tolerance and compliance requirements. The technical controls exist, but success depends on how IT, legal, and business teams collaborate to define policy.

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Risks, limitations, and potential pitfalls​

While Copilot and a centralization strategy can deliver material benefits, the rollout also presents several risks that must be actively managed.

• Hallucination and factual drift: AI summarization can occasionally produce incorrect or misleading statements. Relying exclusively on AI outputs without human verification can propagate errors into decisions. Processes should define when and how to validate AI-sourced summaries.
• Over-indexing confidential content: Broad indexing without tight scope control risks surface of sensitive documents to unintended audiences. Admins must apply granular scope and permission controls before indexing widely.
• Legacy DRM and accessibility blind spots: Documents that remain encrypted or trapped in legacy systems will remain invisible to the AI layer, potentially biasing search results toward more recent or accessible content.
• Cultural resistance and misunderstanding: If employees feel that centralization is surveillance or administrative burden, they may resist, undermining adoption. Framing Copilot as an empowerment tool (time-saver, summarizer) rather than an audit mechanism helps mitigate this.
• Vendor lock-in concerns: Heavy dependence on a single ecosystem for search, protection, and AI may raise strategic questions for organizations that need multi-cloud or multi-vendor portability.
• Licensing and cost: Achieving organization-wide Copilot capabilities can require significant licensing and operational investment; ROI should be calculated against measured time savings and improved outcomes.

These trade-offs underscore the need for a phased, measurable rollout, with clear guardrails for validation, auditing, and human oversight.

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Recommended adoption and governance blueprint​

Based on KT’s journey and general best practices, a recommended blueprint for enterprises considering a similar upgrade:

• Start small with a targeted early access cohort (productivity champions and citizen developers).
• Choose a few high-value use cases: meeting-brief generation, contract summarization, RFP research, and project handoffs.
• Inventory and remediate content that cannot be indexed (DRM, legacy formats).
• Align information protection policies with indexing scope; apply least-privilege principles.
• Build a CoE to share playbooks, templates, and success stories.
• Train managers and users in validation processes so Copilot outputs are used as assistants, not authoritative single sources.
• Monitor and measure: time saved, adoption rates, and changes in storage behavior (OneDrive/SharePoint usage).
• Iterate on retention, metadata, and taxonomy to refine relevance in search and summarization.

This sequence balances speed of adoption with risk controls and governance.

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What KT’s experience teaches the broader market​

KT’s approach holds three important lessons for enterprises and IT leaders planning document modernization with AI:

• Don’t force a platform swap—augment what users already know. KT’s retention of Word, Excel, Teams, and OneDrive lowered the behavioral friction that typically kills migrations.
• Show value early and visibly. Quick wins from early pilots produce the social proof necessary to shift behavior at scale.
• Treat remediation as part of the project, not a blocker. Addressing encrypted PDFs, metadata, and legacy formats upfront pays dividends when you scale indexing across thousands of users.

For telecoms in particular—where regulatory, data privacy, and operational continuity demands are high—this model of incremental, controlled adoption backed by a CoE provides an effective pattern.

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Strategic implications for telecom operators and large enterprises​

• Knowledge as an asset: Centralized, AI-indexed documents transform static archives into living knowledge assets that can be reused across product teams and business units.
• Faster product cycles: Better document discovery cuts time spent in discovery and alignment, accelerating product launch cycles and operational decision-making.
• Platform synergy: Combining collaboration (SharePoint/OneDrive), security (AIP), and AI (Copilot) reduces integration complexity compared to stitching third-party tools together.
• Market differentiation: Operators that enable faster, better-informed decisions across their workforce can move faster in competitive markets and in launch of AI-powered customer services.

However, these strategic benefits are conditional on disciplined governance and careful rollout to avoid inadvertent data exposure or trust erosion.

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Practical checklist before launching Copilot-driven document modernization​

• Conduct a content audit: identify high-value libraries, sensitive repositories, and unreadable/legacy formats.
• Define scope and pilot cohort: limit early indexing to a few business units that can act as demonstrators.
• Configure permissions and AIP policies: ensure Copilot indexing respects access and protection rules.
• Prepare validation protocols: create a simple human-in-the-loop checklist for validating AI-generated summaries.
• Establish metrics: define time-saved KPIs, adoption metrics (percentage of files centralized), and engagement measures.
• Communicate transparently: explain benefits and privacy controls to employees to reduce fear and resistance.
• Budget for licensing and remediation: include costs for Copilot licenses, migration tools, and format remediation.

This checklist reduces surprises during rollouts and accelerates time to value.

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Conclusion​

KT’s experience demonstrates that document modernization is not solely a storage problem—it’s a human problem with technical levers. By coupling document centralization on OneDrive and SharePoint with an AI-first access layer (Microsoft 365 Copilot) and a pragmatic governance plan, KT turned scattered content into an accessible, actionable knowledge base. The approach preserved user familiarity while delivering new capabilities—faster discovery, concise summaries, and tangible productivity gains—that motivated employees to change behavior.
For large enterprises and telecom operators, the lesson is clear: AI-enabled document retrieval works best when it is integrated into familiar apps, governed carefully, and rolled out incrementally with demonstrable wins. The technical platform provides the tools, but the human-centered change strategy determines whether those tools become a force multiplier—or an underused expense.
Enterprises preparing for similar projects should prioritize a measured pilot, remediate inaccessible content, apply strict information protection policies, and build internal champions who can translate early AI gains into lasting organizational habits. When those pieces align, document centralization plus Copilot-style AI becomes not just a search improvement, but a foundation for faster decisions, less duplication, and a more confident, collaborative workforce.

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Source: Microsoft Using Copilot to advance and unify document storage and retrieval at Korea Telecom | Microsoft Customer Stories