Accenture’s decision to scale Microsoft 365 Copilot across roughly 743,000 employees marks one of the clearest signs yet that enterprise generative AI is moving beyond pilots and into everyday work. The rollout, which began in phases in 2023, is now being positioned as Microsoft’s largest enterprise Copilot deployment to date and a proving ground for how AI assistants can reshape document creation, analysis, meetings, messaging, and knowledge retrieval. The headline numbers are striking: Accenture says 97% of surveyed staff completed routine tasks faster, while monthly active usage reached 89% in a roughly 200,000-license tranche. For Microsoft, partners, CIOs, and Windows-heavy enterprise environments, this is less a single customer win than a real-world stress test of the AI productivity stack.
Microsoft 365 Copilot arrived commercially in late 2023 with an ambitious promise: bring large language models directly into the productivity suite where knowledge workers already spend most of their day. Instead of asking employees to leave Outlook, Word, Excel, PowerPoint, Teams, SharePoint, or OneDrive for a separate chatbot, Microsoft embedded AI into the workflow itself. That integration strategy has always been the strongest argument for Copilot, because enterprise productivity gains often depend less on dazzling demos and more on reducing friction in ordinary tasks.
Accenture was an unusually important early adopter because of its scale, consulting influence, and long relationship with Microsoft. The company is not merely a Microsoft customer; through its majority ownership of Avanade and its broad Microsoft services practice, it also helps other organizations implement Microsoft cloud, data, security, and AI technologies. That means Accenture’s internal Copilot journey doubles as a reference architecture for the clients it advises.
The deployment started with selected leaders and early users before expanding to larger groups. That sequencing matters because generative AI tools behave differently at enterprise scale than they do in small pilots. Once a rollout reaches hundreds of thousands of users, the key questions shift from “Can the tool draft a useful email?” to “Can the organization govern access, train users, measure value, manage expectations, and prevent low-quality automation from spreading?”
The move also comes at a crucial moment for Microsoft. The company has invested heavily in AI infrastructure, model partnerships, and Copilot branding across Windows, Microsoft 365, GitHub, Dynamics, Security, and Azure. Yet investors and enterprise buyers have continued to ask whether paid Copilot seats can convert from curiosity into durable, measurable business value. Accenture’s deployment gives Microsoft a powerful answer, but it also raises tougher questions about what real adoption should mean.
The expansion follows a phased model that began in August 2023 and grew through larger cohorts. Early pilots reportedly focused on senior leaders and selected employees, then expanded to tens of thousands of users before reaching a much broader population. That progression suggests Accenture treated Copilot less as a simple license assignment and more as an operating model change.
The more important question is whether activity translates into higher-quality outcomes. Completing routine tasks faster is valuable, but only if the work remains accurate, secure, and aligned with client expectations. For a consulting and services firm, even small errors in proposals, analysis, or internal knowledge sharing can compound quickly.
Key scale factors include:
Copilot can summarize Teams meetings, help draft Word documents, prepare PowerPoint presentations, analyze Excel data, and surface information from SharePoint and OneDrive. Those scenarios may sound mundane, but they represent a huge share of knowledge-work overhead. If AI can reduce the time spent searching, summarizing, reformatting, and rewriting, employees can spend more time on judgment and client engagement.
The integration also gives Microsoft a competitive moat. Rivals can offer powerful models, but they do not necessarily sit inside Outlook threads, Teams meetings, Word drafts, and SharePoint libraries by default. In productivity software, convenience often beats raw model sophistication.
Common Accenture-style use cases likely include:
Self-reported productivity is useful because it captures whether employees feel the tool helps them. However, it does not always map cleanly to measurable business output, revenue, margin, quality, or client satisfaction. The enterprise AI market has already seen a gap between impressive individual anecdotes and mixed macro-level productivity evidence.
The deeper value emerges when AI changes how work is organized. That could mean fewer redundant meetings, faster onboarding, better knowledge reuse, or more consistent proposal development. Those outcomes are harder to measure than task speed, but they are more important for long-term return on investment.
A balanced reading of the claims should consider:
Accenture’s approach reportedly included attention to data strategy, governance, and access controls during the rollout. That detail is critical. Many organizations want Copilot’s benefits without doing the less glamorous work of cleaning up content, tightening permissions, and educating users about responsible AI.
This is why Microsoft 365 governance tools, sensitivity labels, data loss prevention, lifecycle management, and SharePoint access reviews become part of the AI deployment story. Copilot is not just an add-on license; it is a spotlight on the maturity of the Microsoft 365 environment. Organizations that skipped information governance may find the bill coming due.
Important data-readiness priorities include:
Employees need to learn not only where Copilot buttons are located, but also when AI output is trustworthy, when it needs review, and when it should not be used at all. That makes AI literacy a new workplace competency. The best users will not be those who blindly accept generated answers, but those who can ask better questions and evaluate responses critically.
Accenture’s use of internal communities, including employee sharing around daily use cases, is especially important. Generative AI adoption spreads through practical examples: a better prompt for meeting follow-ups, a way to compare documents, or a method for converting notes into action items. The organization learns fastest when successful patterns are visible.
A mature adoption program usually follows a sequence:
The deployment also strengthens Microsoft’s partner ecosystem. Accenture and Avanade can use their own experience to advise clients on governance, adoption, workflow redesign, and industry-specific Copilot extensions. That creates a virtuous cycle: internal deployment informs consulting services, which then drive more Microsoft AI consumption among clients.
However, the market is not settled. Some organizations may prefer model-agnostic platforms, specialized agents, or AI tools that integrate across multiple productivity suites. Others may worry about Microsoft lock-in, licensing costs, or whether Copilot’s value justifies broad deployment to every employee.
Competitive dynamics to watch include:
The Accenture case also suggests that large organizations may need to move faster than traditional software adoption cycles allow. Employees are already experimenting with public AI tools, and business units are pressing for automation. A governed Copilot deployment can give enterprises a safer channel for AI use while keeping data inside managed systems.
That scrutiny may push organizations toward segmented deployment. Not every employee will generate the same value from a paid AI assistant, at least initially. High-value knowledge workers, managers, sellers, analysts, consultants, and legal or finance teams may show stronger early ROI than roles with limited Microsoft 365 usage.
CIOs should evaluate:
For Windows users, the message is that AI assistance will become more ambient. Summaries, drafting, search, file reasoning, and meeting intelligence are likely to feel less like separate applications and more like expected interface capabilities. The operating system, browser, and productivity suite are all becoming surfaces for AI.
This distinction matters because trust is contextual. Users may trust Copilot to summarize a Teams meeting but still hesitate to let AI manage personal documents, photos, or finances. Microsoft will need to communicate clearly where data goes, what is stored, and what controls users have.
For the Windows ecosystem, likely effects include:
If a user already has access to sensitive material, Copilot may help that user find and summarize it faster. That is useful for legitimate work, but it can also magnify the consequences of over-permissioned sites or poorly labeled files. Security teams must think of Copilot as an accelerator of existing access patterns.
Compliance teams also need to understand how Copilot interactions are logged, retained, audited, and discoverable. In regulated industries, AI-generated content can become part of business records. That makes policy clarity essential before broad rollout.
Trust depends on several practical controls:
Microsoft will also keep evolving Copilot from an assistant into a more agentic platform. That means more task automation, more workflow orchestration, more connections to business systems, and more model choice. For customers, this raises both opportunity and complexity because agentic AI requires stronger governance than simple drafting assistance.
Watch these developments closely:
Source: Redmond Channel Partner Accenture Scales Microsoft Copilot Deployment Across Global Workforce -- Redmond Channel Partner
Background
Microsoft 365 Copilot arrived commercially in late 2023 with an ambitious promise: bring large language models directly into the productivity suite where knowledge workers already spend most of their day. Instead of asking employees to leave Outlook, Word, Excel, PowerPoint, Teams, SharePoint, or OneDrive for a separate chatbot, Microsoft embedded AI into the workflow itself. That integration strategy has always been the strongest argument for Copilot, because enterprise productivity gains often depend less on dazzling demos and more on reducing friction in ordinary tasks.Accenture was an unusually important early adopter because of its scale, consulting influence, and long relationship with Microsoft. The company is not merely a Microsoft customer; through its majority ownership of Avanade and its broad Microsoft services practice, it also helps other organizations implement Microsoft cloud, data, security, and AI technologies. That means Accenture’s internal Copilot journey doubles as a reference architecture for the clients it advises.
The deployment started with selected leaders and early users before expanding to larger groups. That sequencing matters because generative AI tools behave differently at enterprise scale than they do in small pilots. Once a rollout reaches hundreds of thousands of users, the key questions shift from “Can the tool draft a useful email?” to “Can the organization govern access, train users, measure value, manage expectations, and prevent low-quality automation from spreading?”
The move also comes at a crucial moment for Microsoft. The company has invested heavily in AI infrastructure, model partnerships, and Copilot branding across Windows, Microsoft 365, GitHub, Dynamics, Security, and Azure. Yet investors and enterprise buyers have continued to ask whether paid Copilot seats can convert from curiosity into durable, measurable business value. Accenture’s deployment gives Microsoft a powerful answer, but it also raises tougher questions about what real adoption should mean.
The Scale of the Rollout
Accenture’s global workforce is roughly the size of a major city, which makes this rollout fundamentally different from the typical enterprise software deployment. A 5,000-seat implementation can rely on champions, training sessions, and close IT monitoring; a 743,000-person deployment requires industrialized change management. Every region, business unit, language group, and client-facing practice brings its own data habits and productivity pain points.The expansion follows a phased model that began in August 2023 and grew through larger cohorts. Early pilots reportedly focused on senior leaders and selected employees, then expanded to tens of thousands of users before reaching a much broader population. That progression suggests Accenture treated Copilot less as a simple license assignment and more as an operating model change.
Why Size Changes the AI Equation
At this scale, usage telemetry becomes as important as feature capability. Accenture’s reported 89% monthly active usage in a 200,000-license group is notable because many enterprise tools suffer from shelfware once the excitement fades. A high monthly active rate suggests employees found repeatable use cases, not just one-time novelty.The more important question is whether activity translates into higher-quality outcomes. Completing routine tasks faster is valuable, but only if the work remains accurate, secure, and aligned with client expectations. For a consulting and services firm, even small errors in proposals, analysis, or internal knowledge sharing can compound quickly.
Key scale factors include:
- Regional complexity across more than 100 countries and many regulatory environments.
- Role diversity spanning consulting, technology delivery, operations, sales, HR, finance, and leadership.
- Data governance pressure because Copilot is only as safe as the permissions and content hygiene behind it.
- Training variance between power users, casual users, and employees skeptical of AI.
- Client confidentiality requirements that demand disciplined handling of prompts, files, and generated content.
Why Microsoft 365 Copilot Fits Accenture’s Workflow
The appeal of Microsoft 365 Copilot is not simply that it can generate text. Its enterprise value comes from being grounded in Microsoft Graph and connected to the documents, emails, meetings, chats, and files employees already use. For Accenture, whose work depends on collaboration, proposals, client deliverables, and knowledge reuse, that context is central.Copilot can summarize Teams meetings, help draft Word documents, prepare PowerPoint presentations, analyze Excel data, and surface information from SharePoint and OneDrive. Those scenarios may sound mundane, but they represent a huge share of knowledge-work overhead. If AI can reduce the time spent searching, summarizing, reformatting, and rewriting, employees can spend more time on judgment and client engagement.
Embedded AI Beats Standalone AI
A standalone chatbot requires users to copy context into a prompt, manage files manually, and decide what data they can safely paste. Microsoft 365 Copilot changes that flow by operating inside authenticated enterprise systems and respecting existing permissions. That makes it more attractive to CIOs who worry about data leakage and shadow AI.The integration also gives Microsoft a competitive moat. Rivals can offer powerful models, but they do not necessarily sit inside Outlook threads, Teams meetings, Word drafts, and SharePoint libraries by default. In productivity software, convenience often beats raw model sophistication.
Common Accenture-style use cases likely include:
- Drafting first versions of client communications and project updates.
- Summarizing long Teams meetings for distributed teams.
- Turning Word documents into presentation outlines.
- Finding internal knowledge across SharePoint and OneDrive.
- Generating starting points for Excel analysis and reporting.
- Improving executive briefings with faster synthesis of scattered material.
Productivity Claims Need Careful Reading
Accenture’s reported figures are impressive, but they deserve careful interpretation. The company says 97% of employees in a 2025 data set involving roughly 200,000 users reported completing routine tasks 15 times faster with Copilot, while 53% reported significant productivity and efficiency improvements. Those are strong indicators of perceived value, but they appear to rely at least partly on self-reported employee feedback.Self-reported productivity is useful because it captures whether employees feel the tool helps them. However, it does not always map cleanly to measurable business output, revenue, margin, quality, or client satisfaction. The enterprise AI market has already seen a gap between impressive individual anecdotes and mixed macro-level productivity evidence.
The Difference Between Speed and Value
Speed is not the same as value. If Copilot helps draft an email in one minute instead of fifteen, the time savings are real. But if the employee spends extra time checking accuracy, revising tone, or correcting unsupported claims, the net gain may be smaller.The deeper value emerges when AI changes how work is organized. That could mean fewer redundant meetings, faster onboarding, better knowledge reuse, or more consistent proposal development. Those outcomes are harder to measure than task speed, but they are more important for long-term return on investment.
A balanced reading of the claims should consider:
- Routine tasks are usually the easiest category for AI to accelerate.
- Complex judgment work still depends heavily on human expertise.
- Survey enthusiasm can be strongest during early adoption waves.
- Quality control determines whether speed gains become business gains.
- Workflow redesign is necessary to capture savings rather than merely filling freed time with more meetings.
Data Readiness Becomes the Hidden Prerequisite
Copilot’s usefulness depends heavily on the quality, structure, and permissions of enterprise data. If SharePoint sites are disorganized, OneDrive folders contain outdated files, or access permissions are too broad, Copilot can surface irrelevant or sensitive material. AI does not magically fix poor information architecture; in some cases, it exposes it.Accenture’s approach reportedly included attention to data strategy, governance, and access controls during the rollout. That detail is critical. Many organizations want Copilot’s benefits without doing the less glamorous work of cleaning up content, tightening permissions, and educating users about responsible AI.
Copilot Reflects the Tenant It Lives In
The phrase “garbage in, garbage out” has new relevance in the Copilot era. If an employee asks for a policy summary and the system finds three conflicting documents, the assistant may produce a confident but problematic answer. If confidential content has been overshared, AI can make that oversharing easier to discover.This is why Microsoft 365 governance tools, sensitivity labels, data loss prevention, lifecycle management, and SharePoint access reviews become part of the AI deployment story. Copilot is not just an add-on license; it is a spotlight on the maturity of the Microsoft 365 environment. Organizations that skipped information governance may find the bill coming due.
Important data-readiness priorities include:
- Permission hygiene across SharePoint, Teams, and OneDrive.
- Retention policies that reduce outdated or duplicative content.
- Sensitivity labels for confidential, regulated, or client-specific material.
- Search relevance tuning so employees find authoritative sources.
- Data owner accountability for high-value knowledge repositories.
- User education around prompt safety and verification.
Change Management Is the Real Deployment Engine
Large software deployments often fail because companies focus on technical enablement and underestimate behavioral change. Accenture appears to have treated adoption as a human process, using training, leader engagement, communications, internal communities, and shared examples. That matters because generative AI is unfamiliar enough to require experimentation but risky enough to demand guardrails.Employees need to learn not only where Copilot buttons are located, but also when AI output is trustworthy, when it needs review, and when it should not be used at all. That makes AI literacy a new workplace competency. The best users will not be those who blindly accept generated answers, but those who can ask better questions and evaluate responses critically.
From Training to Habit Formation
A one-time webinar is rarely sufficient. Sustained adoption requires reminders, role-specific examples, peer stories, and visible leadership use. If senior executives use Copilot to prepare briefings or summarize meetings, employees are more likely to see it as a legitimate part of work rather than an experimental toy.Accenture’s use of internal communities, including employee sharing around daily use cases, is especially important. Generative AI adoption spreads through practical examples: a better prompt for meeting follow-ups, a way to compare documents, or a method for converting notes into action items. The organization learns fastest when successful patterns are visible.
A mature adoption program usually follows a sequence:
- Identify high-friction workflows where AI can help immediately.
- Train early adopters and leaders with role-specific scenarios.
- Publish safe-use guidance and escalation paths.
- Measure both usage and outcome quality.
- Expand licenses based on demonstrated value and readiness.
Competitive Implications for Microsoft
For Microsoft, Accenture’s rollout is a major credibility boost. The company needs marquee enterprise deployments to show that Copilot is not merely a premium feature bolted onto Office, but a strategic layer for modern work. A global consulting firm scaling Copilot across its workforce gives Microsoft a case study that rivals will find difficult to ignore.The deployment also strengthens Microsoft’s partner ecosystem. Accenture and Avanade can use their own experience to advise clients on governance, adoption, workflow redesign, and industry-specific Copilot extensions. That creates a virtuous cycle: internal deployment informs consulting services, which then drive more Microsoft AI consumption among clients.
Pressure on Google, Salesforce, OpenAI, and Others
The broader market implication is that enterprise AI assistants are becoming distribution battles. Google has Gemini for Workspace, Salesforce has Einstein and Agentforce, OpenAI has ChatGPT Enterprise, and many startups offer specialized AI productivity tools. Microsoft’s advantage is the installed base of Microsoft 365 and the deep familiarity of Office workflows.However, the market is not settled. Some organizations may prefer model-agnostic platforms, specialized agents, or AI tools that integrate across multiple productivity suites. Others may worry about Microsoft lock-in, licensing costs, or whether Copilot’s value justifies broad deployment to every employee.
Competitive dynamics to watch include:
- Suite integration versus best-of-breed AI tools.
- Model choice as Microsoft adds more model options beyond OpenAI.
- Agent ecosystems built through Copilot Studio and third-party connectors.
- Pricing pressure as buyers demand proof of ROI.
- Security differentiation around permissions, auditing, and compliance.
- Vertical solutions for industries such as finance, health, energy, and public sector.
Enterprise Impact: CIOs Get a Roadmap
For CIOs, the biggest lesson is that Copilot deployment must be treated as an enterprise transformation program. The technology touches identity, data governance, compliance, records management, user training, security operations, and business process design. That makes it a board-level initiative, not just an IT refresh.The Accenture case also suggests that large organizations may need to move faster than traditional software adoption cycles allow. Employees are already experimenting with public AI tools, and business units are pressing for automation. A governed Copilot deployment can give enterprises a safer channel for AI use while keeping data inside managed systems.
Budgeting for AI at Workforce Scale
The economics are significant. At list pricing, Microsoft 365 Copilot has historically been positioned as a premium per-user add-on, which makes broad deployment expensive for large employers. Enterprise agreements, negotiated pricing, and phased adoption can change the actual cost, but CFO scrutiny will remain intense.That scrutiny may push organizations toward segmented deployment. Not every employee will generate the same value from a paid AI assistant, at least initially. High-value knowledge workers, managers, sellers, analysts, consultants, and legal or finance teams may show stronger early ROI than roles with limited Microsoft 365 usage.
CIOs should evaluate:
- Who benefits first based on daily workflow intensity.
- Which data repositories must be cleaned before rollout.
- How productivity gains will be measured beyond surveys.
- What governance controls are mandatory before scale.
- How AI usage affects compliance, audit, and e-discovery.
- Which training models work for different employee groups.
Consumer and Windows User Implications
Although Accenture’s deployment is an enterprise story, it has implications for everyday Windows and Microsoft 365 users. Microsoft’s AI strategy increasingly blurs the line between work and personal productivity. Features that mature in enterprise Copilot often influence consumer experiences in Windows, Edge, Outlook, and Microsoft 365 subscriptions.For Windows users, the message is that AI assistance will become more ambient. Summaries, drafting, search, file reasoning, and meeting intelligence are likely to feel less like separate applications and more like expected interface capabilities. The operating system, browser, and productivity suite are all becoming surfaces for AI.
The Workplace Sets User Expectations
Employees who use Copilot at work may begin to expect similar capabilities at home. That can accelerate adoption of consumer Copilot features, but it can also create confusion about data boundaries. A work Copilot experience grounded in corporate files is not the same as a personal assistant operating over consumer data.This distinction matters because trust is contextual. Users may trust Copilot to summarize a Teams meeting but still hesitate to let AI manage personal documents, photos, or finances. Microsoft will need to communicate clearly where data goes, what is stored, and what controls users have.
For the Windows ecosystem, likely effects include:
- More AI-first interface design across Microsoft apps.
- Higher expectations for natural-language search and summarization.
- Greater demand for local privacy controls and enterprise policy enforcement.
- More Copilot-aware hardware marketing around neural processing units.
- Increased user training needs as AI features appear in familiar apps.
Security, Compliance, and Trust
Microsoft’s pitch for Copilot rests heavily on enterprise security promises: data access follows existing permissions, prompts and responses are governed within Microsoft 365 commitments, and customer data is not used to train foundation models. Those assurances are essential, but they do not eliminate risk. They shift part of the burden back to organizational governance.If a user already has access to sensitive material, Copilot may help that user find and summarize it faster. That is useful for legitimate work, but it can also magnify the consequences of over-permissioned sites or poorly labeled files. Security teams must think of Copilot as an accelerator of existing access patterns.
AI Security Is Also Information Security
Prompt injection, hallucination, data leakage, and overreliance are now part of the enterprise risk register. Copilot may include safeguards, but users still need to verify outputs and avoid asking AI to produce unsupported conclusions. The more employees use AI in client-facing work, the more important review processes become.Compliance teams also need to understand how Copilot interactions are logged, retained, audited, and discoverable. In regulated industries, AI-generated content can become part of business records. That makes policy clarity essential before broad rollout.
Trust depends on several practical controls:
- Clear acceptable-use policies for client, financial, legal, and HR data.
- Auditability of Copilot interactions where required.
- Human review for external deliverables and consequential decisions.
- Access reviews before and after deployment.
- Incident response plans for AI-related data exposure.
- Transparency about model limitations and output uncertainty.
Strengths and Opportunities
Accenture’s Copilot expansion shows why Microsoft’s enterprise AI strategy has traction: it puts AI where work already happens, pairs it with existing identity and compliance systems, and gives global organizations a path from experimentation to scale. The opportunity is not merely faster documents or cleaner meeting notes; it is the gradual redesign of knowledge work around AI-assisted retrieval, drafting, analysis, and collaboration.- Deep Microsoft 365 integration reduces friction by embedding AI in Word, Excel, PowerPoint, Outlook, Teams, SharePoint, and OneDrive.
- High reported usage suggests employees are finding recurring value rather than treating Copilot as a novelty.
- Accenture’s consulting influence can turn internal lessons into repeatable playbooks for clients.
- Governed AI adoption may reduce reliance on unmanaged public chatbots and shadow AI workflows.
- Role-specific use cases can unlock value in consulting, sales, operations, HR, finance, and leadership.
- Data governance modernization becomes easier to justify when directly tied to AI productivity.
- Partner ecosystem momentum benefits Microsoft, Accenture, Avanade, and implementation specialists.
Risks and Concerns
The rollout also highlights the unresolved challenges of enterprise AI. Impressive adoption numbers do not automatically prove durable ROI, and faster task completion does not guarantee better business outcomes. As Copilot becomes more deeply embedded in work, organizations must guard against complacency, weak governance, and inflated expectations.- Self-reported productivity gains may overstate measurable business impact without independent outcome metrics.
- License costs can become difficult to justify if usage is broad but value is concentrated in specific roles.
- Poor data hygiene can lead Copilot to surface outdated, irrelevant, or overshared information.
- Hallucinated or unsupported output may create risk in client-facing documents and executive decisions.
- Overreliance on AI could weaken writing, analysis, and critical thinking skills over time.
- Change fatigue may grow as employees face constant AI feature updates and new workflows.
- Vendor lock-in could deepen if organizations redesign processes tightly around Microsoft’s AI stack.
What to Watch Next
The next phase will be about evidence. Accenture and Microsoft have strong adoption and sentiment data, but enterprise buyers will want more detailed proof of outcomes: reduced cycle times, lower delivery costs, improved client satisfaction, better proposal win rates, faster onboarding, or fewer hours spent in meetings. The market will increasingly reward AI deployments that can connect usage to operational performance.Microsoft will also keep evolving Copilot from an assistant into a more agentic platform. That means more task automation, more workflow orchestration, more connections to business systems, and more model choice. For customers, this raises both opportunity and complexity because agentic AI requires stronger governance than simple drafting assistance.
Watch these developments closely:
- Whether Accenture publishes deeper ROI metrics beyond self-reported productivity and usage.
- How Copilot agents expand from personal assistance into repeatable business workflows.
- Whether Microsoft adjusts pricing or packaging to accelerate paid seat adoption.
- How competitors respond with Workspace, CRM, model-agnostic, and vertical AI offerings.
- How regulators and clients scrutinize AI-generated work in consulting and professional services.
Source: Redmond Channel Partner Accenture Scales Microsoft Copilot Deployment Across Global Workforce -- Redmond Channel Partner
