Microsoft 365 Copilot limitations: What enterprises need to know before deployment

Microsoft launched Microsoft 365 Copilot in November 2023, positioning itself as the go-to enterprise AI layer. 

At the time of writing, Copilot is among the dominant AI tools in the enterprise. 85% of Fortune 500 companies use it to improve internal operations, and 66% of CEOs reported operational benefits and improved employee satisfaction following the adoption of Microsoft AI solutions. 

At the same time, despite a higher enterprise penetration than that of Claude or GPT, Copilot is struggling to become the default AI solution for organizations. 

Gartner interviewed Microsoft Copilot enterprise customers and found that only 5% of organizations moved from a pilot to larger-scale deployments. 2 out of 3 senior managers at B2B organizations surveyed by TechRadar also said they are not prioritizing investing in Copilot in 2025. 

This post will take a closer look at the limitations that hold enterprise companies back from full-scale Microsoft Copilot adoption. We will offer teams that already own Copilot licenses a roadmap for addressing these limitations.

Microsoft Copilot adoption rates and enterprise deployment challenges

In 2025, Bloomberg published a report that claimed Microsoft was struggling to sell Copilot to organizations. 

At the time of Bloomberg’s report, ChatGPT had over 800 million weekly active users. Copilot only had ~20 million. 

Despite native Windows integration, Copilot’s key selling point, it was still struggling to achieve adoption momentum matching its distribution reach across Microsoft 365’s 440 million paid subscribers. 

When the C-suite agrees to large-scale enterprise Copilot adoption (Accenture, Volkswagen, and Barclays signed deals for over 100,000 seats), there’s allegedly internal resistance because “employees want ChatGPT” over Copilot for daily AI assistance, undermining enterprise mandate effectiveness. 

On Reddit, software engineers also share testimonials of Copilot’s low productivity gains. 

Just this week, my (multi-billion dollar) software company downgraded our Copilot licenses from Enterprise to Business. We just aren’t seeing the benefits from it, company-wide. At least not in software development. For every minute Copilot saves me by writing a line of code, I have to spend 90 seconds to verify that it was right.

 A comment under a post covering the Bloomberg report

Ed Zitron, an independent fact-checker of the numbers AI vendors share, found that in August 2025, Microsoft 365 Copilot had 8 million paying subscribers—a 1.8% conversion rate from the total 440 million paid Microsoft 365 users. 

To understand why, despite Microsoft remaining the go-to enterprise office suite, Copilot is struggling with adoption, we will look into its limitations and explore ways that either Microsoft itself or its power users have found to work around these bottlenecks. 

Five limitations constraining enterprise Copilot deployments

Copilot is now embedded in nearly every Microsoft 365 workflow, making spreadsheets in Excel, designing PowerPoints, transcribing Teams calls, writing Outlook emails, and, most recently, even chatting with Copilot directly in Teams. 

Despite this comprehensive integration, organizations are still struggling to leverage Copilot for meaningful work. Reddit has plenty of anecdotal evidence that makes this clear. 

Analysis of enterprise deployments reveals five recurring limitation categories affecting Copilot adoption:

1. Low accuracy and reasoning capabilities lag behind state-of-the-art models
2. Inconsistent connections with the Microsoft 365 ecosystem. 
3. Accessibility and reliability challenges
4. Limited administration toolset
5. Lack of consistent long-term memory

A disclaimer: Microsoft is actively working on updating Copilot features. The points made in this article are true as of early November 2025. 

Since then, the company may have addressed these issues in new Copilot releases. We encourage readers to consult Copilot release wave reports to see what’s new. 

Limitation #1. Low accuracy 

In September 2025, a tweet noting that Copilot in Excel fails at simple math went viral and sparked discussions about the impact of these failures when the cost of error reaches the scale of millions. 

Similar complaints are popping up in other Copilot use cases. Here’s a brief rundown. 

• In one case, Copilot did a poor job transcribing a Teams call, failing to capture what was said accurately and correcting itself only once. 

• Other users complain about the recent versions of Copilot, powered by GPT-5 getting worse at coding. They claim that their productivity improved after turning Copilot off. 

• A different reviewer was so frustrated with hallucinations and a lack of advanced reasoning that they called Copilot an “unbelievably stupid lobotomized ChatGPT”. 

Why Copilot reasoning is falling behind other LLMs

It’s not instantly clear why people prefer ChatGPT to Microsoft Copilot, considering both use OpenAI models. 

In October 2025, Microsoft Copilot signed an agreement with Anthropic to integrate Claude into Copilot as well, since it has been outperforming GPT on tasks like coding and spreadsheet management. 

But the perception that Copilot is “less intelligent” than the out-of-the-box models it relies on is widespread.

Here’s why teams likely feel the downgrade. 

• Context window limitations. Copilot Chat supports a 64k context window. Considering that state-of-the-art LLMs now have 1 million+ context windows, the loss of awareness will be noticeable. 

• Orchestration tradeoffs. Microsoft runs Prometheus and Orchestrator Pipelines that ground queries before routing them to the LLM. It adds a layer of complexity that increases the risk of data distortion and a mismatch between the expected and actual LLM outputs. 

• Heavy guardrails. The focus on enterprise-grade adoption requires Copilot to fine-tune its LLMs to ensure they are highly compliant. In machine learning, there’s a proven inverse relationship, aka ‘safety tax’, between the safety and accuracy of generative AI models. 

Troubleshooting Copilot reasoning shortcomings

Copilot’s orchestration layers, regulatory guardrails, and limited context windows improve the compliance of AI tools compared to out-of-the-box LLMs but reduce AI’s reasoning capability compared to base models. 

Since enterprise teams want to both stay compliant and deliver reliable, high-quality output, the only option is to design workflows that work around Copilot’s limitations. 

Consider introducing these strategies into your AI-augmented workflows. 

• Implement mandatory human verification for high-stakes outputs. Require manual review and validation for any Copilot-generated content involving financial calculations, legal documents, code deployments, or business-critical data analysis. 

• Use native applications for precision-critical tasks. Rely on Excel’s built-in formulas rather than Copilot for mathematical calculations, use dedicated IDE debuggers instead of Copilot for code validation, and leverage specialized tools for transcription accuracy. Reserve Copilot for ideation, summarization, and drafting where minor errors have lower consequences.

• Provide extensive context in every interaction. Work around Copilot’s context window limitation by explicitly including all relevant information, prior decisions, and constraints in each prompt. 

• Benchmark Copilot against alternative tools for critical use cases. Test Copilot’s performance against ChatGPT, Claude, or specialized copilots like Cursor for specific organizational tasks. When accuracy gaps become significant, consider using alternative AI tools for those workflows and revert to Copilot only for areas where M365 integration provides clear value, despite the trade-offs.

Limitation #2: Integration inconsistencies with the rest of the Microsoft 365 ecosystem 

Microsoft’s ambition to get more Microsoft 365 users on board with Copilot is making the UX inside the Microsoft 365 ecosystem worse. 

Until recently, users could use the Office.com page to access their recent work and apps in one click. 

After an update, the home page features the Copilot Chat window instead. 

While users can still access documents through alternative navigation paths (application-specific URLs, OneDrive interfaces, or Teams file views), removing direct homepage access increases interaction latency for the common workflow of resuming work on recent files.

These UI tweaks disrupt standard workflows and force users to interact with AI even where a purely rule-based interface would be effective. 

Integration inconsistencies are common, too. 

On Reddit, users shared that Copilot frequently fails to connect to SharePoint or Outlook and has no idea what version of Microsoft Office the organization is using. 

How enterprise teams can address the limitation

Here are the workarounds we recommend to ensure your team is not dependent on Microsoft 365 UI changes or inconsistent integrations. 

Limitation #3: Service availability and reliability

In enterprise AI adoption, performance consistency is crucial for error-free operation. Copilot has repeatedly failed to deliver it. 

In September 2025, the company reported three outages that returned 403 messages in response to all queries. 

In October 2025, a fraction of users who updated Copilot were fully disconnected from the system’s internal memory. One user described how this incident led to the loss of “the entire company’s worth of culture and operations.” 

How enterprise teams can troubleshoot Copilot accessibility issues

The general rule of thumb for managing Copilot training files would be to ensure that all the documents you are training Copilot on can be recovered in the event of an outage. Maintain critical institutional knowledge, SOPs, and operational procedures in traditional knowledge bases (SharePoint, Confluence, internal wikis) instead of relying solely on Copilot’s memory. 

This way, operations managers maintain business continuity during outages and protect against data loss from system updates.

To facilitate the recovery of your automations after an outage, deploy uptime monitoring for Copilot services with automated alerts, and create documented escalation paths to Microsoft support for 403 errors. 

Include rollback procedures for updates that cause memory disconnection or data access problems.

Limitation #4: Limited agent governance tooling for enterprise-scale deployments

Despite heavily emphasizing guardrails, Microsoft Copilot is not immune to the errors of other agentic releases—namely, the risk that agents will wreak havoc with limited human supervision. 

In early 2025, Microsoft was among the pioneers in fueling agentic hype. During the Q2 earnings call, Satya Nadella said the company is on the road to “making it as simple to build an agent as it is to create an Excel spreadsheet”. 

Copilot does have a diverse agentic toolset. Teams Toolkit lets non-technical teams quickly create declarative agents for simpler automations. Copilot Studio hosts agentic capabilities for more complex workflows. 

But the tools to manage these agents are not as robust. Here are a few gaps enterprise teams will find significant as their agentic infrastructures evolve. 

Missing controls for agent expiry and duplication

To manage time-sensitive agentic workflows at scale, enterprises would benefit from expiry controls for agents, which are not yet available or part of the feature map. 

At the time of writing, there’s no duplication management to ensure that two siloed teams don’t create agents that overwrite each other’s automations. 

Approval workflows are not built to scale 

In Copilot’s default agent approval workflow, all agents go into a human-in-the-loop approval queue and require admins to manually inspect the workflow, assess risks, and green-light the posting of the agent. 

This process is sustainable when organizations are running small-scale pilots with a few dozen active agents. Once the scale reaches hundreds of agents deployed each month, a fully manual admin approval will create an operational bottleneck. 

Maintaining running agents is also resource-consuming. At the time of writing, changes to running agents will return them to the approval cycle, increasing operational overhead. 

How to troubleshoot agent management in Copilot

In the future, Copilot will likely solve both issues by designing admin APIs that help IT renumerate agents programmatically and introduce guardrails at scale. 

These APIs would support ownership auto-transfer, bulk expiry, duplicate management, and agent approval. They would also enable policy-as-code enforcement and help organizations build unified agent management standards. 

Until Microsoft delivers these platform capabilities, organizations can implement compensating governance controls through the following patterns:

To avoid agent sprawl and prevent high admin sprawl, enterprise teams should treat Copilot agent deployment as infrastructure provisioning. 

Implementing governance controls before scaling beyond the pilot phase helps reduce technical debt and operational chaos as the agent count grows.

Limitation #5: Memory and personalization

Disclaimer: At the time of writing, Microsoft’s new update bringing long-term memory into Copilot still lacks widespread adoption and documented reviews. 

The added feature allows users to save details they want Copilot to retain across conversations and should eliminate the need to repeatedly provide the assistant with context. 

Considering that memory remains a universal unsolved problem for all LLMs, it’s unlikely that the new Copilot fully addresses the issue, which is why we kept it in the post. 

Context retention failures across conversation sessions have been a recurring issue for Copilot users, forcing them to re-establish organizational context, project details, and technical requirements with each new interaction. 

A Reddit user described how Copilot kept forgetting even the details it seemed to have committed to memory. 

Copilot’s memory architecture operates across two distinct persistence layers with different retention characteristics. 

Long-term memory captures user communication style preferences and frequently referenced topics for cross-session personalization. 

Session-specific context tracking maintains detailed conversation history within individual interactions but purges this data upon session termination, preventing context carryover between discrete work periods.

To be truly effective, Copilot would have to memorize nuanced details related to a narrow subset of tasks and retain user data across sessions, which it is not capable of at the moment. 

It’s also worth noting that Copilot is an ecosystem of tools with separate memory stacks. Microsoft 365 Copilot, Copilot Chat, and GitHub Copilot have limited cross-product communication, so context duplication will persist across different versions of the tools. 

Troubleshooting Microsoft Copilot memory limitations

Copilot’s fragmented memory architecture, where context resets between sessions and remains isolated across different product variants, creates reliability challenges for enterprise users who need consistent, context-aware assistance. 

Organizations can mitigate these limitations by implementing workarounds that externalize critical information and adjust workflows to account for the stateless nature of AI copilots. 

Document critical context in persistent storage systems

Maintain project briefs, technical specifications, and workflow requirements in SharePoint, OneNote, or Teams channels that Copilot can reference via search rather than relying on conversational memory. 

Use consistent file naming and tagging conventions so Copilot can retrieve this information when needed, creating a reliable external memory layer.

Implement session continuity protocols within teams

Establish practices where team members explicitly reference prior work in new Copilot sessions by linking to previous conversations, documents, or outputs. 

Create standardized prompt templates that include essential context upfront, reducing dependence on Copilot’s ability to recall information across sessions or product boundaries.

Treat each Copilot product as a separate tool with an isolated context

Recognize that Microsoft 365 Copilot, Copilot Chat, and GitHub Copilot operate independently with no shared memory. 

Design workflows that don’t assume context transfer between products. Explicitly provide the necessary background when switching between Copilot variants, and maintain documentation that bridges these silos for human users.

Microsoft Copilot vs custom AI solutions: Which one should your organization use? 

Considering that Microsoft Copilot does not rely on proprietary foundation models, enterprise consumers who were frustrated by its performance are switching to building custom assistants instead. 

According to user experience, custom chatbots tailored to the organization’s specific needs and built with the latest available models are more helpful than Copilot. 

However, building a custom copilot or using an off-the-shelf platform like AI Copilot is a granular choice for each organization. 

It depends on how tightly your workflows are embedded in Microsoft 365, how familiar your engineers are with building generative AI solutions from scratch, and how complex your workflows are. 

Bottom line

Theoretically, Microsoft Copilot would have been precisely the tool the enterprise C-Suite needs for managing enterprise-grade workflows. It aims to connect all Microsoft 365 tools, give teams powerful out-of-the-box automations, like recording and transcribing Teams calls, and empower employers to build AI agents of their own. 

However, Copilot has yet to find the right blend of strict, compliant guardrails that would not make it less reasonable than the models it uses under the hood—GPT and Claude. There’s little administrative overhead for agentic workflows, and the system itself lacks the reliability to run for long periods unsupervised. 

Performance, governance, and memory limitations, paired with limited flexibility and Copilot’s cost-per-seat billing, encourage enterprise teams to consider building custom internal Copilots instead. 

With a custom copilot, enterprise teams control the model and memory, tailor prompts to their domain, and integrate natively with internal systems.  A custom copilot also allows for shifting from per-seat licenses to usage-based costs, which reduces TCO in the long run. 

Some decide not to make a binary choice at all. 

Keeping Microsoft Copilot for cross-suite productivity and standard workflows and complementing it with custom copilots for proprietary processes and data is a pragmatic approach that delivers both speed and scale without sacrificing governance, flexibility, or cost control.