Reimagining the enterprise: From copilots to agentic AI

Generative AI solutions commonly produce outputs based on patterns learned during training and retrieve and synthesize information from trusted data sources, providing users with responses and reasoning to support decision making. However, agentic AI tools go beyond retrieval by executing tasks, automating workflows and handling repetitive processes to reduce manual workload. They operate independently to chain actions into workflows and execute tasks across systems with minimal human input.

Generative AI can draft an email, generate code and pull answers from a company database. However, agentic AI can create a customer service agent that not only drafts responses but decides on further actions based on company policy while creating invoices, retrieving purchase order data, updating enterprise resource planning systems and contacting suppliers without human interaction.

Organizations are now shifting from isolated AI experimentation to enterprise-scale impact. A successful path to enterprise AI depends on the seamless integration of operational execution and governance oversight, creating a cohesive strategy that enables innovation at scale while ensuring responsible and compliant deployment. AI value realization requires a holistic approach spanning strategy, implementation and ongoing management to drive sustained business outcomes.

From an enterprise Copilot perspective, organizations commonly report productivity gains from two to 10 hours per employee per week. AI augments performance, improving work quality and reducing cognitive load by removing repetitive, mundane tasks. In addition, employees now expect AI as part of their work experience rather than merely a productivity tool, with recent Harvard Business Review research showing motivation drops by 11% and boredom increases by 20% when AI tools are removed.

At the enterprise level, growth is becoming increasingly nonlinear relative to headcount. ROI is strongest when AI adoption is tied to clear business outcomes and the company’s vision, and not just tool deployment. Organizations with high volumes of repeat tasks are looking for point-based AI solutions to assist with automations around that repetition to increase the overall quality of the work that augments staff and improves consistency across processes.

“For one of our clients, ROI unexpectedly came from invoice acceptance by their customers,” says Vollmer. “Therefore, it’s important to look beyond headcount and team member savings to measure AI’s value.” 

Identifying these use cases can be accelerated by using AI itself. For example, RSM utilizes a Microsoft Copilot AI tech stack agent with human-in-the-loop validation throughout the entire process. This agent helps identify quick wins and high-impact use cases in hours rather than weeks. The tool ingests strategic plans, process flow diagrams, meeting transcripts detailing pain points and challenges, and a list of existing technology inventories. The deeper you get with inventorying your applications, the more robust and accurate use cases can be.

“If you use UKG for human capital management, we look closely at which modules you are using, such as learning, payroll, onboarding and offboarding, and performance management,” says Rosenfeld. “The first step is to get the data. Our agent will then put that information in a clear, easy-to-understand way by mapping systems, processes and tasks. From there, we analyze your existing technology to identify available AI features and match them to real work tasks, focusing on practical, real-world use cases.”

These data-driven insights significantly enhance outcomes by outlining quick wins, potential impact, gaps, risks and dependencies.

In addition, a manual approach still applies to organizations that lack such advanced tools. In these circumstances, companies must start by identifying personas and pain points, particularly repetitive or time-consuming tasks and then evaluate where investments will deliver the greatest return. Ultimately, successful prioritization depends on aligning use cases with organizational readiness. Without sufficient people, technology or change readiness, even promising AI initiatives are unlikely to deliver value.

A key element of Microsoft Copilot is its respect for graph permission and not training on tenant content. It reflects an organization’s existing identity, permissions and data-sharing posture. As a result, securing AI starts with fixing oversharing through stronger governance, including reviewing SharePoint sharing and link settings. In addition, SharePoint Advanced Management, which becomes available with a Copilot license, provides reports on existing sharing links to identify where changes are needed to reduce risk.

In addition, if certain users or teams are frequently creating open sharing links, it’s important to understand why and address the behavior to mitigate risk. Microsoft provides robust security capabilities to support these efforts, including data security posture management (DSPM) for AI in the Purview suite. Copilot also mirrors and reflects zero-trust principles and honors existing sensitivity labels and data loss prevention policies, which continue to apply during user interactions.

An often-overlooked risk is poor, unstructured data quality, particularly redundant, obsolete and trivial content. AI tools, such as Copilot, will index these large volumes of unstructured data, especially at the enterprise level, resulting in noisy, outdated or inaccurate responses.

At the enterprise level, organizations are building Copilot agents with stronger guardrails around the content they ingest and how outputs are shared and exposed to users. AI does not introduce entirely new security challenges so much as it exposes existing gaps in data governance.

“The key is to treat Copilot agents as digital workers and tie their identities to the appropriate access control lists and set up a posture for monitoring where they are poking around,” says Grindahl. “It all breaks down to three principles: identity, access controls, and then logging or observability across the enterprise.”

Organizations must acknowledge that employees expect AI as part of their work. Rather than restricting its use, leaders should define what is permitted, designate trusted AI providers, review vendor data-handling practices and establish clear acceptable-use policies.

In addition, leaders should listen to employees in the field and understand the challenges they face. Combined with user education and enablement, these measures help protect sensitive data while supporting responsible and effective AI adoption. On the privacy front, organizations must also carefully review contracts with AI service providers to verify that the use of data aligns with the organization’s privacy standards.

Some additional favorable, action-oriented Copilot agents include:

• SharePoint Agent
  • Quickly turns large amounts of data into tangible information for users 
  • Creates a knowledge base to help IT teams answer customer questions or improve first-call response rates
• GitHub Copilot
  • Accelerates productivity with a generative AI agent coder 
  • Produces code and runs it through sentiment analysis using the open AI endpoints
• Copilot Eros
  • Produces customer-ready proposals within 30 minutes, including product and licensing costs, service fees, and detailed scope material (currently in beta stage)

Successful Microsoft AI adoption

Multiple factors contribute to successful Microsoft AI adoption, including:

• An overall process that is typically structured around a 30-60-90-day framework, including:
  • Tracking cohort growth
  • Establishing governance checkpoints
  • Conducting analytics and adoption reviews
  • Reviewing and refining communication plans
• Adoption maturity when users move beyond one-shot prompts to more conversational usage
• Users creating and sharing their own agents using natural language

Success metrics vary by organization, but dashboards to track adoption and business impact are essential. 

The takeaway

Adopting AI successfully requires a structured approach that balances adoption, governance and measurable impact. By leveraging tools like Copilot and AI agents, business leaders can implement a comprehensive strategy that drives productivity while establishing data privacy and compliance.

An effective framework monitors AI adoption through effective change management, establishes governance checkpoints and enables users with the right training and incentives, enabling your organization to achieve both immediate wins and long-term growth. Ultimately, AI success depends on aligning people, process and technology, while scaling responsibly in a secure environment. Therefore, looking outside the organization and working with qualified and experienced advisors to fully capture AI’s capabilities is often critical to driving stronger ROI across the enterprise.

Ready to get started? RSM’s experienced AI advisory team understands the enterprise AI journey and the critical elements needed to drive long-term value while mitigating risk. Contact our team to learn more about how Copilot and agentic AI can transform your people, business and operations.