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Total Economic Impact
Cost Savings And Business Benefits Enabled By Copilot Studio
A Forrester New Technology Projected Total Economic Impact Study Commissioned By Microsoft, September 2025
Total Economic Impact
A Forrester New Technology Projected Total Economic Impact Study Commissioned By Microsoft, September 2025
Organizations are under ever-increasing pressure to innovate faster, grow revenue, and streamline operations. Two years ago, companies began to use genAI to meet goals at the individual, user level. Now, the emphasis is increasingly shifting to using agentic AI to facilitate enterprisewide, business process transformation. Microsoft Copilot Studio lets organizations multiply capacity by integrating existing data systems with AI to do work or run processes on a user’s behalf. By doing so, organizations can evolve more quickly and inexpensively — achieving their goals and empowering employees.
Forrester defines agentic AI as “advanced AI systems, powered by foundation models, that demonstrate a high degree of autonomy, intentionality, and adaptive behavior, extending beyond the capabilities of traditional and deterministic AI agents. These systems can plan, make complex decisions, and adapt to changing environments, thereby driving toward the highest levels of autonomy in complex process execution.”1 Forrester also differentiates agentic AI from prior technologies, noting, “Building agentic AI into software systems across the enterprise will form the foundation of new knowledge economies and markets. Agentic AI is different because of its unique combination of capabilities that take it beyond existing approaches to automation and insights. What distinguishes agentic AI is that it can plan strategically, reason through complex scenarios, collaborate between different components, and leverage external tools to achieve objectives with remarkable autonomy.”2
Copilot Studio is an agent development platform that enables professional developers (both full stack and natural language) and individual business users (e.g., makers) without technical backgrounds to create AI-powered agents. Using natural language and drag and drop tools, people can build agents that integrate with their organization’s information repositories and business workflows without code.
To explore the above trends and their financial impact, Microsoft commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying Copilot Studio.3 The purpose of this study is to provide readers with a framework to evaluate the potential financial impact of Copilot Studio on their organizations.
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed 13 decision-makers and surveyed 400 respondents with experience using Copilot Studio. For the purposes of this study, Forrester aggregated the interviewees’ and survey respondents’ experiences and combined the results into a single composite organization, which is a $6.25 billion organization with 25,000 employees.
Interviewees said that prior to using Copilot Studio, their organizations struggled across business functions with information overload, unstructured data sources, inconsistent content curation and triaging, and excess operational costs due to overburdened teams. They also said that the widespread deployment of agentic AI was exceedingly difficult if they used only deep-tech tools and methodologies compared to the natural language, no-code features of Copilot Studio. These limitations led to unsanctioned tool use, compliance risks, poor customer satisfaction, missed business opportunities, and an inability to scale.
After the investment in Copilot Studio, the interviewees’ organizations improved their data accuracy and compliance and relieved employee workloads by streamlining manual, repetitive, and time-consuming tasks. They could also deploy agents more broadly because more people could use the natural language/no-code capabilities inside of Copilot Studio. Key results from the investment include revenue generation, operational efficiencies, and onboarding acceleration.
Quantified projected benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
Go-to-market transformation increases top-line revenues by up to 1.4%. As sales and marketing teams leverage agents with Copilot Studio to identify leads and engage customers, the composite organization experiences up to a 1.5% increase in qualified opportunities by streamlining marketing campaign creation, up to a 1.0% increase in win rate with increased sales productivity and high-quality sales proposals, and up to a 1.1% increase in customer retention rate by identifying and addressing customer needs. Taken together and applying a net margin of 7.6%, these improvements increase the composite organization’s projected net income by up to $11.1 million over three years.
Operational transformation decreases total expense by up to 0.2%. Across functional areas such as sales, marketing, IT, finance, procurement, and human resources, Copilot Studio users experience operational efficiencies by using AI agents for tasks that are manual, repetitive, or high cost with low returns. The composite organization can also reduce external costs, such as those paid to agencies and for procurement. The composite organization’s net margin improves from 7.6% to as much as 8.5%. The projected decrease in expenses is worth between $45.6 million to $88.0 million over three years to the composite organization.
People and organization transformation accelerates new hire onboarding time by up to 25.0%. The composite organization increases employee productivity. Through streamlined recruitment and onboarding with AI agents as well as easier access to knowledge through organized repositories and accurate data, the composite organization accelerates onboarding and promotes employee satisfaction. The projected acceleration in onboarding is worth between $880,000 to $1.6 million over three years to the composite organization.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
Improved security and compliance. Copilot Studio, with Microsoft’s broader security and compliance frameworks, enables the composite organization to improve compliance with internal security policies and protect sensitive data.
Data quality improvements and high-quality information repositories. With Copilot Studio, the composite organization improves data quality by curating accurate knowledge repositories, enabling users to navigate environments with high informational integrity.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
Copilot Studio subscriptions. The composite organization incurs subscription costs based on the number of tenants licensed and capacity packages consumed, which increase as adoption of Copilot Studio expands across the organization and more nontechnical business makers begin to create and use AI agents. Over three years, the composite pays $3.1 million in subscription costs.
Planning, development, and ongoing maintenance. As the composite democratizes the use of agentic AI with Copilot Studio, IT developers and nontechnical business makers begin creating AI agents for organizationwide, departmentwide, and personal use. The composite also leverages professional services costs to optimize its use. Over three years, the composite spends $8.2 million on planning, development, and ongoing maintenance costs.
Training and discovery. Employees interacting with agents undergo initial training to become familiar with the process of creating agents. They also engage in ongoing discovery through trial and error. Over three years, the composite spends $13 million on training costs.
Forrester modeled a range of projected low-, medium-, and high-impact outcomes based on evaluated risk. This financial analysis projects that the composite organization accrues the following three-year net present value (NPV) for each scenario by enabling Microsoft Copilot Studio:
Projected high impact of a $76.4 million NPV and projected ROI of 314%.
Projected medium impact of a $52.6 million NPV and projected ROI of 216%.
Projected low impact of a $25.7 million NPV and projected ROI of 106%.
Projected increase in profit margin due to internal and external spend reduction
Projected return on investment (PROI)
Projected benefits PV
Projected net present value (PNPV)
Total costs
| Role | Industry | Headquarters | Number of employees |
|---|---|---|---|
|
Senior manager of technology architecture Director of data and AI Global solutioning lead |
Professional services | United States | 50,000 |
|
Assistant vice president (VP) of IT research technology VP, IT client success and business operations |
Education | United States | 16,000 |
| Digital product strategy senior manager | Financial services | United Kingdom | 7,000 |
| IT director | Manufacturing | United States | 36,000 |
|
Solutions architect Senior strategic product manager of digital customer interaction |
Financial services | Netherlands | 50,000 |
| Director of the global Microsoft 365 Copilot deployment program | Manufacturing | France | 100,000 |
| Technology platforms director | Professional services | United Kingdom | 400,000 |
|
Power platform specialist Senior software developer |
Professional services | United States | 7,000 |
Before Copilot Studio, interviewees experienced challenges including the difficulty of managing and leveraging information, the growing pains of scaling technology, and the tensions between manual processes and automation’s promise. These challenges hindered operational efficiency, employee and customer satisfaction, and growth and innovation potential with agentic AI. Legacy approaches to staffing, knowledge management, and customer engagement impeded progress and pointed to a pressing need for integrated solutions that addressed knowledge fragmentation, resource allocation, and process optimization, while also maintaining ethical and governance standards in the face of technological innovation.
Interviewees and survey respondents noted how their organizations struggled with common challenges, including:
Inefficiencies in knowledge retrieval due to information overload and low search result trust. Interviewees struggled to find relevant and trustworthy content due to the vast volumes of unorganized data in enterprise search systems. Outdated documents and multiple file versions cluttered outputs, leading to inefficiencies with locating up-to-date and high-quality information, especially when preparing sales proposals or project deliverables. Knowledge scattered across multiple repositories and managed by different teams reflected varying standards, styles, and quality. This meant that scaling knowledge access across organizations required significant effort to identify, clean, and integrate new information sources.
Customer frustration and low satisfaction. Notably, employees spent time searching for information during customer interactions, which in turn lengthened and stagnated the interactions and led to customer dissatisfaction. The solutions architect at a financial services organization noted the average customer satisfaction rating for chat was around three out of five, partly due to users getting stuck or bypassing the chatbot. The interviewee said: “We do see that the ratings are on the lower side. We see that people tend to give the ratings based on their frustration because they got stuck somewhere else in the journey before they came to chat. We believe that if we have an optimized genAI chat interaction, people will feel more helped by the mechanism before being connected to a live adviser if needed.” Interviewees anticipated that Copilot Studio would improve conversation intuitiveness and helpfulness, enhancing customer satisfaction.
Inability to scale. Interviewees noted that their previous solutions were difficult to integrate at scale. They said that Copilot Studio offered a more scalable and flexible solution for widespread deployment due to its ease of use and democratization among nontechnical makers.
Use of unsanctioned tools. The assistant VP of IT research technology in education mentioned that researchers and other university members were using unsanctioned genAI tools without IT oversight, raising concerns about data governance. Student workers manually triaged IT service desk tickets, leading to delays outside business hours. Without AI-driven feedback loops, outdated or incorrect content on university websites often went unnoticed until manually reviewed.
Missed revenue opportunities. Interviewees in the manufacturing and financial services industries noted that sales and marketing generated thousands of leads annually, but not all were followed up due to limited sales bandwidth. They highlighted the potential of using agents to prioritize high-value leads, enabling sales teams to focus on the most promising opportunities and drive incremental revenue.
Labor-intensive, manual processes. Interviewees said that many of their internal workflows, particularly in finance, HR, and IT, were highly manual and data heavy with high fixed labor costs for repetitive tasks, limited capacity to scale without increasing headcount, and bottlenecks in follow-up processes when anomalies were flagged due to small teams. Tasks were manual and time-consuming; they also required significant human effort, depended on external services, and had limited scalability. Interviewees noted that Copilot Studio offered an opportunity to shift employees from repetitive tasks to more strategic roles and automate manual tasks involving identifying budgeting inefficiencies or invoicing discrepancies.
Interviewees chose Microsoft Copilot Studio to address a range of operational, strategic, and technical challenges. They identified Copilot Studio as a good fit because of its integration with existing Microsoft infrastructure, its ease of use, and its alignment with organizational goals on efficiency, scalability, and governance. For many interviewees, Copilot Studio was an obvious choice for their organizations due to existing investments in Microsoft 365 as well as Microsoft 365 Copilot and its interoperability with broader platforms. Other major factors that contributed to interviewees’ investment decision include:
Ease of deployment and natural language accessibility. The global solutioning lead in professional services described how a junior offshore developer built a knowledge agent in just a few days, with most of the effort focused on governance and deployment rather than technical complexity. This low barrier to entry enabled rapid experimentation and iteration. The technology platforms director in professional services highlighted the democratization of agent creation across a wide base of business professionals. The power platform specialist in professional services remarked: “Natural language is the way to go. It doesn’t require a developer, and you don’t need to have IT involved all the time. We want people to understand they can build this themselves with best practices and some light guidance.”
With faster development cycles and a reduced reliance on technical staff due to low barrier to entry and no-code capabilities, the solutions architect in financial services estimated that using out-of-the-box genAI features from Copilot Studio could eliminate the need for two full-time developers currently maintaining a custom genAI solution.
Governance and scalability. The director of the global Microsoft 365 Copilot deployment program in manufacturing emphasized that Copilot Studio enabled structured agent development while allowing their organization to maintain control over access and data privacy. Several interviewees saw the ability to scale use through nontechnical business users while maintaining IT oversight as a key advantage.
Security and compliance. Interviewees said that Copilot Studio integrated well with their organizations’ existing security and compliance frameworks, reducing risks like IP leakage.
Interviewees said that their organizations selected Copilot Studio for its technical capabilities and strategic fit, enabling them to prepare for a future where agentic AI plays a central role in business operations.
Base: 419 decision-makers who use or are interested in using Copilot Studio or Microsoft Agent Builder to build AI agents at their organizations
Source: A commissioned study conducted by Forrester Consulting on behalf of Microsoft, June 2025
Based on the interviews and survey, Forrester constructed a TEI framework, a composite company, and an ROI analysis that illustrates the areas financially affected. The composite organization is representative of the interviewees’ and survey respondents’ organizations, and it is used to present the aggregate financial analysis in the next section. The composite organization has the following characteristics:
Description of composite. The global organization has $6.25 billion in annual revenue, 25,000 employees, and a net profit margin of 7.6%.
Deployment characteristics. The composite organization begins using the solution with two initial agents built by IT developers, which increases to 25 agents in Year 1, 75 agents in Year 2, and 150 agents in Year 3. These agents are used more widely across the organization. Nontechnical business makers build 150 agents in Year 1, 200 agents in Year 2, and 275 agents in Year 3 that have personal or department-specific uses. Initially, 15% of employees use agentic AI at the composite organization, which increases to 25% in Year 1, 50% in Year 2, and 75% in Year 3.
$6.25 billion annual revenue
25,000 employees
7.6% net profit margin
| Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|
| Total projected benefits - Low | $5,853,750 | $19,308,750 | $38,324,525 | $63,487,025 | $50,073,018 |
| Total projected benefits - Mid | $12,950,000 | $31,871,250 | $51,720,000 | $96,541,250 | $76,970,605 |
| Total projected benefits - High | $20,054,320 | $40,008,545 | $65,805,000 | $125,867,865 | $100,736,384 |
Evidence and data. Fully autonomous AI agents are on the horizon, and B2B companies that begin integrating AI agents in their go-to-market operations now will see future benefits, such as the increased ability to work with more technical agents and uncover insights that advance their business objectives. By using AI agents to deliver actionable, contextual insights, organizations can elevate their marketing and sales efforts, facilitating more meaningful and relevant interactions rather than simply increasing scale. AI agents can support and enhance human teams, equipping them with innovative ways to engage buyers and drive successful customers outcomes. Both business-to-employee (e.g., agents that salespeople use) and business-to-customer (e.g., agents that interact directly with prospects and customers) use cases contribute to increased revenue.4
Interviewees and survey respondents experienced revenue increases due to improved customer engagement and strategic initiatives in business-to-employee use cases with Copilot Studio through Microsoft 365 Copilot. These improvements led to improved metrics such as increased customer retention, cross-sell and upsell opportunities, and close rates.
Business-To-Employee Agents
The technology platforms director in professional services described a knowledge repository agent that supported deliverable creation by surfacing relevant documentation across global teams. This agent enables teams to create more deliverables and higher-quality outputs, which they expect to lead to increased revenue. The interviewee said, “A 10% increase in revenue would be a very good outcome.”
The director of data and AI in professional services discussed a scheduling agent that assigns the best available resources to project delivery teams. By improving resource matching, they expect the agent to increase utilization rates by 5 to 10 percentage points, contributing to revenue growth.
The director of data and AI in professional services also described an agent that simplifies access to strategic funding programs, which can directly enable new client engagements: “We want to get funding for strategic topics for our clients, but there’s a lot of dependency on the size of the product, project topic, and client category. We want to give our sellers an agent whom they could ask, ‘I have a project this size, this is the client, this is the approach,’ and then [the agent] tells [the seller] for which funding [the client] is eligible.” This capability helps sellers identify funding opportunities they might otherwise miss and enables early-stage workshops or discovery sessions. The interviewee expects the genAI agent to help them meet KPIs by streamlining the funding application process.
The global solutioning lead in professional services shared the development of an RFP response agent that helped generate proposal content. This agent improved the quality of responses and the interviewee expects it to help with win rates, save 9 to 10 hours per week for architects working on responses, and reduce costs per lead.
Business-To-Customer Agents
The senior strategic product manager of digital customer interaction in financial services described an agent built to handle customer interactions. The agentic AI power chatbot currently handles 60% of customer interactions with a future goal of reducing escalations by 20% by the end of 2026. Each escalated interaction costs €14, making even small reductions significant for their organization. The interviewee’s team plans to use conversational AI to increase engagement with younger customers and promote products like mortgages and insurance through personalized digital assistants, thereby impacting revenue: “We can be much more precise in offering sales options like next best action for customers ... especially if you want to grow in particular markets, like mortgages.”
In addition to a product-specific focus, this same interviewee described a genAI chat function that increased interaction volume and therefore increased product and promotion exposure across customer-facing interfaces: “[The chat] is available on every page in the app as well as the overview. We have about three-and-a-half million unique visits per day in the app, which means that we have a lot more exposure when we have it on every page, on the overview, and on the starting page of the app when you log in. And we expect that this will definitely increase the number of interactions to 500,000 unique interactions per month with chat.”
The digital product strategy senior manager in financial services described plans to move virtual assistants from service into sales, enabling proactive engagement with customers browsing the website. The goal is to authenticate users and offer personalized product recommendations based on recent activity, such as mortgages or loans. They said, “We want to upsell in areas that maybe they’ve been browsing from a website perspective … and foresee their needs in the future.” This interviewee expects that the virtual AI assistant will increase lifetime customer value through cross-selling and upselling.
The solutions architect in financial services described conducting a buyer intent analysis using a mobile application chat agent with Copilot Studio to prioritize and focus sales efforts. The interviewee said: “This could be a channel that’s more personalized and also much more capable of understanding what kind of products are useful for the particular customer at that moment in time. So I think we can be much more precise in offering sales options like next best action for customers.”
| Median pre-AI agent improvement | Median post-AI agent improvement | Median percentage improvement | |
|---|---|---|---|
| Cost per lead | $250 | $222.50 | 11.0% |
| Number of opportunities per month* | 1,500 | 1,554 | 3.6% |
| Close rate (percentage points)* | 24% | 26.8% | 11.7% |
| Average deal size* | $2,000 | $2,044 | 2.2% |
| Deal cycle (days)* | 11 | 10.4 | 5.4% |
| Customer retention (percentage points)† | 55% | 60% | 9.1% |
| Increased customer satisfaction (points)† | 5.0 | 6.5 | 29.2% |
| Reduced time to resolve customer concerns (minutes)† | 15.0 | 12.8 | 14.5% |
| Reduced daily call volume† | 1,500 | 1,336 | 10.9% |
Base: 43 decision-makers who indicated that agents built have / will improve marketing processes including reducing cost per lead
*Base: 114 decision-makers who have or expect to use AI agents for business-to-customer use cases and who have experienced or expect to experience top-line revenue increases
†Base: 141 decision-makers who have or expect to use AI agents for business-to-customer use cases
Source: A commissioned study conducted by Forrester Consulting on behalf of Microsoft, June 2025
Base: 114 decision-makers who have or expect to use AI agents for business-to-customer use cases and who have experienced or expect to experience top-line revenue increases due to AI agents
Source: A commissioned study conducted by Forrester Consulting on behalf of Microsoft, June 2025
Modeling and assumptions. Based on the interviews, Forrester assumes the following:
Before using Copilot Studio, the composite has $6.25 million in baseline revenue, with revenue from new sales at 20% and average contract value at $100,000.
The sales win rate before Copilot Studio is 20% and results in 62,500 opportunities.
With Copilot Studio, the number of opportunities increases up to 1.50%; the win rate increases up to 1.00%; and customer retention increases up to 1.10%.
The net profit margin is 7.6%.
Results. This yields a three-year projected PV ranging from $3.6 million (low) to $11.1 million (high).
Increased revenue with Copilot Studio
| Ref | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| A1 | Baseline revenues from won opportunities before Copilot Studio | Composite | $6,250,000,000 | $6,250,000,000 | $6,250,000,000 | |
| A2 | Revenue from new sales before Copilot Studio | Composite | 20% | 20% | 20% | |
| A3 | Average contract value | Composite | $100,000 | $100,000 | $100,000 | |
| A4 | Sales win rate before Copilot Studio | Composite | 20% | 20% | 20% | |
| A5 | Qualified opportunities before Copilot Studio | A1/A3/A4*A2 | 62,500 | 62,500 | 62,500 | |
| A6Low | 0.00% | 0.30% | 0.50% | |||
| A6Mid | Increase in qualified opportunities with Copilot Studio | Interviews and survey | 0.30% | 0.60% | 1.00% | |
| A6High | 0.60% | 1.10% | 1.50% | |||
| A7Low | $0 | $3,750,000 | $6,250,000 | |||
| A7Mid | Subtotal: Incremental revenue with increased qualified opportunities | A3*A4*A5*A6 | $3,750,000 | $7,500,000 | $12,500,000 | |
| A7High | $7,500,000 | $13,750,000 | $18,750,000 | |||
| A8Low | 0.00% | 0.30% | 0.50% | |||
| A8Mid | Increased win rate with Copilot Studio | Interviews and survey | 0.20% | 0.50% | 0.80% | |
| A8High | 0.40% | 0.70% | 1.00% | |||
| A9Low | 20.00% | 20.06% | 20.10% | |||
| A9Mid | Win rate with Copilot Studio | A4*A8+A4 | 20.04% | 20.10% | 20.16% | |
| A9High | 20.08% | 20.14% | 20.20% | |||
| A10Low | $0 | $3,761,250 | $6,281,250 | |||
| A10Mid | Subtotal: Incremental revenue from increased wins | (A9-A4)*(A5+A5*A6)*A3 | $2,507,500 | $6,287,500 | $10,100,000 | |
| A10High | $5,030,000 | $8,846,250 | $12,687,500 | |||
| A11 | Revenue from existing customers | A1*(100%-A2) | $5,000,000,000 | $5,000,000,000 | $5,000,000,000 | |
| A12Low | 0.000% | 0.300% | 0.500% | |||
| A12Mid | Increased customer retention rate | Interviews and survey | 0.200% | 0.400% | 0.700% | |
| A12High | 0.400% | 0.800% | 1.100% | |||
| A13Low | $0 | $15,000,000 | $25,000,000 | |||
| A13Mid | Subtotal: Incremental revenue from retained customers | A11*A12 | $10,000,000 | $20,000,000 | $35,000,000 | |
| A13High | $20,000,000 | $40,000,000 | $55,000,000 | |||
| A14Low | $0 | $22,511,250 | $37,531,250 | |||
| A14Mid | Total revenue | A7+A10+A13 | $16,257,500 | $33,787,500 | $57,600,000 | |
| A14High | $32,530,000 | $62,596,250 | $86,437,500 | |||
| A15Low | 0.00% | 0.36% | 0.60% | |||
| A15Mid | Increased revenue with Copilot Studio | (A7+A10+A13)/A1 | 0.26% | 0.54% | 0.92% | |
| A15High | 0.52% | 1.00% | 1.38% | |||
| A16 | Net profit margin | Composite | 7.6% | 7.6% | 7.6% | |
| AtLOW | $0 | $1,710,855 | $2,852,375 | |||
| AtMID | Business transformation: Go to market | (A7+A10+A13)*A16 | $1,235,570 | $2,567,850 | $4,377,600 | |
| AtHIGH | $2,472,280 | $4,757,315 | $6,569,250 | |||
| Three-year projected total: $4,563,230 to $13,798,845 | Three-year projected present value: $3,556,961 to $11,114,767 | |||||
Evidence and data. Interviewees described how specific agent use cases, ranging from automated invoice review to enhanced knowledge retrieval and efficient resource allocation, drove measurable gains in productivity, external cost savings, and operational efficiency.
The IT director in manufacturing shared how an agent built with Copilot Studio transformed their freight invoice review. The interviewee’s organization processes more than 100,000 freight invoices annually, which were previously sampled manually by a team of 10. The AI agent now reviews invoices, flags anomalies, and enables targeted human follow-up. The pilot has already shown favorable results in identifying surcharges and contract mismatches. The interviewee anticipated saving several million dollars annually just from North American bulk truck freight.
The director of the global Microsoft 365 Copilot deployment program in manufacturing reported that one use case using seven agents resulted in a 10% productivity increase and reduced their external agency costs. Their company also anticipates reducing external services costs by up to 50% over three years, including translation, compliance review, and document classification. The interviewee shared, “We don’t see agents replacing people, but we do see them reducing our reliance on external services.”
The digital product strategy senior manager in financial services described a virtual assistant that supports fraud-related queries, which number between 8,000 and 9,000 per month. The interviewee expects the assistant to reduce resolution time by 20% and decrease costs by 30% by shifting interactions from phone to chat.
Several interviewees highlighted multiple knowledge management use cases. The technology platforms director in professional services noted that one AI agent saved 4 hours per month per user. Another AI agent reduced time spent searching for knowledge by 20%. These efficiencies helped teams produce more deliverables with higher quality.
The senior manager of technology architecture in professional services discussed a scheduler agent that helps assign the best available resources to project delivery teams. With hundreds of schedulers across the organization, they expect the agent to save significant time, resulting in less time on the bench and more reallocation of human effort toward higher value work.
The global solutioning lead at the same professional services organization described a procurement agent that generates RFPs and analyzes responses. They expected this agent to lower procurement costs by 20%.
The director of data and AI at the same organization also shared that a code review agent reduces the time to review 100 lines of JSON code from 10 minutes to just 10 seconds.
The assistant VP of IT research technology in education reported that across all Copilot Studio use cases, users are saving between 2 to 5 hours per month. Additionally, the organization has reduced applicable IT spend on development, maintenance, and support by 30% to 40%, including saving one DevOps role out of a team of eight.
Survey respondents reported expecting a 10.6% improvement in external cost savings using agents.
| Department | Median percentage time saved |
|---|---|
| IT | 25.7% |
| Marketing | 10.2% |
| Customer support | 8.4% |
| Sales | 4.1% |
| HR | 3.3% |
| Finance | 2.7% |
| Legal | 0.4% |
Base:151 decision-makers who have or will use AI agents in IT, marketing, customer support, HR, finance, and legal departments
Source: A commissioned study conducted by Forrester Consulting on behalf of Microsoft, June 2025
Base: 255 decision-makers who use or expect to use AI agents in B2B use cases
Source: A commissioned study conducted by Forrester Consulting on behalf of Microsoft, June 2025
| Question | Median |
|---|---|
| By how much do you expect outsourcing costs (e.g., contractors, professional services, outsourcing contracts) to be reduced? | 10.6% |
| Percentage reduction in other AI-related tools, projects, or external spend costs?* | 6.0% |
| By how much do you expect external marketing agency spend to be reduced?† | 5.1% |
Base: 119 decision-makers who use or expect to use AI agents for business-to-employee use cases
*Base: 263 decision-makers who use or expect to use AI agents for business-to-employee use cases
†Base: 30 decision-makers who have experienced or expect to experience reduced agency spend due to AI agents
Source: A commissioned study conducted by Forrester Consulting on behalf of Microsoft, June 2025
Modeling and assumptions. Based on the interviews and survey, Forrester assumes the following for the composite organization:
Before using Copilot Studio, the composite has $6,250,000,000 in revenue and a net profit margin of 7.6%.
The composite decreases its internal and external expenses by up to 1.0%.
Results. This yields a three-year projected PV ranging from $45.6 million (low) to $88.0 million (high).
Decreased expenses
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| B1Low | $6,250,000,000 | $6,253,750,000 | $6,256,250,000 | |||
| B1Mid | Revenue | A1+A7 | $6,253,750,000 | $6,257,500,000 | $6,262,500,000 | |
| B1High | $6,257,500,000 | $6,263,750,000 | $6,268,750,000 | |||
| B2 | Net profit margin | A16 | 7.6% | 7.6% | 7.6% | |
| B3Low | $5,775,000,000 | $5,778,465,000 | $5,780,775,000 | |||
| B3Mid | Expenses before Copilot Studio | B1*(100% - 7.6% baseline net margin) | $5,778,465,000 | $5,781,930,000 | $5,786,550,000 | |
| B3High | $5,781,930,000 | $5,787,705,000 | $5,792,325,000 | |||
| B4Low | 0.10% | 0.30% | 0.60% | |||
| B4Mid | Decreased expenses | Interviews and survey | 0.20% | 0.50% | 0.80% | |
| B4High | 0.30% | 0.60% | 1.00% | |||
| B5Low | $5,775,000 | $17,335,395 | $34,684,650 | |||
| B5Mid | Reduced expenses with Copilot Studio | B3*B4 | $11,556,930 | $28,909,650 | $46,292,400 | |
| B5High | $17,345,790 | $34,726,230 | $57,923,250 | |||
| B6Low | 7.69% | 7.88% | 8.15% | |||
| B6Mid | Net margin with Copilot Studio | 1-(B3-B5)/B1 | 7.78% | 8.06% | 8.34% | |
| B6High | 7.88% | 8.15% | 8.52% | |||
| BtLOW | $5,775,000 | $17,335,395 | $34,684,650 | |||
| BtMID | Business transformation: Operations | B5 | $11,556,930 | $28,909,650 | $46,292,400 | |
| BtHIGH | $17,345,790 | $34,726,230 | $57,923,250 | |||
| Three-year projected total: $57,795,045 to $109,995,270 | Three-year projected present value: $45,635,864 to $87,986,859 | |||||
Evidence and data. AI agents can improve employee engagement, satisfaction, and productivity by reducing manual and repetitive tasks, resulting in employees with time for other initiatives, such as upskilling. By making advanced AI tools more accessible, organizations can upskill their workforces, lower technical barriers, and drive a culture of innovation. Employees gain hands-on experience with automation and data-driven solutions, enhancing their digital literacy. This investment in skill development improves individual performance and fosters a culture of innovation across teams.
Interviewees said that Microsoft Copilot Studio also enabled upskilling by democratizing access to advanced tools and reducing technical barriers.
The director of the global Microsoft 365 Copilot deployment program in manufacturing reported that 6,000 nontechnical business makers at their organization are now building agents, many of whom are business professionals without formal IT backgrounds. Similarly, the assistant VP of IT research technology in education shared that student workers are now leading chatbot development and client meetings, gaining hands-on experience with enterprise-grade AI tools. The interviewee said: “One of the workers was just transferred to my team and is supporting agents. That’s upskilling. That’s taking one person previously doing one task at the service desk and pulling them in to support multiple agents for the university.”
The IT director in manufacturing described upskilling invoice reviewers to focus on exception handling and supplier engagement. Across interviewees, organizations empowered nontechnical business makers in various departments to build their own agents, reducing reliance on IT and accelerating innovation.
The assistant VP of IT research technology in education described how genAI agents with Copilot Studio were accelerating student worker onboarding at their IT service desk. Previously, three students manually triaged tickets during business hours. Now, an autonomous agent classifies and routes tickets. Deployed on websites like athletics, travel, IT service management, and student health to answer complex user questions, the agent handles sensitive queries (e.g., mental health, assault) and routes users to appropriate resources. Users can also query the agent on university policies and regulations. The interviewee said, “That was roughly $90,000 a year of student workers just triaging tickets … now we’re able to do that automatically.” The agent also helps new hires ramp up faster, with the potential to give an entry-level individual the experience of someone with six months to one year of experience.
The IT director in manufacturing shared that the invoice review agent built with Copilot Studio, which processes more than 100,000 freight invoices annually, flags anomalies such as incorrect surcharges or rates and routes them to humans for follow-up. This agent replaces manual sampling by an estimated 10 people and avoids needing hundreds more, making logistics and finance role onboarding easier for employees.
The director of the global Microsoft 365 Copilot deployment program in manufacturing described a field optimization agent that analyzes equipment data from global field operations to identify optimization opportunities and prioritize maintenance. This agent reduces the learning curve for new field engineers by surfacing actionable insights automatically. The interviewee also mentioned a retirement agent built for HR but expected to be used by all employees, which simplifies access to retirement-related information, reduces dependency on HR staff, and improves onboarding for employees nearing retirement.
The technology platforms director in professional services said that their organization’s content contribution and curation agent automates tagging, categorization, and metadata generation for contributed content. It nudges users at project milestones to contribute knowledge, recommends content for curation, and flags content by tier (e.g., contributed, curated, official). These actions help new employees quickly find and contribute relevant knowledge. The interviewee also highlighted a skills repository agent that reduces time spent updating and searching for skills profiles, which helps new hires and managers quickly identify expertise across the organization.
The digital product strategy senior manager in financial services discussed an HR handbook agent designed primarily for new hires but also used by existing employees. With 6,200 new hires per year, the interviewee expects the agent to save nearly half an hour per employee by simplifying access to onboarding materials.
Interviewees described that agents with Copilot Studio and Microsoft 365 Copilot were reducing onboarding time and simplifying work across departments by automating repetitive tasks, surfacing relevant information, and enabling faster access to knowledge.
Survey respondents also expect streamlined recruitment to be a quick win using AI agents. Longer term, survey respondents expect improvements to people and culture to result in higher employee satisfaction and retention. They also expect streamlined recruitment, onboarding, and training.
Modeling and assumptions. Based on the interviews and survey, Forrester assumes the following:
Ten percent of the employees using Copilot Studio are new hires.
The fully burdened hourly rate (i.e., wages and benefits) for an employee is $35.
Prior to Copilot Studio, new hire onboarding takes 60 days.
After Copilot Studio, new hire onboarding accelerates up to 25%.
A new hire ramp of 50% is applied.
A productivity recapture rate of 50% is applied.
Results. This yields a three-year projected PV ranging from $880,000 (low) to $1.6 million (high).
Acceleration of new hire onboarding
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 | |
|---|---|---|---|---|---|---|
| C1 | Employees using Copilot Studio | Composite | 3,750 | 6,250 | 12,500 | |
| C2 | New hires | Composite | 10% | 10% | 10% | |
| C3 | Onboarding time prior to Copilot Studio (days) | Composite | 60 | 60 | 60 | |
| C4Low | 5% | 10% | 15% | |||
| C4Mid | Acceleration of new hire onboarding | Interviews | 10% | 15% | 20% | |
| C4High | 15% | 20% | 25% | |||
| C5Low | 3 | 6 | 9 | |||
| C5Mid | Time saved per new employee (days) | C3*C4 | 6 | 9 | 12 | |
| C5High | 9 | 12 | 15 | |||
| C6 | Fully burdened hourly rate for an employee | Composite | $35 | $35 | $35 | |
| C7 | Productivity recapture | Composite | 50% | 50% | 50% | |
| C8 | New hire ramp | Composite | 50% | 50% | 50% | |
| CtLOW | $78,750 | $262,500 | $787,500 | |||
| CtMID | Business transformation: People and culture | C1*C2*C5*8 hours a day*C6*C7*C8 | $157,500 | $393,750 | $1,050,000 | |
| CtHIGH | $236,250 | $525,000 | $1,312,500 | |||
| Three-year projected total: $1,128,750 to $2,073,750 | Three-year projected present value: $880,193 to $1,634,758 | |||||
Interviewees and survey respondents mentioned the following additional benefits that their organizations experienced but were not able to quantify:
Improved security and compliance. Interviewees emphasized the importance of choosing Microsoft Copilot Studio due to its alignment with their organizations’ existing security and compliance frameworks. The technology platforms director in professional services noted that their organization’s deep integration with Microsoft ensures that all systems, including Copilot Studio, comply with internal security policies and protect sensitive client data. The interviewee said: “We have a very big alliance with Microsoft. We do have all our systems integrated and our information security policies and artifacts with Microsoft. So in that environment, the context under which you move is very relevant because of integration, security compliance, and risk management.” This alignment was a key factor in adoption, especially for a firm with strict data governance requirements.
Data quality improvements and high-quality information repositories. Agentic AI helped interviewees’ organizations improve data quality by automating validation and surfacing anomalies. The director of the global Microsoft 365 Copilot deployment program in manufacturing mentioned agents that analyze field data to identify optimization opportunities, ensuring that they base decisions on clean, actionable information. Several interviewees’ organizations used agents to curate knowledge repositories and reduce informational noise. The global solutioning lead in professional services described a content contribution and curation agent that automates tagging, categorization, and metadata generation. It also flags content by tier (e.g., contributed, curated, official), helping users distinguish high-quality, authoritative resources from informal contributions. The same interviewee shared: “We’re trying to curate the content a little bit so that we get more trustworthy answers. That’s really one of our biggest problems with knowledge management today: Content from 2016 is still there, and there are 10 variations of the same file. So we’ve been going around trying to find additional repositories to add.”
Base: 54 decision-makers who have experienced or expect to experience time savings in the IT department
Source: A commissioned study conducted by Forrester Consulting on behalf of Microsoft, June 2025
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Copilot Studio and later realize additional uses and business opportunities, including:
Increased creation and deployment of AI agents across functional areas and use cases. Copilot Studio’s ease of use enabled broader participation in AI development across interviewees’ organizations. They described how nontechnical staff could now build agents that address specific departmental needs, from HR documentation to customer-facing services. This democratization of development allows teams to respond quickly to operational challenges without relying solely on IT, while still maintaining governance through publishing controls and training. As more departments gain confidence and conduct training, interviewees expect the number of agents and use cases to grow rapidly, with some organizations projecting thousands of agents in use within a few years. The technology platforms director in professional services said that approximately 1,800 business users, many of whom do not have IT backgrounds, have created more than 2,000 AI agents at their organization.
Microsoft’s continued expansion of AI capabilities. Several interviewees highlighted the potential of Microsoft 365 Copilot Chat and its consumption-based model to drive broader use of generative and agentic AI across enterprises. This model allows users without Microsoft 365 Copilot licenses to access agents on a pay-as-you-go basis, making it easier to scale usage without an upfront investment. Additionally, the integration of Copilot Studio with other Microsoft tools positions organizations to adopt future enhancements seamlessly. As agentic AI matures, interviewees expect agents to become more autonomous, interconnected, and embedded in daily workflows, ultimately transforming how teams collaborate, access knowledge, and deliver services.
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Dtr | Copilot Studio subscriptions | $690 | $775,560 | $1,217,160 | $1,879,560 | $3,872,970 | $3,123,803 |
| Etr | Planning, development, and ongoing maintenance | $225,120 | $1,939,104 | $3,262,800 | $4,745,400 | $10,172,424 | $8,249,760 |
| Ftr | Training and discovery | $630,000 | $2,562,336 | $5,355,336 | $7,455,336 | $16,003,008 | $12,986,598 |
|
Total costs (risk-adjusted) |
$855,810 | $5,277,000 | $9,835,296 | $14,080,296 | $30,048,402 | $24,360,161 |
Evidence and data. Subscription costs vary depending on the licensing model and usage or interaction volume. Some interviewees explained that their organization initially purchased a set number of Microsoft 365 Copilot licenses and later expanded those licenses, with departments funding their own after the pilot phase. They also described a consumption-based model for Copilot Studio, where they purchased interaction licenses in bulk (e.g., 25,000 interactions per month) and used them internally in smaller buckets (e.g., 5,000 interactions per month).
Forrester recommends contacting a Microsoft field sales or partner representative to better understand specific costs.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
The composite licenses one tenant at $200 per month. Its cost per capacity package is $200 per month.
It uses 280 monthly capacity packs in Year 1, 440 in Year 2, and 680 in Year 3.
Risks. Results may not be representative of all experiences, and the cost will vary between organizations depending on the number of tenants an organization licenses and its volume of messages based on the number and types of agents built.
Results. To account for these risks, Forrester adjusted this cost upward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $3.1 million.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| D1 | Tenants | Composite | 1 | 1 | 1 | 1 |
| D2 | Licensing per tenant per month | Composite | $200 | $200 | $200 | $200 |
| D3 | Cost per capacity package (25,000 messages per package per month) | Composite | $200 | $200 | $200 | $200 |
| D4 | Months | Composite | 3 | 12 | 12 | 12 |
| D5 | Employees using agentic AI | Composite | 3,750 | 6,250 | 12,500 | 18,750 |
| D6 | Agents built by nontechnical business makers | Composite | 150 | 200 | 275 | |
| D7 | Agents built by pro developers | Composite | 2 | 25 | 75 | 150 |
| D8 | Capacity packs (monthly) | Composite | 280 | 440 | 680 | |
| Dt | Copilot Studio subscriptions | (D1*D2*D4)+(D3*D4*D8) | $600 | $674,400 | $1,058,400 | $1,634,400 |
| Risk adjustment | ↑15% | |||||
| Dtr | Copilot Studio subscriptions (risk-adjusted) | $690 | $775,560 | $1,217,160 | $1,879,560 | |
| Three-year total: $3,872,970 | Three-year present value: $3,123,803 | |||||
Evidence and data. Development and maintenance costs include initial build time, ongoing support, and governance.
The assistant VP of IT research technology in education shared that building a chatbot typically takes a full-time staff member about 20 hours and is followed by weekly meetings for three months to monitor performance. Student workers often handle maintenance, with support scaled based on demand.
The senior manager of technology architecture in professional services described how building agents in Copilot Studio now takes days to weeks, compared to significantly longer timelines when using custom alternative solutions. They also noted that Microsoft product team collaboration helped reduce development effort by resolving platform limitations.
Additionally, the director of the global Microsoft 365 Copilot deployment program in manufacturing emphasized the importance of governance, with agents requiring approval for system access and productivity reporting.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
IT developers initially build two agents, followed by 23 agents in Year 1, 50 agents in Year 2, and 75 agents in Year 3. IT developers dedicate 25% of their time to developing agents and an additional 20% of this time to maintaining agents.
The fully burdened hourly rate for an IT developer is $65.
Makers spend 20 hours per agent on development and maintenance. The composite spends $120,000 initially on professional services and $720,000 in Years 1, 2, and 3 to set up the program and offer best practices and subject matter expertise.
The fully burdened hourly rate for a maker is $35.
Risks. Results may not be representative of all experiences, and the cost will vary between organizations depending on the following factors:
The organization’s strategy for democratizing AI agent development.
The organization’s IT developers dedicated to developing and maintaining AI agents.
The fully burdened hourly rate for an IT developer and a citizen developer.
Results. To account for these risks, Forrester adjusted this cost upward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $8.2 million.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| E1 | IT developer agents built | D7cy - D7py | 2 | 23 | 50 | 75 |
| E2 | Average time spent developing agents per IT developer (hours) | 2,080*25% | 520 | 520 | 520 | 520 |
| E3 | Average time maintaining/updating apps (hours) | E2*20% | 104 | 104 | 104 | 104 |
| E4 | Fully burdened hourly rate for an IT developer | Composite | $65 | $65 | $65 | $65 |
| E5 | Subtotal: IT development time (hours) |
Initial: E1*E2*E4 Year 1: E1*E2*E4+E1(initial year)*E3*E4 Year 2: E1*E2*E4+(E1 initial year + E1Yearone)*E3*E4 Year 3: E1*E2*E4+(E1 initial year + E1Yearone + E1Yeartwo)*E3*E4 |
$67,600 | $790,920 | $1,859,000 | $3,042,000 |
| E6 | Time to develop and maintain agents for nontechnical business makers per agent (hours) | Interviews | 20 | 20 | 20 | |
| E7 | Fully burdened hourly rate for a maker | Composite | $35 | $35 | $35 | |
| E8 | Subtotal: Citizen developer hours | D6*E6*E7 | $105,000 | $140,000 | $192,500 | |
| E9 | Subtotal: Professional services | Composite | $120,000 | $720,000 | $720,000 | $720,000 |
| Dt | Planning, development, and ongoing maintenance | E5+E8+E9 | $187,600 | $1,615,920 | $2,719,000 | $3,954,500 |
| Risk adjustment | ↑20% | |||||
| Dtr | Planning, development, and ongoing maintenance (risk-adjusted) | $225,120 | $1,939,104 | $3,262,800 | $4,745,400 | |
| Three-year total: $10,172,424 | Three-year present value: $8,249,760 | |||||
Evidence and data. Training costs include upfront onboarding and ongoing enablement for agentic AI users.
The technology platforms director in professional services reported that 1,800 business professionals were building agents, many without formal IT backgrounds, reflecting an investment in democratized training.
The VP of IT client success and business operations in education estimated that initial training for nontechnical business makers takes about 4.5 hours, with additional time for IT staff focused on governance and pricing. The interviewee also described a student ambassador program with structured training modules and ongoing support.
The senior strategic product manager of digital customer interaction in financial services noted that conversational designers and nontechnical business makers work full time on agent development, indicating a need for continuous skill building and platform familiarity.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
Ten percent of Copilot Studio users at the composite organization are new hires.
New users train for 4 hours.
Copilot Studio users undergo 8 hours of additional ongoing discovery, change management, and skill acquisition.
The fully burdened hourly rate for a user is $35.
Risks. Results may not be representative of all experiences, and the cost will vary between organizations depending on the following factors:
The number of Copilot Studio users at the composite organization each year as adoption expands.
The composite organization’s churn rate.
The fully burdened hourly rate for a user.
Results. To account for these risks, Forrester adjusted this cost upward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $13 million.
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| F1 | Users | Composite | 3,750 | 6,250 | 12,500 | 18,750 |
| F2 | New users | F2-F2 previous year plus 10% churn | 3,750 | 2,750 | 6,875 | 6,875 |
| F3 | Training time for new users (hours) | Interviews | 4 | 4 | 4 | 4 |
| F4 | Total time training new users (hours) | F2*F3 | 15,000 | 11,000 | 27,500 | 27,500 |
| F5 | Additional time per user for ongoing discovery, change management, and acquiring new skills (hours) | Interviews | 8 | 8 | 8 | |
| F6 | Total time for employee discovery (hours) | F4+F1*F5 | 15,000 | 61,000 | 127,500 | 177,500 |
| F7 | Fully burdened hourly rate for a user | Composite | $35 | $35 | $35 | $35 |
| Ft | Training and discovery | (F5+F6)*F7 | $525,000 | $2,135,280 | $4,462,780 | $6,212,780 |
| Risk adjustment | ↑20% | |||||
| Ftr | Training and discovery (risk-adjusted) | $630,000 | $2,562,336 | $5,355,336 | $7,455,336 | |
| Three-year total: $16,003,008 | Three-year present value: $12,986,598 | |||||
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | ($855,810) | ($5,277,000) | ($9,835,296) | ($14,080,296) | ($30,048,402) | ($24,360,161) |
| Total benefits (low) | $0 | $5,853,750 | $19,308,750 | $38,324,525 | $63,487,025 | $50,073,018 |
| Total benefits (mid) | $0 | $12,950,000 | $31,871,250 | $51,720,000 | $96,541,250 | $76,970,605 |
| Total benefits (high) | $0 | $20,054,320 | $40,008,545 | $65,805,000 | $125,867,865 | $100,736,384 |
| Net benefits (low) | ($855,810) | $576,750 | $9,473,454 | $24,244,229 | $33,438,623 | $25,712,857 |
| Net benefits (mid) | ($855,810) | $7,673,000 | $22,035,954 | $37,639,704 | $66,492,848 | $52,610,444 |
| Net benefits (high) | ($855,810) | $14,777,320 | $30,173,249 | $51,724,704 | $95,819,463 | $76,376,223 |
| PROI (low) | 106% | |||||
| PROI (mid) | 216% | |||||
| PROI (high) | 314% |
The financial results calculated in the Benefits and Costs sections can be used to determine the PROI and projected NPV for the composite organization’s investment. Forrester assumes a yearly discount rate of 10% for this analysis.
These risk-adjusted PROI and projected NPV values are determined by applying risk-adjustment factors to the unadjusted results in each Benefit and Cost section.
The initial investment column contains costs incurred at “time 0” or at the beginning of Year 1 that are not discounted. All other cash flows are discounted using the discount rate at the end of the year. PV calculations are calculated for each total cost and benefit estimate. NPV calculations in the summary tables are the sum of the initial investment and the discounted cash flows in each year. Sums and present value calculations of the Total Benefits, Total Costs, and Cash Flow tables may not exactly add up, as some rounding may occur.
From the information provided in the interviews and survey, Forrester constructed a New Technology: Projected Total Economic Impact™ (New Tech TEI) framework for those organizations considering an investment in Copilot Studio.
The objective of the framework is to identify the cost, benefit, flexibility, and risk factors that affect the investment decision. Forrester took a multistep approach to evaluate the projected impact that Copilot Studio can have on an organization.
Interviewed Microsoft stakeholders and Forrester analysts to gather data relative to Copilot Studio.
Interviewed 13 decision-makers and surveyed 400 respondents at organizations using Copilot Studio in a pilot or beta stage to obtain data about projected costs, benefits, and risks.
Designed a composite organization based on characteristics of the interviewees’ and survey respondents’ organizations.
Constructed a projected financial model representative of the interviews and survey using the New Tech TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewees and survey respondents.
Employed four fundamental elements of New Tech TEI in modeling the investment’s potential impact: benefits, costs, flexibility, and risks. Given the increasing sophistication of ROI analyses related to IT investments, Forrester’s TEI methodology provides a complete picture of the total economic impact of purchase decisions. Please see Appendix A for additional information on the TEI methodology.
Projected benefits represent the projected value the solution delivers to the business. The New Tech TEI methodology places equal weight on the measure of projected benefits and projected costs, allowing for a full examination of the solution’s effect on the entire organization.
Projected costs comprise all expenses necessary to deliver the proposed value, or benefits, of the solution. The methodology captures implementation and ongoing costs associated with the solution.
Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. The ability to capture that benefit has a PV that can be estimated.
Risks measure the uncertainty of benefit and cost estimates given: 1) the likelihood that estimates will meet original projections and 2) the likelihood that estimates will be tracked over time. TEI risk factors are based on “triangular distribution.”
The present or current value of (discounted) cost and benefit estimates given at an interest rate (the discount rate). The PV of costs and benefits feed into the total NPV of cash flows.
The projected present or current value of (discounted) future net cash flows given an interest rate (the discount rate). A positive project NPV normally indicates that the investment should be made unless other projects have higher NPVs.
A project’s expected return in percentage terms. ROI is calculated by dividing net benefits (benefits less costs) by costs.
The interest rate used in cash flow analysis to take into account the time value of money. Organizations typically use discount rates between 8% and 16%.
New Technology: Projected Total Economic Impact (New Tech TEI) is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The New Tech TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
| Position/Department | |
|---|---|
| IT | 19% |
| Finance/accounting | 18% |
| Operations | 17% |
| Human resources/training | 16% |
| Marketing/advertising | 16% |
| Sales | 14% |
| INDUSTRY | |
|---|---|
| Manufacturing and materials | 7% |
| Energy, utilities, or waste management | 6% |
| Financial services or insurance | 6% |
| Retail | 6% |
| Transportation and logistics | 6% |
| Healthcare | 6% |
| Media or leisure | 6% |
| Construction | 6% |
| Business or professional services | 5% |
| Technology or technology services | 5% |
| Travel and hospitality | 5% |
| Agriculture, food, or beverage | 5% |
| Electronics | 5% |
| Telecommunications services | 5% |
| Chemicals or metals | 5% |
| Consumer product goods | 5% |
| Consumer services | 5% |
| Advertising or marketing | 4% |
| Legal services | 1% |
| ANNUAL REVENUE | |
|---|---|
| $300M to $399M | 5% |
| $400M to $499M | 6% |
| $500M to $999M | 10% |
| $1B to $5B | 25% |
| >$5B | 54% |
1 Source: With Agentic AI, Generative AI Is Evolving From Words To Actions, Forrester Research, Inc., August 8, 2024.
2 Source: Agentic AI Is Rising And Will Reforge Businesses That Embrace It, Forrester Research, Inc., March 7, 2025.
3 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists solution providers in communicating their value proposition to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of business and technology initiatives to both senior management and other key stakeholders.
4 Source: AI Agents: What It Means For B2B Marketing, Sales, And Product, Forrester Research, Inc., April 2, 2025.
Readers should be aware of the following:
This study is commissioned by Microsoft and delivered by Forrester Consulting. It is not meant to be used as a competitive analysis.
Forrester makes no assumptions as to the potential ROI that other organizations will receive. Forrester strongly advises that readers use their own estimates within the framework provided in the study to determine the appropriateness of an investment in Copilot Studio.
Microsoft reviewed and provided feedback to Forrester, but Forrester maintains editorial control over the study and its findings and does not accept changes to the study that contradict Forrester’s findings or obscure the meaning of the study.
Microsoft provided the customer names for the interviews but did not participate in the interviews.
Forrester fielded the double-blind survey using a third-party survey partner.
Nahida S. Nisa
Jonathan Lipsitz
September 2025
ABOUT FORRESTER CONSULTING
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© Forrester Research, Inc. All rights reserved. Unauthorized reproduction is strictly prohibited. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change. Forrester®, Technographics®, Forrester Wave, and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies.
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