The Total Economic Impact™ Of Microsoft’s AI Skilling Portfolio

Improved Employee Productivity Through Higher AI Adoption, Usage, And Effectiveness

Evidence and data. Interviewees reported that Microsoft’s AI skilling portfolio improved employee productivity by increasing AI adoption, use intensity, and effective use of AI tools. Structured learning programs accelerated the transition from access to recurring use, which interviewees said helped employees identify use cases, create more effective prompts, evaluate outputs, and apply AI to everyday business tasks. As the organizations’ workforce AIQ improved, employees gained the ability to use AI more consistently for activities such as taking meeting notes, compiling reports, gathering information, content creation, drafting, and document summarization.

Each interviewee indicated that Microsoft’s structured skilling programs significantly increased adoption rates and helped employees realize value from AI more quickly. The VP of digital strategy and operations at the consumer goods company noted: “We saw a clear increase in usage as a result of training. It’s been a dramatic impact. We’d probably be half as far as we are now if it weren’t for the skilling programs that we had.”

Interviewees also reported that Microsoft skilling increased the intensity and frequency of AI usage, which they said they viewed as a leading indicator of business value. The IT productivity and collaboration leader at the energy and utilities company said: “Training gave us the opportunity to reach a much broader audience of licensed users and helped move employees from occasional use to more consistent engagement. We saw actions per user increase meaningfully over time and continued growth in habitual and power-user segments, indicating that employees were becoming more familiar with the product and integrating it into their daily work.”

The digital enablement leader at the engineering company described how improved usage translated into measurable time savings: “We estimate that users save about an hour per week, which we consider to be a conservative estimate. Much of that value comes from reducing administrative work, such as meeting notes, report compilation, information gathering, and content creation. Skilling helped ensure employees not only used AI but used it effectively and confidently.”

Interviewees also emphasized that skilling improved the quality of AI usage in addition to the amount. The group head of learning and development at the industrial conglomerate described how role-based learning, hands-on experiences, and business-focused enablement accelerated the transition from experimentation to operational use: “Users did not remain in a passive exploration phase. Usage began at scale early and stabilized quickly at high levels. Employees learned how to translate real business processes into AI-supported workflows, reducing the time from curiosity to actionable insight and enabling broader business value from AI investments.”

The learning development leader at the international affairs agency reported similar experiences, highlighting broad adoption of AI across everyday knowledge-work activities: “Copilot was being used extensively for prompting, drafting, meeting summarization, document creation, search, and knowledge discovery. We saw steady increases in adoption over time, supported by both training and experimentation. Teams also began creating their own agents to automate time-intensive local processes with hundreds of local agents eventually deployed across the organization.”

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• The composite organization employs 12,000 AI-eligible office workers who are potential Microsoft Copilot Chat or Microsoft Copilot users.

• For modeling purposes, adoption rates with and without skilling are included to measure the incremental adoption impact of Microsoft’s AI skilling portfolio.

• Adoption among AI-eligible users increases from 55% in Year 1 to 85% in Year 3.

• The productivity analysis reflects productivity gains across all active AI users who receive structured skilling. This includes employees who adopt AI because of skilling and employees who were already using AI but become more effective because of skilling.

• Incremental time savings attributable to Microsoft skilling increase from 0.5 hours per week per active user in Year 1 to 1 hour in Year 2 and 1.5 hours in Year 3.

• Consistent with TEI methodology, Forrester applied a 50% productivity recapture rate to account for the portion of employee time savings that can be converted into productive work.

• Productivity gains are valued using a fully loaded average hourly rate of $41 for an enterprise knowledge worker.

Risks. The scale of this benefit may vary from organization to organization based on:

• The extent of employee adoption.

• Sustained usage of AI technologies.

• Leadership support, organizational commitment, and the maturity of change management and adoption programs.

• The quality and relevance of AI use cases identified for employees.

• Existing levels of AI literacy and digital skills within the workforce.

• The availability of Copilot licenses, AI-enabled tools, and supporting technologies across the workforce.

Results. To account for these risks, Forrester adjusted this benefit downward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $18.7 million.

Accelerated Realization Of AI-Enabled Business Outcomes Through Technical Skilling

Evidence and data. Interviewees reported that Microsoft technical skilling helped their organizations move beyond AI experimentation and deploy AI-enabled workflows, automations, and business solutions more quickly. They said technical teams developed skills in solution design, governance, security, integration, agent development, and workflow automation that enabled them to operationalize AI initiatives and support broader business transformation.

Interviewees explained that Microsoft technical skilling accelerated solution development and deployment and allowed their organizations to build the capabilities needed to design, develop, govern, and scale AI-enabled workflows, automations, and agents. They noted that business alignment increased the impact of technical skilling, reporting that the most successful programs focused on solving business problems rather than teaching tools in isolation. Technical teams worked alongside business stakeholders to identify use cases, automate processes, and implement AI-enabled solutions. 

Interviewees also said that AI-enabled workflows created business value and consistently pointed to workflow automations, business process solutions, and emerging agent use cases as the primary sources of value generated through technical skilling. Use cases included compliance review, procurement support, proposal generation, research activities, and managing administrative workflows. 

The group head of learning and development at the industrial conglomerate explained: “We adopted a use case-driven approach because strong business alignment was critical. Rather than focusing on tool capabilities alone, training centered on real business problems and practical scenarios. We wanted technical employees to develop not only the skills to use the technology, but also an understanding of business context so they could work alongside business teams, solve real problems, and translate business processes into AI-enabled solutions.”

Interviewees noted that as technical capabilities expanded, their organizations measured success through deployed business processes, workflows, and operational use cases rather than training participation or individual technical users.

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• The composite organization employs approximately 2,000 technical users who are enabled to build, configure, govern, or support AI-enabled solutions.

• Activity levels equivalent to 7.5% of technical users in Year 1, 15% in Year 2, and 30% in Year 3 result in 150, 300, and 600 AI-enabled workflows accelerated through Microsoft technical skilling, respectively.

• As the composite organization progresses from discrete productivity use cases to more complex process automation and operational workflows, the average business value attributable to each AI-enabled workflow increases from $35,000 in Year 1 to $75,000 in Year 3.

• Microsoft technical skilling accelerates the composite’s ability to identify use cases, build internal expertise, and operationalize AI-enabled solutions.

• The analysis measures only the portion of business value realized sooner because technical teams are able to develop and operationalize AI-enabled solutions more quickly.

• Forrester assumes Microsoft technical skilling accelerates realization of 40% of the business value generated by these AI-enabled workflows.

Risks. The scale of this benefit may vary from organization to organization based on:

• The organization's ability to identify, prioritize, and operationalize high-value AI use cases.

• The availability and capability of technical resources, AI technologies, and supporting infrastructure.

• Governance, security, compliance, and workflow complexity requirements that may affect deployment timelines and solution scale.

• The extent to which business units adopt and operationalize AI-enabled solutions.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $17.8 million.

Reduced Internal AI Skilling Development And Delivery Effort

Evidence and data. Interviewees reported that Microsoft’s AI skilling portfolio reduced the effort required to build and sustain enterprise AI skilling programs. They said that rather than developing curricula, learning content, and training programs from scratch, their organizations leveraged Microsoft’s learning ecosystem to accelerate enablement efforts and scale training more efficiently. This benefit reflects avoided internal labor from learning and enablement teams rather than direct budget savings.

Interviewees indicated that Microsoft-delivered and partner-delivered learning resources helped their organizations reach larger employee populations across different geographies, languages, and time zones without proportionally increasing internal training resources. They also said Microsoft skilling helped their organizations build internal enablement capabilities. Rather than creating every learning asset independently, their organizations focused internal teams on tailoring content, reinforcing adoption, and supporting learners while leveraging Microsoft's existing resources and learning pathways.

Interviewees also explained that Microsoft skilling reduced the effort required to develop and deliver AI skilling programs. They emphasized that their organizations avoided significant work associated with curriculum development, content creation, instructor preparation, and foundational program design, particularly during the early stages of AI adoption. They also said avoided effort was greatest during the initial stages of AI adoption when their organizations would have been otherwise required to establish foundational AI learning programs, curricula, and supporting materials. The best example came from the learning development leader at the international affairs agency, where a small learning team supported a large global workforce. They said: “People found the content very useful, particularly the videos, although many needed some initial orientation to navigate the breadth of material and identify the learning paths most relevant to them. Together, these resources gave us the reach, penetration, and catalog coverage we simply would not have achieved with our internal team alone.”

Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:

• Rather than building AI skilling programs entirely from scratch, the composite organization leverages Microsoft’s learning ecosystem, including Microsoft Learn, Organizational Skilling resources, certifications, instructor-led training, workshops, digital content, and learning pathways.

• The composite organization avoids 3,120 hours of program management and curriculum-development effort in Year 1, declining to 2,080 hours in Year 2 and 1,040 hours in Year 3. This is equivalent to approximately 1.5 FTEs, 1 FTE, and 0.5 FTE, respectively.

• The composite organization avoids 3,120 hours of technical training content-development effort in Year 1, declining to 1,560 hours in Year 2 and 1,040 hours in Year 3. This is equivalent to approximately 1.5 FTEs, 0.75 FTEs, and 0.5 FTE, respectively.

• The composite organization avoids 800 hours of instructor preparation and delivery effort in Year 1, declining to 640 hours in Year 2 and 480 hours in Year 3.

• Avoided effort is valued using a blended fully loaded labor rate of $95 per hour, representing learning and development professionals, technical trainers, curriculum developers, program managers, and AI enablement resources.

Risks. The scale of this benefit may vary from organization to organization based on:

• The size, maturity, and capability of the organization’s learning and development team.

• The extent to which the organization would have developed AI skilling content, learning pathways, and enablement programs internally.

• The availability of existing internal AI training materials, expertise, and third-party learning resources.

• The scale of the employee population that receives AI skilling.

• The level of ongoing program management, enablement, and support required.

Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $1.0 million.

Unquantified Benefits

Benefits that provide value for the interviewees’ organization but are not quantified for this study include:

• Accelerated AI maturity and workforce readiness. Interviewees said Microsoft’s AI skilling portfolio improved workforce AIQ, employee confidence, practical AI skills, governance readiness, and adoption capabilities. They explained that as employees became more comfortable applying AI to real-world business scenarios, their organizations became better positioned to scale AI adoption and realize value from AI investments. The IT productivity and collaboration leader at the energy and utilities company noted, “We’re seeing growth in habitual and power-user categories, which indicates skilling helps people become more familiar with the product and use it more consistently.”

• Enhanced governance and responsible AI adoption. Interviewees reported that skilling helped increase awareness of AI governance, security, compliance, and responsible use practices, which helped establish stronger foundations for scaling AI initiatives while reducing organizational risk. The digital enablement leader at the engineering company shared: “There wasn’t a smooth rollout where we just gave out licenses. People were nominated, then they had to review the training materials and attest that they had completed acceptable-use and responsible-AI guidance before receiving access.”

• Improved business-IT alignment. Interviewees said role-based learning and gaining a shared understanding of AI capabilities improved collaboration between business and technical stakeholders. They reported stronger alignment around use case prioritization, solution development, and realization of business value from AI investments. The group head of learning and development at the industrial conglomerate described: “Rather than focusing on tool capabilities, we centered training around real business problems and practical scenarios. We wanted technical employees to understand the business context as well so they could work with business units, not just as IT experts, but as team members using their technical skills to solve real business problems.”

• Faster adoption of existing and emerging Microsoft AI technologies. Interviewees said Microsoft’s AI skilling portfolio helped their organizations prepare for broader AI adoption beyond Microsoft 365 Copilot, including Copilot Studio, AI agents, GitHub Copilot, Foundry Tools, and Azure AI Foundry, enabling them to evaluate and operationalize emerging AI technologies more quickly. The group head of learning and development at the industrial conglomerate said: “Technical teams received specialized training in areas such as agent development, integration, governance, security, and platform management. Through Azure, GitHub, Copilot Studio, and other advanced training programs, they developed AI-enabled solutions more quickly, and we accelerated adoption of agents across the organization.”

• Improved AI fluency and organizational readiness. Interviewees consistently described improvements in AI fluency, confidence, and practical application of AI tools. These outcomes align closely with Forrester AIQ, which emphasizes the combination of AI knowledge, skills, behaviors, and organizational readiness needed to generate business value from AI.⁷ Interviewees also said that improvements to AIQ contributed to faster organizational learning cycles and greater curiosity velocity as employees became more capable of translating new AI capabilities into practical business use cases.

Flexibility

The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Microsoft’s AI skilling portfolio and later realize additional uses and business opportunities, including:

• Expanding into emerging AI technologies. Interviewees said Microsoft’s AI skilling portfolio established a flexible foundation for future AI adoption that enabled their organizations to adapt learning strategies, expand into new AI capabilities, and operationalize emerging use cases as technologies and business needs evolve.

• Scaling beyond productivity use cases. Interviewees reported that AI technologies and use cases continue to evolve rapidly and said that Microsoft’s AI skilling portfolio established scalable learning models and foundational AI capabilities that positioned their organizations to expand from productivity-focused AI use cases into workflow automation, AI agents, and future Microsoft AI technologies without fundamentally redesigning learning strategies.

• Continuously adapting to evolving AI capabilities. Interviewees said Microsoft’s AI skilling portfolio provides continuous learning models that evolve with changing AI technologies and business requirements. The VP of digital strategy and operations at the consumer goods organization explained: “The AI landscape is changing every week. We are working in a very fluid way. What is our vision? How do we navigate it? And how do we work in a very agile way?”

Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).