New Technology: The Projected Total Economic Impact™ Of Microsoft AI Solutions For Retail And Consumer Goods Organizations

Go-To-Market Transformation: AI‑Driven Digital Conversion And Revenue Outcomes

Evidence and data. Interviewees told Forrester that Microsoft AI solutions improved marketing execution and customer‑facing shopping experiences, which reshaped how their organizations drive digital revenue. Teams gained the ability to use AI instead of relying on manual workflows, limited creative testing, or static on‑site experiences. This accelerated research synthesis, expanded creative exploration, and optimized how their customers discover, evaluate, and engage with products across digital channels, including being able to use AI shopping assistants to support more conversational guided discovery alongside traditional campaign‑driven acquisition.

Interviewees said these AI‑enabled capabilities allow teams to test stronger ideas earlier, refine messaging more precisely, and respond to performance signals more quickly across the customer journey. As a result, their organizations increased engagement, improved conversion behavior, and captured incremental revenue across digital touchpoints influenced by Microsoft AI. Interviewees and survey respondents provided the following evidence:

• Sixty-three survey respondents at the director level and above reported strong performance gains after adopting Microsoft AI. They reported an average 25% increase in click‑through rates, a 4% to 5% uplift in conversion rates, nearly 20% faster A/B test velocity, and a 10% reduction in time to market for new campaigns, indicating that AI‑assisted workflows materially improved digital campaign outcomes.

• Survey respondents from organizations focused on digital commerce and enablement reported that AI shopping assistants contributed to measurable improvements in conversion behavior and order value across assistant‑influenced sessions. Thirty-six digital experience respondents at the director level and above reported average improvements including an 18.96% increase in conversion rates, an 18.39% reduction in return rates, a 14.67% reduction in cart abandonment, and a 9.69% increase in average order value. Respondents also reported a 6.96% improvement in customer satisfaction scores, indicating that AI shopping assistants influence customers’ purchase decisions and overall shopping experiences as they move from discovery to checkout.

• A senior director of AI transformation at a consumer goods organization emphasized that AI‑enabled insights strengthened targeting and go‑to‑market decision‑making. They said, “Direct‑to‑consumer channels and social signals processed through Azure help us understand consumer preferences better, informing forecasting and go‑to‑market strategies.”

• The consumer strategy and research lead at a consumer goods organization noted how AI improved creative and testing workflows. They said, “In consumer testing, we’ve seen slightly higher purchase intent scores because AI allowed us to put in better creative thought and more ideas into the test.”

• The same interviewee highlighted that AI‑driven automation sped up in‑market optimization and testing cycles. They said, “AI‑powered automation allowed for faster campaign execution and improved testing.”

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

• The composite organization generates $1.25 billion annually from large‑scale digital and omnichannel revenue streams supported by AI‑enabled marketing and shopping experiences, including campaign‑driven acquisition and on‑site digital engagement.

• The composite applies Microsoft AI solutions to a growing share of digital customer journeys each year, increasing from 20% in Year 1 to 60% in Year 3. These AI‑influenced journeys include both marketing‑led interactions and AI shopping assistant–supported discovery and evaluation experiences.

• The composite’s incremental revenue rises by up to 2% to 4% across the low and high states in Year 3 for digital interactions influenced by Microsoft AI solutions, including AI shopping assistant engagement.

• The financial model reflects a conservative, risk‑adjusted view of realized revenue impact across a subset of digital interactions.

• The composite has a 7.71% operating margin.

Results. This yields a three-year projected PV ranging from $1.5 million (low) to $3.4 million (high).

Operational Transformation: Marketing Labor Efficiencies

Evidence and data. Interviewees and survey respondents told Forrester that Microsoft AI solutions materially reduced the time marketers spend on manual, repetitive, or research‑heavy tasks, allowing teams to redirect effort toward higher‑value strategic and creative work. Instead of devoting hours each week to synthesizing research, generating early content drafts, preparing campaign inputs, or coordinating routine workflows, marketers use Microsoft AI solutions to automate large portions of these activities and complete them in a fraction of the time.

The interviewees and respondents explained that by accelerating certain tasks (e.g., data analysis, content generation, summarization, cross‑document knowledge retrieval), AI allowed their marketing organizations to operate with greater efficiency and consistency while maintaining or improving the quality of their output. These time savings accumulate across large marketing departments and translate into meaningful labor efficiencies and enable teams to recapture productive hours that can be reinvested into campaign optimization, creative development, and faster go‑to‑market execution. Interviewees and survey respondents provided the following evidence:

• Based on 63 marketers at the director level and above, the survey shows that Microsoft AI solutions meaningfully reduced the time required to execute core marketing tasks, including a reported 21% increase in assets produced per month, while also accelerating foundational steps such as generating first drafts, which respondents reported improved by more than 16%. These time savings extend across the full campaign workflow. Brief‑to‑launch cycles became roughly 9% faster, and overall marketer productivity improved by more than 10% as teams spent less effort on manual drafting, research synthesis, and early content development.

• Interviewees said Microsoft AI significantly accelerated early‑stage content creation by generating usable first drafts quickly. One marketing leader explained that Copilot helped their team move from ideas to workable content far more quickly than with manual drafting, increasing throughput across campaigns.

• Several interviewees emphasized their organization reinvested time saved through Microsoft AI directly into strategic work, with one explaining that hours reclaimed from manual research, summarization, and content preparation allowed teams to spend more time on message refinement, campaign optimization, and decision‑making for which they previously lacked time. The consumer strategy and research lead at a consumer goods organization stated: “Copilot can summarize hundreds of free-response survey answers in seconds. Before, that was an entire afternoon of work. Now I can reallocate that time to other projects.”
  The same interviewee said AI reduced delays caused by cross‑team information gaps because marketers could instantly search across shared documents, past campaigns, and organizational knowledge. This eliminated the wait time associated with tracking down context from colleagues and contributed to smoother, faster workflows: “If I were to average [out the aggregate time savings across the marketing function], I’d estimate about 2 hours per week saved per person in marketing and insights. That’s pretty notable when you scale it across 400 people.”

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

• The composite organization employs 650 marketers across distributed team. Each uses Microsoft AI solutions to automate portions of research synthesis, content drafting, ideation, cross‑document knowledge retrieval, and campaign management activities.

• After a full rollout in Year 1 and as adoption reaches maturity across the team, the number of marketers who leverage AI remains steady year over year.

• Each marketer who uses Microsoft AI solutions saves a baseline of 7 hours per month and up to 13 hours. The time savings per marketer increases modestly during the analysis period as teams become more proficient with Microsoft AI solutions tools and expand their use of automation into additional workflows.

• Marketers recapture 50% of their time using Microsoft AI solutions and reinvest it into higher‑value work (e.g., campaign strategy, creative refinement, increasing speed of execution cycles).

• The average fully burdened hourly rate for a marketing employee is $58, which includes a fully burned multiplier of 1.25 times.

Results. This yields a three-year projected PV ranging from $4.5 million (low) to $6.7 million (high).

Operational Transformation: External Spend Optimization

Evidence and data. Interviewees and survey respondents reported that Microsoft AI solutions reduced their organizations’ reliance on external agencies and contractors by enabling internal teams to perform work that previously required outsourced creative, research, or production support. Marketing leaders described how AI equipped their teams to generate early‑stage creative concepts, prepare campaign inputs, summarize research, and produce more polished content without waiting for agency cycles or incurring additional project fees. These efficiencies allowed the organizations to optimize their external budgets, avoid incremental spend on outsourced services, and redeploy savings toward higher‑value initiatives or additional AI‑enabled campaign activity. Interviewees and survey respondents provided the following evidence:

• Based on 63 marketing leaders at the director level and above, the survey shows that Microsoft AI solutions reduced external agency spend on copy and creative work by an average of 5.92%.

• Interviewees said the solutions allow their organizations to handle more early‑stage creative development internally, reducing the scope to outsource tasks such as concepting, drafting, and production.

• A consumer strategy and research lead at a consumer goods organization explained that Microsoft AI enabled the team to develop stronger concepts faster: “I’ve even used Copilot to visualize ad concepts for partnerships. Seeing an idea mocked up helps people buy in without paying an agency for a full pitch.”

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

• The composite organization uses Microsoft AI solutions to complete a portion of early‑stage creative development, research synthesis, and content preparation internally.

• The composite organization spends 0.5% of its revenue on outsourced marketing services, of which 35% is addressable by Microsoft AI solutions. This results in 0.18% of revenue representing addressable outsourced marketing spend.

• The share of external services addressable by Microsoft AI solutions remains stable during the three-year analysis.

• The composite optimizes external spend by 1% to a total of 5%

• As marketers expand use of Microsoft AI solutions, the composite makes time and capability gains each year, supporting incremental reductions in outsourced spend over time.

• Savings represent avoided external fees, not the elimination of strategic agency partnerships. The composite continues to use agencies for high‑value creative and production work while reducing project‑based spend.

Results. This yields a three-year projected PV ranging from $433,000 (low) to $881,000 (high).

Operational Transformation: Supply Chain Optimization

Evidence and data. Interviewees and survey respondents told Forrester that Microsoft AI solutions improved the visibility, accuracy, and consistency of their organizations’ supply chain planning processes, which resulted in fewer stockouts, reduced excess inventory, and more efficient allocation decisions across complex retail and CPG networks. Instead of relying on fragmented data sets, manual forecast adjustments, and labor‑intensive inventory reviews, teams used AI‑enabled forecasting and planning capabilities to generate reliable demand signals and optimize where and when inventory should move.

Interviewees described how AI helps planners identify the right buys, anticipate shifts in demand earlier, and standardize decision‑making across markets, which reduces lost sales caused by stockouts while also lowering unnecessary inventory held across the network. They explained that Microsoft AI allows their organizations to operate their supply chains with greater precision and responsiveness, capture avoided lost revenue, and improve overall operating performance. Interviewees and survey respondents provided the following evidence:

• Survey respondents reported substantial performance improvements after adopting Microsoft AI for supply chain planning, including an average 10.81% reduction in lost sales due to fewer stockouts and improvements between 6% and 20% range for most organizations, demonstrating material impact on revenue capture and availability.

• Survey respondents also reported an average 7.83% improvement in inventory turnover, indicating that AI‑enabled planning helped reduce excess stock while maintaining high in‑stock performance across categories.

• Interviewees said Microsoft AI solutions improved forecast accuracy and allocation quality while enabling their organizations to maintain high service levels and reduce over‑inventory. The senior director of AI transformation at a consumer goods organization highlighted how AI‑enhanced decision‑making prevented costly misalignment during market volatility: “In one case, faster decision-making avoided $500,000 to $1 million in lost revenue within a week by preventing excess inventory during a market disruption. … Digital products built on the Microsoft stack enable network optimization and demand-supply alignment, reducing excess stock and improving service levels.”

• A director of demand and store planning at a retail organization described how Microsoft AI powered more advanced clustering and predictive modeling through a partner solution: “[My organization’s supply chain planning solution] uses Microsoft AI through Microsoft Foundry to power its forecasting algorithms. That partnership was key because we wanted advanced clustering and predictive capabilities beyond simple trend-based models. … We saved about $25 million in inventory in our first year and about $18 million additional in the second year. That reduction alone helped pay for the platform.”

• The same interviewee explained how much AI directly reduced stockout‑related losses: “Year to date, we’ve achieved a 37% reduction in lost sales. That’s about $18 million in recovered revenue.” They also described how automated inventory recall workflows reduced excess stock and improved inventory utilization: “Excess inventory reports trigger automated recalls after holidays, redistributing stock to high-performing stores instead of buying new units. That saved $25 million in excess inventory.”

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

• Each year, the composite organization loses a baseline of 3.2% of its sales revenue ($160 million) due to stockouts.

• With Microsoft AI solutions, the composite avoids up to 5% in lost sales due to out‑of‑stock events.

• The composite’s operating margin is 7.71%.

• The composite experiences up to a 2.5% reduction in average inventory carrying costs associated with improved inventory management.

• The composite has a 20% inventory carrying cost rate.

Results. This yields a three-year projected PV ranging from $3 million (low) to $6.3 million (high).

Operational Transformation: Supply Chain Labor Efficiencies

Evidence and data. Interviewees and survey respondents told Forrester that Microsoft AI solutions helped both supply chain planners and frontline retail employees work more efficiently by automating routine tasks, accelerating information retrieval, and reducing the manual effort required to keep operations running smoothly. Supply chain teams use AI to streamline planning work that previously required repeated data pulls, spreadsheet updates, and reconciliation steps, which allows planners to redirect meaningful time each month toward exception management and higher‑value decision‑making.

Interviewees also described how AI‑enabled tools in stores reduced the time frontline retail employees spend on labor‑intensive tasks (e.g., updating in‑aisle information, managing frequent price changes with AI-enabled digital shelf labels). One interviewee explained that digital shelf labels powered by Microsoft AI eliminated the need for repeated manual price adjustments, which freed associates to focus on customer‑facing duties and higher‑priority operational responsibilities. Interviewees and survey respondents provided the following evidence:

• Survey respondents and interviewees reported that Microsoft AI significantly improved supply chain by automating forecasting, inventory management, and reporting tasks. This enabled planners to shift time away from manual activities and toward higher‑value decision‑making.

• The director of AI transformation at a consumer goods organization emphasized that Microsoft AI solutions improved accuracy and productivity. They said, “Forecast accuracy improved more than 10 points compared to statistical forecasting, enabling faster, more informed decisions across the supply chain.”

• A director of demand and store planning at a retail organization highlighted workforce efficiencies: “We reduced our planning workforce from 50 to 60 planners down to 40 to 50 while maintaining strong business results. That efficiency speaks to the ROI of the tool. … The system now automates allocation and replenishment at the SKU‑store level. Planners no longer need to manually set minimum presentation levels or calculate weeks of supply for holidays.”

• The same interviewee noted that Microsoft AI solutions concentrate planner effort on the highest‑impact work: “Alerts pinpoint SKUs outside forecast criteria, so planners can focus on exceptions instead of reviewing hundreds of SKUs manually. That’s a big shift from the old Excel-based process.”

• The same interviewee also described the future potential of agentic AI in planning environments. They said, “We’re excited about agentic AI, which will allow planners to execute changes through natural language commands — like adjusting minimums — without manual input.”

• Interviewees and survey respondents said frontline retail employees experience significant labor savings when AI‑powered digital shelf labels (DSLs) automate price changes. A senior director of enterprise strategy and operations at a retail and consumer goods organization shared, “Digital shelf labels powered by Microsoft AI solutions allow us to update prices across thousands of stores with a single command, saving hundreds of labor hours per store annually.”
  They also explained that DSL automation freed store associates across retail environments from repetitive manual label updates, which they estimated required around 200 hours per store per year. The interviewee said this allows associates to spend more time assisting customers and improving in‑store execution.

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

• The composite organization has a distributed supply chain planning team of 375 FTEs who each use Microsoft AI solutions.

• Supply chain planning FTEs who regularly use Microsoft AI tools experience time savings ranging from 6 to 12 hours per month.

• The fully burdened hourly rate for a supply chain employee is $60.

• Frontline retail employees across 350 stores experience labor time savings for tasks such as updating prices and maintaining shelves.

• Frontline labor savings occur on a per‑store basis, ranging from 9 to 15 hours of total employee time savings per store per month.

• The fully burdened hourly rate for a retail associate is $23

• The composite has a 50% productivity capture rate.

Results. This yields a three-year projected PV ranging from $3.5 million (low) to $5.4 million (high).

People And Culture Transformation: Reduced Frontline Worker Employee Attrition Costs

Evidence and data. Interviewees told Forrester that Microsoft AI solutions helped improve the day‑to‑day experience of frontline retail employees by reducing their manual workloads and removing friction from routine responsibilities. They explained that instead of spending hours on repetitive, low‑value activities (e.g., updating in‑store information, searching for guidance, frequently updating price changes), employees use AI‑enabled tools and automated workflows to complete tasks faster or avoid certain tasks altogether. They said this reduction in operational burden makes frontline teams feel more supported and better equipped to focus on customer interactions and higher‑priority responsibilities. Interviewees and survey respondents provided the following evidence:

• Survey respondents from organizations implementing AI‑powered store automation technologies (e.g., digital shelf labels) reported a 2% to 5% reduction in annual frontline attrition rates, with respondents attributing this improvement to reduced manual labor, higher engagement, and improved working conditions.

• Interviewees highlighted that time savings from AI automation in both store operations and supply chain workflows improved job satisfaction and workforce capacity. They said this is especially valuable in tight labor markets where retaining experienced staff helps limit recruiting and onboarding costs.

• A senior director of enterprise strategy and operations at a retail and consumer goods organization said automation directly reduced the physical and repetitive nature of frontline work: “Automation reduces manual tasks like price changes, allowing employees to focus on higher-value activities such as customer engagement. It also lowers churn by making roles less physically demanding.”

• Interviewees consistently stated that frontline employees responded positively to the removal of tedious, repetitious tasks and that it enhanced morale and reduced the drivers of attrition — particularly during peak seasons when the most amount of repetitive work occurs.

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

• The composite organization has 17,500 frontline retail employees.

• Prior to using Microsoft AI solutions, the composite had a 55% attrition rate for frontline retail employees (including seasonal workers).

• Microsoft AI reduces the attrition rate between 1.7% and 2.4%.

• The fully burdened cost to hire a frontline retail employee is $47,250.

• The composite organization’s hiring cost is 5% of the fully burdened cost per frontline retail employee.

• The composite organization’s baseline attrition rate remains constant during the analysis period.

• The total number of frontline employees remains constant during the analysis period.

Results. This yields a three-year projected PV ranging from $1 million (low) to $1.3 million (high).

Unquantified Benefits

Interviewees and survey respondents mentioned the following additional benefits that their organizations experienced but were not able to quantify:

• Accelerated innovation velocity and shortened time to market for new AI use cases. Interviewees told Forrester that before adopting Microsoft AI, bringing new AI use cases from concept to production required lengthy development cycles and slow deployment processes. After standardizing on Microsoft’s AI stack, teams were able to experiment, build MVPs, and operationalize models much faster, enabling rapid iteration and faster business learning. The senior director of AI transformation at a consumer goods organization explained, “Microsoft’s tech stack enables rapid MVP development and model deployment, cutting time-to-market for AI use cases from months to days.”

• Improved scalability and responsiveness to changing market conditions. Interviewees shared that operating on fragmented technology stacks previously limited their ability to scale digital products consistently across regions, and it slowed their response to shifting market dynamics. By consolidating AI and digital products on Microsoft’s platform, the organizations gained the compute, integration, and governance capabilities needed to deploy solutions globally while remaining agile. The senior director of AI transformation at a consumer goods organization stated: “Most of our digital products run on Microsoft’s tech stack. Azure provides the compute and integration capabilities we need to scale globally and react quickly.”

Flexibility

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

• Enabling exploration of new products. Interviewees described how AI unlocked new forms of strategic exploration that were difficult to achieve through traditional research methods alone. By rapidly synthesizing information and generating novel connections, AI helped teams explore adjacent and emerging product concepts that would not have surfaced through conventional brainstorming. A consumer strategy and research lead at a consumer goods organization said: “AI helped us explore new benefit territories like nootropics and brain food, spaces we wouldn’t have considered before. It’s opening doors to entirely new innovation pipelines.”

• Enabling future‑ready, more profitable delivery models through AI-driven fulfillment automation. Interviewees said Microsoft‑enabled AI supports advanced automation strategies to address the rising cost and complexity of fulfillment, particularly in dense urban environments. They said these initiatives are foundational to long‑term profitability as omnichannel demand grows. A senior director of enterprise strategy and operations explained, “Micro-fulfillment centers will automate picking and packing for 20,000 to 30,000 SKUs, reducing labor costs and enabling profitable urban delivery models.”

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