Introduction
Retail operations are changing quickly as AI improves forecasting, speeds up supply chains, and reduces costs. However, many AI projects fail due to poor communication, misaligned expectations among stakeholders, and resistance to change, rather than technical problems (Deloitte, 2026).
The purpose of this paper is to examine an AI-based inventory forecasting solution for a large national retailer that replaces a significant amount of manual planning work, and to propose a stakeholder communication plan and implementation approach drawing on readings from the course and my professional experience on the Cosmos project at Dell Technologies.
Project and Stakeholder Landscape
The retail company is moving from a traditional planning model to an AI-based inventory management system. This is a major shift from using historical spreadsheet data to using machine-learning tools that analyze sales history, seasonality, regional demand, and market trends to make near real-time inventory recommendations across stores. In theory, this should reduce stockouts and overstocks, making the supply chain more flexible.
McKinsey’s domain-owner framework shows that AI projects work best when business leaders and technical teams share responsibility for the outcomes, because each group defines success differently (McKinsey & Company, 2024).
For this project, the key stakeholders are executive leadership, inventory planning and operations, the data science and AI engineering team, and supply chain and procurement.
Executive leadership: Executives are mainly focused on the performance, cost efficiency, and long-term competitiveness of the business. Their core interest is improving margins, optimizing inventory, and strengthening supply chain responsiveness over the long term. Their primary concern is whether the AI project will deliver a positive return on investment. Their AI fluency is moderate: they understand AI at a strategic level but are less familiar with its technical implementation details.
Inventory planning and operations team: This group serves as the domain owner in McKinsey’s framework because it manages demand assumptions and understands planning workflows. Their core interest is maintaining accurate forecasts and meaningful influence over planning decisions. Their primary concern is that the increased automation will affect their job security and make their current skills less useful. Their AI fluency is low because they know the business processes well, but have limited experience with machine learning.
Data science and AI engineering team: This group includes the product department teams that build and maintain the forecasting models. Their core interest is in delivering accurate, reliable, and scalable models for daily operations. Their primary concern is being pressured to launch models before the data pipelines, monitoring, and downstream business processes are in place, which could undermine model performance and credibility. Their AI fluency is high because they work directly with the models, data infrastructure, and technical implementation details.
Supply chain and procurement team: Forecast accuracy is very important to this group for managing inventory and tenders. Their core interest in maintaining reliable inventory flow to meet service levels and vendor commitments without frequent stockouts or overstocks. Their primary concern is that inaccurate AI forecasting could disrupt operations, damage vendor relationships, or create costly supply shortages. Their AI fluency is moderate at best, since they care more about results than about how the models actually work.
Communication Strategy by Stakeholder Groups
Each of these groups measures success differently, so the communication approach needs to be tailored to what they actually care about, rather than pushing the same message to everyone.
Executive leadership: The main message to this group should be that the AI inventory forecasting system is a long‑term investment that will improve margins, inventory turns, and the supply chain, not something that will create instant results. Monthly executive briefings and quarterly strategy reviews are the best channels because they fit existing leadership routines and make it easier to discuss major milestones and ROI. During the pilot phase, executives should get monthly updates, and once regional rollout begins, the briefings can shift to quarterly check-ins tied to major rollout stages.
The toughest question they are likely to ask is whether this AI project is really worth the cost and disruption. I would respond by presenting early evidence of improved inventory accuracy and cost trends, while setting realistic expectations that the full value will emerge over several iterations.
Inventory planning and operations team: The main message to this group should be that the AI forecasting tool is meant to support their work by automating repetitive tasks, not to replace planners or eliminate their expertise and decision‑making role. Town halls, hands‑on workflow demos, and small training sessions are the best communication channels because they allow planners to ask questions directly, see the tool in action, and build trust through direct interaction rather than top‑down announcements. During the pilot and early rollout phases, planners should receive weekly updates to address concerns and build confidence when uncertainty is highest; once adoption stabilizes, communication shifts to biweekly or monthly check‑ins.
The hard question this team is likely to ask is whether the AI system will make their job obsolete. I would address that honestly by saying that the system will automate some manual forecasting work, but will also create higher‑value analytical and strategic roles that planners can move into, with transition pathways and training available from the start. This approach aligns with the BCG 10‑20‑70 framework, which holds that successful AI adoption depends more on people and processes than on technology itself (Boston Consulting Group, 2024).
Data science and AI engineering team: The main message for this group is that model performance and business adoption are equally important success metrics. The goal is not only technical accuracy, but also operational impact and trust, and they will have adequate time and support to build strong data pipelines and monitoring before production deployment. Regular sprint reviews, governance check‑ins, and shared performance dashboards are the best communication channels because they keep technical teams aligned with business goals and enable clear tracking of both model metrics and adoption progress. Sprint reviews should happen biweekly during the development and pilot phases, governance check‑ins should happen monthly, and dashboards should update in real time so the team can continuously monitor model behavior and business outcomes.
The hardest question this group is likely to ask is why they are being pushed to production before data quality and downstream processes are ready. I would acknowledge that business pressure for speed is real, but make clear that leadership supports a phased rollout with clear quality gates. The team will not be forced to deploy untested models, and technical leaders will advocate to executives for the necessary infrastructure and process readiness.
Supply chain and procurement team: The main message for this group is that AI‑driven forecasts will improve the reliability and timeliness of inventory recommendations, but human oversight and approval will remain part of the process to protect vendor commitments and service levels. Monthly planning reviews and operational dashboards are the best channels because they align with existing replenishment cycles and allow supply chain leaders to review forecast outputs, ask questions, and plan ahead for peak seasons or market shifts. During the pilot, monthly planning reviews keep them informed of forecast performance and required adjustments, while dashboards provide real‑time visibility into inventory recommendations and exceptions requiring human review.
The hardest question this group is likely to ask is what happens if the AI forecast is wrong and causes stockouts or missed vendor commitments. I would answer honestly that no forecast, AI or human, is perfect, but the system includes built‑in human-review checkpoints and clear escalation rules, so supply chain leaders can override recommendations when operational risk is high. Forecast accuracy will also be monitored closely, with corrective action plans if performance falls below agreed thresholds.
Managing the Pilot-to-Production Gap
Pilot fatigue risk assessment: The pilot fatigue risk for this project is moderate to high because planners need to adjust daily workflows, and many employees may initially distrust AI-generated forecasts. It is also a risk that executives could expect results before employees fully adopt the system, which can create disappointment and pressure to cut the pilot short.
A phased rollout with human validation at each stage is the right approach to implementation. Strong executive sponsorship and clear governance checkpoints are important because Deloitte’s State of AI on Enterprise 2026 report finds that moving from pilot to production remains a major challenge, and that clear strategy and governance help reduce pilot fatigue.
Success metric communication: Executives will focus on cost savings, inventory efficiency, and margin improvement, while inventory planners will prioritize reducing manual effort and improving the workflow. Supply chain leaders will focus on service levels and fewer stockouts and overstocks, while technical teams will focus on forecast accuracy, system reliability, and scalability. For non-technical stakeholders, updates will focus on business outcomes such as cost, time, and service levels rather than model precision scores.
Reset strategy: If the project stalls between pilot and production, leaders will arrange cross-functional reset sessions with executives, inventory planners, supply chain leaders, and technical leads to conduct an open assessment of the pilot findings, evaluate the barriers to adoption, and realign the short-term objectives to focus on workflow improvement rather than full automation.
Cross-functional Collaboration Design
Team composition: Research papers from Harvard Business School on Procter & Gamble highlight the importance of cross-functional collaboration in developing AI value propositions (Harvard Business School, 2023). McKinsey’s Gen AI operating model also stresses the importance of breaking down silos, especially by connecting the business and technical sides of decision-making throughout the AI product lifecycle (McKinsey & Company, 2024).
The core team will include AI engineers and data scientists (technical), MLOps engineers (technical), inventory planning and supply chain leaders (business), and product managers, project managers, and governance representatives (cross-functional). This mix aligns with the readings, which emphasize the importance of having both domain and technical expertise on a single team.
Breaking the silos: First, business and technical stakeholders should use the same dashboard to align on outcomes. Second, inventory planners should participate in sprint reviews and model evaluations to better understand operations and build trust in the models.
Decision rights and accountability: There may also be disagreement over how quickly to automate versus how much human oversight to maintain. In this case, supply chain leadership and governance representatives share decision-making authority, while the technical leaders provide feasibility input. Changes that increase operational risk require their joint approval.
Personal Reflection and Professional Application
This assignment challenged my assumption that AI stakeholder communication is primarily about translating technical jargon to a non-technical audience. I now understand that the real challenge is building trust and managing organizational change, not just simplifying the language.
The BCG 10-20-70 framework stood out as the most immediately applicable one to my professional context. In my work on the Cosmos project at Dell Technologies, I saw firsthand how technically sound AI solutions stalled because of insufficient investment in training, role transitions, and process redesign, which aligns with the 70 percent that BCG emphasizes. This is often overlooked in technology-heavy projects. Going forward, I will use this framework to justify upfront investments in people and change management, not just technical infrastructure.
The most difficult part in creating this proposal was balancing honesty about workflow disruption with the need to maintain organizational confidence. Telling employees that AI will automate parts of their work without triggering panic or resistance requires careful messaging and credible commitments to new roles and training. This shows that AI project leadership is as much about managing human anxiety and building coalitions as it is about deploying technology.
If I were to extend this plan, I would add a communication strategy focused on AI governance and ethics, including transparency, accountability, and bias detection in AI applications. This could help build stakeholder trust throughout the project.
AI use disclosure statement
I used AI tools to brainstorm phrasing options, outline the structure, check grammar and clarity, and assist with formatting. All stakeholder analysis, communication strategies, framework applications, and reflections are based on my own reasoning and professional experience.