But, as with every technology, working out where to start is fraught with difficulties. “When all you have is a hammer, everything looks like a nail”—or so the expression goes—but when it comes to business challenges, not every problem warrants an agentic AI approach.

You may have determined candidate areas for agentic AI using a similar approach to that which we described when discussing rapidly ideating on value in a previous blog. However, how do you know if it really warrants an agentic approach, and then, once you’re confident that it does, how do you determine the value it will bring for your organization?

This blog aims to provide guidance on how to address these areas to empower you to make informed decisions and unlock the full potential of agentic AI.

Business and technical criteria

Based on our experience working with retail and consumer goods companies across the globe, there are some common trends that can be considered as criteria for determining if a specific process—or part of a process—is a good use case for agentic AI.

These aren’t considered to be “hard and fast” criteria that must be adhered to—they are merely guidelines.

• Volume. A process with high volumes or number of interactions. For example, a consumer goods company receives many more orders than an aircraft manufacturer, therefore, it’s likely to be far more applicable to apply agentic AI to an order intake process in a consumer goods company. That doesn’t mean that agentic AI cannot help an aircraft manufacturer with this process. It means that the specific process element where it’s applied would be different. For example, in placing an order for an aircraft, multiple detailed configuration documents may be needed, and agentic AI may have a valuable role ensuring those documents are correct.
  
• Interaction. A process that interacts with multiple systems. For example, updates, reads from, or consolidates data between different systems. Processes where users must review, or consolidate, content from multiple systems are prime candidates for the application of agentic AI. Sometimes referred to as “swivel-chair integration,” these types of processes are both tedious and fraught with error.
  
• Human. A process where a high level of human interaction is required. Perhaps involving seeking, reading, considering, and reasoning over multiple pieces of information, documents, or systems. This is typically work that’s mundane and repetitive. Agentic AI can assess and highlight gaps, differences, or anomalies. It can make recommendations to be evaluated by a human and as such, is designed to work alongside or augment the human by reducing the amount of mundane, repetitive activity. The human element is critical here—AI allows the human to focus on exceptions, strategic analysis, and complex decisions while supporting innovation.
  
• Errors. Processes that are error prone—which often occurs with repetitive, mundane human operations. More importantly, one where any errors or issues during the process execution cause adverse downstream consequences such as delayed deliveries, lost sales, compensation claims, or handling by a human that incurs cost or time. This can be a key area of concern and focus.
  

There is an additional requirement, albeit one that must be considered when architecting a solution. This relates to data availability.

It’s critical to ensure that the data required for the agentic AI application is available and accessible without causing challenges elsewhere. It’s common that agentic systems need to refer to data to aid decision-making. For example, it may be necessary to look something up on a customer or supplier master record in a transactional system. Where many of these are required in a very short time, it may be that the agentic solution causes performance issues in the transactional system. Architecturally, this challenge can be avoided by extracting this data into a data lake or other data store to act as a reference location.

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Defining value

Advancements position agentic AI as a cornerstone for creating a more resilient, efficient, sustainable, and autonomous supply chain. When it comes to evaluating the business value of any technology investment, one of the first points to consider is determining the specific drivers of value. In addition, understanding how you’ll measure this is equally important.

From the work we have done relating to agentic AI, value typically falls into three areas:

1. Productivity. You can think of this as “agentic liberated time.” This reflects reducing the non-value-added time associated with human interaction in a process or process step using the “liberated time” for value-added activities. Scoping these additional activities is critical to delivering value from agentic AI. As an example, one retailer was seeking to free up time for their supply chain planners to spend more time with individual suppliers planning future promotional inventories. AI agents can streamline communications with suppliers, monitor contract compliance, and resolve disputes efficiently.
   
2. Process efficiency. This relates to the elapsed time that a process takes. AI agents automate repetitive tasks and optimize operations leading to higher process efficiency levels and lower costs. This in turn has follow-on benefits—for example, reducing the time spent between receiving and processing a customer order translates to improved customer responsiveness.
   
3. Quality. This can often be seen as cliché. However, in this instance, the focus is the reduction of errors or issues. Specifically, those that have a negative consequence downstream within the organization or supply chain. For example, promising inventory that does not exist will adversely impact customer satisfaction scores and may well result in future lost sales.
   

Measurement is key

For each of these value driver areas it’s important to establish the metrics or KPIs that this is likely to impact in your specific case. The graphic above gives some examples, but this is where the value of agentic AI really comes into force.

For the productivity value driver, liberated time can be used to identify additional revenue generating opportunities, which can enhance your revenue per employee KPI. For process efficiency, reducing lost sales can be a relevant metric if, for example, you’re automating your customer order process.

Quality, however, is where it becomes interesting. Determining the downstream negative consequences of a delayed or misinformed decision can be difficult, but it’s worthwhile. One approach to consider is to use Microsoft Copilot to help ideate on this, asking for suggestions as to what the negative downstream consequences of errors in a particular process might be. This may not yield the exact answer for your business, but practice has shown that it usually inspires a new thought or perspective that relates to your business.

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Moving on value

Selecting the right use cases for agentic AI requires a thorough understanding of both the criteria for implementation and the drivers of value. By focusing on high-volume, error-prone processes that require significant human effort and interaction with multiple systems, organizations can identify the most promising areas for AI application.

Additionally, defining and measuring the value of AI investments through productivity, process efficiency, and quality improvements will ensure that organizations can unlock the full potential of agentic AI. With these guidelines, organizations can make informed decisions and navigate the complexities of AI use case selection, ultimately driving innovation and efficiency.

Learn more about agentic AI

• Microsoft’s responsible AI principles
• Retail Ready: Agentic AI built for the future of retail—ready now
• The future of logistics: How generative AI and agentic AI is creating a new era of efficiency and innovation

The post Helping retailers and consumer goods organizations identify the most valuable agentic AI use cases appeared first on The Microsoft Cloud Blog.

Retailers and consumer goods companies have faced constant change, particularly in supply chains. New sales and distribution models, such as online sales, omnichannel approaches, direct-to-consumer sales, and complex ecosystems, have evolved. External disruptions are frequent, with 90% of leaders reporting supply chain challenges in 2024¹. 

Supply chain agility and resiliency rely on fast and accurate decision making. Poor decisions or slow responses lead to missed promises, negatively impacting revenue and customer satisfaction, and increasing costs due to inefficient shipments and higher inventory levels. 

To address these challenges, there is an urgent need to improve both the quality and speed of decision making in supply chain management. 

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Enter agents and agentic systems

Agentic systems offer a revolutionary opportunity to enhance decision making quality and speed. Triggered by business events, agents collect and analyze relevant data to either act directly or recommend actions. 

Microsoft announced the ability to build autonomous agents using Microsoft Copilot Studio during Microsoft Ignite in October 2024. In a supply chain context, this capability could, for example, allow for the identification and action upon alternative supply sources in the event of a delayed shipment, with minimal human intervention.

Overview of agentic systems 

In the context of agentic systems, an agent refers to a system capable of autonomous decision making and action. These systems can pursue goals independently without direct human intervention. Agentic systems have the following characteristics: 

• Autonomy. They operate independently, making decisions and executing tasks without human oversight, escalating to a human when necessary. 
• Context aware. They interpret data and adjust actions accordingly. 
• Goal orientation. They can aim to achieve specific objectives. 
• Learning. They enhance their performance by using new data and past outcomes. 
• Reasoning and decision making. Agents use reasoning to process information, infer relationships, and make decisions. 
• Perception and sensing. Agents perceive their environment through sensors or other means, which allows them to be triggered by changes in the process.  
• Skills and capabilities. Agents possess specific skills or capabilities to perform tasks. These skills can be learned or programmed.   
• Memory. An agent’s memory stores relevant information for decision making and future actions. 

Agents can be programmed to pursue specific objectives once activated. For instance, when searching for an alternative supply source, they can prioritize cost minimization rather than selecting the first available option. 

Agents are already delivering value for customers—for example, one customer has autonomous agents reviewing shipping invoices with more use cases planned. Over time, agents can be developed for various tasks across the organization, with Microsoft Copilot serving as the ‘UI for AI’.  

Have we heard this before? 

This may sound like RPA (Robotic Process Automation). You might also question how an agent differs from a copilot. 

RPA employs rules-based automation, while agents enhance this capability by reasoning over data and using large language models (LLMs) to extract relevant information from extensive datasets. Whereas an RPA-based solution is rigid in terms of the scenarios that it can address and requires programming to make changes, an agent-based process automation solution can learn and improve over time, resulting in more effective outcomes. 

Agents operate autonomously, unlike copilots who assist users in real-time. An agent can work within Copilot, aligning with the Microsoft vision of Copilot as the UI for AI. In the future, users will have one copilot but multiple agents including many working autonomously behind the scenes. 

How agents can operate in the retail and consumer goods (RCG) supply chain 

Agents can be widely applied across the RCG supply chain to automate repetitive tasks, analyze vast amounts of data for insights, and improve supply chain management. An ideal use case involves tasks that are human-intensive, repetitive, and require real-time decision making, where AI can significantly boost efficiency and accuracy. The criteria for an ideal use case includes high data availability, clearly defined achievable outcomes, and the potential for measurable improvements in revenue and cost savings. 

AI agents can play a crucial role in retail store performance and inventory management practices. An agent can autonomously monitor performance data to alert the store manager when store performance metrics fall below a defined threshold. By comparing performance across similar stores, the agent can identify areas for improvement and recommend actions to improve store performance.  

Agents can help to avoid stockout and overstock situations at retail locations. By analyzing data from various sources (such as sales, inventory, promotions, and external events), an agent can identify when a sales spike is misaligned with the forecast, leading to a potential shortage, and alert the supply chain team. The agent recommends a replenishment order which it can automatically generate to help ensure optimal stock levels, lower carrying costs, and reduce the likelihood of stockouts or surplus inventory. 

Mitigating challenges with agentic AI

Disruptions across the supply chain often lead to product shortages and low case fill rate (CFR), leading to the complex daily task of allocating inventory across your customers. An agent can analyze customer orders, current inventory levels, and product substitution options to identify potential CFR situations. The agent allocates inventory by prioritizing orders based on predefined criteria such as customer loyalty, customer segmentation, order value, SLA fines, and urgency. 

One of the biggest challenges facing RCG companies in 2025 is assessing the impact of tariffs. AI agents can evaluate and recommend alternative suppliers from different regions to mitigate the risk of high tariffs. This diversification strategy helps in maintaining a steady supply of materials while minimizing costs. By continuously monitoring tariff regulations and market conditions, an AI agent can suggest cost-saving measures such as bulk purchasing before tariff hikes or shifting production to countries with lower tariffs. An agent can assist in negotiating better terms with suppliers by analyzing market conditions and historical pricing data. This helps to ensure that companies get the best possible deals despite tariff fluctuations.  

What’s next? 

Consider the significant amount of time and effort that it takes today to answer the question: “How can I optimize my supply chain to boost sales by 10%?”. 

Although this might feel like a supply chain question, it involves finance, sales, marketing, and possibly manufacturing. It’s such a complex question that answering it is likely to need days or weeks of analysis. 

Today, agents integrated into Copilot enable users to ask specific questions in defined areas. This capability will expand in scope and complexity over time, eventually leading to a comprehensive redesign of business applications. 

Project Sophia envisions agents, copilot, and business applications converging into an infinite research canvas.   

Designed with an AI first approach, Project Sophia lets you ask business questions by analyzing data from various disparate systems and inputs. The AI guides you to view different perspectives, helping you understand and act on insights holistically. 

Project Sophia reimagines the user experience, supporting each job function to address questions from their perspective while integrating strategic and tactical approaches. 

Getting started with agentic systems 

Increasing AI’s potential to scale value chain optimization in retail, consumer goods 

Agentic AI lends itself well to navigating the complexity of routes to market—integrating manufacturing and sales strategies, selling through multiple channels or direct to consumer, managing multiple product lines and businesses, and integrating marketing and sales efforts globally. 

Agentic AI is an integral tool that gives LLMs agency, with the ability to act autonomously. Whereas LLMs have previously been used to perform tasks including generating text and summarizing documents, they have not been able to act on their recommendations. Agentic AI on the other hand, is designed to drive goal-based optimizations and can dynamically adapt and execute goals with high predictability and minimal human oversight. Together, advancements in generative AI and agentic AI will redefine strategic value and productivity derived from technology, incorporating more advanced decision making processes with greater accuracy and speed. 

Identify business problems and scenarios for more strategic engagement 

As you consider how to use AI agents in a strategic manner, it is vital to frame applications of agentic AI in the larger context of identifying line of business processes that lend themselves to automation: optimizing time-consuming and mundane tasks/scenarios; establishing user trust in the agent’s capabilities and establishing clear operational guardrails for agentic AI including data governance, privacy, security; and instilling confidence in the agent’s value delivery, extending collaborative work management beyond task tracking to planning and execution functions.  

The integration of agentic AI and generative AI into business applications signifies a monumental shift in how organizations can approach problem solving, strategic planning, and operational efficiency. By using advanced AI capabilities, businesses can anticipate a future where decision making is not only faster and more accurate, but also more insightful and holistic. This convergence of technology paves the way for innovative solutions and unprecedented levels of productivity, firmly with AI at the core of tomorrow’s business landscape. 

Learn more about agentic systems

• Delivering your supply chain copilot: Prioritizing areas of ROI 

• Delivering your supply chain copilot: Getting started on ideation 

• Empowering responsible AI practices 

•  Microsoft Cloud for Retail website  

Sources

¹ https://www.mckinsey.com/capabilities/operations/our-insights/supply-chain-risk-survey  

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Now that we’ve explored priority areas of return on investment (ROI) that a supply chain copilot can provide in the context of our supply chain, let’s delve into data considerations and how to get started on ideation for your supply chain copilot. The complex real-time decisions and agility required of today’s supply chains can seem daunting when thinking about transformation, but actually is well-suited to application of AI to selected use cases as you get started. 

Technology requirements  

Irrespective of the target areas for a supply chain copilot, the foundational technology capability requirements are the same. These can be thought of three distinct layers that ensure scalability and maximum extensibility into the future. The three layers are laid out below:  

1. Data platform:
   Data is the fuel that powers AI, consequently it must be the foundation of any application of AI such as a supply chain copilot. Data is likely to exist in multiple places, leveraging different storage approaches. Some could be in distributed databases such as Azure Cosmos DB. Other sources might include SQL databases such as Azure SQL. Irrespective of such sources, the need to unify across multiple locations is essential to make sense of the data. This need to govern, model, and consolidate multiple data sources creates a strong case for a single data platform like Microsoft Fabric—offering unified storage, experience, and governance—Fabric was designed to be the data platform for the era of AI.
2. Analytics and AI:
   Some might say that ‘this is where the magic happens’, and when it comes to making sense of the data they would be correct. The analytics and AI layer is where predictions and detailed—yet actionable—insights are generated. As an example, Azure AI Studio empowers you to simplify the creation of AI-powered solutions, giving access to multiple AI techniques and approaches—including generative AI with Azure Open AI, but also incorporating tools such as AI vision or AI Speech.
3. Application layer:
   While it is easy to think of the application layer as the layer with which users interact with your solution, this is only part of the story. Process orchestration and automation is something that can add significant value to a copilot. For example, when a supply chain copilot recommends actions to a supply chain planner—for example requesting expedition of a shipment—it would add so much more value if the copilot were able to execute the recommendation on behalf of the planner.  This is where Azure Functions, which are event driven, can play their part. Typically, this would require interaction with other execution systems through an API—resulting in the need for careful API management over the lifecycle of the different solutions.  

Your own supply chain copilot: Design and delivery  

Experienced supply chain and technology practitioners know very well that what may look amazing in a conceptual demonstration or video can be very difficult to deliver in the real world.  

Issues such as data sources, system integration, technology choices, and overall architecture can make the prospect of delivering your own copilots feel overwhelming.   

Microsoft teams have seen this and pioneered approaches to help you establish a way forward and deliver results quickly. This is broken down into three steps:  

Rapidly ideating on value  

This is primarily a workshop-driven approach to identify and validate specific challenges or opportunities where AI can deliver value for the customer. From a supply chain perspective, this would focus on the elements that you feel offer the largest potential benefit or cause the largest business pain. It is broken into three parts:   

• Inspiration: Here, a high-level education regarding the capabilities of AI, generative AI, and copilots are covered alongside inspirational examples of where customers are using this technology to deliver value.
• Opportunity generation: In this stage, opportunities within your supply chain organization are examined.
• Prioritization: This is the evaluation stage where opportunity areas are prioritized using a simple four-quadrant matrix with ‘Effort’ on the x-axis and ‘Impact’ on the y-axis. This provides a simple yet effective visualization approach to defining priority focus areas.  

The output of this is to be clear on the ”solution route,” or most appropriate combination of technologies to be applied, to deliver what is required. As an example, Microsoft Copilot Studio, Microsoft Azure AI Studio, and Microsoft Power Automate, when combined together, form a very powerful combination to support copilot delivery especially where there are requirements around information extraction, knowledge mining, and process orchestration.   

There is no “one-size-fits-all” so other approaches may be recommended. This may include leveraging partner offerings—Blue Yonder for example have mature capabilities in the form of its control tower solution which may be appropriate.   

Another key outcome of this stage is to understand which of four AI opportunities is being targeted: enrich employee experiences, reinvent customer engagement, reshape business process, or bend the curve on innovation. This aids identification of the key value drivers that you seek to influence, key performance indicators (KPIs) you seek to influence, and provides a segue into the next stage.  

Envisioning  

Again, this is a workshop driven approach—sometimes spanning several sessions—including all relevant customer stakeholders, to determine details of the solution to be delivered.   

This will include establishing a detailed view of architectural elements, interaction and integration points, value potential, and data requirements. A perspective on the business process impact will further enhance the detail behind the value case. This would leverage benchmarks from existing research and established copilots to create a view tailored to your business.  

An additional key output to complement the value case is a view of the investment required and the way in which the delivery can be structured to maximize return on investment and deliver using an Agile delivery approach.  

Proof of concept, minimum viable product, and beyond  

Following envisioning, there is a choice between moving to deliver a proof of concept (POC) or a minimum viable product (MVP).  

A POC demonstrates that an idea or use case is feasible through the delivery of a specific set of capabilities. It is used to illustrate and prove a concept and is usually self-contained in that it does not connect to live data or other systems. Consequently, it is of limited use beyond demonstrative purposes.  

By contrast, an MVP is deployed into production and integrated to existing systems so offers immediate value to the business and end-user while requiring limited effort to deliver. It can therefore become a foundation for further development and enhancement by adding capabilities based on prioritization using Agile development principles.

Integrate AI across your supply chain  

AI transformation, and specifically copilots, present a remarkable opportunity for you to innovate and compete with renewed vigor. By leveraging AI, businesses can enhance efficiency, mitigate risks, and uncover hidden opportunities.   

The integration of AI into supply chain management through a supply chain copilot, for instance, allows for real-time visibility, optimized data management, and seamless interoperation across multiple elements. This shift from reactive to proactive operations enables organizations to consistently deliver the right products at the right time, while balancing inventory, waste, and transportation costs. Moreover, the use of generative AI offers new possibilities for content and insight generation, further empowering supply chain practitioners. As technology continues to evolve, embracing AI transformation will be crucial for organizations to stay ahead in an increasingly complex and dynamic world.  

Learn more  

•  Microsoft Copilot Studio  
• Azure AI Studio   
• Microsoft Cloud for Retail website  
• Microsoft for Consumer Goods website  
• Supply chain AI for the new era of value realization  
• Unlock the full potential of your next-generation supply chain with Microsoft and Blue Yonder  
• Empowering responsible AI practices  
• Copilot in Microsoft Dynamics 365 Supply Chain Management   

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AI transformation offers you a phenomenal chance to innovate and compete with new vigor—offering previously unimaginable opportunities. It is a term you are likely to hear more over the coming years, and Microsoft aims to place a copilot on every desk, every device and across every role in support of Microsoft’s mission to empower every person and every organization on the planet to achieve more.

As part of this, Microsoft has identified four areas of opportunity for organizations to drive their AI transformation¹:

• Enrich employee experiences.
• Reinvent customer engagement.
• Reshape business processes.
• Bend the curve on innovation.

The value of AI transformation and copilots

While it may feel instinctive that the value of AI transformation lies in its ability to save time, this is only part of the story. Early studies are already showing significant value from AI transformation being derived from not only reducing costs, but also increasing revenue and reducing risk through improved quality of decision making.

Highlights from key studies include benefits of:

• Delivered 25% increase revenue through enhanced efficiency.²
• Increased customer satisfaction by 12%.³
• Increased revenue growth by 4% through improved strategy and engagement.⁴
• Reduced costs of 10%.⁵
• Completed tasks 25% faster.⁶
• Reduced total expenditure by 0.7%.⁷
• Reduced risk through a 40% improvement in quality of decisions.⁸

The supply chain context

In an era of rapid global change, macroeconomic shifts, and geopolitical disruptions, the global supply chain faces unprecedented challenges. Simultaneously, technology is undergoing a transformation fueled by data and AI. These powerful tools and capabilities empower organizations to enhance efficiency, mitigate risk, and discover hidden opportunities.

As the world becomes increasingly complex, leading organizations are gravitating towards technology to accelerate supply chain optimization with greater speed and precision to shift the paradigm from a reactive mode of operating to one that is proactively getting ahead.

It is a foundational concept that supply chain excellence is achieved by consistently and efficiently getting the right products to the right place, in the right quantities, at the right time and at the desired quality, the first time. Doing this while respecting constraints and balancing inventory, waste, and transportation costs is what makes the work of a supply chain practitioner so difficult.

Integral to this challenge is optimized data management, real-time visibility combined with integration and interoperation across supply chain elements—such as production, logistics, procurement, partners, and customer service.

Yet so often, organizations struggle with siloed business processes, communications challenges, disconnected systems, complex planning workflows, transportation disruption, warehouse capacity issues and multiple other challenges leading to high inventory, increased costs, waste, and a lack of overall business resilience.

For a supply chain practitioner there are simply too many information sources to assimilate and consider when making better-informed decisions in real time. The practitioner can get started with a copilot to overcome fragmented data and integrate it into usable insights. Read about how Altana began overcoming fragmented knowledge—establishing a uniform understanding of the data/knowledge gap combining enterprise resource planning (ERP) systems, factory data, enriched with market and external risk factors.

The application of AI across the supply chain

With all the focus on generative AI, it can be easy to perceive that generative AI is the answer to all your problems. This would be incorrect—as ever there are no silver bullets. AI and generative AI are distinct, yet complementary technologies used for supply chain optimization that provide the analytical horsepower to process vast amounts of data that can deliver significant impact.

Non-generative AI techniques can be used for multiple different tasks in a supply chain context, for example:

• Clustering: Route planning for customer shipments and Warehouse slotting optimization.
• Classification: Inventory management approaches (for example, fresh, frozen) and resource allocation.
• Rules and heuristics: Inventory planning and distribution planning.
• Optimization: Inventory optimization, and route optimization and network design.
• Regression: Demand forecasting and supplier performance analysis.

Likewise, generative AI offers some incredible opportunities across the supply chain, which can be broadly placed into three groups:

• Content generation: For example, summarizing multiple contracts and agreements associated with a given supplier.
• Insight generation: For example summarizing multiple sources of external data to provide a perspective of events that could influence your demand forecast.
• User Interaction: Provision of a universal interface with which supply chain practitioners interact and spans multiple systems and allows for both understanding and interaction with systems that control the supply chain.

The control tower concept

You can think of your supply chain function as a central brain orchestrating data and physical movements across your organization. This is critical work, influencing all the key metrics that drive business performance.

The concept of a supply chain control tower appeared a few years ago as a centralized system providing real-time visibility and insights across the entire supply chain. It leverages a unified data platform to deliver next-generation supply chain capabilities, beginning with end-to-end visibility and performance management.

The concept looks to incorporate data from various sources to help you monitor, manage, and optimize your supply chain operations, enabling better decision-making and more rapid responses to disruptions.

Adding AI into this mix offers tantalizing possibilities—the ability to dramatically reduce the quantity of direct decision-making that supply chain practitioners need to be directly engaged in.

Enrich employee experiences

Generative AI is fundamentally changing how we, as individuals, relate to, and benefit from technology. While both generative AI and traditional AI contribute to supply chain optimization, generative AI emphasizes employee productivity and can work with a broader set of data, revolutionizing the types of insights you can glean with better explainability. The gamechanger here is the ability to use a conversational “agent” or copilot to navigate any task and turn data into knowledge through a conversational user interface using natural language. A copilot can enhance supply chain teams by providing real-time insights, automating routine tasks and workflows, and facilitating collaboration. For instance, it can analyze data to identify bottlenecks, suggest optimal routes for shipments, and streamline inventory management. It provides the ability to move beyond static dashboard reporting by extracting actionable insights to empower users.

A copilot for supply chain can help empower teams during their workday by converting predictive insights into specific actions while powering collaboration within a connected ecosystem.

This means organizations are better able to manage the cascading impact of their supply chain with more transparent and collaborative data sharing. Visibility improves because, where once it was restricted by the network it is now enhanced through a wider global context.

Internal data is augmented with real-time connections to partners and external signals—like geopolitical tensions, logistics challenges, and commercial factors like promotional activity or weather events. Data is continuously available and interoperable across the supply chain, giving users simultaneous access to current information, with the ability to pass on insights into the wider organization. Microsoft Teams and Microsoft 365 become engines in the connected ecosystem for greater connectivity and collaboration—empowering team members who may not be using supply chain systems—like a store manager or sales representatives—to be consumers of supply chain insights and information. This improves access to insights that are actionable at the optimal point in the value chain.

Copilots can dramatically improve productivity while accelerating decision-making. For example, take this common scenario where Hillary—an inventory analyst—needs to understand why projected cost and freight (CFR) of a key product has dropped and determine what to do to reduce impact on customer service level agreement (SLA).

Instead of compiling spreadsheets from different data sources and spending hours doing manual analysis, Hillary uses a combination of copilots and a CFR prediction algorithm to quickly identify the root cause, assess alternatives, and share the recommended approach with her manager.

Next steps to apply generative AI across your supply chain

We’ve explored some strategies for applying AI and generative AI across your supply chain, and how a supply chain copilot can support supply chain practitioners. Stay tuned for part two, where we delve into data considerations and how to get started on AI ideation for your organization.

Learn more

• Microsoft Copilot Studio
• Azure AI Studio
• Microsoft Cloud for Retail website
• Microsoft for Consumer Goods website
• Supply chain AI for the new era of value realization
• Unlock the full potential of your next-generation supply chain with Microsoft and Blue Yonder
• Empowering responsible AI practices

¹Embracing AI Transformation: How customers and partners are driving pragmatic innovation to achieve business outcomes with the Microsoft Cloud, Official Microsoft Blog.

²How Netlogic Computer Consulting is Boosting its Sales Performance with Microsoft Copilot for Sales, Tech Community.

³Microsoft: Copilot for Service Boosts Customer Satisfaction by 12 Percent, CX Today.

⁴What Can Copilot’s Earliest Users Teach Us About Generative AI at Work?, WorkLab.

⁵Is Microsoft Copilot Worth the Investment?, Varonis.

⁶Navigating the Jagged Technological Frontier.

⁷Is Microsoft Copilot Worth the Investment?, Varonis.

⁸Navigating the Jagged Technological Frontier.

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In just over a year, it has gone through what you might call the ‘shiny toy’ stage where teams play with it to try and work out what it can do for them. From this, lessons have been learned and applied.  Some of the lessons Microsoft teams have learned have been highlighted in previous blog posts.

Microsoft’s customer teams have undertaken many customer workshops, each focused on identifying the areas that have the greatest opportunity for benefit. 

McKinsey suggests that for retail and consumer goods businesses, the value potential is somewhere in the region of 1 to 2% of the total industry revenue. As for the ‘low hanging fruit’, about “75% of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and research and development (R&D).”¹ But in practical terms what does this look like if you are a retail or consumer goods company? 

From the work Microsoft has undertaken there are three broad groups of use cases that offer the greatest value: 

1. Content and product marketing. 
2. Internal knowledge management. 
3. Customer conversational experience. 

You may wish to explore each of these areas with a view to understanding what others are doing and considering examination of something similar in your organization. 

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Content and product marketing

At the heart of generative AI is the ability to create new content, so it stands to reason that this would be an area of high potential.   

Content marketing traditionally involves a series of iterative loops involving multiple parties—perhaps a brand or product owner and a creative group—be this a copywriter or a creative agency that produces images. 

This approach has several challenges. Firstly, due to the iterative nature between different parties it can take several days or weeks of iterations due to the lags between each create—review—revise cycle. For visual content—with the use of external agencies, complex backgrounds, complex picture, and editing—this can become expensive.   

These two reasons mean that scaling the creation of content becomes very difficult. If you have a very wide range of products or a wide range of customer groups for which you would like to customize the message, it is simply impossible to achieve this with a traditional approach. 

Generative AI changes this. 

One of the first case studies regarding the use of generative AI that Microsoft highlighted was the creation of product related marketing content at a vast scale. Carmax—a used car retailer—wanted to provide a consistent set of product information for all the different makes and model of cars that they sell. Generative AI was used to generate text for car comparisons allowing viewing of specifications, features, highlights, and summary reviews. Carmax estimated that to build this would have required eleven years of effort—which dramatically illustrates how generative AI can address the scaling challenge when a retailer has a wide range of products. Learn more about the Carmax case study alongside example content and a short video. 

Marketers aspire to segment their customers into smaller and smaller groups to make messaging as personal as possible. Customer data platforms such as Microsoft Dynamics 365 Customer Insights allow creation of segments based on customer attributes from multiple sources. Websites and social media platform allow specific messages to be targeted at these groups but the challenge of having the capacity and time to create the relevant content remains a constraint. 

This is where generative AI can be used to fill the gap. A number of organizations are utilizing an innovative approach of aligning keywords to their products and then using generative AI to suggest a series of advertisements, or social media headlines associated with specific consumer profiles.  Following review by a copywriter, to ensure brand alignment and an appropriate tone, these headlines are then approved for use. This approach can enhance overall creativity as well as enabling more granular targeting.

Internal knowledge management

“If HP knew what HP knows, we’d be three times more productive.” This is a quote attributed to Lewis Platt who was Chief Executive Officer of HP between 1993 and 1999 and is well known amongst knowledge management professionals.² 

It is no secret that organizations create and retain a lot of knowledge. The larger the organization the more knowledge. But more knowledge can often add to the problem—understanding what is available can be very difficult. As Lewis Platt suggested, organizations do not know what knowledge they have. Knowledge becomes siloed across the different systems that permeate the organization and pulling it together for specific purposes becomes very difficult.  

Traditional search might be able to help you find something specific within your organization by referring to a particular document. It will even guide you to the source document where the information can be found. But what if you want information from across multiple documents? Or you want the information formatted in a particular way, like providing information in a tabular format? 

Again, this is where generative AI changes things. 

Microsoft Copilot for Microsoft 365 can work across Microsoft 365 applications—Microsoft Word, PowerPoint, Outlook, Excel, and others—to analyze, provide insight, and pull together information allowing you to access and manage all your content in one place. 

While this approach allows you to look across documents you and your colleagues are using today, organizations are also seeking to unlock data in documents going back many years. Examples include understanding recipes and ingredients previously experimented with; attaining insight into previously run marketing programs or attaining perspectives on previous supplier negotiations in preparation for upcoming discussions. These are all use cases where the knowledge is spread across disparate locations and systems. 

Already, several organizations have used generative AI to help improve the employee experience. Heineken, for example, has used Azure OpenAI Service and its built-in ChatGPT capabilities to build chatbots for employees, while also using other Azure AI Services to bring innovation to existing business processes.  

Customer conversational experience

Solving a problem for your customer is a major way to differentiate your business from that of the competition. 

A few years ago, when bots emerged, they offered the opportunity to allow a customer to get help without the need for a human. But the challenge was always that bots were limited by the topics and actions that your bot was configured for. In-short, they did not feel human enough. 

Consumers often want help, advice, or inspiration with their purchases but without visiting a store this can be tricky. These ‘human-like’ interactions are so important that stores have invested heavily to save store associate time—freeing them to help customers.   

Online this becomes difficult. But what if you could replicate a human expert who can help, advise, and inspire? One which could be available 24 hours a day to all your customers online?  

This is where generative AI can power and dramatically enhance your Customer conversational experience. 

In January 2024, Microsoft launched (in public preview) a copilot template on Azure OpenAI Service to build more individualized shopping experiences across existing web sites and applications. With this capability, retailers can build advisor type experiences for their customers who can engage in helpful and natural conversations and be guided to precisely the product they need. Help, advice, and inspiration all in one place.   

Illustrating how this approach can differentiate, Carrefour launched their Hopla bot to help with what many consider a difficult domestic task—menu planning. After selecting the store where you want to do your shopping you can ask Hopla for a meal idea, based on your family size and budget.  When you are happy with the suggestion the ingredients are displayed, considering assortment and availability at your chosen store. From there you can even add the products to your basket and transact for delivery or pick-up. 

Carrefour built this using Azure OpenAI Service to access OpenAI’s GPT-4 technology. The solution respects confidentiality and compliance—leveraging Microsoft Azure data security, reliability, and confidentiality features, to ensure compliance with general data protection regulation (GDPR).³ 

Hopla is a great example of how AI can enhance customer experience and convenience, while also boosting sales and loyalty for retailers. By using OpenAI’s GPT-4 technology, Carrefour was able to create a bot that can generate natural and relevant meal suggestions based on user preferences and store availability.⁴ 

When they announced the launch, Carrefour said that customers will be “able to use this natural-language AI to help them with their daily shopping. They will find it on the site’s home page and will be able to ask it for help in choosing products for their basket, based on their budget, food constraints they may have or menu ideas.”³ 

This is a great example of how AI can help retailers differentiate themselves in a competitive market and offer personalized solutions that meet customer needs. 

Generative AI provides more possibilities than can be addressed in a series of blogs. Understanding what others have done can help guide your thinking and approach. The level of creativity increases daily, and we will all watch the space with anticipation of the most impactful use cases for retail and consumer goods companies. 

Transform your business with AI solutions from Microsoft

Visit the Microsoft Cloud for Retail website to learn more about how AI and generative AI capabilities are helping retailers and consumer goods organizations transform their businesses. Learn about Microsoft’s commitment to making sure AI systems are developed responsibly and in ways that warrant people’s trust.

¹The economic potential of generative AI: The next productivity frontier, McKinsey.

²New technologies to take knowledge management in procurement to the next level, CPOstrategy.

³Carrefour integrates OpenAI technologies and launches a generative AI-powered shopping experience, Carrefour Group.

⁴Hopla,Carrefour.fr.

The post Threefold revolution: The influence of generative AI on retail and consumer goods appeared first on The Microsoft Cloud Blog.

It also changed the conversations Microsoft customer-facing teams were having as organizations scrambled to exploit the technology. There are very few customer conversations and major projects where some form of generative AI does not have a part to play. 

Microsoft teams have worked hard to advise customers on the best way forward and there have been lessons learned from all sides. Conversations are now starting to change, however. They are moving away from being primarily focused on ‘What can we do with generative AI?’ toward ‘How should we approach generative AI?’. 

There are some lessons that have been learned in customer interactions that are now being applied to projects to maximize the return on investment for our customers. Each of these four areas you may wish to give consideration to as you explore possibilities within your organization. 

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1. Be clear on the problem 

There can be a temptation to see generative AI as if it were a ‘new toy’ waiting to be played with. It is critical however to be focused on the problem you are trying to solve and to use this to ‘paint the vision’ of what you want to achieve. 

An approach that can work well is to identify several candidate initiatives or use cases where generative AI may be able to help. Examining these more closely could reveal that there are other ways to solve some of them. There may also be some that you can cluster together because they could warrant similar approaches to address their needs. 

Prioritizing based on likely business impact versus the effort required is an excellent way to determine which use case, or use cases, you should start with. This is a technique Microsoft Industry Architects use with customers engaging with both technical and business teams at the same time. Placing each use case on a simple four-quadrant matrix with ‘Effort’ on the x-axis and ‘Impact’ on the y-axis gives a simple yet effective visualization approach to setting priorities. 

When considering impact, consider things like speed or time saving, improved decision making, and cost savings but also consider how long it will take to reap the benefits. When considering effort, think about the time and money you would need to invest to achieve the desired outcome while being conscious of the different systems and personas that would be involved. 

Once you have determined your target use case or use cases, you must be clear about what ‘good’ looks like. This means determining the metrics and values by which you will measure your success. Using quantitative terms can deliver real focus. Your metric could be based on time, effort, or money—but try to make it as measurable and meaningful as possible. 

2. Work inside-out 

As a customer-centric organization, it might feel counter-intuitive to focus internally first. With new innovative technologies like generative AI, this is a great way to try out the technology and your approaches on a ‘friendly’ audience—your own internal teams—before applying to your external customers. 

This also acts as the foundation for a roadmap of projects and initiatives all centered around generative AI. You can think of this roadmap in three phases—which you could align to a ‘Crawl-Walk-Run’ approach. 

Phase 1: Crawl

Use cases inside the organization

This focuses on ‘human in the loop’ reviews of content that is generated. In a retail and consumer goods environment example use cases might include: 

• Summarization and analysis—call center and customer interaction summarization. 
• Categorization—knowledge management and internal communications. 
• Content generation—creating product images for your e-commerce platform or creating product descriptions based on multiple inputs. 

Phase 2: Walk

Use cases that directly interact with employees and customers

In this phase, you may have human supervision of multiple use cases, each providing information to help the employee or customer, human, make better-informed decisions. Retail and consumer goods examples might include: 

• Summarization and analysis—summarizing product reviews, product descriptions, and specifications. A great example of this is how CarMax is creating their used car listings—saving 11 years of content generation effort. 
• Categorization—analyzing social media trends, sentiment analysis, or making product design recommendations. 
• Content generation—personalized user experience or marketing campaign content generation. 

Phase 3: Run

Increased automation 

This final phase focuses on new product offerings or automation that you might provide as direct interaction with your customers. Examples in a retail or consumer goods setting could include: 

• Summarization and analysis—call-center automation and the contextual automation of customer-facing business processes. 
• Categorization—automating the creation of product descriptions for your commerce platform considering consumer feedback. 
• Content generation—automated creation of personalized marketing and brand content. 

3. Be business (problem) led 

Prioritizing use cases based on effort versus impact is likely to ensure you are maximizing your focus on a problem that can deliver real value for your business. ‘Painting the vision’ of what you are trying to achieve remains key here. 

Ongoing input from business teams remains key—like all initiatives where technology plays a major part, the most successful initiatives tend to be where technology and their business counterparts work most closely—all focused on the specific business problem. 

In any technology-focused initiative, people, processes, and technology all need to be considered together and generative AI is no different. Consequently, it is important to not ignore specific elements. For example, as part of your work focused on each use case you should ensure you: 

• Determine the business process associated with your use case. Understand the triggers that will initiate it along with the points of interaction with different systems and personas. 
• Establish clarity on the personas that interact with the process but also understand who will be impacted when your use case is successfully delivered and used in the business environment. 
• Establish how personas who need to be involved will interact with the process—establish when and how they will need to be notified of interaction or approval they need to undertake. This is especially important when considering the responsible AI principle of accountability. 
• Look carefully at your use case from a responsible AI perspective. Using tools like the Microsoft Responsible Impact Assessment could accelerate things for you here. 
• Change management should be examined carefully. Understanding the personas impacted is essential to aid this. Given how new generative AI is and the large amount of information in the media there are sensitivities that may require careful handling. Again, careful application and consideration of the Microsoft responsible AI principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability will help you in this regard. 

4. Equip yourself (to follow fast) 

It is good to be excited about the potential of generative AI—according to McKinsey the productivity it could add to the global economy equates to $2.6 trillion to $4.4 trillion annually.² These numbers are significant given that in 2021 the GDP of the United Kingdom was $3.1 trillion. Consequently, it is important to be able to innovate using this technology.   

To do this, you need to build skills inside your own organization to be able to deliver use cases quickly. This means nurturing your own teams and in-house capabilities. 

It could be very tempting to adopt a ‘fast follower’ approach where if you see a competitor launch a generative AI-powered solution that aids their customers you rapidly follow with something similar—or perhaps even better. 

There is nothing wrong with this as a strategy, but it needs the in-house skills and experience to have to be nurtured over time to have the capability to deliver against this. Delivering use cases you have prioritized and innovated against is one of the best ways to nurture the in-house skills you need. 

By reaching 100 million users in 64 days, ChatGPT became the fastest-growing consumer application in history.³ This potentially sets the scene for generative AI to become the fastest-growing technology category ever seen. 

While the overall space in terms of possibilities and benefits is likely to evolve rapidly, looking carefully at these four areas will help your organization learn while effectively harnessing the power of generative AI to deliver value for your business. 

Learn more

• Microsoft Cloud for Retail
• Generative AI will revolutionize Revenue Growth Management 
• “What’s my generative AI strategy?” Microsoft is helping consumer goods brand marketers embrace the era of AI 
• How next-generation AI and digital tools transform the retail frontline 
• Microsoft’s responsible AI principles 
• CarMax puts customers first with car research tools powered by Azure OpenAI Service 
• Welcoming the generative AI era with Microsoft Azure 
• Generative AI transforms business processes 

¹ The Guardian, ChatGPT reaches 100 million users two months after launch, February 2023.

² McKinsey Digital, The economic potential of generative AI: The next productivity frontier, June 2023.

³ Reuters, ChatGPT sets record for fastest-growing user base—analyst note, February 2023.

The post From discussion to deployment: 4 key lessons in generative AI appeared first on The Microsoft Cloud Blog.