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  "title": "AI agents in business: three examples of work they take on",
  "hook": "Public accounts from Danone, Regal Rexnord and Uber describe three tasks for AI agents: finding price discrepancies in orders, handling service questions and retrieving financial data. These examples can help you narrow down a task in your own team. Access and human review still need to be agreed for that task. Our graphic shows a proposed division of work, not a client deployment.",
  "summary": "Three documented business examples of AI agents: checking orders, helping customers and finding financial data, with a practical task review for your team.",
  "pillar": "Hybrid Organisation",
  "author": {
    "name": "Marc Seefelder",
    "role": "Co-founder, MING Labs"
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  "datePublished": "2026-10-26",
  "dateModified": "2026-10-26",
  "freshness": {
    "updated": "26 October 2026",
    "nextReview": "November 2026",
    "nextReviewDate": "2026-11-25"
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  "evidenceLabel": "Public case reports and an illustrative MING example",
  "confidence": "B",
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    {
      "id": "S1",
      "title": "Danone: transforming critical business operations at scale with Microsoft AI & Autonomous Agents",
      "publisher": "Microsoft Customer Stories",
      "date": "2025-10-24",
      "url": "https://www.microsoft.com/en/customers/story/25506-danone-microsoft-365-copilot/",
      "supports": [
        "Reported order-price checks and drafts for resolving discrepancies."
      ],
      "method": "Vendor account with a customer voice; no independent outcome audit or complete permissions matrix. Text checked on 16 October 2026."
    },
    {
      "id": "S2",
      "title": "Regal Rexnord goes all in with Microsoft Copilot Studio to streamline operational efficiency and support customers",
      "publisher": "Microsoft Customer Stories",
      "date": "2026-03-10",
      "url": "https://www.microsoft.com/en/customers/story/26197-regal-rexnord-microsoft-copilot-studio",
      "supports": [
        "Separate systems for employee questions, customer support and service staff; RRXy uses product and order information and supports human handoff."
      ],
      "method": "Vendor account; original diagram reviewed on 16 October 2026. No general success rate or verified handover SLA."
    },
    {
      "id": "S3",
      "title": "Unlocking Financial Insights with Finch: Uber’s Conversational AI Data Agent",
      "publisher": "Uber Engineering",
      "date": "2025-07-17",
      "url": "https://www.uber.com/in/en/blog/unlocking-financial-insights-with-finch/",
      "supports": [
        "Described Slack data access with permissions, queries and results; verification warning in the illustrated interface."
      ],
      "method": "Historical operator engineering report; text and six figures checked on 16 October 2026. Its future-state diagram and product comparison are not adopted as current capability or performance evidence."
    },
    {
      "id": "S4",
      "title": "Illustrative price-check role and proposed task review",
      "publisher": "MING Labs",
      "date": "2026-10-16",
      "supports": [
        "Chosen price-check role, permission boundary and proposed questions for selecting a team task."
      ],
      "method": "Invented example and original editorial method; neither Danone’s actual workflow nor measured client results."
    }
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  "faqs": [
    {
      "q": "Which AI agent should our company start with?",
      "a": "These examples do not establish a best first role. Choose a specific task with known inputs, a useful output and a responsible person. Review what existing systems already do and what a supervised agent would add."
    },
    {
      "q": "May the agent change order prices in this example?",
      "a": "No. In MING’s illustrative example it may read approved records and prepare a draft. Sales establishes the applicable price and decides how to proceed. No wider permissions have been agreed. [S4]"
    },
    {
      "q": "Do these cases prove an agent will save us money?",
      "a": "No. Public accounts describe particular systems and circumstances. Their outcomes do not transfer to another team. Check usable results and the actual supervision and correction work for your own task."
    }
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  "subtype": "report",
  "subtitle": "How teams check orders, help customers and find financial data.",
  "category": "Hybrid Organisation",
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  "contentMarkdown": "---\ntitle: \"AI agents in business: three examples of work they take on — MING Labs\"\ndescription: \"Three documented business examples of AI agents: checking orders, helping customers and finding financial data, with a practical task review for your team.\"\ncanonical: https://www.minglabs.com/insights/articles/ai-agent-examples-in-business\nlang: en\nlast-updated: 2026-10-26\n---\n\n# AI agents in business: three examples of work they take on\n\n[Home](https://www.minglabs.com/)  [Insights](https://www.minglabs.com/insights)  [Articles](https://www.minglabs.com/insights/articles)  AI agents in business: three examples of work they take on\n\n[English](https://www.minglabs.com/insights/articles/ai-agent-examples-in-business)[Deutsch](https://www.minglabs.com/de/insights/articles/ki-agenten-beispiele-unternehmen)\n\nHybrid Organisation\n\n\n\nHow teams check orders, help customers and find financial data.\n\nLast updated: 26 October 2026 | Next review: November 2026 Public case reports and an illustrative MING example [Machine-readable record ](https://www.minglabs.com/insights/articles/ai-agent-examples-in-business.json)\n\nMarc Seefelder · Co-founder, MING Labs · October 2026 · 6 min read\n\nAn illustrative MING example\n\nThe agent prepares the price check for the sales team.\n\nOrder\n\nStill uses the old price\n\nApproved price list\n\nContains a new price\n\nAgent\n\nShow the difference with both records\n\nFlag the line that differs and save a draft.\n\n Send for sales review\n\nNamed person in sales\n\nEstablish which price applies to this order\n\nCheck the agreement and decide how to proceed.\n\nThe agent changes no price and confirms no order.\n\nProposed division of work by MING Labs \\[S4\\]. This example depicts neither Danone’s actual workflow nor a measured outcome.\n\nAn order uses a different price from the current price list. Someone needs to find the discrepancy, check the agreement and decide how to proceed. We use this invented situation in the graphic to show what an agent could prepare and where sales makes the decision. That gives the team a specific assignment to discuss.[\\[S4\\]](https://www.minglabs.com/insights/articles/ai-agent-examples-in-business#source-s4)\n\nPublic accounts of business deployments can help you find a suitable task. The three cases below describe different parts of work. The first two come from Microsoft customer stories; the third is Uber’s own engineering account. We have not tested these systems ourselves. The task review that follows is our proposed way to discuss a role with your team.\n\n##  Danone checks orders for price discrepancies\n\nMicrosoft’s October 2025 account describes an agent at Danone that compares order prices with internal pricing and promotion records. It flags differences and helps employees resolve them with suggestions and email drafts.[\\[S1\\]](https://www.minglabs.com/insights/articles/ai-agent-examples-in-business#source-s1)\n\nThe account describes the job but does not establish every approval boundary. It does not, for example, prove that the system may change binding prices itself. For a similar task in your team, that distinction needs an explicit decision: should the agent present a discrepancy or execute a change?\n\n##  Regal Rexnord can hand a customer conversation to a person\n\nMicrosoft’s March 2026 story describes Regal Rexnord’s customer agent, RRXy. It uses product knowledge, retrieves order information from Salesforce and can transfer a conversation with its context to human service staff. Separate agents support employees and service representatives; these are distinct roles.[\\[S2\\]](https://www.minglabs.com/insights/articles/ai-agent-examples-in-business#source-s2)\n\nHandover gives your own team something concrete to test. Who receives the case? Can they see the original question and the information already found? Check that in the channel you actually use and agree who watches the service queue and takes over unresolved conversations.\n\n##  Uber’s finance team asks for data in Slack\n\nIn July 2025, Uber published how Finch handles finance questions in Slack. The system finds relevant data sources, builds queries with access controls and returns results with information about the query. Its illustrated interface asks the user to verify the output.[\\[S3\\]](https://www.minglabs.com/insights/articles/ai-agent-examples-in-business#source-s3)\n\nThis helps distinguish access to data from wider authority. A team may need help finding figures without delegating accounting entries or payments. The historical report alone establishes neither a guarantee of today’s features nor permission to take those actions.\n\n##  Choose a task whose result someone can use\n\nYour team may recognise a similar situation. Take a task that actually occurs and walk through it with the person who needs its result. Where does the work start? What information is available? What must that person receive to continue? Those questions turn an interesting application into an assignment you can check.\n\nFor our price example, the first assignment would be narrow: show the order line that differs and attach both records. Two different prices alone do not tell the agent whether the customer was promised the older one. A named person in sales needs to establish the agreement. Our proposed division of work keeps that unresolved question visible.[\\[S4\\]](https://www.minglabs.com/insights/articles/ai-agent-examples-in-business#source-s4)\n\nWe would begin the discussion with three completed lines:\n\n| What the team agrees | Our illustrative price example |\n| --- | --- |\n| What result do we need? | A flagged discrepancy with the order and the relevant price list. |\n| What may the agent do? | Read approved records and save a draft; do not change prices or confirm orders. |\n| Who decides next? | A named person in sales establishes the applicable price and how to proceed. |\n\nThis is not an account of Danone’s internal workflow. It is our proposal for making a possible role in your own team specific enough to discuss. The actual data, access and responsibilities need to match before it is used.[\\[S4\\]](https://www.minglabs.com/insights/articles/ai-agent-examples-in-business#source-s4)\n\n##  Try the task with an order that needs clarification\n\nNext, walk through an ordinary case and a difficult one: a clear price discrepancy and an order with a special agreement, for example. Can the responsible person follow the evidence? Can the team still find the order if details are missing or the agent gets stuck? Record the review and correction work people actually do.\n\nCompare the proposal with the current process. Which steps can existing rules already cover? Where would an agent add something useful? Our [guide to agents and fixed workflows](https://www.minglabs.com/insights/answers/when-should-you-use-an-ai-agent-instead-of-workflow-automation) helps with that decision. The [completed agent job description](https://www.minglabs.com/insights/articles/hire-dont-deploy) shows how to specify a chosen assignment in more detail.\n\nBring an approved or anonymised task to the first discussion and name the person who would check its result. That gives us a basis for deciding what an agent should prepare and how the team’s responsibilities would change. [Discuss my use case](https://www.minglabs.com/contact?type=project&topic=agent-task-design)\n\nWhat these examples establish\n\nThe three public accounts describe particular tasks and systems at their respective publication dates. They are not independent comparative tests and do not establish a transferable success rate. The graphic, table and review questions are MING’s own proposal; they depict no MING client deployment or measured productivity gain.\n\n## Sources\n\n\\[S1\\]\n\n[Danone: transforming critical business operations at scale with Microsoft AI & Autonomous Agents](https://www.microsoft.com/en/customers/story/25506-danone-microsoft-365-copilot/) Microsoft Customer Stories · 2025-10-24 Supports: Reported order-price checks and drafts for resolving discrepancies. Vendor account with a customer voice; no independent outcome audit or complete permissions matrix. Text checked on 16 October 2026.\n\n\\[S2\\]\n\n[Regal Rexnord goes all in with Microsoft Copilot Studio to streamline operational efficiency and support customers](https://www.microsoft.com/en/customers/story/26197-regal-rexnord-microsoft-copilot-studio) Microsoft Customer Stories · 2026-03-10 Supports: Separate systems for employee questions, customer support and service staff; RRXy uses product and order information and supports human handoff. Vendor account; original diagram reviewed on 16 October 2026. No general success rate or verified handover SLA.\n\n\\[S3\\]\n\n[Unlocking Financial Insights with Finch: Uber’s Conversational AI Data Agent](https://www.uber.com/in/en/blog/unlocking-financial-insights-with-finch/) Uber Engineering · 2025-07-17 Supports: Described Slack data access with permissions, queries and results; verification warning in the illustrated interface. Historical operator engineering report; text and six figures checked on 16 October 2026. Its future-state diagram and product comparison are not adopted as current capability or performance evidence.\n\n\\[S4\\]\n\nIllustrative price-check role and proposed task review MING Labs · 2026-10-16 Supports: Chosen price-check role, permission boundary and proposed questions for selecting a team task. Invented example and original editorial method; neither Danone’s actual workflow nor measured client results.\n\n## Frequently asked questions\n\nWhich AI agent should our company start with?\n\nThese examples do not establish a best first role. Choose a specific task with known inputs, a useful output and a responsible person. Review what existing systems already do and what a supervised agent would add.\n\nMay the agent change order prices in this example?\n\nNo. In MING’s illustrative example it may read approved records and prepare a draft. Sales establishes the applicable price and decides how to proceed. No wider permissions have been agreed. \\[S4\\]\n\nDo these cases prove an agent will save us money?\n\nNo. Public accounts describe particular systems and circumstances. Their outcomes do not transfer to another team. Check usable results and the actual supervision and correction work for your own task.\n\n## More from Insights\n\n[Hybrid Organisation\n\n### What belongs in an AI agent’s job description?\n\nA completed IT-request example shows how to specify the task, access and human responsibility.\n\nRead article](https://www.minglabs.com/insights/articles/hire-dont-deploy)\n\n## Available actions\n\n- **Copy content for my AI**: copies the [AI briefing](https://www.minglabs.com/insights/articles/ai-agent-examples-in-business.md) to the clipboard in the browser.\n\n---\n\nCanonical: https://www.minglabs.com/insights/articles/ai-agent-examples-in-business\nMachine-readable index: https://www.minglabs.com/llms.txt\n"
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