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Agentic AI is being applied to bounded enterprise workflows where software interprets context, selects or sequences steps, and uses company systems to answer questions, recommend actions, or carry out work. Examples range from airline customer service and employee knowledge tools to IT operations, billing, software engineering, and plant logistics. The case studies below show what agents do in practice—and where automation still has limits.
What makes these enterprise workflows agentic?
An agentic workflow connects an AI model to organizational knowledge and tools such as APIs, ticketing platforms, databases, or business applications. Rather than only generating text, it can retrieve information, route work, coordinate steps, or write changes to a system. The scope of action varies: an agent may answer a question, propose a resolution for a person to approve, or execute a permitted operation.
That distinction matters. “Agentic” does not mean unsupervised or universally autonomous. The examples here describe specific workflows, and the results are reported by the vendors or partners involved—not independently audited benchmarks or evidence that the same results will transfer to other organizations.
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| Application | Workflow and systems | Agent role | Reported outcome |
|---|---|---|---|
| Ryanair customer service | Baggage, refunds, bookings, and disruption questions across chat and voice | Answers and supports continuity between channels | AWS reports 120,000 daily answers, 94% accuracy, and 70% fewer service contacts per passengers carried; see the case details below. |
| Iberdrola IT service management | ServiceNow change requests and incident workflows | Validates, adds context, and helps select change models | No quantified outcome stated in the AWS technical post. |
| Sanofi employee knowledge and task routing | Internal information, content, data retrieval, and workflow systems | Finds information and connects users to specialized agents that may write back or automate tasks | AWS reports usage and employee survey results through Q1 2026. |
| Direct Ship ERP billing exceptions | SAP data, operating procedures, and job aids | Answers process questions and recommends exception resolutions | AWS and Accenture say resolution and revenue realization improved within five weeks; no numerical result is stated, and the enterprise is unnamed. |
| Coupa software engineering | Engineering tools, tests, support queries, tickets, pull requests, and databases | Investigates and triages issues, and can take specified actions | AWS reports reductions in investigation and resolution time; planned autonomous remediation is distinct from deployed functions. |
| UNACEM plant logistics | Truck pickup logistics accessed through WhatsApp | Helps coordinate cement pickup and plant-gate flow | IBM reports up to 40% lower driver waiting time at the gate. |
| BMC Helix IT service operations | Observability data, alerts, historical ticket worklogs, and infrastructure tests | Correlates alerts, plans diagnostics, runs checks, and can patch known vulnerabilities | Google Cloud reports a 25% to 35% reduction in mean time to recovery (MTTR). |
The comparison is a practical reading of the cases, not a standardized industry scoring system. The public descriptions do not consistently specify approval thresholds, reversibility, identity configuration, or measured baselines, so those details should not be assumed.
Customer service: Ryanair’s chat and voice assistant
Ryanair uses an assistant for customer questions about baggage, refunds, bookings, and flight disruptions. AWS says the chat solution launched in October 2024 and had generated 10 million chatbot answers by the time of its case reporting. Its case study also reports 120,000 customer answers per day, 94% accuracy, and a 70% reduction in customer-service contacts per passengers carried. These are AWS-published case figures, not independently audited measures.
AWS describes multilingual support in seven languages and integration with Amazon Connect to maintain continuity between chat and voice. In February 2026, AWS reported that the architecture had moved from 12 domain-specific agents to one consolidated agent. In a comparison of five models against 12,000 production customer questions, AWS reports an 84% latency improvement—from 18 seconds to 2.9 seconds—and a 25% accuracy uplift. Those figures apply to that stated comparison, not to a general model benchmark.
Ryanair’s customer-service director framed the business objective as scaling service without costs tracking passenger volumes linearly: “We needed to move from linear cost scaling to a model where customer service costs remain largely fixed as passenger volumes grow.” AWS’s Ryanair case study describes the deployment and its reported measures.
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AWS describes Iberdrola using agents in ServiceNow to validate draft change requests, add context to incident management, and help employees select an appropriate change model through conversational AI. The change-request sequence uses specialist agents for rule extraction, content validation, model analysis, and phase transition. This is a workflow with multiple checks and handoffs, rather than one prompt generating an answer.
The AWS technical account describes REST-based routing, a combination of operational and analytical data, VPC isolation, guardrails, monitoring, and audit traces. Those are design details of this implementation, not requirements that every agent system use the same architecture. AWS’s post does not provide a quantified business outcome. AWS’s Iberdrola technical post was published on 10 February 2026.
Employee knowledge and task routing: Sanofi’s Concierge
Sanofi’s Concierge began as an internal conversational gateway for finding information, creating content, and completing tasks. AWS says Sanofi is adding an agent hub that connects this interface to specialized agents for data retrieval, system write-back, and workflow automation. Examples cited include clinical-manufacturing technology transfers and go-to-market planning. The agent hub’s broader role should not be confused with evidence that every connected workflow is fully automated.
Through Q1 2026, AWS reports 50,000 weekly users, 72,000 monthly users, 11 million conversations, and 90% positive feedback. In an employee survey reported by AWS, 90.5% said they saved time on routine tasks, 78% said they made better decisions, and 77% said Concierge improved work-life balance. These are company case and survey figures, not general evidence about workforce outcomes.
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ERP billing exceptions: Direct Ship with SAP
An AWS for SAP guest post with Accenture contributors describes a Direct Ship billing solution for handling exceptions that would otherwise require manual work. A conversational assistant centralizes standard operating procedures and job aids, answers process questions, recommends resolutions, and accesses SAP data. This makes it a process guide and resolution aid connected to enterprise records, not simply a generic chatbot.
The post says issue resolution and revenue realization improved within five weeks of deployment, but gives no numerical result and does not name the enterprise customer. The scope of the outcome therefore cannot be compared quantitatively with the other cases. The AWS for SAP post with Accenture contributors was published on 4 June 2026.
Software engineering and incident response: Coupa’s Sentinel
Coupa’s internal Sentinel system spans engineering tools. AWS says its agents triage test failures, investigate issues, respond to support queries, and can open pull requests, create tickets, and query databases. These actions can change work systems, so their value depends on clearly scoped permissions and review practices; the case description does not establish a universal level of human approval for each action.
AWS reports 50% reductions in ticket investigation and issue-resolution time, as well as support-query resolution metrics. Because these are vendor case figures and the headline summary does not establish equivalent baselines for every measure, they should be read as Coupa-specific reported outcomes rather than industry benchmarks. Coupa is working toward agentic CI/CD and autonomous issue remediation; that direction is a roadmap, not a claim that the full autonomous remediation capability is already deployed.
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Bradley Simpson, Coupa’s Director of Engineering and Head of the Sentinel Platform, said: “Amazon Bedrock AgentCore is what makes this a production solution rather than a collection of scripts,” and, “Our agents take real actions: opening pull requests, creating tickets, and querying databases.” AWS’s Coupa case study describes the system and reported results.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Plant logistics: UNACEM’s cement pickup workflow
IBM describes a WhatsApp-accessible logistics agent for cement pickup at UNACEM. The workflow targets a tangible operational bottleneck: truck drivers waiting at the plant gate. IBM reports that the deployment reduced waiting time by up to 40%, alongside improved daily load-outs and ETA reliability. “Up to” is the reported maximum reduction; it should not be read as a guaranteed result for every driver, site, or period.
IBM also describes possible extensions of the same foundation into IT support, procurement, customer summaries, help desk workflows, and an “Ask Safety” agent grounded in documented procedures. These are extensions described by IBM, not additional demonstrated outcomes in the case. IBM’s UNACEM article is dated 30 March 2026.
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IT service operations: BMC Helix agents
Google Cloud’s BMC Helix case study describes agents that aggregate observability data, consolidate alerts that were not previously correlated, use historical ticket worklogs to plan diagnostics, and run inspections or tests. It also says agents can automatically patch known vulnerabilities. These functions move beyond summarizing an incident: they use operational context to guide investigation and, in the patching case, execute a remediation action.
Google Cloud reports a 25% to 35% reduction in MTTR. The public case-study result is vendor-reported, and the available description does not specify a publication date or enough baseline detail to treat the range as a directly comparable benchmark. BMC Helix’s Head of Marketing, Jake Adger, said: “A key part of our value proposition is that customers can start using agentic AI to enhance Service Operations out-of-the-box.” Google Cloud’s BMC Helix case study describes the reported result and agent functions.
What these applications show about enterprise automation
- Agents need system access to do more than chat. The cases connect models to sources such as ServiceNow, SAP, engineering tools, observability platforms, internal knowledge, or logistics channels.
- The action boundary is a design choice. Answers and recommendations carry different operational risk from system write-back, opening a pull request, patching a vulnerability, or moving a change request forward.
- Grounded knowledge and workflow rules shape usefulness. Procedures, historical worklogs, company data, and validation rules provide context that a general-purpose response alone would lack.
- Controls matter when actions have consequences. Iberdrola’s case explicitly describes isolation, guardrails, monitoring, and audit traces. Across deployments, teams also need to decide which identities and permissions agents use, what actions require approval, how failures are surfaced, and how to reverse or correct a change.
- Case-study metrics are not cross-company benchmarks. The reported figures come from vendor or partner material, use different measures and baselines, and do not establish comparative ROI or industry-wide adoption.
The most informative way to evaluate an enterprise agent is to ask which bounded workflow it owns, what systems and data it can access, what it is permitted to change, when a person is involved, and how the organization measures the result. The examples above demonstrate several viable patterns; they do not establish a single definition or a universal level of autonomy.
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