Feature
AI agents have quietly moved beyond the hype and headlines to take on real, measurable roles inside enterprise businesses.
From assisting merchants at Walmart to supporting banking workflows at Virgin Money, these autonomous or semi-autonomous tools are reshaping how work gets done. But what are they actually doing on the ground — day to day — and how are businesses ensuring these agents deliver reliable, unbiased results?
Table of Contents
- Understanding AI Agents: The Building Blocks of Autonomous Workflows
- Top Enterprise Use Cases for AI Agents
- How Leading Brands Use AI Agents: Walmart, Virgin Money and More
- How AI Agents Drive Business Value in the Enterprise
- How AI Agents Are Changing Workforce Roles and Team Structures
- How Enterprises Monitor and Control AI Agents in Production
- Build vs. Buy: Choosing the Right AI Agent Strategy for Your Business
- Operationalizing AI Agents: From Proof of Concept to Business Integration
Understanding AI Agents: The Building Blocks of Autonomous Workflows
AI agents are no longer theoretical tools or future promises — they’re already at work inside the enterprise. While generative AI often grabs headlines, it’s these purpose-focused agents, built to perform specific tasks or workflows, that are driving real impact in day-to-day operations. Whether developed in-house or through platforms like Microsoft Copilot Studio, OpenAI’s API ecosystem or enterprise SaaS platforms embedding agentic capabilities, these tools are now being leveraged across sectors — from retail to finance to healthcare.
AI agents are now getting embedded into workflows once dominated by humans — optimizing decisions, handling routine tasks and providing insights in real time. These aren’t general-purpose bots; they’re purpose-built, often domain-specific tools designed to operate with autonomy or semi-autonomy under human oversight.
Fergal Glynn, chief marketing officer at Mindgard, said that at his company, they've integrated AI agents into customer support to resolve basic queries, manage inventory and streamline document processing. "We have observed faster response times for customers, reduced manual errors and a boost in employee productivity." He also noted that by automating repetitive tasks, they've seen improved operational efficiency, reduced costs and more time for teams to focus on complex challenges.
Related Article: What Are AI Agents? The Autonomous Software Changing How Work Gets Done
Top Enterprise Use Cases for AI Agents
| Department | Use Case | Agent Function |
|---|---|---|
| Customer Service | Live chat support, post-call summaries, escalation routing | Conversational agents that assist reps and automate FAQs |
| IT Operations | Incident detection, ticket triage, root cause suggestions | Autonomous monitoring agents with remediation logic |
| Finance | Invoice matching, risk flagging, internal fraud monitoring | Task-based agents that extract insights from financial systems |
| Human Resources | Candidate screening, onboarding support, internal queries | Support agents that interface with HRIS platforms and employees |
| Sales & Merchandising | Assortment planning, pricing analysis, performance reporting | Generative AI copilots that respond to natural language queries |
How Leading Brands Use AI Agents: Walmart, Virgin Money and More
At Walmart, a generative AI assistant named Wally is helping merchants make smarter decisions about product assortment, pricing and replenishment. Rather than sorting through dashboards or waiting on analysts, teams can ask the assistant direct questions, like “What’s driving inventory turnover in the midwest region?” and receive contextualized, data-backed responses. The AI agent isn’t just retrieving data; it’s reasoning across multiple inputs to guide decisions faster and more consistently.
In the financial sector, Virgin Money deployed a conversational AI agent named Redi to handle thousands of routine customer interactions, such as resetting passwords, answering account queries or updating personal details. But behind the scenes, the agent is also connected to internal systems and knowledge repositories, allowing it to support human agents and automate portions of fraud detection workflows. These hybrid agents are proving to be force multipliers — improving speed while freeing human agents to handle more sensitive cases.
Australian law firm MinterEllison implemented an AI agent named Lantern to expedite the legal discovery process. Lantern processes documents at a rate of 3,500 per hour — 58x faster than manual review — which has significantly reduced costs. The law firm seeks to integrate AI thoughtfully, with the goal of augmenting human capabilities rather than replacing them.
Mercedes-Benz's MBUX Virtual Assistant, in partnership with Google Cloud's Automotive AI Agent, offers vehicle owners the ability to have personalized, conversational responses on navigation, points of interest and more. Drivers can converse naturally with the agent to get answers to questions, like "Where is the nearest Italian restaurant?" and ask follow-up questions like, "What's their signature dish?" or "Do they have good reviews?" The bot has the ability to handle complex dialogue and retain memory of conversations.
The #ConceptCLAClass features the all-new #MBUX Virtual Assistant, which uses advanced software combined with generative AI for an even more natural and intuitive in-car experience. #MercedesBenz #CES2024
— Mercedes-Benz (@MercedesBenz) January 9, 2024
Other Deployments Span Sectors:
- In healthcare, many hospital systems have integrated agentic AI to coordinate pre-op preparation and insurance verifications, automating what was once a highly manual back-office function.
- In enterprise IT, AI agents monitor infrastructure, flag anomalies and, in some cases, remediate low-risk incidents without human intervention. John Tito, co-founder at Game Host Bros, said their agent system proactively detects resource spikes and automates server-level corrections, which previously would have required manual diagnosis and downtime.
- In customer service, agents are used not only for chat support but to summarize interactions, draft follow-up actions and route escalations based on tone, context and urgency.
- In financial services, AI agents streamline compliance and fraud detection. For example, some banks deploy agentic systems to monitor transactions in real time, flagging suspicious activity and auto-generated suspicious activity reports (SARs) for human review.
- In manufacturing, agentic systems monitor sensor data from machinery, detect anomalies and trigger preemptive service tickets, avoiding unplanned downtime.
What ties these use cases together is not just automation — it’s intent-driven assistance. These agents are increasingly capable of interpreting context, adapting across systems and making micro-decisions that align with business goals.
How AI Agents Drive Business Value in the Enterprise
As enterprises move beyond experimentation with AI agents, they’re beginning to report measurable returns — particularly in efficiency, cost reduction and agent productivity. While early use cases focused on containment and deflection (especially in customer service), today’s AI agents are driving value across a broader operational environment, including finance, IT, HR and logistics.
For many businesses, efficiency gains come from reduced time spent on repetitive decision-making. At scale, even small improvements eventually compound. For example, enterprises deploying internal agents to assist with scheduling, report generation or task prioritization often report reduced administrative workloads. In contact centers, generative agents supporting live reps have been shown to cut handle time, while also improving the speed and accuracy of post-call documentation.
Cost savings are often indirect but substantial. Automating portions of back-office workflows reduces error rates, minimizes manual rework and enables leaner staffing models without sacrificing performance. AI-powered agents can operate 24/7 without any downtime, helping businesses meet customer expectations for round-the-clock support without a proportional increase in labor costs.
That said, the real shift may lie in augmentation rather than pure automation. In the majority of deployments, AI agents aren’t replacing people — they’re working alongside them. Agents suggest next best actions, generate drafts or provide real-time coaching. Human employees remain in the loop, validating decisions or applying judgment to more complex edge cases. This collaborative model has also improved job satisfaction in some environments, particularly where AI reduces cognitive overload or monotonous task-switching.
When implemented with clear oversight, AI agents can dramatically improve customer service performance without sacrificing quality. Kyle Sobko, CEO at SonderCare, claimed that AI agent use at his company has allowed them to "reduce wait times by 30% for customers…thus greatly reducing errors." He added that AI chatbots now handle common product inquiries, freeing his team to focus on more complex issues. Future plans include expanding agents into inventory management with predictive analytics to minimize stockouts and inefficiencies.
While hard ROI numbers vary by industry and use case, early adopters consistently report improvements in service speed, consistency and employee focus. In coming years, as monitoring and benchmarking tools mature, businesses will be better equipped to track AI agent contributions to KPIs across departments.
How AI Agents Are Changing Workforce Roles and Team Structures
The rise of AI agents isn’t just transforming workflows — it’s reshaping the workforce itself. As enterprises adopt agent-based systems, new roles are emerging while existing ones are being redefined. From prompt engineers who refine language-based interactions, to AI operations managers who oversee performance and governance, businesses are building out the infrastructure to support a future where AI and humans work in tandem.
Some businesses have begun hiring or repurposing staff into roles such as digital workplace architects, who focus on designing frictionless collaboration between employees, AI agents and enterprise systems. These roles serve as the connective tissue between IT, business operations and frontline teams, ensuring that AI integration enhances productivity rather than disrupt it.
As AI agents become more integrated into contact center operations, businesses are beginning to reorganize roles and responsibilities — not just to improve efficiency, but also to protect employees and enable strategic growth. Nikola Mrkšic, CEO and co-founder at PolyAI, said, "AI-powered agents can help convert the contact center into a command center that draws from deep wells of customer data… These sophisticated AI agents can help with reorganization — by automating high-churn positions and upskilling human agents to AI architects who oversee operations and further develop AI strategies." Mrkšic noted that AI agents are not only boosting service efficiency but also transforming the workplace dynamic. In some cases, they act as a buffer against volatile customers, helping to resolve issues before escalation — protecting human agents and giving them space to focus on higher-value work.
But these internal shifts are not always seamless. Change management remains one of the most underestimated challenges in AI agent deployment. Employees may view automation as a threat to job security, especially in roles that historically relied on process ownership. Others may resist using agent tools they perceive as opaque or unreliable. Transparency around agent limitations — and emphasizing augmentation over replacement — can go a long way in easing adoption.
When AI agents reduce repetitive work or minimize context switching, many brands report increased employee engagement and productivity. Enterprises that involve teams in the rollout process — rather than imposing it from the top down — are often better able to capture long-term value from AI adoption.
Related Article: Is Your Data Good Enough to Power AI Agents?
How Enterprises Monitor and Control AI Agents in Production
As AI agents take on more responsibility within the enterprise, the need for robust oversight has become critical. Businesses can’t afford rogue outputs, biased decisions or hallucinated responses — especially when agents interact with customers or influence operational decisions. That’s why many enterprises are investing heavily in monitoring systems, guardrails and human-in-the-loop workflows to reduce risk and preserve trust.
Webinar
Oct
16
Agentic AI Playbook: Real-World Customer Service Use Cases You Can Deploy Now
Boost self-service by 30% and slash call volume by 63% with agentic AI.
Register
Webinar
Oct
22
Beyond Storage: Smarter Content, Bigger Impact with DAM + AI
Discover how the DAM + AI duo makes content smarter, stronger and more accessible.
Register
Webinar
Nov
6
Fix the Content Bottleneck: Build a Better WebOps Strategy
Content stalled? Dev overloaded? You’re not the only one. Learn how streamlined WebOps bridges the publishing gap.
Register
Webinar
On demand
CMS Briefing: A Live Look at What’s Next in AI-Driven Platforms
Learn how leading organizations are using AI‑driven tools to publish faster, personalize smarter and stay secure.
Watch Now
Webinar
On demand
Ready or Not: How Data-First Organizations Are Unlocking Agentforce Potential
Learn how to cut through the noise, activate Agentforce and build a Salesforce AI strategy that actually delivers.
Watch Now
Webinar
On demand
AI in Customer Service: Faster Resolutions, Happier Customers
Don’t let rising demand burn out your team. See how to build a smarter, more resilient support org.
Watch Now
Enterprises are layering in oversight mechanisms to ensure AI agents operate within guardrails, especially in support roles that impact customer trust and uptime. "Everything our staff handles gets recorded in audit logs and human operators stay involved with billing and account-related actions," explained Tito. He added that, while AI agents handle 70% of support tickets and some backend processes like node restarts, sensitive actions remain in human hands. This mix has reduced support response time from two hours to 25 minutes and significantly lowered ticket volume.
One key strategy is the use of feedback loops that evaluate agent outputs in real time. This may involve direct end-user feedback (such as thumbs-up/down ratings or satisfaction scores), but increasingly includes automated scoring against enterprise benchmarks. Agents can be trained to recognize uncertainty and escalate when confidence is low — minimizing the risk of hallucinated or misleading information making it into production workflows.
The choice between in-house vs. vendor-based monitoring tools often depends on maturity and risk appetite. Enterprises using platforms such as Azure OpenAI, Anthropic or Google’s Vertex AI may rely on built-in safety and moderation layers — but many are layering on proprietary governance stacks for additional control. This often includes custom logging, escalation thresholds or red teaming exercises aimed at stress-testing agent behavior under adversarial or ambiguous prompts.
Governance frameworks are also becoming more formalized. Enterprises are increasingly adopting AI-specific policies that mirror data governance programs — covering transparency, explainability, model documentation and usage boundaries. In regulated industries, internal review boards are being created to evaluate AI deployments before launch.
And across all deployments, the human still matters. Human-in-the-loop (HITL) designs remain standard in high-risk use cases — ensuring that AI agents augment, rather than override, human decision-making. This model not only preserves accountability, but also allows agents to continue learning safely within set boundaries. As AI agents become more embedded in business logic, the guardrails around them are likely to prove just as important as the models themselves.
Build vs. Buy: Choosing the Right AI Agent Strategy for Your Business
Enterprises deploying AI agents today are facing a familiar strategic crossroads: build custom agents in-house or buy from external vendors and platforms. Both paths offer compelling benefits — and meaningful tradeoffs. The decision often comes down to internal capabilities, speed to market and the complexity of the use case.
| Factor | Build In-House | Buy From Vendor |
|---|---|---|
| Speed to Deploy | Slower; requires internal engineering resources | Faster; pre-built tools with minimal setup |
| Customization | High; tailored to internal workflows and data | Moderate; constrained to platform capabilities |
| Cost | Higher upfront; ongoing maintenance required | Lower up-front; subscription-based pricing |
| Scalability | Flexible, but requires infrastructure planning | Inherently scalable with vendor infrastructure |
| Vendor Lock-In | None; full internal control | High; dependent on vendor's roadmap and uptime |
On one side are in-house innovation labs and AI teams, building custom agents using foundational models from OpenAI, Anthropic or open-source providers like Mistral and Cohere. These enterprises often use internal data, proprietary workflows and domain expertise to create tightly aligned agents that reflect specific business needs. The advantage is control — over behavior, integrations and performance tuning — but it comes with a higher investment in engineering, prompt design, model testing and risk mitigation.
On the other side are businesses adopting agent-building platforms such as Microsoft Copilot Studio, Google Vertex AI Agent Builder or Salesforce’s Einstein 1 Studio. These low-code or no-code environments offer prebuilt agent scaffolding, security and native integration with existing enterprise tools. They allow non-technical teams to build task-oriented agents faster and scale them with less overhead. But they also introduce vendor dependency, and often limit flexibility when it comes to behavior customization or adapting to niche workflows.
Integration is a key challenge in both models. Whether building or buying, AI agents must plug into CRMs, knowledge bases, data lakes and user interfaces — without creating new silos or process gaps. For enterprises with legacy systems or strict compliance requirements, this can slow deployment timelines or limit which agents can be trusted in production.
Customization is another pressure point. Off-the-shelf agents may cover the majority of use cases but lack the specificity required for industry-specific tasks or branded experiences. On the other hand, custom-built agents risk becoming brittle or resource-intensive if not maintained with a long-term product mindset. Some businesses are opting for a hybrid approach — buying a base agent framework, then customizing it with proprietary data, behaviors or plugins. This lets them move quickly without sacrificing differentiation or domain specific data.
Related Article: How to Build Multi-Agent Workflows That Don't Fall Apart
Operationalizing AI Agents: From Proof of Concept to Business Integration
AI agents are no longer simply pilots or proof-of-concepts — they’re active participants in how enterprises operate, serve customers and evolve their internal systems. Whether supporting back-office processes or augmenting human decision-making in real time, these agents are proving their value across industries. As businesses refine their agent strategies, the next wave of value may come not from the technology itself, but from how well it’s integrated into the human systems around it.
