5 Enterprise Workflows Ripe for Agentic AI – And How to Start

5 Enterprise Workflows Ripe for Agentic AI – And How to Start

In the fast-evolving AI landscape, agentic AI refers to intelligent systems that plan, reason, and act autonomously in multi-step processes. These AI agents go beyond simple chatbots or scripts; they can “perceive their environment, make decisions, and take actions — often without needing constant human oversight”. For enterprise leaders, agentic AI opens new possibilities to automate complex workflows and accelerate innovation. In 2025, adopting AI agents can yield quick wins in everyday processes. Below we highlight five key workflows – both customer-facing and internal – where agentic AI is already showing real business impact. We pair each with a brief case example and action steps so decision-makers can get started effectively. (For deeper AI strategy, see our AI Consulting Services and Generative AI blog post.)

1. Intelligent Customer Support and Service

Customer support is a classic fit for agentic AI. Instead of routing every inquiry to a human, an AI agent can automatically handle routine questions 24/7, escalating only complex cases. Many organizations report dramatic improvements: an IBM-backed study found that chatbots handling routine queries can cut support costs by up to 30% by automating self-service. For example, companies like Vodafone and Klarna have deployed AI-driven chatbots (often with retrieval-augmented generation) to answer FAQs and process simple requests, freeing human agents to tackle harder problems. The result is faster response times and higher first-contact resolution for customers.

Agentic support bots can also personalize interactions. Powered by customer data and generative AI, they can proactively suggest help based on user history (e.g. “It looks like you had an issue with your last order. Can I help check the status?”). This kind of AI assistant can improve satisfaction while trimming headcount, as NexGen Cloud notes that automating routine inquiries has helped telecom and e-commerce firms slash service costs.

How to Start: Begin by identifying your highest-volume support tasks (e.g. password resets, order tracking, billing queries). Work with your AI team or a consultant to build a pilot chatbot or voicebot. Key steps:

• Catalog top FAQs: Pull transcript logs or ticket tags to see which questions recur frequently.

• Define success metrics: Set target KPIs (response time, cost savings, customer NPS).

• Choose AI tools: Use a RAG-enabled LLM (such as OpenAI GPT-4o with knowledge connectors, or IBM Watsonx) linked to your FAQ/knowledge base.

• Train and integrate: Feed the agent historical data (previous tickets, help articles) so it learns your products/services.

• Pilot in one channel: Launch the bot on a low-risk channel (e.g. chat window on your website) and monitor performance.

• Iterate: Refine prompts and add handoff logic (when to escalate) until accuracy and satisfaction meet your goals.

By starting small and iterating, you can quickly demonstrate value and then scale the solution across more support channels. Our software engineering team can help integrate AI agents into your CRM, live chat, and IVR systems, ensuring smooth rollout and ongoing refinement.

2. Smarter Inventory & Supply Chain Management

Inventory management and supply-chain optimization are ripe for AI agents. Retailers and manufacturers struggle to balance stock: stockouts cost sales, overstocks tie up capital. Agentic AI can dynamically analyze real-time data (sales, supplier status, weather, local events) to rebalance inventory across locations and channels. For example, Walmart uses AI-powered forecasting year-round, especially for the holidays. As Walmart’s tech team explains, their AI-driven system leverages historical data and predictive analytics to strategically place products in stores and warehouses, ensuring customers find items when and where they need them. By syncing physical and digital sales data, the AI “completes the heavy lifting” of inventory planning before customers even start shopping.

The payoff is huge: machine learning models can detect anomalies (sudden demand spikes) and autonomously trigger transfers or reorders across distribution centers. This lowers out-of-stock incidents and markdowns. In one pilot, an AI solution reduced lost sales by rebalancing inventory between regions in real time. Overall, faster, data-driven restocking means happier customers and leaner operations.

How to Start: Turn your supply chain data into an AI project in stages:

• Collect and integrate data: Bring in sales history, POS data, and supplier lead times into one platform. Ensure your ERP/WMS is accessible.

• Build predictive models: Work with data scientists to train forecasting algorithms (leveraging ML or LLM planning) on historical demand.

• Deploy a monitoring agent: Use an agentic AI framework (e.g. an LLM with memory/tools) to continuously scan inventory levels and signal alerts.

• Automate actions: Connect the AI to your logistics tools. For instance, let it auto-generate inter-store transfer requests or purchase orders when thresholds hit.

• Start small: Pilot in a single product category or region. Measure fill-rate improvements and cost savings.

• Scale and refine: Roll out model improvements broadly, adding more data sources (like promotions or competitor pricing).

Our consultants can help by building custom prediction models and integrating them with your supply-chain software. Combined with our cloud engineering expertise, we ensure the new AI logic slots into your existing infrastructure securely.

3. Automated Financial Reporting & Compliance

In finance, regulatory reporting and compliance workflows are pain points begging for agentic AI. Tasks like compiling audit reports, risk disclosures, or tax filings typically require laborious data gathering across departments. AI agents can automate these end-to-end: they pull data from accounting systems, validate figures, format results per regulations, and even draft narrative summaries. The benefits are clear: fewer errors, faster turnaround, and greater agility to adapt as rules change.

A leading example is in banking: Wells Fargo recently adopted an AI-based platform (TradeSun) to digitize its trade finance and compliance processes. The bank leverages this agentic AI to harvest and classify unstructured data from documents, strengthening its risk framework. As Wells Fargo’s team noted, the new AI partnership gives them “digitisation and automation tools to strengthen our risk framework” while streamlining compliance checks. Similar approaches in utilities and insurance have also seen regulatory reporting time cut in half by letting AI handle repetitive compliance tasks.

How to Start: To launch agentic AI in finance:

• Map your processes: Identify high-volume, rules-based tasks (e.g. weekly risk reports, audit-prep paperwork, expense approvals).

• Choose a compliance use case: Start with a reporting area that has clear inputs (e.g. monthly financial KPIs or regulatory filings).

• Ingest your data: Use Robotic Process Automation (RPA) plus NLP to automatically extract data from ERP/CRM, databases, or spreadsheets.

• Define agent rules: Embed regulations or business rules into the agent (for example, thresholds, formulas). Agents can use LLMs with plugins (like Copilot for Finance) to run logic.

• Generate deliverables: Let the AI draft the report or submission, then have a human review for final accuracy.

• Iterate and expand: Once one report is on autopilot, move to others (e.g. compliance, taxes). Continuously train the agent on newly updated policies.

Our firm specializes in building compliant, auditable AI pipelines. We can integrate an agent into your financial systems (e.g. SAP, Oracle) and ensure it meets internal controls, so you get the efficiency without sacrificing governance.

4. Proactive IT Incident Management (AIOps)

IT operations are transforming with AIOps agents that predict and fix issues before users notice them. Traditional monitoring floods teams with alerts; an agentic approach groups signals and acts. In practice, AI agents ingest logs from servers, networks, and applications, detect anomalies, and even trigger automated remediations. For example, a global financial services company used an AI incident-management platform to gain full-stack observability. The results were dramatic: incident detection time fell by 80% and MTTR dropped from 3 hours to about 20 minutes, yielding near-100% uptime during peak periods. In e-commerce, another AI system identified a hidden API bottleneck in minutes (vs. 45 minutes manually) and scaled resources automatically, reducing checkout failures by 55% during flash sales.

These agentic IT workflows are no longer science fiction. Enterprises now leverage LLMs tied to tools like ServiceNow, Splunk, or Azure Monitor: an agent can triage tickets by reading incident descriptions, consult a knowledge base, and even execute self-healing scripts (like restarting a hung service). The outcome is far fewer all-hands-in-the-data-center emergencies and far faster mean-time-to-resolution.

How to Start: Move from reactive to proactive IT:

• Enhance observability: Ensure your monitoring tools (logs, metrics, APM) are centralized. More telemetry means smarter AI.

• Set up an AI Ops platform: Deploy an AI-driven ITOM tool (such as Dynatrace, Moogsoft, or an LLM-based bot) that can correlate alerts.

• Automate RCA: Program the AI agent with playbooks: e.g., if high CPU is detected on Server X, run diagnostics or restart service automatically.

• Train the agent: Use historical incident data for supervised learning. The agent learns which patterns led to particular fixes.

• Pilot critical services: Start with a non-customer-facing system (e.g. internal dev environment). Confirm the agent's suggestions before full auto-fix.

• Expand to customer systems: Once trusted, let the agent intervene in live production (with human-in-the-loop safeguards).

Our custom software team can help design these monitoring integrations. By embedding agentic AI into tools like Slack or Teams, we can even have the agent proactively notify on-call engineers with its findings and proposed fixes.

5. AI-Powered Procurement and Strategic Sourcing

Procurement workflows – from contract review to vendor negotiations – benefit greatly from agentic AI. Routine tasks like matching invoices, tracking purchase orders, or even haggling with suppliers can be automated. Walmart provides a real-world example: the retail giant uses an AI-powered procurement chatbot to negotiate with thousands of tail-end suppliers. According to a case study, this agent conducts multi-party negotiations, saving time and securing better terms across the supply chain. The AI system parses quotes from suppliers, proposes counter-offers, and finalizes deals in an automated, data-driven way. Similarly, AI-driven invoice processing can cut labor by ~92%, as seen in Landsec’s accounts-payable automation pilot.

More broadly, AI agents in procurement can extract data from contracts, compare terms, and flag non-compliance (e.g. missed SLAs or hidden fees). They can even forecast material costs by pulling market data. The key is that these agents don’t require a software overhaul; they can sit on top of existing e-procurement platforms and ERPs.

How to Start: Automate your buying process step-by-step:

• Automate invoice matching: Implement an AI invoice processing bot. Train it to read invoices, verify amounts against POs, and enter data into your ERP. This frees staff from data entry.

• Use chatbots for inquiries: Deploy a procurement helpdesk bot (via Teams/Slack). The bot can answer status questions or forward approvals to managers.

• Pilot a negotiation agent: For simple RFPs, try an AI agent that can solicit bids and analyze offers. Even off-the-shelf AI tools or Copilot plugins can compare supplier proposals in minutes.

• Integrate with contract management: Use NLP to extract clauses from contracts. Let the agent highlight risky terms (non-standard payment dates, etc.) for legal review.

• Measure ROI: Track time saved (e.g. invoice processing time, negotiation cycle-time) and cost reduction. Use these wins to expand agent scope.

Our solutions architects have helped clients integrate procurement AI with systems like Coupa or SAP Ariba. We can build the custom connectors and AI logic so that agentic workflows like the above work seamlessly within your procurement platform.

Conclusion

Agentic AI is no longer a distant vision – it’s delivering measurable ROI across industries today. From 24/7 customer-service bots to inventory bots and intelligent IT operations, enterprises are already reaping the benefits of AI agents. By focusing on specific workflows where data and rules are well-defined, decision-makers can achieve fast wins without a giant transformation.

As a custom software engineering partner, we combine deep business domain knowledge with AI expertise. We help enterprises identify the right use cases, choose suitable AI technologies (from LLMs to automation tools), and execute proofs of concept quickly. Whether you need help building a chatbot, predictive supply-chain model, or AI-driven report generator, our team can deliver a solution tailored to your needs.

Get started by picking one of the workflows above, and reach out to discuss how to integrate an AI agent into your processes. Together, we can transform those workflows into autonomous, data-driven engines of efficiency.

Sources: Authoritative industry reports and case studies have been cited throughout, including IBM’s AI research and real-world company examples (see references). Each cited source provides further insight into the results achieved with agentic AI. For related content, see our [AI & Automation Services]and [Generative AI insights blog].