The latest shift in AI takes us beyond data analysis and content generation. With agentic AI we are now seeing systems that can plan, reason, act autonomously, and adapt based on outcomes. This shift marks a practical turning point for operational execution and strategic agility.
Smart On-Ramps for Agentic AI
Technology providers are rapidly maturing their agentic AI offerings, increasingly packaging them as pre-built agents designed for quick deployment. These often require minimal customisation and target common enterprise needs – onboarding assistants, IT helpdesk agents, internal knowledge copilots, or policy compliance checkers – integrated with existing platforms like Microsoft 365, Salesforce, or ServiceNow.
For example, a bank might deploy a templated underwriting agent to pre-screen loan applications, while a university could roll out a student support bot that flags at-risk learners and nudges them toward action. These plug-and-play pilots let organisations move fast, with lower risk and clearer ROI.
Templated agents won’t suit every context, particularly where rules are complex or data is fragmented. But for many, they offer a smart on-ramp: a focused, contained pilot that delivers value, builds momentum, and lays the groundwork to scale Agentic AI more broadly.
Here are 10 such opportunities – five cross-industry and five sector-specific – ideal for launching agentic AI in your organisation. Each addresses a real-world pain point, with measurable impact and momentum for broader change.
Horizontal Use Cases
1. Employee Onboarding & Integration Assistant
An AI agent that guides new hires through their critical first weeks and months by answering FAQs about company policies, automating paperwork, scheduling introductory meetings, and sending personalised reminders to complete mandatory training, all integrated with HRIS, LMS, and calendaring systems. This can help reduce the administrative load on HR teams by handling repetitive onboarding tasks, potentially freeing up significant time, while also improving new hire satisfaction and accelerating time-to-productivity by providing employees with better support and engagement from day one.
Consideration. Begin with a specific department or a targeted hiring wave. Prioritise roles with high turnover or complex onboarding needs. Ensure HR data is clean and accessible, and policy documents are up to date.
2. Automated Meeting Follow-ups & Action Tracking
With permission, AI agents can listen to virtual meetings, identify key discussion points, summarise decisions, extract and assign action items with deadlines, and proactively follow up via email or collaboration platforms like Slack or Teams to help ensure tasks are completed. By integrating with meeting platforms, project management tools, and email, this can reduce the burden of manual note-taking and follow-up, potentially saving team members 1-2 hours per week, while also improving execution rates and accountability to make meetings more action-focused.
Consideration. Deploy with a small, cross-functional team that has frequent meetings. Clearly communicate the agent’s role and data privacy protocols to ensure user comfort and compliance.
3. Intelligent Procurement Assistant
An agent that interprets internal requests, initiates purchase orders, compares vendor options against predefined criteria, flags potential compliance issues based on policies and spending limits, and manages approval workflows, integrating with ERP systems, vendor databases, and internal policy documents. This can help accelerate procurement cycles, reduce manual errors, and lower the risk of non-compliant spending, potentially freeing procurement specialists to focus more on strategic sourcing rather than transactional tasks.
Consideration. Begin with a specific category of low-to-medium value purchases (e.g., office supplies, standard software licenses). Define clear, rule-based policies for the agent to follow.
4. Enhanced Sales/Outreach Research Agent
Given a target account, citizen segment, or potential beneficiary profile, this agent autonomously gathers and synthesises insights from CRM data, public financial records, social media, news feeds, and industry reports. It then generates tailored talking points, personalised outreach messages, and intelligent discovery questions for human operators. This can provide representatives with deeper insights, potentially improving their preparation and boosting early-stage conversion rates, while reducing manual research time significantly and allowing teams to focus more on building relationships.
Consideration. Train the agent on a specific sales vertical or a targeted public outreach campaign. Ensure robust data privacy compliance when accessing and synthesising public information.
5. Proactive Internal IT Helpdesk Agent
This agent enables employees to describe technical issues in natural language through familiar platforms like Slack, Teams, or internal portals. It can intelligently troubleshoot problems, guide users through self-service solutions from a knowledge base, or escalate more complex issues to the appropriate IT specialist, often pre-filling support tickets with relevant diagnostic information. This approach can lead to faster issue resolution, reduce the number of common support tickets, and improve employee satisfaction with IT services, while freeing IT staff to focus on more complex problems and strategic initiatives.
Consideration. Start with a well-documented set of frequently asked questions (FAQs) or common Tier 1 IT issues (e.g., password resets, VPN connection problems). Ensure a clear escalation path to human support.
Industry-Specific Use Cases
6. Intelligent Insurance Claims Triage (Insurance)
This agent reviews incoming insurance claims by processing unstructured data such as claim descriptions, photos, and documents. It automatically cross-references policy coverage, identifies missing information, and assigns priority or flags potential fraud based on predefined rules and learned patterns. This can speed up initial claims processing, reduce the manual workload for claims adjusters, and improve the early detection of suspicious claims, helping to lower fraud risk and deliver a faster, more efficient customer experience during a critical time.
Consideration. Focus on a specific, high-volume, and relatively standardized claim type (e.g., minor motor vehicle damage, simple property claims). Ensure robust data integration with policy management and fraud detection systems.
7. Automated Credit Underwriting Assistant (Banking)
An AI agent that pre-screens loan applications by gathering and analysing data from internal banking systems, external credit bureaus, and public records. It identifies key risk factors, generates preliminary credit scores, and prepares initial decision recommendations for human loan officers to review and approve. This can significantly shorten loan processing times, improve consistency in risk assessments, and allow human underwriters to concentrate on more complex cases and customer interactions.
Consideration. Apply this agent to a specific, well-defined loan product (e.g., unsecured personal loans, small business loans) with clear underwriting criteria. Strict human-in-the-loop oversight for final decisions is paramount.
8. Clinical Trial Workflow Coordinator (Healthcare)
This agent monitors clinical trial timelines, tracks participant progress, flags potential non-compliance or protocol deviations, and coordinates tasks and communication between research teams, labs, and regulatory bodies. Integrated with Electronic Health Records (EHRs), trial management systems, and regulatory databases, it helps reduce delays in complex clinical workflows, improves adherence to strict protocols and regulations, and enhances data quality, potentially speeding up drug development and patient access to new treatments.
Consideration. Focus on a single phase of a trial or specific documentation compliance checkpoints within an ongoing study. Ensure secure and compliant access to sensitive patient and trial data.
9. Predictive Maintenance Scheduler (Manufacturing)
By continuously analysing real-time IoT sensor data from machinery, this agent uses predictive analytics to anticipate potential equipment failures. It then schedules maintenance at optimal times, taking into account production schedules, spare part availability, and technician workloads, and automatically assigns tasks. This approach can significantly boost machine uptime and overall equipment effectiveness by reducing unplanned downtime, optimize technician efficiency, and extend asset lifespan, resulting in notable cost savings.
Consideration. Implement for a critical, high-value machine or a specific production line where downtime is extremely costly. Requires reliable and high-fidelity IoT sensor data.
10. Personalised Student Success Advisor (Higher Education)
This agent analyses student performance data such as grades, attendance, and LMS activity to identify those at risk of struggling or dropping out. It then proactively nudges students about upcoming deadlines, recommends personalised learning resources, and connects them with tutoring services or academic advisors. This support can improve retention rates, contribute to better academic outcomes, and enhance the overall student experience by providing timely, tailored assistance.
Consideration. Start with a specific cohort (e.g., first-year students, transfer students) or focus on a particular set of foundational courses. Ensure ethical data usage and transparent communication with students about the agent’s role.
Pilot Success Framework: Getting Started Today
As we have seen in the considerations above, starting with a high-impact, relatively low-risk use case is the recommended approach for beginning an agentic AI journey. This focuses on strategic, measured steps rather than a massive initial overhaul. When selecting a first pilot, organisations should identify projects with clear boundaries – specific data sources, explicit goals, and well-defined actions – avoiding overly ambitious or ambiguous initiatives.
A good pilot tackles a specific pain point and delivers measurable benefits, whether through time savings, fewer errors, or improved user satisfaction. Choosing scenarios with limited stakeholder risk and minimal disruption allows for learning and iteration without significant operational impact.
Executing a pilot effectively under these guidelines can generate momentum, earn stakeholder support, and lay the groundwork for scaling AI-driven transformation throughout the organisation. The future of autonomous operations begins with such focused pilots.
