GenAI pilots are everywhere. Production-ready systems aren’t.
Most enterprises have tested generative artificial intelligence (GenAI) in some form: summarising reports; drafting content; or embedding a chatbot. That part is easy. Scaling GenAI inside real environments is where it gets hard. These systems need to follow rules, process signals, and make decisions in complex workflows. Traditional architecture isn’t built for that.
In our recent webinar, From Idea to Impact: Fast-track your AI Proof of Concept for Success, we explored what it takes to move from pilot to production, and why so many GenAI projects stall just before they get real. We also introduced the GenAI Kickstart Program, DXC’s two-week engagement that helps teams build a governed, audit-ready proof of concept (PoC) aligned to real enterprise workflows.
How to build agentic AI that works in the wild
Agentic AI doesn’t behave like traditional software. It acts on context, signals, and partial data. It triggers tasks across multiple systems without fixed input-output rules. These traits make it powerful. They also make it hard to control without the right operational scaffolding.
This is where most GenAI projects stall. DXC has already helped global enterprises deploy agentic AI systems in high-stakes environments, from finance and health to government. Here are some of the key insights we covered in the webinar:
What agentic AI actually needs to succeed
Agents don’t live in isolation. They need rules, feedback, visibility, and systems that supervise and respond in real time.
That means:
- clear control points between human input and agent execution
- continuous telemetry to tune behaviour and improve outputs
- coordination across agents, systems, and data environments
- governance that shapes action, not just reports on it.
Most GenAI failures aren’t due to model performance. They’re caused by weak orchestration, disconnected systems, and governance that doesn’t run in real time. If an agent makes decisions on live data, static policy won’t cut it. Governance must function as part of the execution layer.
Client spotlight 1: automating finance reporting with reasoning agents
During the webinar, we shared a use case on how GenAI is used to streamline month-end reporting. The finance agent, powered by chain of thought reasoning models, generates a management report, including Earnings Before Interest, Taxes, Depreciation, and Amortisation (EBITDA) analysis, risk commentary, and executive summaries.
Finance analysts interact with the draft, enriching data and adjusting tone as needed. Once published, stakeholders query the report through voice or text to extract deeper insights.
Every interaction is logged, queries are grouped by topic, and feedback loops train the agent to improve future outputs. The system runs with role-based access, audit trails, and semantic telemetry from start to finish. The result is not a chatbot or static summariser; it’s a dynamic system that adapts to human input, maintains compliance, and scales across reporting cycles.
Client spotlight 2: coordinating resident check-ins with inter-agent orchestration
We also presented a use case to enhance customer service. In this specific example, aged care providers use virtual assistants to coordinate resident wellbeing check-ins, escalate maintenance issues, and resolve billing queries. These tasks are handled across two specialised agents: one focused on resident engagement and the other managing real-time billing resolution.
Together, the agents operate under a shared interaction protocol. Conversations are transcribed, summarised, and tagged. Maintenance issues generate automatic work orders, while billing queries are resolved through secure, authenticated interactions with the finance system.
Agent decisions are context-driven, actions are linked to downstream systems, and each interaction is governed by role-based permissions, trigger constraints, and audit trails. The result is a coordinated, compliant orchestration layer that supports both resident experience and operational efficiency.
Build your AI PoC in just two weeks with DXC
These use cases demonstrate how different agentic patterns—reasoning agents and orchestrated multi-agent systems—are already driving real impact. Together, they show what’s possible when GenAI is built with the right foundations.
From Idea to Impact: Fast-track your AI Proof of Concept for Success shows how to move GenAI out of pilot mode and into production, with the governance, observability, and integration enterprise systems demand.
Watch the webinar on demand to see how agentic AI works in the real world, then talk to us about building your AI PoC through the DXC GenAI Kickstart Program. Two weeks. Fully governed. Ready to run.
We look forward to helping you turn AI ideas into impact—quickly, securely, and confidently.