Make AI useful. Make automation work.

Start with one process that has clear ownership, measurable friction and sufficient data or system access. Assess manual effort, exception rates, business impact, integration requirements and control needs before deciding whether rules-based automation, AI or a combination is suitable.

Robotic process automation (RPA) generally follows defined steps to complete structured tasks. AI can help interpret unstructured information, classify content, generate drafts or identify patterns. Many enterprise workflows combine deterministic rules with targeted AI assistance.

Potentially. A solution can retrieve information from approved knowledge sources and present it within an authorised assistant experience. The design must account for document permissions, content quality, data handling, source attribution and the risk of incorrect answers.

Useful safeguards may include constrained tasks, approved data sources, grounded retrieval, structured outputs, validation checks, evaluation datasets and human review. No design makes generative AI infallible, so controls should reflect the impact of an incorrect result.

First classify the information involved and assess provider terms, access controls, processing locations, retention, data flows and applicable policies. Some use cases may require alternative architectures or should not use sensitive information at all.

Not in every scenario. Routine low-risk steps may be suitable for straight-through processing, while exceptions, sensitive information and material decisions often require human approval or escalation. The decision should follow a documented risk assessment.

Often, yes. Integration approaches can include supported APIs, workflow platforms, event-driven interfaces and, where suitable, user-interface automation. We would assess your existing systems and security requirements before choosing an integration pattern.

Agree the baseline before a pilot—for example processing time, manual touches, error rates, turnaround, service quality or employee experience. Compare post-deployment results while including licensing, infrastructure, monitoring and ongoing support costs.

Timelines depend on scope, integration complexity, data readiness, approvals, testing and adoption. A focused pilot can establish feasibility and the evidence needed for a phased rollout, but production readiness should not be assumed from a successful demonstration.

Agentic AI describes systems that can plan steps or use tools to work towards a defined goal. It may help with multi-step work, but requires limits on tool access, clear responsibilities, testing, action logging, fallback routes and human approval for consequential operations. Simpler workflow automation may be preferable where tasks are predictable.

The choice depends on security requirements, integration, data sensitivity, ownership, operational costs and the desired user experience. We favour selecting a solution architecture from the business need rather than assuming one platform or model is suitable for every task.