- RPA systems require dedicated maintenance resources and fail when processes, systems, or data formats change
- RPA systems require dedicated maintenance resources and fail when processes, systems, or data formats change
- Traditional automation offers no pathway to continuous improvement; it executes exactly as programmed, no more
- The productivity ceiling of RPA is well understood; organisations require a fundamentally different paradigm to drive the next wave of operational efficiency
Process adaptability
- Legacy RPA
- Rigid , breaks when process variables change
- DOT AI Agent
- Adaptive , learns from changing inputs and edge cases
Exception handling
- Legacy RPA
- Exception handling Requires manual human intervention for all exceptions AI Agents escalate, resolve, or learn from exceptions autonomously
- DOT AI Agent
- Exception handling Requires manual human intervention for all exceptions AI Agents escalate, resolve, or learn from exceptions autonomously
Natural language processing
- Legacy RPA
- Cannot read or interpret unstructured text or documents
- DOT AI Agent
- Natively processes emails, reports, contracts, and free-form data
Maintenance burden
- Legacy RPA
- High , every process change requires developer intervention
- DOT AI Agent
- Low , agents update behaviour based on feedback and training
Scalability
- Legacy RPA
- Linear cost increase with volume
- DOT AI Agent
- Intelligent AP Automation — 10,000+ Invoices/Month
Time to value
- Legacy RPA
- Typically 6–12 months for meaningful deployment Working POC within 6 weeks; scaled deployment within 12 weeks
- DOT AI Agent
- Typically 6–12 months for meaningful deployment Working POC within 6 weeks; scaled deployment within 12 weeks
Discovery & Scoping
Build & Integration
Controlled Pilot
Handover & Scale Planning
- AI Agent
An autonomous software entity capable of perceiving its environment, reasoning about inputs, and executing a sequence of actions to achieve a defined objective , without continuous human directionanual transformation or intervention.
- Agentic Workflow
A business process managed end-to-end by one or more AI Agents, operating with defined autonomy and escalation protocols.
- Agentic Efficiency Ratio
DOT’s metric measuring the proportion of operational workflows managed by AI Agents versus manual human effort , a primary KPI of the Autonomous Operations practice.
- Proof of Concept (POC)
A bounded, time-limited implementation of an AI Agent designed to validate commercial viability and technical feasibility before full-scale deployment
- GreenOps
The application of AI to automate and optimise environmental, social, and governance (ESG) reporting, carbon accounting, and sustainability performance management.enOps
- LLM (Large Language Model)
The AI model architecture underpinning DOT’s AI Agents , capable of reading, interpreting, and generating natural language across structured and unstructured data formats.
Autonomous Operations , FAQ
Every AI Agent deployed by DOT operates within a defined governance framework that specifies decision rights, escalation thresholds, audit logging requirements, and human review checkpoints. Agents are never deployed to own decisions that fall outside their defined authority parameters , all material exceptions are escalated to human reviewers with full contextual documentation. The Agent’s decision log is accessible in real time via the operational monitoring dashboard.
DOT’s AI Agents are designed with explicit uncertainty handling. When an Agent encounters an input that falls outside its confidence threshold, it escalates the item to a human reviewer rather than proceeding with a DOT’s AI Agents are designed with explicit uncertainty handling. When an Agent encounters an input that falls outside its confidence threshold, it escalates the item to a human reviewer rather than proceeding with a
DOT’s AI Agents are designed to be system-agnostic, with integration capabilities across all major ERP platforms (SAP, Oracle, Microsoft Dynamics), HRIS systems (Workday, SuccessFactors), cloud infrastructure (AWS, Azure, GCP), and communication platforms (Microsoft Teams, Slack, ServiceNow). Custom API integrations are developed as required during the scoping phase.
Yes. DOT structures Rapid AI Pilot engagements as fixed-fee, fixed-scope contracts with defined deliverables and success metrics agreed at the outset. This provides commercial certainty for clients and aligns DOT’s incentives to delivering demonstrable outcomes within the six-week timeline.
GreenOps is DOT’s AI-driven approach to environmental and sustainability performance management. Our GreenOps Intelligence service automates ESG data collection, carbon footprint calculation, and regulatory reporting , supporting compliance with the EU Corporate Sustainability Reporting Directive (CSRD), the EU Taxonomy Regulation, GRI Standards, and TCFD frameworks. Clients receive real-time sustainability dashboards and audit-ready annual reports.
DOT approaches workforce transition as an integral component of every Autonomous Operations engagement. Our programme includes a structured redeployment analysis identifying higher-value roles for affected team members, an AI literacy programme that equips employees to supervise and collaborate with AI Agents effectively, and a change management framework that maintains operational continuity throughout deployment. Our client experience consistently demonstrates that AI Agent deployment enables , rather than diminishes , the contribution of skilled employees.