Enterprise AI and Process Intelligence Glossary

Accounts Payable (AP) is the business function managing payment obligations to suppliers, covering invoice processing, approvals, and payment execution. Skan AI observes how AP teams handle invoices, identifying the manual touchpoints and exception patterns with the greatest automation potential.

An action is the smallest observable unit of work captured by Skan AI, such as opening an application, entering data, or clicking a field. Skan's computer vision classifies actions automatically, transforming raw desktop activity into structured process data that makes previously invisible work measurable.

An activity is a group of related actions that together complete a defined step in a business process. Skan AI identifies activities automatically from desktop observation, replacing manual process interviews with real-time data on how work actually happens.

The agency gap is the divide between knowing what to do and being able to act on it autonomously. Gartner coined this to describe the limitation of GenAI: it informs but does not act. Skan AI closes the agency gap for enterprise processes by grounding agents in operational ground truth.

An AI agent is an autonomous software system that perceives its environment, makes decisions, and takes actions to reach a goal without step-by-step human instruction. Skan AI builds agents from operational ground truth, real observed work data rather than assumed process maps, so agents perform accurately from day one.

Agent memory is the capability of an AI agent to retain and build on information from previous interactions and tasks, improving performance over time. Skan AI's context graph of work provides persistent process memory, ensuring each agent task is informed by the full history of how that process has been observed to behave.

Agent Operating Procedures (AOPs) are structured, machine-readable process definitions governing how AI agents execute tasks, including workflow steps, decision rules, escalation thresholds, and success criteria. Skan AI generates AOPs from observed work data, ensuring agents receive accurate operational playbooks rather than idealized documentation.

Agent washing is the practice of rebranding existing automation tools as AI agents without implementing genuine agentic reasoning. Gartner coined this to warn buyers about inflated vendor claims. Skan AI recommends evaluating any claimed agent on whether it reasons from context, adapts to change, and is grounded in real process data.

The Agent-to-Agent Protocol (A2A) is an open standard enabling AI agents from different platforms to share context and coordinate tasks securely. Gartner pairs A2A with MCP as foundational enterprise agentic architecture standards.

An autonomous form of artificial intelligence that independently makes decisions, learns in real-time, and adapts to dynamic environments with minimal human intervention. Unlike traditional automation, Agentic AI enables businesses to shift from human-driven processes to agile, self-sustaining operations that drive efficiency and long-term value.

An agentic enterprise is an organization where AI agents autonomously handle routine decisions and workflows, freeing human workers for judgment and strategy. Skan AI serves as its operational intelligence foundation, providing an accurate, continuously updated picture of how work happens through the context graph of work.

The Agentic Ontology of Work (AOW) is a standardized vocabulary developed by Skan AI for classifying human and AI work across enterprise environments. Launched in February 2026, it provides a common language for actions, activities, processes, and agents, enabling consistent human-AI collaboration at scale.

Agentic orchestration is the coordination of multiple AI agents, human workers, and enterprise systems to accomplish complex, multi-step goals. Skan AI's process intelligence provides the operational context orchestration requires, ensuring agents are built on accurate process knowledge rather than assumed workflows.

An agentic orchestration platform is enterprise infrastructure that coordinates AI agents, human workers, and systems into a governed execution layer. Gartner identifies this as a $550 billion market and the control plane that determines whether enterprise AI scales from pilots to production.

Agentic Process Automation (APA) is enterprise automation where AI agents independently plan, execute, and improve complex workflows without predefined scripts. Skan AI's APA platform trains agents on operational ground truth, real observed work data, so they perform accurately from day one.

Agentic process intelligence is the discipline of combining continuous process observation with AI agent deployment into a closed loop, where observed work data powers, governs, and improves agents executing enterprise workflows. Skan AI delivers this through its Observation-to-Agent pipeline, grounding every agent in the context graph of work built from first-party desktop observation.

The agentic runtime fabric is the execution layer of an agentic orchestration platform, carrying out agent plans with speed and cost discipline. It allocates resources, selects optimal models per task, and ensures every agent action is auditable.

Agentic training data is the structured, process-level operational data used to train AI agents to execute enterprise workflows accurately. The quality of this data is the primary determinant of agent reliability. Skan AI's continuous desktop observation produces the most comprehensive agentic training data available: first-party, real-time, and complete at the action level.

AgentOps is the operational practice of managing the full AI agent lifecycle, covering evaluation, testing, deployment, monitoring, drift detection, and improvement. Skan AI supports AgentOps by providing the work telemetry baseline against which agent performance is measured and the observation data that feeds agent improvement.

AI agents are autonomous software systems that perceive their environment, make decisions, and take actions to complete goals without step-by-step instructions. Skan AI builds enterprise agents grounded in operational ground truth, ensuring agents start with an accurate, process-native understanding of the workflows they will operate in.

AI Agent Mining is the continuous observation and analysis of how AI agents execute tasks within enterprise environments, producing behavioral intelligence used to audit performance, detect drift, and verify conformance against defined playbooks. It applies the core methodology of process and task mining to AI agent behavior rather than human work. Skan AI delivers AI Agent Mining through its desktop observation layer, making agent behavior as measurable and improvable as human processes.

AI governance is the set of policies, controls, and accountability structures ensuring AI systems operate reliably and in compliance with regulations. Skan AI's governed autonomy framework ensures every agent action is bounded, traceable, and auditable from day one.

An AI operating model is the organizational structure, governance framework, and capabilities defining how an enterprise manages and scales its AI and automation initiatives. Skan AI supports this by providing accurate process baselines before AI deployment and continuous performance monitoring after.

AI process automation applies AI capabilities including machine learning and autonomous decision-making to automate complex business processes that rule-based tools cannot handle. Skan AI delivers this through its agentic process automation platform, combining process observation with agents trained on operational ground truth.

AI-driven insights are actionable findings generated by analyzing data about how work is actually performed. Skan AI produces these by observing every desktop interaction continuously, capturing patterns and bottlenecks that system logs miss.

AI-Powered Process Discovery utilizes artificial intelligence algorithms to automatically discover, document, and analyze business processes. It accelerates the identification of process variations, inefficiencies, and improvement opportunities.

Application Portfolio Rationalization is the process of evaluating and optimizing the collection of software applications within an organization. It aims to eliminate redundancies, improve efficiency, and reduce costs by retaining only those applications that deliver value and align with business objectives.

An as-is process map documents how a business process currently executes in practice, capturing actual steps, exceptions, and deviations rather than the intended design. Skan AI generates as-is maps automatically from desktop observation, with no workshops or interviews required.

Attended automation supports a human user in real time; unattended automation executes complete processes independently. Skan AI's process observation identifies which steps require human judgment, making the attended-versus-unattended decision data-driven rather than assumed.

Automation discovery is the process of identifying which tasks and workflows are best suited for AI or automation deployment. Skan AI scores every candidate automatically by observing real work across all applications, delivering a data-backed automation prioritization without manual assessment.

An automation maturity model defines the stages organizations progress through as they advance from basic RPA toward autonomous AI-driven operations. Skan AI anchors maturity assessment in process intelligence: accurate observed data on how processes work determines how confidently each stage can be achieved.

An automation pipeline is the end-to-end sequence for moving an automation opportunity from identification through deployment and monitoring. Skan AI compresses discovery and design phases by automatically observing and scoring automation candidates.

Automation ROI is the quantified return from an automation investment, comparing cost savings and productivity gains against implementation costs. Skan AI's Unit Cost of Work metric provides the accurate process baseline that makes ROI calculations precise, based on observed rather than estimated process costs.

An autonomous enterprise is an organization where AI agents handle routine decisions and workflows autonomously, freeing its workforce for judgment and strategy. Skan AI supports this by providing accurate, observed process data to identify which processes are agent-ready and which require redesign first.

Autonomous execution refers to the ability of a system to perform tasks independently, without human intervention. In business terms, it means automating repetitive tasks and workflows within your processes.

The process of identifying stages in a workflow or system that slow down operations or create inefficiencies. This analysis helps organizations pinpoint and address obstacles that hinder optimal performance, leading to smoother, more streamlined processes.

BPMN is the standardized visual language for modeling enterprise processes, using defined symbols to represent steps, decisions, and handoffs. Skan AI can validate BPMN documentation by comparing designed process models against observed execution behavior.

Business process automation uses technology to execute recurring business processes with minimal human intervention, from simple rule-based scripts to AI agents that handle complex decisions. Skan AI provides the process intelligence foundation that makes automation reliable by grounding every deployment in observed operational data.

Business process intelligence is the capability to analyze and generate insights from enterprise process data to support operations decisions and transformation programs. Skan AI delivers this by combining desktop observation with AI analytics, producing a complete picture of how work is actually performed.

Business Process Management (BPM) is the discipline of designing, executing, monitoring, and continuously improving the operational processes that run an enterprise. Skan AI adds an observation layer that replaces documented assumptions with accurate, real-time data on how processes actually execute.

A Center of Excellence (CoE) is a team that governs and scales an organization's automation and AI initiatives. Skan AI provides the operational ground truth CoEs need to prioritize investments with evidence rather than assumptions.

Process compliance is verification that business processes are executed in line with regulatory requirements and internal policies. Skan AI monitors compliance in real time by observing actual work execution and flagging deviations, such as skipped approval steps, the moment they occur.

Computer vision is an AI technology that interprets visual information, including screen content. It is the foundational technology behind Skan AI's process intelligence platform, enabling observation of desktop activity across any application without system integrations.

A confidence threshold is the minimum certainty level an AI agent must reach before acting autonomously without human review. Above it the agent acts; below it the workflow escalates to a human. Skan AI's governed autonomy framework allows threshold configuration by process and risk level.

Conformance checking compares how a business process actually executes against its intended design, identifying deviations and compliance gaps. Skan AI extends this beyond ERP event logs by observing work at the desktop level, detecting workarounds and policy bypasses that system logs cannot record.

Context engineering is the discipline of designing and managing the information context that AI agents need to reason and act accurately. Skan AI's work telemetry and context graph of work automate this for process agents, capturing the operational context agents need without manual curation.

A context graph of work is a real-time, structured representation of how work actually happens across an enterprise, capturing the flow of actions, decisions, and handoffs rather than just system events. Skan AI builds this from continuous desktop observation, creating a living organizational memory for AI agents.

Context graphs capture the directional flow of decisions, workflows, and actions across an organization, not just entities and relationships. AI agents use them to understand how work actually flows rather than relying on static knowledge. Gartner predicts 50% of AI agent systems will leverage context graphs by 2028.

Context grounding is the practice of providing AI agents with accurate, current information about a specific operational environment before they reason or act, preventing hallucinations. Skan AI's context graph of work, updated continuously from desktop observation, provides each agent with a current model of how the processes it operates in actually behave.

Continuous improvement is an ongoing effort to enhance products, services, or processes incrementally. It involves gathering feedback, making adjustments, and striving for excellence over time.

The Controls and Compliance Cockpit is Skan AI's compliance monitoring feature applying a structured Identify-Map-Monitor-Report-Remediate methodology to regulated workflows and approval processes. It generates real-time alerts for control violations, captures every approval action with screen-level evidence, and converts audit preparation from retrospective reconstruction to a real-time query.

Process data discovery is the capture and analysis of data generated by how work is actually performed. Skan AI collects this first-party data directly from the desktop, revealing manual workarounds and undocumented process variants that system logs cannot surface.

Days Payable Outstanding (DPO) measures the average days an organization takes to pay suppliers after receiving goods or services. Skan AI's process intelligence identifies the specific P2P workflow bottlenecks extending payment cycles.

Days Sales Outstanding (DSO) measures the average days to collect payment after a sale, reflecting order-to-cash process efficiency. Skan AI identifies the specific collection workflow steps extending DSO, enabling targeted AI agent deployment to accelerate collections.

Decision traces record the reasoning pathway through which an agent or human arrives at an action, including which data and logic contributed to the outcome. Skan AI captures decision lineage through process observation, providing a complete audit trail for every agent action.

Descriptive analytics answers the question: what actually happened? Skan AI delivers this by recording every process step in real time, producing an accurate picture of how work flows across teams and applications, including steps no standard dashboard captures.

Desktop telemetry is the real-time capture of user interaction data at the screen level, including application navigation, data entry, and task execution. Skan AI collects this through computer vision, producing structured process data without requiring integration with the observed applications.

Diagnostic analytics identifies the root cause of a performance problem or process deviation. Skan AI performs this automatically by correlating observed work data across applications and roles, pinpointing whether a bottleneck stems from a system issue, policy gap, or process design flaw.

Digital adoption measures how effectively employees use the technology tools an organization has invested in. Skan AI quantifies this by observing actual application usage, identifying which features are used, which are bypassed, and where retraining or redesign will have the greatest impact.

Digital Adoption Monitoring is Skan AI's capability for continuously tracking how enterprise applications are actually used across the workforce, including detection of unsanctioned AI tools and AI-generated code outside approved governance frameworks. It surfaces shadow AI inventory and governance baselines without requiring endpoint agents or application integrations.

Digital transformation is the shift to AI-powered, data-driven operations that let enterprises move faster and outperform their competition. Skan AI provides the process intelligence foundation that makes transformation measurable: accurate data on how work happens today and where AI will deliver the most value.

A digital twin of operations is a continuously updated virtual model of how an enterprise actually operates, built from observed work data rather than assumed process maps. Skan AI constructs this by capturing every action across all applications in real time, enabling performance analysis and scenario testing without disrupting live operations.

Direct modeling of human actions is the practice of observing and structuring exactly how employees interact with enterprise applications, including steps, data entry, navigation, and decisions, as the primary input for process intelligence. Skan AI's computer vision observation is purpose-built for this, providing the most complete representation of enterprise work behavior available.

Domains modeling is the practice of building structured representations of how work flows within a specific enterprise business domain, capturing the processes, decision rules, entities, and relationships that define how operations actually function. Skan AI constructs domain models from continuously observed work data rather than manually curated schemas, grounding AI agents in domain-specific operational knowledge rather than generic training.

Enterprise AI refers to the application of artificial intelligence technologies within organizational settings. It includes AI-driven solutions tailored to improve decision-making, operational efficiency, and customer experiences across various business functions.

Enterprise transformation is the strategic redesign of how an organization operates and creates value in the age of AI. Skan AI gives transformation leaders an accurate operational data baseline and clear evidence of where AI agents will deliver the highest return.

An event log is a structured record of timestamps and activities captured by enterprise systems, used as the primary input for process mining analysis. Event logs miss the manual steps and decisions that occur between system touchpoints. Skan AI adds desktop observation to capture what event logs cannot.

A federated Center of Excellence distributes AI governance across business units, with a central CoE setting standards while local teams execute. Skan AI supports this by providing a unified process intelligence platform giving both central and distributed teams visibility into enterprise-wide process performance.

First-party process data is work activity data captured directly from an organization's own operations through real-time desktop observation, not inferred from third-party system logs. Skan AI produces this continuously, creating a proprietary operational dataset that powers all downstream process intelligence and agent training.

The future of work describes the shift to AI-augmented enterprise operations, where agents handle routine decisions and humans focus on judgment and strategy. Skan AI supports this transition by providing the work telemetry that makes the shift measurable and the process intelligence that shows where AI should act first.

Generative AI is a subset of artificial intelligence that focuses on creating new content or data by learning patterns from existing data. It uses algorithms, such as generative adversarial networks (GANs) and transformers, to generate text, images, music, and other forms of content that are indistinguishable from human-created output. This technology has applications in various fields, including art, entertainment, healthcare, and business process automation.

Governed autonomy is the principle that AI agents operate independently within explicitly defined boundaries, escalating to human judgment when actions exceed authorized thresholds. Skan AI's framework sets clear operational boundaries for agents and logs every decision against the observed ground truth that informed it.

Guardian agents are specialized AI agents that monitor, evaluate, and constrain the behavior of other agents, functioning as an autonomous oversight layer. Gartner predicts guardian agents will replace almost half of incumbent risk and security systems protecting AI activity by 2029.

Human-in-the-loop (HITL) is the design principle of embedding specific human review points within automated workflows, ensuring AI acts with oversight where judgment matters. Skan AI implements HITL through confidence thresholds that automatically escalate agent decisions when certainty falls below the defined level for a task or risk level.

Hybrid process intelligence combines system-based process mining from ERP event logs with desktop task mining from observed user activity, providing a complete operational picture. Skan AI delivers both natively through a single platform.

Hyperautomation is a strategy combining RPA, AI, and process mining to automate as many business processes as possible. The field has evolved toward agentic process automation, where agents reason and adapt rather than follow scripts. Skan AI supports every stage by grounding automation in accurate observed process data.

A knowledge graph of work is a structured representation connecting people, processes, applications, decisions, and outcomes into a queryable model of how an organization performs its work. Skan AI's context graph of work extends this with real-time decision traces from observed activity, creating an organizational memory that is both semantically rich and operationally grounded.

Large Action Models (LAMs) are AI foundation models trained on human application interaction data to understand and execute complex multi-step tasks within graphical user interfaces without explicit scripting. Skan AI's work telemetry and desktop observation create the large-scale, structured action data required to train robust LAMs.

Large Process Models (LPMs) are AI foundation models trained on large-scale structured process data to understand and reason about enterprise workflows. Skan AI's continuous work observation creates the first-party process datasets required to train robust LPMs, analogous to how text data trains large language models.

A lineage and provenance graph records how each decision or output in an agentic system was produced, documenting the data, models, and reasoning that contributed to it. Skan AI's process observation provides the foundational data for comprehensive lineage graphs across all agent actions.

A living system of record is a continuously updated operational model reflecting how an organization actually functions today, not a static snapshot from a previous assessment. Skan AI creates this by observing work at the desktop level and keeping the operational model current with actual process execution.

Manual process discovery maps business processes through workshops, interviews, and manual observation. It captures only an idealized snapshot of how processes should run, not how they actually do. Skan AI replaces manual discovery with continuous desktop observation, delivering accurate process maps in days.

The Model Context Protocol (MCP) is an open standard for how AI agents securely access enterprise tools and systems through a standardized interface. Gartner endorses MCP as a must-have to avoid vendor lock-in. Skan AI integrates with MCP-enabled systems as part of its O2A pipeline.

A multiagent system is an architecture where multiple autonomous AI agents collaborate, each with a defined role, to accomplish goals too complex for a single agent. Skan AI's context graph of work provides the shared operational knowledge multiagent systems need to coordinate accurately across enterprise workflows.

Neurosymbolic AI combines neural network pattern recognition with symbolic rule-based reasoning in a single architecture. Skan AI uses this approach to deliver process intelligence that is both accurate, capturing complex real-world work, and auditable, producing decisions explainable to regulators.

Object-centric process mining analyzes how multiple business objects, such as orders, invoices, and customers, interact within a single process rather than following one linear case. Skan AI adds the desktop observation layer that captures how employees handle these multi-object processes, revealing human activity no event log records.

Object-centric task mining captures how users interact with multiple business objects simultaneously in a single work session, reflecting the reality of knowledge worker multitasking. Skan AI's desktop observation captures this multi-object complexity at the activity level.

The operational data layer is the foundational infrastructure that captures and continuously updates an organization's real-time work activity, providing AI agents and analytics systems with an accurate operational picture. Skan AI constitutes this layer by observing what humans do across every application, creating the context agents need to reason accurately.

Operational Excellence refers to the continuous improvement of processes and systems to achieve sustainable business growth, efficiency, and customer satisfaction. It focuses on eliminating waste, optimizing resources, and delivering high-quality products/services.

Operational ground truth is an accurate, real-time picture of how work is actually performed, captured from desktop observation rather than inferred from system logs or process interviews. Skan AI establishes this continuously, giving enterprises a reliable data foundation for AI deployment and process improvement.

Operational intelligence is continuous, real-time visibility into how enterprise operations are actually performing. Skan AI delivers this by observing work at the desktop level, capturing telemetry that system dashboards cannot provide.

The orchestration control plane defines how work is structured, how agents interact, and which policies govern decisions across all automated workflows. It translates business intent into repeatable execution blueprints.

Order-to-Cash (O2C) is the end-to-end process from customer order through to payment receipt, covering order management, fulfillment, invoicing, and cash application. Skan AI's Observation-to-Agent (O2A) platform takes its name from this transformation, identifying where agents can compress cycle times across the O2C workflow.

Predictive analytics uses data to forecast future process performance, such as when a bottleneck will form or an SLA will be breached. Skan AI applies predictive models to continuously captured work telemetry, surfacing risk signals before they become failures.

Prescriptive analytics recommends the specific actions that will produce the best operational outcome. In Skan AI, this identifies not just where a process underperforms but exactly what to change, whether a workflow redesign, a training intervention, or an AI agent deployment on a specific task.

Privacy-first AI observation captures process-level activity data without collecting personally identifiable information about individual employees. Skan AI masks all PII at the point of desktop capture, ensuring process intelligence reflects how work flows rather than who performed it.

Process analysis is the systematic examination of a business process to identify areas for improvement. It often involves techniques like process mapping, process mining, and unit cost analysis. Process analysis helps businesses streamline workflows, reduce costs, and improve overall efficiency.

Process analytics applies data analysis to process performance data to identify inefficiencies, measure cycle times, and quantify improvement opportunities. Skan AI delivers process analytics from first-party desktop observation, providing complete process data that includes human activity beyond what system records contain.

Process automation involves using technology to execute recurring tasks or processes in a business where manual effort can be replaced. This automation increases efficiency, reduces errors, and frees up human workers to focus on more strategic activities. It includes the use of robotic process automation (RPA), AI, and other digital tools to streamline operations.

Process benchmarking compares process performance within an organization against internal data across teams, industry standards, or peers. Skan AI enables benchmarking with observed performance data at the task and activity level.

A process data fabric is the integrated infrastructure connecting, harmonizing, and continuously updating process-level operational data from across an enterprise's applications and workflows. Skan AI builds this foundation through desktop observation, creating a unified work data layer that is always current and always grounded in actual operations.

A Process Definition Document (PDD) is a structured specification of a business process including each step, decision points, application interactions, and exception paths, used as the reference for automation development. Skan AI generates PDDs automatically from observed work data rather than relying on stakeholder recall.

A process digital twin is a virtual model of a business process that continuously mirrors real-world execution using live operational data. Skan AI builds process digital twins from desktop observation, capturing human activity, workarounds, and exceptions that make the model an accurate reflection of operations.

Process drift is the gradual deviation of a business process from its intended design, typically invisible until performance has already degraded. Skan AI detects process drift in real time by comparing observed work patterns against the baseline context graph of work.

The process of identifying and mapping out business processes using data and analytics. For example, a company might use process discovery to understand how customer orders are processed, identify bottlenecks, and optimize the workflow.

Process Intelligence refers to the use of AI and advanced analytics to monitor, visualize, and analyze business processes in real-time. It helps organizations gain an end-to-end view of their processes, identify inefficiencies, optimize workflows, and improve operational performance.

A process map is a visual representation of a business process, outlining the sequence of activities, decisions, and agents involved. Process maps are often flowcharts or diagrams that help visualize the steps involved in achieving a specific outcome. They are a valuable tool for understanding, documenting, and improving business processes.

Process mapping creates a visual representation of how a business process flows, documenting steps, decision points, and handoffs. Skan AI generates process maps automatically from observed desktop activity, producing accurate, real-time maps that reflect how processes actually run rather than how they were designed.

A more advanced process discovery technique that analyzes event logs to visualize and understand business processes in detail. For example, a bank could use process mining to analyze customer onboarding processes, identify inefficiencies, and improve customer experience.

Process mining use cases are the operational scenarios where analyzing event logs delivers value, including conformance checking, bottleneck analysis, and automation discovery. Skan AI extends standard use cases by adding desktop observation, enabling task-level analysis that log-based mining cannot support.

A process model is a more formal representation of a business process, often created using specific modeling languages or software tools. Process models can be more detailed than process maps and can be used for simulation, analysis, and optimization purposes.

Process optimization involves improving business processes to achieve higher efficiency, lower costs, and better-quality outcomes. It encompasses redesigning workflows, eliminating redundancies, and adopting best practices.

Process orchestration coordinates automated tasks, AI agents, human activities, and system interactions into an end-to-end workflow that delivers a defined business outcome. Skan AI's process intelligence identifies the optimal orchestration design by observing how end-to-end processes actually flow.

Process orchestration software coordinates automated tasks, agents, and human activities from a central control point across an end-to-end workflow. Skan AI's process intelligence complements orchestration platforms by providing the accurate operational model governing how work is routed and monitored.

A process reasoning engine evaluates the current state of a business process, determines the next best action, and selects the appropriate agent to execute it. Skan AI functions as the process reasoning foundation for enterprise agentic deployments by maintaining an accurate, real-time operational model through continuous observation.

Process simulation models a business process digitally and runs scenario tests to predict how changes would affect performance before production implementation. Skan AI's digital twin of operations provides a simulation foundation grounded in real observed data, ensuring scenarios reflect actual process behavior.

Process transparency is full, real-time visibility into how work actually happens across an enterprise, not just how it is documented. Skan AI achieves this through continuous desktop observation, capturing every process variant, exception, and decision point as it occurs.

Process variants are the different execution paths a business process takes in practice. Skan AI captures all variants, including those that occur entirely outside system logs, by observing work at the desktop level.

Process-native agents are AI agents trained on observed enterprise process data rather than generic instructions, understanding how work actually flows including exceptions and workarounds. Skan AI builds process-native agents from the context graph of work, producing agents that operate with enterprise-grade precision.

ProcessGPT is Skan AI's generative AI capability that lets users query process intelligence data through natural language. Users can ask where bottlenecks are forming, which processes have the highest automation ROI, or how teams differ in executing the same workflow. Answers are grounded in first-party observed process data.

ProcessPilot keeps your process documentation and standard operating procedures (SOPs) readily available at your fingertips. This helps improve efficiency and reduce errors by ensuring everyone on your team has easy access to the latest process information.

Procure-to-Pay (P2P) covers all activities from identifying a purchasing need through to supplier payment, including requisition, approval, purchase orders, goods receipt, and invoice processing. Skan AI observes the complete P2P workflow to identify bottlenecks and exception patterns, enabling precise AI agent deployment without disrupting governance controls.

Robotic Process Automation (RPA) uses software bots to execute repetitive, rule-based tasks by mimicking human interactions with digital systems. RPA breaks when processes change and cannot handle unstructured inputs. Skan AI helps enterprises identify where agentic automation will deliver more reliable, adaptable results.

RPA use cases are specific processes suited for software bot automation, typically high-volume, rule-based, and stable. Skan AI eliminates guesswork from RPA use case identification by automatically scoring every observed process for automation suitability based on volume, variability, and ROI potential.

Self-healing automation describes workflows and agents that automatically adapt when underlying applications or processes change, without requiring manual re-scripting. Skan AI detects process changes in real time through continuous observation, alerting teams before performance degrades.

Skan Agents is the agentic execution layer of the Skan AI platform, deploying AI agents built on the process intelligence established by Skan Blueprint and Skan Intelligence. Agents operate inside deterministic playbooks with embedded guardrails, produce a full audit trail, and are continuously monitored post-deployment.

Skan Blueprint is the strategic discovery and assessment layer of the Skan AI platform, designed to give C-suite leaders a quantified business case for AI transformation. Blueprint observes work across thousands of roles, scores automation opportunities by value, and produces a board-ready investment roadmap showing where AI will deliver the highest return.

Skan Intelligence is the operational intelligence layer of the Skan AI platform, providing CISOs and operations leaders with continuous visibility into how work is actually executing. It captures process variants, bottlenecks, application utilization, compliance adherence, and remediation velocity: the data needed to turn operational assumptions into evidence.

A Standard Operating Procedure (SOP) is a documented set of steps required to complete a task consistently and correctly. Skan AI's ProcessPilot automatically generates and updates SOPs from observed work data, so documentation always reflects how the process is actually performed.

Structured observation data is work activity data captured through real-time desktop observation and transformed into an analyzable format with defined schemas and classifications. Skan AI's computer vision converts the raw desktop activity stream into labeled activities, timed actions, and process variants ready for analysis and agent training.

A System of Action (SOA) is a platform that actively directs work and executes decisions rather than just recording events. Gartner introduced this to distinguish agentic orchestration platforms from systems of record like ERP or CRM. Skan AI's O2A platform is a system of action, closing the loop from observation to execution.

Task mining captures user interactions with software applications to analyze and identify patterns in task execution. It provides a narrow, task-level view of processes, focusing on unstructured data, which is ideal for low-complexity robotic process automation (RPA). This technique helps in understanding how specific tasks are performed, uncovering inefficiencies, and pinpointing opportunities for automation and improvement. However, it does not offer an end-to-end process view.

Technology insights are analytics derived from observing how employees actually interact with enterprise applications. Skan AI captures application usage at the desktop level, revealing which systems are used as intended, which are bypassed, and which licenses are underutilized.

Touchless invoice processing handles supplier invoices from receipt to payment authorization entirely through automated systems without manual intervention. Skan AI identifies the exception patterns preventing straight-through processing, providing the process intelligence needed to achieve high touchless rates.

Tribal knowledge is the undocumented process expertise held by experienced employees, including informal workarounds, unwritten decision rules, and expert shortcuts that never appear in official process documentation. Skan AI captures this by observing how experienced employees actually work, transforming it into structured process intelligence.

Turnaround time (TAT) is the total elapsed time to complete a process from start to finish. Skan AI measures TAT from observed work data, identifying exactly which step or queue is responsible when performance degrades.

A unified process view is a single, end-to-end picture of how a business process flows across all applications, systems, and teams. Skan AI builds this through desktop observation, connecting every action into a complete process model via the context graph of work.

A unified semantic layer ensures AI agents and applications share consistent definitions of business terms and process concepts across an enterprise. Without one, agents create silos. Skan AI's Agentic Ontology of Work provides the process-specific semantic layer that standardizes how work is described across all systems.

Unit cost analysis determines the true cost of completing one unit of work within a business process. Skan AI enables precise unit cost measurement by observing actual time and steps at every process stage, producing cost data based on how work is done, not ERP estimates.

Unit Cost of Work (UCOW) is a Skan AI metric that calculates the observed cost of completing one unit of work, including labor time, application interactions, and process steps captured from the desktop. It provides an accurate baseline for measuring the financial impact of process improvements and AI deployments.

Unstructured data is information that does not conform to a predefined schema, including documents, emails, and screen content. Skan AI's computer vision extracts structured process intelligence from the unstructured stream of desktop activity, making it analyzable for process improvement.

Skan's desktop observation agent is a lightweight software component installed on a user's device that uses computer vision to observe workflow activity in real time, classifying every action without disrupting the user. All personally identifiable information is masked at the point of capture.

What-if analysis in process intelligence is the ability to model the impact of a proposed process change before implementing it in production. Using Skan AI's digital twin of operations, leaders can simulate redesigned workflows with results grounded in observed work data.

Work intelligence is the continuous capture and analysis of data about how human work is performed across an enterprise. Skan AI's platform observes every action across every application, producing structured insights that reveal inefficiencies and automation opportunities that system-based analytics cannot detect.

Work telemetry is the continuous capture of data signals from how employees interact with applications and workflows. Skan AI collects work telemetry through desktop observation, transforming user interactions into structured process intelligence that reveals where delays accumulate and where agents should be deployed.

Workflow automation automates repetitive tasks and workflows using technology, such as AI and robotic process automation (RPA). It aims to increase productivity, reduce errors, and accelerate process execution.

Workforce insights are data-driven analytics derived from Skan's process intelligence platform. These insights provide valuable information about how employees work, measured through key metrics like productivity, proficiency, and utilization.

Workforce productivity intelligence uses process observation data to understand how work is performed across teams and identify efficiency improvements. Skan AI takes a privacy-first approach, observing process-level activity rather than individual behavior, with all personally identifiable information masked at capture.