Simulation XPLR
Last updated
Last updated
Simulation XPLR is a dashboard designed to help enterprises evaluate the potential of GenAI adoption across key business functions. It allows users to identify the total number of high-impact GenAI solutions, AI agents, and enterprise processes relevant to their industry and functions. The tool enables simulation of GenAI possibilities by combining filters, taxonomies, and solution mappings to give a structured, data-backed foundation for feasibility and value discussions.
Simulation XPLR is divided into three phases—Inform, Ideate, and Build—enabling a progressive approach to AI adoption that guides users from process identification to actionable GenAI solutions.
Simulation XPLR uses several filter dimensions to contextualize simulation results. These filters include:
Industry: Allows users to select the relevant industry to tailor the solution set. Examples include aerospace and defence, automotive, consumer packaged goods, pharmaceuticals, telecommunications, software and services, and many more.
Impact: Lets users choose the desired scale of transformation. This filter includes three impact levels: breakthrough, transformative, and incremental.
Function: Facilitates users to filter processes and solutions by business function. Examples include finance, HR, IT, legal, procurement, sales and supply chain.
Benefit area: Helps users focus on key business goals such as revenue growth, customer experience, process productivity, employee productivity and cost savings.
All metrics in the inform, ideate, and build sections are computed based on users' filter configuration. Changing filters will adjust solution mappings, AI agent listings, and processes accordingly.
Provides visibility into the structure of enterprise operations, showing how many end-to-end (E2E) flows, processes, subprocesses, and worksteps are identified based on the selected filters.
Metrics:
# of E2E: Number of end-to-end business processes identified.
# of process: Individual processes within E2E flows.
# of subprocess: Detailed subprocesses that further break down each process.
# of worksteps: Granular operational tasks within subprocesses.
These are the foundational units used to locate where in the business operations AI solutions might apply. They also form the structure for categorizing existing use cases and AI agents.
The number displayed here reflects how many elements have been identified that match the selected filters. These are not fixed numbers—they change dynamically with each new combination of filter settings.
The ideate phase surfaces potential GenAI solutions applicable to the filtered operational areas. These solutions are curated to match the selected industry, functions, and business objectives (e.g., productivity, cost savings).
GenAI solution metrics:
Total GenAI solutions: Overall number of relevant GenAI solutions identified for the current configuration.
Breakthrough solutions: These solutions target large-scale automation opportunities that often replace or significantly reduce human intervention in complex workflows. Examples include AI-led decision-making systems or end-to-end automation of processes that cut across multiple departments. They aim at exponential ROI and performance gains.
Transformative solutions: These solutions reshape how existing business models operate. They use AI to improve or evolve key operations, such as automating a portion of decision-making or dynamically optimizing how resources are allocated. Such solutions aim at strong ROI through process improvements and business model adaptation.
Incremental solutions: These solutions bring value by automating repetitive, task-level activities that improve individual employee productivity. They don't require major system changes but still contribute measurable efficiency gains. The expected outcome from such solutions is modest ROI and productivity improvements.
The numbers reflected in each metric highlight the count of suitable solutions found under the current simulation parameters.
The build phase quantifies GenAI opportunities and maps them to executable AI agents. It helps you assess implementation scope and prioritize agent development based on business criticality.
Build metrics:
GenAI opportunities: Identified from GenAI solutions in the ideate phase.
AI agents: This represents the total number of implementable AI agents associated with the GenAI opportunities.
Essential agents: They are core agents that deliver primary value and are prioritized for implementation.
Optional agents: They are supportive or secondary agents that can enhance core solutions but are not mandatory.
Agent classification is designed to support phased implementations based on enterprise readiness.
A curated collection of GenAI-powered business solutions identified across various functions and industries. The library helps users explore and shortlist relevant AI solutions aligned to business priorities. Users can also mark solutions as favorites for quick access and further consideration during simulation or agent building.
Click the star icon in any solution entry in the ‘AI solution library’ to add it to favorites. These starred solutions are saved under ‘My favorite AI solutions’ for quick reference and tracking.
Each favorited solution includes the following data points:
Solution name – Name of the selected GenAI solution.
Impact type – Indicates whether the solution offers a breakthrough, transformative, or incremental impact based on the scale of transformation it brings.
Benefit area scores – Each solution is evaluated across five benefit areas:
Revenue growth
Customer experience
Process productivity
Employee productivity
Cost savings
Each benefit area is scored as high, medium, or low, helping users quickly assess the potential value of a solution. This structured view supports decision-making by aligning solution impact with strategic business goals.
Users can favorite any number of solutions while browsing the ‘AI solution library’. These marked solutions automatically appear in the favorites section of Simulation XPLR for easy access during simulation and agent planning. Users also have the option to remove an AI solution from the favorites list.
