Be an Enterprise Architect who is an expert in AI

I am going to give you a short description of AI Needs that need to be developed. I need your help determining the following:

1. if I should Buy a Solution, Build a solution, or some Hybrid of that solution. (Rate each Buy and Build on a scale of 1 to 10 with 1 being fully purchase and Build being fully build. Hybrid solutions should be a combination of each whose total is 10.)
2. If this solution will require Custom Prompt Engineering, Custom Business Processes, or Custom data Models. (rate each on a scale from 1 to 10 with 1 being standard out of the box, and 10 being fully custom frameworks.)
3. If this approach will require a novel or unique Agentic Framework (Rate from 1 - 10 with 1 being very standard and 10 being very novel)
4. If this solution will require Knowledge Management Outcomes (Rate from 1 - 10 with 1 being very standard and 10 being very novel)
5. If this solution will require a Common API layer for either Data Calls, Model Calls, or for Security and Cost accounting (Rate from 1 - 10 with 1 being very standard and 10 being very novel)
6. if this solution should trigger a need for an AI TRISM platform(Rate from 1 - 10 with 1 being very standard and 10 being very novel)

Output the above as a table in the following format.

[AI Needs]

1. Work Order Exception Reviewer - Work orders are generated for various maintenance, repair, and operational tasks. Occasionally, exceptions occur that require manual review and intervention. These exceptions can be due to various reasons such as discrepancies in data, unexpected equipment failures, or safety concerns. The manual review process can be time-consuming and prone to human error. - Automate the detection, prioritization, and resolution of work order exceptions, improving efficiency, accuracy, and decision-making while reducing costs. This system integrates with existing work order management systems and provides data-driven recommendations for resolving exceptions
2. Engineering Design Generator - Engineering design is a critical process that involves creating detailed plans for infrastructure projects such as power plants, substations, and distribution networks. This process can be time-consuming and requires a high level of expertise. - Automate the creation, customization, and optimization of engineering designs, improving efficiency, accuracy, and decision-making while reducing costs. This system integrates with existing CAD software and provides data-driven design recommendations.
3. Permitting Virtual Assistant - Obtaining permits for various projects such as construction, maintenance, and upgrades is a critical and often time-consuming process. It involves navigating complex regulatory requirements, submitting detailed documentation, and coordinating with multiple stakeholders. - An AI-powered permitting virtual assistant automates the generation, compliance checking, and management of permit applications, improving efficiency, accuracy, and decision-making while reducing costs. This system integrates with existing project management and regulatory systems and provides real-time feedback and coordination with stakeholders.
4. Automated work package creation with predictive material forecasting: - Creating work packages for maintenance, repair, and construction projects involves detailed planning and accurate material forecasting. This process can be time-consuming and prone to errors, leading to delays and increased costs. - An AI-powered solution for automated work package creation with predictive material forecasting streamlines the planning process by generating detailed work packages and accurately predicting material requirements. This system integrates with existing project management and ERP systems, improving efficiency, accuracy, and decision-making while reducing costs.
5. RFI and Supplier Contract Generator: - Managing Requests for Information (RFI) and supplier contracts is a critical task that involves significant manual effort and time. The process includes drafting, reviewing, and finalizing documents to ensure compliance with regulatory standards and project requirements. - An AI-powered RFI and supplier contract generator automates the creation, review, and finalization of RFIs and supplier contracts, improving efficiency, accuracy, and compliance with regulatory standards
6. Bid Package Generator - Preparing bid packages for projects is a complex and time-consuming process. It involves gathering project requirements, creating detailed specifications, and ensuring compliance with industry standards. - An AI-powered bid package generator automates the creation of bid packages, improving efficiency, accuracy, and consistency while reducing manual effort
7. Automated Work Scheduling - Scheduling work for maintenance, repair, and construction projects is a critical task that requires careful planning and coordination. Manual scheduling can be time-consuming and prone to errors, leading to delays and increased costs - An AI-powered automated work scheduling system optimizes the scheduling of maintenance, repair, and construction projects, improving efficiency, accuracy, and resource utilization while reducing delays and costs
8. Construction Standards Generator and QC - Adhering to construction standards and ensuring quality control (QC) is essential for the successful completion of projects. This process involves creating and maintaining construction standards, as well as conducting QC checks to ensure compliance. - An AI-powered construction standards generator and QC system automates the creation and maintenance of construction standards and conducts quality control checks, improving efficiency, accuracy, and compliance
9. GIS-Based Work Visualization to Optimize Design and Engg Calculations - Visualizing work and optimizing design and engineering calculations are crucial for efficient project planning and execution. Geographic Information Systems (GIS) provide valuable spatial data that can enhance these processes. - An AI-powered GIS-based work visualization system integrates spatial data with design and engineering calculations, optimizing project planning and execution
10. Automated Schedule Analysis & Drafting - Creating and analyzing schedules for various projects is a critical task that requires significant manual effort and time. This process involves drafting schedules, analyzing timelines, and ensuring that all tasks are aligned with project goals - An automated schedule analysis and drafting system automates the creation and analysis of project schedules, improving efficiency, accuracy, and alignment with project goals
11. Automated Energy Contract Drafting - Drafting energy contracts is a complex and time-consuming process that involves ensuring compliance with regulatory standards and negotiating terms with suppliers. - An automated energy contract drafting system automates the creation of energy contracts, improving efficiency, accuracy, and compliance with regulatory standards while reducing manual effort
12. Risk P&L Analysis / Earnings Analysis - Analyzing risk, profit and loss (P&L), and earnings is essential for informed decision-making and financial planning. This process involves evaluating financial data, identifying potential risks, and forecasting earnings. - Risk P&L analysis and earnings analysis system automates the evaluation of financial data, identification of risks, and forecasting of earnings, improving decision-making and financial planning
13. Design Engineer / Service Planner Assistant - Design engineers and service planners are responsible for creating detailed plans for infrastructure projects. This process involves significant manual effort and time. - A design engineer/service planner assistant automates the creation and optimization of infrastructure project plans, reducing manual effort and improving efficiency and accuracy
14. In-Field Technician Assistant - Field technicians perform critical maintenance and repair tasks. They often face challenges such as accessing information, diagnosing issues, and ensuring safety. An in-field technician assistant can provide real-time support, enhancing their efficiency and effectiveness. - An in-field technician assistant provides real-time support to field technicians, improving efficiency, accuracy, and safety during maintenance and repair tasks
15. Image and Sensor-Based Asset Health Monitoring - Monitoring the health of assets is crucial for preventing failures and ensuring reliability. Traditional methods can be time-consuming and less accurate. - An image and sensor-based asset health monitoring system provides real-time, accurate monitoring of assets, improving reliability and preventing failures
16. Interconnection Studies (Draft/Review): - Interconnection studies are essential for integrating new power generation facilities into the grid. These studies involve complex calculations and regulatory compliance. - AI enabled system for interconnection studies automates the drafting and review process, ensuring accuracy, compliance, and efficiency
17. Automated Job Scheduling - Scheduling jobs for maintenance, repair, and construction projects is a complex task that requires careful planning and coordination. - An automated job scheduling system optimizes the scheduling of maintenance, repair, and construction projects, improving efficiency, accuracy, and resource utilization
18. Asset Info QC - Ensuring the quality and accuracy of asset information is critical . Inaccurate data can lead to compliance issues and increased costs. - An asset information QC system automates the validation and correction of asset data, improving accuracy, compliance, and reducing costs
19. AI Optimized Emergency Response - Emergency response requires quick and accurate decision-making to minimize downtime and ensure safety. - An emergency response system provides real-time insights and recommendations, optimizing decision-making and improving response times during emergencies
20. Mutual Assistance Crew Tracking - During large-scale emergencies, utilities often rely on mutual assistance agreements to share resources and crews. Tracking these crews and coordinating their efforts can be challenging. - A mutual assistance crew tracking system streamlines the coordination and tracking of crews during large-scale emergencies, improving efficiency and response times
21. Automated Job Close-Outs - Closing out jobs involve verifying that all tasks are completed, and documentation is accurate. This process can be time-consuming and prone to errors. - An automated job close-out system ensures that all tasks are completed, and documentation is accurate, improving efficiency and reducing errors
22. Safety Monitoring & Alerts - Ensuring the safety of workers is paramount. Traditional safety monitoring methods can be reactive rather than proactive. - An AI enabled safety monitoring and alert system provides real-time monitoring and proactive alerts, improving worker safety and preventing accidents
23. Optimized Work Bundling (Geographical) - Bundling work tasks geographically can improve efficiency and reduce costs. - An optimized work bundling system analyzes geographical data and project requirements to bundle tasks efficiently, improving efficiency and reducing costs
24. Robotics for High-Risk Work - High-risk work such as inspections and maintenance in hazardous areas, poses significant safety risks to workers. Robotics can perform these tasks, reducing the risk to human workers. - An AI enabled system for robotics in high-risk work optimizes the use of robotics for inspections and maintenance in hazardous areas, improving safety and efficiency “25. - I know you were probing on AI – one area I need help is with the Transformer MPFR form. Need a good tool that links into our asset management DB and can be used to report failures of transformers for field people to fill out. Mario Munoz is leading the effort from supply chain. Sounds like they are bottle necked with IT resources. Not sure what solution they were thinking

Also, they are visually inspecting all incoming transformers at a Fontana warehouse before we take delivery. I wonder if you you could take pictures of every transformer and use AI to find failures so we can maintain 100% inspections.

My next request will be a robot to do electrical testing of them

• ”
  1. ORCA Document Chatbot - AI-powered chatbot that ingests manuals, SOPs, and internal documents to support Q&A for teams. -
  2. ORCA Information Retrieval - Splunk Agent - Agents designed to facilitate communication and integration with Splunk systems through Text2Splunk capabilities for specific use cases e.g., environmental factors for a device -
  3. Cut Fiber Detection System - System to detect and identify common cables during a cable cut in the network to reduce MTTR -
  4. Job Package Scoping from ATP - Parse Approval to Proceed (ATP) emails from T&D department to identify Telecom Scope - inform telecom department of any projects requiring scope with a summary of the expected scope required including level of effort and cost estimate -
  5. ATP Forms Experience-Based Job Provisioning Engine - Tool to provision jobs dynamically based on historical experience and contextual user profiles. When a work order is received, the system analyzes its complexity and automatically assigns it to the most suitable engineer for efficient resolution -
  6. ORCA Information Retrieval - BMC Agent (Remedy/Helix) - Integrating ORCA chatbot with agents using Remedy data to identify similar tickets related to queried incident from a given time frame -
  7. Change Managment Request - Integrating ORCA with the ability to analyze change impact for approval at a given time (redundant circuit check) at a given time -
  8. Situational Awareness Assistant - Safety requirements for fire threats - Notifying a technician in a working area if there are any fire threats in the area -
  9. Ticket Enrichment Assistant - Agent designed to recognize similarities in alarms and incidents, consolidating resolution steps, helping reduce MTTR -
  10. Who’s Close - Technician Dispatch agent - Identifying the right technician with right level of experience to dispatch to location x has an event at time y -
  11. Patching Verification System - Reads emails on patched Palo Alto PADU Updates and checks on firewalls to determine if updates were in fact completed. -
  12. Self Healing Alarms - Enhancing ticket assistant to be able to auto-heal alarms to eliminate noise for recurring low priority incidents e.g., wireless authentication, configuration errors -
  13. Evidence Collection Agent - Pre and Post Evidence collection during Change Request events and capabilities of generating evidence documents to be stored. -
  14. Coordinating Agent - Establish an architecture for agentic rag with a coordinating agent -
  15. TBS AI Trainer - Creating Training Platforms that will guide users through TBS frontend and Data/Job entry. Will allow users to asks questions during training session as they interact with TBS frontend. -
  16. Palo Alto Rules Scanning - Palo Alto Running Configs are very difficult to parse using traditional programing to look for rules like Any Any or rules that are noncompliant. We have over 600 firewalls that each have rules that are set up by Panorama. Have an LLM to scan running configs XML files and pull out rules that are noncompliant. - “42. Waveform Analysis - ✅ Fault Detection & Prediction:
• Use ML models to detect anomalies in waveform patterns that indicate impending faults such as arcing, insulation degradation, or transformer issues.
• Time-series anomaly detection to predict equipment failure before it occurs. ✅ Harmonic Analysis
• AI models can analyze harmonic distortion to detect power quality issues like voltage sags, swells, or flicker.
• Detect equipment causing waveform disturbances (e.g., faulty capacitor banks or motor drives). ✅ Load Signature Identification
• Use AI to classify waveform signatures for specific appliances, enabling granular load disaggregation for demand-side management. ✅ Event Classification
• Distinguish between benign events (e.g., lightning strikes) and serious issues like line faults or equipment failures. - “ “43. Power Flow Monitoring and Prediction - ✅ Dynamic Load Forecasting. - S
• Use AI models (e.g., LSTM, GRU) to predict short-term and long-term power flow with enhanced accuracy.
• Incorporate weather data, demand profiles, and distributed energy resources (DER) integration. ✅ Voltage Stability Prediction - S Predict voltage instabilities and proactively dispatch control actions to mitigate voltage collapse. ✅ Real-Time Contingency Analysis -L
• AI models can simulate ““N-1”” or ““N-2”” contingency scenarios to evaluate grid resilience and recommend preventive measures. ✅ Renewable Energy Forecasting - S
• Predict solar and wind power generation using ML models, integrating environmental data for higher accuracy. - “ “44. Transmission Operations - ✅ Line Sag and Thermal Overload Prediction - S
• AI models can predict line sag or conductor overheating based on temperature, wind speed, and load conditions. ✅ Grid Topology Optimization - M
• AI-based optimization algorithms can dynamically reconfigure grid topology to minimize power loss and improve stability. ✅ Predictive Maintenance - L
• Use computer vision models on drone footage or sensor data to detect corrosion, broken insulators, or vegetation encroachment. ✅ Enhanced Fault Localization -S
• AI can pinpoint the exact location of faults along transmission lines by analyzing current and voltage waveforms. - “ “45. Distribution Operations - ✅ Outage Prediction and Restoration - S
• AI models can predict outage risks based on environmental factors, maintenance records, and equipment health.
• Optimize crew dispatch with route planning and predictive analytics for faster restoration. ✅ Load Balancing and Optimization - S
• AI-driven load forecasting models can dynamically shift loads across feeders to improve voltage regulation. ✅ EV Charging Load Management - M
• Predict EV charging behavior and optimize charging station deployment to mitigate grid congestion. ✅ Distributed Energy Resource (DER) Management - M
• AI models can optimize the dispatch of solar, battery storage, and demand response resources for grid stability. - “ “46. Digital Twin for Utility Operations - ✅ Grid Resilience Simulation
• Digital twins combined with AI can simulate wildfire scenarios, identifying optimal de-energization strategies to mitigate risk. ✅ Asset Health Management
• Predict the remaining useful life (RUL) of transformers, circuit breakers, and other assets by feeding digital twin models with real-time IoT data. ✅ Operational Efficiency
• AI can run complex simulations on digital twin environments to test new grid configurations, DER integrations, or restoration strategies without impacting the live grid. ✅ Energy Market Optimization
• Simulate energy trading scenarios, optimize power purchase agreements (PPAs), and forecast wholesale energy pricing. - “ “47. Electric Grid Troubleshooting Automation - AI-driven diagnostic tool for faster fault identification and resolution. Use Case: • Develop a knowledge graph integrating historical outage data, asset information, and GIS data to guide troubleshooting. • Implement natural language processing (NLP) models that allow field engineers to query a system for guidance. • Deploy computer vision models to analyze drone or sensor footage for visual signs of failure (e.g., corrosion, equipment tilt). • Integrate with ticketing systems (e.g., ServiceNow) to automate issue escalation and resolution. Outcome: Faster diagnosis of faults with reduced manual effort, improving field technician efficiency. - “ “48. Ai-Assisted Switching - Automating and optimizing switchgear control for grid reconfiguration. Use Case: • Use reinforcement learning (RL) models that predict optimal switching sequences during fault isolation or grid congestion. • Integrate with Distribution Management Systems (DMS) for real-time switching control. • Factor in load balancing, voltage control, and DER integration to ensure grid stability. Outcome: Faster response to outages, improved grid stability, and efficient DER integration. - “ “49. Zonal Control Grid Management - AI-driven grid partitioning and localized control for enhanced stability. Use Case: • Develop AI models that dynamically segment the grid into self-sufficient zones for fault isolation and localized optimization. • Use graph neural networks (GNNs) to analyze electrical topology and identify optimal zones. • Predict overload risks or instability points in each zone and proactively balance power flow. Outcome: Enhanced grid resilience, especially during extreme weather or wildfire risks. - “ “50. Predictive Outage & Storm Management - Forecasting storm impact and automating crew readiness. Use Case: • Develop predictive models that combine weather data, vegetation indices, and asset health metrics to forecast outage risks. • Use geospatial analytics to model storm paths and predict the most vulnerable grid segments. • Automate crew dispatch optimization by integrating with workforce management platforms. Outcome: Reduced restoration times, improved resource allocation, and proactive outage prevention. - “ “51. System Operator Co-pilot - AI-powered assistant for real-time decision support. Use Case: • Deploy an AI copilot interface that uses NLP and ML models to analyze SCADA data, recommend control actions, and automate reports. • Integrate with Digital Twin models for real-time simulation and impact analysis. • Enable voice-based or text-driven guidance for system operators during critical grid events. Outcome: Faster, data-driven decisions with improved situational awareness. - “ “52. AI-Assisted FLISR (Fault Location, Isolation, and Service Restoration) - Enhanced FLISR capabilities using AI for faster service restoration. Use Case: • Train ML models to analyze historical fault data, power flow patterns, and topology to predict likely fault locations. • Use reinforcement learning models to optimize FLISR sequences by prioritizing key feeders or critical loads. • Automate sectionalizing switches to reroute power and minimize outage impact. Outcome: Faster restoration, reduced outage duration, and improved customer satisfaction. - “ “53. Auto Dispatcher - AI-optimized field crew dispatching for efficient response. Use Case: • Develop AI-driven route optimization models that prioritize dispatch orders based on fault severity, crew availability, and geographic proximity. • Incorporate geospatial data for real-time traffic, terrain conditions, and access constraints. • Enable two-way communication with crews for dynamic re-routing. Outcome: Reduced downtime, improved crew efficiency, and cost savings. - “ “54. AI-Enhanced Contigency Analysis - Predictive models for proactive contingency planning. Use Case: • Implement AI models that simulate “N-1,” “N-2,” or “N-x” contingency scenarios. • Use graph-based models to evaluate cascading failures across transmission and distribution systems. • Integrate AI with EMS (Energy Management Systems) to automate contingency plan recommendations. Outcome: Improved grid resilience and proactive risk mitigation. - “ “55. Generative Design & Engineering - AI-driven design optimization for infrastructure projects. Use Case: • Utilize Generative AI models to generate optimal substation layouts, transmission tower designs, and cable routing. • Use physics-informed neural networks to ensure designs adhere to load, thermal, and mechanical constraints. • Automate design comparison for efficiency, material cost, and environmental impact. Outcome: Faster engineering design cycles with cost-effective and optimized designs. - “ “56. Engineering Expert Assistant / Copilot (Engineering RAG Tool) - Retrieval-Augmented Generation (RAG) tool for engineering support. Use Case: • Implement an AI copilot trained on utility standards, engineering manuals, and technical documentation. • Use vector search and knowledge graphs to retrieve relevant engineering best practices and solutions. • Enable conversational interfaces to assist engineers in complex design or troubleshooting tasks. Outcome: Enhanced decision-making for engineers with quick access to complex technical information. - “ “57. AI-Enhanced Interconnection Assessment - AI-driven assessment for DER integration and grid impact analysis. Use Case: • Develop AI models that analyze proposed solar, wind, or battery installations to predict grid impact. • Simulate power flow changes, voltage fluctuations, and fault current impacts in real-time. • Provide automated recommendations for optimal interconnection points and capacity limits. Outcome: Faster DER integration with improved grid stability. - “ “58. Intelligent Asset Management (Predictive Maintenance & Vegetation Management) - AI models for asset health monitoring and vegetation risk reduction. Use Case: • Deploy AI models that use vibration, temperature, and electrical stress data to predict asset failure. • Use drone-based computer vision models to monitor vegetation encroachment along power lines. • Integrate with maintenance platforms to prioritize high-risk assets for proactive repair. Outcome: Improved asset lifespan, reduced maintenance costs, and improved reliability. - “ “59. Event Impact Modeling - AI-powered simulations for analyzing grid stress events. Use Case: • Develop simulation models that predict how wildfire threats, earthquakes, or cyberattacks will propagate across the grid. • Use agent-based modeling to simulate the behavior of customers, DERs, and utility crews in response to events. • Generate automated impact reports and response recommendations. Outcome: Improved emergency preparedness and faster recovery strategies. - “ “60. Incipient Faults Predictive Analytics - Detecting early-stage faults before they escalate into major outages. Use Case: • Build a data pipeline that aggregates waveform data from sensors like PMUs (Phasor Measurement Units), smart meters, and SCADA systems. • Utilize AI models (e.g., LSTM, CNNs) to analyze waveform distortions for signs of insulation breakdown, arcing, or partial discharge events. • Integrate streaming analytics platforms like Apache Kafka for real-time data ingestion and alert generation. • Use pattern recognition and signal decomposition techniques to filter noise and accurately identify anomalies. Outcome: Early detection of failing transformers, underground cable issues, or failing circuit breakers to improve grid reliability. - “
  1. Job Package Automation - Automatically perform engineering analysis tasks and create job packages which are technical documents that are utilized by field technicians to perform installation work for DGS Infrastructure and services. -
  2. Outage Reporting Automation - Automation and AI to analyze data to perform impact analyais of DGS assets and subsequently create standardized communications to report impact to clients. Able to classify outages as non-service impacting or service impacting. Able to repeat outage analysis real time before taking outage and compare to initial analysis to validate impact is the same. -
  3. Itential - Orchestration tool that supports workflow automation between multiple systems. Will integrate with BMC to follow ITIL standards while making or validating changes. Will create data governance rules during data input to source sytems (operational and cmdb systems). Will be integrated for agentic workflows to perform actions to do troubleshooting and analysis and resolve incidents -
  4. Advanced Knowledge Management for Agents - Customer service agents often deal with complex queries that require detailed and accurate information. - Implementing an advanced knowledge management system can significantly enhance the efficiency and effectiveness of customer service agents by providing them with quick access to relevant information and resources at their fingertip.
  5. Automated Intent / Root Cause Analysis - By using AI and machine learning algorithms, utilities can analyze customer interactions to determine the intent behind each query and identify common root causes - Automated intent and root cause analysis can help in quickly identifying the underlying issues behind customer inquiries and complaints, leading to faster resolution times and improved customer satisfaction.
  6. Customer Facing Chatbot - Customers need their queries about billing, service outages answered quickly, They expect real-time or near real-time responses. - Deploying a customer-facing chatbot can provide customers with instant support and assistance, reducing the workload on human agents and improving response times.
  7. Personalized Energy Use Recommender - Customers are seeking information to reduce energy bills and a way to get get tailored advice on optimizing their energy consumption. This is particularly valuable in times of rising energy costs. Customers want convenience by simplifying the process of identifying energy-saving opportunities, making it easier for customers to implement these measures. - A personalized energy use recommender can provide customers with tailored advice on how to optimize their energy consumption, leading to cost savings and increased customer satisfaction. By analyzing individual energy usage patterns and comparing them with similar households or businesses, AI can generate personalized recommendations for energy-saving measures. Additionally, it supports environmental sustainability by helping customers reduce their carbon footprint through more efficient energy use.
  8. Automated QA / Compliance Monitoring and Agent Coaching - Ensuring that customer service interactions comply with regulatory standards and company policies is a significant challenge. Manual quality assurance (QA) processes are time-consuming and may not cover all interactions, leading to potential compliance risks and inconsistent agent performance. - AI can automate QA and compliance monitoring by analyzing customer interactions in real-time, identifying non-compliant behaviors, and providing immediate feedback to agents. This ensures consistent adherence to standards and enables targeted coaching to improve agent performance
  9. Fraud Detection / Prevention - Fraudulent activities, such as identity theft and billing scams, pose a significant threat to utilities, leading to financial losses and damaged customer trust. Traditional fraud detection methods may not be effective in identifying sophisticated fraud schemes. - AI can enhance fraud detection and prevention by analyzing large volumes of data to identify unusual patterns and anomalies that may indicate fraudulent activities. Machine learning algorithms can continuously learn from new data, improving their accuracy in detecting and preventing fraud
  10. Bad Debt / Revenue Recovery - Managing bad debt and recovering outstanding payments is a major challenge for utilities, impacting cash flow and financial stability. Traditional debt recovery methods may not be effective in identifying high-risk customers and prioritizing recovery efforts. - AI can help manage bad debt and improve revenue recovery by analyzing customer payment histories and behavior to predict the likelihood of default. This allows utilities to prioritize recovery efforts and implement targeted strategies to recover outstanding payments more effectively
  11. Automated Collateral Generator - Creating marketing and informational materials manually is time-consuming and may lead to inconsistencies in branding and messaging. This can affect the effectiveness of customer communications and marketing campaigns. - AI can automate the generation of collateral by using predefined templates and content to create high-quality materials quickly and consistently. This streamlines the process, ensuring that all materials are aligned with the company’s branding and messaging standards
  12. Customer Service (Agent) Co-Pilot - Customer service agents often struggle to find relevant information quickly during interactions, leading to longer call times and reduced customer satisfaction. Providing real-time assistance to agents can significantly improve their efficiency and effectiveness. - Co-pilots can assist customer service agents in real-time by analyzing ongoing conversations and providing relevant information, suggestions, and guidance. This helps agents resolve customer issues more quickly and accurately, enhancing the overall customer experience “73. CPaaS Response Trending analysis - CPaaS Responses from WebEx is 4 to 5 times of the request message sent like acknowledegement, delivered, submitted,failures at each data point. Analysis of failure is manual in ISU for hypercare period. Need trending analysis of response to request in snowflake system
  13. Acknowledged but not delivered in 2hr, 4hr, 6hr,10hr,20hr
  14. Acknowledgement Error
  15. Message on hold : Data.status = “wait” or “catchup”
  16. Message Dropped : DLR_CODE >= “70000” and DLR_CODE < “80000”
  17. automate daliy response reporting–> this will include the time duration for ack and delivery
  18. Automated reports capturing hard bounce response
  19. Automated report to show number of notifications per DLR code - “
  20. Marketing Knowledge Assistant (query past campaign briefs, company docs.) - Marketing teams often struggle to quickly access and retrieve information from past campaign briefs and company documents, leading to inefficiencies and delays in campaign planning and execution. - AI can serve as a marketing knowledge assistant by indexing and organizing past campaign briefs and company documents, allowing marketing teams to query and retrieve relevant information quickly. This enhances efficiency and ensures that valuable insights from previous campaigns are utilized effectively
  21. Content Generator (briefs, blogs, etc.) - Creating high-quality content such as briefs, blogs, and articles can be time-consuming and resource-intensive for marketing teams. - AI-powered content generators can automate the creation of various types of content by analyzing existing materials and generating new, relevant content. This helps marketing teams produce high-quality content more efficiently and consistently
  22. Content Standardizer - Inconsistent branding and messaging across different marketing materials can dilute a company’s brand identity and confuse customers. - AI can standardize content by ensuring that all marketing materials adhere to predefined branding guidelines and messaging standards. This helps maintain a consistent brand identity and improves the overall effectiveness of marketing communications.
  23. Customer Segmenting / Targeting - Identifying and targeting the right customer segments is crucial for the success of marketing campaigns, but it can be challenging to analyze large volumes of customer data manually. - AI can analyze customer data to identify distinct segments based on behavior, preferences, and demographics. This enables marketing teams to target their campaigns more effectively and personalize their messaging to resonate with specific customer groups.
  24. Marketing ROI Attribution Modelling - Measuring the return on investment (ROI) of marketing campaigns is often complex and requires analyzing data from multiple sources. - AI can create advanced attribution models that accurately measure the impact of different marketing activities on overall ROI. This helps marketing teams optimize their strategies and allocate resources more effectively.
  25. Customer Sentiment / Feedback / Program Churn Analyzer - Understanding customer sentiment and feedback is essential for improving customer satisfaction and reducing churn, but analyzing large volumes of feedback can be challenging - AI can analyze customer feedback from various sources to identify sentiment trends and potential issues. This helps marketing teams address customer concerns proactively and improve overall satisfaction, reducing program churn.
  26. Community Stakeholder Analysis - Engaging with community stakeholders is important for building strong relationships and ensuring the success of customer programs but identifying and analyzing stakeholder interests can be complex. - AI can analyze data from various sources to identify key community stakeholders and their interests. This helps marketing teams tailor their engagement strategies and build stronger relationships with stakeholders.
  27. Ad Copy Generation - Creating compelling ad copy that resonates with target audiences can be time-consuming and requires creativity and expertise. - AI can generate ad copy by analyzing successful ads and identifying patterns that resonate with target audiences. This helps marketing teams create effective ad copy more efficiently and consistently.
  28. AI Led Product Recommender - Recommending the right products to customers based on their preferences and behavior is crucial for driving sales and customer satisfaction. - AI can analyze customer data to provide personalized product recommendations. This helps marketing teams deliver more relevant suggestions to customers, increasing the likelihood of purchase and improving overall satisfaction.
  29. Market Assessment / Product Strategy - Conducting market assessments and developing product strategies require analyzing large volumes of data and identifying trends and opportunities. - AI can analyze market data to identify trends, opportunities, and potential threats. This helps marketing teams develop informed product strategies and make data-driven decisions.
  30. Frontline Co-Pilot - Customer service agents often struggle to find relevant information quickly during interactions, leading to longer call times and reduced customer satisfaction. - AI-powered co-pilots can assist customer service agents in real-time by analyzing ongoing conversations and providing relevant information, suggestions, and guidance. This helps agents resolve customer issues more quickly and accurately, enhancing the overall customer experience.
  31. Legal Q&A Assistant - Legal teams often face a high volume of queries that require quick and accurate responses. Manually handling these queries can be time-consuming and may lead to delays. - AI can serve as a legal Q&A assistant by providing instant, accurate answers to common legal questions based on a vast database of legal knowledge. This reduces the workload on legal teams and ensures timely responses.
  32. Compliance Assistant - Ensuring compliance with various regulations and standards is a complex and ongoing challenge for utilities. Manual compliance checks are prone to errors and can be resource-intensive. - AI can automate compliance monitoring by continuously analyzing operations against regulatory requirements. It can flag potential compliance issues in real-time, helping the organization stay compliant and avoid penalties.
  33. Automated Drafting & Redlining - Drafting and reviewing legal documents manually is a time-consuming process that can lead to errors and inconsistencies. - AI can automate the drafting and redlining of legal documents by using predefined templates and historical data. This ensures consistency, reduces the time required, and minimizes errors.
  34. Auto Claims Creation - Creating claims manually is a labor-intensive process that can lead to delays and inaccuracies. - AI can automate the creation of claims by extracting relevant information from documents and generating accurate claims quickly. This streamlines the process and reduces the likelihood of errors.
  35. Contract Risk - Identifying and assessing risks in contracts manually is a complex and time-consuming task. - AI can analyze contracts to identify potential risks and provide insights into risk mitigation strategies. This helps legal teams make informed decisions and manage risks more effectively.
  36. Regulatory Response Generation - Responding to regulatory inquiries and requirements manually can be slow and prone to errors. - AI can automate the generation of regulatory responses by analyzing the requirements and generating accurate, compliant responses quickly. This ensures timely and accurate communication with regulators.
  37. Local Law Research / Summarization - Researching and summarizing local laws manually is a time-consuming process that can lead to incomplete or outdated information. - AI can automate the research and summarization of local laws by continuously analyzing legal databases and providing up-to-date summaries. This ensures that legal teams have access to accurate and current information.
  38. Vendor Billing Reconciliation - Reconciling vendor bills manually is a labor-intensive process that can lead to discrepancies and errors. - AI can automate vendor billing reconciliation by analyzing billing data and identifying discrepancies. This ensures accurate billing and reduces the time required for reconciliation.
  39. Location Specific Document Generator (Reg. / Tax Docs) - Corporate Real Estate - Creating location-specific regulatory and tax documents manually is a complex and time-consuming task. - AI can automate the generation of location-specific documents by using predefined templates and local regulatory data. This ensures accuracy and consistency while reducing the time required.
  40. Email Responder - Handling a high volume of emails manually can lead to delays and missed communications. - AI can automate email responses by analyzing the content of incoming emails and generating accurate, contextually relevant replies. This ensures timely communication and reduces the workload on legal teams.
  41. Env. Regulations Chatbot - Navigating and understanding complex environmental regulations can be challenging for employees, leading to compliance risks and inefficiencies. - AI can provide an environmental regulations chatbot that offers instant, accurate answers to regulatory questions, helping employees stay compliant and informed without extensive manual research.
  42. ESG Reporting Draft - Creating Environmental, Social, and Governance (ESG) reports manually is time-consuming and prone to errors, which can affect the accuracy and timeliness of reporting. - AI can automate the drafting of ESG reports by analyzing relevant data and generating accurate, comprehensive reports quickly. This ensures timely and accurate reporting while reducing the manual effort required.
  43. Permit / Environmental Assessment Generator - Preparing permits and environmental assessments manually is a labor-intensive process that can lead to delays and inconsistencies. - AI can automate the generation of permits and environmental assessments by using predefined templates and analyzing relevant data. This streamlines the process, ensuring consistency and reducing the time required.
  44. Intelligent Site Analysis - Conducting site analyses manually is time-consuming and may not always capture all relevant environmental factors, leading to incomplete assessments. - AI can perform intelligent site analyses by analyzing data from various sources, such as satellite imagery and environmental sensors, to provide comprehensive and accurate assessments. This helps in making informed decisions and ensuring environmental compliance.
  45. Project Cost Estimator - Estimating project costs manually can be inaccurate and time-consuming, leading to budget overruns and delays. - AI can provide accurate project cost estimates by analyzing historical data, current market trends, and project specifications. This helps in better budgeting and resource allocation, reducing the risk of cost overruns.
  46. IMT Assistant - Incident Management Teams (IMTs) often face challenges in coordinating responses to environmental incidents, leading to delays and inefficiencies. - AI can assist IMTs by providing real-time data analysis, incident tracking, and decision support. This enhances coordination and response times, ensuring effective management of environmental incidents.
  47. Finance Productivity Assistant - Finance teams often spend a significant amount of time on repetitive and administrative tasks, reducing their productivity and ability to focus on strategic activities. - AI can serve as a productivity assistant by automating routine tasks such as data entry, report generation, and scheduling. This allows finance teams to focus on higher-value activities and improves overall efficiency.
  48. SOX Reporting - Ensuring compliance with the Sarbanes-Oxley (SOX) Act involves extensive documentation and monitoring, which can be time-consuming and prone to errors. - AI can automate SOX compliance by continuously monitoring financial transactions and generating accurate reports. This reduces the manual effort required and ensures timely and accurate compliance.
  49. Lease / PPA Analysis / Review - Analyzing and reviewing leases and Power Purchase Agreements (PPAs) manually is a complex and time-consuming process. - AI can analyze and review leases and PPAs by extracting relevant information and identifying key terms and conditions. This streamlines the process and ensures accuracy and consistency.
  50. Automated Contract Drafting - Drafting contracts manually is a labor-intensive process that can lead to errors and inconsistencies. - AI can automate contract drafting by using predefined templates and historical data. This ensures consistency, reduces the time required, and minimizes errors.
  51. Monthly Performance Reporting - Generating monthly performance reports manually is time-consuming and can lead to delays and inaccuracies. - AI can automate the generation of monthly performance reports by analyzing financial data and generating accurate, comprehensive reports quickly. This ensures timely and accurate reporting.
  52. Unbilled Variance Analysis - Identifying and analyzing unbilled variances manually is a complex and time-consuming task. - AI can analyze financial data to identify unbilled variances and provide insights into their causes. This helps in resolving issues quickly and improving billing accuracy.
  53. Invoice Coding - Coding invoices manually is a repetitive and error-prone process that can lead to delays and inaccuracies. - AI can automate invoice coding by analyzing invoice data and assigning the correct codes. This reduces manual effort and improves accuracy and efficiency.
  54. Journal Entry Generator - Creating journal entries manually is a time-consuming process that can lead to errors and inconsistencies. - AI can automate the creation of journal entries by analyzing financial transactions and generating accurate entries quickly. This ensures consistency and reduces the time required.
  55. Business Case Benefits Realization Checker - Evaluating the realization of benefits from business cases manually is a complex and time-consuming process. - AI can analyze financial data to evaluate the realization of benefits from business cases. This helps in identifying areas of improvement and ensuring that expected benefits are achieved.
  56. HR / Payroll Chatbot/Virtual Assistant - HR and payroll departments often face a high volume of repetitive queries from employees, leading to inefficiencies and delays in response times. - AI can provide an HR/payroll chatbot or virtual assistant that offers instant, accurate answers to common employee queries, reducing the workload on HR staff and ensuring timely responses.
  57. HR Reporting Automation - Generating HR reports manually is time-consuming and prone to errors, which can affect decision-making and strategic planning. - AI can automate HR reporting by analyzing relevant data and generating accurate, comprehensive reports quickly. This ensures timely and accurate reporting while reducing the manual effort required.
  58. Auto Comm. Drafting - Drafting communications manually is a labor-intensive process that can lead to inconsistencies and delays. - AI can automate the drafting of HR communications by using predefined templates and analyzing relevant data. This ensures consistency, reduces the time required, and minimizes errors.
  59. Talent Data / Engagement / Sentiment Analytics - Understanding employee engagement and sentiment is crucial for improving retention and satisfaction, but analyzing large volumes of data manually can be challenging. - AI can analyze talent data to provide insights into employee engagement and sentiment. This helps HR teams identify areas for improvement and implement targeted strategies to enhance employee satisfaction.
  60. Performance / Career Coach - Providing personalized performance and career coaching to employees manually is time-consuming and may not always be consistent. - AI can serve as a performance and career coach by analyzing employee data and providing personalized recommendations for career development and performance improvement. This ensures consistent and tailored coaching for all employees.
  61. Recruiting Automation – Auto. Job Desc., CV Screening, Candidate Assistant - The recruiting process involves several repetitive tasks such as creating job descriptions, screening CVs, and assisting candidates, which can be time-consuming. - AI can automate various aspects of the recruiting process, including generating job descriptions, screening CVs, and aiding candidates. This streamlines the process, reduces manual effort, and improves the overall efficiency of recruiting.
  62. Employee Recognition Tool - Recognizing employee achievements manually can be inconsistent and may not always capture all deserving employees. - AI can automate employee recognition by analyzing performance data and identifying achievements. This ensures consistent and timely recognition of employee contributions, boosting morale and engagement.
  63. HR Training (Upskill/AI) - Providing training and upskilling opportunities to employees manually can be resource-intensive and may not always be tailored to individual needs. - AI can personalize HR training and upskilling programs by analyzing employee data and identifying skill gaps. This ensures that training is tailored to individual needs, improving the effectiveness of upskilling efforts.
  64. Code Gen / Test / Documentation - Writing, testing, and documenting code manually is time-consuming and prone to errors, leading to inefficiencies and potential bugs. - AI can automate code generation, testing, and documentation by using advanced algorithms to write code, run tests, and generate documentation. This improves efficiency, reduces errors, and ensures consistency.
  65. Regulatory Knowledge Assistant - Keeping up with regulatory requirements and ensuring compliance is challenging and time-consuming for IT teams. - AI can serve as a regulatory knowledge assistant by continuously analyzing regulatory updates and providing relevant information to ensure compliance. This helps IT teams stay informed and compliant with minimal effort.
  66. Report Generation (SOX, NRC) - Generating reports for compliance with regulations such as SOX and NRC manually is labor-intensive and prone to errors. - AI can automate the generation of compliance reports by analyzing relevant data and generating accurate, comprehensive reports quickly. This ensures timely and accurate reporting while reducing the manual effort required.
  67. User Story Builder - Creating user stories manually can be time-consuming and may not always capture all necessary details. - AI can automate the creation of user stories by analyzing project requirements and generating detailed, accurate user stories. This streamlines the process and ensures that all necessary details are captured.
  68. IT Risk Assessment - Assessing IT risks manually is a complex and time-consuming process that may not always identify all potential risks. - AI can perform IT risk assessments by analyzing data from various sources to identify potential risks and provide insights into mitigation strategies. This helps in making informed decisions and managing risks more effectively.
  69. AI Enabled IT Self-Service (IT VA) - IT departments often face a high volume of repetitive queries from users, leading to inefficiencies and delays in response times. - AI can provide an IT virtual assistant (VA) that offers instant, accurate answers to common IT queries, reducing the workload on IT staff and ensuring timely responses.
  70. Automated IT Tutorials - Creating IT tutorials manually is time-consuming and may not always be up-to-date with the latest technologies and practices. - AI can automate the creation of IT tutorials by analyzing current technologies and practices and generating accurate, up-to-date tutorials. This ensures that users have access to the latest information and training materials.
  71. System Failure Triage - Diagnosing and resolving system failures manually can be time-consuming and may not always identify the root cause accurately. - AI can perform system failure triage by analyzing data from various sources to identify the root cause of failures and provide insights into resolution strategies. This helps in resolving issues quickly and accurately.
  72. Project Assistant - Managing IT projects manually can be complex and time-consuming, leading to potential delays and inefficiencies. - AI can serve as a project assistant by automating various aspects of project management, such as scheduling, task assignment, and progress tracking. This improves efficiency and ensures that projects stay on track.
  73. Data Viability and Availability Assistant - Ensuring the viability and availability of data manually is a complex and time-consuming that may not always be accurate. - AI can analyze data from various sources to ensure its viability and availability, providing insights into potential issues that can help in maintaining accurate and reliable data.