As energy demand continues to surge due to population growth and economic expansion, the utility industry faces mounting challenges. Key among them is the increasing complexity of utility systems, driven by advancements such as microgrids and the rise of prosumers—individuals who both produce and consume electricity. This shift requires utilities to move away from traditional top-down planning and adopt a more decentralised, bottom-up approach.
In this evolving landscape, utilities are grappling with vast amounts of data, which must be collected, analysed, and acted upon within increasingly tight operational windows. To keep pace, utilities need to become more agile and responsive by integrating advanced technologies like AI to enhance decision-making and streamline operations.
The question now is: where should utilities focus their efforts to unlock the full potential of AI and maximise its benefits?
Key Focus Areas for Utilities Providers
Customer Experience
- Enhance customer interactions through intelligent chatbots and conversational AI that provide real-time assistance.
- Replace traditional IVR systems with AI-driven, natural language solutions that allow customers to engage intuitively.
- Automatically transcribe customer calls to improve service quality and derive actionable insights from conversations.
- Analyse customer behaviour and preferences to deliver hyper-personalised product and service recommendations.
- Use AI to predict and proactively address customer service issues, reducing wait times and improving satisfaction.
Generation
- Incorporate synthetic data to train models that can anticipate and resolve operational issues faster.
- Streamline the interconnection process by using AI to optimise scheduling and resource allocation.
- Improve construction design by leveraging AI for optimal placement of renewable energy sources like solar panels and wind turbines.
- Use predictive modelling to forecast energy production and manage generation capacity more effectively.
Transmission and Distribution
- Refine demand response strategies by using AI to predict energy usage patterns based on real-time data and historical trends.
- Detect equipment failures before they occur by monitoring assets with AI and real-time analytics, reducing downtime.
- Provide field engineers with AI-generated insights for more effective maintenance and on-site decisions.
- Use AI-assisted design to support infrastructure projects, improving both cost-efficiency and scalability.
Security & Compliance
- Ensure continuous compliance with automated monitoring that delivers real-time insights into security threats and regulatory adherence.
- Use AI-driven log analysis and auditing to automate compliance reporting and flag potential issues before they escalate.
- Implement AI tools that scan code for security vulnerabilities and provide real-time remediation suggestions to protect against cyber threats.
- Deploy predictive analytics to detect anomalies in system behaviour, preventing security breaches and ensuring compliance with industry regulations.
Productivity
- Leverage AI for code generation, automated testing, and continuous integration to speed up software development cycles.
- Automate query generation and optimise data retrieval processes for improved decision-making.
- Use AI to resolve support tickets faster by predicting issues and suggesting solutions based on historical data.
- Streamline workflows by using AI to generate content, augment datasets, and support repetitive tasks, allowing employees to focus on higher-value activities.
- Employ AI to identify inefficiencies in internal processes and recommend productivity improvements.
Selecting the Right Use Case
Demonstrating the end to end feasibility of initial AI use cases is paramount. A steel thread approach finds a feasible path, and unearths key risks in the end-to-end delivery of demonstrable value through AI.Ecosystem
Choose a mature data domain.
Opt for a business domain with sound data maturity. This will enable the right focus on AI, rather than addressing existing operational issues.
Process
Simple means feasible.
Demonstrating early value from the AI use case is more important than scale. A simpler process to execute the AI use case will contribute to delivering meaningful outcomes.
People
Find a business champion.Find and collaborate with a business champion, who can drive the AI initiative forwards, and act as the evangelist in driving the AI adoption within your organisation.
Technology
Don’t over engineer. Focus on delivery of the AI use case to deliver business value. Build the technical foundations once initial business value is delivered, and there is support to scale.Data
Ensure data quality is right. Bad data = Bad AI. Ensure the use case leverages datasets with fit for purpose data quality to give the best chance of success for the use case.Cost
Low cost means low risk.Don’t start with a high cost use case. A use case which is feasible, delivers tangible business value and is low cost, lowers the delivery risk and makes funding approvals easier.
By partnering with Codex, our team of experts can help you with AI strategy, use case identification, model development and operationalisation helping you realise value from your AI investments.
Reach out to Codex today to learn how we can help you harness the power of AI to transform your operations.
Talk to Us
We would love the opportunity to connect and understand more about the problems you are trying to solve.
Adrian Cambpell
Associate Partner, AI
Get in touch to coordinate a meeting with one of our technical experts.
Australia: +61 7 3132 3002.