Use Cases and Examples of applying AI and Machine learning
Field Services operations
Moving from strategic decision making to automated operational decisions creates significant operational efficiency.
80% increase in capacity from day one
Over 75% cheaper operations
$1.8 million revenue increase in first 6 months
Smart Virtual Power Plant
Transitioning from smart metering to IoT- and AI-led smart energy management and demand response management.
By focusing on both demand and generation, a virtual power plant(VPP) generates revenue and efficiencies by performing subtle energy changes without impacting business operations.
Integrating the capabilities of energy measurement, management and monetising enables transformation to EaaR(“Energy as a revenue”).
Central to this approach is automated decision-making by feeding real-time sensor data into AI models to identify and prioritise maintenance requirements.
This not only makes the decision-making processes more efficient, it can significantly improve capacity through better utilisation of skilled resources and reduced operational costs, ensuring an optimal balance between maintenance and business continuity.
Some business-critical assets deployed in the field (and in remote locations) may need ongoing monitoring and supervision to ensure operational continuity. This could be for the purposes of verification, security, safety or simply ensuring continuity. Such operations are typically performed manually. Switching to using AI models as your eyes and ears on the ground to automate critical analysis, decision-making and process automation results in better quality, increased accuracy and reduced costs compared to manual activities.
Client retention and engagement
Better understanding of customers and improved customer service, satisfaction and loyalty to improve margins and growth.
40% increase in customer satisfaction
Over 25% increase in profits
Over 28% reduction in churn
Utilising an AI-driven model to get actionable intelligence and take pre-emptive action can ensure renewals and continuity.
These models go beyond generic churn rate predictions to analysis and continuous monitoring of customer satisfaction and loyalty, market conditions and other key parameters.
We then extend these models and integrate them with operational business processes to create tasks with actionable recommendations to intervene where necessary.
This prioritises and drives the work management activities of your client retention team.
Chatbots and speech analytics are not just about the speed of responses. Utilising AI-driven models to make these responses effective is critical for customer service teams to maintain customer satisfaction.
An effective multi-channel service model can bring consistency and effectiveness.
AI models enable the right balance of automation and human touch, providing guidance on next best actions, the associated risks and risk-mitigating insights, which helps customer service teams to improve capacity and response time.
Today, customers expect brands and companies to know them; to know not just what they buy, but also why. Strategies like creating customer segmentation and combining additional data points, such as purchase history, user preferences and brand interactions, are used to create customer-value personalisation and propensity-to-purchase models.
These AI models are integrated with operational business processes to ultimately enable better service offerings, upscaling of product & services and improved customer loyalty.
Improving operational efficiencies to reduce working capital spending to increase financial sustainability.
Over 50% reduction in goods disposal
35% more service capacity
Over 22% increase in utilisation
Move away from outdated forecasting and business strategy development methods by implementing IoT- and AI-based solutions.
These solutions collect real-time data to get an accurate and complete picture. These data points are then used in the AI models to provide operational decision-making and to optimise inventory management, workforce scheduling, stock maintenance and production planning.
It is common for service teams to review documents and contracts, which takes significant time and effort.
AI models can review these documents using pre-selected criteria, evaluate and perform risk assessments and make recommendation for an expert user to consider. These models are designed to learn and improve their accuracy based on the final decisions of the expert users. This results in a reduction in review time by over 80%.
Documenting policies, procedures and their associated benefits is often a time-consuming task. However, it is even more challenging to recall these when necessary which ultimately limits their impact and benefits.
Utilising an AI-based model to capture operational knowledge
significantly reduces the effort required. It can improve company's abilities to answer questions about benefits, policies and procedures. It can also enhance staff training and conflict resolution, making it equally effective for internal staff, suppliers and customers.