Written by Mark Brewer, VP Service Industries at IFS
As generative Artificial Intelligence (AI) and machine learning become increasingly integrated into various operations and processes within the service sector, Mark Brewer, Vice President of Service Industries at IFS, outlines his five foremost predictions for AI-driven field advancements in 2024. Additionally, he provides insights into the potential intersections and opportunities arising from these distinct trends.
By 2025, half of contact centers are expected to implement an AI copilot system, allowing each agent to rely on an expert counterpart. Contact centers have long grappled with staffing challenges, with a staggering 42% attrition rate reported in a recent global survey by NICE WEM in 2021. Nearly one-third of surveyed agents were actively seeking new employment, and only 60% of those desired another role within a contact center. This presents a significant number of disenchanted agents.
Transitioning to AI-enabled technology stacks in call centers has the potential to address these issues. It is positioned to enhance customer satisfaction and alleviate the strain from increasing voice call volumes by providing continuous support to agents, keeping them interested and engaged. Handling customer calls concerning issues like vehicle breakdowns or broken boilers requires agents to gather complex and high-stress information. Often, necessary details and logistical variables are dispersed across multiple systems, demanding a comprehensive consideration for real-time call resolution.
Enhancing Agent Performance: Artificial Intelligence (AI) offers a means to assist agents in real-time by introducing a virtual assistant, essentially a copilot, during their call processing tasks. For instance, an AI copilot can autonomously prioritize and visually highlight the most urgent calls for agents on a dashboard by utilizing voice recognition to identify key phrases in a request. Similarly, it can provide context-sensitive information during the call, such as prompts to aid in fault diagnosis or details on the proximity and travel time of the nearest qualified engineer. In this scenario, intelligent autonomous technology is not substituting the agent but rather enhancing their capabilities, enabling them to deliver an improved, more reassuring, and efficient customer experience. With the support of an AI-powered copilot, every agent, including those who are new or relatively inexperienced, becomes proficient.
By 2026, an estimated 70% of organizations are anticipated to adopt a “circular by default” approach, responding to the growing emphasis on sustainability globally. There is a notable shift in the sustainability paradigm from obligatory compliance with regulations to a proactive desire to inherently embrace sustainability. Recognizing consumer preferences for environmentally conscious companies, organizations are increasingly motivated to incorporate circular economy principles.
Simultaneously, the outcome-based service model is gaining traction, with consumers showing interest in subscription-based offerings like heating-as-a-service. This model allows suppliers to proactively maintain assets, extending their lifespan, and consequently reducing emissions, waste, and the need for recycling.
Anticipated advancements also include the integration of self-healing capabilities in new products such as appliances and vehicles. This innovation aims to minimize the costs, time, and environmental impact associated with unnecessary field service visits.
By 2027, around 30% of asset-centric organizations are expected to employ computer vision for observation and analysis. While artificial intelligence enables computers to think, computer vision empowers them to see and comprehend visual information. In settings like manufacturing or processing, AI can be trained to scrutinize video imagery, identifying faults or safety risks. Industries such as oil and gas and maritime are already utilizing computer vision to monitor and detect issues like corrosion, facilitating timely inspections and preventive maintenance. In addition, autonomous robots equipped with multiple cameras oversee operations in factories and warehouses.
The integration of cameras in both passenger and commercial vehicles has become commonplace, capturing video during travel. By applying suitable image recognition algorithms, this continuous data stream supports computer vision applications. For instance, it can identify maintenance issues for infrastructure, such as obscured road signs, tree growth interfering with overhead cables and phone lines, and vehicles automatically detecting and reporting pothole locations.
By 2028, approximately 30% of service organizations are expected to experiment with autonomous vehicles in pursuit of achieving “total productivity.” Traditionally, a key metric in field service has been productivity or utilization, representing the percentage of a field engineer’s time spent on actual tasks. This “wrench time” typically ranges from 50% to 95%, with most organizations aiming for 70 to 80% utilization.
Driving consumes around 30% of an engineer’s day, and administrative tasks add an additional 20-30%. The introduction of autonomous vehicles transforms travel time into productive time, allowing engineers to prepare for upcoming visits while the vehicle handles the driving.
Autonomous journeys, planned digitally and efficiently routed, consider factors like electric vehicle range and charging requirements. Unlike human drivers who may struggle with such planning, AI-driven IFS Planning & Scheduling Optimization (PSO) eliminates guesswork. The application not only plans optimal schedules and routes but also dynamically adjusts based on real-time vehicle telemetry data. For instance, a 10-minute charging stop could be strategically incorporated, enabling an engineer to complete two additional jobs, thereby boosting daily productivity by 50%.
By 2026, AI is anticipated to evolve into the ultimate fleet manager for 40% of asset-centric service providers. Utilizing streamed video data from assets and interpreting it through computer vision will significantly enhance the visibility for fleet managers. For instance, wind turbines equipped with IoT remote sensing provide real-time performance and operational data to turbine manufacturers from numerous assets across customer wind farm installations. Through AI, intelligence derived from cameras, sensors, service records, and digital twin models offers fleet managers unparalleled visibility.
AI’s capabilities include quickly identifying wind farms that outperform others in terms of efficiency and energy generation. It can compare specifications to pinpoint the reasons behind the superior performance, such as a more effective gearing calibration. Recognizing a competitive advantage, AI automatically generates proposals for recalibration services, presenting a sales opportunity to underperforming wind farm operators. This entire process, including service pricing, is initiated by AI with minimal human intervention and no sales calls. This shift from transaction-based sales to a consultative, fact-based, value-added partnership is facilitated by AI.
From downtime prevention to driving improvement
Similarly, within a production setting, AI analysis enables machine manufacturers to optimize efficiency, productivity, and throughput for customers throughout the lifecycle. This transcends traditional maintenance approaches focused solely on preventing downtime, extending to actively driving operational improvements.
By eliminating human subjectivity and proactively anticipating changes, AI empowers every company to potentially lead in their industry. Variables such as weather, sales trends, demand, and usage duty are automatically integrated into an AI-informed performance overview. Even service provision can now be tailored, as AI generates instance-specific documentation and recommendations, ensuring the precise service part, optimal procedure, and interval on every occasion.
Illustratively, the Rolls Royce Blue Data Thread program utilizes AI to analyze engine performance data from diverse customer aircraft fleets. This enables instance-specific maintenance intervals for similar aircraft models, personalized based on engine usage and wear. This value-added service not only reduces unnecessary Aircraft On Ground (AOG) time but also enhances operational profitability.
In summary, significant industry shifts are anticipated, made possible by AI, leading to a genuine revolution in the sector. Future posts will delve into these developments further as they unfold, with service standing at the forefront. Your thoughts on these advancements are welcomed.
For more information on how IFS leverages AI to manage and enhance service, visit IFS.ai.