Harnessing Generative AI in Aftermarket and Field Services
A recent McKinsey analysis investigated the long-term performance of over 50 industrial firms and revealed that those emphasizing services yielded 1.7 times greater total shareholder returns (TSR) than their product-centric counterparts. In sectors pertaining to aftermarket and field services, the scope of services spans traditional offerings—such as equipment installation and maintenance—to innovative models encompassing data monetization through digital solutions and “as-a-service” frameworks.
The Digital Transformation of Service Value Chains
Service value chains present significant opportunities for accelerated digital transformation, particularly with the integration of AI technologies. Many service-oriented businesses already possess extensive datasets, including asset histories, IoT information, technical documentation, maintenance records, and service interactions. These datasets serve as a valuable foundation for extracting actionable insights.
Service-focused organizations are recognizing this potential. Notably, McKinsey’s digital strategy survey indicated that 70% of top-performing companies are leveraging advanced analytics for insights, with 50% utilizing AI to streamline decision-making.
The Challenge of Scaling Generative AI
Despite the promising pilot projects developed over the past year using generative AI solutions, many organizations struggle to realize tangible profits from these initiatives. A recurring issue within tech-driven innovations is the phenomenon known as “pilot purgatory,” where companies find it challenging to escalate their digital and AI strategies beyond the initial experimentation stage.
Common pitfalls include fragmented data landscapes, dated technological infrastructures, insufficient expertise, change management hurdles, feeble business cases, and inadequate strategic leadership. Companies involved in aftermarket and field services face unique vulnerabilities concerning these obstacles, which impede the realization of substantial value from generative AI implementations. Below are five pivotal actions to minimize transition obstacles from pilot projects to substantial profits.
Redefining the Service Paradigm
The journey towards achieving profit and loss (P&L) impact commences with a complete rethinking of service delivery. Instead of simply integrating digital solutions into existing processes, successful organizations map out a future service continuum. This approach allows stakeholders—including service providers, sales teams, technicians, and supply chain partners—to understand how value is created and identify necessary changes to operational workflows.
When a leading industrial firm undertook this analysis, it became evident that realizing an AI-enhanced service model would necessitate restructuring roles and establishing stronger connections between service, sales, contracting, supply chain, and financing functions.
Identifying High-Impact Use Cases
Among various generative AI applications, certain use cases have demonstrated notable effectiveness within service value chains. Consider the following examples:
1. Enhanced Sales and RFP Responses
Numerous industry leaders have experienced growth through utilizing AI for sales lead generation and enhanced bid responses. For instance, a water technology OEM used aftermarket analytics to optimize sales data, generating over $350 million in leads for nearly 45,000 customers, revealing significant growth prospects.
2. Improved Troubleshooting and Self-Service
Organizations are optimizing customer interactions by employing AI-driven troubleshooting solutions. A global machinery provider leveraged a generative AI platform to analyze data from 13,000 documents, resulting in a 50% increase in first-contact resolutions and drastically cutting troubleshooting time from 30 minutes to less than a minute.
3. Optimized Planning and Scheduling
AI technology enhances demand forecasting and workforce scheduling efficiency. A prominent water treatment firm adopted a digital scheduling solution, achieving a remarkable 40% increase in technician capacity and a concomitant reduction in overtime expenditures.
4. Streamlined Contract Management
AI tools can uncover new service opportunities within existing contracts. A global truck OEM automated contract evaluations to eliminate excessive manual processes, resulting in over 5 million euros in annual savings by employing an AI-driven contract management agent.
Creating Synergies Among Use Cases
Maximizing the value of AI entails linking multiple use cases. One industrial organization combined five distinct applications to transform its equipment maintenance approach. By integrating documentation searches, root cause analysis, and AI scheduling algorithms, the company significantly enhanced operational efficiency while minimizing redundant customer visits and travel time.
Establishing Robust Data Foundations
Effective AI utilization depends heavily on the quality of data. Organizations must ensure their data ecosystems are adequate, protected, and accessible, which often requires upgrading existing systems and developing new infrastructures. Essential data components include:
- Process Data: Maintenance documentation, troubleshooting guides, and operating procedures.
- IoT Data: Comprehensive sensor data for predictive maintenance.
- Equipment History: Historical maintenance and configuration data.
- Supply Chain Data: Parts inventory, shipping timelines, and vendor details.
- Personnel Data: Skills, availability, and locations of service teams.
Effective data governance is essential to safeguard sensitive information and intellectual property while ensuring data quality and compliance.
Embrace the Future with Generative AI
The integration of generative AI has the potential to revolutionize aftermarket and field services. Projections indicate that deploying such technologies could lead to a substantial reduction in operational costs, increased revenue, and enhanced customer satisfaction over the next 12 to 24 months.
For organizations aiming to capitalize on generative AI, thoughtful consideration of the following queries is essential:
- What are your primary objectives for generative AI? Clearly outline expected outcomes, whether focusing on revenue growth or operational efficiency.
- Where will generative AI deliver the most value? Assess the overall service journey to pinpoint areas ripe for AI integration.
- Are your digital foundations strong enough? Ensure the organization has the necessary technological and governance frameworks in place.
By systematically addressing these questions, companies can fortify their readiness for next-gen aftermarket and field services, ultimately promoting value for customers, employees, and shareholders alike.
