Aquant, a leading provider of AI-powered solutions for the field service industry, announced a new strategic vision for its Service Co-Pilot platform, marking a significant leap towards hyper-personalized AI in the service industry. Aquant’s latest advancements focus on personalizing AI to uniquely understand and address the complex challenges faced by service organizations in the manufacturing industry.
“by 2027, over 50% of the GenAI models used by enterprises will be domain-specific, tailored to individual industries or business functions—an increase from just 1% in 2023.”
The motivation behind this shift is the surge in AI and IoT adoption. The global AI in manufacturing market is projected to expand significantly to $20 billion by 2028 and IoT technologies in manufacturing is expected to reach $538.09 billion in 2028. As a result, machinery is expected to become exponentially more complex. This complexity extends beyond just the design of the machinery; it also impacts the service teams responsible for maintaining machine uptime, making the need for domain-specific, personalized AI to help avoid downtime and keep up with customer expectations more crucial than ever.
Shahar Chen, CEO of Aquant, says, “Every service situation is unique, each with its own challenges and requirements, making personalized AI crucial for addressing specific needs effectively. Generic AI models only offer recommendations based on the frequency of the solution in the data, which is only helpful with simple, common issues. Complex machinery requires AI that understands the context of the challenge. Our Service Co-Pilot platform stands out by personalizing every response based on a deep understanding of the organization’s service business, including the specific asset, part, customer, and user.”
Forecasts by industry analysts at Gartner® predict that “by 2027, over 50% of the GenAI models used by enterprises will be domain-specific, tailored to individual industries or business functions—an increase from just 1% in 2023.” Gartner also emphasizes the importance of maintaining a human-in-the-loop (HITL) to enhance the effectiveness of AI systems – this is particularly true for the service sector given the complex nature of the business.1
Deniz Mullis, Technical Operations Leader, Global Service Engineering at Cytiva, says, “I’ve seen Aquant’s offerings over several years and two different companies in which we’ve implemented them. Service Co-Pilot has been a game-changer in tackling our engineers’ ability (both in-house & in-the-field) to access critical technical information when needed. Aquant significantly improves ease-of-implementation so that organizations can quickly see the benefits of AI and how these technologies will help us meet our strategic objectives.”
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Aquant’s Approach to Personalized AI:
- Data Integration and Analysis: Effective personalization starts with the integration of vast amounts of raw data from various sources—work orders, technical notes, and expert insights—converted into trusted data using advanced algorithms.
- Advanced Pattern Recognition: Utilizing AI to analyze service data allows for the discovery of connections and behavioral patterns. This analysis is essential for understanding complex relationships between assets and issues, distinguishing this approach from other solutions that may lack depth in context awareness.
- Expertise Integration: Service Co-Pilot converts subject matter expertise into reliable data that the AI uses to improve response accuracy. The platform features an intuitive interface for straightforward expert contribution and utilizes a predictive algorithm designed to forecast outcomes, based on academic research and behavioral decision-making studies.
- Feedback-Driven Adaptation: A robust AI system must adapt based on ongoing feedback and business goals. This involves evaluating the effectiveness of solutions in real-time, considering factors like cost and operational impact, and making adjustments to continually enhance and personalize recommendations.
Product updates include:
- Personalized Recommendations: This feature analyzes cost-efficiency and the historical performance of assets to deliver the best recommendations, based on the root cause of problems rather than just immediate fixes.
- Advanced Guided prompts: With multiple questions available for Triage – Service Co-Pilot’s equipment troubleshooting application – users can get to the core of the problem much faster with better accuracy.
- Continuous Learning: We introduced an improved feedback framework that allows real-time interaction and editing of recommendations, as well as improved AI modeling that automatically adapts and learns from user interactions, ensuring alignment with business strategies.
- All-in-One Experience: Users can effortlessly transition from initial queries to in-depth investigations and insights, all within a unified view accessible with a single click.
These improvements to Aquant’s Service Co-Pilot are designed to personalize the AI engine and enable it to uniquely address the complex and varied challenges faced by service organizations, by streamlining processes, enhancing decision-making with relevant insights, and adapting continuously to align with strategic goals
