Is Your Data Smart Enough to Power the AI You Are Using in Field Service?

AI’s effectiveness is entirely dependent on the quality of the data it processes. Without a solid foundation of accurate, reliable, and high-quality data, even the most advanced AI tools will fall short.

Therefore, the true challenge for organizations is not the AI technology but the ecosystem that supports it, especially the data infrastructure. Field service leaders need to prioritize data governance, quality, and integration before AI deployment. So, what risks come with poor data, and how can organizations mitigate these to ensure AI delivers on its promises?

The Growing Impact of Gen AI on Field Services

According to McKinsey, Gen AI is expected to reduce content creation costs by up to 80%, increase operational efficiency by 30%, and automate 25% of customer interactions within the next 12 to 24 months. These advancements could significantly boost customer satisfaction, service productivity, and revenue, with potential growth ranging from 10% to 30%. 

Additionally, a separate McKinsey analysis of 50 industrial companies over 15 years found that service-focused businesses outperformed their product-centric counterparts, generating 1.7 times more total shareholder returns (TSR).

The automation of multilingual customer support, training, and documentation is already boosting efficiencies across service operations. However, the impact of AI depends on how well it integrates with existing systems. 

The Real Risk Is Data, Not AI

If data is incomplete, inaccurate, or biased, AI predictions will be flawed and thus result in service inefficiencies, poor decisions, and missed opportunities. For example, in predictive maintenance, inaccurate data can lead to unnecessary service calls or missed maintenance, both of which decrease customer satisfaction and increase costs.

McKinsey’s research emphasizes that AI can only deliver value when supported by a strong, well-structured data ecosystem. While organizations rush to implement AI, focusing too much on the technology itself and not enough on the underlying data infrastructure poses the real risk. Neglecting the data foundation before deployment can result in underwhelming outcomes for AI.

Data Strategy Before AI Strategy

Because AI is only as effective as the data it’s trained on, and poor data can amplify existing problems, a strong data strategy is necessary. And it should ideally include considerations on:

• Data quality: Is the data accurate, complete, and up-to-date for reliable AI predictions?
• Data governance: Are there clear policies in place for data usage, privacy, security, and compliance with regulations like GDPR?
• Data integration: Have data silos been addressed and is the data consolidated from different systems to create a unified, accessible ecosystem?
• Data accessibility: Is high-quality data easily accessible to relevant teams for the best predictive capabilities?

To unlock AI’s full potential, field service leaders need to focus on a foundation that will allow AI to thrive. Here’s how to lay the groundwork for a successful deployment:

1 – Audit your historical data

Before implementing AI, it’s necessary to understand the data you have. Conduct a thorough audit to evaluate the quality of your historical data. Check for completeness, accuracy, and relevance. Identify the usable datasets and separate them from incomplete or inconsistent ones. Conducting such an audit will ensure that AI is working with reliable, high-quality data to generate accurate insights and predictions.

2 – Invest in master data management (MDM)

Create a centralized system to store and manage data, with designated roles responsible for maintaining its accuracy and consistency. Establish clear naming conventions and standardized categories to ensure that everyone understands the data in the same way. Creating a “single source of truth” makes it easier to trust and use AI.

3 – Build cross-functional data literacy

Everyone should be able to “speak data” in the organization, not only the IT department. Encourage data literacy across all levels of your workforce, from field technicians and customer service agents to back-office teams. Everyone should understand the importance of clean, accurate data entry and how their actions affect the quality of AI outputs.

4 – Pilot with purpose

Rather than implementing AI across all operations at once, start small with a focused pilot. Choose a specific use case where you have clean, well-documented datasets. This could be a specific product line or a region with comprehensive historical records. A targeted approach helps you learn quickly, measure results, and adjust your strategy before scaling AI to broader areas of your business.

5 – Treat AI as a strategy, not a tool

AI should be seen as a long-term investment rather than a quick solution. As service expert Venkata Reddy Mukku put it, AI is not a “silver bullet”. To be effective, it must grow and adapt, and transform the organization gradually. If AI is used only for isolated applications, its potential is limited, and it may not deliver lasting benefits. Successful AI adoption requires a shift in company culture, continuous data management, and ongoing process improvements. While AI can enhance operations, it delivers true value when it is aligned with broader business goals and long-term strategies.

What to Do if AI Fails After Implementation

Not all implementations go as planned. If AI systems fall short of expectations, it’s important to address the issues systematically.

Diagnose the root cause. Check if the data feeding the AI is outdated, inaccurate, or poorly structured. Next, assess whether the AI model is suited to the problem and adaptable to real-world complexities.

Evaluate the data infrastructure. A unified data platform is essential for easy access, cleaning, and data sharing. If there are gaps or silos in data collection, they can hinder AI’s performance. Improving this infrastructure may require investment, but it’s critical for the long-term.

Update your AI models and algorithms regularly. Periodically reviewing and adjusting the data inputs (features) helps keep up with new data and changing conditions, and it also improves accuracy.

Engage with stakeholders for feedback. Gather insights from end-users about usability issues or real-world challenges the AI might not be addressing. Collaboration with IT, operations, and data science teams is also key to resolving technical problems and fine-tuning the system.

Moving forward, set realistic expectations. AI adoption is a gradual process that requires continuous updates and improvements to meet your organization’s goals. If needed, consider bringing in external expertise. Expert consultants can provide fresh perspectives and industry best practices to help improve your system.

Finally, maintain transparent communication with stakeholders. Clearly share the challenges you are facing and the steps being taken to address them. Transparency fosters trust and keeps everyone on the same page and towards the same goal.

Realistic Expectations Moving Forward

To fully leverage AI and drive long-term growth, organizations must prioritize data quality, governance, and integration from the start. Smart AI starts with smart data, so it is crucial to ensure that data is accurate, well-managed, and integrated.

Setting realistic expectations from the beginning is also key. AI requires a strategic, phased implementation. So it is best to start with smaller, well-defined projects to assess AI’s effectiveness and the quality of datasets, manage risks, and adjust processes as needed. 

Finally, ensuring that everyone is data literate will enhance cross-functional collaboration and enable AI to integrate seamlessly with broader business strategies. The future of AI in field services is promising, but only for organizations that invest in the data infrastructure required to unlock its full potential.