Blog Posts

Yuliya Shcherbachova

The Human Element in AI: Blending Expertise with Machine Learning for Superior Service

As machine learning (ML) practices advance, users can complete more tasks with fewer resources and less time. 

For instance, we can use structured and unstructured service data to get a holistic view of service and make better decisions. We can save time by analyzing contextual information and generating easy-to-read summaries. We can also derive value from piles of books and manuals in seconds.

Overall, it’s simple enough to embed ChatGPT into daily workflows. But to get the best results from your input, there are several best practices that you should be mindful of. 

Fine-Tuning Your AI Approach

AI tools deliver the best results with thoughtful prompting.

The accuracy of an AI system’s answer directly correlates with the quality of the prompt. The best way to glean the correct answer is by asking the right questions and providing as many helpful details as possible.

For example, imagine you are looking up today’s weather forecast to determine what to wear. To generate the most accurate answer, you have to add a location. You can also add the time you anticipate being in the area. Additionally, you can ask to have the results delivered in Celcius or Fahrenheit.

The weather forecast is a straightforward example, but the same principle applies to complex questions, problems with multiple answers, and elaborate decision trees. You can quickly feel overwhelmed if you don’t have knowledge or prior experience, so it’s essential to map out your questions and processes to determine the best prompt. 

Human knowledge is still front and center.

There is a lot of skepticism about AI replacing specific jobs. However, while AI systems shape our workflow and experiences, they don’t take unique knowledge from us. 

If you crave your aunt’s world-famous apple pie, you might find many recipes online—you can even ask ChatGPT to create a shopping list for you. However, it’s unlikely that anyone else can duplicate your aunt’s exact recipe because she has developed skills from years of baking. For example, your aunt can tell, by touch, what the ideal pie dough consistency feels like; she can even determine doneness from the color of the pie crust once baked. 

As a result, your aunt’s fantastic apple pies result from her baking experience and knowledge. These aspects of baking won’t be explicitly found in a cookbook—so if she doesn’t share her expertise, it gets lost. 

Ultimately, you can follow the recipe for your aunt’s apple pie down to the final instruction, but you will probably have to make a few pies until you get a feel for the process. This aspect is the “human in the loop” component in AI. Similarly, combining human knowledge and resources can achieve the best results in service AI models. Aquant research has shown that 30% of service solutions are not found in historical service data. Instead, the best answers are provided by veteran service experts.

Trust is essential.

It’s in human nature to want control over the decision-making process. Even when we look up answers in public documents, we still rely on tried-and-tested best practices and advice from senior colleagues. We care about IP and internal data and trying to protect our organizations and users from irresponsible use of private information. Ask yourself if your solution is trustworthy, and be prepared to fact-check your outputs. 

Close the Skills Gap with Service Co-Pilot for Knowledge 

At Aquant, we’ve dedicated time and resources to building Service Co-Pilot, a platform that corresponds to today’s service needs and beyond. Service Co-Pilot for Knowledge approaches the skills gap issue by: 

  1. Ingesting all available data. Organizations typically need help with many data-related challenges, including sorting through poor-quality data, corralling isolated data sources, or managing diverse data types. Plus, if a company is represented globally, it also requires multi-language support. Service Co-Pilot supports all of the aforementioned items—and in its eyes, there is no such thing as insufficient data! Bring your data—wherever you are in your process—and we will help you get started. We can also provide feedback and recommendations on managing your data strategy across your service business to improve outcomes.  
  2. Reviewing and optimizing outputs to get results you can trust. Service Co-Pilot speaks the service language. We combined the best Natural Language Processing (NLP) and Generative AI practices with a human-in-the-loop component to achieve trustworthy results. By involving subject matter experts, we transfer your tribal knowledge to improve results and protect your unique IP. Service Co-Pilot also ingests user feedback, so the tool gets even more accurate through continuous use!
  3. Accelerating your learning and reducing service costs. Keep upskilling your employees through data and AI tools to help them perform at the highest level. According to Aquant’s 2024 Field Service Benchmark Report, an organization’s bottom-performing workers can 80% more than their top-performing counterparts. However, if organizations empowered all employees to perform like the top 20% of the workforce, service costs could be reduced by as much as 22%.

Start Your Journey Today

We live in a world where technology amplifies our capacity to make informed decisions and preserve invaluable knowledge. Aquant’s Service Co-Pilot stands at the forefront of this revolution, offering a robust platform that understands and respects the nuances of human intelligence and organizational data. 

By bridging the gap between data complexity and decision-making simplicity, we pave the way for a future where every individual has the tools to excel, and every organization has the means to thrive in the ever-evolving landscape of information and technology.

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About the Author

Yuliya Shcherbachova, Senior Product Marketing Manager, Aquant

As a Product Marketing Leader at Aquant, I’m passionate about using technology and AI to tackle complex problems. I’m on a mission to make the world a better place through innovative, high-impact projects. Over the last decade, I’ve worked with hyper-growth AI companies—helping them grow, increase their revenue, and develop new product strategies.