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A Robotic Future: How to Choose an FSM AI Solution That Actually Works

Benjamin Fielding, Research Editor at CompareSoft, discusses how field service organisations can choose the right AI solution that’s most suitable for their operations.

As each day passes, we continue to see the range of digital transformation across all industries. Many businesses have transitioned from conventional modes of operation to more flexible and highly efficient modes using artificial intelligence.

As a result, these businesses have taken giant strides in pursuing their goals and have achieved maximum productivity in the process.

The field service industry is one industry that has leveraged the potency of artificial intelligence. A 2021 report by digital.ai found those field service businesses that adopted an AI solution recorded a 47% boost in productivity.

AI solutions open the door to multiple opportunities and benefits, such as:

● Streamline tasks

● Improving in-field communication

● Organising resources and personnel in real-time

Optimise worker planning and scheduling

● Enhancing maintenance tasks

While these are glimpses of how artificial intelligence has improved field service management, there’s much more that can be achieved. However, to do so, field service businesses must choose the right AI solution that’s most suitable for their operations.

This article will examine crucial factors and must-knows when choosing an AI solution that works for you. Ensuring you’re left with the solution that poses minimal pain points to your operations.

But first, let’s discuss the problems linked to choosing the wrong AI tools and how these can pose challenges when it comes to managing your field service activities.

Potential Challenges Field Service Businesses Face Using AI Tools

Joining the strategic trend of incorporating AI into your field service operations isn’t enough to maximise its benefits. Therefore, it’s important to familiarise yourself with some of the challenges faced when using AI tools. Four common challenges include:

Integration Issues

Integrating AI tools into your existing operations is necessary for growth. However, shifting from a conventional business operational model to a modern and AI-driven model isn’t an easy task.

One of the challenges field service managers face is the problem of integrating the AI tool. Integration goes beyond the addition of new plugins for existing models. It’s a complex process many field service businesses struggle to resolve independently and most require outside assistance.

The Right Infrastructure

AI-driven solutions are designed to provide a daily utility that makes tasks easier. This function thrives on infrastructures like high internet speed and extensive processing capacities. However, many businesses lack these infrastructures or rely on outdated ones, and that slows their operations while using AI solutions.

Therefore, in adopting AI, field service businesses must be willing to erect infrastructures that complement modern tools. Due to the expenses involved, infrastructure remains a big problem when choosing the right AI solutions to work with.

Lack of Appropriate Data

AI relies on big and pure data availability to create the value field services businesses can leverage for operations. However, many companies cannot source clean, well-secured, and high-quality data. This presents problems in adopting AI solutions and integrating them into operations.

This problem in sourcing appropriate data can be traced to the lack of investment in the data management system required to power AI solutions.

Shortages of Skills

The shortage of technical staff with the requisite training and experience to effectively deploy AI tools is a growing problem. An extension of this is the lack of will amongst field service businesses to invest in hiring people with the right skill-set, let alone explain its findings for decision-making processes.

5 Factors to Consider Before Implementing AI Tools in Business Operations

As efficient as AI tools can be, particularly for growth, certain factors must be considered before implementing these solutions into your business operations. Consider the following:

1. Use-case

As noted, the purpose of AI solutions is to solve complex business problems. Therefore, as tempting as it might be to jump on the train of using AI, you must first determine the unique use case for your field service business.

In other words, you must identify the problem you intend to solve with it. Create a clear objective and determine how you want it to demonstrate value. Failure to do this may attract negative repercussions or, at the very least, offer no result.

2. Value Prioritisation

Another essential factor is how the use cases identified can offer business and financial value. For example, some SMEs have little to non-existent use for AI solutions, such that by virtue of their size, incorporating AI solutions makes no difference in terms of value. Upon this basis, you must tie a value to the initiative you intend to execute.

3. Internal Capabilities

As excited as you might get about what you want to achieve using AI solutions, you must consider your capabilities to attain these achievements.

As an organisation, you must identify and institute what you require to reach goals for using AI solutions. You must also determine whether existing teams or infrastructure are capable, or you need to erect specific infrastructures and bring in experts to set the tool on a course before implementing AI solutions.

4. Data Availability

Data has a far-reaching role to play in using AI. AI thrives on a large amount of data, so you must consider the data your organisation uses.

If the data is low, you may need to explore other business solutions other than AI because failure to do this will render implementation useless. Additionally, don’t just consider the availability of data; you must consider the availability of quality data.

5. Human Factor

Also, consider how much an AI tool will affect human contributions. Regardless of how efficient AI solutions can be, you must retain human intervention.

According to a report on hbr.org, firms that prioritise collaborative intelligence between humans and AI achieve more productivity. While those that displace human contribution achieve less.

How to Choose an AI Solution That Works For You

We can’t overemphasise the need to choose an AI solution unique to your use case. To make the right choice, it’s best to prioritise the following:

Research Strengths and Limitations

Undoubtedly, AI tools are highly efficient. However, certain tools aren’t fit to achieve specific objectives. So to choose the one that works for your goals, you must research the tool’s strengths and limitations.

Begin with Small Integration Experiments

As tempting as it might be to go all in when integrating AI solutions, it might end up counterproductive. This is why it’s vital to begin small experiments while slowly incorporating the solutions into business operations.

Tie AI Solutions to Specific Objectives

Don’t just implement AI solutions into your operations without tying each product to a specific objective. This has proven to be the most effective way to maximise the function of the tool.

Prioritise Gradual Implementation

Avoid making an overhaul in business operational shifts. Even if you have the infrastructure, resources, and technicians to front the process, you should implement it gradually to understand how the solution affects each stage of business operation.

Outsource Support and Establish Employee Training

The best way to avoid the problem of underusing an AI solution is to outsource the support of experienced experts. While using their services, you can slowly integrate internal teams, after which you can conduct full training on using the AI solutions for your business needs.

Final Thoughts

Before jumping on the trend of incorporating AI solutions into your field service business, be sure you’re familiar with what it entails, and that you have a timely need for it. This is the surest way of ending up with an AI tool that actually works for you.

About the Author

Benjamin Fielding is the Research  Editor at Comparesoft. With a meticulous background in content creation, and working in some of the most complex industries, he has built a great reputation around his work.