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AI opportunities for field services

Luke Matcham

Any organization that employs a field force will be aware of the myriad challenges around organizing employee routes for the week, ensuring they have the right tools and equipment to complete their jobs, and dealing with last-minute changes to task priorities. For years, software providers have claimed their solutions can deal with these challenges and offer significant benefits. Still, recent advances in artificial intelligence (AI) technology are starting to unlock new opportunities and make existing benefits much more tangible.

How do you separate the hype from the reality and determine which AI tools and solutions are appropriate for your organization?

AI implementation

Our recommended approach

Step 1

Define the AI vision and guiding principles

Step 2

Identify AI use cases and define a strategic blueprint

Step 3

Prioritize pilots and define an AI roadmap

Identify potential AI use cases within your field force function 

Field force functions can vary drastically from one organization to another, e.g. one organization deploys engineers to fix broken-down assets while another has a fleet of travelling salespeople. While this article focuses more on asset-related field force activities than those associated with customer service/sales, there are many common characteristics and challenges that AI use cases look set to tackle.

Promising recent AI developments for field services

Evaluating feasibility and implementation risks

Dedicated field service management software incorporates increasing AI functionality, yet several bespoke tools also exist that may tackle one or more of your organization’s challenges. A good evaluation of available AI offerings will encompass both categories and weigh the potential benefits against both organizational and technical feasibility challenges.

Organizational feasibility factors include:

  • User adoption, which may be challenging for longstanding field force members who have set ways of working
  • Developing the internal technical skills required to run your AI initiative after implementation
  • Strategic alignment to wider business goals rather than tactical deployments
  • Potential need for a new operating model to take advantage of AI benefits
  • Pushback from users who may feel threatened by a collection of location data.

 Some of the technical feasibility challenges include:

  • Lack of human decision-making capabilities, e.g. a planning system could prioritize based on job type criticality but would not be able to replicate the operator’s local knowledge and experience
  • Lack of supporting technology, e.g. advanced telematics devices in vans or accurate GPS tracking on all user devices
  • Strong and stable internet connections, i.e. all field-based solutions are likely to require a solid connection to be effective, but internet connectivity remains limited in large parts of the world
  • Ease of access to, and quality of, the data required, i.e. organisations may be guarded over their data and hesitant to share with others
  • Complexity involved in tailoring generic AI solutions to your individual business needs.

Leaders implementing AI initiatives should also be aware of external factors, such as rapidly changing regulations around AI, which may impact the direction of any initiatives. AI use within a field force setting is also open to potential challenges from operatives or unions who may question the need for and use of personal data about employee whereabouts throughout the day, for example.

Calculating the return on AI investment

Determining the best use cases for AI within your organization can feel overwhelming. Developing a business case for any AI initiative, which weighs potential benefits against the risks and costs, can help you make an informed choice by comparing which are likely to return the most value from your initial investment. 

Key benefit categories for an AI business case

A pragmatic approach for deploying AI within your field force

Any organization aiming to roll out AI within its field force needs to take a pragmatic approach to its implementation. 

This means that trialling AI initiatives on a smaller, or regional, scale may be a safer initial approach. Such an approach allows the benefits and risks to be tested without subjecting the organization to the undue risk of negatively impacting their entire operational force. Trialling may also be a pragmatic option given the speed at which technology advances in this area are being made. Embarking on a multi-year program could mean that, by the time it has been implemented, the technology and related benefits are already behind the latest developments.

Always ensure that your field force is bought into the change journey for any AI initiative, as end-user failure to adopt the solutions could spell disaster for any forecast benefits.

Ultimately, you should treat the implementation of AI within your field force as you would any other project. If you align AI use cases with your overall strategy, balance the potential risks and rewards, and don’t neglect the importance of effective change management, then AI looks set to radically alter the way that the field force of the future operates.