I recently attended ISM World 2023, one of the year’s preeminent supply chain and procurement events.  One of the major themes and sources of discussion in the break out sessions was harnessing AI and robotic process automation (RPA) to gain efficiency and optimize resource deployment.  Firms of all sizes are all over the spectrum in terms of their automation journey.  For those that are looking for a place to start, I’ve outlined a brief roadmap summarizing how we help clients with this very issue.

The first step is to take an enterprise view of the kind of problems you are trying to solve through automation. The goal could be increased productivity, reduced costs, improved product quality or better service delivery, but it’s critical to establish what the problem is before you can work out if automation is the answer.

A committee approach, that brings together business function and IT experts, can help companies to understand the interdependencies between their key business processes and functions, and to define the required adjustments to their processes, in order to create a feasibility assessment well before a business case is made.

The next stage is to create a clear automation pipeline, using a structured methodology to evaluate opportunities and impacts. For example, when assessing different categories of automation opportunity, ask questions such as:

  • Would this simplify or improve an existing business process or function?
  • Would this transform a function or the enterprise at large?
  • Could this optimize a key function or business process?

Assessment of impacts should involve an understanding of how people will be affected across the organization, leveraging cross-industry research wherever possible. For example, the European Robotics Industry’s Good Work Charter sets out some of the principles for how humans and robots can work side by side.

For every automation opportunity, functional leaders must be able to articulate where it sits on both feasibility and risk spectrums. This requires an understanding of both the technical implications (e.g. how it complements the existing IT capabilities and roadmap) and the business implications (e.g. impacts on organizational structure, downstream stakeholders, and the level of change management required to implement and sustain a particular path).

Avoid high-risk, high-return projects to start with

To begin with, it is better to start slow, especially if the business is new to automation. Avoid the urge to embark on projects that have the highest return, but also involve a high risk of failure and a high degree of complexity.

Organizations can work towards more complex projects over time, using real-time experience to build automation integration capabilities within your enterprise. One of the issues companies are likely come across is skills gaps. Taking a measured approach will help identify these gaps and develop the right approach to filling them.

Making a successful transition to automation, however, isn’t just about recruiting or reskilling individuals to perform key functions. There needs to be honesty about whether the capability and experience exists within the enterprise to identify opportunities and select and implement the right technology.

Organizations will have to weigh up whether bringing in full-time expertise, partnering with consultants, or a hybrid approach presents the best path to de-risking the project and delivering success.

The final piece of the jigsaw is sustaining momentum once the integration is underway. This requires fostering a culture of continuous learning throughout the enterprise, through organizational training and development programs, and through capturing key perspectives on matters such as customer experience, analytics and organizational design.

It is important that ongoing requirements and their associated costs, such as maintenance, documentation and training, are established at the outset. This should include identifying how existing roles may need to be modified (even if those roles don’t directly intersect with the automation application or process) and how key learnings along the way will be captured - especially the root causes of any failures or setbacks.