Despite business anticipating the fourth industrial revolution there is still a definite need to understand the process.
“The hype around robotics and intelligent automation (IA) programmes has many organisations launching into these and very quickly finding that the return on investment (ROI) is just not there,” says Seugnet van den Berg, a founding partner at Bizmod consulting.
“Leadership teams need to be implementing the correct processes and support structures required by IA and robotic programmes,” says van den Berg. She cites the following reasons that ROI is being affected:
Robotics initiatives and larger IA initiatives are still frequently viewed as a technology intervention with little or no attention being spent on processes, practices, people and most importantly company culture.
The current trend is for organisations to embark on robotics programmes by focussing on obtaining and implementing the technology. However, once completed, project teams are finding that they encounter resistance from individuals who have been performing the tasks that are now to be automated. This results in the entire approach having to be re-evaluated.
Van den Berg says that they have found that the main reason for disgruntlement developing is that little or no attention is focused on people, structure, process and practices as part of the implementation. To make the programme sustainable and to generate a return, employees need to understand why the programme is being implemented, the processes required to accommodate digital workers and how the organisation practices will need to be adapted to accommodate this. It requires an overall change in the thinking about “the way we do things around here”, which ultimately means the culture of the organisation.
A standard first level approach by business is to assess the ‘low hanging fruit’ across the processes.
These are elements that are typically easy to automate. However, automation is then undertaken in an isolated process at a task level, and even though it may be a good approach when first starting out with IA, it becomes very difficult to calculate ROI. For many organisations, one of the key reasons to embark on an automation journey is to reduce the number of human resources involved in the process. When tasks across a variety of processes are automated, the “benefit” is calculated in terms of minutes saved per process. But as human resources work across a variety of processes scattered in the value chain, this approach rarely results in reducing the required human resources.
Process visibility and having a clear understanding of where in the process the work is being done by digital workers is also overlooked by project teams who tend to focus on implementation from a technical perspective.
The technical teams understand the processes and have an even better understanding of which parts of the process are automated, but somehow this knowledge is not transferred back into the business. The “business” people that work in the process on a daily basis don’t have a clear view of their process and an understanding of which elements of work are being done by human workers and which by “digital workers”. To keep track of the automating process steps across functions, process visibility is key. It is difficult enough to monitor what is being automated where, but when you are doing this across functional automation it is an absolute necessity.
Historically work has always been done from a human perspective with the basic assumption that humans do the work or manage other humans doing the work.
Robotics and IA projects are slowly replacing the need for humans to do the actual work but very little attention is being given to what it will take to manage “digital workers”.
The technical skills required for the maintenance of “digital workers” often lies with the technical implementation team and in most cases the business is not equipped or skilled to deal with this maintenance. The challenge being that the business needs to create these skills in the business whilst implementing the programme. This requires large scale upskilling of existing human workers to be able to build and maintain bots and in this way retain visibility of “work” being done in their area. It also opens up a host of questions not currently being addressed by businesses on “what do we do with our people now?” In this case imagination is the limitation.
One of the main reasons that businesses decide to embark on an IA or robotics process is for cost reduction and improved efficiency.
When evaluating cost reduction, the leadership team will typically look at a headcount reduction to generate savings. As much as these initiatives have the potential to reduce headcount, there is a definite requirement for additional headcount – individuals who are data and data clean-up professionals are a precursor for IA, as well as individuals with the technical skills required to maintain what has already been built.
The business may find itself saving costs on the volumes of lower skilled employees, but at the same time it will need to introduce higher skilled technical resources. Opportunities will open up for humans to focus on the skills that only humans possesses and these can be applied to create more innovative, customer centric advantages.
The focus of the business is usually on launching the IA and robotics initiative.
This is a short term objective that is aimed at technology adoption. The longer-term view is ignored. This is needed to focus on determining the impact and cumulative effect of all the IA initiatives on employee skills. If the decision is taken at a business level to reskill employees it is important to realise that this is a process that will take time. Therefore, the sooner a clear view is established on what kind of skills are required, the better.
If the mundane, repetitive, large volume, data capturing, process steps are automated – what is the organisation doing with the human capacity that it will be releasing? Some of the future skills identified talks to improved customer service, data cleaning and management, upskilling business users to develop and maintain their own intelligent automation initiatives. If the business choice is to re-skill people these kinds of initiatives have to be formalised and directed.
The focus on data, prior to embarking on an IA initiative, cannot be overstated.
The pace at which ROI can be achieved is directly linked to the quality of data available. Many IA initiatives come to a grinding halt because of data that has not been cleaned and maintained previously. Theoretically the technical part of the project can be “ticked off” because the bot was built and it is working, but the ROI can’t be achieved because the quality of the data is insufficient.