It’s also about people

While a majority of business leaders globally are optimistic about the potential of Intelligent Automation to augment the workforce, rather than replace jobs, there is still work to do to convince employees.

An overwhelming 79% of business leaders believe that internal resistance to change is limiting the implementation of IA.

In our work with our clients around the world, we repeatedly see some key challenges:

Fear and Uncertainty:

There is always a varying appetite for the organization to understand and accept the changes that will come along with IA initiatives. Often, management fails to calibrate this resistance accurately.

Then there are other questions around the financial benefits and monetizing the the customer experience, efficiency and productivity gains.
There are also significant and present fears that stakeholders have around the impact on their roles and change in job profiles and content.
Very often there is the human dimension of having a stake in the failure of the IA initiatives. Business teams are often waiting for the Automation CoE team to fail at the first step and use this as a reason to not proceed.

Impact on employees:

There are usually two distinct groups of people with opposing KRAs. One is the implementation team and the other those who are affected by automation.

Usually, there is no clear, consistent and continuous communication around the intention behind automation. Any concern around what automation means to people’s jobs aren’t adequately addressed. People also have the real concern whether they will have the requisite skills to move to higher value work.

Then there is the aspect of behavioral change that needs to be addressed. Automation needs time to implement. In addition to changes in process and the accuracy and quantum of data being processed over time, it also required significant behavioral changes among the people on the process, both direct and indirect.

How does this all fit in and work together? It’s not easy to find the right balance between all these challenges.

Adequate and influential sponsorship:

It is imperative to secure sponsorship from the relevant business areas which are willing to support the automation initiative. This can take some time and stakeholder management effort, especially in organizations with a culture of consensus building.

This also helps mitigate any political and cultural risks as the automation project progresses, especially if there are any slips.

Adequate Governance:

It is critical to establish intent and support at every level, from developer to CEO.

How will Intelligent automation impact the relevant stakeholder’s team in terms of their performance and KRAs? If the right structure to execute isn’t in place, Intelligent Automation projects will not succeed. There is always a need for a ‘middleman’ between the automation, business and other teams to orchestrate this transformation. This needs to be someone who understands the initiative and translates this appropriately between various stakeholders and defines matching, relevant KRA’s.

The bot is a virtual person as opposed to just software that is running in a process in the back-end. Therefore this is not classical IT. How does one manage process change?

Besides the people challenges, other risks must be identified. What are the risks of managing BOTs? How does one manage the BOTs? Will the BOTS be treated as agents? How does one change the process to interface BOTs with humans and how does this impact decision making?

In summary, change management for “Intelligent Automation” programs should definitely entail managing and addressing the people factor. This along with adequate sponsorship and a robust governance framework are key success factors that need to be in place for any IA program to succeed.