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Enterprise management

Organizational change management – the missing link in intelligent automation

New ways of working and embedding change drives AI and automation success

If you have ever been asked one of the following questions, this short paper is the perfect read:

  • Will robots take my job?
  • Can a robot go rogue and change data in the system?
  • Why this chatbot does not understand me?
  • Does AI make mistakes?
  • How you will ensure compliance with business policies?
  • What is your approach to ensure that people will use self-service instead of asking someone else by email?
  • How will you ensure a return on investment (ROI)?

According to Prosci, the business consultancy, organizational change management (OCM) comprises “the process, tools and techniques to manage the people side of change to achieve a required business outcome.” Despite this impressive definition, OCM does not get the critical recognition it deserves as a driver of value. In fact, in many organizations it is often confused with internal employee communication.

Little wonder, then, that projects continue to fail to achieve the adoption levels that are essential to meeting targets for return on investment (ROI).


What is OCM?


In its very essence, OCM is about encouraging and enabling people to use the tools in which the organization has invested, so as to achieve true business value, and a return on that investment.
Here’s an example. An organization requested an investigation why its HR Virtual Assistant had very low usage across the business. The IT and HR teams had invested a significant amount of money, time and resources to build the solution, which was meant to reduce the processing time for each employee from almost 30 minutes of HR service desk calls to five minutes with a virtual assistant – something that seemed like a big win-win for everyone.

We investigated the solution from a technical perspective – logs, code, solution design, use experience – and could not find many major points that were missing. The virtual assistant had a funny and helpful persona, leading through the process in an organized manner, and the technical testing and logs suggested it was working as intended.

However, we found one aspect that had been forgotten by the organization’s technology-focused teams. The only place where the virtual assistant was mentioned to employees was a small-print reference in a booklet of over 300 pages that is given to each new recruit. As a result, they were spending time locating the right service team, booking a slot with an agent, and then going through everything on a long call. The result? A waste of time for them, an unnecessary use of service resources, and an inability to achieve the expected return on investment.


How does OCM help?


organizations face are. OCM can help to remove many of these barriers by effectively addressing the people side of change.OCM is able to help meet innovation and transformation objectives by focusing on critical aspects of the people side of change, instead of facilitating communication and training alone. Those aspects may include the creation of the change vision, which comprises making a practical case for change, effective stakeholder management, transformation scaling, and an approach to roll-out that reinforces positive change.

Part of this vision addresses the WIIFM – the employee’s “What’s In It for Me?” – and is the beginning of achieving buy-in, continuous use, and ROI. It’s the most often overlooked component of many change management projects.

How change is embedded across many silos in an organization is very important. In the context of artificial intelligence (AI), it’s less about introducing new tools and software, and more about changing the way everyone thinks, operates and does business. Mere excitement for the new technology will not win people over. Automation anxiety (the fear that machines will soon replace people and take their jobs away from them) might distract them from the benefits that change can bring to them, and how it’s impacting clients.

Organizations need to make sure they frame and sell AI as an evolution of something that employees are doing already, not merely as “just the next logical step”, but as a reinvention of the whole process. Instead of throwing out what’s familiar to people, employers should reassure them that the new technology adds to their existing capabilities, and that it allows them to fulfill their true potential – which is something that is not automatable.


Barriers to the adoption of intelligent automation


Intelligent process automation (IPA) can be defined as a combination of robotic process automation (RPA), artificial intelligence (AI), and analytics. These are often seen as very complex technologies that might threaten work stability for employees who are currently executing the same processes manually. Resistance to change is therefore not surprising.

Capgemini has conducted a detailed analysis of the typical implementation paths and challenges of intelligent automation adoption across many industries and business processes. What we have found in our “Reshaping the Future: Unlocking Automation’s Untapped Value” report is that only a minority of organizations (16%) are deploying multiple automation use cases at scale, by which we mean implementation that goes beyond pilot and test projects. The most common challenges in IPA that organizations face are.

OCM can help to remove many of these barriers by effectively addressing the people side of change.

BARRIERS TO THE ADOPTION OF INTELLIGENT AUTOMATION


OCM outcomes


Successfully deployed OCM empowers employees across organizations to improve and refine the new solution continually – which can stimulate revenue growth and secure ROI. People need to be both aware of how the new solution is changing their work and that they are active in driving it – and, thanks to that, they are actively making the organization a powerhouse for continuous innovation.

Moreover, OCM can reduce tool or software implementation complexity by effectively identifying, engaging and managing stakeholders and their needs. Each stakeholder or stakeholder group will likely find itself in a different place when it comes to perspective, concerns and willingness to accept new ways of working. It is often the skills with which organizations communicate, persuade and involve these people that will determine the success of the AI project. If the stakeholders don’t get it, they won’t buy into it, and it’s then very likely the project will fail, and its ROI will not be realized.


Case studies in context

Generally speaking, the “software robots” of robotic process automation (RPA) can run in fully unattended mode, and are never seen by employees. But there is a growing trend to introduce RPA as a hybrid model, where the robot is working hand-in-hand with its human counterpart. The hybrid model allows employees to focus on what’s most important for the business, while the robotic solution handles data processing and system automation. This new model of fulfilling business processes requires a new approach to adoption, training and managing the workforce.

Another major trend is to use RPA in self-service solutions, where chatbots or virtual assistants act as the front end of a process, and RPA is working in the background, automating many legacy systems and data sources. Such an approach enables user requests to be fulfilled without any manual work. Everything is done in real time, and the solution is always available and fully automated, even with disjointed legacy systems.

We see these trends at work in the case of a major transportation client of ours, which has introduced virtual workers working hand-in-hand with human counterparts fulfilling the accounts payable  process. Robots were made responsible for intelligent document processing (extraction of data from unstructured documents) and automating all relevant systems, while people manage decisions and business communication. As much as 90% of the entire work was automated, while at many points within the process flow there is continuous communication between robots and humans. As a result, OCM was required to ensure people would stop conducting the manual processing themselves and allow robots to handle it instead, trusting the solution and thereby growing the business.

For a major business services client, we have deployed an AI-based solution to categorize incoming queries and automate the extraction of key details from multiple systems. Such an advanced solution, utilizing state of the art AI and machine learning approaches would usually create distrust among process owners and employees. However, in this case, introducing OCM early on enabled our client to increase employee participation in developing the necessary training. Using gamification made the process more accessible, resulting in a greater sense of team ownership, a positive impact on the AI solution’s performance, and the emergence of new ideas for improvement and further development.

Another organization has felt the benefit of combining OCM with IPA within its cash collections function . The approach involved combining RPA, AI, and analytics into a single cash collection assistant solution that dynamically generates a human-like voice and that automatically makes collection calls in 24 languages, with local accent variants. This effectively automates the first-call part of the cash collection process. By using state-of-the-art AI capabilities for natural language processing (NLP) and business process automation, this solution enables the whole cash collections process to be reimagined. In this project, OCM had to be extended even beyond the organization itself, and out to accommodate external stakeholders and their own culture of work.

Case studies in context OCM INTELLIGENT PROCESS AUTOMATION


Critical success factors for OCM


Effective OCM will help organizations achieve adoption levels that are essential to meeting ROI targets on digital transformation programs that include IPA. It achieves this, first, by factoring in all the areas and people in the organization that will be affected by the project, directly and indirectly; and second, by taking steps to minimize any negative impacts of the change.

In order to make sure that the new automation solutions will be implemented successfully across the organization and that employees will actually use them, companies should follow change management best practice and tailor their approach to the unique challenges presented by technology, corporate culture and external uncertainty. The following examples of best practice in OCM can help to achieve the best possible benefits of an AI and IPA strategy by addressing the “people” side of change – all while making a significant difference to the bottom line:

  • Integrated OCM – OCM will be most effective when integrated with project management, and when it is included right from the planning/defining phase
  • Active and visible sponsorship – organizations should provide executive leadership and change sponsorship in creating new but safe environment for employees to get used to new tools and learn different practices. Throughout the project, leaders should make it a top priority to demonstrate their commitment to the transformation process, to reward risk-taking, and to incorporate new behaviors into the day-to-day operations of the organization.
  • A tailored case for change – businesses should make the new solution desirable and relevant for their employees by presenting the big picture, outlining the organization’s goals, and illustrating how the solution will help achieve them. In other words, for each stakeholder group, the case for change needs to be given, and the “What’s in it for me?” question needs to be answered. Wherever possible, it’s best to use overwhelming evidence, including real data, compelling stories, and a clear vision.


Sustaining new ways of working drives success

The transformation of work is enabled by technology, but it relies on people not just changing, but wanting to change.

That’s why OCM plays such a critical role in ensuring that the return on investment of any major AI project will be realized. After all, technology might be considered as the easy part. It can be counted on to do as it was designed.

Conversely, the success of the project depends on how well organizations have managed to change people’s behavior, and whether the new ways of working are sustained and delivering business results over time.


About the author


Marek Sowa helps clients to transform their business operations leveraging the combined power of AI and RPA to create working solutions that deliver real business value.

Patrycja Mulder is a former change manager at Capgemini, currently leading organizations through successful digital transformations and GBS transitions.

 
Contact Business Services

To learn how organizational change management can drive AI and automation success for your organization, contact: marek.a.sowa@capgemini.com

To learn more, contact Capgemini’s Business Services at: businessservices.global@capgemini.com
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