What Are Key Challenges That Stand In The Way Of Automation Progress?

To drive return on their automation investments, leaders need a bold vision and a clear roadmap to build momentum and bring the organization behind them.

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To drive automation at scale, organizations need to overcome a range of business, technology, and talent-related challenges, a new study from Capgemini Research Institute has found.

Automation is revolutionizing business processes, both customer-facing, and back-office. At Amazon Go – Amazon’s brick-and-mortar outlets – shoppers can enjoy checkout-free shopping. In Volvo’s accounts payables department, 2,000 supplier invoices are automated every day.

According to the study, automation has significant potential, from the radical transformation of efficiency to new revenue opportunities.

Developments in automation technologies – from robotic process automation (RPA) to artificial intelligence (AI) – are transforming operational efficiency, productivity, and creating new revenue and customer experience opportunities.

Companies are expected to invest significantly in pursuit of these gains. For example, the market for cubicle artificial intelligence – by which we mean AI for office and administrative, sales, professional, and management jobs – is expected to grow to $48.5 billion by 2021.


However, the study claims that few organizations are currently realizing the full upside, and many are struggling to accomplish scale and value.

Initiatives are carried out in pilots and in silos, and only a few are focused on targeting the right use cases and driving them at scale.


While rule-based technologies are significantly contributing to efficiency gains today, emerging intelligent automation technologies bring in a plethora of exciting opportunities to drive customer experience as well as top-line growth.

To drive return on their automation investments, leaders need a bold vision and a clear roadmap to build momentum and bring the organization behind them.


However, leaders of organisations need to deal with key challenges in implementing automation.


To drive automation at scale, organizations need to overcome a range of business, technology, and talent-related challenges.

Key challenges


As the slide above shows, talent challenges are a major concern, with the majority (57%) saying that they struggle to find talent with a deep understanding of automation technologies.


As the program leader for accounting operations at a leading consumer products goods company says: “To start automation, you need people with some experience. Your process experts know the functional processes, but they aren’t trained, or they don’t have a mindset, that is more algorithmic in nature.”


The study also discovered that organisations also face internal resistance, with 42% saying that their employees are hesitant to embrace automation because of fear of potential job losses.

Christian Gottswinter, head of Central Business Excellence at Siemens says: “Employees need the confidence of their job environment to support automation. The adoption of an agile workforce includes eliminating the fear of new technologies. Resilience is a major topic in our VUCA (volatile, uncertain, complex, ambiguous) world and is important to create momentum for digitalization. If an organization doesn’t care, you will not be successful.


“We have to foster an agile and digital culture with open-minded people to drive value in our digital transformation.”

In this study, Capgemini Research Institute define automation as a combination of rule-based and artificial intelligence technologies.

Automation technologies included in our research
Automation technologies included in our research

It is important to note that the research does not cover mechanical automation (e.g., physical robots used in vehicle production).

Rule-based technologies automate high-volume, repeatable tasks and mimic human actions and include both IT process automation (ITPA) and robotic process automation (RPA). Artificial intelligence (AI) encompasses a range of technologies that learn over time as they are exposed to more data.

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