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The automation ecosystem – unlocking the full potential of an automation estate

Marek Sowa
Oct 05, 2023

As organizations continue their journey into intelligent process automation powered by data and AI at scale, it’s crucial to view automation as an interconnected ecosystem. Prioritizing reusability, scalability, and cost-effectiveness will pave the way for an agile, efficient, and resilient future.

For well over a decade, businesses have recognized the indispensable role of automation tools in optimizing their operations. These tools have evolved from basic scripts and Excel macros to sophisticated enterprise-grade automation platforms, serving as the bedrock of operational efficiency and a testament to technological advancement within organizations.

But before delving into the captivating world of automation-driven business landscapes, let’s take a moment to reflect on the journey to where we are with automation today.

In the past, when business or IT systems fell short of meeting evolving needs, organizations would introduce additional tools, such as robotic process automation (RPA), to patch the gaps. When extensive system modifications were infeasible, they layered on low/no code applications. When creating new business experiences was the goal, a suite of tools like conversational AI or natural language processing (NLP) entered the scene. To infuse AI capabilities, IT departments had to enable adoption of prebuilt AI services through APIs. This list extends to encompass numerous technological components, all contributing to the realization of “straight-through processing” within organizations.

The question remains: should organizations persist in constructing large automation technology assets in isolated silos, or should they prioritize consolidating their entire automation ecosystem into a unified estate? Or seek improved methods of collaboration and integration to improve agility, resilience, the number of transformative projects, and lower total cost of ownership (TCO)?

In this article, we will introduce three pivotal mindset shifts that leaders must consider when shaping the future of intelligent automation within their organizations. These aspects are often overlooked when organizations adopt an automation-as-an-ecosystem approach, emphasizing the entire enterprise automation estate over individual components.

There’s more to automation than a single bot

It can be tempting to believe that a small automation script in Excel, a desktop-based robot, or a data transformation in a report suffices, as long as it serves its primary purpose. However, beneath the surface lies a complex web of dependencies between the tools that constitute the enterprise automation estate.

Consider the case of an Energy Generation & Distribution company engaged in a large transformation program. Their finance and accounting departments relied heavily on a single extremely complex Excel macro developed and carefully maintained over the years. This seemingly solitary automation was, in reality, a vital cog in the larger automation machinery. Hundreds of lines of code, integrated security, API connectivity, and complex data processing built into a single reporting Excel sheet. Such interdependencies become increasingly prevalent as automation matures, while also introducing new business risk and technological debt within an organization.

Historically, automation projects have often started with a single challenge: a tedious task, a repetitive process, or a specific business bottleneck. But as automation maturity advances, every automated function inevitably interacts with, relies on, or impacts another business process and associated technologies underpinning them.

For instance, an RPA bot designed to process invoices might rely on a conversational AI tool to gather missing information from vendors. While growing in popularity and self-service design, such model introduces a new set of technical and governance changes required for long-term success. As businesses scale, these small technical interdependencies multiply, creating a large web of interconnected tools, all playing their part in the larger automation estate.

The quest for reusability and total cost of ownership

In the fast-paced business and technology landscape, reinventing the “solution and tool design” wheel for every automation need is impractical, particularly with the emergence of AI-driven innovation triggered by large language models (LLMs) and AI.

Organizations must prioritize governance, security, reliability, and reusability to harness the potential of new technologies in their automation estates. This involves creating automation tools and components that can be reused across different processes and departments, enabling faster deployment, adherence to best practices, and reducing redundancy.

Take the example of an Asia-Pacific-based Financial Services Company establishing an automation center of excellence driven to increase their automation maturity, modernize their operating model, and ensure they were ready for a wave of AI-related project demands from the business.

When Capgemini introduced its STARDUST maturity methodology, the team realized that their point-based and siloed approach to automation hindered wider adoption, governance, and cost oversight across their diverse technology stack. To address this, they formed a unified Automation Design Authority responsible for setting and enforcing standards, guidelines, best practices, and roles to enhance reliability, scalability, and reusability.

Figure 1: Capgemini’s STARDUST maturity methodology

Capgemini’s STARDUST maturity methodology

With growth imperative for any forward-looking organization, automation solutions should not merely cater to current business and IT needs but also adapt to future challenges. They must be scalable in tandem with the organization’s growth trajectory, avoiding potential bottlenecks, and offer a favorable total cost of ownership over time. They also need to factor in expenses related to maintenance, upgrades, training, infrastructure, and potential replacements.


Intelligent process automation has evolved far beyond its initial definition of robotic process automation (RPA). It now encompasses a wide array of technologies and integrations with enterprise platforms, data estate, cloud services, and AI-driven capabilities such as intelligent communication processing, advanced and predictive analytics, and applied AI. Yet, many organizations treat automation as isolated silos, missing out on the potential insights and efficiencies that can be unlocked through interconnected automation.

The leadership of a European-based Automation Center of Excellence of a Life Sciences organization recognized the need to enhance automation technology interconnectivity and adoption to enable business stakeholders to benefit from an entirely transformative technology portfolio. They discovered that numerous underlying technologies, while individually solutioned and connected for specific projects, lacked predefined, prebuilt, and scalable integration paths. By introducing definitive interconnection routes, they made these underlying technical services visible to business stakeholders, enabling new automation projects, legacy automation migration, and enterprise-wide innovation.

True benefits from an automation-rich landscape can only be realized when integration is integral to the strategy. Whether integrating legacy systems with new tools, connecting RPA with AI, or ensuring that low code applications communicate effectively with the business and IT estate – highly reusable integration paths must be at the very core of your automation strategy. A well-defined, interconnected, and governed automation landscape serves as the foundation for growth and transformation opportunities.

Conclusion – shaping your automation estate for a unified purpose

Each automation tool has its distinct role, but they should all align with the unified business strategy. Mere possession of these tools is insufficient; they must seamlessly communicate, synchronize, and integrate on an enterprise-wide scale. This holistic approach not only enhances operational efficiency but also fosters strategic agility, resilience, and lower total cost of ownership.

As organizations journey into the realm of advanced and truly intelligent process automation, fueled by data and AI at scale – it’s crucial to view automation as an interconnected ecosystem, where each part, no matter how small, plays a critical role in the larger landscape of business processes. Prioritizing reusability, scalability, and cost-effectiveness will pave the way for an agile, efficient, and resilient future.

This article is published in the new edition of our Innovation Nation magazine. Read more from our special feature on “Automation and the data-powered organization” and download the full magazine.

Meet our expert

Marek Sowa

Data & AI for Enterprise Management leader
Marek Sowa is head of Capgemini’s Intelligent Automation Offering & Innovation focused on adopting AI technologies into business services. He leverages the potential hidden in deep and machine learning to increase the speed, accuracy, and automation of processes. This helps clients to transform their business operations leveraging the combined power of AI and RPA to create working solutions that deliver real business value.