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Using Ecosystem Thinking To Build An AI-Powered Enterprise


It is now obvious that we need to start thinking about ecosystems that serve both our customers and the internal processes of our businesses as AI awareness and available technology have developed. In this post, I’ll discuss three ways ecosystem thinking—thinking in terms of the group of organisations and systems that carry out business processes—can assist you in creating an enterprise powered by AI.

Functional Capabilities Mapping

Mapping the interactions and connections among the many functional systems in your firm is the first step in putting ecosystem thinking into practises. Review your whole business process to determine how your ecosystem of components will need to work together to satisfy that process, rather of investing in segregated capabilities that are intended to be connected later when necessary.

For instance, business intelligence and dashboarding systems now rely heavily on data exploration and very few regressions; they are not typically AI-aware. Similar to data lakes, data lake deployment is not designed to prevent data duplications in the future because the data may be required by several enterprise departments to power AI and business intelligence (BI) systems. Additionally, more businesses are adopting customer data platforms, although these programms frequently do not include machine learning operations (MLOps) or the integration of business applications.

Although quick to install, this capability-driven deployment may make it more difficult for an organisation to successfully integrate AI into its business operations. The end-to-end process being mapped out should make it simpler to anticipate and deal with these problems, which will make it simpler to operationalize AI.

Therefore, when it comes to the individuals employing them, an ecosystems approach may identify whole processes and their interlinkages. Design thinking, the next phase of our strategy, will be built on this understanding.

Think creatively

Design thinking is essential to comprehend the top-down AI use cases that can help the organisation, as I explained in my previous essay. I think the best way to develop a holistic vision for AI integration is to involve stakeholders from all departments in a process that is results-driven.

Design thinking considers the viewpoints of stakeholders and what they require for success. It entails recognising the issues that must be resolved, comprehending the situation in which they arise, and investigating alternative fixes that satisfy the demands of the organisation.

Therefore, design thinking can enhance the business process perspective that we created in the first stage. Together, these two processes support the alignment of business requirements and guarantee that AI integration is done in a way that is relevant, valuable, and usable for the end user. For instance, let’s say you examine the need to give better customer experiences and learn that personalization, recognising when a consumer is disengaged or disappointed, and figuring out when they are likely to respond to an offer are crucial.
With these requirements in mind, you may now create a combination of digital tools designed to collect zero-party data and create generic AI models that must be connected in real time with all customer channels. However, the use of AI models in conversation is not limited to digital channels; email and phone calls can also be used.

Integration of Technology

How we view technology is the third and last aspect of ecosystem thinking. In my opinion, creating an agile AI-powered organisation must result in the data and technological ecosystem becoming simplified. We do have to consider reducing data silos and establishing a single, authoritative source of truth when we transition to a microservices-based design. Effective AI requires high-quality data.

There are options for choosing data systems that allow for sufficient extensibility without necessitating difficult interconnections. For instance, software that combines business intelligence and AI model hosting rather than requiring them to be separate can help to simplify the technology architecture and streamline the data operations. By doing this, you might be able to simplify your company’s data management and integration processes, which account for 45% to 60% of most analytics investments.

Simplifying the environment is a difficult undertaking, especially in light of legacy systems, specialised programming, and the overall expense of change management. Technology is always developing, enabling two-way integration behind the scenes, making reconciliations much more tolerable. Therefore, I believe that for any important endeavour, gradual simplification should be a component of the enterprise architecture (EA) governance process.

Despite the existence of sophisticated technologies, understanding enterprise data flows and tracking data history is a difficult task today. In addition to integrating various data sources to support better AI adoption, simplification can also lead to improved compliance and less risk.


To sum up, using an ecosystem thinking approach can help us take a step back and sort through the complex network of players, systems, and processes. This is why I think it’s essential to apply ecosystems thinking when creating a company that uses AI. Enterprises may make sure that AI is included into the overall business plan and not only perceived as a collection of stand-alone technological components by using the three-pronged approach of functional systems mapping, design thinking, and simplicity. This may make it possible for businesses to fully utilise this revolutionary technology.

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