The growing need to have a deeper understanding of the connections within Ecosystems has taken me into the world of Artificial Intelligence (AI) with the ability to quickly search for connected areas that “make” ecosystems.

So what is the value of AI when you are researching a subject? I have found applying a selected “natural language generation” technique as helpful.  It has accelerated and streamlined my research, making any analysis faster, it has been sometimes fairly puzzling in some of the outputs but I feel you are gaining from “real-time” data and keyword associations that provide new insights. The end results will certainly help shape my thinking into the future of Ecosystem Management.

For the past six or so weeks I have been looking into Ecosystems and one of my strands of research took me to “Natural Language Understanding” and I read an article by Mark Seall, Head of Digital Communications at Siemens called “How AI is shaping the future of marketing communications” and it got me curious. I got in contact with him and he encourage me to dig a little deeper by using the Siemens AI tool as I have a collaboration agreement in place with them.

Ecosystems are made up of many connected areas

Applying AI to my Ecosystem work has given me a new way to relate”what makes up ecosystems” and especially learning different ways of looking specifically at the links between innovation and ecosystems.

I have been applying AI through access to this interrogation tool on the following aspects of “Ecosystems”. I generated different results for:

• Ecosystems
• Innovation Ecosystems
• Business Ecosystems
• Ecosystem Management

I then have extended this out to cover off searches into

• Knowledge Management with a specific ecosystem enquiry,
• Digital Platforms and
• Sustaining Innovation.

I wrote about the initial AI work on my posting site paul4innovating.com with a recent article ” Innovation Ecosystem Understanding through an AI-driven approach”

Let me repeat some of this article to build more context.

Initial output for “Ecosystems” in general

I narrowed down the general use of “Ecosystems” in my searches to more Ecosystem-related or orientated to Business are still providing interesting triggers like “Novel Ecosystems”, “Ecosystem Models”, “Ecosystem Service”, “Ecosystem Thinking” and “Ecosystem Design”

In my early rounds, I decided in taking out “Sustainability”, “Circular Economy”, “Life-Cycle Assessment,” to gain a better amplification of the points above. These different searches have given some good food for thought on future approaches to “Ecosystems” Both outputs or rounds offer different approaches to Ecosystems.

Then investigating the Outputs for “Innovation Ecosystems”

As for “Innovation Ecosystems” that yielded a veritable host of strands of associated thinking. These included “Value Networks”, “Transferring Knowledge”, “Diffusions of Innovation”, “Network Design and Theory”, “Co-creation, “Platform Design”, “Competitive Positioning,” “Dynamics of Innovation” and “Innovation Impact” among some others that have taken my thinking in new directions.

For this post, I have added an output visual from the tool to show these.

The size of the bubbles is the “relative”  number of related counts (websites, posts etc) giving the greater association with ” Innovation Ecosystems”

Innovation Ecosystems output (v4)

Then I found comparing “Innovation Ecosystems” with “Business Ecosystems” really separated them in some unique ways.

What I did find was that much as “Innovation Ecosystems” was certainly a rich data set, the “Business Ecosystems” one was much richer. This is where I pick up from by focusing on Business Ecosystems

Business Ecosystems output (v4)

Again, the size of the bubbles is the “relative”  number of related counts (websites, posts etc) giving the greater association with “Business Innovation” This is a little misleading on actual content size but to get the total bubble content on one page you need to click on the name, within the tool, to see the actual count.

The different colours refer to breaking out different subjects into focus relevancy, including trending areas, niche, mainstream etc., etc.

So what do these AI-generated outputs tell me?

Clearly, areas that have importance to understand when discussing or researching ecosystems. This could be an end result or platform understanding that will enable ecosystems, or to areas that need to be applied to ecosystem thinking or design.

Another very useful design of this specific tool is offering search recommendations on what you should do more or less of.

I have gone on with additional versions, learning to screen out certain keywords that are mainstream, such as Digital Transformation is an (accepted) result of applying Business Ecosystems.

Mapping back to Ecosystem Management

At the moment I am mapping back to exploring “Ecosystem Management” and looking at the “returns on content” in different visual ways of relative importance to Ecosystem Management, for example:

You have to be careful, like on any tool, you can keep playing but arguable your knowledge and the AI thinking behind the tool improves but you do have to stop and think about how you relate AI outputs into what you as a “human” are wanting to learn and apply. More to what and where you want to invest your knowledge and experience, in this case on Ecosystems to guide and advice on where to focus.

So I now need time to absorb and reflect on what I am learning here

The end results will certainly help shape my thinking into the future of Ecosystem Management.

As I have already mentioned in my previous post I have found growing insights and triggers. Applying this learning and converting this into “future understanding” is my next step.

 

 

**Siemens has a collaboration with Storyroom.ai. Storyroom provides a set of unique datasets and AI to meaningfully connect concepts, ideas or thoughts, to drive awareness and engagement.  Storyroom is a venture-backed AI startup, based in California.