An AI Ecosystem Framework for Strategic Project Success

This post re-examines the concept of ecosystems around organizations from an AI perspective.  It explores ideas around an ‘AI ecosystem’ and discusses impacts for strategy and project management.

Review: What Is an Ecosystem?

AI ecosystemAn ecosystem in biology is a geographic area where plants, animals, and other organisms, as well as weather and landscape, work together to form a bubble of life. In business, government, and all forms of organizational life, there is also an ‘ecosystem’, although it is not so much defined in biological terms.

An organizational, or business ecosystem, is the overall supporting environment for an organization. For example, in industry, the ecosystem would include such parties as workers, transportation networks, suppliers, service providers, and resellers and dealers.

The ecosystem can also be extended to such stakeholders as regulators and enforcers, as they will have an impact on the environment (a proxy for ecosystem) within which the organization does business.

Business ecosystems vary, depending on the industry structure and related factors. Business ecosystems feed a value chain in the industry. For AI, it is more of a digital value chain, but it largely works similarly to any type of industry value chain. It increasingly seems that in order for companies to realize value from AI, they need to leverage an AI value chain in some way.  It’s an infrastructure strategy problem.

The next section delineates one take on the parties that make up the value chain in an AI ecosystem.

What Is an AI Ecosystem?

AI technology has evolved into a multi-player universe – or ecosystem. Here’s a take on the key players and what they do to add value:

  1. Foundation model builders – These are the organizations that build the core AI functionality that, when provided a set of data, learns and can then generate responses to questions about that data. The core functionality is the essence of AI; it is the simulation of human intelligence processes by machines.
  2. Data platform providers – These organizations provide software solutions that enable businesses to develop and deploy AI-powered applications. The solutions include capabilities for managing data, developing applications, a run time environment, and a user-friendly interface. Top AI data platform providers include the Databricks Lakehouse Platform, Vertex AI, MATLAB, Azure Machine Learning Studio, and SAS Visual Data Mining and Machine Learning.
  3. Hyperscaler innovators – This term implies the massive scaling of AI. AI is only effective to the extent that it is fed by massive amounts of data, and it requires massive computing power to sort through that data and identify the myriads of patterns almost instantaneously. The biggest players in this group include Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), IBM cloud, and Oracle.
  4. Academic institutions – Academic institutions provide a strong research component to the AI community, and can be of help to anyone else in the AI ecosystem, as well as users and organizations trying to leverage AI to advance their organizations. Top institutions in the AI space currently include Massachusetts Institute of Technology (MIT), Stanford, Georgia Tech, University of Michigan, and Columbia.
  5. Application providers – These organizations provide AI as a Service (AIaaS). They provide specific applications to enhance functionality with AI in areas such as language translation, image recognition, credit scoring, and e-commerce.

Strategy Implications of an AI Ecosystem

ecosystem strategy

The key for organizations is to identify which are the key pieces of the AI infrastructure that they need to put into place. Depending upon how much they want or need to scale, they will have some combination of these players in the AI ecosystem. In the case of large digitally driven businesses, they will need all.

AI forces businesses to rethink themselves. They need to look at where AI can have the largest impact on their overall value chain. Here are just a few of the areas where AI can have high impact:

  • Product/Service Capabilities – Is there an information component that would add value? Is there an information component that could be enhanced with AI?
  • Operational Efficiencies – Is the organization structured to take full advantage of the potential of AI? What pieces of the organization might need to be reconfigured, or even eliminated? What new organizational components might need to be added to the structure to gain full advantage from AI?
  • Market Focus and Reach – How are we segmenting the market now? How could we segment the market using AI? Can AI help to adapt to rapidly shifting market needs, so to better fill the needs of shades of gray among customers?

I previously posted about the idea of an ecosystem strategy. One key takeaway is that in a digital world, there are almost infinite possibilities for mixing and matching to create value.

One of the key areas to think about is the value chain for AI. Where is value added currently, and how can it be enhanced with AI? Are modifications to the value chain needed to deliver that?

Even Porter’s Five Forces framework is worth considering here. The list of players in the AI ecosystem – foundation model builders, data platform providers, hyperscaler innovators, academic institutions, and application providers – has some commonalities with the Five Forces.

Project Management Implications of an AI Ecosystem

A growing number of projects are evolving out of the move to build an AI ecosystem. Embedding AI into the organizational infrastructure and culture takes projects!

Here are some ways that project management plays a role in the implementation of AI:

  • PMO – Is there expertise related to AI? Does there need to be an AI competency in the PMO? Are there services the PMO provides that can be better done using AI? Are there services that the PMO no longer needs to provide because of the availability of AI? What can the PMO contribute to the AI ecosystem of the organizaiton?
  • Portfolio – Is there a thrust toward AI that needs to show itself in the portfolio? Are there specific projects related to AI, or parts of every project related to AI? In what ways can AI be used as a tool to hep manage the portfolio?
  • Program – Should the strategy behind the program be modified? What new opportunities for the program exist with AI? What approaches to managing the program should be modified with the use of AI?
  • Individual projects – In what ways can projects be implemented more efficiently using AI? What planning techniques can be enhanced using AI? What about risk management, budgeting, tracking, and reporting?

In addition to the above, much is being written about how AI is increasingly reshaping the job of the project manager and PM careers. Antonio Nieto-Rodriguez has written much about it and has a seminar planned – see AI-driven Project Management Revolution.

Conclusion and Further Resources

This post has re-examined the concept of ecosystems around organizations, explored a new one – an AI ecosystem, and discussed ideas on impacts for strategy and project management.

How is AI impacting your strategic and project management activities?

To add some further perspective, I recommend this short (< 4 min) video, Defining the AI Ecosystem.  It has a technical, political, and security but illustrates the points clearly and efficiently.

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