Hello, Thought Leaders, We have something special for Episode 50: We will discuss Artificial Intelligence, Business Models, and Barriers, based on Gartner’s categorization as the most influential technology trend in the 2023 CIO Agenda.
Let me start by saying this episode is a consequence of fascinating interactions with Thought Leaders like you in public forums I attended in Houston and Austin, TX, this January and February.
To begin, let’s discuss the 2023 Gartner CIO Agenda Report. Over 2000 WW IT Executives were interviewed belonging to sixteen Industry verticals.
Q1: Which Technologies do CIOs think are the most likely to be implemented by 2025?
IT Leaders were asked to pick from 14 choices.
The #1 Choice in all industry segments was Artificial Intelligence.
No surprise, Jose!
Q2: Where do CIOs think investments will go in 2023?
IT Leaders were asked to pick from 10 choices.
Artificial Intelligence was the #4 choice based on the number of votes.
Hmm, interesting…
Lessons learned on adopting AI in Enterprise Customers
Think of this; Artificial Intelligence is a disruptive technology. It provides new ways to process data, respond to events, and build content for organizations: it requires essential efforts to create, develop, maintain, and support these solutions.
Everyone sees the value of using and adopting it, but only some have the organizational maturity to embrace it.
As a result, it is beneficial to talk about the different Artificial Intelligence Business Models. I will illustrate the many obstacles Enterprise organizations face.
Am I explaining myself?
Yes, Jose, let’s go!
Let’s do something hilarious. I’ll ask Chat-GPT to show up and give us a hand.
What can ChatGPT show us about Artificial Intelligence Business Models?
Many of you know that I am a big fan of Chat-GPT.
I have already published several Episodes about it. (Ep42, Ep43, Ep49).
This time, I want to share my interaction with ChatGPT about Artificial Intelligence Business Models and try to make an analogy about making Pizza.
I will cut and paste the interaction, so you will see how ChatGPT has improved by building tables and responding to multiple categories.
Lessons Learned by Interacting with ChatGPT about AI Business Models.
As the table shows, each business model represents a different level of business disruption, each harder for the organization to adopt.
Our current scenario is to evaluate new functionalities that can be enriched with AI and to build a powerful IT-Platform to support new business models and customer preferences.
This is what Gartner recommends: Build your upgraded IT platform based on Composable Applications connected by Application Program Interfaces (APIs)
However, all AI business models have some level of IT Enterprise adoption.
Here are a couple of recommendations for different types of business models.
| Business Model | Customer Profile |
|---|---|
| Home Made (on Premise) | A Business model that makes sense where the customer typically is a large organization with large amounts of sensitive data that cannot be stored in the cloud due to security or compliance concerns or organizations that require a high degree of control and customization over their AI infrastructure. AI on-premises can also be a good fit for organizations with high technical expertise and resources to manage their own AI infrastructure. · Think of Government (Military, national security, Law enforcement), · Healthcare (protecting sensitive patient data), · Financial (processing large amounts of sensitive financial data and meeting strict regulatory requirements), or Manufacturing processing large amounts of industrial data and integrating AI into existing production processes. |
| AI Infrastructure as a Service | AI Infrastructure as a Service is an excellent model for organizations that want the benefit of cloud-based AI deployment but also want to retain control over the AI algorithms and models. Typically, the showstopper is the need for more technical skills. In many cases, those skills are acquired by creating a small organization that develops AI piloting solutions inside the organization and gaining experience in models, algorithms, and governance via an iterative approach. Andrew Ng (AI Thought Leader) strongly supports this maturity model. · Healthcare, There are several scenarios where Customers need to process large amounts of sensitive patient data (electronic health records, among others) while retaining control over the AI algorithm and models. · Financial Services, same scenario, customers need to process a large amount of sensitive data but keep control of the algorithm and models, such as new payment solutions. · Digital-born organizations, often, this AI business model provides the most flexibility to develop innovative ways to build value for tech-based organizations. |
| AI Platform as a Service | The typical use cases for AI PaaS are organizations that want to develop and deploy AI applications and models rapidly but need more technical expertise or resources to build and maintain the AI infrastructure. AI PaaS can be a good fit for organizations that want to focus on developing and deploying their AI algorithms and models while leaving the management of the AI infrastructure to the solution provider. In addition to the previous niches, I would like to mention the following: Retail, there is a trend in the Retail industry to develop and deploy AI applications and models for customer behavior analysis and personalized marketing. |
| Dining Out (SaaS Solution enriched by AI) | The typical use cases for SaaS solutions enriched with AI are organizations that want to quickly and easily leverage AI capabilities in their business processes and operations but need more technical expertise or resources to build and maintain their AI infrastructure. This approach is often evolutionary rather than disruptive, so the risk of adopting it is much smaller than other business models. Everybody. Enterprise and consumer. (Including myself). |
One final comment, in any business model, the goal is to improve your capability to respond and adapt to your ecosystem in real time without increasing your business and organizational risk.
I got you, Jose. Any final thoughts?
Yes, just one.
Often, Enterprise Customers decide to partner with a Managed Service Provider (MSP), so they can be focused on business needs. At the same time, the MSP stays focused on the tactical side of implementing and supporting disruptive technologies and digital transformation initiatives.
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