Today AI’s impact and potential value are undebatable. Many businesses have already evaluated and experimented with AI in various forms. To the extent that applications of AI are actively investigated in healthcare, recruitment, law, and government. However, the adoption rate varies across industries and companies, but AI is becoming increasingly prevalent in the business world. However, AI boost and bust cycles are seen over time, which makes potential adopters wary. On one side, there are greater success stories of AI implementation, especially within the technology and healthcare domain. There are other traditional businesses where AI adoption is much slower than expected.
Technology adoption is an ongoing process of operational use of technology, according to Neumann, Guirguis & Steiner. I prefer this definition because it clearly defines adoption as not being achieved at a single point in time. It is also emphasized for the operational success of AI, especially in the business context of AI adoption. Any technology adoption depends on not just technological advancement but also external factors such as social, economic, and environmental influences. Technological advancement, for instance, is the ease of use, efficiency, value drivers, etc.; external factors are social, economic, and environmental factors. All these factors are unequivocally applicable to AI today, evidently enforcing their effects on us today, creating an environment that demands either adoption or failure.
Given the potential impact and value of AI, enterprise AI practitioners and managers need to approach adoption in a thoughtful and strategic manner. This may involve carefully evaluating business needs, identifying appropriate use cases, and considering the ethical and social implications of AI adoption.
By focusing on operational success and understanding the broader contextual factors influencing AI adoption, businesses can navigate the potential challenges and maximize the benefits of this transformative technology.
There are several frameworks and tools that exist to support it. There are a few popular frameworks to which one can refer.
- Gartner’s AI Maturity Model: This framework provides a structured approach for businesses to assess their current AI capabilities and chart a path for AI adoption at the organizational level.
- Microsoft’s AI Business School: This framework is designed to help businesses understand AI concepts and provide guidance for AI adoption through a series of case studies, videos, and tutorials.
- IBM’s AI Ladder: This framework helps businesses to understand the key stages of AI adoption, starting with defining the AI strategy and ending with scaling AI across the organization.
- Deloitte’s AI in a Box: This framework provides a modular approach to AI adoption, allowing businesses to choose the components that best suit their needs.
- McKinsey’s AI Enablement Framework: This framework helps businesses to develop a strategy for AI adoption, define use cases, build the necessary infrastructure, and scale AI across the organization.
The main challenge with AI adoption lies in its execution, as it has remained theoretical and difficult to implement in practical situations. The primary issue is the inability to simplify complex concepts to a level that can be effectively executed.
In most AI adoption models or frameworks, best practices for organizational readiness are suggested for three key areas: people, process, and technology. It is important to note that technology readiness also includes data readiness. These three areas are often referred to as the “golden triangle” of AI adoption.
The golden triangle of people, process, and technology is widely recognized as a useful framework for analyzing socio-technical systems. Each component of the triangle – such as the development of people’s skills, the importance of processes, and the fit of technology to business needs – is equally critical to the success of AI adoption. These components are intertwined and can impact each other in numerous ways. For instance, in the AI technology context, the technology selected by a project team may influence the nature of AI tasks and, consequently, the processes to which these tasks contribute, which in turn can impact the people involved, including project stakeholders. Understanding the interplay between these three components is essential for successful AI adoption.
The data is an extension of technology in this golden triangle. The importance of data, its availability, quality, and governance are pre-requisite for the concept of a data-driven culture in an organization. The successful adoption of AI requires data to be first understood, available, and managed. The importance of data can be better illustrated by separating Data and Technology in the golden triangle as below.
The people who drive processes can decide the relevance of data. Data also determines the output of AI technology and drives the direction of AI development in an organization. To reflect all this importance of data, let us extend the triangle to a square, including data at the apex.
Stages of AI Maturity
AI Adoption within an organization depends on mechanisms that vary based on the current level of AI maturity. There are three stages of AI project maturity such as AI Experimentation, AI Adoption, and AI Production.
- AI Experimentation:
An early stage of AI, where use cases need to be identified by business stakeholders, the success criteria need to be defined for each use case and overall. Then, a strategy to execute these use cases in terms of people, process, and technology needs to be evaluated.
- AI Adoption:
A transitioning stage from research to deployment. This is the hardest phase to deploy an AI model in an operational setting. There could be several operational challenges to overcome at this stage, such as ensuring operational data is readily available, quality controls are in place, etc. Many organizations also need to gain all the required skills to operationalize AI models.
- AI Production:
This is a production phase where proven AI models are integrated with production applications.
Let us understand how interaction of these PPDT model influence AI project maturity.
At the experimentation stage, it is required to match the technology to the kinds of data available to address the business problem that stands out (model testing and selection). During the adoption phase, the link to people is emphasized, as the data which captures expert judgment of the quality of AI outputs is needed to prove the technology’s value. Once the technology is mature, the link between data and processes must be built into the organization’s MIS infrastructure to maintain the supply of operational data.
New age AI platforms have significantly reduced the challenges to AI adoption in an enterprise allowing them to be confident about adopting AI in an organization based on their AI maturity. Subex’s cutting edge platform (Such as HyperSense AI) enables enterprise customers to make faster, better decisions by leveraging AI across the data value chain. The platform allows users without coding knowledge to easily aggregate data from disparate sources, turn data into insights by building, interpreting, and tuning AI models, and effortlessly share their findings across the organization, all on a no-code platform.
8 Keys to AI Success in an Enterprise
Zabi Ulla heads products at Subex AI Labs. He comes with over 15+ years of experience in applied artificial intelligence and data science. He has developed winning AI products for enterprise customers and has successfully led digital transformation projects for multi-national clients including Lenovo, Intel, Microsoft, YouTube, Wrigley, and T-Mobile. Zabi comes from an applied statistics background and has a Master's in statistics. He has a passion for machine reasoning, causal inference, and experimental designs. Zabi has been featured as a key speaker in several industry webinars, events and was recognized as one of the Top 40 data scientists in India by Analytics India Magazine in 2019.