Building AI Capability

By Wayne Madhlangobe  
   January 12, 2021

The concept of artificial intelligence (AI) becoming real predates the current definition. Classical philosophers described human thinking as a symbolic system. This observation makes sense because humans utilize symbols to communicate and represent information. The definition applies to computers as well. The ability to achieve given goals is the computational part of a symbolic system, and this is the definition of intelligence (McCarthy John, 2001). McCarthy defines AI as the

… science and engineering of making intelligent machines, brilliant computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to biologically observable methods. ” (2001). 

There is still no explicit agreement on the definition of intelligence. Given today’s advances in computational processes, we have systems that possess advanced symbolic capabilities and can achieve goals to solve real-world problems (Cattral et al., 2002).  It does not matter if we agree on the definition of AI or not, we have the computational capacity to create systems that can perceive (sense), learn, think (formulate ideas), interact, and act. As result, AI has the potential to be a game-changer for most businesses to optimize and streamline their operations. The opportunities presented by AI adoption are significant. It is imperative for organizations to pivot from an opportunistic to a strategic orientation to view AI as an enablement capability (Joshi & Wade, 2020). 


Building an AI capability requires three fundamental elements:

        a) Technology infrastructure

        b) Talent

        c) Agile processes


Technology infrastructure: 

AI is an amalgamation of many advances in mathematics, statistics, and computer science (Joshi & Wade, 2020). Advancements in computing have been impactful by significantly reducing the cost and speed of calculations. Investing in a reliable end-to-end infrastructure to manage your data assets and AI solutions must be a top priority to jumpstart AI capability. According to Joshi and Wade (2020), “This infrastructure must include support for the entire data value chain — from data capture to cleaning, storage, governance, security, analysis, and dissemination of results — all in close to real-time. It is not surprising, then, that the AI infrastructure market is expected to grow from $14.6 billion in 2019 to $50.6 billion by 2025.” Defining a technology infrastructure starts by defining your desired end state and mapping the journey to get there from your current state in the organization. This exercise will inform you how to structure your AI strategy and align it with your organization’s goals. This is the foundation of a reliable capability. It is crucial to have an agile and robust infrastructure that can change with technological advances. 


The importance of talent goes beyond AI, and it is a necessary need for any organization. It is human talent that drives innovations. We argue a strategic asset for any organization. In the AI domain, it is estimated that the need for specialized resources will continue to rise and grow by 74% annually (LinkedIn, 2020). This trend is supported by the growth observed by Indeed over the last few years (2020). Demand for AI talent is a reliable indication of adoption. A robust talent strategy is needed to compete and retain the best resources. A talent strategy must focus on attraction, retention, and training. This function needs to be ingrained with your human resources policies and functions. Talent is the support pillar of a reliable AI capability. It is imperative to have a talent strategy addressing all aspects of human resources that can adapt to demographical changes.  

Agile Process: 

AI projects are data-driven to resolve a business problem. The promise of agility and lean processes from agile methodologies is a natural fit. However, some methodologies miss the mark (Walch, 2020). An AI solution is only as reliable as the data and algorithms powering the solution. A robust organizational AI process and methodology are powerful tools to ensure the reliability of the solution. For AI projects, the CRISP-DM (cross-industry process for data mining) approach is well worth considering. CRISP is based on solid theory and can be extended to suit an organizational structure. There are several Agile approaches in the market. It is essential to understand the organizational culture and strategy before adopting any method. An agile process is the last mile function required to deliver a reliable AI capability. 

Capability building is not a trivial task. It is necessary if an organization is serious about transforming its operations, leveraging AI. Given the speed of innovation and advances in technology, it is vital to build an AI capability designed for learning. An innovative culture must be at the center of the organization with the intent of providing a fail-safe environment. It is astonishing the power of consequences in promoting current actions. Connecting the current work to future results and then measuring them closes the loops and promotes continuous learning.  All this work must be housed in a community that shares the same mission, and the organization must support and promote.


Cattral, R., Oppacher, F., & Deugo, D. (2002). Evolutionary data mining with automatic rule generalization. Recent Advances in Computers, Computing, and Communications, 296–300. 

Indeed. (n.d.). Here Are the Top 10 AI Jobs, Salaries, and Cities. Retrieved December 23, 2020, from 

Joshi, A., & Wade, M. (2020). The Building Blocks of an AI Strategy. MIT Sloan Management Review. 

LinkedIn. (n.d.). 2020 Emerging Jobs Report. 

McCarthy John. (2001, February). What Is Artificial Intelligence? KurzweilAI.Net. 

Walch, K. (2020). Why Agile Methodologies Miss The Mark For AI & ML Projects. Forbes Media LLC.–ml-projects/?sh=d98490021ea9 

Wayne Madhlangobe
Strategic AI Advisor & CTO

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