In a time where data drives decision making and innovation, the journey of artificial intelligence [AI] has transformed from a technical concept to a cornerstone of modern business strategies. And today, we’ve reached the point in the evolution of data’s ‘AI-dentity’ that demands careful navigation to ensure that organisations are able to unlock real business value, but also maintain ethical and responsible practices.
Climbing Maslow’s AI hierarchy
Phil Anderson, Sales Manager for Digital Business Solutions at Datacentrix, places significant focus on having the right building blocks in place to manage risk versus deriving business value.
Anderson parallels Maslow’s hierarchy of needs to a business’s journey with AI, emphasizing the need to build foundational layers. These include responsible and ethical AI policies, robust data management and governance, thoughtful platform choices, and human factors, all essential for driving sustainable innovation. The current market reflects this, as most organizations struggle to scale beyond the proof of concept [POC] stage. This challenge not only hinders business value realization but also poses significant risks to reputation and legislative compliance.
“Before deploying advanced AI capabilities, companies must ensure ethical and transparent practices, balancing innovation with the human impact on customers and employees. We must take responsibility for ensuring that AI enhances the human experience and generates outcomes free from unfair bias.
Anderson further highlights the risks of unfettered AI advancements, referencing the rise of publicly available AI tools that operate without sufficient guardrails and protections. “It’s not just the dark web anymore; AI’s risks are surfacing in public platforms every day. This makes it crucial for African organisations to adopt a robust stance on ethical AI now, ensuring their risks are managed.”
The Unsung Hero – Data Management
Data management and governance often take a backseat in AI conversations, yet they are critical for success. Arno Hanekom, Digital Strategist for Digital Business Solutions at Datacentrix, underscores this point: “In the past few months alone, as Datacentrix, we’ve engaged in over 80 discussions with clients on AI, and rolled out a number of proof-of-concept projects. Everyone wants AI, but few are prepared to address the personal and invasive nature of data management.”
However, this process can be simplified by taking a stepwise approach, managing metadata and creating scalable models. “The goal here is to make data governance less daunting while building collaborative models with our clients,” says Hanekom.
Choosing the Right Platform
Companies are facing a choice between product-specific AI extensions and deploying enterprise-wide AI platforms to cover wider scope challenges. While the former is quick to implement and has pre-defined use cases, it lacks the scalability of the latter.
“Enterprise-wide platforms provide the flexibility to govern, manage and exploit data across the organisation, ensuring many different use cases can be managed to the same standards,” explains Anderson. “The key is to carefully evaluate use cases and make smart choices on the chosen platform on which to deploy. In today’s market, OEM licencing models are moving and changing every day so it is important to have a partner that can help you dissect this landscape and cut through the complexity.”
The Human Element in AI
Despite its technical underpinnings, AI remains a profoundly human endeavour. “Our clients value trust and credibility,” Hanekom notes. “Technology is effectively just a byproduct of effective communication and collaboration. Thus, companies should foster a culture of critical thinking, problem-solving and cross-functional collaboration to ensure that their AI implementations resonate with human values and organisational goals.”
Scaling MVPs to Business Value
Scaling minimum viable products [MVPs] and proofs of concept [POCs] into enterprise-wide solutions remains a persistent challenge in AI adoption. “Why do so many AI projects falter at the scaling stage?”. He emphasizes that companies must resist being easily swayed by the shiny outcomes presented in today’s demonstrations and POCs. Instead, they need to address early how these projects impact organizational policies, processes, and the people involved. These factors are crucial to overcoming barriers to scaling and achieving full value realization.
“By addressing these organisational barriers. For example outdated policies and inconsistent governance practices, right at the outset, then the path to scale becomes significantly smoother.”
A Future Powered by AI-dentity
“The evolution of data’s AI-dentity is as much about technological prowess as it is about ethical stewardship and strategic foresight. At Datacentrix, we are committed to guiding our clients on this journey. We ensure that their AI solutions are as responsible as they are transformative,” Hanekom concludes.