AI has been generating a great deal of excitement around its potential to transform various sectors and industries across South Africa. Despite all the buzz, the business value of AI will only be fully realised when enterprises evolve beyond simply consuming the technology to wielding it for competitive advantage, because this is where the magic happens. In other words, AI’s true enterprise power is leveraging it for projecting and acting upon trends – such as proactively uncovering shifts in customer purchasing behaviours and preemptively pivoting to meet forecasted opportunities ahead of the competition.
Most local enterprises have significant AI ambitions — almost 70 percent of organisations in Sub-Saharan Africa are deploying cloud and AI technology to improve productivity and drive growth. However, for these businesses to truly start developing a competitive AI advantage, they first need to lay a solid foundation for AI transformation. A recent global survey, commissioned by HPE and conducted by Sapio Research, provides critical insights as to where current gaps lie, that can help local business leaders create a strong AI strategy[i].
The survey shows that the majority of organisations have thus far focused on tactical projects around organisational efficiency rather than strategic initiatives that promote growth[ii].
The same research also reveals that enterprises are overly confident in their readiness to shift from tactical AI projects to strategic ones. Most notably, fewer than half of enterprise IT leaders reported having a full understanding of enablement needs across the complete AI lifecycle. As the foundation for any successful technology initiative, AI or otherwise, is fully understanding the needs, this is a significant deficiency.
Within that deficit, two of the most critical gaps occur in the areas of talent and technology. For the latter, this is specifically around the compute, storage, and networking infrastructure required for supporting AI. To help enterprises overcome these disparities, let’s dig deeper on each and discuss how organisations can respond effectively.
Gaining the right talent
When considering talent for AI projects, enterprises seem to hold some fundamentally invalid assumptions about the breadth of individuals required at the table. Companies in the Sapio survey named the obvious cast of characters, including the C-Suite, IT leads, AI/ML engineers, data scientists, and network managers, among others.
However, less than a quarter have legal teams involved in their business’ AI strategy conversations and, similarly, only about a third have looped in their HR department. This mirrors generally held views that legal, compliance, and ethics are the least critical elements for AI success — selected by only 13% and 11% of research participants respectively.
Of course, this is a serious blind spot. From a legal and compliance perspective, new regulations are in the pipeline worldwide for which the requisite expertise is necessary to ensure an AI initiative complies. For HR, the ability to fill such critical positions relies upon strong representation on each AI initiative team to prevent talent acquisition and retention bottlenecks that lead to delays or even outright project abandonment.
Recognising IT infrastructure gaps
Like talent, enterprises are also overly confident in their AI-related technology capabilities, according to the Sapio report, as 93% of IT leaders say their network infrastructure is ready to support AI traffic. Further, 84% indicate their systems have enough flexibility in compute capacity to handle the unique demands of AI applications at scale.
Yet, the certainty breaks down upon deeper examination. For example, 40% or fewer of IT leaders fully grasp the unique networking and compute needs for either AI model tuning or inferencing. Clearly, this poses a danger for ensuring AI applications have the technology resources required.
Take a pilot-to-production approach
To discover what talent and technology your enterprise needs to move from tactical to strategic AI adoption, start by pursuing the well-accepted path of using smaller pilots to uncover the gaps within your talent and technology pools.
Pilots also provide the opportunity for balancing cross-functional business and IT teams to determine what gaps need addressing. Regardless, it’s imperative that every functional area — business and IT — be represented right from the start, to ensure sufficient expertise for identifying and addressing blind spots and roadblocks during the pilot and when transitioning to a larger production initiative.
In this way, AI initiatives can be shaped appropriately to ensure insights gleaned from them are fit for purpose and solve critical problems, as this ultimately enables delivery of the envisioned business-outcomes results.
Infrastructure: go with private or hybrid clouds
From a technology infrastructure perspective, it’s tempting to consider starting a pilot in the public cloud. However, utilising public cloud platforms introduces significant latency into computing processes, preventing AI models from operating at the necessary speeds for completing real-time computations at scale.
This makes a public cloud approach unattractive for an enterprise AI initiative as it will ultimately necessitate moving to a private cloud and retooling your AI applications, creating considerable inefficiencies that markedly delay production rollouts and substantially increase costs.
To gain both the capacity, performance, and scalability required, it’s recommended that enterprises start with private cloud infrastructure at the outset. Private clouds are designed to handle the unique workloads of an inherently more complex strategic AI initiative.
In addition, private clouds are more cost-effective as you can begin with a solution sized for early piloting phases and then grow seamlessly to include the appropriate supercomputing solutions as you move into production. This approach enables you to avoid retooling costs and ensure the fast, efficient rollouts necessary for gaining business advantage in the marketplace.
For enterprises with AI pilots already underway in an existing public cloud, the most effective way to add private cloud is using a hybrid cloud approach. A hybrid cloud provides you with a centralised interface for managing both public and private clouds, significantly reducing administrative costs.
Open, flexible, and adaptive fuels AI innovation
Regardless of how your journey begins, plan to adopt open systems solutions for your infrastructure. This will provide your AI initiative with the ability to use the best tools for any given circumstance, rather than being locked into a specific vendor’s offerings.
Even better, look for solutions that are also cloud-agnostic, as this permits IT staff to use a unified management interface for monitoring every AI workload, no matter where they are, or which cloud they reside in.
Most importantly, choose a solution with an adaptive architecture that can scale rapidly and appropriately as your AI maturity progresses. This will enable harnessing vast amounts of both public and private data to fuel analytics applications and AI model development, from training to tuning.
Taking the next steps
With common gaps illuminated, and tips on how to close them, South African enterprises can take the next steps in their AI journeys. By constructing a solid talent and technology foundation, they can transition from operational to strategic AI for making the most of this exciting AI era.
[i] Architect an AI Advantage, Sapio Research, January 2024
[ii] Ibid.