AI is not a futuristic concept. This is a powerful tool that shapes the way businesses operate today. The key to unlocking that potential lies in a thoughtful strategic approach that moves beyond hype and focuses on tangible results. By applying AI to the most impactful processes, organizations can drive meaningful improvements across the enterprise.
This is especially true in South Africa. New research from Dell Technologies shows that South African businesses are increasingly viewing AI as a strategic priority. The global survey surveyed 2,850 business and IT decision makers, 50 of whom were from South Africa, and found that 92% of South African businesses now consider IT to be a “key part” of their business strategy. Additionally, 32% of South African organizations report seeing measurable productivity and economic returns from their initial investment in AI.
This research was conducted in June 2025 by independent research firm Vanson Bourne on behalf of Dell Technologies. A total of 2,850 business and IT decision makers from around the world were surveyed.
“At Dell Technologies, we see AI as a vehicle for human progress. Our journey to date has provided a clear blueprint for making our AI vision a reality. This includes asking the right questions, establishing a solid data foundation, and implementing solutions that deliver real business value.”
Explore how you can create your own path to successful AI transformation.
Charting your career path: Top-down and bottom-up approaches
A successful AI strategy begins with introspection. Before getting into technology, you need to understand what makes your organization unique.
- What makes us special? Identify your key differentiators.
- What problem are we trying to solve? Define the specific problem you want to address.
- What specific processes will AI change? Pinpoint areas of your business for improvement.
This top-down approach ensures that your AI efforts are directly tied to your business goals. It is important that it is doable and strategic from the beginning.
At the same time, a bottom-up approach is required to identify common patterns across the organization. Not all use cases require unique tools. By identifying these patterns, South African companies can develop versatile capabilities that serve multiple functions, increasing efficiency and eliminating the need to create bespoke snowflakes for each new project.
Prioritize your AI projects for maximum impact
There are countless possibilities for AI applications, so prioritizing is important. We recommend evaluating use cases based on two core criteria: business value and feasibility.
business value Measure how much your project contributes to your bottom line. What will your team, product, and processes look like?
- Is it more effective?
- better quality?
- faster Do you want to do it?
- cheaper Would you like to operate it?
feasibility Assess the practical and technical feasibility of the project. This includes assessing readiness across key areas.
- data: Is the data you need available, accessible, and of high quality?
- AI model: execute Do you have a suitable model or can you build or obtain one?
- Process: can Can existing processes be adapted to AI integration?
- person: do Does your team have the skills to implement and manage the solution?
- Platform: Is the underlying technology infrastructure ready to support AI?
By plotting potential projects on a high/low business value, high/low feasibility matrix, you can quickly identify initiatives that promise the greatest return on investment and are most likely to succeed. High-value, high-achievability projects are a clear starting point.
Fundamentals of AI: Modern data management
Data is the lifeblood of any AI system. Without a solid data foundation, even the most advanced models will fail. Establishing this foundation requires a multi-step process.
- Data Discovery: Start Uncover all relevant data sources within your organization. The goal is to identify datasets that can be combined to provide rich context to AI models. Treat your data as a product that can be used in many applications and think about broad reuse.
- Data preparation: this Critical phases include data cleaning and sanitization to ensure data quality. It also includes managing data access, classification, and security to maintain governance and compliance regardless of where the data resides.
- implementation: Once you have clean and well-organized data, you can move on to implementation. This ranges from using pre-trained models for inference, to augmenting and fine-tuning models with search augmentation generation (RAG), or more complex tasks such as training a new model from scratch.
Our AI journey: From process to platform
Our own AI transformation followed this same path. We started by identifying what makes Dell special. In our case, it's a go-to-market engine, world-class service, market-leading products, and a supply chain.
From there, we determined which processes would benefit most from AI and what capabilities they would need.
- Go to market: To improve sales meeting preparation, we developed a RAG-based chatbot that provides product and solution content and advanced search/investigation.
- service: To provide more efficient and effective support, we built a hybrid AI and RAG-based chatbot to support service professionals by suggesting the next best action.
- Product development: To accelerate innovation, we introduced coding assistance tools to help software developers work faster.
- supply chain: To create a more predictive system, we leveraged hybrid AI and agent systems to enhance our supply chain intelligence.
This structured approach, moving from differentiators to processes and ultimately to specific projects, ensures that every AI effort is purposeful and aligned with our core mission.
Lessons learned on the road to AI transformation
Our journey has been one of continuous learning and adaptation. We discovered some key lessons that can guide any organization embarking on an AI transformation.
- Evaluate the project comprehensively. The success of an AI project is not determined by technology alone. Assess not only its fit within your individual AI strategy, but also against your broader business strategy and its technical viability.
- Focus on core functionality. Projects evolve, but the underlying patterns and required functionality often remain the same. Once you understand the core functionality you need, you can probably support many outcomes with some AI platform.
- Accept ecosystem partners: Enterprise AI projects tend to be complex, unique “snowflakes” that are difficult to scale. Rely on ecosystem partners to provide expertise and help standardize and scale your solutions.
- Embrace platform evolution: Technology platforms have a short shelf life. Embrace the “two-year rule,” knowing that you will need to reevaluate and adapt your platform to keep up with innovation.
By turning your vision into a structured, actionable plan, you can harness the power of AI to not only solve problems but move your organization forward. The age of AI is upon us, and with the right strategy, there are endless opportunities for advancement.
Jonathan Allmayer, Senior Account Executive, Dell Technologies South Africa
