In the first wave of artificial intelligence (AI) adoption over the past four years, companies had a relatively simple equation. They would sign up to OpenAI’s ChatGPT, Microsoft’s Copilot, or Google’s Gemini, upload large amounts of data, automate repetitive tasks, and move on.
But as AI becomes embedded into core operations such as market research, customer relationship management, and logistics, some companies are increasingly questioning who controls the intelligence behind the technology.
Increasingly, these companies are deciding that relying entirely on external AI providers is no longer sufficient. Instead, they’re investing in what tech executives call “sovereign AI.” It’s about building or deploying AI systems in a way that allows organizations to maintain control of their own data, infrastructure, and AI models, rather than relying entirely on third-party platforms.
This shift comes as companies are increasingly concerned about data privacy, intellectual property (IP), and regulation as they entrust sensitive internal information to AI.
Unlike consumer AI tools that process much of the information in public cloud environments, Sovereign AI allows businesses to store sensitive corporate and customer data within secure private infrastructure. This helps businesses comply with local privacy laws and data residency requirements.
It also gives companies more control over how their AI models are deployed, reducing their dependence on a single technology vendor while preventing proprietary information from being used to train public or competing AI systems.
According to a recent report by NTT Data, a global IT services and consulting provider, the top reasons for organizations to invest in sovereign AI are to gain competitive advantage, increase bargaining power, and build internal expertise.
According to the survey, 35 percent of chief information officers cite these factors as their primary motivations, ahead of meeting country-specific regulatory requirements (26 percent) and simplifying the IT environment and reducing operational risk (23 percent).
This trend is mainly evident in areas where reliability and data confidentiality are important. According to NTT Data, global interest in sovereign AI is being led by the public sector, followed by healthcare, mining, natural resources such as oil and gas, and manufacturing.
In government, AI is increasingly interfacing with national records, public services, and in some cases national security systems. In the medical field, used for diagnosis, treatment planning, and clinical research, the question of where patient data is stored and processed is becoming increasingly important.
“At the heart of everything I do is confidence that the AI and data we have is consistent with the laws and cultures of the countries and is used responsibly in everything we do,” Alan Turley-Jones, chief executive officer of NTT Data’s Middle East and Africa division, told Business Daily in an interview.
“Citizens and organizations really want the data used within these AI models to be used responsibly.”
Analysts say the concerns are even greater for companies that have a competitive advantage in their information. This is compared to the rest of the AI users who may be using public AI tools every day without giving much thought to where their prompts are handled.
For companies in financial services, e-commerce and retail, and digital media and entertainment, proprietary data and advanced information systems drive performance, pricing, and customer loyalty.
“For many companies, their data is their intellectual property, and that’s what differentiates them from their competitors,” says Turley-Jones. “If you’re using AI in a corporate environment, you need to make sure that that data is protected.”
“Employees also need to understand what information belongs in a protected environment and what is available in the public domain.”
This debate is becoming increasingly important in Africa as governments develop AI regulations and companies accelerate investments in emerging technologies.
Approaches to sovereign AI vary widely around the world. In the European Union, for example, investment is often driven by regulatory requirements regarding privacy and data protection. In some parts of the Middle East, national strategy and technological independence are stronger drivers. However, cost is also an important factor for African companies.
Building cutting-edge AI models from scratch requires vast amounts of computing power, thousands of specialized graphics processors, vast amounts of electricity, and highly paid AI researchers, making it prohibitively expensive for most organizations.
Instead, many companies are building applications on top of existing open source models, such as Meta’s Llama and Mistral, or integrating powerful AI models through application programming interfaces (APIs) provided by companies such as OpenAI and Anthropic.
This strategy gives organizations greater control over their data without incurring the significant costs of developing underlying AI systems from scratch. For Kenya and the continent as a whole, Turley-Jones argues that the success of sovereign AI will ultimately depend on investment in digital infrastructure.
“AI requires a lot of data and a lot of bandwidth, so we need continued investment in connectivity. We need data centers to run these large language models and other AI platforms,” he says.
“The importance of cyber cannot be underestimated. Ensuring organizations have an appropriate cybersecurity posture is critical to ensuring that all work related to AI is done responsibly.”
