In the next five to ten years, he anticipates multilingual AI workflows becoming standard practice in progressive African newsrooms and community stations.
Lowering the Cost and Time of Production
Drawing from his deep roots in community radio, Olufemi highlighted another major shift already underway which is production efficiency.
Small radio stations across Africa often struggle with limited staffing, tight budgets, and heavy production timelines. AI, he argued, directly addresses these constraints.
He explained that AI tools can now accelerate scriptwriting, speed up audio editing, convert government documents and datasets into broadcast-ready scripts, generate local-language versions of programmes, and improve archiving and retrieval of past content.
For small newsrooms and community broadcasters, this could be revolutionary. What once required days of manual work can increasingly be completed in minutes, freeing journalists to focus more on editorial judgment, field reporting, and audience engagement.
In Olufemi’s view, the real story is not automation for its own sake, but capacity expansion for under-resourced media houses.
From Guesswork to Audience Intelligence
A third major shift he identified is the growing role of AI in audience insight and revenue intelligence.
Historically, many African radio stations have relied on limited or expensive audience measurement systems, often controlled by a few dominant providers. AI, Olufemi argued, is beginning to democratise this space.
With AI-driven analytics, stations can now better understand listener preferences, analyse engagement patterns, tailor programming more precisely, and make smarter advertising and revenue decisions.
This transition from intuition-based programming to data-informed broadcasting could significantly strengthen the sustainability of African radio in the coming decade.
The Ethical Imperative: Keeping Humans in the Loop
While enthusiastic about AI’s potential, Olufemi repeatedly returned to a central caution saying human oversight must remain non-negotiable.
He stressed the importance of what technologists call human-in-the-loop systems. In journalism terms, this simply means that broadcasters, editors, and producers must retain final editorial control over AI-generated outputs.
Verification, he noted, must happen at every stage.
To illustrate, he referenced the development of the Goloka AI system for smallholder women farmers. Even after the system generated translations, the team still sent outputs to language experts in Kenya to confirm accuracy and cultural nuance.
In a nutshell, what he meant was that “AI can assist, but humans must validate.”
Transparency as a Trust Safeguard
Another safeguard Olufemi emphasised is radical transparency with audiences.
Broadcasting has always depended on trust. In the AI era, he warned, that trust could erode quickly if listeners feel deceived.
Radio organisations, he advised, should clearly disclose when AI-generated voices are used, the extent of automation in programmes, and the sources of AI-assisted content
If a station uses voice cloning, for instance, it should not present the output as live human broadcasting. Misrepresentation, even unintentionally, could damage credibility built over decades.
Transparency, in his framing, is not optional, it is institutional self-preservation.
The Need for Clear Protocols and Data Protection
Beyond transparency, Olufemi called for formal AI protocols within broadcast organisations.
Traditional radio already operates with well-established procedures for research, call-ins, and editorial workflows. AI, he argued, requires the same level of structured governance.
Key areas he highlighted include verification protocols for AI-generated content, data protection and anonymisation standards, clear internal guidelines on AI usage, and cross-sector collaboration when working in unfamiliar languages or domains
Importantly, he warned against the assumption that professionalism alone is sufficient protection. Without written policies and technical guardrails, even well-meaning newsrooms can make costly mistakes.
What Excites Him Most: Radio as AI’s Natural Partner
When asked to summarise his biggest source of optimism, Olufemi did not hesitate, he noted that radio may be the media sector best positioned to lead the AI transition.
For decades, radio stations across Africa have been quietly building vast archives of local language recordings, voice datasets, cultural expressions, idioms and proverbs, and community narratives.
These archives, he explained, are precisely the raw materials AI systems need to function effectively. In other words, African radio is not merely a consumer of AI, it is a critical supplier of the linguistic and cultural data that powers it.
He argued that radio can shape the direction of AI rather than simply adapt to it.
He also expressed strong excitement about AI’s role in democratising data storytelling, pointing to Dataphyte’s Nubia AI as an example. Tools like Nubia can convert datasets into ready-to-use audio, video, podcast scripts, and infographics, dramatically lowering the barrier for data-driven journalism across the continent.
What Worries Him Most: Over-Automation and Deepfakes
According to Olufemi, his optimism is tempered by two serious concerns.
The first concern is the risk of over-automation. Olufemi warned against the temptation to automate the very elements that make radio powerful, which is human emotion, storytelling nuance, and cultural expression.
