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Tag: generative AI

  • From Generative to Agentic: How Kenya’s AI Future Will Be Built on Trust, Data and Practical Automation

    From Generative to Agentic: How Kenya’s AI Future Will Be Built on Trust, Data and Practical Automation

    Over the past two years, Generative AI (GenAI) has captured global attention, including here in Kenya, thanks to its ability to draft content, summarise reports, and offer conversational assistance.

    These tools provide meaningful value, especially for teams looking to boost productivity and ease administrative workloads. However, GenAI represents  only one part of the broader AI ecosystem. For most Kenyan organisations, the real opportunity lies in understanding how generative and agentic technologies complement rather than replace one another, and how each can be applied at different stages of digital maturity.

    The effectiveness of any AI system, whether generative or agentic, depends heavily on the quality of the data and workflows it operates on. This is where many Kenyan organisations face their greatest challenge. Manual processes, inconsistent data entry, fragmented systems and limited integrations between various systems remain common issues across sectors. These realities make it difficult to leap directly into advanced AI use cases. Without clean, organised and accessible data, even the most sophisticated AI systems can produce inconsistent or misguided outputs.

    For this reason, the most practical starting point for many Kenyan businesses is not the immediate adoption of advanced GenAI models but the digitisation and automation of core processes. Tasks such as routing customer-service tickets, reconciling mobile-money transactions, managing field-officer reports or processing sensor data may seem modest compared to futuristic AI visions. Yet these workflow-driven improvements provide immediate, tangible value. They reduce errors, improve consistency and create a clearer picture of how information flows through an organisation. As these processes stabilise, they naturally highlight areas where AI can actually make a difference.

    Once these foundations are in place, AI becomes especially powerful. While GenAI helps teams create and be more productive, agentic AI helps organisations act and be more efficient. It proposes actions, verifies them and then executes based on predefined business rules. This distinction matters greatly in sectors such as BFSI or public services in Kenya, where trust, compliance and accountability are central.

    A loan approval system powered by agentic AI, for instance, might recommend an action but will only execute it after confirming that KYC rules have been met, thresholds respected and documentation verified. This combination of intelligence and verifiable guardrails enables fast and reliable decision-making.

    As Kenyan enterprises grow more comfortable with AI-enabled systems, another important layer emerges: context. Global AI LLM models, despite their power, often struggle with the nuances of local regulations, business practices, cultural norms and sector-specific terminology. This is where contextual AI and sovereign LLMs become essential.

    These are models fine-tuned with local data and designed to operate within specific regulatory frameworks, ensuring that the insights and actions they generate reflect the realities of the Kenyan market. Such models do not replace global systems; rather, they complement them by adding the local intelligence required for accuracy, relevance and regulatory alignment.

    Beyond the technology itself, the rise of AI presents an exciting opportunity to strengthen what Zoho calls transnational localism, the idea that global technology can fuel local innovation and economic empowerment. No-code and low-code tools, embedded with AI capabilities—allow SMEs, NGOs and governments in regions like Kisumu, Eldoret or Turkana to build their own automations without needing specialised data-science expertise.

    A micro-insurer can automate risk assessments based on local claims patterns; a county office can streamline citizen services; an agritech startup can create workflows around farmer support. The result is a decentralisation of digital innovation that allows solutions to emerge from the communities that understand their challenges best.

    For leaders charting their AI journey, the path forward becomes clearer when viewed through this practical lens. The most sustainable strategy is to begin with workflow automation, build strong data foundations, introduce GenAI where it offers productivity improvements and gradually adopt agentic AI when the organisation is ready for secure and auditable automation. As maturity grows, contextual and sovereign AI models add the essential layer of local relevance.

    Kenya’s AI future will not be defined by a race toward the most advanced model. Instead, it will be shaped by organisations that take a balanced approach. Those that invest in good data, well-designed workflows, and systems designed to act responsibly will see the greatest returns, through improved customer experience, reduced operational costs, and empowered teams who spend less time on repetitive tasks and more time on meaningful work.

    Ultimately, the future belongs to businesses that embrace AI not as a flashy tool, but as a dependable partner in delivering lasting impact.

    Veerakumar Natarajan is the Country Head, Zoho Kenya

  • Music industry giants continue to battle AI deepfakes

    Music industry giants continue to battle AI deepfakes

    The music industry is fighting on platforms, through the courts and with legislators in a bid to prevent the theft and misuse of art from generative AI — but it remains an uphill battle.

    Sony Music said recently it has already demanded that 75,000 deepfakes, simulated images, tunes or videos that can easily be mistaken for real, be rooted out, a figure reflecting the magnitude of the issue.

    The information security company Pindrop says AI-generated music has “telltale signs” and is easy to detect, yet such music seems to be everywhere.

    “Even when it sounds realistic, AI-generated songs often have subtle irregularities in frequency variation, rhythm and digital patterns that aren’t present in human performances,” said Pindrop, which specializes in voice analysis.

    But it takes mere minutes on YouTube or Spotify, two top music-streaming platforms, to spot a fake rap from 2Pac about pizzas, or an Ariana Grande cover of a K-pop track that she never performed.

    “We take that really seriously, and we’re trying to work on new tools in that space to make that even better,” said Sam Duboff, Spotify’s lead on policy organisation.

    YouTube said it is “refining” its own ability to spot AI dupes, and could announce results in the coming weeks.

    “The bad actors were a little bit more aware sooner,” leaving artists, labels and others in the music business “operating from a position of reactivity,” said Jeremy Goldman, an analyst at the company Emarketer.

    “YouTube, with a multiple of billions of dollars per year, has a strong vested interest to solve this,” Goldman said, adding that he trusts they’re working seriously to fix it.

    “You don’t want the platform itself, if you’re at YouTube, to devolve into, like, an AI nightmare,” he said.

    Litigation

    But beyond deepfakes, the music industry is particularly concerned about unauthorised use of its content to train generative AI models like Suno, Udio or Mubert.

    Several major labels filed a lawsuit last year at a federal court in New York against the parent company of Udio, accusing it of developing its technology with “copyrighted sound recordings for the ultimate purpose of poaching the listeners, fans and potential licensees of the sound recordings it copied.”

    More than nine months later, proceedings have yet to begin in earnest. The same is true for a similar case against Suno, filed in Massachusetts.

    At the center of the litigation is the principle of fair use, allowing limited use of some copyrighted material without advance permission. It could limit the application of intellectual property rights.

    “It’s an area of genuine uncertainty,” said Joseph Fishman, a law professor at Vanderbilt University.

    Any initial rulings won’t necessarily prove decisive, as varying opinions from different courts could punt the issue to the Supreme Court.

    In the meantime, the major players involved in AI-generated music continue to train their models on copyrighted work, raising the questions as to whether the battle isn’t already lost.

    Fishman said it may be too soon to say that: although many models are already training on protected material, new versions of those models are released continuously, and it’s unclear whether any court decisions would create licensing issues for those models going forward.

    Deregulation

    When it comes to the legislative arena, labels, artists and producers have found little success.

    Several bills have been introduced in the US Congress, but nothing concrete has resulted.

    A few states, notably Tennessee, home to much of the powerful country music industry, have adopted protective legislation, notably when it comes to deepfakes.

    Donald Trump poses another potential roadblock: the Republican president has postured himself as a champion of deregulation, particularly of AI.

    Several giants in AI have jumped into the ring, notably Meta, which has urged the administration to “clarify that the use of publicly available data to train models is unequivocally fair use.”

    If Trump’s White House takes that advice, it could push the balance against music professionals, even if the courts theoretically have the last word.

    The landscape is hardly better in Britain, where the Labour government is considering overhauling the law to allow AI companies to use creators’ content on the internet to help develop their models, unless rights holders opt out.

    More than a thousand musicians, including Kate Bush and Annie Lennox, released an album in February entitled “Is This What We Want?” – featuring the sound of silence recorded in several studios – to protest those efforts.

    For analyst Goldman, AI is likely to continue plaguing the music industry, as long as it remains unorganised.

    “The music industry is so fragmented,” he said. “I think that that winds up doing it a disservice in terms of solving this thing.”