Speaking at a conference hosted by Commonwealth Bank of Australia in Sydney on Tuesday, Sam Altman said he expected entry-level office jobs to disappear much faster after the launch of ChatGPT in 2022.

Instead, he said the reality has been different because many jobs still depend heavily on human interaction.

“I’m delighted to be wrong about this,” Altman said during a discussion with CBA chief executive Matt Comyn. “I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened.”

Altman added that he now understands why the disruption has been slower than expected.

“I now think I understand more about why it hasn’t, and I’m obviously grateful but that is an area where my intuitions were just off,” he said.

The OpenAI boss explained that while AI tools can handle technical tasks, many people still prefer dealing with humans directly. He said he once experimented with using AI to reply to Slack and email messages but later returned to answering some personally.

“We really do care about people,” Altman said. “We really do care about our interactions with people.”

That experience, he said, changed how he thinks about the future of work and the role AI will play inside companies.

“I don’t think we’re going to have the kind of jobs apocalypse that some of the companies in our space advocate or talk about,” he said.

Even so, several large companies have already linked job cuts and restructuring to AI adoption. Firms including HSBC, Amazon, Standard Chartered and Commonwealth Bank of Australia have said automation and AI tools are changing staffing needs in some departments.

Matt Comyn said AI would likely lead to smaller teams in some parts of the economy, although workers may also progress faster as technology handles routine tasks.

CBA has been investing heavily in AI and staff training as banks prepare for wider adoption of the technology. According to the bank, it plans to spend about A$90 million on reskilling programmes while annual technology investment has reached A$2.4 billion.

Altman also said AI technology is advancing faster than many businesses and institutions can absorb. While AI tools have improved rapidly, he believes enterprise adoption is still at an early stage.

He said OpenAI had been “roughly right” about the pace of technological development but “pretty wrong” about the social and economic consequences.

The remarks come as OpenAI prepares for a possible stock market listing in the United States. Reuters reported last week that the company plans to confidentially file for an initial public offering in the coming weeks.

The report said OpenAI could seek a valuation of about $1 trillion and raise at least $60 billion, which would place it among the world’s most valuable technology companies.

The venture, called The Deployment Company, brings together 19 investors, including TPG Inc., Brookfield Asset Management, Advent International and Bain Capital. SoftBank Group and Dragoneer Investment Group are also involved.

People with direct knowledge say the new company is valued at about $10 billion, not counting the new capital raised, while OpenAI will keep control of the business.

OpenAI wants its tools used inside more companies, not just tested. So it will place its engineers, who will help redesign workflows, automate routine tasks and ensure wider use of its software, directly within organisations backed by these investors.

This approach changes direction from simply selling access to software to focusing on hands-on deployment. It is closer to a service model, where companies pay not just for tools, but for implementation and ongoing support.

The investors backing the venture control more than 1,000 companies between them. That gives OpenAI a ready pipeline of clients without relying on long sales cycles. It also means faster rollout across sectors.

OpenAI has committed about $500 million upfront, with the option to increase that to $1 billion later. The rest of the funding will come from private equity firms over the next few years.

Interestingly, OpenAI is offering investors a 17.5% annual return, and if the venture doesn’t meet expectations, it will cover the gap, creating risk. On a $4 billion commitment, the shortfall could run into hundreds of millions each year if returns disappoint.

The development comes after Anthropic secured about $1.5 billion for a similar initiative. Its backers include Blackstone Inc., Goldman Sachs and Hellman & Friedman, who plan to deploy AI tools across their own investment portfolios.

Both companies are trying to prove that their technology can deliver value inside large organisations even as they move closer to potential public listings.

OpenAI is on track for about $30 billion in annual revenue this year, and at the same time, heavy spending on infrastructure could push losses as high as $14 billion.

“South Africa is not short on ambition when it comes to artificial intelligence. What we are short on is execution,” announces Joshua Harvey, head of Growth at Specno, South Africa’s leading digital innovation agency, the company responsible for launching South Africa’s very own Innovators Den.

While global benchmarks show South Africa is around 35–40% behind the United States in AI readiness, enterprise data reveals an even wider gap in execution, with AI implementation rates in South Africa sitting at roughly half the level of the US:

“This gap reflects not a lack of will, but differences in skills, data infrastructure, organisational alignment, and the integration of AI into core business strategy” adds Harvey.

He argues that South Africa’s AI challenge stems from a number of execution barriers, not a single missing piece. These barriers are well documented in recent national and academic analyses:

Skills Shortage and Workforce Readiness

Last year, SAP reported findings that South Africa faces a critical shortage of AI-related skills, which threatens to limit the country’s competitiveness and the ability of organisations to realise value from AI technologies. Without coordinated investment in training, certification, and workplace upskilling, this gap will widen rather than close.

Organisational and Data Readiness

Successful AI implementation is not simply about acquiring tools; it requires robust organisational infrastructure and data readiness.

Research on AI adoption frameworks shows that readiness factors, including data quality, executive leadership support, IT capacity, and available resources, are core determinants of whether AI initiatives succeed or fail.

The study also shows that where organisations lack integrated data systems or strong governance structures, AI pilots often stall and fail to scale.

Low Adoption Despite Acknowledged Value

Even where business leaders recognise the benefits of AI, adoption lags. Studies of South African organisations reveal that many executives understand AI value in theory but are constrained in practice by limited IT maturity, risk aversion, and organisational culture factors that inhibit more transformative adoption.

What the United States is doing Differently

“U.S. firms and institutions have aggressively pushed AI into operational workflows, talent development, and business strategy. Even amid challenges, including debates about deployment scale and workforce impact , American companies maintain strong investments in practical AI applications, cross-functional teams, and data architectures that support industrialisation” says Harvey.

In the U.S., a concerted focus on problem-first deployment, where AI is aligned to cost drivers and operational impediments, has influenced both innovation and productivity.

While adoption is still uneven across sectors, the integration of AI into core functions such as supply chains, customer service, and decision support is demonstrably more advanced than in South Africa.

What can South Africa do to catch up?

Harvey believes that we cannot work from an aspirational future state:

“We must benchmark honestly against where we are today and commit to concrete shifts in capability and practice.”

AI literacy at executive and board level must improve, but this must be matched with practical implementation skills. From data engineers and machine learning operators to product managers who can translate business needs into technology outcomes.

South Africa’s strongest opportunities lie in sectors such as financial services, healthcare, energy, and logistics;  areas where inefficiency is measurable and improvements deliver real value. AI must address tangible, locally relevant problems that matter to the economy and citizens.

Government, Industry, and Academia must Collaborate

“Filling the skills gap and strengthening data ecosystems requires coordinated action across all sectors. National initiatives that support training, certification, and research collaborations can accelerate readiness and ensure South Africa’s workforce is prepared for the demands of AI-driven economic participation. South Africa stands at a pivotal point. The next 12 months will determine whether we remain followers, or whether we narrow the execution gap and build AI systems that are robust, ethical, and commercially viable in our context” concludes Harvey.

Businesses looking to operationalise AI responsibly, pragmatically, and at scale are encouraged to engage with Specno, a local digital innovation agency focused on turning complex technology into viable, production-ready solutions that solve real-world problems.

What gets far less attention is the layer that determines whether any of that technology actually works in real-world operations, language.

Modern defense operations are inherently multilingual. Coalitions, allies, humanitarian partners, intelligence sources, and civilian populations all operate across languages and dialects.

Yet as AI adoption accelerates across defense and government agencies, one risk remains largely unaddressed: operational miscommunication caused by poor or uncontrolled translation.

According to new 2026 research from LILT, an AI translation company used by governments and enterprises in high-stakes environments, the gap between AI adoption and language governance is widening, and the consequences extend far beyond brand voice or customer experience.

Language Is the Invisible Infrastructure of Defense

Defense communication today moves faster and across more borders than ever before. Mission updates, threat assessments, humanitarian directives, procurement documents, and crisis communications are exchanged continuously across agencies and allied partners.

The problem isn’t a lack of AI. It’s a lack of guardrails around how language is handled at scale.

LILT’s nationwide survey of more than 400 professionals responsible for translation and localization found that 96% say translation quality is mission-critical, yet only 57% believe their organization maintains a consistent voice across languages.

In civilian contexts, inconsistency leads to confusion. In defense contexts, it leads to misaligned actions, delayed responses, and eroded trust between partners.

AI Is Already Embedded – Whether Agencies Admit It or Not

The survey also reveals that AI translation is no longer experimental:

• 79% say AI translation is part of a broader organizational AI transformation, led largely by IT teams
• 81% plan to increase their use of AI translation in the next two years
• 49% are already using large language models like ChatGPT or similar tools for rapid translation

The implication is clear: AI is already shaping how information moves inside defense-adjacent organizations. But adoption without governance creates risk.

Why Hardware-First AI Coverage Misses the Point

Much of today’s “AI in defense” coverage assumes that intelligence failures stem from flawed algorithms or insufficient computation. In reality, language is often the bottleneck.

LILT’s data shows that:

• 70% of organizations use multiple translation vendors, increasing fragmentation
• 34% report quality issues tied to vendor sprawl
• 31% struggle with communication breakdowns across providers

In high-stakes environments, fragmented language workflows mean inconsistent terminology, lost institutional knowledge, and reduced accountability.

Human Oversight Is Still the Standard, That’s for Good Reason

Despite fears of automation replacing humans, the survey shows strong consensus on one point: AI alone is not enough.

• 79% say they will continue to keep a human-in-the-loop for AI translation
• 52% rely on in-house linguists for post-editing
• Only 8% ship raw AI translations without review

This isn’t resistance to innovation but also recognition that precision, nuance, and context still require human judgment, especially in defense and government settings.

The Case for “Operational Language Readiness”

Rather than debating whether AI belongs in defense workflows, the more urgent question is how language systems are governed.

Based on the survey findings, LILT outlines four priorities defense and government teams should standardize now:

1. Terminology control – Shared glossaries across allies and agencies
2. Secure translation workflows – No consumer AI workarounds
3. Human-verified QA frameworks – AI speed with expert oversight
4. Vendor consolidation – Centralized systems to reduce risk and drift

Together, these form what LILT calls Operational Language Readiness, which is the foundation for reliable AI-enabled communication.

The Quiet Risk Leaders Can’t Ignore

As global tensions rise and coalitions become more complex, defense readiness isn’t just about hardware or software. It’s about whether every party involved understands the same message, the same way, at the same time.

AI will continue to reshape defense operations. But without disciplined language governance, miscommunication may become the most underestimated risk in national security.

Stuart Dorman, chief innovation officer at Sabio Group, delivered this sobering message to delegates at the UK National Contact Centre Conference earlier this week, challenging the industry’s approach to AI adoption whilst revealing why voice interaction could become the dominant customer service channel within three years.

“Big tech wants you to believe AI deployment is simple, just plug in and transform your operation,” said Stuart. “The reality is starkly different. Anyone can build a demo bot in an afternoon, but scaling AI to handle thousands of voice interactions at high performance levels requires expertise that many organisations simply don’t possess.”

Stuart’s session at the conference exposed the uncomfortable truth behind why so many AI implementations fail. Successful deployment demands a rare combination of skills: user experience design, linguistics expertise, AI prompt engineering, data science capabilities, systems integration knowledge, and in the contact centre, a deep understanding of operations.

Stuart explained.

“The cost of AI technology is plummeting, ChatGPT tokens have reduced 1,000-fold in just three years for example, but expertise to exploit this technology is difficult to find as demand outstrips supply. It’s particularly becoming harder in the contact centre, where organisations are struggling to find the skilled resources required to bring AI to life within a CX transformation.”

This creates a paradox: whilst underlying AI model costs decrease 10-fold annually, the specialist knowledge required for implementation becomes increasingly expensive and scarce.

Stuart added:

“Most AI deployments are internally facing ‘knowledge agents’ and these are great as a starting point, but they are only scratching the surface of what AI can do. There are lots and lots of exciting potential use cases where AI has an external focus and this is where the true potential for AI technology lies. That’s something that we’re actively discussing with our customers at the minute.”

Voice: The biggest opportunity for productivity gains

Contradicting predictions of text-based dominance, Stuart argued that voice will become the fastest-growing customer service channel over the next three years, driven by a fundamental human preference.

“That’s providing organisations stop trying to prevent customers from speaking to them by hiding phone numbers and forcing the use of poorly implemented chatbots,” said Stuart.  

“We speak at 150 words per minute but type at only 40,” he told delegates. “As AI eliminates traditional channel boundaries, customers will naturally gravitate towards voice interaction. The future isn’t about replacing phone support, instead it’s about making every digital touchpoint conversational.”  

The conference tackled a provocative proposition: can AI actually deliver superior customer experiences compared to human agents for specific interaction types? Early implementations suggest this isn’t just possible but inevitable.

“What happens to your operation when you can deliver better experiences at 10 times lower cost?” Stuart asked delegates. “This isn’t about doing more with less, it’s about unlocking entirely new service possibilities.”  

The Implementation Reality Check

For the estimated 30% of Fortune 500 companies planning single AI-enabled channels by 2028, the message is clear: success depends more on implementation expertise than technology selection.

“The organisations thriving in AI transformation aren’t necessarily those with the biggest budgets,” concluded Stuart. “They’re the ones who recognise that expert guidance through the complexity of real-world deployment is non-negotiable.”

The UK National Contact Centre Conference was held on 30th September at the QE11 Centre, Westminster.

The conference brings together industry leaders to address modern contact centre challenges and trends, including practical AI implementation opportunities.

By 2030, Africa’s AI market will surpass $16.5 billion, and 230 million jobs in sub-Saharan Africa will require new skills because automation and AI adoption are moving faster than most education systems or training programmes can keep up. 

In other words, the workers who fail to adapt may not just lose opportunities, they may lose entire careers. The AI economy does not reward tradition; it rewards transformation. 

For Africa, survival in this new world will depend less on raw labour and more on how quickly we can reskill, retool, and rethink what work means. 

The continent risks being trapped in a global skills gap that could erase accomplishments made in the past two decades.

Straight from the agricultural sector to finance, healthcare and logistics sectors, Africa’s economy is on the verge of a huge change. 

The question we should be asking is: how do we prepare Africa’s people, not just its infrastructure, for the economy that is already here? How do we reskill Africa’s workforce for the AI Economy?

The Macro-Economic Lens

The impact of automation is double-edged. On one hand, studies warn that millions of low-skill jobs in retail, clerical services, and even agriculture could vanish or shrink. With weak social protection and a large informal workforce, this could increase inequality at scale.

In countries like Nigeria and Kenya, the risk of job displacement runs close to 50%. 

On the other hand, there is opportunity. Africa’s AI market is projected to grow from $4.5 billion in 2025 to $16.5 billion by 2030. PwC estimates that if properly harnessed, automation could unlock over $1 trillion in productivity for the continent. 

This growth could create entirely new categories of work, from digital agriculture platforms to AI-driven health diagnostics. 

Here’s the choice: ignore the shift and sink deeper into unemployment, or embrace reskilling and ride the wave of digital resilience.

The Business Playbook

Companies know what is coming. A 2025 SAP report shows that:

• 85% of African organisations rank AI development skills as their top priority
• 83% specifically demand generative AI expertise
• Yet, 90% of African firms report talent shortages are already having negative business impacts and causing revenue losses

Africa’s businesses know where the future lies, but cannot find the people to build it. 

Some are beginning to act. Two-thirds of African companies say they are already introducing career development programmes to reskill workers in data analysis, digital collaboration, and cybersecurity. 

This is encouraging, but far from enough. To succeed, reskilling must move from being a one-off initiative,  a “CSR project” to becoming a core business strategy, woven into corporate culture as much as tax compliance or quarterly reporting.

Governments also have a role, as today, many African universities still teach outdated curricula that prepare students for jobs that no longer exist. 

What we need are public-private partnerships that reform education systems, build digital infrastructure, and make continuous training accessible to both urban and rural communities. Without this, even the best corporate training will not be enough.

The Human Element

Let’s bring this down to the individual. If you are a worker in Lagos, Nairobi, or Accra, the most urgent question is: what should I learn today to stay relevant tomorrow?

The evidence points in three directions:

• Technical literacy: digital tools, data analysis, and yes, AI literacy, including skills like prompt engineering.
• Cybersecurity and ethics: privacy and digital trust, these skills will be indispensable.
• Soft skills and adaptability: creativity, critical thinking, and collaboration, which no algorithm can fully replicate.

This is not about becoming a software engineer overnight. It is about building resilience, learning how to adapt, how to work alongside new technologies, and how to stay employable in a labour market that is shifting beneath our feet.

Policy and Ecosystem

Governments cannot leave reskilling entirely to the private sector. Curriculum reform is urgent, particularly in vocational and technical schools. Regional institutions such as the African Union should coordinate a continental digital skills pact, pooling resources and setting clear targets.

Public-private partnerships can turbocharge this transition either through coding academies, low-cost broadband expansion, or incentives for companies that invest in reskilling their employees. The infrastructure challenge is real, but the skills challenge is even greater.

Africa stands at a crossroads. By 2030, millions of jobs will look nothing like they do today. Some will vanish, others will evolve, and entirely new ones will be born. The outcome depends on how quickly we equip our people to reskill, relearn, and adapt.

If we fail, automation will increase inequality and exclude entire populations from the global economy. But if we succeed, Africa could emerge as a global talent hub, exporting skills and innovation to the world, not just raw materials. These and more will help reskill Africa’s workforce for the AI Economy.

Highlights – How to Reskill Africa’s Workforce for the AI Economy

• Shift from degree obsession to skill certification: employers should hire for proof of skill, not just paper credentials.
• Mandate corporate reskilling quotas: just like tax compliance, companies should be required to retrain a percentage of their workforce annually.
• Turn mobile phones into classrooms: scale affordable micro-learning apps that deliver AI and digital skills directly to workers’ devices.
• Reskill the informal economy: mechanics, traders, and farmers need digital toolkits to stay relevant, not just office workers.
• Incentivise “second careers”: provide funding and tax breaks for mid-career professionals to pivot into tech-driven roles instead of being left behind.
• Create continental skill passports: allow Africans to certify and use their digital skills across borders in the free trade era.
• Put the gender and age gap: targeted reskilling programmes must bridge the gender and age divide in tech adoption.

The choice is ours. The clock is ticking.

Speaking on the report during the launch, the Managing Director/CEO of eTranzact International Plc, Mr. Niyi Toluwalope, said as a leading fintech company, eTranzact views Artificial Intelligence not as a distant prospect but as an immediate opportunity for Nigerian businesses.

“This research bridges the knowledge gap by helping SMEs understand their current AI adoption status and what they need to compete both locally and globally,” he stated. The study also reinforces eTranzact’s expanding role as an enabler of digital growth and innovation beyond just payments, he noted. 

Explaining further, eTranzact CEO said the report offers SMEs a fact-based assessment of their AI readiness, guiding informed investment and capacity-building decisions. 

For Nigeria, he said, the findings serve as a strategic blueprint to enhance competitiveness, job creation, and innovation-driven economic growth.

Mr. Toluwalope highlighted that policymakers, financiers, and educators will be better equipped with data in the report to design support programs tailored to SME needs.

The comprehensive study, conducted over several months across key regions including Enugu, Kano, Lagos, Abuja, and Port Harcourt, highlights the current state of Artificial Intelligence adoption readiness among Nigeria’s over 39 million Small and Medium Enterprises (SMEs) and Micro, Small and Medium Enterprises (MSMEs). 

While there is evident enthusiasm for AI among SME entrepreneurs, the study also identifies critical gaps in awareness, access, and tailored solutions necessary to harness AI’s full potential.

The managing director emphasized that the report offers more than just data, it provides a strategic roadmap to foster an inclusive and intelligent digital economy in Nigeria. 

He said, “Through direct engagement with entrepreneurs across diverse geo zones, we have gained invaluable insights into their hopes and challenges. 

“At eTranzact, innovation is core to our values, and we are committed to collaborating with policymakers, innovators, and SME leaders to develop practical AI solutions that address real business needs.”

The organizations also noted that both electronic copies and hard copies are available.

Director of Programs & Partnerships at the Enterprise Development Centre (EDC), Pan-Atlantic University, Dr. Olawale Anifowose,stated, “The study adopted a mixed-method research approach, involving the use of both quantitative and qualitative data collection and analysis methods.

“The quantitative approach was implemented through a sample survey of SMEs with 5,290 responses across the 36 states of the federation, including the Federal Capital Territory. 

“The qualitative aspect was implemented through focus group discussions/summits with 212 participants across five states in the country, aimed to collect rich, in-depth information about issues in the study.”

The session themed “Beyond Compliance: AI-Powered Resilience for Nigeria’s Financial Future” held on Wednesday this week at The George Hotel, Ikoyi, Lagos.

Delivering the keynote address, Francis Ofungwu, chief executive officer, DevSecFlow, noted that the future of cybersecurity in Nigeria lies not just in regulatory compliance but in building intelligent, responsive systems that understand our local context and respond with speed, scale, and precision.

According to Ofungwu, DevSecFlow has developed an advanced Security Operations (SecOps) platform (SECOYA), designed to help financial institutions and highly regulated organizations streamline their security operations through intelligence, clarity, and control, in response to the increasing complexity of cyber threats facing Nigeria’s digital economy.

“The SECOYA platform handles critical cybersecurity functions such as threat hunting, incident response, and alert triage with greater accuracy and endurance than human analysts. It reduces manual workflows, provides 24/7 protection and still requires human oversight and governance to ensure transparency and ethical safeguards,” he said.

Speaking on the broader challenges in Nigeria’s cybersecurity ecosystem, Abdel Sy Fane, co-founder, DevSecFlow, said SECOYA was built to address these issues by embedding security seamlessly into existing workflows and powering it with AI that understands both user behaviour and operational context.

Fane added that SECOYA’s SecOps automation capabilities enable teams to focus on real threats, while its accessible design ensures that even SMEs with limited resources can deploy enterprise-grade protection at scale.

“We built SECOYA to solve the trust and collaboration gaps we kept seeing across tech teams. Real security does not just come from tools, it comes from understanding how people work and then designing systems that support and elevate them without getting in their way,” he said.

The event also featured a panel session moderated by Ofungwu, which had an impressive list of cybersecurity leaders from some of Nigeria’s top fintech companies.

The discussants included Ebuka Onyejegbu, Senior Business Relationship Manager, Moniepoint; Demi Babajide, Back-End Developer, OPay; Paul Oludele, Information Systems & Security Manager, PalmPay; and Sopriye Iketubosin, manager, Information Security, Kuda MFB.

Onyejegbu highlighted the ongoing transition from rule-based systems to AI-driven threat detection, noting that while AI adoption is on the rise, effectiveness depends on thoughtful, contextual integration.

While Babajide, on his part, emphasized the importance of multi-layered AI systems built with prevention at their core, underscoring the value of proactive security engineering from the design stage.

Bringing different perspectives to the discourse, Oludele focused on how AI is enhancing user security and the end-to-end user experience, while Iketubosin stressed the need to shift human involvement from manual detection to strategic oversight and governance.

He echoed the consensus that AI-driven systems when allowed to operate at scale can significantly improve efficiency, reduce incident response times, and enhance overall security outcomes.

As adoption spreads from the bottom-up, companies are losing control over how sensitive information is being handled, what models are being used, and who has access to what.

Unbound Security AI has raised $4 million to fix this. The oversubscribed seed round was led by Race Capital, with participation from Wayfinder Ventures, Y Combinator, Massive Tech Ventures and others including notable angel investors.

Unbound gives IT teams the visibility and controls they need to safely introduce and manage AI tools in the enterprise. Its AI Gateway plugs into commonly used tools, like Cursor, Roo, Cline or internal document copilots, and provides real-time protection, model routing, and usage analytics. 

From blocking sensitive information leakage to managing model costs and performance, Unbound helps organisations roll out AI on their own terms.

The founding team brings deep experience in both enterprise security and infrastructure. CEO and co-founder Rajaram Srinivasan previously led data security products at Palo Alto Networks and Imperva, and earlier worked on SaaS security at the onset of the AI wave. 

He teamed up with Vignesh Subbiah, a seasoned engineer and former founding team member at Tophatter and Shogun, who scaled engineering teams and platforms from seed to growth stage. 

After working together at Adobe, the two reconnected to build a system that could meet the urgent security gaps emerging in the new AI stack.

The need became clear quickly. In the early days of GPT-3.5, teams were already sending sensitive prompts into AI tools without oversight, leaking secrets, exposing PII, and consuming costly licenses with no guardrails. Existing DLP tools either blocked the tool altogether or failed to adapt to newer AI workflows.

Unbound takes a different approach. It has already prevented the leakage of 100s of secret credentials, including passwords, API keys, and connection strings, as well as more than 500 instances of personally identifiable information such as customer names, phone numbers, and patient records. 

Rather than simply blocking prompts, Unbound redacts sensitive content in real-time and reroutes high-risk requests to internal, open-source models hosted in the organisation’s cloud. This ensures employees get their answers without ever seeing a security speed bump.

The platform also gives companies fine-grained control over model access and cost. Rather than buying a one-size-fits-all license, teams can allocate premium model access to high-stakes workflows, like engineers building core infrastructure, while routing lighter tasks, like content editing, to smaller open-source models. 

Mid-market customers using Unbound have already saved more than $10,000 annually on unnecessary AI seat licenses. And when new models outperform old ones, as with Gemini 2.5 recently overtaking Claude Sonnet for certain coding tasks, Unbound allows IT to roll them out incrementally, test their effectiveness, and swap them in without breaking employee workflows.

The product is already being used by a growing base of mid-market and enterprise customers across sectors including tech and healthcare. One customer, a leading tech company, recently used Unbound to safely introduce Gemini 2.5 into production AI tools for more than 100 engineers within the same week.

“As AI tools become mainstream, enterprises are turning to flexibility and control,” said Rajaram Srinivasan, co-founder and CEO of Unbound. “They want visibility into what’s being used, assurance that their data is protected, and the ability to swap in better models as the space evolves. Unbound is the bridge that makes that possible.”

Reflecting on Unbound’s early days, CTO and co-founder Vignesh Subbiah said, “Defaulting to blanket bans on AI tools is like being in the times of GPT 3.5. Unbound enables surgical security controls into every AI request so teams can innovate freely without putting corporate secrets at risk.” 

He added, “In just a few months, our customers have prevented over 7,000 potential data leaks and cut AI tooling costs by nearly 70 percent.”

The market is shifting fast. What started as shadow IT is quickly becoming mission-critical infrastructure. Generative AI is embedded in everything from customer support to software engineering, but the tooling around it is still stuck in early-stage chaos.

CIOs and CISOs are looking for ways to support AI adoption without compromising security or governance. Unbound is building that foundation. 

“At THG Ingenuity, we see the security team as an enabler, not a blocker. Unbound empowers us to roll out AI tools to employees with confidence. Unbound AI Gateway’s data protection controls and intelligent routing have been instrumental in safeguarding sensitive data while helping us optimize costs,” says Abraham Ingersoll, chief information security officer (CISO) of The Hut Group (THG), a customer of Unbound.

“AI is projected to reach $4.8 trillion in market value for the enterprise by 2033 globally — but without proper guardrails, that value is at risk. From shadow models to data leaks, the dangers of unmanaged AI are very real.  

“We are excited to back Rajaram Vignesh and the Unbound Security team as they create a new category of AI infrastructure: one built for safety, observability and cost discipline from day one,” said Edith Yeung, general partner at Race Capital. 

“We’re proud to back Rajaram, Vignesh, and the team building a new category of AI infrastructure, one that makes enterprise adoption safe, observable, and cost-efficient from day one.”

Unbound is just getting started. The team plans to expand integrations across the AI ecosystem, deepen model routing capabilities, and support internal model orchestration for enterprises adopting open-source LLMs. Their mission is simple: to ensure every organisation can embrace AI without losing control in the process.

Other investors in the round included Alpha Square Group, Northside Ventures, Liquid2, Pioneer Fund, Scale Asia Ventures, SBXI and notable angels including Ram Shriram (founding board member at Google), Dr Trishan Panch (CSO LuminHealth), Dr John Brownstein (chief innovation officer, Boston Children’s Hospital), Taro Fukuyama (CEO, Fond), Eli Brown (CEO, Guilded, acquired by Roblox), Chris Siakos (CEO Sinefa, acquired by Palo Alto Networks), Joe Vadakkan (CISO, Ex- CRO), Zain Rizavi (Cloudflare, Ridge VC), Finbarr Taylor (CEO, Shogun) alongside other silicon valley and cybersecurity veterans.

The study, “Our Life with AI: From innovation to application,” surveyed 21,000 people across 21 countries, finding that global AI usage has jumped to 48% and excitement about its potential now exceeds concerns (57% vs. 43%, up from 50% / 50% last year).

In Nigeria, AI adoption and enthusiasm are even higher. 70% of the online population have used generative AI, surpassing the 48% global average. Moreover, 87% are excited about AI’s potential and see its benefits outweighing the risks.

“AI is starting to deliver magic at scale, making people’s lives easier and better,” said Kent Walker, President of Global Affairs, Google & Alphabet. “Today’s survey results show the more people use these tools, the more excited they get about the possibilities and about the personal, professional, and scientific breakthroughs on the way.”

AI Optimism on the Rise

The survey results indicate that optimism about AI is growing within the surveyed online community in Nigeria. Key findings include:

• Generative AI Use: Among survey participants in Nigeria, 70% reported using generative AI in the past year, which is significantly higher than the global average of 48%.
• Positive Outlook: A substantial 87% of Nigerian respondents feel that AI’s potential benefits outweigh the associated risks, suggesting a strong belief in the positive impact of AI.
• Economic Impact: A significant 81% of surveyed Nigerian adults believe AI will positively change the economy.
• Societal Impact: Furthermore, 90% of the survey respondents in Nigeria anticipate AI having a positive impact on science and medicine, demonstrating the widespread belief in the potential of AI to drive progress in these sectors.

How Nigeria Fares Versus Other Regions

Nigeria’s online population demonstrates a higher level of excitement and adoption of AI when compared to other regions. The survey indicates:

• Nigeria is among the top countries in terms of AI usage and excitement about its potential.
• This contrasts with more cautious sentiment in some European and North American countries.

AI Opportunity for Science and Medical Breakthroughs

The Nigerian online community sees immense potential for AI in science and medical advancements:

• A significant 90% of survey respondents expect AI to have a positive impact on science and medicine. This is one of the highest rates globally, highlighting the strong anticipation of breakthroughs in these fields through AI.

AI as a Tool for Personal and Professional Growth

The surveyed online population in Nigeria recognises AI’s potential to enhance personal and professional development:

• Many believe AI can make people’s lives better by boosting productivity and providing access to resources.

Pro-Innovation Sentiments Prevail

Within the Nigerian online population surveyed, there is a prevailing sentiment that supports the fostering of AI advancement rather than restrictive regulations.

This suggests that those surveyed are keen to embrace innovation.

Global Comparison of 2023 and 2024 Results

“Our latest multinational survey with Google shows that people view the continuing development of AI as important and exciting, but something that should be done thoughtfully,” said Ben Page, CEO, Ipsos. “We find that as more people use and gain experience with AI, they are better able to see how AI can help them and the possibilities that AI gives them.

These results highlight the strong enthusiasm and optimism of the surveyed online population in Nigeria about the role of AI in various aspects of life, particularly in the economy, healthcare, and scientific advancement.

The findings suggest that Nigeria’s online community is among the most enthusiastic globally about the transformative potential of AI.

Charting the Course of AI in Nigeria’s Business Terrain

Google’s approach to AI

Google has been pursuing AI boldly and responsibly for years – in fact, in 2018, the company was one of the first companies to establish AI Principles grounded in beneficial use and avoidance of harm.

Two years ago, Google unveiled its Opportunity Agenda, which shares concrete recommendations for governments to ensure AI benefits the broadest range of people possible.