Rwanda

The Minister argued that publishing AI strategies and adopting governance frameworks, while important milestones, are insufficient on their own to bring about societal transformation. What is needed are capable institutions equipped with the right mandate, talent, and partnerships to convert strategic vision into concrete outcomes. She illustrated this by noting that a strategy cannot, by itself, diagnose a patient or support a farmer.

The Minister stated explicitly that 'strategies alone cannot transform societies' and that 'institutions do', framing this as the heart of the morning's conversation . She used the example that a strategy will not diagnose a patient or support a farmer, emphasising that real outcomes require capable institutions with a mandate, the right talent, and the right partnerships .

The Minister explained that Rwanda's decision to establish the Rwanda National AI Agency was not about adding another bureaucratic body but rather a deliberate investment in building national capability focused on execution and delivery. This agency is intended to serve as a national engine for deploying AI across priority sectors, expanding talent, strengthening infrastructure, and attracting investment. It builds on years of foundational work in policy, data governance, digital infrastructure, and skills development.

She stated that for Rwanda, establishing the agency 'is simply not creating another institution' but 'a deliberate investment in building national capability' . She referenced the foundations already laid, including the national AI policy, data governance frameworks, digital public infrastructure, digital skills investments, and the work of the Rwanda Center for the Fourth Industrial Revolution in agriculture, healthcare, and education . The agency is described as serving as 'a national engine for delivery' covering deployment, talent, compute, data infrastructure, investment, and commercialisation .

The Minister emphasised that the Rwanda National AI Agency is conceived not as an isolated national body but as a collaborative platform that connects local, regional, and international partners. She stressed the value of national AI agencies forming a network to share best practices and collectively address shared challenges. The agency is intended to attract partnerships and stakeholders to accelerate AI in a way that delivers genuine public value.

She highlighted that the agency is 'designed as a platform for collaboration locally, regionally, and internationally, that it's bringing together different partners and stakeholders to accelerate AI in a way that delivers real public value' . She also noted the value of being part of a network of similar bodies across the world, sharing best practices, connecting policy to implementation, research, deployment, and innovation .

The Minister challenged participants to move beyond treating responsible and ethical AI as a rhetorical aspiration and instead embed it into the actual design of AI solutions. She also raised the concern that developing countries risk remaining mere consumers of AI technologies designed elsewhere, and argued that they must be co-creators of solutions that reflect their own priorities, contexts, and languages. This requires trusted partnerships and an enabling policy environment.

She posed the question of 'how we make responsible AI happen, not just merely as an aspiration, but as an operational reality', noting that it is 'one thing to say ethical, responsible AI deployment, but how much of that is woven into the design of how we build many of these solutions' . She stressed the importance of ensuring that 'developing countries and consumers from developing countries don't just remain consumers, that we can be part of designing and co-creating solutions that reflect our very own priorities, our very own context, and also reflect our very own languages' .

The Minister clarified that the Rwanda National AI Agency is not intended to displace or duplicate existing institutions but to complement them by providing a dedicated engine for execution and delivery. The agency's role is to accelerate AI deployment across priority sectors, expand talent, strengthen compute and data infrastructure, and attract investment, all while operating responsibly and for the benefit of citizens.

She stated that 'the agency comes in to complement rather than replace existing institutions' , and described its functions as accelerating deployment across priority sectors, expanding AI talent, strengthening compute and data infrastructure, attracting investment, and thinking about commercialisation of applications .

The Minister argued that the value of Rwanda's AI agency extends beyond its national borders, as it can become part of a global network of similar bodies. Such a network would enable countries to share best practices, connect policy to implementation, and collectively address problems that are common across nations. This collaborative approach is seen as essential to advancing AI for public good at a global scale.

She noted that 'the idea is that the agency can be part of a network of similar bodies across the world' and highlighted 'the value in us being able to share best practices', 'the value in us coming together to solve for problems that are shared across the different countries', and 'the value in us being able to connect policy to implementation, research, deployment, and innovation' .

The Minister challenged the assumption that only countries with the largest AI models and biggest budgets can benefit from AI. She argued that smaller nations can still generate real value and impact if they have the right institutions, talent, coordination, and global and regional partnerships. The key is scaling innovations from proofs of concept to solutions that truly impact people.

She stated that 'it will not only benefit the countries with the largest models and the biggest budgets' and that 'there's value in even smaller countries that don't have the bigger models or quite deep pockets that they can still generate real value and real impact' . She added that to achieve this, countries require 'the right institutions, the right people, the right talent, the right coordination across the board, the right partnerships globally and regionally' and the ability to 'scale innovations from simply proofs of concept to something that is truly impacting the rest of the world' .

The Minister contextualised the forum as occurring at a pivotal moment — the first major gathering following the WSIS Plus 20 review — when the global conversation must shift from articulating digital ambitions to actually delivering on them. She argued that AI embodies this challenge more profoundly than almost any other technology, particularly in relation to governance. The moment calls for moving from commitment to implementation.

She described the forum as 'a very significant forum' and 'the first gathering following the WSIS programme, the PLUS20 review, a moment where the global conversation must evolve from a digital ambition to digital delivery' . She noted that 'a few technologies embody the challenge more profoundly than AI' .

The Minister called on participants to move beyond high-level dialogue and into tangible, ground-level impact for citizens. She stressed that all the collaborative ambitions around AI will only materialise through trusted partnerships that deliver meaningful and mutually beneficial results. The Rwanda National AI Agency is presented as a concrete step in this direction.

She expressed the hope that the agency and the partnerships it fosters would allow everyone to 'move beyond the boardroom conversations to delivering impact on the ground and for our people' . She also stated that 'all of this is going to happen if we can have trusted partnerships across the board and partnerships that can deliver meaningful and win-win results for all of us' .

The Minister raised two interlinked questions that she argued must guide the design of AI institutions: how to ensure AI augments rather than replaces human capability, and how to build institutions agile enough to keep pace with the unprecedented speed of AI's technical advances. She noted that the speed of AI development already outpaces what regulation and governance can realistically achieve, placing additional demands on institutional design.

She asked 'how do we ensure that AI strengthens human capability rather than replacing it', describing this as 'the biggest concern that everyone has with the autonomous capabilities that AI has' . She also noted that 'the unprecedented speed at which the technical advances AI has seen over the last years very much outpaces what regulation can truly do or governance can do', raising the question of 'how agile enough are these institutions going to be to keep pace with the technical advances' .

Dr. Baraka argued that the most important lesson from Egypt's experience is that an AI strategy is not self-executing and requires a clearly defined operating model. This model must answer specific institutional questions about who is responsible for coordination, regulation, capacity building, piloting use cases, measuring progress, and accountability when risks emerge. Without this clarity, strategies remain aspirational documents rather than drivers of change.

She stated that 'the most important lesson from what we are doing in Egypt and from Egypt experience is that an AI strategy does not implement itself' and that 'it requires a clear operating model' covering who will coordinate, regulate, build capacity, pilot use cases, measure progress, and be accountable when risks emerge . She referenced Egypt's first national AI strategy from 2020 and the second launched in January 2025, noting the evolution in understanding of AI's opportunities and challenges over that period .

Dr. Baraka shared Egypt's deliberate choice to adopt a governance framework and guidelines rather than enacting hard law, in order to avoid stifling creativity and innovation in the AI ecosystem. She argued that governance must be treated as an enabler of innovation rather than a constraint, and that this balance between promoting innovation and protecting society is a critical institutional lesson. Egypt's framework was developed in alignment with international standards while being localised to its context.

She explained that Egypt's choice not to have a law is 'a choice' made to 'make this kind of balance between, from one side, promoting innovation and creativity and supporting SMEs, and from the other side, having a framework, a governance framework, that will help us to protect our society' . Egypt's governance framework was aligned with international frameworks including Huderia, NIST's AI RMF, OECD ethical principles, and UNESCO, before arriving at a 'localised version of AI governance framework' .

Dr. Baraka highlighted the gap between having a well-crafted ethical charter and actually implementing its principles in practice. She argued that bridging this gap requires practical tools, and described Egypt's establishment of an AI audit lab — functioning as a sandbox — to test, validate, and ensure that principles such as fairness, accountability, explainability, and transparency are operationalised in real AI systems. This is presented as a key step in moving from theory to ground-level impact.

She noted that Egypt had a well-written ethical charter published in 2023 that was 'very aligned with all the international norms, but nothing on ground', and posed the question of 'how to move from the theoretical aligned ethical principles to real work that will be on ground' . Egypt established what it calls an 'AI audit lab', described as 'a sandbox so that we are sure that all the nice principles that we are talking about, from being fair, from being accountable, explainability, transparency, accountability, comes into reality' , with support from partners including GIZ and the African Union .

Dr. Baraka argued that while a central body is necessary for coordinating AI governance and policy, implementation of an AI strategy must be distributed across multiple specialised institutions that reach down to the grassroots level, including ministries and agencies. Egypt's experience shows that different functions — data governance, safety, investment, capacity building — require dedicated institutional homes. Centralising policy while decentralising implementation is presented as a key design principle.

She described Egypt's institutional architecture, noting that the National Council for AI was established in 2019 for centralised governance, while implementation was distributed: the Information Technology Development Agency handles capacity building and the ecosystem, the PDPC handles data governance, the Egyptian CERT handles safety and security, the Ministry of Investment handles investment, and various institutes handle capacity building . She stated that 'when we talk about the policies, about the governance, this is centralised, but when we talk about the implementation of the strategy itself, then we need to go to the grassroots, we need to go to all the ministries, we need to go to different agencies' .

Dr. Baraka stressed that capacity building for AI must go beyond the usual focus on youth and university students to include public servants and employees, who are often overlooked but are critical to effective AI deployment in the public sector. She noted that public servants often do not understand what ethical AI principles mean in practice, and therefore need targeted training. Procurement guidelines are highlighted as a particularly important practical tool, enabling public servants to write effective RFPs and evaluate AI systems from vendors.

She noted that 'when you go to the public servants and talk to them about the great ethical charter, they don't really know what does it mean', leading Egypt to focus capacity building on public employees as 'one important segment that we have to focus on' . Egypt developed procurement guidelines to help public servants understand 'what are the conditions that they have to put in the TOR so that they have the right systems' and 'what kind of success measures that they can put together' .

Dr. Baraka argued that one of the most important lessons from Egypt's experience is the need to balance national sovereignty with international partnership when adopting AI technologies. She emphasised that imported technologies must be adapted to reflect local cultural norms, values, traditions, and languages, and that this is especially important for African countries, which share many cultural characteristics. Failing to ensure this alignment risks imposing foreign values and norms through technology.

She stated that 'how to balance sovereignty with international partnership is really a very important point', noting that Egypt has 'Arabic language, cultural norms, values, religion' and must ensure that 'when we import technologies, we are sure that it reflects our norms, our values, our traditions, and our language' . She extended this concern to African countries broadly, noting that 'African countries, somehow we are alike, so we need to make sure that it reflects all our values and our traditions in whatever technologies we are importing from other parts of the world' .

Crystal Rugege described Rwanda's approach to AI as a deliberate, phased journey that began with laying strong governance foundations before moving to implementation. This included establishing key instruments such as the data protection and privacy law and the national AI policy, which were developed through the foundational work of the Centre for the Fourth Industrial Revolution (C4IR). Only after these foundations were in place did Rwanda move towards piloting and scaling AI solutions.

She described Rwanda's journey as 'a deliberate journey in trying to harness technology for the benefit of society aligned with national priorities' , and noted that C4IR's foundational work included 'laying the foundational governance instruments from the data protection and privacy law that the minister mentioned, as well as the national AI policy' . She also noted that Rwanda has 'come to this moment with AI where we see tremendous potential' having 'been really laying quite deliberate foundations towards that' .

Rugege identified compute as one of the most significant and cross-cutting barriers to scaling AI, noting that it is a challenge for the entire world but disproportionately complex for countries like Rwanda that lack domestic compute capacity. She described the ongoing debate between investing in building in-country infrastructure — which is costly — versus moving quickly by relying on cloud-based solutions. This tension is particularly acute for smaller nations seeking to scale AI applications.

She stated that 'compute is one obvious barrier and it's a challenge for really the entire world', but noted that 'it disproportionately is a complex problem for countries like ours that don't have that domestic capacity' . She described 'the debate around whether or not we should be building in-country infrastructure, the cost it takes to do that versus trying to move quickly and relying on cloud-based solutions' .

Rugege argued that investing in skills at every layer of the AI ecosystem is essential to avoid countries being permanently locked into the role of AI users and adopters rather than creators. Without this investment, nations will be unable to develop solutions that are genuinely fit for their own contexts and responsive to local challenges and opportunities. She acknowledged that building skills is a long-term endeavour requiring sustained commitment.

She stated that 'if we don't invest in skills, and we don't invest in the right tools, really skills at every layer, then we're definitely going to be stuck in this cycle of just being users and adopters of the AI and not really empowering a generation of people who can create solutions that are really fit for their context and responsive to the challenges and opportunities that are on the ground' . She acknowledged that 'building skills is a long game' and 'not something that will happen overnight, but it's something that we certainly have to invest in' .

Rugege identified funding as the biggest barrier to scaling AI, but argued that it can be addressed through the right partnerships and a coordinated whole-of-government approach. She expressed optimism that the establishment of the Rwanda National AI Agency would help align government efforts and position Rwanda strategically to attract the right partners and funding. The agency is seen as a vehicle for mobilising the resources needed to scale AI solutions.

She described funding as 'the biggest one' among the barriers, but noted that 'funding can be addressed through also the right partnerships' . She expressed excitement about the AI agency because 'it's also helped align government to have this kind of whole of government approach to how are we attacking this AI problem' and 'positioning ourselves strategically to be able to attract the right partnerships, the right partners, the right funding so we can scale' .

Rugege argued that agility in governance is essential because it is nearly impossible for government to keep pace with the speed of innovation. She used Rwanda's experience with Zipline as a concrete example of the value of being willing to pilot new technologies even when no specific regulation exists, provided the right mindset and willingness to adapt are in place. This agile approach is presented as a model for how countries should approach AI governance.

She noted that Rwanda is 'fortunate to have a government that has the right agile mindset, knowing that it's nearly impossible for government to keep pace with innovation', and that 'while you can put the right guardrails in place, you still have to be agile enough to adapt' and 'willing to test some things where there is absence of a governance framework' . She cited the example of Zipline, which 'wasn't able to actually pilot their drone delivery technology anywhere in the world, but they came to Rwanda and they found a willing partner, even though we didn't have a regulation in place' .

Rugege emphasised that developing AI skills is not a short-term endeavour but a long-term investment that must be sustained over time. The goal of this investment is to empower a generation of people who can create AI solutions that are genuinely appropriate for their own contexts, rather than simply adopting and using solutions developed elsewhere. This is presented as a fundamental requirement for countries to move from AI consumption to AI creation.

She stated that 'building skills is a long game' and 'not something that will happen overnight, but it's something that we certainly have to invest in' . She warned that without investing in skills at every layer, countries will remain 'stuck in this cycle of just being users and adopters of the AI and not really empowering a generation of people who can create solutions that are really fit for their context' .

Roberts observed that across many countries, AI has already become a visible national priority and political attention is abundant. However, she argued that the far more difficult task — building the institutional mandates, coordination frameworks, and delivery systems needed to implement these priorities responsibly — is significantly lagging behind. This gap between political attention and institutional capacity is a consistent pattern seen across UNDP's AI landscape assessments.

She noted that 'the political attention on AI is not in short supply' and that 'across many contexts, AI is already a visible national priority', but that 'the harder task of building the mandates, the systems, the coordination frameworks to deliver these priorities responsibly is really lagging' . She framed implementation as requiring answers to questions such as who leads AI deployment across government ministries, how priorities are financed, how systems are procured, which institutions supervise deployment, and where accountability sits when systems fail .

Roberts identified data as a decisive factor in determining whether AI pilots can be reliably scaled. She argued that the quality, interoperability, access, and governance of data are critical enablers of scale, and that data is also the domain where inclusion is decided — determining which populations are visible and which languages are represented. Weaknesses in data can cause even strong innovation models to stall or fail.

She stated that 'data is often a decisive factor' and that 'the quality, the interoperability, the access, the governance of the data really determine whether a promising pilot can become reliable at scale' . She also noted that 'data is often also where we see where inclusion is decided - which populations are visible, which languages are represented - and where these are weak, even strong innovation models can stall out or fail' .

Roberts warned that AI adoption is not waiting for governance frameworks to be established; it is already entering through existing procurement systems, sector programmes, and digital public infrastructure initiatives. This means that highly consequential, path-dependent decisions about AI deployment are being made before they are recognised as policy choices, often appearing to be merely technical or procurement decisions. This invisibility makes them particularly difficult to govern retrospectively.

She noted that 'AI adoption is really entering already through existing systems - your procurement systems, your sector programs, DPI initiatives - it's not waiting for us to get our national strategy and our governance frameworks and all of our ducks in a row' . She warned that 'very important consequential path-dependent decisions are being made before they're visible as policy choices' and that 'they seem like a technical procurement decision' but 'actually really arc into your overall national strategy for how you're deploying AI' .

Roberts argued that trust and safety must be treated as integral components of AI readiness from the outset, rather than as features to be added after deployment. The capacity to monitor AI system performance, detect harms, and correct systems is particularly critical in health and education applications, where errors affect people directly and almost instantly. This framing positions safety not as a constraint but as a prerequisite for equitable benefit.

She stated that 'trust and safety are a part of AI readiness - they're not an add-on once you have AI' and that 'an innovation can really only benefit everyone equally when we have that assurance in place, so the capacity to monitor performance, to detect harms, to correct systems' . She noted this 'matters most in some of the applications where we see real promise in health care and education because these are the places where errors affect people directly and almost instantly' .

Roberts argued that institutional ownership is a critical determinant of whether AI innovations can scale and deliver impact. This means having clear mandates, secured financing, and established delivery routines in place. Without these elements, even technically excellent AI innovations that have proven transformative in other contexts can fail to deliver at scale.

She stated that 'institutional ownership is very important' and that 'clear mandates, financing, delivery routines must be in place in order to see innovations really scale and deliver' . She also noted that 'AI adoption will not automatically advance human development on its own, and even AI innovations that look excellent on paper, that have been transformative in other contexts, can fail if you're not matching the investments and the innovations themselves with investments in the ecosystem into which you're deploying the systems' .

Roberts argued that AI adoption does not automatically translate into human development gains, and that the assumption that good AI innovations will naturally deliver impact is flawed. She stressed that investments in the broader ecosystem — including institutions, governance, data, and capacity — must match and accompany investments in the AI innovations themselves. Without this ecosystem investment, even proven innovations can fail.

She stated that 'AI adoption will not automatically advance human development on its own, and even AI innovations that look excellent on paper, that have been transformative in other contexts, can fail if you're not matching the investments and the innovations themselves with investments in the ecosystem into which you're deploying the systems' .

Roberts drew on UNDP's extensive AI landscape assessment work across 36 countries to highlight consistent global patterns in the challenges of AI deployment. These patterns — including lagging institutional capacity, data governance gaps, and the invisibility of path-dependent procurement decisions — demonstrate that matching institutional readiness with AI deployment is a shared global challenge. This evidence base supports the case for coordinated multilateral support and knowledge sharing.

She noted that UNDP has 'over the last three years, completed 26 AI landscape assessments in 26 different countries' with '10 more underway', and that she would speak to 'a few patterns that we've seen across these assessments that really shape whether an AI innovation can deliver impact and scale' . UNDP is described as 'present in 170 countries around the world and providing digital and AI programming and partnerships in 130 countries' .

Kamara argued from a private sector perspective that there is a major disconnect between the high-level strategy and policy conversations that dominate AI forums and the actual buyers of AI in Africa — large enterprise corporations. He noted that enterprise Africa, which represents the companies with the financial resources to implement AI at scale, is almost entirely absent from these discussions and has little understanding of or interest in AI policy, focusing instead on whether AI delivers business value.

He stated that 'enterprise Africa is never shown anywhere in any conversation' and that 'the big companies' are not in the room because 'they have to run the business' . He noted that '87% of enterprise Africa buy all their AI from vendors globally' and that 'most of the top 150 CEOs in Africa, if you ask them about AI policy, it's not very relevant - the bottom line is do we make money, does AI work for us' . He described this as 'a massive disconnect between the whole strategy conversations that we consistently have, the policy great, and the real buyers' .

Kamara argued that the AI industry is a whole ecosystem requiring a much broader range of talent than is typically recognised, extending well beyond data scientists to include mathematicians, physicists, philosophers, and AI ethics officers. He highlighted that demand for AI ethics officers is already emerging from companies in South Africa, yet universities are not producing graduates with this profile. This gap between industry talent needs and educational supply is presented as a significant challenge.

He noted that 'it's not just about the data sciences - it's about the scientists, the mathematicians, the physicists, the philosophers, and the ethics' . He described a concrete example where a client company in South Africa asked him to hire two AI ethics officers, prompting the question 'where do we find that?' and 'what university teaches AI ethics officers?' . He described AI as 'a whole industry' that 'starts from power generation all the way to application' .

Kamara argued that private sector investment — including from sovereign wealth funds — is essential to scaling AI in Africa, yet the entities that hold this capital are consistently absent from policy and strategy forums. He noted that sovereign wealth funds need to understand AI as an investable industry before they will commit capital, and that bridging the gap between AI for public good and enterprise AI is critical to unlocking this investment. He also described a concrete initiative to raise $150 million for distributed compute infrastructure to make AI accessible to smaller companies.

He stated that 'the people with the money is the private sector in Africa' and that enterprise Africa is 'never shown anywhere in any conversation' . He described working with '11 sovereign wealth funds in the continent' whose question is 'what is the industry for investment in AI' and 'can this thing help us make money' . He also referenced a distributed network project to raise '$150 million so that we can actually bring AI closer to the small companies because they don't necessarily need hyperscalers' .

Kamara argued that the absence of the private sector — particularly large enterprise corporations and sovereign wealth funds — from AI policy and strategy forums fundamentally undermines the potential for these discussions to produce actionable outcomes. Since the private sector holds the financial resources needed to scale AI in Africa, their exclusion from the conversation means that the funding and implementation capacity required to translate strategies into reality is not being mobilised. He called on the Rwanda AI Agency to find ways to bring these actors into the room.

He stated that 'enterprise Africa is never shown anywhere in any conversation' and that these companies 'have to run the business' and are not engaged with AI policy discussions . He expressed hope that 'the agency would really find a way to get these guys into the room and say, how do we help you help us to help these startups that we're trying to build' . He noted that he has 'attended over 132 sessions in the past four years' and consistently observed the absence of enterprise Africa .

Kainamura opened the panel discussion by reading a quote from Claude AI that characterised international AI conferences as producing no binding rules, realistic outputs, or agreed principles, and suggested that participants' time would be better spent watching proceedings online. He used this provocation to challenge the panellists and audience to demonstrate that the session could produce meaningful and binding outcomes, framing it as a contest between human agency and AI scepticism.

He read a quote attributed to Claude AI stating 'skip the plenary theater, watch it on UN Web TV' and that the conference 'won't produce binding rules, realistic outputs, or agreed principles' and that 'the hard questions will be deferred over months and years' . He described this as 'what AI thinks of this conference' and used it as a 'call to action for us as human beings versus this AI, to challenge this AI and for us to come out with meaningful and binding agreements and for this not to be, in its words, a plenary theater' .

Janet framed the workshop as occurring at a particularly significant juncture in the global digital governance calendar, being the first major YCIF forum following the WSIS Plus 20 review. She argued that this timing makes the discussion especially important, as the focus must now shift from making commitments to actually implementing them. This contextualisation positioned the session as part of a broader, ongoing global process of accountability and follow-through.

She described the event as 'the first YCIS forum following the YCIS Plus 20 UN General Assembly Review' and stated that 'this discussion comes at an important moment as countries move from commitments to implementation' .

Janet articulated the central problem that the workshop was convened to address: the gap between having an AI strategy and having the institutional capacity to deliver on it. She argued that strategy development, while necessary, is insufficient on its own, and that the real work lies in constructing institutions capable of converting strategic ambitions into tangible outcomes for citizens. This framing set the intellectual agenda for the entire panel discussion.

She stated that 'while many nations have developed AI strategies, the next challenge is building the institutions that can translate strategies into real, measurable public impacts' , directly establishing the core tension that the panel was convened to explore.

Janet highlighted Rwanda's specific institutional response to the challenge of moving from AI strategy to delivery, presenting the Rwanda Artificial Intelligence Agency as a model case study for the workshop. She noted that this agency builds on prior foundational work, including the 2020 National AI Policy and the Rwanda AI Scaling Hub, demonstrating a sequential and deliberate approach to institution-building. By foregrounding Rwanda's experience, she positioned the host country as both a subject of study and a potential model for other nations.

She noted that 'Rwanda is taking this step through the establishment of the Rwanda Artificial Intelligence Agency, building on its 2020 Three National AI Policy and Ongoing Initiative, such as the Rwanda AI Scaling Hub' , and described the agency's mandate as covering acceleration of AI adoption, innovation, talent development, investment attraction, and responsible deployment across sectors .

Following the Minister's opening remarks, Janet invoked the spirit of continuous progress and stakeholder engagement as guiding principles for the discussion. She referenced a paraphrase of a Martin Luther King quote about the importance of keeping moving regardless of pace, using it to underscore that progress on AI governance requires sustained effort and broad engagement from all stakeholders. This framing encouraged participants to view the forum as part of an ongoing process rather than a one-off event.

She reflected on the Minister's remarks by saying 'for us, it's to keep moving' and noted that 'she mentioned about engagement, stakeholder engagement' , invoking the spirit of a Martin Luther King quote about maintaining forward momentum regardless of the pace at which one can move .