Through my work deploying AI systems in production-grade enterprise environments both internationally and across the African continent, I’ve learned that successful implementations require local expertise who understand resource constraints as design parameters. While global best practices from established markets provide valuable frameworks, local talent creates the innovations that make technology truly work under real-world conditions.

As Stanford’s Fei-Fei Li, co-director of the Human-Centered AI Institute, emphasizes: “The idea is to recognize that AI technology is very important and will be affecting human lives and society. There are also a lot of unknowns still to be explored in AI. How do we put guardrails around AI? How do we develop tomorrow’s AI? How do we move into the future, so that this technology can maximally benefit humanity and we can mitigate and govern the guardrails and the risks?” This perspective is particularly relevant for African contexts where responsible implementation can determine whether AI becomes a tool for empowerment or further marginalization.

Africa’s Real Problems Demand AI Solutions

The challenges facing our continent are urgent and solvable through intelligent AI deployment. Sub-Saharan Africa has 1.55 healthcare workers per 1,000 people compared to the WHO threshold of 4.45². AI-powered diagnostic agents can connect community health workers to specialist networks, providing expert-level diagnosis where doctors are unavailable.

In agriculture, where 70% of Africans depend on farming³, AI agents analyzing satellite imagery can provide personalized crop recommendations in local languages. Water scarcity affects 400 million people⁴—AI systems predicting drought patterns can transform water management from reactive crisis response to proactive optimization.

Financial inclusion remains critical with 43% of adults lacking bank accounts⁵. AI credit assessment agents understanding community vouching systems can extend services to populations Western banking models ignore.

The Brain Drain Crisis: Innovation as Retention Strategy

Africa loses approximately 70,000 skilled professionals annually⁶—a $2 billion annual loss⁷. This exodus isn’t just about salary; it’s about intellectual stimulation and cutting-edge challenges. The loss is particularly acute among women technologists, who face additional barriers to career advancement.

Erik Brynjolfsson, director of Stanford’s Digital Economy Lab, advocates using AI to augment human capabilities, avoiding the “Turing Trap” where developers focus on creating AI that mimics human behavior. Instead, “We can have humans do the things that we’re good at… and machines do what they’re good at.” This collaborative approach creates meaningful career opportunities where African professionals work alongside advanced AI systems while contributing irreplaceable human insight.

When fintech companies use AI to assess credit through alternative data sources, they’re creating innovation that attracts global attention. This innovation pipeline creates career trajectories competing with overseas opportunities.

Strategic Protocol Choices for African Sovereignty

Understanding AI governance frameworks and technical protocols becomes crucial for technological sovereignty. Meta’s Chief AI Scientist Yann LeCun emphasizes: “Open source is necessary,” because no country will “have AI sovereignty without open-source models.”

Responsible AI Governance Frameworks from organizations like the Partnership on AI emphasize transparency and accountability—principles aligning with African values. However, most existing frameworks are designed by Global North institutions and may not adequately address African contexts.

Model Context Protocol (MCP) standardizes AI-to-tool connections through JSON-RPC messaging. While enabling rapid deployment, MCP creates strategic dependency on external platforms—an important consideration in technical framework selection.

Agent-to-Agent Protocol (A2A) offers multi-agent orchestration capabilities. A2A enables complex agent collaboration with enterprise-grade security, but the proprietary nature means organizations become consumers rather than contributors to protocol development.

Constraint-Driven Architecture: Africa’s Hidden Advantage

Africa’s resource limitations drive different design approaches than those common in abundance-rich environments. Intermittent power encourages edge computing solutions. Limited bandwidth promotes efficient algorithms. Capital constraints demand measurable returns.

Experience with distributed federated learning models at scale for African enterprises reveals that constraint-driven systems often demonstrate superior resilience and efficiency. Federated learning architectures allow organizations to train AI models collaboratively without centralizing sensitive data—particularly valuable where data sovereignty and connectivity are concerns. AI systems designed to work within infrastructure limitations through event-driven architectures that handle intermittent connectivity gracefully often outperform solutions designed for optimal conditions.

Responsible Implementation: Humans First

Sustainable AI adoption requires keeping humans central to deployment. As Brynjolfsson notes, the goal should not be creating AI that “perfectly mimics human behavior” but rather designing systems where “humans do the things that we’re good at… taking care of kids and talking to each other and interacting, and machines do what they’re good at.”

Stakeholder engagement and cultural integration significantly impact adoption success. Involving existing expertise and systems in AI implementation creates better outcomes than replacement approaches. Importantly, the rise of agentic AI systems makes subject matter expertise even more critical—human experts must serve as verifiers and validators of AI-generated insights to ensure accuracy and contextual relevance.

This collaborative approach addresses talent retention challenges. Rather than viewing AI as job displacement, successful implementations frame it as capability enhancement, creating compelling career paths while building local technical capacity.

The Path Forward: Innovation as Continental Strategy

Africa’s constraints, properly leveraged through human-centered AI innovations, become competitive advantages enabling leapfrog development. The emerging generation of African AI developers—representing diverse backgrounds and perspectives—shows instinctive understanding that our path to technological leadership runs through our constraints, not around them.

By addressing real African challenges through technically sound AI deployment while creating meaningful innovation opportunities, we can transform limitations into competitive advantages. Developing indigenous ethical frameworks offers alignment with sovereignty goals, while adopting existing enterprise solutions requires careful consideration of long-term technical implications.

The choice of technical architecture and implementation approach will determine success. Organizations across the continent considering AI implementation need frameworks that balance global best practices with local technical realities. Having architected these systems across diverse African markets, I continue to work with enterprises seeking to build AI capabilities that create lasting competitive advantage rather than temporary solutions.

Africa cannot afford to wait, but we must be strategic about technical implementation approaches. Resource constraints, when properly architected into system design, create innovative solutions that serve both local needs and broader markets—but only with the right technical expertise guiding the implementation.

References:

1. International Data Corporation. (2024, August 19). Worldwide Spending on Artificial Intelligence Forecast to Reach $632 Billion in 2028, According to a New IDC Spending Guide. Retrieved from https://www.businesswire.com/news/home/20240819177906/en/
   
   
2. WHO African Region. (2022). The health workforce status in the WHO African Region: findings of a cross-sectional study. BMJ Global Health. Retrieved from https://www.afro.who.int/news/chronic-staff-shortfalls-stifle-africas-health-systems-who-study
   
   
3. World Economic Forum. (2016, May). 70% of Africans make a living through agriculture, and technology could transform their world. Retrieved from https://www.weforum.org/stories/2016/05/70-of-africans-make-a-living-through-agriculture-and-technology-could-transform-their-world/
   
   
4. Brookings Institution. (2022, March 9). Addressing Africa’s extreme water insecurity. Retrieved from https://www.brookings.edu/articles/addressing-africas-extreme-water-insecurity/
   
   
5. World Bank Group. (2025, January 14). Financial Inclusion in Sub-Saharan Africa: Overview. Global Findex Database. Retrieved from https://www.worldbank.org/en/publication/globalfindex/brief/financial-inclusion-in-sub-saharan-africa-overview
   
   
6. Mo Ibrahim Foundation. (2018). Brain drain: a bane to Africa’s potential. Retrieved from https://mo.ibrahim.foundation/news/2018/brain-drain-bane-africas-potential
   
   

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How Do We Feel About AI Now?

The perception of AI depends on an individual’s perspective of AI, says Marais Neethling,  Principal for Artificial Intelligence at Synthesis Software Technologies. Some I might view AI as the next best thing to revolutionise the world, and others as a mechanism for taking jobs away and making people lazy.

Any negative connotation can be removed by simply changing perspective. If the perspective is to focus on the negative, that will be your belief. The reality is that with any disruptive piece of technology, there will be good and bad that come from it. An increasing number of people can now easily recognize AI-generated content, raising concerns about its use without proper disclosure. When should you use it? How should you use it? Is it okay to use it?

In the same way, the value extracted from AI largely depends on an individual’s intentions and motivations. When used thoughtfully to enhance or refine your work, it can become an incredibly powerful tool. If it is used with malicious intent, that could be a different conversation. All we know for sure is that it is here, and it is here to stay, explains Neethling.

Neethling anticipates seeing five AI trends in 2025.

1. AI Agents and AI Capabilities Will Become More Pervasive

In the day-to-day lives of consumers, the adoption of AI learning and understanding comes from the need of individuals to upgrade devices. Apple, for example, recently launched Apple Intelligence which is now available on the iPhone 16. This upgraded version of Siri can perform more tasks on your phone than the existing assistant. Phone and PC manufacturers are also bringing AI straight into the desktop, enabling users to access and learn how to use it.

2. Multimodal Advances in AI

The nature of the latest models will provide different modes of media consumption. Video, images and audio. Imagine an assistant that looks at your computer screen in real-time, all the time, and can make suggestions or automate tasks while you work. Depending on what is being worked on a, the tool suggests options, logs time against tasks, and automatically tracks it. This takes away the grudging admin of your day by automating it.

3. Impact of AI on Energy Consumption

For large enterprises and companies, AI models require a huge amount of computing to run, both during training and execution. Massive data centers are required which results in the need for enormous amounts of power to function effectively.  Understanding the amount of power and electricity needed is the responsibility of all individuals to be environmentally conscious. Changes to the economy, renewables, and other types of energy sources are expected with the high demand for energy supply needed to support AI transformation, first in the AI hubs around the world, and later in South Africa.

4. Virtual Reality (VR) Advancing into Wearable Tech

VR headsets are big and bulky; however, companies are now starting to launch smaller and smaller ones. Meta and other companies are working on reducing the size of wearable computers to fit into spectacles or sunglasses. AI can be integrated into these augmented reality (AR) devices to understand what the wearer is doing in the real world. The AI should be able to identify objects and people in the wearer’s field of view and supply interesting information about the subjects in real-time. It is a bit futuristic and maybe a little creepy but has the potential to unlock new opportunities as it evolves. It’s unlikely that we will see some of this in 2025, but it is definitely something that is on the horizon.

5. Industry Use Cases for AI

The medical and healthcare industry leverages AI to provide improved patient care, from accurate diagnoses to supporting patients in managing symptoms. Multi-modality introduces an exciting new dimension, particularly in analysing X-rays and other sensory inputs. Multimodal models enhance diagnostic capabilities by integrating diverse data types, offering richer insights compared to traditional methods that relied solely on textual inputs. Although medical procedures are regulated, it does not stop individuals from getting a second opinion and using AI to help diagnose patients could prove to be accurate.

There are many more use cases for AI across industries covering many topics to improve security, automate manual processes, create content, and develop innovations. This is just the start, I can’t wait to see what’s next, concludes Neethling.

The post The Latest View on the Horizon of AI – 5 Things to Expect in 2025 appeared first on Synthesis.

Synthesis began its Microsoft Azure journey with cloud opportunities arising from customers.  Some key success stories include Synthesis’s involvement in payment modernisation and the development of PayShap, the rapid payments platform for real-time payments. The architecture required high performance and latency. The solution leverages Microsoft Azure’s cloud infrastructure to ensure robust scalability and maintains sub-second transaction processing through its distributed architecture. Globally, Synthesis has digitalised and transformed the logistical system of one the largest logistics shipping companies in Singapore. The transition evolved from a manual process incorporating different partners, departments, and countries to an automated digital platform hosted on Microsoft Azure, explains Rui.

Microsoft Azure recently hosted the Ignite session, from November 18 to November 22, 2024, at the McCormick Place Convention Center in Chicago, to share innovation on data and AI. The rise of AI agents and security was very topical. Microsoft is investing in data, AI, and some of the custodian products such as data fabric. Some of these automated tools can be used to streamline and centralise data. Orchestrated agents will be integrated with Microsoft products to enable automated day-to-day tasks, understand, and look for trends and patterns within the data. It’s like having your own personal assistant, says Rui.

Microsoft already has Copilot which enables users to use prompts to access information easily. Add in a prompt that says, “Give me a short write-up that explains what Microsoft Azure is” and within seconds, the tool generates text; “Microsoft Azure is a comprehensive cloud computing platform and service created by Microsoft. It offers a wide range of cloud services, including computing, analytics, storage, and networking. Users can choose and configure these services to develop, deploy, and manage applications through Microsoft’s global network of data centres. Azure supports various programming languages, tools, and frameworks, making it a versatile solution for building and scaling applications in the cloud.” To add to this, orchestration agents will further enable prompts that will review lengthy documents and only pull-out necessary information.

Security is a priority in discussion with Microsoft investing in tools to enable data safety. Guard rails can ensure that data is user-specific and not exposed to everyone. The sensitivity of the data needs to be considered to ensure that the right guardrails are put in place for AI agents, adding an additional layer of security advises Rui.

Everyone has an opinion of AI and what it means for future business. There are both positive and negative connotations around it. Rui believes that any malicious intent around AI will be addressed by regulations. The concept of the Industrial Revolution that came into effect resulted in the automation of machines that have replaced human involvement and streamlined processes. It was not a case of simply replacing the workforce but working in conjunction to equip the workforce with the skills to manage the machines. Replacing jobs with robots and developing skills amongst resources. Mundane tasks can be replaced by AI or robots and, eventually, there will be an equilibrium.

Think of the advancement of transportation and the concept of when cars came to be. Individuals were using horse carriages and there were jobs in farming horses and building carriages that were taken away. But at the same time, there were new jobs created with the need to build cars. There’s always this sort of equilibrium that forms a balance concludes Rui.

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Project success hinges on many factors, but the human element of development teams should not be overlooked. While the team primarily delivers code, software systems and intricate system architectures, the journey to project success can be streamlined by prioritising the soft skills and relationships amongst developers – your fellow warriors in the trenches. With the right foundations, unbreakable bonds are formed that are worth striving for, ensuring a more cohesive and productive team.

“I have gained valuable experience through years of experience, collaborating with diverse clients and enterprises, and working within several development teams. The insights within this article stem from operating in both Technical Lead and Sales Engineering roles,” says Gabriel Eisenberg, Senior Data Engineer at Synthesis Software Technologies.

Leadership goes beyond titles

Regardless of the role, be it a Technical Lead or Developer, everyone can lead by taking ownership of their domain and contributing to the project’s success. Taking on responsibility goes a long way and empowers everyone. In my experience, I’ve seen that fostering a culture where everyone embraces ownership and actively contributes not only enhances individual growth but also strengthens the collaborative fabric of the entire team.

Be on the same page

Teams perform best when communication is frequent, and individuals lean on each other for help. As my friend Lara Timm pointed out, although communication is critical, comprehension is what will ensure that the team is truly on the same page. While meetings are a wonderful way to bring the team together, it is worth taking the time to ensure that the team is aligned in these sessions. This simple act can save a lot of time over the duration of a project and can prevent large scale changes.

Often, people can be shy to speak up in meetings when they don’t understand what is expected of them. Hopefully, they will ask for clarification in private. I have found that encouraging questions in a supportive environment, while still being patient, can encourage more introverted types to open up and contribute their valuable opinions in a group setting.

Asking for help is a strength

Engaging with the team throughout the day is fantastic for alignment and ongoing communication. It also encourages asking for help when necessary. Willingly seeking aid recognises the team’s eagerness to help and foster collaboration for more effective problem-solving than solo efforts.

Asking for help is critical when consulting across industries and enterprises, as you will be exposed to numerous technologies which can be overwhelming. You cannot possibly know every technology and its nuances as there is a massive scope of technologies that is ever-increasing. Matt Turck’s 2023 MAD (Machine Learning, Artificial Intelligence & Data) Landscape shows the sheer scale of available technologies.

By asking for clarification or assistance from a knowledgeable teammate, the time to understand and implement a solution can be shortened drastically. There is humility in asking for help and doing so helps build relationships with the team. Hopefully, by getting help, one can empower others too.

Turning to project managers for aid

Not all members of the team are technical, but their contributions are just as valuable. Such roles include project managers, product owners, business analysts and others.

Project managers play a crucial role as intermediaries between the business and the development team, adeptly navigating the inherent human challenges in projects. Leveraging their people and administrative skills, project managers ease project progression by addressing obstacles and streamlining communication channels. For example, they can advocate for the team when developers may struggle to present their work or need to engage with business or client stakeholders, alleviating these burdens and allowing developers to focus on their core tasks.

Despite the more subtle nature of their impact, project managers are instrumental in ensuring project success and should be leaned on by developers. Their involvement is critical to supporting project alignment with budget, schedule, and scope.

Own up to mistakes early

Mistakes must be raised early. The sooner this is done, the quicker the team can join and manage the problem. This is especially important if it is a large-scale problem with far-reaching impact.

The reality of development work is that there will always be problems and mistakes that happen – even in the most established companies. We have seen examples of this at Meta (Facebook, Instagram, and WhatsApp), GitHub, GitLab, and many others.

While there might be frustrations from the team when mistakes happen, if they are not raised as they occur, they could compound into greater challenges.

Mentorship

One of the most meaningful things I have done in my career is mentoring juniors technically and in terms of their conduct in the workplace. As we’ve already discussed, this field can be overwhelming, particularly for someone new to it. Through mentorship, juniors can accelerate from an uncertain start to more stable productivity and growth.

In my opinion, technical mentorship is best conducted within a project. Not only will the senior have context of the junior’s challenges but also their day-to-day experiences. By investing time into their growth, the mentee will feel more comfortable. In turn, they will also be able to take on greater responsibility for the project which can ease the load across the team. Additionally, by taking the time to engage with your juniors (or really anyone in the team) you can notice their gaps which might be surprising. 

For example, I was working on the development of an enterprise scale data platform with a highly skilled team. One of our teammates was a graduate student who had studied Actuarial Science and through conversation with him, I realised that he did not have foundational systems thinking. Systems thinking is emphasised throughout engineering studies and is critical to understand technical architectures and the flow of data throughout the various systems. 

Without this core skill, he found it challenging to understand how the various pieces of the overarching architecture fit together. We took the time to delve into systems thinking and to his credit, he picked it up very quickly. To this day I support that he is one of the most intelligent and gifted people I have ever met, and my hope is that the time I took to address some of his gaps will allow him to take on greater challenges and accelerate his growth, skills and achievements over time.

Cameras on

In this era of remote working, many individuals opt to keep their cameras off, leading to a loss of crucial non-verbal communication cues such as body language and facial expressions. This absence of visual feedback significantly affects our ability to understand others’ thoughts and reactions.

Consider a scenario where you’re presenting to a client, and a comment provokes a bristling reaction. If cameras are switched off, this crucial non-verbal feedback will be missed and if the client chooses to say nothing, there’s a risk of a worsened relationship and assumptions may be made. Conversely, having cameras on allows for the prompt identification and addressal of such reactions, effectively handling any concerns that may arise.

Moreover, maintaining camera visibility encourages others to follow suit, facilitating a more connected and engaged virtual environment. This practice proves beneficial not only with clients but also with the development team. As Gabriel explains, “I always keep my camera on, even if I am the only one to do so.”

Give credit where it is due

It’s essential to acknowledge the efforts of the team’s great work and the impact they’ve had. When discussing features or problem-solving approaches that the team has implemented, it’s crucial to give credit to those who contributed to these solutions. This is especially important for team leaders. By recognising the efforts of the team, cohesion is strengthened, and team members feel valued for their contributions.

Gabriel concludes by saying, “We can never escape the human element, but cultivating stronger relationships and maintaining clear communication enables more effective management of the unpredictable aspects of projects, allowing us to devote greater attention to the technologies we enjoy”.

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Agile is a project management methodology that is meant to promote flexibility and adaptability in software development or any business process.

While Agile has gained popularity, there are companies that claim to be Agile but in fact, are faking it. This can happen when companies introduce Agile practices but do not adopt an Agile mindset. But how can companies do Agile the right way?

To start, it is important to understand what Agile is and what it is not. Agile is an umbrella term that encompasses various approaches, including Scrum, Kanban, and Extreme Programming, to name a few. Agile is all about being able to respond to change, fail fast, and deliver a better product. Agile is not a rigid process that controls everything and allows no wiggle room. The point of Agile is to allow teams to adapt to changes that occur during the development process and improve how they work.

One common misconception about Agile is that having a Scrum master is anti-Agile. This is not true. A Scrum master is there to help facilitate and be a servant leader for an Agile team. They can help the team be more Agile and coach them to a certain degree. However, it is important to note that calling oneself a Scrum master or adopting the roles within Scrum does not make one Agile.

To do Agile the right way, it is crucial to have an Agile mindset. An Agile mindset means embracing change and being willing to fail fast. It means being open to feedback and collaboration and continuously looking for ways to improve. Companies that adopt an Agile mindset are more likely to succeed in Agile practices.

Another crucial aspect of doing Agile right is to focus on delivering value to the customer. This means identifying what the customer needs and delivering it quickly. Agile emphasizes delivering value in small increments, known as sprints. By delivering value in small increments, the team can get feedback from the customer and adjust accordingly, leading to better outcomes.

Communication is another critical component of Agile. Agile teams communicate regularly and openly, which helps to identify and address problems quickly. Communication also promotes collaboration, which is essential for Agile to succeed. Agile teams should have daily stand-up meetings where team members can discuss progress, identify problems, and plan for the day ahead.

Finally, it is crucial to measure progress when doing Agile. Agile teams should track progress by measuring how much work is completed, how much is remaining, and how long it takes to complete a task. By measuring progress, the team can identify areas where they can improve and adjust accordingly.

Doing Agile the right way requires an Agile mindset, a focus on delivering value to the customer, regular communication, and measuring progress. Companies that adopt an Agile mindset and implement Agile practices are more likely to succeed in Agile. It is essential to remember that Agile is not a rigid process but a way of thinking and working. By embracing change, failing fast, and continuously improving, companies can do Agile the right way and reap the benefits of this methodology.

Ends

For more information on the innovative work Synthesis has done for its clients, contact us on 087 654 3300

About Synthesis

Synthesis uses innovative technology solutions to provide businesses with a competitive edge today. Synthesis focuses on banking and financial institutions, retail, healthcare and telecommunications sectors in South Africa and other emerging markets.

In 2017 Capital Appreciation Limited, a JSE-listed Fintech company, acquired 100 percent of Synthesis. Following the acquisition, Synthesis remains an independent operating entity within the Capital Appreciation Group providing Cloud, Digital, Payments and RegTech services as well as corporate learning solutions through the Synthesis Academy.

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Every day as an ML Engineer I seek new ways to provide value to further enhance our lifestyles through AI and while there are several problems seeking solutions and we may think that AI could be these solutions, very often a glass ceiling is reached where the limitations are glaringly obvious.

As an AI enthusiast, I speak for many when I say that AI is great, but AI is never a replacement for human beings. The very nature of AI is artificial and while there are benefits that AI may offer, we need to always ground our thinking around what this means for human beings and the society we live in, and it is even more important to implement these human-centric solutions sooner rather than later.

Our saving grace now is AI ethics and Responsible AI – but what exactly does this mean for us as a society?

Taking it back to first principles: “Ethics are the moral principles that govern a person’s behavior or the conducting of an activity” another definition is “the branch of philosophy that involves systematising, defending, and recommending concepts of right and wrong behavior.”

For the sake of not debating morality, I’d like to take a step into what determines whether something is wrong or right and by extension what parameters determine the use of AI in a morally sound manner.

Considering that AI is attempting to mimic human intelligence (often unsuccessfully) would it not make sense to use concepts such as the universal declaration of human rights (UDHR) to govern AI ethics – a recent report titled “Artificial Intelligence and Human Rights: Opportunities and Risks” seems to agree and this is a basic argument “The UDHR provides us with a (1) guiding framework that is (2) universally agreed upon and that results in (3) legally binding rules—in contradistinction to “ethics”, which is (1) a matter of subjective preference (“moral compass,” if you will), (2) widely disputed, and (3) only as strong as the goodwill that supports it. Therefore, while appealing to ethics to solve normative questions in AI gets us into a tunnel of unending discussion, human rights are the light that we need to follow to get out on the “right” side. Or so the argument goes”.

As a practitioner in the field of AI, I can say that purely relying on UDHR is not going to solve the ethical dilemma we have with AI ethics since I believe  the argument is an overestimation of the capabilities of UDHR. Ethics are severely underestimated and we may be more aligned than we think we are when it comes to a moral compass.

A good example of highlighting this conundrum between UDHR and ethics is the classic case of utilising an AI system as a diagnostic tool within the healthcare industry.

There are lists of potential positives and negatives that can be addressed; the positives being the accuracy and efficiency of diagnostic AI tools have a positive impact on the right to life, liberty, security, adequate standards of living, and education. The contrast being that data is required to construct these tools which has a negative impact as it infringes on the right to privacy. The solution could be developing methods through which these diagnostic tools could be created without violating privacy and this may be the obvious alternatives that several AI practitioners are taking, however what if the situation arises where we may not necessarily be able to respect privacy to its full extent and the patients who require the diagnostic tools are suffering/dying right now?

UDHR may not necessarily solve that problem but ethical reasoning such as the Kantian framework may be a start for example in the context of de-identified data that could be traced back to individuals only through sophisticated means and whether there would be a strict rule against it. And in theories of social justice, we would look at the effects of these decisions on different segments of society.

In essence, we have established that it is absolutely essential that we combine ethical reasoning in combination with human rights in order to establish a case by case understanding around AI ethics and Responsible AI, however having a loosely composed framework is not comforting enough to ensure that AI is used ethically.

In an attempt to rectify this lack of structure, several organisations such as the European union have proposed “AI regulation.” However, attempts towards such a framework have drawbacks since the bill assesses the scope of risk associated with various uses of AI and then subjects these technologies to obligations proportional to their proposed threats – thereby promoting companies to adopt AI technologies as long as operational risk is low which does not necessarily align with human rights risk.

The obvious gap with regulations is that AI systems are not obliged to respect fundamental freedoms similar to the rights constructed to protect against government intrusion. Although there have been attempts to rectify this utilising “Europeans Unions General Data Protection Regulation” which allows users to control their personal data and ensures that companies respect those rights – the solution does not permit users to mobilise their human rights and there has been no education in terms of understanding the value of safeguarding their personal information.

The regulations that have been created try to address the balance between subjective interests of humans as well as those of the industry. They try to protect human rights while ensuring that the laws adopted don’t impede technological progress. But this balancing act often results in merely illusory protection, without offering concrete safeguards to citizens’ fundamental freedoms.

To achieve this, the solutions adopted must be adapted to the needs and interests of individuals, rather than assumptions of what those parameters might be. Any solution must also include citizen participation. A good example of how ethics and human rights can be balanced out to solve the AI ethics conundrum is the “human rights by design” approach. The approach covers the following: “A “human rights by design” approach needs to be capable of addressing human rights issues with short-, medium-, and long-term implications. These might include:

• How do we interpret key human rights concepts, such as informed consent or remedy, in the context of big data analytics, the internet of things, and artificial intelligence?
• How do we involve rights holders in the innovation process for products or services that might have millions or billions of users?
• What are the most effective ways to address the known human rights risks of disruptive technologies, such as the risk that algorithm-based decision-making can result in discriminatory outcomes (such as in the areas of housing, credit, employment, health, and public services)?
• How do we address novel human rights risks that emerge from the use of disruptive technologies, such as land rights and airspace in the age of drones?
• What use cases for disruptive technologies in different industries—such as agriculture, financial services, healthcare, or mining—will present the most salient human rights risks and opportunities?”

With all these variations and attempts to solving the AI ethics dilemma, it has become clear that as a human race we must be more conscious when adopting AI solutions as well as when creating AI solutions, remembering not only the data-centric approach but also the human-centric approach that will not only ensure that we are securing ourselves for a successful future but also protecting those that are important (the inspiration behind AI) – humans. Leveraging on ethical reasoning and human rights with a more inclusive approach is the start to truly ensuring that AI ethics and Responsible AI is a reality but the decision to incorporate this approach must be decided from the start and the responsibility lies with all of us i.e. the solution builders as well as the consumers.

Now I would like to make the alteration to the Charles Wheelan quotes:

“Change is inevitable; but progress depends on what we do with that change and who we include in that change.”

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Part of this can be attributed to the view that the likes of AI and machine learning (ML) are just products that can be installed and forgotten about. But like any technology, these innovations can only truly become useful if they are embedded in a system where the people inside the organisation can use them effectively.

An example of this is the work Synthesis has done for Nedbank Insurance. Previously, employees at the financial services provider spent eight hours a day reading and categorising emails. But by introducing sophisticated AI and ML features as part of an overarching software development project run by Synthesis, a robot now does this 24×7 to make sure clients get a quick response. This has seen Nedbank Insurance clear its main client inbox almost eight times faster, getting rid of the backlog and speeding up client service.

Similarly, Synthesis used ML to speed up the Absa and Barclays brand separation in 2018. With a limited period to identify and replace the logo in key documentation, Web sites, contracts, database structures, marketing, and communications. It would not have been possible to be reliant on human-driven processes. Synthesis used an agile approach to deliver a software-based solution using ML and a micro-services cloud-based architecture to rapidly identify 40 000 artefacts that needed replacing.

Part of a whole

It comes down to applying AI and ML models to software development processes that look at business operations in its entirety. Being able to leverage the high-performance computing capabilities of cloud providers like Amazon Web Services (AWS) for example, AI and ML have the potential to positively disrupt any organisation.

But to do so requires several elements. Firstly, data scientists need to draw up the right model for a particular business objective. The data engineers then need to build that model and ensure data is processed correctly. Finally, it requires AI and ML to be applied to a modern, open-cloud framework that can scale securely according to the business requirements. AI and ML is not a bolt-on service but must be architected into a solution capable of delivering the advanced value needed.

While these technologies can also help in decision-making processes, the model used must be capable of learning from the human elements in the organisation. Human intervention at the early stages of the model development is crucial to ensure there is an effective feedback loop and an understanding of how the machine-driven insights are generated.

Business-focused

Ultimately, it is not just about the technology, but the entire business approach involved. AI and ML are not stand-alone concepts but must be built into an effective use case. Of course, being able to implement more sophisticated AI and ML opportunities depends on the level of maturity of the organisation to the technology.

But with every business wanting to improve efficiencies and maximise the potential of employees, AI and ML can help unlock value previously left untapped. With significant opportunities for these technologies across sectors in South Africa, companies are well primed to start taking advantage of what AI and ML can offer. However, they must remember to take an integrated approach to these technologies if they are to be truly effective.

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The purpose of digital transformation is to allow an industry to perform at an optimal pace, as well as to leverage newer technologies out there. This results in a more effective working environment.

If we look at the banking sector as an example, digital transformation has assisted the industry to transform by using new technologies. The adoption of new technologies helps change the way in which operational structures work.

As an example in the banking sector, a customer applying for a loan would physically have to go into their financial services provider; manually filling out forms that would get filed away in a cabinet; and the branch would take 2-3 months to process the application. Enter Artificial Intelligence (AI). Now using new technology it can be done in seconds. Or milliseconds.

Object Character Recognition (OCR)

An interesting development in the AI space is Object Character Recognition (OCR). In the not so distant past a person applying for a loan could scan the form; now with OCR, a photograph on a mobile phone is good enough. Thanks to machines that recognise objects and characters and extract the content, transform it into plain text, there is no longer any need to go into the institution.

With AI we are becoming smarter with how we work by optimising the way businesses function, and we are doing this is less time too.

Machine Learning can propel digital transformation

For some, AI is a buzz word and there certainly is a lot of fear around it, but the reality is, it can propel organisations forward, especially in areas they are struggling with. For example, fraud detection and customer profiling. The benefits and advantages specifically in these two areas are obvious when the role of machine learning is clearly understood. Fraud can be detected quickly; and in the case of customer profiling – done accurately there are many ways business can use this information to reach the individuals who make up their customer base.

Clean data and data-driven decision making

Using machine learning and AI depend on the foundation of the company’s data. Like everything – if data is accurate, the results are excellent. However, organisations do have legacy issues, and data can be inaccurate and misconstrued.

This is a challenge as businesses need to fix their data before they can reap the enormous benefits of clean data. Here, interestingly enough, humans are needed to step in and correct information. Humans will always be needed, but we need to get to a place where we use technology to our advantage. AI can help to get the data to the place it needs to get to.

Let’s start using AI to augment humans so we can fix what exists. It can’t be one or the other – we need a combination of AI and human interaction.

Why AI matters

It’s not only machines getting smarter, but people are also becoming more tech savvy and more demanding. In order to keep up, we need to start leveraging technology and infrastructure. Not only are we going to be using AI to enforce digital transformation, but we are catering for a market that demands it. AI is not something in the future – AI is already here, and if your organisation is not already using it, you are falling behind!

We are a bunch of numbers

Gone are the days where one person has one footprint. Today, the average person has three devices on which they are interacting. On average this one person has about five accounts, such as social media, banking apps, fitness apps, shopping apps etc. This means that there are more digital footprints than there are existing human footprints.

Bot nor not

In South Africa, we are reaching a point where AI has matured to the point where human interaction is no longer necessary in some instances. For example, Google has released an AI model Generalised Pre-Transformed Trainer (GPT3) that can understand language and talk back to you (in a call centre environment), and you will not be able to differentiate whether it is a bot or a human!

In conclusion, the fear around AI is null and void; if you look at the good it definitely outweighs the bad – AI can be so useful. Yes, fear is driven by the unknown, but the reality is, that people will always be needed.

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