However, in a world where Artificial Intelligence (AI) is no longer a trend but infrastructure, the baseline for what counts as valuable work has changed dramatically.

What used to take a department a week now takes an intelligent tool an hour. This is not theoretical, it’s already the norm in marketing, finance, legal operations, management, customer service, and even procurement.

Relevance decay happens quietly. It is the product designer who hasn’t tested a generative AI tool for ideation, the Human Resource (HR) manager still scheduling interviews manually, and the team leader using slide decks while competitors are building interactive dashboards in minutes.

Contrary to many opinions, none of these employees are doing anything wrong, but the work they are doing is no longer aligned with what modern performance looks like in this age of speed, scale, and tech-integrated economy.

The hardest part is that these employees often look like your best people on paper. They meet deadlines, show up early, and know the company’s system inside out. But they are not learning new systems, questioning processes, or exploring more efficient alternatives.

They have unknowingly become great at yesterday’s work, while tomorrow’s work is already here, and because they are not visibly underperforming, most companies do not see the risk until it is too late.

According to the World Economic Forum (2025), the average skill today remains relevant for just 2.5 years, and generative AI is not just automating repetitive tasks anymore; it is redefining entire workflows, and employers expect 39% of workers’ core skills to change by 2030.

More strikingly, technical skills may now become obsolete in as little as 2.5 years, down from a previous average of five years. These figures make clear that skills are perishable, and staying still is a risk.

At the heart of the issue is a simple truth: jobs haven’t vanished, but the value within them has shifted. Learning cycles have been compressed.

The people you are paying to perform must now also be learning as fast as they execute. If they are not growing, they are quietly becoming irrelevant, even if they are still in the room.

Another contributing factor is that job expectations are evolving, but performance reviews are not. Most organizations still assess employees on historical KPIs, without evaluating how well they’ve adapted to current tools or if they’ve redefined their role in light of industry change

Some employees know this and are already adapting. KPMG found that over two-thirds of enterprise teams plan to spend between $50 and $250 million on GenAI in the next year. While others assume their experience is enough.

The difference? One remains in motion, while the other quietly slips behind, and in this context, staying still is moving backwards.

Leaders need to re-audit what roles require, re-scope jobs around AI-augmented workflows, and reset expectations across the board. This includes creating environments where employees are expected to experiment, integrate, and evolve, not once, but continuously.

The organizations that are winning in this era (2025) will be those that help their people adapt not once, but continuously, to unlock agility, engagement, and innovation, because what is useful today could be obsolete in six months. This is not about job loss; it is about a value shift, and in 2025, relevance is your most important performance metric.

*Ruby Igwe is the Country Director, at ALX Africa (Nigeria) and Co-Founder of Archivi.ng. She is a dynamic leader passionate about driving Africa’s growth through innovation, education, and strategic leadership. At ALX Africa, she spearheads initiatives that empower the next generation of African innovators. Beyond this, her work with Archivi.ng is preserving Africa’s creative heritage for future generations.

Meanwhile, many countries are adhering to globally recognised regulations or establishing their own and are even in the business of building their own models.

In Nigeria, for example, the government has partnered with the private sector to develop an open-source language model for local languages.

AI has the potential to bring the region’s public sectors and institutions into the future, relieving them of obstacles caused by limited infrastructure, manual processes and outdated systems.

That said, AI is not a one-size-fits-all solution, and its implementation does come with its own challenges, especially as projects move from the proof of concept to the production stage and organisations need to start scaling and extracting value from their deployments.

But the future of AI in the public sector isn’t defined by the models you use. It’s how you use them, and for any organisation, that’s the right place to start.

Modernising IT and putting data to good use

It’s important to keep in mind that when we refer to AI or ML, we are using a single term to describe a whole host of technologies, everything from generative AI (GenAI) and intelligent automation to natural language processing and optical character recognition.

These technologies form the basis for government agencies to leverage any data, structured or unstructured, that they have access to.

They also represent the chance for agencies to modernise their IT, culminating in use cases across all kinds of departments and services.

Sitting at the heart of all these technologies is software that enables AI applications to communicate with large language models (LLMs) and generate responses on their data.

This is a process known as inference and it is the operational phase of AI. However, running complex LLMs can be expensive, and running inference can end up accounting for the majority of an organisation’s budget.

That’s where software solutions like Red Hat AI Inference Server come in. Powered by virtual large language model (vLLM), it provides fast and cost-effective inference by maximising infrastructure (i.e., GPU) utilisation, is certified for all Red Hat products, and can be deployed across other supported Linux and Kubernetes platforms.

Modernising IT involves keeping operating costs as low as possible, and how government agencies run AI models becomes part of that effort.

Agencies can also use AI to modernise legacy systems, identify bugs in existing code and move to newer programming languages. From there, it’s a free-for-all.

In government procurement, models can collect information about potential suppliers and select preferred ones based on strictly outlined criteria. In law enforcement, pattern recognition can help agencies identify suspects and track specific threats (within the confines of relevant privacy regulations).

In healthcare, AI can analyse patient data to streamline approvals and research processes, as well as collate data to identify broader health trends or issues in the general population.

Across any use case, the advantages of AI are constant. Agencies can deliver more effective services and better allocate resources, while citizens enjoy an enhanced and more seamless experience when interacting with those services.

At the same time, improved data distribution and collation means agencies can streamline dependent processes and better predict citizens’ changing needs, thus increasing their agility and overall service availability.

The cost question

Deploying and maintaining AI models can be an expensive exercise, with cost variables encompassing GPU clusters and other hardware, human expertise and ongoing fine-tuning.

Even in the face of avoiding potential exorbitant licensing fees by training and deploying their own models, costs for organisations can escalate as they have to fork out for computation, storage and talent.

According to one survey published by Benchmarkit, 80% of enterprises miss their AI infrastructure forecasts by more than 25%, while 24% miss theirs by 50% or more.

Model costs can also be unpredictable without centralised governance. Agencies and development teams benefit from having a streamlined experience for managing AI models, achieving improved productivity and consistency, as well as guaranteed lifecycle management.

In terms of reducing expenses associated with model deployments, government agencies in West Africa need to choose the right model for the right job.

They need to evaluate the complexity of the task the model needs to perform and then fine-tune it. By doing so, they elevate the model’s efficiency and optimise its cost.

Agencies should also utilise monitoring tools that are typically available on cloud platforms to track resource usage. They should also prioritise open-source tools and platforms that enable greater flexibility and tailor-made solutions.

AI by the people, for the people

As the global market for AI models matures, organisations are focusing less on training and more on operations. In other words, the focus is now on being able to take a model and apply what it’s learned to the real world.

That is the difference between AI training and AI inference, and by transitioning between the two, government agencies can start to unlock value from their projects.

Inference does have its unique challenges, particularly as model complexity increases and hardware evolves at an accelerated pace. Agencies should note that models are only as cost-effective and high-performing as the inference server.

What makes a solution like Red Hat AI Inference Server impactful is its ability optimise model inference across hybrid environments, as well as its open source nature.

vLLM is the standard in enterprise AI inference, and the versatility it offers through open source means agencies can tailor it according to their model needs.

When it comes to AI in the public sector, it needs to be developed by the people, for the people. With the help of platforms that centralise model development, management and governance, institutions in West Africa can put their best digital foot forward, all while keeping costs in check and delivering reliable and consistent services.

Flam is building the infrastructure to make that possible. The company has raised $14 million in Series A funding to scale its AI Infra, making it easy for marketers to turn any touchpoint into an interactive, app-less digital and 3D experience.

The round was led by RTP Global, with participation from Dovetail and other existing investors, bringing Flam’s total funding to $22 million.

To date, Flam has been transforming advertising by turning traditional ads into interactive MR experiences. A simple QR code Scan or a link will let users instantly immerse in an experience that can showcase a product, tell a story, or unlock a deeper layer of the brand — all without needing to download an app.

Flam’s platform allowed brands to launch interactive content via QR codes or link on any touchpoint – Digital, Broadcast TV, Mass Media, Retail, OOH, packaging, even WhatsApp messages.

One scan or a link click, and consumers are instantly immersed in an experience that can showcase a product, tell a story, or unlock a deeper layer of the brand — all without needing to download an app.

Starting this year, Flam has been accelerating R&D on its app-less GenAI infrastructure that enables brands to create, publish and measure high-fidelity MR, 3D & Digital experiences in <300 ms on any smartphone.

The same infra already powers campaigns for Google, Samsung, Emirates and hundreds of global enterprises and agency powerhouses.

“Our mission is to turn every touch-point — Digital, Broadcast TV, Mass Media, Retail, Stadium Fan engagements —into an interactive digital experience,” said Shourya Agarwal, co-founder & CEO of Flam.

“We are laser-focused to ship the GenAI tools that brands and enterprises have been yearning for. Flam has galvanised marketers around the world now we’re taking it to the next level with a full stack enterprise suite of products across channels; to make them engaging, measurable, interactive.”

The platform is already being used by 100+ global brands, including Google, Samsung, Emirates, Britannia, and Mahindra, with real-time mixed reality campaigns that have reached over 380+ million users.

From turning product packaging into shareable stories to activating 3D demos on TV ads and billboards, Flam is helping brands create experiences that feel native to how people consume media today.

Flam will expand its partner program for creative studios and global platforms, enabling Fortune 500 brands to move from pilot to rapid global roll-out. Upcoming product development includes GenAI-driven 3D asset generation, Democratising MR deployment at scale, Enterprise Suite of Products across Industries, and Infrastructure for broadcasters and fan engagement.

With its Series A secured, Flam aims to redefine how consumers interact with ads, retail aisles, live broadcasts and fan moments—turning content and interfaces into shoppable, shareable experiences that deliver measurable ROI. 

“This capital unlocks the next chapter of Flam’s deep‑tech roadmap. Our edge‑compute architecture already streams hyperreal mixed‑reality in under 300 ms; the next milestone is a fully generative pipeline that lets brands create, personalise, and publish Digital & 3D experiences on the fly—secure and at scale,” Amit Gaiki, co‑founder & CTO, added. 

Nishit Garg, partner at RTP Global, commented: “The time for MR is now — and Flam is uniquely positioned to lead this wave. What excites us is not just the technology, but the clarity of vision and speed of execution. Shourya, Malhar and team are building a category-defining company—and we’re excited to be part of their journey in this next phase of growth”.

While, Amal Parikh, managing director at Dovetail added: “With Limitless applications, strong execution and clear vision we believe Flam is set to redefine how brands connect with consumers.” 

Flam currently employs 120+ people across engineering, AI, creative tech, and go-to-market teams. The company expects to grow to 180+ employees by the end of 2025, with expansion across the U.S., Europe, and Asia already underway.

“The World is meant to be experienced. Immersive media shouldn’t just be a video,” added Shourya Agarwal. “That said, the creation of immersive media should be as easy and ubiquitous as a video. Flam is here to power enterprises precisely for this.”

With the insurance landscape ever evolving, it is becoming increasingly crucial for insurance companies to embrace innovative technological advancements that introduce convenience, enhance customer engagement, and redefine marketing strategies to stay competitive.

Africa’s population remains largely underserved by traditional insurance models, underscoring the urgent need for accessible, customer-centric solutions.

This highlights an opportunity for insurers to serve this market with innovative tools and solutions that can meet the changing demands of African customers.

Delivering a strong insurance product is no longer sufficient; insurance companies must also focus on delivering a robust omnichannel customer experience.

Modern customers expect instant, real-time, and personalised support experiences. They want to buy insurance easily, file a claim, access basic information, and have questions answered anytime and anywhere over their preferred communication channel.

However, it is not only about technology, the key is to also create connected customer experiences that are humanised. This should be at the heart of the digital transformation efforts of insurance companies, and this is where knowing who your customers are, their channel preferences and always being there for them becomes essential.

With the rise of digitalisation, insurance companies are exploring new ways to increase customer engagement, streamline processes, reduce costs, introduce convenience, and boost satisfaction, and one effective strategy is through conversational customer experience.

A conversational experience

To exceed customer expectations, insurance companies must implement new technology, practices and processes.

A conversational customer experience ticks all the boxes and focuses on building long-term customer relationships that ultimately result in greater customer loyalty, improved brand image, and more revenue.

For example, conversational experiences use tools like Artificial Intelligence (AI), chatbots and virtual assistants for conversations initiated via QR codes from advertising on social media pages or click to conversation promotional campaigns. This provides customers with instant and personalised two-way communication over WhatsApp.

By integrating conversational experiences, insurance companies can scale communication efforts and unify internal operations, allowing marketers, agents and chatbots to work together to deliver one customer experience.

AI, particularly Generative AI (GenAI), has democratised innovation, enabling businesses of all sizes to harness its power. GenAI-powered interactions hold tremendous promise for insurers to deliver an enhanced, differentiated customer experience, particularly with its advanced natural language capabilities to support customer service and enhance engagement and satisfaction.

While Africa’s aggregate insurance penetration is still very low, with a penetration rate of 2.78% in 2019 compared to the global average of 7.23%, the industry has leapfrogged traditional services to some extent.

This was the result of harnessing digital technology and driving innovation to find unique solutions that suit the continent’s demographics.

Overcoming digitalisation challenges

Despite this, insurance companies in Africa face several challenges related to digitising processes and meeting customer expectations. For example, complex products and policies can make it difficult for agents to effectively communicate and explain the details of policies, leading to customer confusion and dissatisfaction.

Similarly, many insurers lack a comprehensive, 360-degree view of their customers or the tools to engage them effectively at key moments in their journey, thus limiting potential for maximising customer lifetime value. Some struggle to implement the right technology to meet rising customer expectations for quick, accessible and personalised communication.

These insurance companies can enhance their customer service and provide fast, accessible, and personalised support by leveraging AI technology. Introducing AI will open up the potential to make this more accessible and efficient.

By embracing technology and adopting digitalisation and omnichannel communications strategies, insurance companies on the continent can boost customer engagement, streamline processes, reduce costs, introduce convenience, and increase satisfaction.

Finding the right omnichannel communications partner with a full stack of digital channels and conversational solutions is essential to effectively digitise customer communication.

South African retail and e-commerce businesses are stepping up their game by weaving Generative Artificial Intelligence (GenAI) into their omnichannel strategies to meet evolving customer expectations.

Customers here tend to favour brands that deliver a smooth shopping experience, whether online or in-store.

With e-commerce sales set to hit R225 billion by 2025, retailers are keen to boost customer engagement and loyalty by adding more digital touchpoints.

They’re tapping into smart technologies like AI-powered chatbots and GenAI-driven personalised SMS campaigns. These innovations offer real-time support and tailored promotions, helping keep customers coming back for more.

The rise of GenAI is helping retailers supercharge their customer experience (CX) by allowing them to hyper-personalise interactions.

Now, businesses can create content and engage with customers based on the information they gather during their interactions. This means retailers can relate and connect with customers on a personal level.

By using AI-powered chatbots, retailers can provide instant support and be available for their customers around the clock. This not only improves the CX but gives retailers a competitive edge. The bottom line is that the better their GenAI strategies are, the more they’ll stand out.

Challenges persist

Despite the advantages of GenAI, South African retailers are up against some challenges when implementing omnichannel solutions powered by GenAI technology.

One major hurdle is the fragmented nature of some retail solutions, with businesses often relying on different service providers for each channel. This can make it challenging to deliver a consistent customer experience.

Another challenge many retailers face is that their digital infrastructure is often lacking because they haven’t kept up with digital transformation.

This can hold them back from truly realising their omnichannel strategies. To tackle this issue, they must embrace a unified solution on a single platform that supports a seamless customer journey.

Despite these challenges, some local retailers are successfully integrating GenAI into their omnichannel strategies. Take a prominent South African hardware retailer, for example; they have developed a robust e-commerce platform that lets customers start their shopping journey online.

Customers usually interact with a chatbot on this platform. If an issue arises – like receiving the wrong items or an order not being fulfilled – they can chat with the bot again.

At this point, the interaction can be handed off to a human agent who can assist the customer more effectively, thanks to having a line of sight of the entire customer journey up to that moment.

GenAI is also very good at detecting customer sentiment, so when a customer starts using words such as “unacceptable” during the chat, this can raise a red flag, enabling a human agent to take over and resolve the situation faster.

Leveraging customer data

By capturing data from their chatbots, cloud contact centre solutions, and customer data platforms and feeding it into their GenAI application, retailers can keep their connections with their customers more personal, customised, and bespoke.

Plus, GenAI can guide the customer through all key steps of their journey – discovery, consideration and purchase, service, retention, and loyalty.

Data shows a significant shift towards mobile commerce, with 60% of online purchases now happening on smartphones.

As retailers once focused mainly on desktop portals, this trend has led many to rethink their strategies and adopt a mobile-first approach.

Retailers are prioritising mobile-optimised websites and creating apps specifically designed for mobile.

At the same time, they also keep in mind other features mobile-enabled features, like the growing popularity of mobile payments and digital wallets.

In the end, a seamless CX can significantly boost revenue growth. Retailers can improve customer engagement and satisfaction by being available across multiple touchpoints – online, mobile, or in-store.

This leads to higher conversion rates and increased sales, all driven by a refined CX and the benefits of an omnichannel strategy.

This is also where GenAI plays a crucial role, as it can process all customer data and help businesses cross-sell and upsell effectively.

By signing up for unified, scalable CX solutions, retailers can achieve effective omnichannel communication and customer engagement. It will also enable them to leverage GenAI technology better.

These solutions are engineered to address retailers’ challenges and bring out the best in each channel.

This approach improves revenue and cuts costs, paving the way for personalised customer experiences and sustainable business growth in the digital age.

[Featured Image Credit]

Yet, most companies struggle to deploy large AI models in production. With the steep costs and complexities involved, nearly 90% of machine learning projects are estimated never to make it to production. 

Addressing this pressing issue, Simplismart is today announcing a $7m funding round for its infrastructure that enables organisations to deploy AI models seamlessly. 

Like the shift to cloud computing, which relied on tools like Terraform and mobile app development fueled by Android, Simplismart is positioning itself as the critical enabler for AI’s transition into mainstream enterprise operations. 

The series A funding round was led by Accel with participation from Shastra VC, Titan Capital, and high-profile angels, including Akshay Kothari, Co-Founder of Notion. This tranche, more than ten times the size of their previous round, will fuel R&D and growth for their enterprise-focused MLOps orchestration platform.

The company was co-founded in 2022 by Amritanshu Jain, who tackled cloud infrastructure challenges at Oracle Cloud, and Devansh Ghatak, who honed his expertise on search algorithms at Google Search. 

In just two years, with under $1m in initial funding, Simplismart has outperformed public benchmarks by building the world’s fastest inference engine. This engine allows organisations to run machine learning models at lightning speed, significantly boosting performance while driving down costs.

Simplismart’s fast inference engine allows users to leverage optimised performance for all their model deployments. For example, Its software-level optimisation helps run Llama3.1 (8B) at an impressive throughput of >440 tokens per second. 

While most competitors focus on hardware optimisations or cloud computing, Simplismart has engineered this breakthrough in speed within a comprehensive MLOps platform tailored for on-prem enterprise deployments – agnostic towards choice of model and cloud platform.

“Building generative AI applications is a core need for enterprises today. However, the adoption of generative AI is far behind the rate of new developments. It’s because enterprises struggle with four bottlenecks: lack of standardised workflows, high costs leading to poor ROI, data privacy, and the need to control and customise the system to avoid downtime and limits from other services,” said Amritanshu Jain, co-founder and CEO at Simplismart

Simplismart’s platform offers organisations a declarative language (similar to Terraform) that simplifies fine-tuning, deploying, and monitoring genAI models at scale. 

Third-party APIs often bring concerns around data security, rate limits, and utter lack of flexibility, while deploying AI in-house comes with its own set of hurdles: access to computing power, model optimisation, scaling infrastructure, CI/CD pipelines, and cost efficiency, all requiring highly skilled machine learning engineers. 

Simplismart’s end-to-end MLOps platform standardises these orchestration workflows, allowing the teams to focus on their core product needs rather than spending numerous manhours building this infrastructure.

Amritanshu Jain added: “Until now, enterprises could leverage off-the-shelf capabilities to orchestrate their MLOps workloads since the quantum of workloads, be it the size of data, model or compute required, was small. As the models get larger and the workload increases, it will be imperative to have command over the orchestration workflows. Every new technology goes through the same cycle: exactly what Terraform did for cloud, android studio for mobile, and Databricks/Snowflake did for data.”

“As GenAI undergoes its Cambrian explosion moment, developers are starting to realise that customising & deploying open-source models on their infrastructure carries significant merit; it unlocks control over performance, costs, customisability over proprietary data, flexibility in the backend stack, and high levels of privacy/security”, said Anand Daniel, partner at Accel. 

“We were happy to see that Simplismart’s team saw this opportunity quite early, but what blew us away was how their tiny team had already begun serving some of the fastest-growing GenAI companies in production. It furthered our belief that Simplismart has a shot at winning in the massive but fiercely competitive global AI infrastructure market.”

Solving MLOps workflows will allow more enterprises to deploy genAI applications with more control. They want to manage the tradeoff between performance and cost to suit their needs.

Simplismart believes that providing enterprises with granular Lego blocks to assemble their inference engine and deployment environments is key to driving adoption. 

Despite the notable progress in mobile banking and fintech, the continent continues to face a significant financial inclusion gap, with over half the population remaining unbanked.

This paradox is particularly evident in South Africa, where a sophisticated financial sector coexists with deep inequality.

To bridge this divide, South African banks are leapfrogging legacy hurdles by embracing technologies like generative AI (GenAI), focusing on socioeconomic trends and using advanced tools to reshape customer interactions and underwriting processes.

Breaking down barriers to the unbanked

Despite the fact that a high percentage of South Africans have access to formal financial products, a significant portion of the population remains effectively unbanked due to distrust of financial institutions, particularly within the informal sector.

To appeal to this market, the Banking, Financial Services, and Insurance (BFSI) sector is pursuing digital strategies that build trust by offering more accessible financial solutions.

In leveraging conversational banking and prioritising customer experience, South African BFSI players can attract tech-savvy users while retaining traditional customers, ultimately driving greater financial inclusion across the country.

By integrating AI, South Africa’s BFSI sector can enhance customer experiences and operational agility, ultimately driving financial inclusion and sustainable growth in the digital age.

The role of Generative AI in digitising access

GenAI is playing a decisive role in transforming the BFSI sector in South Africa. Banks are now leveraging AI-powered chatbots to provide 24/7 customer support, answer frequently asked questions, and personalise customer interactions at every touchpoint.

This not only improves customer satisfaction but also frees up human agents to handle more complex inquiries.

Furthermore, AI is revolutionising credit scoring, especially for underserved populations. By analysing alternative data sources like mobile phone usage and utility payment history, AI can assess creditworthiness for individuals who may not have a traditional credit score.

This helps to expand financial inclusion by providing access to credit for those who were previously excluded.

First address risks associated with AI

While AI offers significant benefits, it is essential to address the risks associated with its use. Organisations must be cautious when partnering with cloud-enabled service providers.

These providers should prioritise data privacy and security by adhering to strict regulations like General Data Protection Regulation (GDPR) and Protection of Personal Information Act (POPIA).

They should also employ advanced encryption techniques to protect sensitive customer data. Transparency is key; providers must inform customers about data usage and offer options for data control.

By partnering with reputable providers who prioritise these factors, organisations can mitigate the risks associated with AI and ensure the ethical and responsible use of technology.

The rise of fintech and digital-first banks

To serve diverse customer segments, South African banks are implementing tailored digital strategies. For affluent customers, banks offer personalised financial services through advanced digital platforms, including wealth management tools and investment advisory services.

For underserved populations, banks are focusing on accessible options like Unstructured Supplementary Service Data (USSD) banking and micro-lending.

By partnering with financial technology (fintech) companies, banks can reach underserved segments and offer innovative financial solutions.

The emergence of fintech and digital-first banks has increased competition and forced traditional banks to innovate.

These newer banks often offer more digital-friendly services and a focus on customer experience.

Driven by the need to innovate to survive, traditional banks have responded by investing in digital platforms and partnering with fintech companies to offer more convenient features like instant transfers and AI-powered customer support.

With the emergence of Rich Communication Services (RCS), companies are now able to offer a more enhanced customer experience compared to traditional Short Message Service (SMS).

Key features include multimedia support, file sharing, branding, and two-way communication. RCS also provides stronger security features, such as encryption and verification, to reduce the risk of fraud.

Future advancements in AI and digital banking

AI-driven credit scoring for the unbanked, voice and conversational banking in local languages, and AI-powered financial literacy tools are key areas for future development.

These advancements can further drive financial inclusion and growth in Africa’s BFSI sector. By leveraging AI and digital technologies, South Africa’s BFSI sector can overcome legacy challenges, foster financial inclusion, and contribute to the country’s economic growth.

This requires prioritising customer experience, addressing AI risks, and tailoring strategies to serve diverse populations.

Companies are increasingly turning to artificial intelligence technologies to personalise interactions and provide seamless self-service options for customers. 

During Infobip’s recent Exclusive Business Dinner, Orediretse Molebaloa, head of Solution Engineering at Infobip discussed how generative AI is being leveraged to hyper-personalise the customer journey. 

He highlighted the importance of AI integration and collaboration with major tech players. “We are partnering with service providers like Microsoft to power our AI technologies and current use cases that are essential for our customers,” he said while further noting two methodologies involved, the rise of generative AI and the concept of Super Apps. 

“WhatsApp for Business, for example, has significantly improved customer experience by enabling businesses to create self-service options through chatbots,” he explained.

Ingesting company-specific data, generative models can gain insights into individual preferences and needs. This allows businesses to preemptively address issues and provide tailored recommendations at each touchpoint.

Using real-world examples, Molebaloa illustrated the applications of AI in customer interactions. “In South Africa, I’ve seen WhatsApp being used by the traffic department to send invoices directly to my house. Another example is DSTV, which sends notifications for subscription payments via WhatsApp, offering self-service options even for routine tasks.”

Addressing the issue of app updates, he noted, “Restaurants often face the challenge of app versions being outdated when customers want to place orders. With Super Apps, all user experiences from the traditional app are made available seamlessly, enhancing customer satisfaction.”

Molebaloa also delved into the role of AI in improving productivity and user experience. “Generative AI can significantly enhance productivity by automating repetitive tasks, allowing employees to focus on more complex work,” he stated. “For example, AI can scan, understand, and categorise documents, transforming handwritten notes into digital files efficiently.”

Expect More AI’s Impact on Mobile, Customer Experience and Workflows, says Taiwo Bashorun at Infobip’s Business Dinner

In discussing AI-driven customer service, he pointed out, “AI-driven chatbots and virtual assistants provide 24/7 support, resolving issues efficiently and enhancing the overall customer experience. This is especially essential in industries like financial services, where AI helps in fraud detection and financial planning.”

Sharing insights on the integration of AI into existing systems, Molebaloa explained the need for businesses to adopt a personalized approach. “By leveraging generative AI, businesses can ingest company-specific data to create tailored experiences for customers,” he said.

“For instance, banks can use AI to provide personalised financial advice based on the customer’s transaction history.”

He also highlighted the importance of omnichannel strategies in customer engagement, stating that “It’s vital to ensure that customers can access services through their preferred channels, whether it’s WhatsApp, web, or mobile apps. This omnichannel approach enhances the overall user experience,” he added.

To illustrate the impact of generative AI, Molebaloa shared an example from his personal experience. “I once had to send money using USSD, and the switch to WhatsApp for the same transaction was seamless and efficient,” he recounted.

“This transition improved the user experience and also reduced the pressure of session-based interactions.”

Molebaloa conclusively explained the importance of understanding and adopting AI technologies. “It’s essential to educate customers about the technology behind AI solutions to ensure they fully benefit from it. We must hold their hands through this journey, combining technology and empathy to create the best user experiences. 

“It’s about the journey, it’s about the user experience, it’s about leveraging generative AI and other technologies to ensure that the user experience of your customers is at its best,” he said.

Infobip has been recognised for its Ability to Execute and Completeness of Vision^.

The Gartner report Top 10 Trends in Enterprise Communication Services 2024 notes the growth of GenAI as “The year 2023 saw the rise of generative AI (GenAI) as a major disruptor impacting almost all technology areas”.

The Gartner CIO and Technology Executive Survey identifies customer experience, improving margins, revenue growth, ensuring compliance/minimising risk, and increasing employee effectiveness as the top critical outcomes expected from enterprise digital technology investments.

Infobip has invested in its AI Hub for AI-driven conversational customer experiences that solve business problems.

The firm was also among the first globally to launch Camara-compliant Network APIs under the GSMA Open Gateway initiative.

Silvio Kutić, CEO at Infobip, said:

“As Gartner explains, “businesses prioritise business outcomes, buying experiences, and cloud consumption models when buying communications services.” That’s why we are innovating across all layers of the tech stack to enable businesses to digitally transform their interactions with customers. As the CPaaS market continues to grow, Infobip remains the full-stack omnichannel communications platform for every platform.”

But what regions and countries are leading in the use of GenAI? China is in the lead according to a recent global study SAS commissioned with Coleman Parkes Research Ltd.

China business decision makers report that 83% of their organisations are using the technology. That’s more than in the United Kingdom (70%), the United States (65%) and Australia (63%).

But organisations in the United States are ahead in terms of maturity and having fully implemented GenAI technologies at 24% compared to China’s 19%, and the United Kingdom’s 11%.

What does this mean in terms of the global economic impact of AI and GenAI? In a 2023 report, McKinsey estimated GenAI could add the equivalent of $2.6 trillion to $4.4 trillion annually across a variety of use cases. That’s comparable to the entire GDP of the United Kingdom in 2021.

This impact would increase the overall influence of artificial intelligence (AI) by 15% to 40%.

Considering these economic implications, SAS and Coleman Parkes targeted 1,600 decision makers across key global markets.

Respondents work in a range of industries including banking, insurance, the public sector, life sciences, healthcare, telecommunications, manufacturing, retail, energy and utilities, and professional services.

The smallest organisations surveyed employed a workforce of 500 – 999 people, and the largest employed more than 10,000.

“While China may lead in GenAI adoption rates, higher adoption doesn’t necessarily equate to effective implementation or better returns,” said Stephen Saw, Managing Director at Coleman Parkes. “In fact, the US nudges ahead in the race with 24% of organisations having fully implemented GenAI compared to 19% in China.”

Global regions charge ahead with GenAI

Highlights from the global survey results include indicators that signal different regions are on board and starting to adopt GenAI in meaningful ways but at different rates.

“With any new technology, organisations must navigate a discovery phase, separating hype from reality, to understand the complexity of real-world implementations in the enterprise. We have reached this moment with GenAI,” said Bryan Harris, Executive Vice President and CTO at SAS. “As we exit the hype cycle, it is now about purposefully implementing and delivering repeatable and trusted business results from GenAI.” 

Where do regions rank in fully using and implementing GenAI into their organisation’s processes?

• North America: 20%
• APAC: 10%
• LATAM: 8%
• Northern Europe: 7%
• South West and Eastern Europe: 7%

Which regions have implemented GenAI use policies?

• APAC: 71%
• North America: 63%
• South West and Eastern Europe: 60%
• Northern Europe: 58%
• LATAM: 52% 

To what extent do those planning to invest in GenAI in the next financial year have a dedicated budget?

• APAC: 94%
• Northern Europe: 91%
• South West and Eastern Europe: 91%
• North America: 89%
• LATAM: 84%

Industries and functional divisions embrace GenAI at varying rates

Sabine VanderLinden, CEO and Venture Partner, Alchemy Crew, sees much potential for industries investing in GenAI.

“The future of business is being reshaped by GenAI,” she said. “Indeed, the integration of GenAI into business processes – from dynamic profiling in marketing to precision claims insurance – offers unparalleled opportunities for efficiency, personalisation, and strategic foresight. Embracing this technology is essential for staying ahead in a highly uncertain and unpredictable competitive market.”

When split into industry segments, the data shows banking and insurance leading other industries in terms of incorporating GenAI into daily business operations across a variety of metrics. Highlights from those findings are below.

How do specific industries rank in terms of fully implementing GenAI and fully implementing it into regular business processes?

• Banking: 17%
• Telco: 15%
• Insurance: 11%
• Life sciences: 11%
• Professional services: 11%
• Retail: 10%
• Public sector: 9%
• Healthcare: 9%
• Manufacturing: 7%
• Energy and utilities: 6% 

Which industries indicate they already use GenAI daily to some extent?

• Telco: 29%
• Retail: 27%
• Banking: 23%
• Professional services: 23%
• Insurance: 22%
• Life sciences: 19%
• Healthcare: 17%
• Energy and utilities: 17%
• Manufacturing: 16%
• Public sector: 13%

Which departments inside organisations are using or planning to use GenAI?  

• Sales: 86%
• Marketing: 85%
• IT: 81%
• Finance: 75%
• Production: 75%

Early adopters are finding plenty of obstacles in using and implementing GenAI

No. 1 on the list of challenges organisations face in putting GenAI to routine use is the lack of a clear GenAI strategy.

Only 9% of leaders responding to the survey indicate they are extremely familiar with their organisation’s adoption of GenAI. Of respondents whose organisations that have fully implemented GenAI, only 25% say they are extremely familiar with their organisation’s adoption strategy.

Even those decision makers responsible for technology investment decisions aren’t familiar with AI – including those at organisations that are ahead of the adoption curve.

Nine out of 10 senior technology decision makers overall admit they don’t fully understand GenAI and its potential to affect business processes.

At 45%, CIOs lead the way with executives who understand their organisation’s AI adoption strategy. But only 36% of Chief Technology Officers (CTOs) say they’re fully in the know.

Yet despite this understanding gap, most organisations (75%) say they have set aside budgets to invest in the next financial year.

Other challenges organisations face include:

• Data: As organisations adopt GenAI, they realise they have insufficient data to fine tune large language models (LLMs). They also realise – once they’re deep into deployment – they lack the appropriate tools to successfully implement AI. Organisations’ IT leaders are mostly concerned about data privacy (76%) and data security (75%).
• Regulation: Only a tenth of organisations say they are fully prepared to comply with coming AI regulations. One third of organisations that have fully implemented believe they can comply with regulations. Only 7% are providing a high level of training on GenAI governance. And only 5% have a reliable system in place to measure bias and privacy risks in LLMs.

Although there are obstacles, some early adopters have experienced meaningful benefits already: 89% report improved employee experience and satisfaction; 82% say they’re saving operational costs; and 82% state customer retention is higher.

*Find the full research report and an interactive data dashboard. [Featured Image Credit]