In Africa, approximately 40% of organisations are either experimenting with or deploying generative AI tools.

This adoption is already yielding measurable success: 51% of South African businesses believe generative AI has improved productivity and competitiveness.

Governance: The foundation for reliable innovation

To ensure this growth is reliable and responsible, organisations must build a foundation of trust. AI runs on data, and data runs on trust. Building a healthy data culture involves knowing what information is held, where it lives, who can use it, and for what purpose.

This is where governance comes in, providing structure and discipline. Governance establishes the standards and controls necessary to ensure information accuracy and security, as well as the accountability to uphold them.

Crucially, when governance works as it should, it doesn’t slow innovation – it makes it safer and faster. Clear rules give businesses the confidence to move quickly, use data creatively, and make better decisions.

African nations are proactively establishing strong legal foundations for trust in the digital space. For instance, South Africa mandates that boards are held responsible for managing data risk and ensuring lawful usage through its  Protection of Personal Information Act (POPIA) and the King IV Code. Similarly,  Nigeria’s Data Protection Act demands essential principles like transparency, consent, and human oversight in data handling. Meanwhile, in Kenya, the Ministry of Information, Communications and the Digital Economy (MICDE) is the primary governmental body actively driving cybersecurity and AI governance efforts.

Strategic security for sustainable growth

Africa’s AI market is projected for significant expansion, expected to reach around $6.4 billion by the end of 2025, supported by more than 2,400 AI-focused companies. This growth won’t be sustainable without strong foundations.

While the technology that makes work smarter can also be used by cybercriminals (e.g., forging images or cloning voices), organisations are implementing strategic solutions to mitigate these risks. Boards and executives must look at information governance as a strategic priority, not merely a technical one.

Technology offers robust support for this strategic focus: platforms can automate data protection, monitor activity, and simplify compliance, helping with the heavy lifting of security.

Empowering the human element

Cybersecurity, data governance, and training must all work together to maintain a secure system. Employees are essential, remaining the first and last line of defence.

Top down organisational culture is key to empowering employees with necessary skills. These essential skills include recognising manipulated voices, spotting deepfakes, avoiding suspicious links, and questioning urgent payment requests.

The safest way to work with AI is to treat data with the same care afforded to money or reputation. By integrating rules, oversight, and discipline, organisations keep the system honest.

The principle guiding this growth remains clear: progress is nothing without trust.

The possibilities for enhanced productivity and innovation are immense. Using AI agents, businesses around the world are unlocking a piece of the potential $6 trillion digital labour market opportunity.

Businesses that fail to adopt agentic AI, however, risk disruption by competitors or savvy upstarts. This demands a proactive and strategic response from leaders.

In this new era of human-AI collaboration, leaders must center their efforts around two key pillars: large-scale employee reskilling and establishing a trustworthy AI ecosystem.

Reskilling for the agentic AI era

With just 15% of workers saying that they have the education and training necessary to use AI effectively, reskilling must be a priority for every business leader.

Employees must be given access to learning opportunities so they can adopt human-AI collaboration skills, including a foundational understanding of agentic AI and prompt engineering — a way to provide clear and effective instructions to AI systems.

Consider, for instance, the evolving role of developers. With AI agents capable of handling routine coding, developers can focus on bigger-picture tasks like system design and future planning.

According to Salesforce’s latest State of IT survey of software development leaders‌, more than nine in 10 developers are excited about AI’s impact on their careers, and an overwhelming 96% expect it to change the developer experience for the better. More than four in five believe AI agents will become as essential to app development as traditional software tools, the survey found.

In addition to technical abilities, cultivating human and business skills is vital for fostering a trusted environment where teams feel comfortable experimenting with AI.

And, as every employee increasingly manages individual or even teams of agents, developing basic managerial skills across the workforce will become increasingly important.

Identifying the skills is just the first step. To succeed in the agentic AI era, businesses need to develop a comprehensive strategy that incorporates these skills into their workforce plan. This includes setting clear, measurable goals and actively tracking progress.

Managers need to provide active guidance and support to employees throughout this transformation, ensuring the workforce remains relevant and engaged.

Adopting trusted AI across the ecosystem

As the capabilities of agents grow, so too does the responsibility to manage ‌associated risks. It’s imperative to ensure these systems are fair and prevent stereotypes or alienation. The very qualities that make AI transformative can also lead to biases and erode trust if not managed.

To fully harness the potential of agentic AI, businesses must prioritize trust and safety at every stage of development and deployment. This means implementing strong security measures and adhering to ethical AI practices to safeguard data and ensure responsible use.

Guardrails for AI agents can be established using natural language topics and instructions specifying when an agent should escalate or transfer a task to a human.

Concerns around data privacy and potential biases must be proactively addressed through strong data protection protocols and transparent communication.

Equally important are tools that foster transparency and empower users to make informed decisions regarding task delegation to AI. Employees need a clear understanding of the capabilities and limitations of the AI agents they collaborate with, alongside having control over the tasks being automated.

A key feature of Agentforce is its capacity for autonomous operation within specifically defined guardrails. This means that while ‌AI agents can operate independently, making decisions and taking actions, they do so within boundaries established by human teams, ensuring alignment with business objectives and policies.

The Einstein Trust Layer enables Agentforce to use any LLM safely by ensuring that no Salesforce data is viewed or retained by third-party model providers.

The power of reskilling and trust to drive innovation

The transition to an AI-powered future will bring challenges, particularly ensuring employees have access to the right infrastructure, high-quality data, and relevant skills.

However, by investing in reskilling and comprehensive training programs, organizations can empower teams to work effectively alongside AI agents, adapt to the evolving nature of work, and ultimately drive innovation in this age of digital labor.

Building a robust infrastructure that prioritizes trust and safety, and fosters transparency, will also be instrumental in mitigating disruptions and unlocking new opportunities for growth.

Ultimately, investing in both AI agents and human employees, and actively fostering their collaboration in a trusted way, will enable businesses to operate at scale and realize their full potential in the agentic AI era.

As organisations refine their AI strategies, we are witnessing the next chapter of work and the emergence of digital labour with agentic AI.

Since the launch of ChatGPT many business leaders focused on what they thought was the right topic – the Large Language Models (LLMs). But these models are quickly becoming a commodity, as each one races to build the best for a specific use case.

To truly unlock value from AI, you need to focus on everything around the model such as the orchestration, the low code / no code approach to building and refining, the metadata framework and a data engine that compliments the data strategy.

It’s this platform advantage that is seeing agents across the globe stand up and deliver value with real data, leveraging real integration in a few short weeks.

To unlock the action and value of generative AI requires  a deeply integrated and connected platform with a one code base, but this takes significant time and money to build unless you have already been empowering your human employees on the Salesforce platform.

Our platform leverages everything you have built to empower your digital workforce. Its a win-win where even for those who are not quite ready for a digital workforce – will be unlocking their ability to pivot to an agentic workforce with every flow, cloud, integration and build – Ultimately  future proofing their business.

Agentic technology is a multi-trillion-dollar industry opportunity. The agentic enterprise  will operate with unprecedented independence capable of responding to queries and handling complex tasks autonomously.

This autonomy will optimise workflows, drive innovation, and break down barriers related to the need for continuous human intervention.

By 2028, Gartner predicts that 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, allowing 15% of day-to-day work decisions to be made autonomously.

Yet, AI agents are only as good as the data they have. They need connected data—both structured and unstructured—to understand user queries and make informed decisions. That’s where integration and APIs come in, building a solid foundation for these agents.

While 93% of IT leaders are either implementing or planning to implement AI agents within the next two years, they face significant integration challenges that hold back the full potential of these agents.

According to the latest MuleSoft Connectivity Benchmark Report, which surveyed more than 1,000 IT leaders globally, 95% struggle with data integration across systems.

On average, only 29% of applications are connected, which really affects the accuracy and usefulness of AI agents.

The report found that, on average, enterprise organisations are using 897 applications, and those with AI agents are using even more—1,103 applications. 90% of IT leaders say data silos are creating business challenges.

The more applications and AI models there are, the harder it gets to integrate everything. Data silos make it even tougher, limiting agents’ access to the data they need and leading to less accurate and useful outputs.

Disconnected data also places major strain on IT resources. IT leaders are looking for ways to boost efficiency and productivity, but they expect their teams’ workload to increase in the next year.

Balancing current capabilities with integrating AI agents across hundreds of unique applications while maintaining those systems, is a real challenge.

To unlock the full potential of AI agents, businesses need to align their integration and AI strategies. APIs and integration solutions can simplify and unify data infrastructure, allowing AI agents to access critical data and interact with existing systems and automations.

This can significantly improve IT infrastructure, enable data sharing across teams, and integrate disparate systems.

Organisations that have successfully integrated their data and systems using APIs are reaping the rewards: increased productivity (49%), faster response to business needs (49%), and higher revenue generation (45%).

On average, half of an organisation’s internal software assets and components are available for reuse, which means companies can leverage their existing investments, instead of starting from scratch.

The reliance on IT teams highlights the need for a clear automation strategy, along with robust governance and monitoring to ensure everything runs smoothly and securely.

A well-rounded automation strategy is crucial for integrating AI effectively, but many teams are still working on theirs.

One key part of this strategy is making AI accessible to non-technical users, which is essential for broader adoption and creating a solid foundation for employees to build on, and this is where agents are changing the game.

Every company, team, and employee will soon have an agent. But how useful is a team of agents if they can’t interact with other systems or agents to coordinate and take action across the entire business?

AI must have a smooth handoff to a human, and if that transition isn’t well-coordinated and seamless, any benefits are quickly undone.

As AI, integration, automation, and API use continue to drive transformation and performance, organisations that invest in these technologies to harness unlimited digital labour are best placed to stay agile, efficient, and ultimately succeed.

Linda Saunders, country manager and senior director of Solution Engineering for Africa said: “Among the biggest benefits of agentic AI solutions will be on-demand, instant access to personalise financial recommendations that may otherwise require appointments and taking time out of busy schedules.”

In addition to taking the pulse of consumers’ sense of financial security and goals, the report delves into how customer experiences — including those increasingly powered by artificial intelligence (AI) agents — are changing individuals’ relationships with their FSIs.

“For humans and agents to work together, it’s critical for financial service institutions to implement with trust, transparency, and the highest levels of regulatory compliance as core to their strategy — not an afterthought,” adds Saunders.

Key insights from the research include:

• Differentiated service and experience can outweigh rates and fees. 45% would stay with a provider that offers excellent service, even if fees increased. This is especially true for 56% of high earners.
• Consumers have big expectations for AI in financial service: 43% of FSIs in EMEA, including 54% of Gen Z and 50% of millennials, expect AI to play a bigger role in financial services than in other industries. FSIs are most interested in use cases that prevent fraud, lower prices, and complete routine tasks.
• Building trust is essential as agentic AI is rolled out: 49% of consumers at least somewhat trust the use of AI agents in financial services, but only 8% are fully on board. The top factors that would build consumers’ trust in AI agents are transparency into their use, validation of their outputs, and explainability of their outputs.

Today, 60% of consumers believe that advances in AI make trust even more important, and with AI agents on the rise, the findings point to real opportunities for companies to win back consumers with trustworthy AI agents this holiday season.

This opportunity is greatest with Gen Zers, with almost a third of Gen Z consumers saying they’d be comfortable having an agent shop for them.

Faced with a challenging holiday shopping season and sinking consumer trust, brands can’t afford to get AI wrong—especially as more than $200 billion in global online sales will be influenced by AI this holiday season.

AI agents, or intelligent software that understands and responds to customer inquiries without human intervention, can help companies drive higher margins and keep consumers buying by delivering incredible customer service.

From alleviating clunky purchase experiences to difficult return processes, there’s an agent for that. But to build trusted customer relationships, brands need trusted AI agents that are grounded in transparency and the right data.

Consumers trust less, expect more

Consumer trust is at its lowest point in eight years, and advances in AI make earning that trust more critical than ever.

• Nearly three-quarters (72%) of consumers trust companies less than they did a year ago

• 65% feel companies are reckless with customer data

It’s not just about trust; consumers also expect best-in-class experiences.

• 69% of consumers expect consistent interactions across departments

• Nearly 60% of consumers prefer using fewer touchpoints to get information or complete a task

While better deals are a top driver for consumers to switch to a new brand, customer service experience, convenience, and consistent product or service quality drive more long-term brand loyalty.

• 43% of consumers say poor customer service experience will stop them from making a repeat purchase from a company or brand

• More than a third of consumers say that inconvenience, such as a difficult return process or clunky purchase experience, will cause brands to lose them

Younger consumers are most open to AI agents

The research shows Gen Zers and millennials are more willing than older generations to use AI agents to improve their customer experience by creating more personalised, or useful content.

Younger generations, in particular, hold companies to a higher standard when it comes to adapting to and anticipating their needs—43% of Gen Zers and millennials say AI raises the bar for customer experiences compared to just 32% of baby boomers.

Gen Z and millennial consumers are more likely than older generations to consider the benefits provided by agents.

Transparency is key to building consumer confidence in the AI agent era

Despite the promise of young shoppers, many consumers haven’t made up their minds on AI yet. Nearly half of consumers are neutral about AI’s impact on their lives, whether personal or professional.

In fact, many consumers feel a mix of suspicion (44%) and curiosity (41%) about the future of AI —revealing a ripe opportunity for companies to help consumers see and understand the benefits of AI agents.

• Over a third of consumers would work with an AI agent instead of a person to avoid repeating themselves

• 30% of consumers—even more among Gen Z and millennials (37%)—would work with an AI agent instead of a person for faster service

• A quarter of consumers — even more among Gen Z and millennials (roughly one-third)—would share their personal information with an AI agent so it can better anticipate their needs

To build confidence in the agent experience, businesses need to bridge the trust gap through more transparency.

• Nearly 75% of consumers want to know if they’re communicating with an AI agent

• 45% are more likely to use an AI agent if there’s a clear escalation path

• 44% are more likely to use an AI agent if its logic is clearly explained

“Retailers face a much more competitive shopping season this year, as they look to deliver higher margins in the midst of increasing customer demands. AI agents can help brands deliver consistent, personalised experiences for shoppers across every channel – deepening customer loyalty and ultimately driving more sales,” says Linda Saunders, Salesforce director Solutions Engineering Africa.

While workers feel most comfortable when AI and humans work together, they are beginning to trust AI to complete time-saving tasks autonomously, like writing code, uncovering data insights, and drafting written communications.

Workers aren’t delegating everything to AI just yet.

According to the research, today’s workers prefer that humans oversee tasks like onboarding, training, and data security. But this likely won’t last for long.

The data also suggests that investing in knowledge and education builds trust in an autonomous AI future.

As technology advances, people are getting a glimpse into a future in which AI can operate entirely on its own.

The Salesforce research, which surveyed nearly 6,000 people around the world, revealed that workers are excited about an AI-powered future.

Additionally, they also stressed the importance of a human touch as they build trust, knowledge, and experience with AI tools.

Global workers, especially leaders, believe in an autonomous future and are already offloading tasks to AI

Workers today already trust AI to do roughly 43% of their work tasks, indicating a shift among workers to offload tasks to AI.

Leaders trust AI to do more of their work than employees do — leaders trust AI to do 51% of their work, while rank-and-file workers trust AI to do 40%.

• 77% of global workers will eventually trust AI to operate autonomously. This number includes:
  • 10% of global workers trust AI to operate autonomously today.
  • 26% of global workers will trust AI to operate autonomously in less than three years.
  • 41% of global workers will trust AI to operate autonomously in three or more years.

While workers prefer AI-human collaboration, they’re starting to trust AI to handle certain tasks alone

• Today, 54% of global workers trust humans and AI to do most work tasks together.
• When asked if these workers trusted AI to do any of these same tasks autonomously, the answer, for a small group, was some. Tasks they felt comfortable offloading to autonomous AI included:
  • 15% trust AI to write code autonomously.
  • 13% trust AI to uncover data insights on its own.
  • 12% trust AI to develop internal and external communications without a human.
  • 12% trust autonomous AI to act as their personal assistant.
• Other tasks, according to respondents, require having a human involved right now. Global workers are most likely to trust humans alone to do the following:
  • 47% trust humans alone to be inclusive.
  • 46% trust humans alone to onboard and train employees.
  • 40% trust humans alone to keep data safe.

Human involvement and enablement can pave the way to an autonomous AI future

• Human involvement is needed to build trust in AI.
  • 63% of global workers say more human involvement would build their trust in AI.
• Concerns about AI may come from a lack of understanding. Fifty-four percent of global workers say they do not know how AI is implemented or governed in their workplace.
  • Workers who are knowledgeable about how AI is implemented and governed in their workplace are five times more likely to say they will trust AI to operate autonomously within the next two years than those who are not knowledgeable.

The AI Knowledge Gender Gap: Males are 94% more likely to say they are knowledgeable about how AI is implemented and governed in their workplace compared to females.

Training may be another key to trusted autonomy

62% of workers say more skill-building and training opportunities would build their trust in AI.

“Workers are excited about an AI-powered future and the research shows us that human engagement can help us get there. By empowering humans at the helm of today’s AI systems, we can build trust and drive adoption – enabling workers to unlock all that AI has to offer,” says Linda Saunders, Salesforce Director, Solutions Engineering Africa.

This new solution streamlines the launch, management, and oversight of energy efficiency, conservation, electrification, and EV charging programs to help electric, gas, and water utilities increase customer participation in clean energy programmes.

Energy & Utilities Cloud is built on the Einstein 1 Platform, Salesforce’s trusted customer platform.

The Einstein 1 Platform helps organisations safely take advantage of their data to create better customer experiences and enhance employee productivity with AI to improve profitability for customers with CRM, AI, and data.

Clean Energy Program Management helps utilities manage clean energy initiatives with connected data — like energy usage and billing information — and personaliSed engagement to promote relevant programmes.

It digitises and simplifies the application process for customers, participating contractors, and utility program staff to help increase clean energy and energy efficiency program enrollment, electric vehicle adoption, and more.

Program Management Console makes it easy to manage residential, commercial, and industrial programmes with targets for energy savings, water conservation, compliance, number of homes served, and more.

From the console, program managers can track and manage applications, as well as monitor program performance and budgets.

Automation from Salesforce Flow, built-in data validations, and prebuilt formulas to calculate energy savings and incentives, help utilities reduce human error by collecting regulatory-required data during the application process, while dashboards track program milestones, performance, and goals.

Customer Self-Service Portal helps utility customers discover and learn about energy efficiency programs and offers.

The portal provides an intuitive experience, empowering customers to easily search and access available rebates, incentives, new rates, and income-qualified offers. Customers can then apply for programs, be matched with a service provider, and even receive their benefits — right from their energy retailer or utility’s website.

Contractor Portal makes it easy for outside contractors to communicate and collaborate with utilities, providing a central hub to enroll in programmes, discover leads, and upload work in bulk.

Automation simplifies the management of rebate applications and invoices to help contractors and their customers get reimbursed faster, even as the volume of work grows.

“The energy and utilities sector has a once in-a-lifetime opportunity to guide their customers toward a clean and sustainable future. With Clean Energy Programme Management on Salesforce’s trusted AI, data, and CRM platform, energy and utility companies can create strong and lasting partnerships with their customers and communities, helping us all get to net zero faster,” said Linda Saunders, Salesforce Director Engineering Solutions Africa.

IT leaders across Africa are grappling to establish the governance and processes required to master the basics with widening digital skills gaps, disconnected systems, and compliance concerns amongst their top concerns.

This is according to MuleSoft’s 2024 Connectivity Benchmark Report, which found that among 1,050 IT Leaders worldwide, 98% say they are facing challenges regarding digital transformation.

81% of IT Leaders report the persistence of data silos, and 72% cite the fragility of tightly coupled and highly dependent systems as top challenges holding them back from AI adoption.]

For businesses looking to stay ahead in an AI-powered future, integration and automation will be essential. The role of the CIO and other IT leaders is becoming more critical than ever.

The savviest business leaders are turning to their IT leaders to help drive their businesses’ AI strategy forward.

While 85% of IT leaders expect AI to boost developer productivity, they flag that both security and trust remain as barriers to adoption. An additional 64% of IT leaders are concerned with ethical AI usage and adoption.

This includes establishing and communicating a clear strategy for execution that addresses both compliance and skills gap concerns.

Integration is the foundation to connected customer experiences

With adoption of AI tools rising rapidly among the general public, demand for AI-first customer experiences will follow.

Today’s customers have come to expect exceptional experiences supported by well-connected data through integrated systems.

Nearly three-quarters (70%) of customer experiences are now entirely digital, but only 26% of organizations report providing a completely connected user experience across all channels.

This is why a single, unified, and real-time view of every customer, at scale, is the intelligent heart of customer engagement.

Across all industries, there’s a greater need for better integration to unify all structured and unstructured business data to power and deploy trusted, relevant AI across business functions.

While AI has the power to drive efficiency, it is dependent on integrated data, and it’s creating more complexity for integration strategies. Organisations having to balance nearly 1000 applications to create a cohesive experience for end users.

IT Leaders acknowledge that data silos and systems fragility are holding their companies back from AI adoption. Over 90% of IT leaders are experiencing integration issues.

A significant minority of organisations are architected for AI success, where only 2% report no significant barriers to utilising their data for AI use cases.

Concerns around integration are twofold: the difficulty integrating generative AI features with other software systems, and the need for integration between existing systems.

Organisations that have adopted an integration strategy have reported a vast array of benefits. From customer experience, more significant ROI, and automation implementation, integration positively impacts the organisation.

Failure to close the gap between integrated/connected applications will prevent AI from meaningfully improving employee or customer experiences for most organisations for the foreseeable future.

Democratising automation and establishing data governance will unlock greater productivity

Automation remains a source of contention for IT leaders. IT relies on automation solutions to drive efficiency and provide business users with autonomy.

According to McKinsey, current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees’ time today.

Yet IT teams are still largely responsible for governing and maintaining the automation process, and the workload that is required to implement solutions can counter the intended benefits.

To scale, automation solutions highlight an opportunity for business teams to self-serve and ease the burden on IT. As businesses increasingly look to automation to drive efficiency, APIs can become a powerhouse for productivity and revenue.

IT leaders report that APIs allow them to drive agility and promote self-service (54%), increase productivity (48%), and even benefit business teams and help meet their demands (46%).

Managing and securing the data that underpins these APIs at scale has become increasingly complex. By establishing data governance – setting the rules or policies by which information is collected, managed, stored, measured, and communicated – companies can set the foundations for success.

With the right governance parameters in place, automation can be democratized, which would free up IT teams to tackle technology challenges with increased complexity.

With the support of the wider business they can unlock the benefits of AI applications and data integration and governance, paving the way for a more productive, efficient AI-powered future.