“Organisations are leveraging powerful business AI capabilities to achieve greater efficiency, improve decision-making, support innovation and unlock new market opportunities. By building AI into their core business processes, from finance to supply chain, HR and procurement, African enterprises can increase their performance across a range of activities and unleash a new wave of innovation potential.”

Analysts estimate听that GenAI could add between $2.6-trillion and $4.4-trillion in productivity gains annually. A separate study predicts AI will contribute up to $15.7-trillion to the global economy听by the end of the decade.

, Deputy Group CEO, , says the time for AI-powered innovation is now as Africa is reshaped by rapid technological advances. “Eleven of the twenty fastest-growing economies in 2024 are in Africa, spurred on by innovation across a range of industries and use cases. The continent also boasts a vibrant startup ecosystem with a growing list of innovative companies addressing issues ranging from clean energy, e-commerce, health and financial services. The entry of business AI solutions will likely accelerate these innovation efforts and speed up time to market for a growing list of new solutions.”

Strong business case for AI in Africa

Karageorgiades believes there is a strong business case for AI within African enterprises, most notably as a tool for accelerated decision-making. “Enterprises that leverage business AI to enhance decision-making gain greater competitiveness, increased operational efficiency, and more powerful innovation capabilities. Provided there is a strong alignment with broader business objectives, organisations can confidently make use of AI as a key business driver.”

Gumede also notes the opportunity for African enterprises to leverage broader digital transformation as a growth driver.

“Improved internet penetration thanks largely to the proliferation of mobile technologies is connecting African companies to more customers than ever before. By developing mobile apps, e-commerce platforms and digital services tailored to the unique needs of local businesses and consumers, enterprises can unlock new market opportunities while enhancing their customer engagement efforts.”

In addition, Gumede believes business AI solutions will enable new productivity gains, empowering employees with capabilities that can make their job functions more efficient and effective.

“Considering our continent’s unique landscape, organisations must localise emerging technologies such as AI to ensure they meet the needs of local companies and customers. Care should also be taken to ensure organisations have the necessary technology infrastructure in place to truly unlock the full value from their AI investments.”

Unleashing business AI potential

Despite interest in AI reaching unprecedented levels over the past two years, some questions still remain over its enterprise readiness. Karageorgiades explains that poorly trained AI solutions could become a liability to the business, instead of an asset.

“The tendency of AI solutions to hallucinate – where the algorithm produces false information in response to user prompts – poses a direct risk to enterprises that business leaders cannot ignore. An AI solution is only a good as the data it is trained on, making a strong case for companies to invest in comprehensive data setup, data strategy, and governance capabilities.”

Gumede adds that organisations that focus on quality data will achieve more effective decision-making capabilities. “The hype around AI is settled; now it’s about the practical implementation. African enterprises are currently exploring the role that AI could play in supporting their sustainability efforts, bringing greater stability to their supply chains, and improving the business’ ability to sense, recognise and respond to disruptions or opportunities in novel ways.”

Enterprise-grade business AI solutions such as SAP’s , which draws on the trusted data of enterprise resource planning platform, are more suited to powering the innovation and efficiency gains that African enterprises seek. According to Karageorgiades, businesses also benefit from higher levels of collaboration when users trust the output of their AI-powered applications.

“Business AI solutions that draw on trusted, accurate and real-time information about the business deliver actionable insights that support business users in fulfilling essential work tasks faster and with fewer errors. While we are still in the early days of understanding the true potential of AI in modern enterprises, any business that seeks to benefit from this emerging technology must take care to train their AI solutions only on trusted business data.”

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According to a joint statement by the companies, the intention is to make it easier for customers to adopt solution on AWS and improve the efficiency of 51风流workloads running in the cloud.

The companies also aim to embed GenAI into an enterprises鈥� entire portfolio of business-critical applications.

, incoming CEO at AWS, said, 鈥淎WS was the first听cloud听听provider certified to support the 51风流portfolio and today, thousands of enterprise companies run 51风流solutions on AWS to get the most out of their mission-critical applications. Now, AWS and 51风流are making it faster and easier for companies to apply generative AI to their core business听data to become more efficient, responsive and sustainable.鈥�

, CEO of SAP, added: 鈥淧artnerships like this collaboration with AWS are critical as we embed generative AI solutions across our ERP applications so that customers can drive innovation at an accelerated pace. In addition to delivering modern cloud ERP to our joint customers, we are excited to support Amazon on their own transformation journey as they adopt RISE with 51风流for pioneering areas such as , Amazon鈥檚 satellite initiative intended to increase global broadband access.鈥�

The GenAI hub in 51风流AI Core infrastructure provides customers with secure access to a range of large language models (LLMs) that can be integrated into 51风流business applications.

The companies announced that 51风流customers will now have access to high-performance GenAI models from Amazon Bedrock, including the and models. This integration allows customers to build applications customised with their own data and is expected to help them accelerate GenAI adoption and modernise key business processes built on 51风流solutions.

These听 innovations听can be used in embedded use cases within RISE with 51风流and intelligent scenario lifecycle management, either as an integration component or side-by-side on the (51风流BTP).

51风流and AWS plan to expand the use of Bedrock capabilities in the GenAI hub to further enable embedded AI features within SAP鈥檚 cloud solutions and applications, including finance and product lifecycle management.

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However, while everyone agrees the technology is a game-changer for business, the question being asked more and more in the boardroom is whether AI is truly ‘enterprise-grade’.

Business AI is not new

While hype about AI has reached unprecedented levels, the technology itself is not entirely new.

Nick Bostrom, in his book 鈥淪uperintelligence鈥�*, first published over ten years ago, provides an excellent summary of the progression of AI technology. This contemporary work captures the stages of AI – the inflated expectations; the plateauing; and the breakthroughs.

Enterprises have long relied on machine learning to power advanced analytics and predictive capabilities in a broad range of use cases – from manufacturing to finance operations to procurement and supply chain. These algorithms have equipped business leaders with information to drive greater operational efficiency.

AI has also been used extensively in traditional forms of algorithms, for example search engines, which defined an entire era of our technological development and transformed entire sectors, most notably the advertising industry.

Mounting issues with AI’s enterprise readiness

But what works on the web does not necessarily work in business. The Internet doesn’t care about authorisations. The C-suite does.

As concerns over privacy and data protection mount – especially in light of ongoing regulatory pressures – many companies have implemented restrictions over the use of open GenAI tools.

This is for good reason. Imagine an employee shares financial statements, vendor contracts or salary information with a GenAI tool that then reuses that information in answering prompts from other users.

A GenAI tool without an authorisation element is simply not enterprise-grade, and probably doomed to be confined to a single use case or department, limiting its ability to deliver value to the broader business.

Security issues also arise with the concept of data lakes, which combine enterprise and external data sources for AI purposes. Data lakes can be treacherous to enterprises, especially when data must be exported outside of large enterprise applications.

Here, a federated approach that leaves enterprise data at their origin and does not copy or transfer data is needed. Critically, organisations must maintain the semantic layer of the data, which can be the Achilles heel of every data lake project and, consequently, the GenAI models trained on that data.

Beware of hallucinations

However, the greatest danger in AI that is not enterprise-grade lies in its tendency to hallucinate.

Gen AI is an excellent algorithm that fundamentally learns by looking at what is available within its domain, usually the internet. Let鈥檚 be honest 鈥� you can鈥檛 trust every piece of information found on the internet anymore.

In an enterprise environment, CEOs are seeking the 鈥榮ingle version of truth鈥�. That means fact checking is important but begs the question: 鈥渨hich is the dataset on which I should train my Gen AI?鈥�. The simple truth is that business leaders cannot build products or develop innovations using models that make things up or use insights based off false or inaccurate information.

Here, application suite vendors have the upper hand. The business applications that power the world’s enterprises have a wealth of business data that can be mined by AI algorithms to produce accurate, relevant, and reliable insights. Vendors in this space also have significant expertise in business processes and contextualised data – the perfect sources for training effective GenAI.

There’s no doubt that enterprises will benefit from the power of AI in the coming years. Whether it is enterprise-ready comes down to individual systems and tools. While some already have enterprise-level capabilities, others may not yet meet all the requirements for reliability and security. 听Business leaders must take care to build out AI use cases that can deliver value to the business, rely on robust datasets, and live up to expectations. These guardrails will ensure business AI solutions that are relevant, reliable, and responsible.

*Superintelligence – Paths, Dangers, Strategies was written by author, Nick Bostrom, and first published in 2013 by Oxford University Press

听

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At听 in Bangalore, India, the company announced AI-powered pro-code tools and the AI Foundation, offering developers the means to create GenAI extensions and applications on the 51风流Business Technology Platform.

Juergen Mueller, SAP’s CTO and executive board member, said in today鈥檚 technology and business landscape, every developer needs to be an AI developer.

The newly announced AI tools provide developers with the resources they need to 鈥渢ransform the way businesses run鈥�, he said.

The enterprise software company also introduced 51风流Build Code solutions which aim to facilitate collaboration with business experts, complementing their low-code 51风流Build solutions from 2022.

The upgraded 51风流Build Code offers AI-powered productivity tools optimised听 optimised for Java and JavaScript development.听 It draws on the power of SAP鈥檚 new GenAI copilot Joule, embedding code generation capabilities for data model, application logic and test script creation.

51风流maintains that good data is the foundation of good AI.

To support AI development, the company鈥檚听51风流HANA Cloud now includes vector database features in its multimodel offerings. Vector data stores handle unstructured data like text, images, or audio, improving AI context and quick object retrieval.

These features enhance interactions between large language models and听an organisation鈥檚 mission-critical data. According to SAP,听 using industry-specific customer data reduces听the risk of AI ‘hallucinations’ or false information being generated.

51风流aims to upskill two million software developers by 2025.

In addition to the AI Foundation, it has also announced role-based certification and free learning resources for ABAP Cloud developers. Released in 2022, ABAP (advanced business application programming (ABAP) is said to听simplifies the development of business applications within 51风流environments.

51风流has also partnered with the Stanford Institute for Human-Centred AI to collaborate on research into the intersection of generative AI and business.

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At the听, hosted this week at the company鈥檚 headquarters in Walldorf, Germany, 51风流executives said industries continue to converge and core business models are being adapted to adopt and leverage AI.

In line with its strategy to champion the business case for AI investment and integrate the technology into business process management, Joule will be embedded throughout SAP鈥檚听cloud听enterprise portfolio.

The intention, according to SAP, is to deliver proactive and contextualised insights from across the breadth and depth of its solutions, as well as third-party sources.

The company added that by quickly sorting through and contextualising data from multiple systems to surface smarter insights, Joule drives faster productivity and better business outcomes in a secure, compliant way.

鈥淲ith almost 300 million enterprise users around the world working regularly with 51风流cloud solutions, Joule has the power to redefine the way businesses 鈥� and the people who power them 鈥� work,鈥� said , CEO at SAP.

According to SAP, Joule will be embedded into 51风流applications including HR, finance, supply chain, procurement, as well as into 51风流Business Technology Platform.

The GenAI co-pilot is designed to support digitised, automated supply chain operation, which is another key area of focus for SAP.

51风流said Joule can identify underperforming regions; link to other data sets that reveal a supply chain issue; and automatically connect to the supply chain system to offer potential fixes for the manufacturer鈥檚 review. Joule will continuously deliver new scenarios for all 51风流solutions.

Joule will be available with solutions and later this year, and with early next year.

group vice president, worldwide thought leadership research, , said: 鈥淎s generative AI moves on from the initial hype, the work to ensure measurable return on investment begins. 51风流understands that generative AI will eventually become part of the fabric of everyday life and work and is taking the time to build a business copilot that focuses on generating responses based on real-world scenarios 鈥� and to put in place the necessary guardrails to ensure it鈥檚 also responsible.鈥�

According to SAP, over 26,000 51风流cloud customers have access to across multiple scenarios and听partner听solutions.

The company鈥檚 strategy to build an enterprise AI ecosystem includes direct investments, such as those announced in July with , and , as well as third-party partnerships including those with , and , which were announced in May.

Global software venture capital firm, , is backed by 51风流and has earmarked over $1 billion to fund AI-powered enterprise technology startups.

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