Companies across sectors are racing to embed AI into operations, but proving return on investment (ROI) remains a key hurdle.

While 84% of businesses investing in AI听, only a fraction have achieved sustained value at scale. For South African executives navigating budget constraints and talent shortages, the question is not 鈥渟hould we invest in AI?鈥� but 鈥渉ow can we prove it works?鈥�

According to Dumi Moyo, marketing director for Africa at 麻豆原创, there鈥檚 good news: AI doesn鈥檛 have to be a leap of faith.

鈥淲ith the right strategy and the right tools, AI deployments deliver measurable ROI quickly across a range of business functions. One of the most effective ways to get there is by embedding AI directly into business applications, avoiding costly integrations and accelerating time-to-value.鈥�

Embedded AI upends traditional implementations. Instead of requiring custom data pipelines, standalone platforms, and complex integrations, embedded AI delivers intelligence directly into the flow of work.

鈥淎 native approach means faster deployment, less disruption, and clearer outcomes,鈥� says Moyo. 鈥淔or example, 麻豆原创 has built more than 295 AI-powered scenarios into business applications that span supply chain, procurement, finance, customer experience and HR, empowering business users to complete navigational and transactional tasks up to 90% faster.鈥�

听Strategy for AI ROI

A recent study of 1600 businesses in eight countries听听found that businesses investing in AI expect on average a 16% ROI in 2025, nearly doubling to 31% in 2027.

Moyo says businesses typically have high expectations of the ROI they will receive from AI projects, with nearly half of global companies expecting AI initiatives to deliver positive ROI faster than other investments.

鈥淚t is critical that organisations align their AI projects with the key pillars of cost savings, decision time, risk and revenue. By prioritising embedded AI, companies can secure significant ROI while unlocking听 broader innovation benefits across the organisation.鈥�

He shares insights into how AI initiatives built on four key pillars can show provable, measurable return-on-investment for organisations:

听Pillar 1: Reduce manual effort and waste to drive cost savings

Cost reduction is often the first benefit that organisations receive from AI investments. 鈥淎I allows teams to redirect hours toward higher-value work by automating repetitive and rule-based tasks,鈥� says Moyo. 鈥溌槎乖� data shows that companies achieve up to 20% operational cost reduction in key functions like accounts payable and HR through effective AI deployments.鈥�

In procurement functions, organisations are cutting administrative workload and improving sourcing outcomes by automating statements of work (SOWs) and supplier research, while in finance, AI accelerates reconciliations, optimises cash collections, and automates journal entries, thereby reducing close cycles and improving liquidity.

鈥淎 good approach here is to quantify the time saved across manual processes and to calculate the cost of labour hours that have been freed up and reinvested into more strategic work thanks to AI,鈥� says Moyo.

听Pillar 2: Improve decision time for faster insight and action

AI tends to thrive when speed and accuracy matter most. By analysing large datasets, surfacing trends, and recommending next steps, AI shortens the gap between information and action.

鈥淒ata shows that embedded AI improves forecasting and demand planning in the supply chain, resulting in less downtime and a 25% productivity bump for planners,鈥� says Moyo.

Organisations are also using generative AI to accelerate their hiring processes and producing job descriptions and candidate rankings in real-time. Finance also benefits from AI-generated insights that help CFOs and analysts make faster, more confident decisions using cleaner data.

鈥淥rganisations should measure reductions in cycle times, from planning to resolution, and link them to improved responsiveness or reduced delays,鈥� says Moyo. 鈥淭his provides a clear path to ROI and its impact on core business performance.鈥�

听Pillar 3: Reduce risk through better controls and compliance

In a volatile business climate, organisations typically seek greater risk mitigation across their operations. According to Moyo, AI plays a growing role here.

鈥淎I embedded into core business processes can flag anomalies, detect fraud patterns, and monitor compliance thresholds in real time. AI agents are increasingly being used in risk reduction, for example by scanning procurement data for irregularities and highlighting non-compliant purchases.鈥�

AI agents such as 麻豆原创鈥檚 Joule can also bolster procurement processes by suggesting suppliers with stronger reputations, and support finance teams by reducing bad debt write-offs through improved collection strategies. In HR, AI-driven insights reduce legal risk by providing bias-aware hiring recommendations and fairer performance reviews.

鈥淭o quantify the benefits that AI brings to risk reduction, companies should track aspects such as risk events avoided, compliance costs reduced, or error rates minimised,鈥� says Moyo.

听Pillar 4: Grow revenue through personalisation and conversion

According to Moyo, organisations should look beyond back-office efficiency to maximise AI value. 鈥淎 smart AI strategy will consider front-end growth by prioritising customer experiences. For example, AI fuels personalisation by recommending products, predicting churn, and guiding service agents with next-best actions to enhance customer satisfaction and drive retention.鈥�

Organisations that embed AI into their core business processes can connect sales, service and marketing data to build richer customer profiles, increase upsell success and reduce attrition. Organisations also accelerate their campaign rollouts through AI-powered content generation, while smart selling tools shorten deal cycles and increase the average basket size.

鈥淭his results in higher revenue per customer interaction, as well as more predictable sales pipelines,鈥� says Moyo. 鈥淗ere, ROI is readily quantified through tracking lead conversion rates, customer churn, and average order value.鈥�

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AI is becoming increasingly pervasive. AI-powered innovations are seamlessly integrating into everyday products and services, from e-commerce and virtual assistants such as Siri to AI-driven healthcare and financial solutions, it鈥檚 becoming impossible to go through a day without interacting with some form of AI.

In business, AI offers significant benefits to organisations of all sizes, both in the private and public sectors. Many large companies already leverage the power of AI to drive business value, both internally and for their customers. One study predicts that , more than the economic output of China and India combined.

AI is enhancing efficiency and fostering innovation across industries, from automating routine tasks to enabling complex data analysis and providing predictive insights, while also improving decision-making and optimising business operations. Experts believe AI could add $1.5-trillion to Africa鈥檚 economy if the continent can capture even 10% of the global AI market by 2030.

Sectors like healthcare, agriculture, retail, and financial services can benefit significantly from AI specifically and digital technologies in general. In healthcare, innovations can address challenges like accessibility and resource management. In agriculture, tackle issues like climate change and food insecurity by improving crop yields and sustainability. In retail and e-commerce, drive hyper-personalisation and boost customer experiences. In financial services, promote financial inclusion, access and financial literacy, particularly for Africa鈥檚 unbanked population.

To understand AI鈥檚 potential impact, it鈥檚 important to differentiate between Business AI and the more mainstream application of Generative AI.

Understanding Business AI

For many people, the mention of AI often brings ChatGPT to mind, a Generative AI tool that launched in 2022 and quickly became one of the most rapidly adopted new technologies in history. However, Business AI encompasses much more than just Generative AI, which refers to AI systems designed to create and generate content.

Business AI is a more specialised application of AI that focuses on solving specific business challenges, such as optimising processes, enhancing customer experiences, and improving decision-making.

Generative AI uses Large Language Models (LLMs) trained on vast amounts of data to generate creative outputs based on patterns it has learned. In contrast, Business AI draws mostly relies on structured data and real-time inputs from internal and external business systems to deliver business value, for example by improving efficiency.

All business functions generate data 鈥� from HR and supply chains to CRM and ERP applications 鈥� that can be processed by AI-powered tools to reveal new insights or automate business processes. Organisations also draw on external, publicly available data, for example market data and social media analytics, to better understand their operating environment. When integrated to Business AI applications, the combination of internal business data and external public data provides a more holistic view of a company鈥檚 operations, enabling better, more informed and context-based decision-making.

The application of AI in Business

Broadly speaking, AI can deliver value for companies in three categories, namely: (1) automating business processes; (2) gaining valuable insights through the analysis of data; and (3) enhancing how businesses engage with both customers and employees.

In practice, the use of AI unlocks value for example through AI-driven inventory management that reduces wastage and boosts profitability, or through data-driven insights that enhance customer engagement and enables hyper-personalisation.

AI has the potential to transform a wide range of sectors, and the benefits span across multiple industries. In agriculture, emerging farmers leverage AI-powered tools to analyse soil conditions, weather patterns, and pest control, leading to higher crop yields. In supply chain management, AI helps optimise operations by predicting demand, efficiently managing logistics, and reducing costs. In retail, particularly in South Africa鈥檚 expanding e-commerce marketplace, AI applications like dynamic pricing and customer behaviour analysis enable businesses to effectively respond to rapidly changing market conditions.

The benefits of AI are not limited to enterprises. SMEs can leverage a growing range of AI use cases to unlock competitive advantages and power their growth and innovation. As AI continues to evolve, it鈥檚 evident that it has become a vital tool for sustainable growth and a powerful catalyst for competitive advantage in a fast-evolving digital marketplace.

Building a Strong Foundation for Effective AI Adoption

听Companies seeking to unlock the benefits of AI and other emerging technologies must develop a deep understanding of the complexities and potential pitfalls of adding them to their technology stack.

听Adopting technology should be focused on maximising business value while minimising complexity. Here, a solid data foundation and a single source of truth for operational data are essential starting points. This could involve implementing cloud-based ERP systems to manage transactional and operational data, CRM to track interactions with current and potential customers, and supply chain management systems to manage the flow of goods and services from suppliers to end customers. By ensuring accurate, real-time information, these systems provide AI tools with reliable data, establishing a single-source-of-truth across the business and creating a strong foundation for future AI-powered innovation.

Businesses should prioritise areas where AI can provide the most value, such as reducing operational costs, increasing revenue, or improving efficiency. Focusing on high-impact areas like automating repetitive tasks or leveraging AI-driven analytics for customer insights can deliver immediate benefits. This targeted approach enables businesses to achieve quick wins and build momentum for broader AI initiatives.

Throughout it all, it鈥檚 essential to drive adoption with sound integration and comprehensive change management efforts. By experimenting with simple automation before scaling to more complex AI applications, businesses can innovate without overwhelming complexity, ensuring long-term success and continuous improvement.

The time for AI in business is now

The rise of AI is comparable to the Industrial Revolution of the 19th century, transforming and disrupting businesses across industries. With its ability to streamline operations and fuel innovation, AI is a powerful tool for business success. As the technology continues to advance and mature, it will unlock even more opportunities, particularly through automation and integration with other emerging technologies.

The key question is: Are businesses ready to embrace AI-powered solutions and challenge the status quo? The stakes are high鈥攖hose that do will gain a competitive advantage in the rapidly evolving marketplace, while those that don鈥檛 risk being left behind.

In today鈥檚 dynamic world, business success hinges on innovation, and AI is leading that transformation.

The post Demystifying Artificial Intelligence for Business appeared first on 麻豆原创 Africa News Center.

From record-high gold prices to the stunning growth in renewables driving demand for minerals and the global transition to NetZero, the mining industry is in an era of intense reinvention and disruption.

Africa鈥檚 mining sector isn鈥檛 spared this volatility. The continent has more than half of the world鈥檚 reserves of platinum group metals and diamonds and nearly half of manganese and cobalt reserves, both critical elements in the batteries powering the global transition to cleaner energy.

Searching for greater productivity

In the current business environment, mining companies are under intense pressure to increase efficiencies, reduce costs and boost productivity.

Autonomous technologies have ushered in a broad range of economic benefits for the mining industry over the past decade. By leveraging automation to perform more hazardous tasks, mining companies have minimised worker exposure to dangerous conditions and bolstered overall safety standards within their operations.

The emergence of digital technologies such as machine learning and artificial intelligence (AI) has introduced new gains in efficiency and productivity. One market estimate predicts that investment into AI by the mining sector will reach $7.264 billion by 2033, driven by a compound annual growth rate of 22.7%.

The ethics of AI in mining

While it鈥檚 undeniable that AI holds immense promise for the mining industry, the introduction of any new technology is typically accompanied by several ethical dilemmas.

In the mining context, fears around AI range from the displacement of human labour by automation to the increased surveillance compromising data privacy and encroachments on autonomous decision-making.

One of the biggest fears of AI鈥檚 impact in the mining sector relates to the potential of the technology to displace workers by automating their job roles and essentially making them redundant.

In the African context, job fears will remain, specifically considering the important historical role the sector plays in employment creation, especially in markets such as South Africa.

There are also concerns over explainability, that is, how AI decisions can be explained in a way that makes sense to human workers. For example, if AI is used in surveillance at mining sites to improve safety and security, questions may arise over how the algorithm determines which actions can be considered to be safety or security incidents.

While AI has the potential to improve efficiency and safety in mining, its deployment introduces new risks that must be carefully managed. Mining companies, technology developers, and regulatory authorities must collaborate to establish robust safety protocols, provide comprehensive training, and establish clear lines of accountability to mitigate the risks associated with AI use in mining operations.

Practical use cases

Despite the concerns, AI will unquestionably play a leading role in the mining sector鈥檚 success in the coming years. AI lends itself to a myriad of applications across the mining value chain, including:

1 Exploration

AI-driven prospecting mapping models are emerging that analyse geological, geochemical and geophysical data sets to pinpoint promising areas for mineral exploration. By amalgamating diverse data sources, these AI models can enhance the successful discovery of promising resource deposits.

2 Geotechnical monitoring

Geotechnical monitoring and analysis are crucial to ensure ground and infrastructure stability at mining operations. Here, AI can be integrated with sensor networks to detect early signs of instability or failure, while predictive models can forecast ground behaviour and assess potential hazards. AI can also be applied to create detailed simulations of rock masses that can help guide the design of tunnels, underground layouts, and slope stability.

3 Mine planning and optimisation

AI technologies enable dynamic, datadriven decision-making to optimise mine plans and production schedules. Mining operations can leverage AI to predict performance under various conditions, helping decision-makers identify optimal productivity strategies while minimising costs. Predictive maintenance systems can also optimise the performance of mining equipment, reducing downtime and improving overall operational efficiency.

4 Supply chain management

AI鈥檚 benefits extend beyond mining sites to improve supply chain management. Predictive inventory management leverages AI for a variety of tasks, including reducing inventory planning time, minimising costs, optimising repair schedules, and determining the optimal times for reorders. By using techniques such as time-series analysis and probabilistic modelling, mines can gain real-time visibility over their supply chain. This can help mining operations optimise their logistics operations, including transportation routes and distribution networks.

AI is a transformative force in the mining industry, introducing a broad range of innovative applications to solving complex challenges across various facets of modern mining operations. By embracing AI-driven innovation and collaboration, mining companies can pave the way for a more efficient, sustainable and responsible mining future.

The post Artificial Intelligence: Unearthing Greater Efficiency in the African Mining Sector appeared first on 麻豆原创 Africa News Center.

Kenya, Nigeria and South Africa are among the countries showing enough aggression to make AI part of their economies.

Responding to questions from听, to Systems Applications and Products (麻豆原创), the German headquartered software developer, which forecast AI would help stimulate development mainly in the areas of agriculture, fintech, healthcare and logistics. The majority of these sectors are already AI-driven.

鈥淎frica is making significant progress in adopting emerging technologies, though the level of adoption varies across the continent,鈥� said Dumisani Moyo, 麻豆原创 Africa Marketing Director, in an interview with听CAJ News Africa.

The executive noted that despite disparities in AI adoption, embracing of AI was taking shape in parts of the continent including Kenya, Nigeria and South Africa.

鈥淭his disparity is largely driven by differences in the maturity of tech ecosystems, particularly in relation to the adoption of Business AI technologies,鈥� Moyo added.

He cited key areas in the fields of fintech, healthcare, agriculture and logistics as having already benefited from AI driven innovations.

鈥淔or example, AI is being used to improve financial inclusion, enhance crop yields, and optimize supply chains across the continent,鈥� Moyo said.

He said the main reasons Africa is adopting emerging tech was because of the growing ecosystems in above-mentioned countries that have already emerged as hubs for innovations.

Moyo commended mobile penetration and connectivity as creating a foundation for AI applications, particularly in areas like mobile banking, which now includes those that were unbanked or financially excluded before.

He also indicated that AI was instrumental in supporting government policies.

鈥淪ome African governments are implementing policies to promote AI research and development,鈥� he said.

鈥淔or example, here in Kenya AI has been identified as a key thematic focus by the Kenya National Innovation Agency鈥檚 Strategic Plan 2023-2027, especially its application in developing innovative solutions for use in biotech, urban planning and climate change efforts.鈥�

He also noted the importance of international partnerships, which he said had seen African countries benefit from ties with global tech companies, fostering the growth of Business AI capabilities.

However, the official noted challenges that also hindered adoption. These include infrastructure limitations

While some regions have good connectivity, many rural areas still suffer from limited access to reliable internet, electricity and the necessary infrastructure.

There are also data accessibility issues. The cost of AI adoption in infrastructure and expertise could be expensive.

鈥淎I relies on large datasets, and in many African countries, data collection and management systems are either underdeveloped or fragmented,鈥� Moyo said.

鈥淢any African businesses, particularly small and medium-sized enterprises, face these challenges. However, with consumption-based models in the cloud, this is becoming less of a challenge,鈥� he said.

Regulatory concerns also hinder adoption.

鈥淔or many African countries, regulatory frameworks surrounding AI are still evolving, and there are concerns about data privacy, job displacement and ethical use of AI,鈥� Moyo stated.

This article first appeared on .

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While there is apprehension regarding job displacement, income reductions from shortened work hours, and the threat of identity theft, the narrative is far from gloomy. The advent of AI is not a harbinger of joblessness but a beacon for the creation of novel employment opportunities, necessitating a pivot towards embracing and integrating AI into the workforce.

The potential is limitless, particularly in developing economies such as ours. By leveraging AI tools for data analysis, farmers can gain insights into crop health, soil conditions, and weather patterns, leading to more informed decisions and higher yields with lower resource inputs. In areas where healthcare infrastructure is underdeveloped and there is a scarcity of medical professionals, AI can significantly improve access to quality healthcare services.

AI-driven diagnostic tools, telemedicine platforms, and health monitoring apps can extend healthcare services to remote areas, reducing the need for patients to travel long distances for basic healthcare.

Many developing economies suffer from low levels of banking penetration and financial inclusion. AI has the capacity to change this through fintech innovations. By utilising AI and machine learning algorithms, fintech companies can offer personalised financial products, improve credit scoring methods, and extend banking services to previously unbanked populations through mobile platforms.

But there is an important caveat. Paul Daugherty, chief technology officer of Accenture, encapsulates this transition best, advising to 鈥渋nvest more in the people than in the technology鈥�. This principle underscores the importance of human capital in the age of AI, advocating for a workforce that is not only technologically proficient but also adaptable and innovative.

Generative AI, a focal point of Accenture鈥檚 Tech Vision 2024, exemplifies the symbiotic relationship between human ingenuity and artificial intelligence. It is set to redefine the essence of work, transforming roles into that of advisors, creators, developers, and protectors within the enterprise sphere. For example, Accenture鈥檚 initiative to automate 麻豆原创 documentation exemplifies how AI can streamline productivity and tackle complex challenges, highlighting a shift towards more engaging and meaningful work.

The transformation does not stop at job enhancement; it extends to the genesis of new career paths. As generative AI permeates various sectors, there is a burgeoning demand for roles that blend technical savvy with creative and strategic thinking. This shift is catalysing a comprehensive re-skilling movement, spearheaded by organisations keen on nurturing a workforce that is resilient and future ready.

Amid these technological strides, the essence of human-AI collaboration becomes ever more pronounced. AI technologies, including semantic search engines, transformer models, and large language models, are revolutionising how we interact with digital systems, making these interactions more intuitive and contextually rich.

This is not just a leap in technology but a paradigm shift in human-computer interaction, fostering a more integrated and seamless blend of digital and human intelligence.

However, the journey towards a harmonious human-AI coexistence is paved with ethical considerations. Privacy, transparency, and inclusivity stand at the forefront of this venture, ensuring that AI development is aligned with societal values and norms. It is about crafting AI systems that are not just intelligent but also ethical and human-centric, reflecting our collective aspirations for a future where technology amplifies human potential.

In South Africa, this evolution presents a unique set of challenges and opportunities. The key to navigating this landscape lies in fostering a culture of continuous learning, ethical inquiry, and inclusive design. By doing so, we can ensure that the benefits of AI are universally accessible, bridging the gap between technology and humanity.

So, what types of job am I talking about? Consider the following skills to which local companies need to start applying their minds.

As AI systems become more integrated into daily life and business operations, there is a growing need to ensure these technologies are developed and deployed responsibly. An AI Ethics Officer is responsible for creating guidelines that govern the ethical use of AI within organisations.

AI in healthcare is not just about data analysis; it is also transforming patient care through AI-enhanced medical devices and personalised medicine. AI-enhanced Healthcare Technicians specialise in operating, maintaining, and interpreting data from AI-powered medical equipment, such as diagnostic imaging machines and robotic surgical systems.

The accuracy of AI models depends heavily on the quality of the data on which they are trained.

Data annotation specialists play a crucial role in the AI development process by labelling data accurately, which is then used to train machine learning models. This can involve anything from identifying objects in images for computer vision tasks to annotating speech for natural language processing systems.

Integrating AI into existing business processes requires not just technical knowledge but also a strategic vision. AI business integration specialists work at the intersection of AI technology and business strategy, helping organisations identify opportunities for AI implementation that align with business objectives.

With the rise of chatbots and virtual assistants, there is a growing need for professionals who can design AI-powered conversational interfaces that provide a seamless and engaging user experience. Conversational AI designers combine expertise in AI, linguistics, and user experience design to create systems that can understand and respond to human language in a natural and intuitive way.

The focus must now shift from merely adapting to AI to actively shaping its trajectory in a manner that enriches human society. It is a call to action for all stakeholders 鈥� businesses, educators, policymakers, and individuals 鈥� to embrace this era of human-AI collaboration with optimism and a shared commitment to a future where technology and humanity coalesce to unlock unprecedented possibilities.

Ntsako Baloyi is a senior manager within the technology business at Accenture, Africa

This article first appeared in .

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Agriculture in Africa is being transformed by artificial intelligence, but there is still one element it cannot replace.

Despite dramatic technological advances in the sector in recent decades, the role of the farmer remains indispensable. This was a message delivered, surprisingly, at a recent conference in Europe, by a world leader in the field 鈥� from Eswatini.

Rob Coombe, group IT manager of Royal Eswatini Sugar (RES), took part in a听keynote speech at the Sapphire conference hosted by software giant 麻豆原创 in Barcelona, with a case study that encapsulates 21^st听century farming.

鈥淵ou really can鈥檛 take the farmer out of farming,鈥� Coombe told us during Sapphire. 鈥淪o much depends on the farmer鈥檚 intuition. Farming is incredibly scientific, with various measurements and readings. However, even with the best data, natural elements can change outcomes drastically.鈥�

The sugarcane producer uses drones and satellite imagery to enhance productivity and efficiency with 麻豆原创 software.

麻豆原创鈥檚 S/4HANA system is a leading tool for enterprise resource planning in enterprises, and is used by 85 of the 100 largest companies in the world.

In a keynote address, 麻豆原创 chief revenue officer by Scott Russell said: 鈥淭he reality is that, with business AI imbedded in your business, the opportunities are endless.鈥�

The biggest opportunity at present, said Coombe in response, was improving the bottom line.

鈥淲e have reduced the cost of IT by 15%,鈥� he told the audience of several thousand delegates. 鈥淲e鈥檙e spending more with 麻豆原创, but less overall.鈥�

Coombe has been at RES for more than 12 years, but has worked with 麻豆原创 systems since 1991. In that time, he said, the way technology is implemented has changed.

He told Business Times: 鈥淭he two big things are always people and data. People are immune to change, so getting the change in people鈥檚 mind is key. And getting your data sorted is equally important. When your data is not right, it doesn鈥檛 matter what the system is, you get a lot of rubbish out of it.鈥�

RES applies AI and Machine Learning (ML) to 15 years of satellite imagery to create a 鈥渄igital twin鈥� of its operations, in collaboration with 麻豆原创 systems.

鈥淭his helps us predict the yield and manage cash flow and budgeting more accurately. By integrating this data with 麻豆原创 Intelligent Agriculture and S4/HANA, RES can monitor crop health in real-time and make necessary adjustments. We don鈥檛 have to wait until harvest to see that there is a problem; we can see it on a day-to-day basis.鈥�

RES is using historical climate data and forward-looking weather data, combined听 with ML models, to predict future conditions, aiding in better planning and simulation. It is deploying drones for a range of agricultural tasks, enhancing both efficiency and precision.

鈥淚nstead of getting the guys in there, we send out a drone, and do an analysis with AI. Scouting 鈥� going out to look for pests and diseases 鈥� will now become an electronic thing, so you get quicker responses.鈥�

For climate monitoring, RES is using historical climate data and forward-looking weather data with ML models to predict future conditions, aiding in better planning and simulation.

鈥淲e are building a model with Swiss partners to use forward-looking weather data to improve our predictions. After we finish with S4, we are moving into the planning side of it, by the end of this year. This will help us determine the anticipated crop, the amount of sugar we will produce, the people needed, tractor hours, and diesel consumption.鈥�

Instead of randomly fertilising fields, the operation use satellite data to determine which areas need more fertiliser.

RES uses sensors to predict vibration and heat, allowing it to fix machines before they break. This information is fed into the 麻豆原创 software to manage maintenance tasks efficiently.

The transition to public cloud technologies has simplified aspects of operations.

鈥淚n the old 麻豆原创 days, it was a lot more difficult,鈥� says Coombe. 鈥淏eing on the public cloud, technology-wise, it has been easy. The amount of content available to us from a customer perspective to learn what to implement, how to implement, and so on, is completely different to what it was in the early 2000s.鈥�

This change has also facilitated the rapid onboarding of new employees.

鈥淲e employed some graduate trainees, and two days later they鈥檙e working. You could not do that in the past. They can bring their Java, and scripting tools, and start just start working.鈥�

RES鈥檚 strategic approach involves engaging all organisational levels.

鈥淚t was key to start at the top, but also work hard at the bottom, and then the frozen middle melted. At the bottom, we have super users. We mentor them once a month, addressing their problems and quickly dealing with them.鈥�

RES is also using generative AI with the 麻豆原创 digital assistant, Joule, to handle routine tasks like managing debtors, creditors, and reconciliations.

鈥淛oule can work at 2 o鈥檆lock in the morning, and the analysis can be read upon waking up. You can simply ask it about outstanding creditors, without needing to know specific program numbers.鈥�

RES is not waiting to be told what is possible. It is introducing a 鈥測outh AI council鈥� to explore further applications of AI.

鈥淭he council will help us identify new areas where AI can make a difference, leveraging the fresh perspectives and skills of younger employees.鈥�

Coombe says AI was not about replacing people.

鈥淚t鈥檚 about empowering people and making them capable. We are innovative in Southern Africa; we do stuff differently.鈥�

• Arthur Goldstuck is CEO of World Wide Worx and editor-in-chief of听. Follow him on social media on @art2gee.
• Jason Bannier is a data analyst at World Wide Worx and writer for听. Follow him on Twitter and Threads at @jas2bann.听

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1. 麻豆原创 leads with ethics

麻豆原创 appreciates the complexity that AI introduces into technology, and they approached the topic by forming an AI ethics steering committee comprised of senior 麻豆原创 leaders who review their approach, processes, and product capabilities to ensure operationalization and alignment with their policies and guidelines. Read about 麻豆原创鈥檚听.

2. Joule is not limited to SuccessFactors

Joule, announced in September 2023, is a natural-language, generative AI co-pilot that is embedded across the entire 麻豆原创 portfolio, and is designed to transform the way a business runs by leveraging intelligent technology across the various business applications. I鈥檝e heard it referred to as both a Co-pilot and a Digital Assistant, but the magic of it is that it is embedded in every application area across the business. It is first available as part of SuccessFactors; however, it will become available across the entire 麻豆原创 ecosystem.听

3. New AI innovation is delivered each quarter

You can keep an eye on what is available today and what is coming soon, by quarter, by viewing the 麻豆原创听. The Roadmap Explorer makes it easy for you to enter filters for the key areas that you are interested in, so you see only the areas of interest. For example, in the picture below I filtered on SuccessFactors and Artificial Intelligence:

4. 麻豆原创 SuccessFactors resources

There are large teams of people at 麻豆原创 SuccessFactors that focus on Artificial Intelligence, here are a couple that I like to follow to stay in the know on the topic specific to SuccessFactors:听,听,听听and听. Also I share a weekly AI Byte on LinkedIn that introduces something new in the area of AI, please follow me at听. Lastly, please be sure to check out this interesting White Paper听听(valid 麻豆原创 credentials required).

5. Partners play a critical role in 麻豆原创 SuccessFactors鈥� AI strategy

麻豆原创 and SuccessFactors are known for their superior technology, but they also acknowledge that Partners are an important part of the ecosystem, and having the ability to integrate with their technologies quickly and seamlessly to deliver better employee experiences is important. Here at EPI-USE Labs we have created a division dedicated to developing BTP and AI integrations for SuccessFactors. See my blog听听for more information.

听to enrich, enhance and extend your applications, landscapes and processes.

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By leveraging AI technologies, SMEs can achieve performance and innovation boosts across multiple areas of the business, from financial management to sales and marketing to human capital management and product development.

The global AI market is expected to grow from $208-billion in 2023 . For SMEs, the growth of AI holds huge promise: one study found that .

However, AI is still in its relative infancy. While technology providers have made huge strides in the power and quality of their AI tools, the technology has not yet reached its full potential and can be prone to error if not deployed in an optimised environment.

The intelligent SME

The adoption of AI forms part of a broader shift among businesses to establish intelligent enterprise capabilities. An intelligent enterprise consistently applies advanced technologies and best practices听within agile, integrated business processes.

For SMEs, this means establishing a powerful core that links departments, integrates business processes, connects data, and embeds intelligence to accelerate innovation and growth. And AI is the latest essential technology in the intelligent enterprise mix.

However, a successful deployment of AI in an SME requires accurate, relevant and reliable data. The better the data, the better the AI-generated outcomes.

For example, deploying AI to make decisions based on unstructured third-party data – such as social media posts by potential customers – can lead to inaccurate or downright incorrect outcomes. In contrast, deploying AI to trusted business systems, such as an SME’s enterprise resource planning solution, means the algorithm is basing its recommendations and outcomes on data that is accurate, up-to-date, reliable and relevant, leading to higher-quality outcomes that can truly drive the business forward.

Practical applications of AI

Despite the technology still being in its relative infancy, AI holds huge potential to SMEs. Unlocking its value will be a critical aspect of the success of any business in the coming years.

For SMEs, the journey to value-generating AI for business really starts with a digital transformation process that establishes a single source of truth for all business data. Here, enterprise resource planning (ERP) systems are key: cloud ERP systems enable greater speed and predictability across all of an SME’s core business processes and can scale as the business grows or expands to new markets.

With the core ERP in place, SMEs can choose to deploy AI to a wide range of business processes to accelerate daily tasks, minimise human error, cut costs and boost innovation, including:

Human Capital Management: Generative AI integrated with human capital management software can streamline content creation and provide access to in-depth analytics that provide valuable insights into the talent management and hiring process.

Finance: SMEs can increase the performance of their finance operations by leveraging AI for expense management, invoicing, auditing, financial forecasting and payments. One of the biggest challenges for SMEs is managing cash flow. AI can reduce the sales outstanding days with intelligent invoice matching to improve cash flow and keep the balance sheet healthy.

Supply Chain: In the wake of ongoing supply chain challenges, SMEs may want to leverage AI to better predict customer demand and increase production efficiency with intelligent auto-dispatching. In the manufacturing sector, SMEs can mitigate risk with preventive maintenance guided by AI that is integrated to the core production systems.

Procurement: Buying and procurement can become more agile, with AI simplifying the buying process while maintaining compliance with various internal and external controls. The use of AI can also automate the creation of sourcing events, drawing on past successes and real-time business information to provide an intuitive automated sourcing capability.

Sales & Marketing: Considering the importance of customer engagement and CX in the sales process, SMEs may also want to leverage AI for hyper-personalised product recommendations across multiple channels, with AI predicting customer behaviour to enable companies to minimise costly customer churn.

Innovation: Finally, SMEs can accelerate their innovation efforts by deploying and running AI models at scale without compromising data privacy. By leveraging the work by leading technology providers such as 麻豆原创, SMEs can also build AI into their core business applications with a library of pretrained models, accelerating the time-to-value of new innovation projects.

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听Vimercate-Bologna, February 26, 2024 鈥� is the first multi-utility company among the big players in the Italian market to choose the solution to accelerate its digitalization journey, enable a major business transformation that will involve the main business processes, with a view to sustainability.

In particular, will enable Hera Group to further improve its business continuity and operational efficiency by adopting cutting-edge technology solutions that complement the major innovation investments that the multi-utility has always made. 麻豆原创鈥檚 solution will also enable the company to simplify the management of a large volume of data and accelerate the ability to analyze and correlate it to optimize and speed up strategic choices and deliver higher performance.

鈥淪ince the birth of Hera Group, in 2002, we have always worked with an innovation-oriented vision to ensure high standards of quality and continuity of our services. The decision to work with 麻豆原创 now also confirms our willingness to equip ourselves with cutting-edge technologies to support the infrastructure and operational processes that underpin our business. Utilities is an evolving and fast-paced industry, but the great flexibility and scalability of the RISE with 麻豆原创 solution allows us to more easily seize future opportunities and address the changes in business models required by the dynamism of the market,鈥� said , Hera Group鈥檚 Chief Innovation Officer.

鈥淲e have been supporting the Hera Group since the beginning of their history in a challenging market environment with very strict regulatory aspects, demonstrating that we have always been a reliable technology partner, able to accompany the company in its constant growth and respecting all industry compliance,鈥� said , CEO of 麻豆原创 Italy. 鈥淏uilding a robust system of automated operations and processes based on RISE with 麻豆原创 will provide Group Hera with intelligent data and insights to seize new business opportunities, reach new customers, serve existing customers with higher levels of satisfaction, and support its strong commitment to sustainability.鈥�

An application solution that strengthens the strategic partnership between the Hera Group and 麻豆原创

The Hera Group has been working with 麻豆原创 for over twenty years, a strategic partnership that has expanded and strengthened over time. The path to digital transformation to adopt RISE with 麻豆原创 is a move to new technology with a multi-stage project over a horizon of more than three years, involving all business areas and staff functions from 2025 onwards and will be articulated in a program that ensures a migration that does not impact operations.

This approach will lead to the redefinition of all 麻豆原创 application and infrastructure components, combining the strategy and benefits of the cloud with the technology and architecture of 麻豆原创 S/4HANA. A choice that focuses on the reliability, efficiency and infrastructure flexibility guaranteed by the RISE with 麻豆原创 offering, on scalability and application evolution, with the possibility of integrations with AI.

An evolution in line with the multi-utility digitalization process

The adoption of RISE with 麻豆原创 represents a technological evolution fully in line with the Hera Group鈥檚 recent Business Plan, which allocated over 鈧�1 billion to digitalization and innovation of infrastructure, processes, and solutions for customers. These initiatives include numerous digitalization and automation projects and the use of predictive models and Artificial Intelligence algorithms based on the enormous amount of data managed by the Hera Group. The Group has long used artificial intelligence tools in the development and management of assets and processes in the energy supply chain, both on the distribution network and in back office activities and marketing services. In the field of water networks, digitization improves monitoring of the quality of service and helps predict anomalies; as regards the environmental supply chain, the Group uses AI to identify recyclable materials in waste and optimize their collection routes.

Hera Group

It is one of the largest multiutilities in Italy and operates in the environment, energy and water sectors, with about 10,000 employees, engaged every day in responding to the multiple needs of about 5 million citizens located mainly in Emilia-Romagna, Veneto, Friuli-Venezia Giulia, Marche, Tuscany and Abruzzo. Listed since 2003, it is among the top 40 Italian companies by capitalization (it is part of the Ftse Mib index) and since 2020 has entered the Dow Jones Sustainability Index, World and Europe.

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Does the entry of AI into every facet of our job functions represent a threat? On the whole, AI is largely expected to augment human capabilities, not replace them outright. The most likely impact of AI in the workplace is a transformation of our jobs and a shift in focus toward higher-value tasks.

This is reflected in some of the latest industry research.

One survey found that nearly half of European workers expect AI . A McKinsey analysis found that the impact of Generative AI on productivity , with 75% of that impact stemming from value generated in customer operations, marketing and sales, software engineering, and R&D.

What does AI hold for the world of work in 2024? My pick for the top three trends are:

Trend 1: Meeting demands for new types of skills

The rapid adoption of Generative AI in the business landscape in 2024 will drive demand for specialised Generative AI Prompt Engineers. Similar to how a sommelier is better at describing and pairing the unique characteristics of wines than the average person, so too will Generative AI Prompt Engineers excel at prompting Large Language Models to achieve more relevant, reliable and responsible business results.

For employers, this creates an opportunity to provide employees with valuable new skills that can transcend traditional job roles, especially within coding and software development. As Generative AI grows in popularity, the need for expert AI skills including good prompt writing will become indispensable to companies.

Trend 2: A new era for software developers

As the builders and engineers behind all of the world’s critical and most popular software applications, developers hold a special place within modern technology-led enterprises. In the year ahead, expect AI to become more embedded in the software development process, bringing rapid and dynamic changes.

In light of a surge in the use of Generative AI by developers, we will see the creation of new products and services, the automation of repetitive tasks, improvements in the quality of software, the emergence of entirely new ideas and business concepts, and personalised software development.

For companies, the opportunity is to rekindle creativity within their development teams and to redefine the boundaries of software development as we know it.

Trend 3: The indispensable value of managers

Is there a future where AI ‘manages’ teams of people? Highly unlikely. In fact, 2024 may very well see a resurgence in the role of managers in modern enterprises, especially in the delicate balancing act between implementing tighter budgets and greater bottom-line focus on the one hand and the motivating and engaging employees on the other.

Following a tenuous period in the wake of the pandemic and associated lockdowns where managers were largely cut off from their teams, the role of middle and frontline managers is back in focus.

While AI will inevitably be leveraged to support and improve management functions, it cannot adequately handle more complex tasks such as balancing the needs of neurodiverse employees with those of the business, for example.

However, this leaves managers under immense pressure from the top as well as from those under their management. Organisations will need to implement practices and processes to support the success of managers and empower them to navigate the complex problems they will inevitably deal with.

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