Real business value: The state of AI in 2024

18 June 2024
Written by Andrich van Wyk

Andrich van Wyk has 16 years of experience in Artificial Intelligence (AI) Research and Development, and consulting. He has broad experience as a Software Engineer and Architect, specialising in enterprise systems and Cloud technology. He holds an M.Sc. in Computer Science from the University of Pretoria, with a focus on neural networks and evolutionary algorithms. Andrich enjoys writing about machine learning engineering, and the software engineering industry at large.

As we navigate through 2024, the rapid evolution of artificial intelligence (AI) continues to reshape the business landscape. Enterprises across various sectors increasingly leverage AI to enhance operational efficiency, drive innovation, and deliver superior customer experiences. This blog post explores the current state of AI in 2024, highlighting key trends, the value of SAP's Joule, and the challenges and risks businesses face in unlocking AI's potential.

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As we navigate through 2024, the rapid evolution of artificial intelligence (AI) continues to reshape the business landscape. Enterprises across various sectors increasingly leverage AI to enhance operational efficiency, drive innovation, and deliver superior customer experiences. This blog post explores the current state of AI in 2024, highlighting key trends, the value of SAP's Joule, and the challenges and risks businesses face in unlocking AI's potential.

Key trends

The surge in Generative AI Adoption

The adoption of generative AI has almost doubled since 2023, with 65% of organizations now regularly using these technologies, according to the latest McKinsey Global Survey on AI. This surge is driven by the significant business value generative AI offers, such as cost reductions and revenue growth, particularly in marketing, sales, and product development functions.

AI is showing ROI

Reports and surveys show that investments in AI are showing rapid Returns on Investments (ROI). Estimates indicate an ROI of $3.5 for every $1 invested, with some organizations realizing an average of $8 in return. Value is also realized quickly, with most AI deployments taking 12 months or less.

Increased focus on AI in workforce planning and development

AI transforms workforce planning by emphasizing skills over traditional credentials. Organizations are targeting specific AI skills and investing in reskilling and upskilling initiatives to meet the demands of AI integration. This trend is especially prominent in state governments, where there is a strong focus on preparing the workforce for AI through comprehensive training programs.

Integration of AI in HR practices

AI is revolutionizing HR practices by making skills the center of HR strategies and enhancing employee experiences. AI-powered tools identify skill gaps, improve recruitment processes, and offer personalized learning opportunities, helping create a more engaged and skilled workforce. However, there is increased scrutiny of AI's ethics and safe use, especially in the HR domain.

Widespread adoption of AI tools in software engineering

AI tools such as ChatGPT, GitHub Copilot, and Gemini are becoming staples in the developer toolkit. Most developers (estimated between 60% and 70%) report using AI for various tasks, including coding, writing documentation, conducting research, writing tests, troubleshooting, and managing CLI commands. Generative AI tooling is significantly enhancing software development efficiency by automating routine and repetitive tasks, freeing engineers to focus on more complex and creative problems. Importantly, having the engineer in the loop remains critical for overseeing complex decision-making and correctness. This necessity is reinforced by the most common risks associated with Generative AI tools, such as biases, inaccuracies, and ethical concerns, which require the nuanced expertise and judgment of human engineers to manage and mitigate

Generative AI is proving the key to unlocking data

AI profoundly transforms the data and big data landscape by enhancing data processing capabilities, primarily through multi-modal AIs capable of understanding unstructured and previously inaccessible data (such as audio and video). 2024 is also seeing the rise of copilot-based tooling for major data platforms, such as Snowflake Copilot, that lowers the entry barrier and increases productivity in working with large data warehouses.

Increased focus on AI safety and governance

With the growth of AI adoption, there is a heightened focus on establishing robust governance frameworks to ensure responsible and ethical AI use. With some legal questions still outstanding (especially surrounding the issue of copyright), organizations and state governments are developing policies and risk management practices to address data privacy, security, and bias concerns. There has also been a significant expansion of the AI observability and monitoring space, with a strong focus on safety, to enable the auditable and traceable use of AI models in the industry.

SAP's AI innovation: Joule

SAP's Joule showcases SAP’s ongoing investment in AI innovation in 2024. Joule acts as an AI copilot, revolutionizing how users interact with SAP business systems. By enabling conversational interactions, Joule simplifies access to information and automates business processes, enhancing employee and customer satisfaction.

 

Joule helps users efficiently navigate functionalities, complete tasks, and retrieve relevant information, significantly boosting productivity. Joule is also capable of delivering answers and intelligent insights on demand, enabling informed decision-making and reducing bottlenecks. By leveraging data from various sources, Joule helps enterprises make data-driven decisions quickly and accurately. Joule also features integrated AI ethics and privacy controls to minimize the risk of AI usage in the organization.

 

Joule’s abilities will continue to expand in 2024 with integration into SAP Build Process Automation, Ariba Category Management, and Analytics Cloud, all planned to be released this year.

Challenges and risks

Despite the promising advancements, businesses face several challenges and risks in adopting and unlocking value from AI. Chief among these are the ethical, legal, and regulatory concerns surrounding using new generative AI systems. The penetration of AI systems into critical business systems, such as HR and finance, heightens the need for robust risk frameworks and AI policies within enterprises.

 

Another significant barrier to AI adoption is the shortage of skilled professionals who can develop, implement, and manage AI technologies. Many organizations struggle to attract and retain talent with AI-related skills due to competition with higher-paying private-sector jobs.

 

Data quality and management issues remain prevalent in the age of Generative AI. The effectiveness of AI systems heavily depends on data quality and accuracy. Many organizations face challenges related to data governance, ethics, data privacy, and ensuring data accuracy, which can undermine the reliability and value of AI applications. This is exacerbated by the accessibility to new forms of data (audio and video), which poses new privacy and ethical concerns.

 

AI-based systems also pose unique security and scalability challenges. New attack vectors and vulnerabilities, such as prompt injections and leaking, model poisoning, and model hallucinations and inaccuracy, remain active areas of research with limited mitigation strategies. AI systems also potentially carry significant costs, and may require specialized hardware in the form of GPUs, posing new budgetary challenges for IT organizations.

Summary

The state of AI in 2024 is marked by rapid adoption and transformative impacts across various sectors. Key trends highlight the growing integration of AI in business-critical functions such as HR and finance, significant ROI through increased efficiency, and product and data innovation. SAP's Joule exemplifies how AI can streamline tasks, provide intelligent insights, and enhance user experiences. However, safety, security, scalability, skills shortages, and data quality must be addressed to unlock AI's potential fully. As organizations navigate these challenges, AI's strategic and ethical implementation will be crucial in realizing its full business value.

 

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References

 

 

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