2024 has seen massive technological advancements, especially in AI and Data Analytics space. Industries adopted these technologies early started realizing the benefits. From healthcare to finance and retail, AI and data analytics have emerged go-to tools for business insights and improved productivity that are fundamental to business growth.
This blog looks at the current state of AI usage, trends in data analytics, key problems, and AI and Data Analytics prospection.
Current AI Adoption Landscape & trends in data analytics
A recent McKinsey Global Survey found that 72% of organizations have had some levels of AI adoption across different business functions than in previous years. Moreover, half of these organizations still implement AI in two or more functional areas, which shows that AI is embedded throughout the firm.
As the general uptake of AI has risen, so has the amount of investment in AI in equal measure. The survey also revealed that 40% of organizations adopting AI spent over 5% of their budget on the technology in 2018. By 2023, this was at 52%. The global investment in AI technology will exceed 200 billion US dollars in 2024, demonstrating a healthy approach to fully tapping into the power of AI for operational improvement and product development.
Also, a report by Teamlease Digital reveals that the integration of AI solutions differs in various industries. In the pharmaceutical and healthcare sectors, AI is adopted by 52% of organizations.
The FMCG and retail industries are not far behind at 43% in terms of the adoption of data technologies. In manufacturing, the adoption rate has been reported to be low at 28 percent. In transport, the current AI adoption is between 20 percent and 22 percent. The lowest percentage across all industries belongs to the media and entertainment industries, where only 10% to 12% of firms take advantage of this technology.
Mirror view at 2024 Key Trends in Data Analytics
Here are some of the notable data analytics and artificial intelligence trends of the year 2024 that continue to influence how organizations use data. The following is a list of some of the trends observed below.
Augmented Analytics: Augmented analytics reduces IT intervention, allowing business users to perform analyses, detect anomalies, or generate insights in real-time. Some augmented analytics examples include IBM Cognos Analytics, Microsoft Power BI, and Oracle Analytics Cloud.
Edge Computing: Edge computing means analysing data at the network’s edge or the end device instead of at a central data centre. This trend is important for real-time analysis and result processing, low latency, and high speed. It’s happening now. Future estimates show that by 2025, more than 50 percent of the most important data will be dealt with in the regional edges.
Data-as-a-Service (DaaS): As the data grows more complex, DAAS is becoming a necessary solution to organizations that have no proper systems to deal with data.
Data Fabric: In its simplest definition, data fabric is a structure that interconnects your data across all platforms, tools, and ecosystems to govern and utilize it efficiently. It assists in attaining a better view and usage of data without requiring movement to different systems.
Analysts have assessed the data fabric market to reach $2.1 billion globally. They estimate that this figure will rise to approximately $8.9 billion U.S. dollars by 2032.
Synthetic data: Synthetic data generation is another data analytics trend quickly emerging and standing out as one of the most important ones. Synthetic data is a fake data created by computers but like actual data. This type of data is employed in testing, when training AI models, or for conducting research when ‘real’ data cannot be obtained or is insufficient. This information suggests that the market for synthetic data was at $288.5 million in 2022 as per various industries.
Widespread Adoption of AI Across Industries
Industries have accepted and applied artificial intelligence (AI) in many areas and as a result, it has changed the way businesses operate, make decisions and communicate with customers. Some of the key industries that has adopted AI are as follows:
1. Healthcare
Computer-aided mechanisms is used to enhance diagnostics by analysing medical images to define abnormalities that can hasten condition eventualities such as cancers and cardiovascular diseases. Personalized treatment is accomplished using predictive analytics to dissect the massive quantities of genetic and clinical data concerning the patient.
Recently, Google researchers have presented a new toolbox by the name of Health AI Developer Foundations (HAI-DEF) to support new weight models that enable developers to build AI models for healthcare applications.
2. Finance
Real-time claim and transaction analysis enables AI systems to detect fraud, which, in turn, increases overall account security for both providers and patients. For example, PayPal uses artificial intelligence to track transactions to detect signs of irregular activity.
Highlighted in the latest KPMG research, in September 2024, 71 percent of companies are implementing AI in finance, although at various degrees, with 41 percent applying it to a moderate or large scale.
3. Retail
AI commissioned in purchasing examines buyers’ habits to offer recommendations into the market that can assist in the marketing approaches. These systems make recommendations of products to customers in line with their tastes and previous orders has greatly increased sale. For instance, Amazon, uses artificial intelligence algorithms to study customers’ behaviour in relation to products, and they will provide recommendations of products that will improve customers’ experience in shopping.
A recent report, (AI) in retail stores market size is expected to see rapid growth in the next few years. It shall reach $2.01 billion in the year 2028 with a CAGR of 14.3%.
4. Manufacturing
By analysing data from machinery, AI foresees equipment breakdown and thereby spares manufacturers expensive repairs and lost time due to faulty machine.
Using real-time data, AI systems maintain consistent quality, leading to fewer defective products and improved productivity. For example, the integration of artificial intelligent robots to work alongside humans driving efficiencies in the manufacturing floor.
5. Transport
Self-driving cars and trucks that are based on artificial intelligence can be safely considered as the vehicles where technical errors do not affect the driving ability.
For instance, Tesla’s self-driving feature uses AI that is designed for route direction and safety aspects with an intention of improving road safety. Furthermore, the current AI system is useful in traffic analysis for efficient traffic flow by avoiding congestion in urban areas.
Logistics, utilize AI to estimate the most plausible routes rather than traversing through maps manually, thereby helping cut down on both time and expenses of fuel.
The Future Outlook
As Artificial intelligence technology continues to improve every day, the future lies nothing short of marvellous. Several emerging trends are already shaping the landscape of AI and data analytics, including:
Growth of Autonomous Decision-Making Systems: Decision-making capacity of AI own its own is called as Autonomous decision making. As firms intensify their spending on artificial intelligence, automated systems that operate independently and make decisions independently without being controlled by other individuals will become widespread. By 2030, this ADM will rapidly increase and the global AI market could be $1.81 trillion.
No-Code Solutions/platforms: A no-code platform or solution is a software technology that enables individuals to build and integrate applications, sites, or programs using little or no actual code. This will make it easy for people who are not so conversant with handling advanced analytics tools, thus creating a level playing field for organizations. Some of the no-code app builders are Softr, Bubble, Zapier Interfaces, & Glide.
Human-AI Collaboration: It has been estimated that by 2030, the use of AI will enhance human activity to a very large extent in various fields, including health care and financial services. This means specific job characteristics will change, meaning that workers will need to adopt to new technologies while simultaneously performing other tasks that call for the use of both emotions & rationality.
Conclusion
AI advancements made a record in 2024, impacting the industry at large. Thus, by entering 2025, there is a new chance that combining human and artificial intelligence in a new way will help envision new opportunities in work and living.
But this must be achieved while applying and maintaining organizational adaptability emerge as part of the solutions to identified hurdles such as data governance and ethical issues.
At Prescience Decision Solutions, we understand that those are facts and figures, and AI is only as good as the data the models are based on. Using Data Sentinel, businesses can safely and effectively use correct and bias-free data.
2024 brings not just evolution but a revolution in every aspect of our lives, our businesses, and how we connect, so let’s build this smarter world together.
