Released
The New Skills in Finance Report 2022
In a digital-transforming era, there is a widening skills gap for those who cannot adapt to the new digital world in finance.
CFTE and Elevandi published the report with the discussion with leading experts to help governments, organisations and individuals address the current skills gap in finance and build a digital-resilient workforce in the industry.
Key Insights
Key takeaways from
"2023 Global Trends in AI Report" published by Weka
CFTE summarised “2023 Global Trends in AI Report” by Weka. The Global Trends in AI Report examines the state of AI adoption in 2023 and delivers key data-driven insights on likely developments.
Key Aspects
- The report looks at the use cases and value drivers of successful AI projects across industries and the data management and sustainability challenges organizations face as they attempt to scale their AI applications, particularly those that have not invested in modernising their data architectures and infrastructures.
Table of Contents
- Introduction
- AI is rapidly maturing at enterprise scale, impacting business outcomes
- Organizations’ IT infrastructure and data architectures are unfit for the AI revolution
- AI and modern infrastructure provide a path to sustainability despite near-term costs
- Enterprises with their data/infrastructure houses in order are poised to be future AI leaders
- The value derived from AI varies by region, industry and size
Key Findings and Insights
This report will give you an insight into:
- 1. AI/ML is accelerating across industries and within organizations, but few have reached true enterprise scale.
More than two-thirds (69%) of surveyed organizations have at least one AI project in production (“AI pioneers”), whereas 31% of respondents’ AI projects are still in pilot or proof-of-concept stages (“AI explorers”). Additionally, 28% of survey respondents cite reaching enterprise scale with AI projects widely implemented and driving significant business value. - 2. AI has shifted from a cost-saving measure to a revenue driver, and it is redefining markets.
Of 5,400+ responses received from over 1,500 survey respondents, 69% of responses regarding the motivations behind AI/ML projects cite revenue-focused drivers, as opposed to 31% that are cost-focused. Among AI pioneers, 70% of responses correspond to revenue drivers, compared with 66% of responses from AI explorers, suggesting that the focus on AI’s potential to drive revenue increases as AI initiatives mature. - 3. Data management is the top technical inhibitor to AI/ML.
The most frequently cited technological inhibitor to AI/ML deployments is data management (32%), outweighing security challenges (26%) and compute performance (20%). This points to many organizations’ current data architectures being unfit for the AI revolution. - 4. AI pioneers leverage a hybrid approach and more deployment locations to support the demands of AI/ML workloads.
AI/ML workloads can operate in a wide variety of deployment locations, ranging from the public cloud to enterprise datacenters and, increasingly, edge sites.AI pioneers (respondents with AI/ML projects in production environments) leverage more of these locations on average (3.2 deployment locations for training, 2.5 for inference) than AI explorers (2.9 and 2.3 locations, respectively). However, with data-intensive and complex AI applications, the public cloud provides an easier lift to start; the public cloud is the top primary deployment location for training AI/ML models (47%) and inferencing (44%). Additionally, those who use the public cloud to run AI/ML are more likely to use a hybrid approach incorporating more locations for training (4.2 on average) and inference (3.2), as opposed to respondents who do not use the public cloud (2.2 and 1.9 locations, respectively). - 5. AI/ML’s energy use and carbon footprint are straining corporate sustainability goals, but the cloud presents a path to improvement.
Organisations are challenged by AI/ML’s toll on their corporate sustainability goals. More than two-thirds (68%) of respondents indicate they are concerned about the impact of AI/ML on their organization’s energy use and carbon footprint. The cloud provides a path to greater AI sustainability: 74% of total respondents say sustainability is an important or critical motivator for moving workloads to the public cloud. - 6. Aging data infrastructures and legacy architectures directly impact AI/ML’s sustainability performance.
More than three-fourths (77%) of respondents believe data architectures impact their sustainability performance. - 7. Organisations that have their data and infrastructure “houses” in order will be well-positioned to lead with AI in the future.
Companies leveraging a modern data architecture to overcome significant data challenges (sources, types, requirements, etc.) can accommodate AI/ML workloads operating across multiple infrastructure venues.
Key infographics
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