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
"The state of AI in 2022—and a half decade in review" report published by McKinsey & Company
CFTE summarised “The state of AI in 2022—and a half decade in review” report by McKinsey & Company. The report shows the expansion of the technology’s use since we began tracking it five years ago, but with a nuanced picture underneath.
Key Aspects
- The report “The State of AI in 2022 – And a Half-Decade in Review” provides a comprehensive overview of AI’s evolution and current status.
- It focuses on the expansion of AI adoption in businesses, the financial returns from AI, and the development of AI capabilities like natural-language processing and computer vision.
Table of Contents
- Five years in review: AI adoption, impact, and spend
- Mind the gap: AI leaders pulling ahead
- AI talent tales: New hot roles, continued diversity woes
- About the research
Key Findings and Insights
This report will give you an insight into:
- Integration of AI into Business Processes: AI adoption is not merely about the technology itself but also involves integrating it into business processes and decision-making. This integration requires a clear understanding of the specific tech talent roles needed and how AI engines and human efforts can collectively create more value.
- Diversity in AI Development Teams: There is a notable performance difference in AI development based on the diversity of the team. Organizations with at least 25% of their AI development employees being women or racial or ethnic minorities are significantly more likely to be high performers in AI.
- Long-term Perspective in AI Adoption: Companies that view AI adoption as a long-term journey, rather than a quick fix, tend to be more successful. These organizations evolve into learning entities, gradually incorporating AI capabilities and learning from both successes and failures.
- AI Strategy Linked to Business Outcomes: High-performing organizations in AI often link their AI strategy to tangible business outcomes. They engage in advanced practices like modular data architecture and automated data processes, enabling rapid development and deployment of AI applications at scale.
- AI and Data Governance: High-performing organizations in AI are also more proactive in managing AI-related risks, such as data privacy and equity. They engage in practices like standardized processes, automated quality control, and ongoing model validation and monitoring.
- Investment in AI Technologies: High performers in AI are likely to allocate a significant portion of their digital-technology budget to AI-related technologies, indicating a strong commitment to leveraging AI for business growth and innovation.
- AI High Performers Expanding Their Lead: The report identifies a group of 'AI high performers' who are seeing significant bottom-line impact from AI, primarily through revenue growth rather than cost reduction. These organizations are more likely to engage in foundational practices that unlock AI value.
- Talent Sourcing Strategies: High-performing organizations in AI employ various strategies for sourcing AI talent, including reskilling existing employees and recruiting from diverse sources. This approach contrasts with the more limited strategies of other organizations.
Key infographics
Be AI-ready in this digital era
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