Creativity is as important as data literacy in the AI era, says IBM exec

Speakers at Fortune’s Innovation Forum warned that AI is reshaping jobs faster than workers can retrain, shifting employer demand away from pure STEM toward data literacy, prompt engineering, human‑machine collaboration and creativity, and urging firms to adopt a startup, experimental mindset and break down silos or risk remaining in “pilot purgatory.” Supporting evidence of rapid enterprise adoption, Gartner’s 2025 AI in Finance survey of 183 CFOs found 59% of finance functions use AI, led by knowledge management (49%), accounts‑payable automation (37%) and error/anomaly detection (34%), pointing to near‑term productivity and cost‑efficiency gains. Nvidia’s blowout quarter — ~62% revenue growth and an eye‑popping January revenue guide of $65 billion versus the Street’s $61.7 billion — reinforces expectations of escalating AI infrastructure spend, heightening pressure on firms and investors to prioritize talent, tooling and scalable deployment.

Analysis

Speakers at Fortune’s Innovation Forum emphasized that AI is reshaping labor markets faster than workers can retrain, with IBM Consulting APAC senior partner Charu Mahajan saying data literacy, human–machine collaboration, prompt engineering and creativity are now core competencies rather than pure STEM credentials. Mahajan argued technology is increasingly commoditized and the differentiator is people who can use tools creatively; Achmad Zaky of Init 6 urged an experimental, "startup" mindset and highlighted the value of learning from failure as firms try to move beyond 'pilot purgatory.'

Gartner’s 2025 AI in Finance survey of 183 CFOs reinforces adoption momentum: 59% of finance functions use AI, led by knowledge management (49%), accounts-payable automation (37%) and error/anomaly detection (34%), implying near-term productivity and cost-efficiency upside where deployments scale. The panel noted many large corporations still struggle to operationalize AI across functions and must break down silos and invest in talent to capture value.

Nvidia’s blowout quarter — roughly 62% revenue growth and January guidance of $65 billion versus the Street’s $61.7 billion estimate — and its forecast of industry-wide trillions in AI infrastructure spending heighten the urgency for companies and investors to prioritize scalable deployment, tooling and workforce readiness rather than isolated pilots. This dynamic favors firms that can translate infrastructure demand into recurring revenue and demonstrable efficiency gains while exposing those slow to build talent and cross-functional adoption to execution risk.

Key Decisions for Investors

• Consider increasing tactical exposure to AI infrastructure leaders such as NVDA given the company’s ~62% revenue surge and raised January guidance to $65 billion versus the Street’s $61.7 billion estimate,
• Prioritize companies with concrete plans to scale AI in finance and operations (knowledge management, accounts-payable automation, anomaly detection) because Gartner’s survey shows 59% adoption and early scale should drive measurable productivity gains,
• Underweight or engage with firms that remain in 'pilot purgatory' and lack clear cross-functional deployment and data-literacy training programs; monitor corporate disclosures on training, prompt-engineering initiatives and CIO/CFO execution metrics,
• Use corporate guidance, CFO commentary and reported AI use-case rollout metrics as primary triggers to add or trim positions rather than relying solely on thematic enthusiasm about AI infrastructure spending