Four Traits Separate High AI Adopters From Laggards
Forrester's survey of 1,500 AI decision-makers points to customer focus, CEO leadership, data readiness and workforce skills.
Updated on Nov. 5, 2026 in Artificial Intelligence
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Forrester analysts outlined four traits that distinguish companies with high AI adoption at the firm's Technology & Innovation Forum East in New York. The traits are customer focus, CEO leadership, data and platform readiness, and workforce AI skills, drawn from Forrester's April 2026 report Accelerate Your AI Voyage, which surveyed 1,500 AI decision-makers. These are findings from an analyst report and forum keynote, not a shipped product.
Why it matters
Scaling AI across a business requires teams to work toward shared goals and fund needs that span departments, which Forrester identifies as a key difference between high and lower adopters. The framework points to a shift from counting AI pilots toward measuring customer value and workforce readiness.
The figures come from a Forrester survey of 1,500 AI decision-makers, with high adopters 1.3 times more likely to focus on customer experience and twice as likely to test applicants' AI skills than lower adopters. Forrester's measures of workforce AI readiness barely moved between 2024 and 2025, and the link between these traits and measured business returns is not quantified in the source.
The players
Forrester
A research and advisory firm whose analysts publish technology market research, including the Accelerate Your AI Voyage report on enterprise AI adoption.
DBS Bank
A Singapore-based bank cited for deploying personalized AI-driven financial guidance that solicits customer feedback on each suggestion.
The details
Forrester suggests companies assess customer focus by dividing the number of AI use cases that create customer value by those aimed at internal gains, with a ratio of 1-to-1 as a conservative floor and 1.5-to-1 or higher signaling stronger customer orientation. High adopters also invest in AI literacy and training for existing employees, not only new hires. DBS Bank, based in Singapore, offers personalized financial guidance and asks customers whether each suggestion helps them, an example of customer-facing AI tied to feedback.
Timeline
April 2026: Forrester publishes the Accelerate Your AI Voyage report
2024 to 2025: Workforce AI readiness measures improve only slightly
November 5, 2026: Forrester Technology & Innovation Forum East keynote held in New York
The Tech Race
Forrester's April 2026 report, based on 1,500 AI decision-makers, frames enterprise AI competitiveness around customer focus, leadership and workforce skills rather than tool adoption alone. The framework sits alongside the firm's own measurement of workforce readiness, which improved only slightly between 2024 and 2025.
Companies adopting these practices may begin asking job applicants to demonstrate AI skills and measuring AI use cases against customer value, changing hiring and internal investment decisions. No timeline is given for when these practices become standard across organizations.
The takeaway
Watch whether companies adopt a customer-to-internal use case ratio and report it, since Forrester offers 1-to-1 and 1.5-to-1 as benchmarks. Forrester's next updates to its workforce AI readiness measures will show whether the slight 2024 to 2025 gains continue.
Further reading
For broader context on how companies are putting these tools to work, see Artificial Intelligence coverage in New York City.
Source note: This article includes information reported by TechTarget.
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