Explainer Collection

AI Strategy

Mertens et al. (2026)

AI is not crashing over jobs in waves, it is rising as a tide across nearly all of them

Across 17,000 worker evaluations of more than 3,000 real labor-market tasks, frontier models improve broadly across task lengths, not in sudden bursts. By 2029 most text-based tasks could hit 80–95% success rates.

3.8 mo: doubling time for the task length frontier models can complete at a 50% success rate
60%: average rate at which model outputs are accepted by domain-expert evaluators without edits

Zhang et al. (2025)

Agile teams want AI to be a teammate, but the tools, skills, and rules aren't ready yet

A workshop of 30+ researchers and practitioners at XP2025 catalogued six categories of frustration with GenAI in agile software development and co-created a five-theme research roadmap to move from isolated experiments to human-centered integration.

78.6%: of voters flagged lack of prompting skills as their top frustration, the highest single sub-challenge across all categories
73.3%: voted 'too many tools, unclear which to use' as the most pressing tooling challenge

Singla et al. (2025)

Nearly every company now uses AI, but most still can't turn pilots into profit

McKinsey's 2025 global AI survey of nearly 2,000 respondents finds that 88% of organizations use AI, yet only 39% report any enterprise-level EBIT impact. The companies capturing real value treat AI as a catalyst for transformation, not just a cost-cutting tool.

88%: of respondents say their organizations regularly use AI in at least one business function, up from 78% a year ago
62%: of respondents report their organizations are at least experimenting with AI agents