Enterprise adoption and operating models
How organisations actually absorb AI: vendor selection, usage patterns across sectors, and the operating-model changes that decide whether any of it lands.
Research sweeps
2026-04-09 · standard
Enterprise Agentic AI Adoption Criteria
Enterprise agentic AI adoption in operational processes November 2025–present: procurement criteria, model drift risk, version stability, availability SLAs, and how enterprises manage dependency on AI vendors in production workflows
Claude Opus 4.8- financial
- frontier
- academic
- +1
2026-04-13 · standard
Enterprise LLM Vendor Selection and Consumption Models
Enterprise LLM vendor selection and consumption patterns (April 2025–present): how companies choose between OpenAI, Anthropic, Google, hyperscaler-hosted model access, and direct API relationships; what decision metrics they use across availability, quality, price, governance, and SLAs; and how adoption differs by company size, workload criticality, and realtime versus offline use cases
Claude Opus 4.8- financial
- frontier
- academic
- +2
2026-06-20 · deep
Comparative LLM Usage Across Sectors
Comparative real-world usage of LLMs and adjacent AI technologies from June 2025 to June 2026: which models (GPT-5, Claude, Gemini, Llama, Mistral, DeepSeek, Qwen) dominate which sectors, how they are deployed (hosted API, Bedrock/Azure, self-hosted vLLM/Ollama, RAG, agents, fine-tuning), what workloads they serve, and how organisations measure, budget, and publicly report token cost and actual spend.
Claude Opus 4.8- financial
- frontier
- academic
- +3
2026-06-16 · deep
Designing AI Operating Models Around Humans
How humans are adapting to AI between June 2024 and June 2026, weighing measured benefits and harms, and how organizations should design operating models around human cognitive load and behavioural patterns rather than forcing adoption, covering cognitive overload from supervising multiple agents at machine speed (context switching, automation complacency, vigilance fatigue), the poor budget and value outcomes of top-down AI mandates and token-maximizing usage, the gap between model welfare functions (such as Anthropic's) and any equivalent human or worker welfare function, and how much good human outcomes depend on model training versus orchestration and deployment design.
GPT-5.5- financial
- frontier
- academic
- +3
Explainers
- Research Explainer · 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.
- Research Explainer · 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.
- Research Explainer · 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.
- Research Explainer · Hitzig (2026)
Coding agents make a software background optional, but expertise still decides who succeeds
An analysis of roughly 400,000 Claude Code sessions finds a clear split of labour: people decide what to build, the agent decides how. Command of a domain, not the ability to write code, is what makes sessions succeed.
- Research Explainer · Cambridge CCAF (2026)
Finance has gone all-in on AI, but the supervisors watching it have not
A 628-organisation, 151-jurisdiction survey finds 81% of financial firms now using AI, while regulators trail on adoption, data collection and the supervisory tools needed to keep up.
- Research Explainer · Leite & Audretsch (2026)
Firms don't fail from one big shock, they fail when invisible tensions quietly compound
A new framework argues organisations behave like complex adaptive systems, where small erosions in trust, ideology, or politics cascade into structural collapse. The authors test it against a 16th-century financier and Tesla.