Special Report
Analysis of how artificial intelligence, automation and agentic software are changing work, company strategy, markets and the businesses built around them.
34 public articles
Mid-market firms are increasingly using indemnity clauses and specialised insurance to transfer liability from third-party AI vendors. This analysis examines the contract structures, coverage gaps, and commercial implications for buyers and vendors.
Mid-market firms are inserting agentic procurement clauses into vendor contracts to define liability, data ownership and termination rights when AI agents autonomously purchase software and services. The clause shifts risk allocation and may reshape procurement workflows.
Mid-market firms are building dedicated budgets for AI model drift, allocating funds for monitoring, retraining, and fallback systems to maintain production accuracy.
Mid-market operations teams are adopting runbooks for agentic workflows. Learn how decision logs and rollback procedures reduce risk and build audit trails.
Mid-market firms face a complex procurement decision between API-based AI models, fine-tuned open-source alternatives, and embedded automation modules. This analysis benchmarks total cost of ownership across the three approaches, examining usage patterns, infrastructure costs, and operational trade-offs.
Mid-market firms are adopting agentic software but face a critical governance question: when must a human intervene? This explainer unpacks the escalation matrix, its design principles, and the commercial implications for buyers and vendors.
Mid-market firms are forming internal AI audit teams to validate outputs, monitor bias and certify compliance before regulators act. Analysis of costs, structure and commercial impact.
Mid-market firms are creating internal AI model registries to catalogue algorithms, data lineage, and version control. This case study examines the drivers, practical steps, and commercial implications of this emerging practice.
Mid-market firms are building model audit trails to document AI decisions for insurers and regulators. Analysis of methods, commercial impact, risks and outlook.
Mid-market firms are increasingly running exit audits on fine-tuned AI models before renewing vendor contracts, testing whether weights, training data, and deployment paths can be moved without disruption.
Mid-market firms are absorbing revenue losses from unmonitored AI pricing systems as traditional insurance products fail to cover model drift. This analysis examines the self-insurance strategies emerging and the commercial implications.
Mid-market firms are deploying AI models without central inventory, creating hidden liability in compliance, security, and cost control. This analysis examines the blind spot and its commercial consequences.
Employees in mid-market firms are deploying unauthorised AI agents without IT oversight, creating data exposure, compliance gaps, and operational blind spots. This analysis examines the scale of the risk, the commercial implications, and what leaders can do.
AI models in pricing and inventory systems degrade over time. This 'model drift tax' can erode margins by 5-15% before detection. Analysis of causes, commercial impact and mitigation strategies.
Mid-market software companies face a new budget challenge as AI model providers shift to usage-based pricing. This analysis examines the commercial impact, risks, and strategic responses.
CFOs are reallocating significant portions of labour budgets toward AI infrastructure. This analysis examines the drivers, commercial implications, risks and outlook for businesses and investors.
As enterprises deploy agentic AI systems that make autonomous decisions, a critical audit trail gap has emerged. This analysis examines why existing verification methods are insufficient, the commercial risks involved, and what businesses must consider to maintain accountability.
Enterprises are spending heavily on AI software licenses but using only a fraction of them. This analysis examines the causes, the financial impact, and what procurement teams can do to close the gap between purchase and adoption.
Agentic software promises autonomous task execution but introduces unpredictable operational costs. Finance leaders must budget for failure, retries, and human oversight to avoid budget overruns.
AI tools are being adopted in financial auditing to improve speed and coverage, but questions of accountability, regulatory alignment and model risk remain unresolved.
AI-linked enterprises, especially server manufacturers like Foxconn and Quanta, are driving a massive share of Wall Street gains, fueled by unprecedented demand for high-performance GPU infrastructure. This surge in hardware investment signifies a critical, capital-intensive new layer emerging within the global AI economy.
As global powers vie for semiconductor supremacy, the battleground has shifted from cloud datacenters to local edge devices.
Forget GPT-5; the real revolution is happening in high-performance models that run entirely on your smartphone.
Fortune 500 companies are increasingly appointing AI agents to advisory roles, transforming corporate governance forever.
Scaling intelligence requires massive power. We examine the nuclear and fusion startups racing to fuel the AI revolution.
A new generation of entrepreneurs is building billion-dollar companies with teams of three humans and a thousand agents.
Automation is no longer just for blue-collar tasks. How the 'agentic workflow' is reshaping the corporate hierarchy.
When high-quality human data runs out, AI must learn from itself. The risks and rewards of synthetic training sets.
From the UAE to France, countries are investing billions in national AI clusters to ensure data and cultural sovereignty.
The 2026 AI Safety Treaty has fundamentally changed how frontier models are developed and deployed across borders.
Standard GPUs are no longer enough. The rise of LPUs and neuromorphic chips is defining the next era of compute.
As AI answers queries directly, the multi-billion dollar SEO and referral economy is facing an existential crisis.
Moving beyond 'p-doom', the conversation has shifted to practical alignment and the prevention of agentic drift.
Moving beyond chatbots: how enterprise AI is shifting from experimentation to core infrastructure.
