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OpenAI has finalised the most structurally novel enterprise AI deal of 2026: a $10bn vehicle anchored by TPG, with 19 investors and a 17.5% guaranteed annual return over five years. The strategy is to make PE portfolios a captive distribution channel. We wrote about the venture’s outline last month;
Saudi Arabian AI firm Humain is expanding its partnership with Amazon Web Services. This collaboration introduces Humain One, a new generative AI enterprise operating system.
Enterprise AI teams are hitting a wall — not because their models can't reason, but because the workflows underneath them were never built for agents. Tasks fail, handoffs break, and the problem compounds as organizations push agents deeper into back-office systems. A new architectural layer is emer
According to SAP, enterprise AI governance secures profit margins by replacing statistical guesses with deterministic control. Ask a consumer-grade model to count the words in a document, and it will often miss the mark by ten percent. Manos Raptopoulos, Global President of Customer Success Europe,
Enterprise software has no shortage of AI enthusiasm, but the gap between AI potential and production-ready results continues to frustrate organizations investing in enterprise process automation. As generative AI matures, building agents is no longer the hard part — keeping them accurate, auditable
Enterprise AI transformation is clearing the proof-of-concept stage for many organizations, with execution at scale becoming the new challenge that IT departments alone can’t handle. Governance, talent and organizational structure are required to meet C-suite mandates, according to Daniel Prager (pi
AI scaling challenges are intensifying as enterprises continue commiting serious budgets. However, cultural and structural barriers — not technology — are now the biggest obstacle to closing the gap between experimentation and production. The forces responsible for that gap are converging at once. A
Enterprises are pushing past AI experiments and demanding production-grade deployments, but fragmented data and poorly scoped agents are stalling progress. The question is no longer whether AI agents can execute tasks — it’s whether the underlying data fabric can give them enough context to execute
Mistral AI has launched Workflows, an orchestration layer for enterprise AI that is now in public preview. This release addresses a significant challenge as AI models and agents become more advanced, while reliably deploying them in production remains difficult due to a lack of infrastructure for co
Building LLM-powered applications starts simple. You pick a model, connect an API, and ship a feature. Maybe it’s a chatbot, a summarizer, or an internal tool. At this stage, everything feels manageable. Then things grow. Another team wants to use a different model. Someone asks for cost tracking. S
Enterprise AI orchestration has become the defining challenge of the agentic era — not because the models aren’t ready, but because most enterprises aren’t. The whole software stack is being reimagined for an agentic world, and the companies that win will be those that build the right controls. That
Without trusted data as the foundation, even the most sophisticated models will produce outcomes that enterprises can’t act on with confidence. As a matter of fact, enterprises are paying a steep price because of fragmented, problematic data stores, according to Alyson Welch (pictured), chief revenu
As enterprises navigate the complexities of scaling AI initiatives, the agentic AI blueprint for success lies in combining deep process intelligence with powerful cloud platforms, enabling organizations such as Genpact Ltd. to transform finance and operations workflows through reimagined processes a
Enterprise AI is shifting from hype to measurable outcomes, with success now depending on seamless integration and balancing automation with the human element. But the gap between a compelling AI demo and a deployment that actually changes outcomes isn’t the model — it’s everything built around it,
Enterprise AI scaling is accelerating as organizations shift from experimentation to full deployment, embedding intelligence into core workflows. At the same time, agentic systems are driving a broader move toward AI-native operating models. With operating models under pressure from every direction,
Enterprise IT is shifting from AI experimentation to enterprise AI enablement, pushing companies to modernize systems and unlock data value. At the same time, cloud-first strategies and evolving governance are reshaping how organizations stay competitive. This next moment for AI is focused on real-w
Google LLC has emerged as the only cloud “hyperscaler” with a leading frontier artificial intelligence large language model – Gemini – and today it issued a raft of announcements designed to capitalize on that current advantage. The search giant’s cloud unit launched the Gemini Enterprise Agent Plat
As enterprises race to adopt AI without sacrificing control or flexibility, open-source infrastructure is the stable foundation that enables organizations to modernize while maintaining full digital sovereignty. The promise of cloud computing was flexibility — but for many enterprises, years of deep
Enterprise AI is moving fast, but not fast enough to outrun the governance blind spots that are quietly undermining the rush from controlled pilots to at-scale production. With one in five organizations reporting a breach tied to shadow AI and only 37% having policies in place to manage or detect it
For the past year, early adopters of autonomous AI agents have been forced to play a murky game of chance: keep the agent in a useless sandbox or give it the keys to the kingdom and hope it doesn't hallucinate a catastrophic "delete all" command. To unlock the true utility of an agent—scheduling mee
