
Technology Finance: A Guide to Sustaining AI’s Return on Investment
Maximise AI ROI by fusing FinOps speed, TBM rigour and OKRs via TAONexus for agile, outcome-based financial governance.

Maximise AI ROI by fusing FinOps speed, TBM rigour and OKRs via TAONexus for agile, outcome-based financial governance.

Leaders are prioritising operational optimisation and procurement, whilst navigating AI governance to future-proof their digital strategies.

Microsoft Ignite ushers in autonomous agents, creating an immediate need for financial oversight and zero-trust security to manage unpredictable operational costs.

Unsustainable AI vendor economics demand a strategic focus on governance, true costs, and open-source models for lasting innovation and competitive advantage.

Microsoft’s new sandboxed Copilot extends its reach from data synthesis to autonomous execution, creating governance and compliance challenges, especially around paywalled data and security.

Core AI will soon be embedded in core business platforms, making complex custom projects redundant and shifting ICT strategy focus to vendor roadmaps.

StarCoder2, an open-access code LLM, offers customisation for enterprise development, but requires governance for DevOps integration.

Lately, the big questions for CIOs have revolved around strategic ICT challenges, ensuring proper governance and maximum value from IT investments. The key trend is artificial intelligence (AI), with senior leaders keen to understand the AI landscape, including practical uses for generative AI, to guide investment decisions and ensure the new tools align with established principles

CustomGPT.ai’s new visual processing allows AI agents to incorporate images (diagrams, screenshots) as visual citations into responses, leveraging existing multimodal large language model (LLM) technology.