AI as the Engine of the Innovation Economy: Part 5 – Value Realisation
Realise value by treating AI as a portfolio: balance risky experimentation with scalable adoption to bridge the ROI gap.
Realise value by treating AI as a portfolio: balance risky experimentation with scalable adoption to bridge the ROI gap.
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.
Artificial intelligence is moving from isolated experiments to the foundation of enterprise operations, demanding that CIOs redefine digital transformation by placing a strategic focus on data utilisation, ethical governance, and deep workforce literacy
The AI industry is experiencing an unprecedented surge, with neo-cloud firms like Iren playing a pivotal role in reshaping the AI infrastructure market.
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.
Adobe’s unified Firefly AI platform streamlines creative production, offering choice via a multi-model ecosystem while shifting vendor lock-in to the overarching Creative Suite.
AI projects often fail not because of the technology, but because of misalignment with strategy, poor data foundations, and low organisational readiness. To succeed, IT leaders must shift focus from hype to value, build robust data ecosystems, and foster cross-business collaboration.