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Your company needs a digital twin
As AI begins to generate change faster than enterprises can track it, there’s a growing risk of operating models simply falling apart under the deluge. One wya of looking at is by treating the enterprise itself like a game.
As AI accelerates change, one of the biggest risks is that organisations lose sight of how work actually flows. That is why the SimCity metaphor is useful. Imagine your organisation as a living digital model: people, processes, data, AI agents, workflows, bottlenecks, and dependencies all visible in one place. Then you can simulate changes before implementing it.
- What happens if this task is automated?
- If this decision moves to an AI agent?
- If this department disappears? If transaction volume triples?
- If one system fails?
Digital transformation stops being a sequence of technology projects and becomes the continuous redesign of the enterprise.
- https://diginomica.com/handle-ai-impact-make-cto-says-enterprise-look-like-simcity
The leadership question
Do you understand your organisation well enough to simulate what happens when AI starts changing it?
The next era of digital transformation: Forward to the intelligent enterprise
Adoption moves through four stages: scattered pilots, agent proliferation, a modernization reckoning, and finally the intelligent enterprise.
https://www.pwc.com/us/en/technology/alliances/library/the-next-era-of-digital-transformation.html
The agentic transformation office: Redefining the economics of change
The transformation office (TO) has a dirty secret. The very function charged with driving radical change across the enterprise is often one of the slowest to adopt modern tools and technologies
How Legacy Data Storage is Holding Back Your AI Push
Legacy storage bottlenecks AI with capacity gaps, slow performance, and poor integration. Modern NVMe arrays deliver speed, scale, and compliance.
https://aibusiness.com/data-centers/how-legacy-data-storage-is-holding-back-your-ai-push
AI’s next enterprise risk is generated sprawl
This is the next frontier for CIOs: the layer of working but ungoverned systems – an array of tools that were useful enough to launch, but not mature enough to own, maintain, secure or even properly retire. And they also might not look like what CIOs are used to seeing. They could begin as a prompt, a low-code workflow, an automation or an agent configured for a narrow task
https://www.ciodive.com/spons/ais-next-enterprise-risk-is-generated-sprawl/831090/
Avoiding the ERP hangover
Reorient the operating model for continuous improvement
https://www.cio.com/article/4223967/avoiding-the-erp-hangover.html
Gartner Predicts that Guardian Agents will Capture 10-15% of the Agentic AI Market by 2030
Guardian agents are AI-based technologies designed to support trustworthy and secure interactions with AI. They function as both AI assistants, supporting users with tasks like content review, monitoring and analysis, and as evolving semi-autonomous or fully autonomous agents, capable of formulating and executing action plans as well as redirecting or blocking actions to align with predefined agent goals.
When Robots Learn to See: RaaS and Spatial AI Reach the Factory Floor
A study from Deloitte and the Manufacturing Institute projects that as many as 2.1 million manufacturing jobs could go unfilled by 2030, a shortfall estimated to put $1 trillion in output at risk. That pressure is now colliding with two technologies maturing together: Robotics as a Service (RaaS) and spatial AI. Together they are changing not just how automation gets paid for, but how machines perceive the world they work in.
How to effectively govern third-party AI agents across the enterprise
As AI agents spread across enterprise systems and providers, organizations need a governance model that controls what agents can do without limiting the value they can deliver
8 ways poor data governance changes AI’s effectiveness
Make your institutional knowledge AI-ready, your AI is only as good as your data, why grading data quality boosts agentic AI reliability, AI-generated data ownership presents enterprise risks, agentic AI meets data debt first in contact centers, the data fitness test: Why copilots stall after the demo, data silos undermine CX insights; AI discovery tools can help, and personal AI assistants present organisational data risks
https://www.nojitter.com/data-management/8-ways-poor-data-governance-changes-ai-effectiveness
