A specialist AI agent company recently raised $950 million at a valuation above $15 billion, signalling that enterprise AI has moved beyond experimentation, according to a TechRadar report by Sergii Gorpynich. But the same report warns that many organisations may be applying new technology to outdated operating models, risking project failure.
The productivity trap
The debate within enterprises still focuses too often on a single question: "Is AI delivering productivity gains?" The answer is yes. Across technology, operations, marketing, service and back-office teams, AI is helping employees automate workflows and use agents to perform tasks that previously consumed large amounts of human time. However, productivity is not the most important point, according to the report.
Gartner has predicted that more than 40% of agentic AI projects will be cancelled by the end of 2027 because of rising cost, unclear business value or weak risk controls. The warning points to a deeper issue: many organisations are applying a new technology to an old operating model, putting copilots, assistants and agents on top of workflows that were designed for a slower, more predictable business environment.
When stripped away from hype, an AI strategy comes down to two key priorities: business optimization and business transformation. Optimization means using AI to do what you already do better — improving efficiency, reducing redundancy, eliminating manual effort, strengthening existing revenue engines and helping people make better decisions. Transformation means using AI to do something different — creating new products, new services, new revenue models and new ways to generate value that were not viable before.
Five levels of enterprise autonomy
For CIOs and CTOs, the immediate question is where the organisation sits on the maturity curve. The report defines enterprise AI transformation across five levels of autonomy:
| Level | Name | Description |
|---|---|---|
| L1 | Assisted Automation | Initial AI adoption with assistive AI copilots; decisions and execution still by humans; enterprise system interaction by humans |
| L2 | Partial Autonomy | AI takes over bounded decisions and execution in clearly scoped domains with guardrails; humans handle exceptions and provide supervision; AI agents own system interaction within domains |
| L3 | Cross-Functional Autonomy | Multiple agents coordinate across functions, using outcome-driven (not fixed-workflow) optimization |
| L4 | Near-Autonomous Enterprise | Enterprise runs in purely AI agentic mode; AI agents plan, execute, monitor and correct within policy constraints; people define strategy, ethics and governing policies |
| L5 | Fully Autonomous Enterprise | AI sets sub-goals, reconfigures organisational execution and autonomously refines strategy within agreed bounds; people act as board, ethics and risk authority |
A realistic path forward
The report suggests that a realistic goal over the next two to five years is progress from Level 2 to Level 3 in high-volume, well-instrumented domains where data quality, process ownership, controls and ROI metrics are already strong. The CTO and co-founder at Star (the AI agent company that raised $950m) contributed to the analysis, but the report does not provide their name.
Three capabilities are required to move up the maturity curve, though the full third capability description was cut off in the source. The first two capabilities implied are: strong data quality and process ownership, and clear ROI metrics.
The shift from workflow automation to autonomous enterprises requires a deliberate strategy that moves beyond simply layering AI onto existing processes. Organisations must choose between optimizing the present or transforming for the future — and invest accordingly. The Gartner warning underscores that without clear business value and risk controls, even well-funded AI initiatives may fail.
For trade executives, the implications are clear: AI adoption in logistics, customs, and supply chain management will likely follow a similar maturity curve. Companies should assess their current automation level and target Level 3 capabilities in high-volume, data-rich trade operations to avoid project cancellations and to capture meaningful efficiency gains that can be reinvested into transformational solutions.
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