The misuse of “AI” is undermining enterprise credibility
Artificial Intelligence has become a catch-all term for any system that exhibits automation or data-driven output. But in the enterprise, this linguistic inflation creates confusion and risk.
According to Gartner’s 2025 Market Insight on Generative AI Adoption, over 58% of organisations reporting AI use are in fact deploying deterministic automation or data analytics — not cognitive or generative systems. Misclassification doesn’t just mislead the market; it obscures the governance, security, and operational design principles needed to deploy these technologies responsibly.
We’re not surrounded by artificial intelligence.
We’re surrounded by assistive systems — tools that enhance human capability without autonomy.
Defining the distinction: AI vs. Assistive Technology
The ISO/IEC 22989:2022 standard defines artificial intelligence as a system capable of “perceiving environments and taking actions with some degree of autonomy to achieve specified goals.” By contrast, assistive technologies — including Robotic Process Automation (RPA), algorithmic decision support, and generative co-pilot tools — are bounded systems. They operate within deterministic rulesets or supervised learning loops, not autonomous cognition.
| Category | Example Use Cases | Autonomy Level | Appropriate Label |
| Rule-based automation | Workflow routing, invoice matching, report generation | None | Assistive Technology |
| Machine learning models | Forecasting, fraud detection, image recognition | Low to medium | Assistive Intelligence |
| Generative co-pilots | Code assistants, document drafting, summarisation | Medium (bounded prompts) | Assistive Intelligence |
| Cognitive or autonomous agents | Adaptive logistics, dynamic resource optimisation, self-learning control | High | Artificial Intelligence |
Most enterprise “AI projects” today exist in the first three rows — useful, but not autonomous.
Labelling all of them as AI dilutes technical integrity and overstates maturity.
The governance implications of over-labelling
This distinction isn’t semantic; it has regulatory and operational consequences.
Under the EU AI Act, risk classification depends on autonomy, opacity, and decision-making scope.
If an automation system is incorrectly labelled “AI,” it may fall under obligations it doesn’t meet — or worse, escape necessary oversight because the label trivialises real risk.
Gartner (2024) identified misclassification as a top-five factor driving “AI governance fatigue” — boards unsure what they are actually responsible for.
Clarity of language leads directly to clarity of accountability.
Human-machine interaction: augmentation, not replacement
The most transformative enterprise technologies today are not replacing humans — they are assisting them.
A MIT Sloan / BCG 2024 study found that human-AI collaboration models delivered a 21% performance uplift compared with automation-only deployments, primarily through error reduction and decision speed.
Reframing these systems as assistive intelligence aligns better with their operational intent:
- They improve human precision and throughput.
- They accelerate repetitive cognitive tasks.
- They preserve accountability with human-in-the-loop oversight.
This model is not artificial intelligence; it’s human-centred intelligence — the architecture of augmentation.
Why precision in language matters
1. Governance and risk alignment
Mislabelled “AI” systems distort compliance structures and blur where risk sits. Accurate taxonomy improves traceability and assurance.
2. Procurement clarity
Boards are beginning to challenge vague vendor claims. The UK’s Crown Commercial Service (2024) now requires suppliers to declare whether systems exhibit true autonomy or rule-based logic — a direct response to AI-washing.
3. Market credibility
Investors and clients increasingly differentiate between AI-enabled and AI-adjacent. Firms overstating capability lose technical credibility and erode long-term trust.
The case for Assistive Intelligence
Adopting a vocabulary of Assistive Intelligence recognises the true function of most enterprise systems today: human-centred augmentation.
It also restores balance to the public discourse — acknowledging that genuine AI (autonomous learning and reasoning systems) remains rare, expensive, and tightly scoped.
This framing doesn’t diminish progress; it grounds it.
It allows organisations to design responsibly, measure impact realistically, and build adoption strategies around verifiable capability rather than inflated expectation.
Closing reflection
Not every data-driven tool is AI — and that’s fine.
The most valuable technologies in modern business aren’t the ones that replace people; they’re the ones that make people more capable, consistent, and confident.
Precision in language creates precision in governance, design, and trust.
Perhaps it’s time to reserve “Artificial Intelligence” for what truly earns the name — and celebrate the rest for what it is: Assistive Technology powering an intelligent enterprise.
Surtori. Intelligent Transformation. Proven Impact.