In reality, AI adoption is neither a simple switch nor a competitive sprint. It is a test of how well an organisation understands its own processes, data, and decision making. What AI exposes is not just technical capability but organisational reality. This is why some AI efforts in trading are failing for the reasons people do not expect. They don’t collapse because the models are inadequate or the technology immature. They falter because AI brings hidden assumptions into the open, and many organisations are not yet prepared to confront them.
WAITING DOES NOT PRESERVE SIMPLICITY — IT ALLOWS COMPLEXITY TO BUILD OUT OF SIGHT.
STAGE ONE: NOT STARTED YET, BUT NOT STANDING STILL
There are legitimate reasons why some trading businesses have not yet deployed AI in live operations. Regulatory scrutiny is intense, margins are volatile, and the cost of mistakes is high. In that context, caution can feel like responsible governance.
The danger is confusing inaction with neutrality.
Even organisations that have not launched formal AI programmes continue to change. Processes evolve organically. Manual workarounds become part of everyday practice. Data is reconciled through experience and judgement instead of explicit rules. Critical knowledge settles in people rather than systems.
Much of this remains invisible. Human expertise quietly absorbs inconsistency and ambiguity as part of routine work. When AI is eventually introduced, these hidden dependencies surface almost immediately, and early initiatives tend to stumble not because the technology is weak, but because the organisation struggles to answer simple questions with certainty. Which figure is authoritative. When it becomes final. Who owns it. Under what circumstances it can change.
Such questions are manageable when people bridge the gaps informally, but they become unavoidable when a system must operate consistently, at scale, and under scrutiny. For those that have not yet begun, the warning is straightforward - waiting does not
preserve simplicity, it allows complexity to build out of sight and when AI finally arrives, it meets not a clean slate but years of unexamined assumptions.
STAGE TWO: THE PROOF OF CONCEPT TRAP
At the opposite end are organisations that have embraced experimentation. They have run multiple AI proof of concepts and can demonstrate technical feasibility with some even showcasing impressive results in controlled settings.
Yet daily operations often look much the same.
This is the proof of concept trap. POCs are designed to answer narrow questions quickly. They sidestep governance, streamline data flows, and operate outside production constraints. That is precisely why they succeed. It is also why they struggle to scale.
Over time, organisations accumulate a portfolio of successful experiments without a clear route to operational value. Each POC carries its own assumptions and solves a local problem, and few address the harder issues of accountability, trust, or ownership of decisions.
Repeated experimentation can also introduce fragility. Point solutions multiply. Architectural coherence begins to erode. Confidence weakens, not in AI itself, but in the ability to translate insight into impact without increasing risk. At this point, progress slows not because the technology has disappointed, but because the organisation has reached the boundary between experimentation and responsibility.
5 | ADMISI - The Ghost In The Machine | Q1 Edition 2026
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40
