\begin{abstract}

Policy and industry discourse often reduce artificial intelligence
(AI) to machine learning framed as “supervised, unsupervised, or
reinforcement learning.”  This triad omits long-standing AI traditions
(symbolic expert systems, search \& planning, probabilistic inference,
control/estimation, and evolutionary/collective computation). We
formalize the \emph{Operational-Premise Taxonomy}~(OPT), classifying
AI by its dominant computational mechanism: \Lrn, \Evo, \Sym, \Prb,
\Sch, \Ctl, and \Swm. For each class we provide core mathematical
operators, link them to canonical biological mechanisms, and survey
hybrid compositions. We argue that OPT yields a principled,
biologically grounded, and governance-usable taxonomy that avoids
category errors inherent in training-signal–based labels, while
remaining compact and readable with a short, compositional naming
code. We propose a grammar for decription, evaluation process methods,
and argue for the utility of OPT in both post-hoc classification of
systems and as a design-time adjunct that aids in software development
and lifecycle risk management processes. OPT can serve as an adjunct
and complement to existing frameworks of software risk management
given its basis in the operational mechanisms of the software rather
than more superficial characteristics commonly used.

\end{abstract}