Abstract

The Ad Hoc Weaving Framework (AHWF) was introduced as a design-science meta-

artifact that turns deep commitments about ontology, value, agency and

communication into practically usable tools for innovation management,

entrepreneurship and technology governance (Bonatti et al., 2025). It provides five

tightly interwoven pillars – a trope-based meta-ontology with explicit life-cycles and

time-modes; a layered distinction between natural processes and human processings;

a unified account of persons and organisations as Selfs’ Complexes; a Value–Life

Complex (VLC) that ties mission to governance, learning, planning, doing and facts;

and a multi-level communications pragmatics (CI–CV) supported by an engineering

stack that shifts attention from message transmission to completion – together with a

Minimal Working Set (MWS) of three one-page instruments that teams can run in

practice.

At the same time, large language models have matured to the point where they can

serve as AI amanuenses: persistent conversational companions that can read and

write across many documents, maintain a memory of a project’s evolving conceptual

weave, detect inconsistencies, recover forgotten commitments, and prompt reflexive

re-organisation. This paper explores how AHWF and an AI amanuensis can be

combined into a second-order meta-artifact for innovation: AHWF provides the

grammar and instruments; the AI amanuensis provides continuous assistance and

vigilance that lighten the cognitive and organisational burden of applying that grammar

correctly over time.

We first recapitulate AHWF’s pillars and MWS as defined in earlier work, situating them

in design-science research and innovation studies. We then characterise the AI

amanuensis as a project-specific conversational partner whose capabilities in

classification, memory, pattern detection, narrative reconstruction and meta-reflection

are mapped, pillar by pillar, onto AHWF’s demands. We describe the resulting

AHWF+AI meta-artifact, detailing the assistance affordances that can reduce drift

between intentions, operations and facts, preserve the layered distinction between

processes and processings, reveal gaps in Selfs’ Complexes, keep Value–Life

complexes coherent, and maintain CI–CV handshakes as completable conversations.

Stylised vignettes in technology transfer and regulatory sandboxes illustrate how this

combined artefact works in practice. We propose an evaluation roadmap, building on

the FEDS framework for design-science evaluation (Venable et al., 2016), and discuss

implications and risks. The central claim is that AHWF, when paired with a carefully

configured AI amanuensis, can move Technovation’s “from theories to tools” ambition

one step further: not only are first principles turned into tools, but those tools

themselves are supported by a conversational intelligence that helps practitioners

apply them correctly, consistently and reflexively.

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