Search for a command to run...
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.