Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0111
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-13T05:23:46.372192Z
status: published
lens: p_space
sector: business
headline: Governing Enterprise AI Investments: A Decision-Centric Portfolio Framework
dek: Despite enterprises continuing to invest heavily in AI, many initiatives fail to scale or generate sustained business value. Terming this the AI-investment paradox, we argue that it persists because firms govern AI as a broad technology program rather than as a set of discrete, investable decision opportunities embedded within workflows. We address this issue by developing a decision-centric portfolio framework for governing enterprise AI investments. Our framework introduces AI-Investable Process Nodes (AIPNs)…
gain_title: (none)
problem_title: Enterprises governing AI as a broad technology program see many initiatives fail to scale or generate sustained business value
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Enterprises governing AI as a broad technology program see many initiatives fail to scale or generate sustained business value
problem_evidence: many initiatives fail to scale or generate sustained business value | govern AI as a broad technology program rather than as a set of discrete, investable decision opportunities
quick_read: By July 2026, the authors described an AI-investment paradox in enterprises: continued heavy investment alongside initiatives that fail to scale. They proposed a decision-centric portfolio framework that reframes governance around discrete investable decision opportunities within workflows, introducing AI-Investable Process Nodes as bounded points where benefits, risks and costs can be assessed ex ante.

Linking investments to identifiable and governable decision nodes matters because it shifts evaluation from broad technology programs to specific workflow outcomes. What remains uncertain from the text is whether the Expected Net Benefit formalization and real options staging actually resolves the paradox in practice, as no empirical implementation or measured business value outcome is reported.
limitation: Text describes a conceptual framework and pathway, not an observed implementation outcome with measured business value
tag: Evidence-backed problem
key_points: Authors term the pattern the AI-investment paradox where heavy investment does not lead to scaled value | Framework introduces AI-Investable Process Nodes (AIPNs) as bounded decision points where AI can alter expected outcomes | Node-level value is formalized through Expected Net Benefit and staged using real options logic | AIPNs are assembled into a broader portfolio through risk-return principles
rundown: By July 2026, the authors described an AI-investment paradox in enterprises: continued heavy investment alongside initiatives that fail to scale. They proposed a decision-centric portfolio framework that reframes governance around discrete investable decision opportunities within workflows, introducing AI-Investable Process Nodes as bounded points where benefits, risks and costs can be assessed ex ante.

Linking investments to identifiable and governable decision nodes matters because it shifts evaluation from broad technology programs to specific workflow outcomes. What remains uncertain from the text is whether the Expected Net Benefit formalization and real options staging actually resolves the paradox in practice, as no empirical implementation or measured business value outcome is reported.
sources:
- peer_reviewed | Journal of the Association for Information Systems | https://aisel.aisnet.org/pacis2026/practioner/practioner/8 | 2026-07-05
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

e2d45f5e56fbd0ed4926593ee712e110e9cfe210ec879e4ef2d310a7af37de6f

previous

0000000000000000000000000000000000000000000000000000000000000000