Data · dataset · 2026
<b>The Growth Orchestration Model: An AI-Era Operating Model for SMB Lead Generation</b>
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<p dir="ltr">For decades, business-to-business (B2B) lead generation has run on a linear funnel: find prospects, contact them, follow up, qualify, and hand them to sales.
Description
Successive waves of technology—marketing automation, customer relationship management (CRM) platforms, and intent data—have accelerated each stage without changing the underlying model. Artificial intelligence (AI) is now accelerating the next shift, and it exposes a problem: we are applying AI to a model built for slower information flow and narrower buyer behavior.
Trade press and industry research increasingly declare the B2B funnel obsolete, but nearly all of that conversation—and the operating-model literature it draws on—assumes the dedicated revenue-operations (RevOps) and sales-operations (SalesOps) functions, specialized sales-development teams, and enterprise-scale growth infrastructure available to large enterprises. Small and mid-market businesses (SMBs) face the same shift with a fraction of the resources.
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Drawing on the author's 13-plus years of practitioner experience building patented conversational lead-routing technology and scaling SMB acquisition, this article introduces the Growth Orchestration Model: five capabilities—sense, sequence, synchronize, surface, and strengthen—that let resource-constrained organizations detect demand signals, coordinate engagement across channels, recognize when human judgment is required, and improve from outcomes.
The model is grounded in sense-and-respond, dynamic-capabilities, and ecosystem-orchestration theory; extends recent Engineering Management Review scholarship on AI adoption into SMB customer acquisition; and is illustrated with six anonymized, published client cases across five sectors. For managers building or buying growth technology, the central implication is that competitive advantage lies in orchestration architecture, not in any individual AI tool. </p><p dir="ltr">NOTE: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no longer be accessible.</p>
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Where it is published
- DOI doi.org/10.6084/m9.figshare.34020624.v1 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
Provenance · 1 source records, 13 field assertions
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