AI in B2B Marketing
Summary: AI in business-to-business contexts requires a distinct competency model (not just tool deployment) and enables both strategic capability-building and operational automation. Two 2023–2025 papers ground this with empirical evidence: Mikalef et al. on the organizational competency model, NEC on applied negotiation automation.
Sources: Academia/1-s2.0-S0148296323003569-main.pdf · Academia/JOI-Article-XMProFINAL.pdf
Last updated: 2026-05-06
Why B2B is different
B2B marketing differs from B2C in ways that make AI particularly valuable and particularly difficult to implement:
- Complexity: fewer customers, each managed individually; high informational requirements per relationship
- Trust dependence: B2B relationships require higher reliability than consumer markets; AI-generated errors are more visible and costly
- Long sales cycles: planning and implementation capabilities must integrate across time horizons that mass-market tools are not designed for
These characteristics mean that off-the-shelf AI tools transfer poorly. The value of AI in B2B is determined by organizational competency, not by the sophistication of the tools. (source: mikalef-ai-competencies-b2b)
The competency model (Mikalef et al., 2023)
AI competency = creative bundling of:
- Infrastructure: data systems, integration, technical capacity
- Business-spanning ability: cross-functional deployment, not siloed
- Proactive stance: continuous experimentation, anticipation
This competency enhances three B2B marketing capabilities:
- Information management: understanding customers, competitors, stakeholders
- Marketing planning: translating market signals into strategy
- Marketing implementation: executing, controlling, evaluating
The effect on organizational performance is fully mediated by these capabilities. There is no shortcut from “we deployed AI” to “we perform better.” (source: mikalef-ai-competencies-b2b)
Applied case: automated negotiation (NEC, 2025)
NEC’s procurement automation demonstrates what the Mikalef model looks like operationally. The GenAI negotiation agent embodies all three pillars:
- Infrastructure: multimodal time-series forecasting integrated with procurement data
- Spanning ability: bridges demand forecasting, supplier management, and contracting
- Proactive stance: the system generates target plans from predicted future demand, then negotiates to meet them — not merely responding to inbound offers
Classical automated negotiation systems (game-theoretic, rigid protocols) failed precisely because they lacked the information management and spanning capabilities the Mikalef model prescribes. (source: nec-genai-negotiation)
The organizational implication
Most organizations that report AI disappointment have invested in the tools but not the competency. The Mikalef findings mean:
- AI pilots that work in one business unit rarely transfer — because transfer requires spanning ability
- Technical teams that deploy models without marketing domain integration will not see performance gains
- “Proactive stance” is an organizational culture property, not a tooling property
This framework is applicable as a consulting diagnostic: which of the three pillars is missing?