AI Competencies for B2B Marketing
Summary: Mikalef et al. (2023) develop and test a model showing that AI competency — the creative bundling of technology, org knowledge, and institutions — drives B2B marketing capabilities (information management, planning, implementation), which in turn drive organizational performance. AI tools alone are not enough; competency is the differentiator.
Sources: Academia/1-s2.0-S0148296323003569-main.pdf
Last updated: 2026-05-06
Citation
Mikalef, P., Islam, N., Parida, V., Singh, H., & Altwaijry, N. (2023). Artificial intelligence (AI) competencies for organizational performance: A B2B marketing capabilities perspective. Journal of Business Research, 164, 113998.
The problem
Despite growing AI investment, most organizations are not generating the value they expected. The gap is not technical — it is organizational. AI tools are being deployed without the competency structure needed to make them generate sustained value. This gap is especially pronounced in B2B marketing, where decision environments are complex, relationships are high-stakes, and informational demands are intense.
AI competency vs. AI technology
Drawing on Prahalad’s (1993) core competency theory, the paper distinguishes between:
- AI technology: the tools themselves (ML models, NLP systems, etc.)
- AI competency: the creative bundling of AI technology, organizational knowledge, and institutional processes as a “harmonious whole”
An AI competency must meet three criteria to be strategically valuable:
- Technical orchestration: ability to deploy AI effectively and differentiatingly
- Trans-unit scope: the competency must span multiple business units, not be siloed
- Hard to imitate: requires continuous experimentation and a proactive stance
The three pillars of AI competency
The paper operationalizes AI competency as a second-order construct comprising:
- Infrastructure: the technical foundation — data, systems, integration
- Business-spanning ability: the capacity to apply AI across functions and processes
- Proactive stance: ongoing experimentation, anticipation of AI use cases, willingness to invest
B2B marketing capabilities
The paper focuses on spanning marketing capabilities (which integrate both inside-out and outside-in processes):
- Information management: acquiring and analyzing stakeholder information for marketing decisions
- Marketing planning: anticipating market changes; translating AI insights into strategy
- Marketing implementation: executing, controlling, and evaluating marketing strategies
AI competency does not directly produce performance — it works through these capabilities.
The model and findings
Tested using PLS-SEM with 155 survey responses from senior IT executives at European companies.
Confirmed: AI competency → information management → performance
Confirmed: AI competency → marketing planning → performance
Confirmed: AI competency → marketing implementation → performance
All three B2B marketing capabilities fully mediate the relationship between AI competency and organizational performance. There is no confirmed direct path from AI competency to performance — the effect is entirely indirect.
Practical implication: Organizations should invest not just in AI tools but in building organizational structures and cultures that enable the creative deployment of those tools. Information management capability was the strongest mediating pathway.
Relevance for teaching and consulting
This framework is directly applicable when advising organizations on AI adoption. The distinction between AI technology (what you buy) and AI competency (what you build) is the central diagnosis for most failing AI initiatives. The three-capability model (information → planning → implementation) provides a structured audit framework.