AI kompetens projektledning är inte top priority för project managers, visar Q-methodology studie (35 professionals). AI literacy viewpoint emerged as least salient – fyra viewpoints identifierades (performance-oriented, people-centered, strategy-focused, AI-literacy), men AI-skills ranked lägst. Samtidigt: McKinsey 2025 visar 80% organisations ser ingen tangible EBIT impact från AI. Paradoxen: organisations investerar i AI men inte i competence som unlocks värdet. För dig som projektledare betyder detta: gap mellan AI investment och AI capability skapar failed implementations. 62% companies rate AI training ≤4/10. Solution requires competence frameworks, targeted training, strategic alignment.
Paradoxen: 80% ingen AI-värde MEN AI literacy inte prioritet
McKinsey global study (2025):
- 80% respondents: organisations ser ingen tangible enterprise EBIT impact från AI/GenAI
- Key reason: implementation challenges + necessary organisational restructuring
Men samma organisations:
- 62% rate AI training provision ≤4/10 (scale 1-10)
- 65% employees possess no or basic AI knowledge
Q-methodology findings (35 PM professionals):
- AI literacy viewpoint = least salient av fyra identified perspectives
- Project managers prioritise traditional competencies (performance, people, strategy) över AI skills
För projektledare: Classic chicken-egg problem. No AI value utan AI competence. No AI competence prioritisation because no perceived immediate success need. Breaking cycle requires leadership mandate.
Fyra viewpoints: Var hamnar AI literacy?
Q-sort method forced distribution (25 statements, -4 to +4 scale): “Sort according to extent each represents important competence for project manager”
Factor 1 – Performance-Oriented (14.2% variance):
- Top priorities: Risk/quality management, resource allocation, time/cost control
- AI literacy: Låg ranking
Factor 2 – People-Centered (13.9% variance):
- Top priorities: Leadership, team motivation, conflict management, interpersonal skills
- AI literacy: Låg ranking
Factor 3 – Strategy-Focused (13.9% variance):
- Top priorities: Customer needs understanding, market dynamics, long-term perspective
- AI literacy: Låg ranking
Factor 4 – AI-Literacy/Data-Proficiency (9.9% variance):
- ONLY här AI competencies ranked högt
- Men denna viewpoint = smallest factor, least explanatory power
För projektledare: Majority project managers filter AI skills through traditional lens. Performance delivery, people management, strategic vision dominate. AI seen as “nice to have” not “must have”.
Varför AI literacy inte prioriteras: Three explanations
1. Immediacy bias: Traditional competencies deliver immediate visible results:
- Risk management prevents failures (measurable)
- Team leadership drives delivery (observable)
- Cost control impacts budget (trackable)
AI literacy benefits är delayed och indirect:
- Data quality improvement (invisible until problem)
- Bias recognition (preventive, not reactive)
- AI tool evaluation (enables future decisions)
2. Lack of AI implementation experience: Study participants use Microsoft Copilot, men limited exposure till:
- AI-driven risk assessment
- ML-based scheduling
- Automated stakeholder classification
Without seeing AI in action på critical tasks, competence seems theoretical.
3. Training gap reinforces perception: 62% companies provide minimal AI training → Managers don’t develop fluency → Don’t recognise value potential → Don’t prioritise skill development Vicious cycle
För projektledare: Break cycle genom pilot projects där AI competence directly impacts success metrics. Make invisible benefits visible.
Sex AI literacy levels (Bloom’s Taxonomy adaptation)
Study framework identifies progression:
Level 1 – Remember: Recall AI definitions, recognise applications Level 2 – Understand: Explain AI mechanisms, interpret outputs Level 3 – Apply: Use AI tools effectively, employ libraries Level 4 – Analyze: Evaluate AI outputs, identify biases, assess limitations Level 5 – Evaluate: Judge AI appropriateness för tasks, compare solutions Level 6 – Create: Design AI applications, build custom models
Current PM baseline: Majority at Level 1-2 Required för value realisation: Level 4-5 minimum
Gap: 3-4 levels mellan current state och value threshold
För projektledare: Don’t aim för Level 6 (technical expertise programming – statement #21 ranked lowest). Aim för Level 4-5: evaluate outputs, recognise biases, understand limitations, know where AI excels/fails.
Fem critical AI literacy statements från study
Statement #16 (Data sources & biases): “Ability understanding where AI data comes from and recognising potential biases is essential. Awareness ensures fair, accurate output and supports ethical decision-making.”
Statement #18 (AI strengths/limitations): “Ability understanding where AI excels and where it falls short is essential för maximising potential.”
Statement #20 (Evaluate AI outputs): “Ability evaluating AI and outputs essential för informed decision-making. Clear interpretation helps leaders use insights effectively, reduce risks.”
Statement #19 (Leverage AI tools): “Ability effectively leveraging AI tools/libraries essential as they streamline development, simplify tasks, boost productivity.”
Statement #17 (Data cleaning): “Ability knowing how clean data before using with AI essential. Clean data ensures accuracy, reduces errors, improves output quality.”
För projektledare: These five competencies = minimum viable AI literacy för PM context. Not programming (statement #21), but evaluation, interpretation, ethical awareness.
Practical pathways: Closing gap
1. Competence framework integration: Embed AI literacy i standard PM certification/training (PMI, PRINCE2, IPMA) Currently: AI conspicuously absent från major frameworks Future: AI evaluation skills alongside risk management
2. Role-specific training design: Not generic “Introduction to AI” kurser Instead: “AI för Project Risk Assessment”, “Data Quality för PM Decisions”, “Evaluating AI Scheduling Tools”
3. Pilot-driven learning: Deploy AI i ONE critical area (risk, scheduling, forecasting) Require managers develop evaluation competence Success builds priority perception
4. Strategic alignment mandate: Senior leadership explicitly prioritises AI literacy Links competence development till performance reviews Allocates budget för targeted training
5. Community of practice: Cross-project forums där managers share AI experiences Collaborative learning reduces individual learning curves Makes benefits visible through peer examples
Bottom line
AI kompetens projektledning ranked lowest priority trots 80% organisations ser ingen AI value. Paradox: no value utan competence, no competence priority utan perceived value need. Q-methodology visar AI literacy viewpoint = least salient (9.9% variance vs 14% för performance/people/strategy factors). 62% companies provide minimal training, 65% employees possess basic/no knowledge. Gap between AI investment och capability development explains implementation failures. Six AI literacy levels identified (Bloom’s Taxonomy), majority PMs at Level 1-2, value realisation requires Level 4-5. Critical competencies: evaluate outputs, recognise biases, understand limitations, leverage tools, ensure data quality. NOT programming expertise. Practical pathways: integrate i PM frameworks, role-specific training, pilot-driven learning, strategic mandate, communities of practice. Breaking vicious cycle requires making delayed benefits visible through targeted implementations.
Källa: “Mind the gap: project managers, AI literacy, and the future of project management competences” av Costanza Mariani, Mauro Mancini & Kirsi Aaltonen, Politecnico di Milano & University of Oulu, publicerad 26 mars 2026.