Italy's AI Push in Project Management: Security Drives Buying Decisions

Project Management in Italy: Betting on AI, Buying for Security

A new global study from Capterra shows a clear signal for managers in Italy: AI influences purchase intent, but security decides the deal. Teams want smarter tools, yet leadership prioritizes risk control and data protection.

The research surveyed 2,545 respondents globally, including 227 professionals in Italy. The takeaway is practical-adopt AI where it creates leverage, but anchor it in security and human skills to convert spend into outcomes.

Key numbers you can use

• AI as a driver: 43% of Italian PM software buyers cite adding AI and improving integrations as top reasons to buy. Globally, 55% say AI drove their most recent purchase.
• Security leads: 64% in Italy say security is "critical" in evaluating and implementing PM tools. For 26%, a security issue triggered their most recent purchase.
• Adoption friction: 35% in Italy expect AI adoption challenges; globally it's 41%.
• Human skills matter: 51% of Italian project managers increased use of emotional intelligence after adopting AI.

What this means for management

AI features help you move faster-forecasting, resource allocation, and automation. But PM software houses sensitive data (budgets, contracts, vendor info), so security must lead the buying process.

Value realization depends on two things: clean implementation and people who can interpret, adjust, and communicate. That is why emotional intelligence, conflict resolution, and strategic communication now sit beside data literacy and automation setup.

Security-first buying: the non-negotiables

• Data flows: Map where data is stored, processed, and integrated, including AI-driven features (predictive or generative).
• Access control: Enforce SSO/MFA, role-based access, least privilege, and audit trails.
• Vendor posture: Verify certifications (e.g., ISO 27001, SOC 2), pen-test cadence, incident response SLA, and breach history.
• Model behavior: Clarify if vendor models train on your data, how PII is handled, and options to opt out.
• Integration risk: Review third-party connectors and APIs for data exposure points.
• Encryption and logs: Require encryption in transit/at rest and immutable logs for forensics.

If you need a reference framework for AI risk, review the NIST AI Risk Management Framework for governance guardrails. Read NIST AI RMF

Close the adoption gap

• Choose for usability: Favor tools with in-app onboarding, templates, and low-friction automation setup.
• Pilot, then scale: Run a 6-8 week pilot with one or two teams. Measure adoption, cycle time, and error rates before a wider rollout.
• Align workflows: Document current workflows and update them for AI features (assignments, approvals, dependencies).
• Own change management: Appoint champions, set office hours, and define success metrics tied to business outcomes.
• Upskill continuously: Train on interpreting predictions, configuring automations, and communicating decisions.

People make AI useful

Automation can remove busywork. It doesn't build trust, resolve conflict, or align stakeholders. That's your job-and it's where outcomes are won or lost.

• Raise EQ on purpose: Active listening, negotiation, and framing trade-offs for executives and contributors.
• Make data legible: Turn model outputs into clear decisions with risks, assumptions, and next steps.

Vendor demo checklist (bring this)

• Show a live run of AI features on sanitized data: task suggestions, predictions, risk flags.
• Export of audit logs and permission change history.
• How integrations are sandboxed; API rate limits and scopes.
• Data retention policy, regional hosting options, incident response process.
• Ability to disable training on our data and to isolate tenant data.
• Admin controls for AI feature rollout (who gets what, when).
• Onboarding plan: training assets, success manager, measurable milestones.

30/60/90 rollout plan

• Days 0-30: Security review, configure access, pilot with one team, define 3 metrics (e.g., cycle time, on-time delivery, rework).
• Days 31-60: Expand to 2-3 teams, refine automations, publish updated workflows, start EQ-focused coaching.
• Days 61-90: Organization-wide enablement, quarterly security review cadence, KPI dashboard tied to portfolio outcomes.

Where to upskill fast

If your team needs pragmatic AI skills-prompting, workflow automation, and role-based learning-these resources can help:

• AI courses by job role for PMs and team leads.
• Certification in AI automation to operationalize use cases.

Read the full study

For the complete data set and methodology, see Capterra's report. Visit Capterra

Bottom line: In Italy, AI is winning attention, but security earns the purchase order. Pair security-first evaluation with disciplined rollout and people-first skills to turn features into results.