Project management software: How PM leaders can use AI securely and see ROI
AI features are now standard in project management software. Budgets are rising to match, yet skills and security often stall progress. The result: wasted spend, messy rollouts and data exposure. The fix is a clear plan, tighter security checks and focused training.
Budgets follow AI - ROI does, too
Most buyers are leaning in: 59% say AI influenced their last PM software purchase, and 36% plan to raise budgets to adopt new tools. The top expected gains are simple: automate repetitive tasks, better forecasts and faster content creation. It's paying off for many, with 85% reporting positive ROI. The catch is execution.
Enthusiasm meets execution
Almost half of PMs (48%) expect challenges adopting AI features. The usual blockers are skill gaps, weak vendor onboarding and workflows that don't fit the new tools. If you can't scale beyond a pilot, value stalls. Training and upfront process design prevent that.
A simple plan to put AI to work
- Pick 3-5 high-impact use cases (e.g., automated status reports, risk summaries, schedule variance alerts). Write a one-line success metric for each.
- Map the data these features need, the systems they touch and who can access what. Decide what must never leave your tenant.
- Pilot with one team and two projects. Baseline current performance, then compare post-rollout. Track time saved, forecast accuracy and cycle times.
- Assign owners: one product lead, one security lead, one training lead. Set a cadence for reviews and issue cleanup.
- Plan the handoff to operations early: playbooks, FAQs, training assets and a simple feedback path into the backlog.
Security first: what to check
Security drives buying decisions. 72% consider it critical, and 51% say it influenced their last PM software choice. AI features increase exposure because they touch more data and more APIs. Tighten the basics before rollout.
- Vulnerable Internet of Things (IoT) devices feeding site data.
- API connections to ERPs, design tools and data lakes.
- Access control settings across projects, vendors and clients.
- Sensitive data shared, generated or stored by the system.
Use proven guidance where it applies. For API risks, review the OWASP API Security Top 10. For supplier risk, see the UK's NCSC supply chain security.
Vendor due diligence checklist
- Recent breach history, transparency and remediation speed.
- Certifications and audits (e.g., ISO 27001, SOC 2) and scope of coverage.
- SSO/MFA support, least-privilege roles and project-level permissions.
- Data handling: retention, deletion SLAs and whether models train on your data (default should be opt-out from training).
- API protections: OAuth 2.0, rate limits, token scopes and secret rotation.
- Encryption in transit and at rest, with clear key management.
- Audit logs you can export, plus clear incident response terms in contract.
Upskill your team so the tools actually get used
Skills are the bottleneck. Plan ongoing training, not a one-time workshop. Build a small group of internal champions and make their playbooks reusable across projects.
- AI literacy for PMs: prompt tactics, result evaluation and quality checks.
- Data basics: model inputs, PII handling and labeling standards.
- Workflow design: where automation starts and where human review sits.
- Guardrails: acceptable use, red flags and escalation paths.
- Tool-specific certifications for admins and power users.
If you need structured options, explore job-focused programs and certifications that keep pace with new features: AI courses by job and an AI automation certification.
Measure ROI like a CFO
- Hours saved from automated tasks (reporting, meeting notes, submittal tracking).
- Forecast accuracy: schedule and cost variance before vs. after AI.
- Cycle time: issue resolution, change approval and document turnaround.
- Quality: rework rate, error count and RFI volume.
- Adoption: weekly active users, feature usage and seat utilization vs. licenses.
Common failure patterns to avoid
- Buying features without a problem statement and metric.
- Letting sensitive data reach external models without controls.
- No integration plan, so work gets copied between tools by hand.
- Underfunding change management and training, then blaming the tech.
- Ignoring API limits, leading to flaky automations and broken syncs.
Move forward
AI won't fix a broken process. Start with one clear use case, secure the data path, and train the people who will use it every day. Prove value, then scale. That's how budgets turn into outcomes, not shelfware.
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