Why this matters now

Enterprise AI adoption is accelerating into 2026, but outcomes hinge on tools that are safe, well-integrated, and simple for people to use every day. Leaders are moving from pilots to platforms and need confidence in security, governance, integrations, and agent roadmaps.

Key points / benefits

• Enhance productivity with AI tools that surface knowledge, summarise content, and automate routine steps.

• Evaluate software security and integration depth to protect data and reduce friction.

• Boost user adoption with clear enablement, governance, and role-based training.

What “AI collaboration software” means in 2026

Today’s platforms go beyond chat. They index enterprise knowledge with permission awareness, ground responses in your data with citations, and—in 2026—increasingly coordinate multi-step tasks via policy-aware agents. Validate any “agentic” claims against real, measurable use cases.

Evaluation criteria CIOs should use

1) Security, privacy, and compliance (non-negotiable)

Choose platforms built on zero trust and least privilege. Require encryption in transit/at rest, tenant isolation, and recognised certifications (e.g., SOC 2 Type II, ISO 27001). Confirm GDPR/HIPAA coverage where relevant. Ensure RBAC, permission-aware retrieval, audit logging, and data residency options.

Checklist

• Independent attestations (SOC 2 Type II, ISO 27001)

• Role-based access controls with audit trails

• Permission-aware search and grounding

• Data residency and clear DPA/sub-processor lists

2) Breadth and depth of integrations

Your AI layer should plug into where work already happens: Microsoft 365, Google Workspace, Slack/Teams, Jira, Confluence, GitHub, ServiceNow, Salesforce and more. Rich, permission-respecting connectors unlock accurate answers and reduce shadow IT. Ask for a published connector catalogue and governance controls.

Checklist

• Native connectors to core suites, comms, dev, CRM/ITSM

• Source-level permission mapping

• Admin controls for connector governance

3) Governance and explainability

Demand transparency: source citations, traceable reasoning, and central policy controls. Every answer or action should be grounded in your data, with links back to originals. Admins should manage models, prompts, retention, and approval flows from a single pane.

4) Agent readiness without the hype

If you’re exploring agents, prioritise policy-aware orchestration, human-in-the-loop approvals, and least-privilege tool access. Start with narrow, auditable tasks (tickets triage, meeting actions, scheduling) and expand only when KPIs prove value.

5) Adoption, change management, and measurability

Adoption is the multiplier. Choose vendors with role-specific enablement, prompt/playbook libraries, and admin analytics for usage and impact.

Checklist

• Role-based onboarding and training paths

• Built-in analytics (queries answered, time saved)

• Champions network; iterative prompt libraries

A closer look at Glean for enterprise AI collaboration

Glean positions itself as a Work AI platform that connects to enterprise data and provides search, an assistant, and a framework for secure agent orchestration. It emphasises enterprise-grade security (e.g., SOC 2/ISO 27001), permission-aware retrieval, and an expanding library of connectors and agents—aligning well with CIO evaluation criteria for 2026.

Where Glean can help

• Secure knowledge access: Permission-aware search and answers across Microsoft 365, Google Workspace, Slack/Teams, Jira, Salesforce and more.

• Work AI assistant: Summarise, draft, and retrieve with clear source citations.

• Agents and orchestration: Build policy-constrained automations with approvals and observability.

Practical steps or examples

1. Assess integration with existing systems
   Map high-value scenarios (support resolution, sprint planning, policy Q&A, RFP responses) and the apps where the data lives. Prioritise by impact and risk.

2. Review security features and compliance
   Request documentation on certifications, architecture, data handling, RBAC, audit logging, and model governance. Involve Security, Legal and Data Protection early.

3. Encourage user adoption through training
   Design a role-based enablement plan with prompts, playbooks, and clear guardrails. Establish a champions network and measure time saved, decision speed, and deflection.

Summary

The right AI collaboration platform in 2026 should be secure, permission-aware, deeply integrated, and adoption-ready. Glean is a strong candidate for enterprises prioritising governance and measurable outcomes. For a structured selection process and a hands-on pilot, speak to Generation Digital.

Ready to compare platforms and design a secure pilot?

FAQs

Q1: What should enterprises consider when choosing AI collaboration software?
In 2026, prioritise security (SOC 2/ISO 27001), permission-aware retrieval, RBAC, and proven integrations to your core stack. Evaluate vendor governance, auditability, and data residency.

Q2: How can AI-driven tools improve productivity?
They surface answers faster, summarise content, automate routine steps, and—when safely orchestrated—coordinate multi-app tasks via agents. Measure time saved and decision speed during pilots.

Q3: Why is security important in AI collaboration tools?
Security ensures sensitive enterprise data is protected from breaches and unauthorised access. Certifications, zero-trust design, and RBAC enable productivity without sacrificing control.

Related links

• Explore Glean

• Asana for AI-assisted workflows

• Miro for collaborative planning

• Notion for knowledge workflows