Local Innovation Is Valuable. Fragmentation Is Not.

When practice groups pursue AI independently, the firm can quickly develop:

• Duplicate tools solving the same problem in different ways
• Inconsistent quality and review standards
• Conflicting vendor relationships
• Uncoordinated data practices
• Governance gaps across groups
• Redundant spending on overlapping solutions
• Knowledge silos where successful approaches are not shared
• Difficulty scaling what works in one group to others
• Technology decisions made without enterprise context

The challenge is not to stop experimentation. It is to connect it.

Coordination does not require centralization. It requires shared boundaries.

A coordinated model defines common ground across the firm while leaving room for practices to innovate within it. Boundary areas may include:

• Approved technology platforms and tools
• Data handling and confidentiality standards
• Governance and risk thresholds
• Quality review expectations
• Vendor evaluation and procurement processes
• Use-case documentation and sharing
• Training and support resources
• Reporting and visibility requirements

Within these shared foundations, practice groups retain the freedom to develop solutions that reflect their specific workflows, clients, and expertise.

Depending on where the organization stands, support may include:

• Cross-practice AI landscape assessment
• Coordination framework design
• Enterprise and practice-level AI strategy alignment
• Use-case inventory and prioritization
• Shared governance model development
• Technology evaluation and consolidation planning
• Practice group AI advisory
• Knowledge-sharing structure design
• Pilot coordination across groups
• Ongoing strategic alignment support

The objective is coordination that strengthens innovation rather than replacing it.