A firm can encourage lawyers to learn AI while maintaining incentives that make learning difficult.
When professional development competes with client work for the same hours, experimentation may become something lawyers are expected to fit into evenings or quieter periods.
Ropes & Gray is addressing that tension through target credit. Reuters reported on October 8 that more than 1,000 associates and counsel can count up to 100 hours of AI experimentation and training toward annual billing requirements. The firm also reported investing more than 35,000 lawyer hours in AI time during 2026. Reuters
The distinction is important: credit toward an internal target does not mean clients are being billed for the training.
Incentives turn encouragement into capacity
An internal message supporting innovation has limited effect if performance systems reward only other work.
Giving training recognized credit changes the practical decision. A lawyer can devote time to developing a skill without treating every hour as a deficit against the annual target.
That is the management significance of the programme. It allocates resources to learning.
It does not, by itself, show that the training improves legal work or produces financial returns. Those outcomes require separate assessment.
Experimentation should produce transferable knowledge
A useful training programme needs more than access to software.
Lawyers must learn when a tool helps, where it fails and how much checking its output requires. They also need an approved environment for experimentation and a way to share lessons.
One lawyer discovering that a workflow is unreliable can save colleagues from repeating the same mistake. Another identifying a useful method can help the firm develop a consistent approach.
The institutional benefit depends on whether individual experiments become reusable knowledge.
Legal competence includes reviewing the output
AI training should develop the ability to evaluate a result.
A fluent draft may omit an important qualification. A research summary may answer a nearby question instead of the question asked. A document comparison may identify textual changes without explaining their legal significance.
A lawyer needs enough subject knowledge and context to recognize those limitations.
Training that concentrates exclusively on generating output risks confusing speed with competence. Review, verification and escalation deserve equal attention.
What Canadian firms can learn
The programme offers a management model to examine rather than a number every firm should copy.
A smaller practice might begin with a defined training allowance, a limited set of approved tasks and short sessions where lawyers explain what they tested.
Useful evaluation would consider whether participants can:
- Identify appropriate and inappropriate uses.
- Recognize errors and missing context.
- Explain the review required before relying on output.
- Share a workflow others can reproduce safely.
Hours measure the investment. Demonstrated capability measures what the firm gained.
Professional development needs a place in the business model
The wider question is how firms make room for skills they expect lawyers to acquire.
If AI competence becomes part of service delivery, developing it requires time, supervision and feedback. Leaving that work entirely to individual initiative produces uneven results.
Ropes & Gray’s decision makes the investment visible. The next question is what lawyers can do better because of it.
Training credit creates room to learn; sound judgment remains the result to measure.
Related coverage: read our analysis of the relevance and review problems raised by AI-assisted court filings.
Sources: Reuters · Ropes & Gray’s AI practice information.