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For lawyers, that is also the
threshold for listening: give a
practical answer they can use
today, and they will come back
for more, even if they know they
must invest time before putting it
into practice.
And that, folks, is the magic
moment when you overcome the
hump of lawyer skepticism and
are on your way to a successful
program. This conclusion, then,
begs the question: what is the
right, practical content?
THE RIGHT CONTENT
At the core of any training
program is a clear definition of
the competencies the participants
should gain. GenAI remains an
emerging technology, and part of
Stradley Labs' goal was to initiate
a conversation about industry
training standards (https://www.
stradley.com/publications/gen-
ai-legal-drivers-training-course-
syllabus-2025/). We propose that
any comprehensive attempt at
AI transformation within a legal
organization should address the
following competencies:
Use Case Assessment
• Division of Tasks – consider
your process and push only
the subtasks best suited for
GenAI to the machine.
• Risk Assessment – identify
privacy and ethical concerns,
client sensitivities, or
potential biases.
• Impact vs. Investment – where
the machine is faster, use it
to create more time to apply
human judgment.
• Verification Needs – an interpretation of dense legal rules requires an
intense audit. Brainstorming does not.
• Engagement – consider the level of human involvement needed –
training, oversight, and feedback loops.
Tool Assessment
Include the scope of the training data for various tools as you choose the
right tool for the use case.
Large Language Model (LLM) Features
• Self-Learning – LLMs expose your inputs; users must understand if they
are public or secure for private use.
• Probabilistic – LLMs generate likely responses from patterns in data.
We call inaccurate output hallucinations.
• Biased – LLM output reflects the bias of its training data.
• Conversational – LLMs communicate confidently, even when providing
likely but inaccurate information.
• Customized by workflow – Legal GenAI tools can ground outputs in
the law and cite back to legal databases, allowing lawyers to audit
generated output for accuracy and protect sensitive client data.
For lawyers, that is also the threshold for
listening: give a practical answer they can
use today, and they will come back for
more, even if they know they must invest
time before putting it into practice.
