Legal AI ROI & Operating Models
Build vs. Buy vs. Outsource: Legal AI Operating Models Compared
By Anthony Villanueva ·
Law firms have three primary options for acquiring legal AI capability: build proprietary systems, buy off-the-shelf tools, or outsource to an embedded engineering partner. The right choice depends on the firm's scale, competitive strategy, and available resources.
The build vs. buy vs. outsource decision for legal AI capability is the central operating model question for law firm leaders in 2025. Each model has different cost structures, capability profiles, and strategic implications, and the right choice is not universal — it depends on the firm's scale, competitive aspirations, and available resources.
Building proprietary capability involves heavy investment
Build: A firm that builds its own proprietary AI capability — as Kirkland & Ellis is doing — invests heavily in engineering talent, data infrastructure, and custom model development. The advantages are proprietary capability that competitors cannot replicate, deep integration with the firm's specific workflows and institutional knowledge, and the ability to iterate and improve continuously without vendor dependencies.
The disadvantages are high capital investment, long time-to-value, and the need to attract and retain engineering talent in a highly competitive market. This model is viable for Am Law 50 firms with the financial scale to sustain the investment.
Purchasing off-the-shelf tools offers speed
Buy: Most law firms deploy AI by purchasing off-the-shelf tools from vendors like Harvey, CoCounsel, or Lexis+AI. The advantages are fast time-to-deployment, predictable cost, and access to well-tested technology without requiring engineering investment. The disadvantages are limited customization, vendor dependency, and the risk that competitors can access the same tools. The off-the-shelf model is appropriate for firms that want to deploy AI quickly for standard use cases without a major capital commitment.
Embedded engineering provides a middle path
Outsource/Embed: The embedded legal engineering model provides a middle path — a firm gets custom-built AI systems without needing to build an internal engineering team. An embedded engineering partner brings the technical capability and legal domain knowledge to design and deploy AI that is customized to the firm's specific workflows and institutional knowledge, at a cost and timeline that is more accessible than the build model. The result is proprietary capability without the permanent overhead of an internal engineering organization.
CLOC's operating model research identifies this model as the most common approach among mid-market legal departments that are achieving advanced AI maturity.
Frequently asked questions
For which type of law firm is the build model primarily considered viable?
Build: A firm that builds its own proprietary AI capability — as Kirkland & Ellis is doing — invests heavily in engineering talent, data infrastructure, and custom model development. The advantages are proprietary capability that competitors cannot replicate, deep integration with the firm's specific workflows and institutional knowledge, and the ability to iterate and improve continuously without vendor dependencies.
The disadvantages are high capital investment, long time-to-value, and the need to attract and retain engineering talent in a highly competitive market. This model is viable for Am Law 50 firms with the financial scale to sustain the investment.
What are the primary disadvantages associated with purchasing off-the-shelf AI tools?
Buy: Most law firms deploy AI by purchasing off-the-shelf tools from vendors like Harvey, CoCounsel, or Lexis+AI. The advantages are fast time-to-deployment, predictable cost, and access to well-tested technology without requiring engineering investment. The disadvantages are limited customization, vendor dependency, and the risk that competitors can access the same tools. The off-the-shelf model is appropriate for firms that want to deploy AI quickly for standard use cases without a major capital commitment.
Which model is the most common approach for mid-market legal departments reaching advanced AI maturity?
Outsource/Embed: The embedded legal engineering model provides a middle path — a firm gets custom-built AI systems without needing to build an internal engineering team. An embedded engineering partner brings the technical capability and legal domain knowledge to design and deploy AI that is customized to the firm's specific workflows and institutional knowledge, at a cost and timeline that is more accessible than the build model. The result is proprietary capability without the permanent overhead of an internal engineering organization.
CLOC's operating model research identifies this model as the most common approach among mid-market legal departments that are achieving advanced AI maturity.