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How to Outcompete Your Client’s AI

Andy Carter/Ikon Images The in-house lawyers at real estate investment firm Alturas Capital Partners used to rely on outside counsel for much of its lease work. Thanks to generative AI, it can now do that work internally, saving the firm hundreds of thousands of dollars in spending and compressing lease work that once stalled for […]

How to Outcompete Your Client’s AI

Law firms once relied heavily on outside counsel for various legal tasks, but generative AI has enabled them to handle these tasks internally, resulting in significant savings and faster turnaround times. However, this shift is also impacting the service providers, as AI has lowered the cost of producing legal documents, market analyses, creative assets, and software. In-house legal teams equipped with AI can now replicate the work that was previously outsourced.

To remain relevant, service providers must rethink their sales strategies and focus on cost advantages. They should aim to improve unit economics by building infrastructure that allows them to produce work at scale, enabling them to price each finished output below what a client could achieve by doing the work in-house themselves.

Companies like WPP have successfully implemented this approach by creating agentic marketing platforms that leverage proprietary intelligence and proprietary data, allowing brands to generate assets and activate media campaigns more cost-effectively.

Another crucial aspect is making the buying process as seamless as possible. Traditional buying processes involve significant effort, including finding the right expert, negotiating terms, explaining needs, reviewing drafts, and integrating results. Generative AI can streamline this process by creating agents that take requests, perform the work, and deliver results directly into tools customers are already using.

This ease of interaction reduces the perceived value of building in-house capabilities, as clients can achieve similar results without the added effort.

Finally, providers must address the quality assurance and maintenance costs associated with in-house work. While AI-enabled in-house teams can streamline processes, they still require oversight to ensure accuracy and compliance. Service providers can differentiate themselves by offering comprehensive quality assurance and ongoing support, ensuring that clients benefit from the expertise and experience that AI cannot easily replicate.

Written by urgent.news from MIT Sloan's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

Read the original at sloanreview.mit.edu →

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