As before, the diffusion of such technology comes with newer, more robust client expectations. Meeting such expectations requires charting a forward-looking path, and introspection among the legal community.

Understanding Client Expectations: Three Principles

Between 2006 and 2007, scholars at the Harvard Law School and the University of Miami surveyed 166 Chief Legal Officers across S&P 500 companies, attempting to identify the factors behind a robust client-provider relationship. A large client typically has 10-15 preferred providers, they found. Relationships among such providers were enduring and did not solely focus on star firms or lawyers. They also focused on the qualities of teams and departments that clients worked with.

Client expectations regarding technology are best understood as an expectation on quality. In breaking down this expectation, consequently, a three-pronged approach is most amenable. Expectations on technology must respond to three principles: speed, cost efficiency, and transparency.

First, speed. It is trite that Artificial Intelligence has demonstrated efficiency gains across document review and management. However, these expectations require calibration. Uses have come at the peril of AI hallucination. This has meant augmented diligence, Partner-and-Firm led accountability and the identification of use-cases that do not work well with AI.

Passing on this benefit to clients while balancing the above objectives requires business planning. Gains may most readily be passed in transactions or advisory work, while the use of AI in disputes must continue to be restrained and supervised. A recent case involving a UK-based law firm citing hallucinated statutory text in court filings serves as a warning; missteps on AI may involve absorbing additional costs and judicial scrutiny over professional misconduct.

Second, cost-efficiency. Client expectations are most advanced along this prong, as businesses recruit strategists to identify costs. To solve, consider the maths involved. What you can pass on is the revenue over hours saved, minus the cost of adopting these technologies. Today these costs remain substantial; once clarity over both variables in this equation emerges, lawyers should consider formulating an appropriate fee model.

Third, transparency. Firms must readily communicate AI use to clients. In parallel, the development of a governance mechanism (marked by internal AI Governance policies, and incident reporting and response mechanisms) must ensure that logs of use are readily available for inspection. Knowing how AI use leads to errors or gains forms the trust layer to the lawyer stack.

Way Forward

Substantial investments, followed by a pivot for practicality, underscore tech adoption at professional services firms. This is routine; law firms’ adoption of computers was similarly marked by profound cooperation with technology solutions providers.

That experience contained two insights. One, facilitative technological deployment involves robust strategic partnerships between the developer and the deployer. For AI, this has meant understanding Firm and client-level expectations around data, infrastructure and talent, and coding them into the partnership. This is a mindset shift: lawyers are now co-founders, and their legal work is proprietary.
Two, sustainable use. Systems built with the best intentions may still not work. In the AI era, experimentation comes at a cost, with firms needing to identify the right model that burns tokens most efficiently to finish tasks.

Identifying the right model and model provider forms the first step in addressing client expectations on AI. In time, a governance plan centred on speed, value and trust shall help meaningfully meet them.

This article was originally published in Fefutech on 15 June 2026 Written by: Dr. Shardul S. Shroff, Executive Chairman. Click here for original article

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