AI Adoption Roadmap

Looking at a market where top firms were rapidly adopting AI for research, intake, contract drafting, and document review, our client – a mid-sized law firm – knew it risked falling behind. Despite growing pressure from peers and clients, the firm had no clear AI strategy, no internal infrastructure to support it, and growing concerns among partners about confidentiality, output quality, compliance, and ROI.

To move from hesitation to action, the firm engaged Sphere to design a practical AI adoption roadmap – building a secure data foundation, establishing governance, and launching pilot use cases powered by Sphere GPT to deliver quick, measurable wins.

The engagement ran over sixteen weeks and moved deliberately from clarity to infrastructure to real, working pilots. Sphere began by interviewing over twenty people across the firm – partners, associates, practice leads, BD staff, and operations – to map daily workflows and surface friction points. Several themes emerged quickly: attorneys were spending hours on repetitive document review, contract drafting dragged due to scattered precedents, and client intake involved slow, manual data entry.

In parallel, we ran a comprehensive audit of the firm’s technology stack. Every core system was cataloged – the document management system, CRM, billing platform, HR tools, and internal file shares – to see where data lived, how it flowed, and how secure it was. We examined existing security, access, and retention policies, then facilitated a prioritization workshop with firm leadership to identify three high-impact, low-risk use cases for pilots.

With priorities defined, Sphere laid the groundwork for safe AI adoption. We consolidated siloed data sources from the DMS, CRM, and shared drives into a single secure repository with consistent metadata. We implemented role-based access controls, encryption (at rest and in transit), and full audit logging. At the same time, we introduced firm-wide data governance standards covering document retention, version control, data ownership, and privacy – effectively creating a secure “AI sandbox” environment that could safely support sensitive legal work.

Laying the Foundation Discovery interviews revealed where AI could create real impact, while the audit exposed how fragmented the data landscape had become. Sphere unified key data into a structured, governed repository with consistent metadata, access controls, encryption, and audit trails – giving the firm a secure, compliant base on which AI tools could safely run.

Governance and Pilots with Sphere GPT With the foundation in place, Sphere created an AI Steering Committee made up of partners, operations leaders, IT, and risk/compliance officers to oversee decisions and budgets. We drafted firm-wide AI policies around confidentiality, model output verification, prompt logging, and risk thresholds, and trained 45 attorneys and staff on responsible AI usage.

Client Intake Assistant: Sphere GPT was deployed as an embedded assistant inside the firm’s intake platform. It pulled from internal policies, conflict-check rules, and past intake forms to help staff handle new matters faster. When a new client inquiry arrived, the assistant could:

Contract Clause Suggestion Tool: Sphere GPT was also integrated with the firm’s document management system as a drafting-sidekick for attorneys. It operated entirely on the firm’s internal clause library and past contracts, with no exposure to public models. Attorneys could:

Both tools ran inside a secure private cloud instance with full audit logs and access controls. All outputs were routed through a human-in-the-loop review step: paralegals for intake, and senior associates or partners for contracts. This not only ensured quality and compliance but also helped staff trust the system – knowing it was augmenting, not replacing, their work.

• Pre-populate matter intake forms using details extracted from emails and attached documents

• Flag potential conflicts of interest by cross-checking parties against the CRM and DMS

• Suggest the appropriate engagement letter template based on matter type, jurisdiction, and fee arrangement

• Summarize intake data into a structured brief for partner review This reduced back-and-forth emails, accelerated conflict checks, and gave partners clean summaries without additional admin work.

• Draft first-pass agreements by inserting suggested boilerplate clauses pulled from the firm’s approved precedent bank

• Compare an uploaded contract draft against the firm’s standard – highlighting missing, unusual, or risky clauses

• Generate redline-style commentary on how proposed edits differ from previous versions in similar matters

• Surface recent negotiated language for specific counterparties to guide consistent risk positions This shifted much of the initial drafting and comparison work to the AI layer, letting attorneys focus on negotiation strategy instead of mechanical drafting.

Within ~5 months, the firm had moved from having no formal AI plan to running secure, useful AI tools in selected workflows. Sphere’s roadmap, combined with data infrastructure investment and governance in place, meant the firm was ready for broader rollout of AI tools (document review, research assistants, etc.).

By introducing Sphere GPT only once the foundation was secure, Sphere helped this law firm avoid the pitfalls of premature adoption while gaining tangible efficiency wins and preserving client trust.