Is your firm ready to put AI to work?

Mostly paper, email, spreadsheets, or personal drives A case system exists, but data is inconsistent or duplicated A modern case system is the source of truth for most cases One system has clean, consistent, mostly complete case data Case data is clean and current signals feed the record

Key fields are usually missing or unknown Some staff fill them in, but not consistently Most core fields are populated with occasional gaps Core fields are reliably populated and trusted Data quality is monitored and improved over time

Legacy or closed systems with no practical integration path Cloud case system, but other tools are siloed Core systems are cloud-based with a few integrations Modern tools with APIs and working integrations Data flows automatically across intake, case, docs, comms, and accounting

Mostly analog or manual handoffs Tools exist, but staff re-key or copy-paste between them Some workflows connect, such as e-sign or document management VoIP, documents, and case system mostly work together The stack is integrated enough for automation to act safely

Every intake is handled differently The team mostly knows what to do, but it is not documented There is a consistent informal flow Intake has a written script or checklist that is followed Intake is documented, measured, and continuously improved

They live in people's heads A few steps are known, but handoffs vary by person Core steps are informal but reasonably consistent Key workflows have SOPs or checklists Workflows have clear owners, handoffs, and improvement loops

No metrics are tracked reliably A few numbers are known anecdotally Leads, signed cases, and sources are tracked Speed-to-lead and intake conversion are tracked Funnel KPIs are dashboarded and tied to revenue

Decisions are mostly gut feel The team can name examples but not quantify them Basic cycle time or stall points are reviewed sometimes Operational KPIs are reviewed regularly KPIs guide staffing, capacity, and investment decisions

Staff may paste client data into consumer AI tools with no policy No written policy or vendor diligence exists yet People know to be careful, but guidance is informal There is a realistic written AI-use policy Policy, vendor diligence, training, and transparency are in place

Data handling is not checked Questions come up only during purchase conversations Confidentiality and data handling are discussed informally Vendor data practices are reviewed before rollout Controls for PHI, retention, training use, and access are documented

Leadership is not bought in, or staff are actively hostile with no champion The owner is curious, but no one owns adoption Leadership is supportive and one person could champion it There is clear sponsorship and a designated owner Leadership, champion, and training rhythm are already in place

It failed or staff worked around it Adoption was uneven and required constant pushing Some teams adopted it, others lagged The team adopted it with reasonable training The firm has a strong habit of training, measuring, and improving

"We should do AI" but no specific problem There is interest, but no priority problem A pain point is named but not quantified A specific, valuable problem is quantified The problem is tied to revenue, capacity, budget, and expectations

No budget or realistic expectation is defined Budget is unclear and expectations are broad There may be budget for a narrow first project Budget and human-in-the-loop expectations are defined AI investment is prioritized as part of a broader capacity plan