01Agentic service triage
Create an AI agent that prioritises faults, requests and client issues
How it should workThe agent reads approved incoming messages, classifies urgency and impact, gathers missing details and routes each case to the right owner. A person approves high-impact or ambiguous decisions.Prove value withTime to triage, urgent cases identified, reassignment rate and resolution time.First controlled movePilot one service queue using historical cases, explicit escalation rules and a named operational owner.02Proposal intelligence
Build a proposal copilot that turns expertise into stronger first drafts
How it should workThe copilot retrieves approved case studies, credentials and service language, then produces a tailored proposal outline for expert review—without inventing experience or commercial claims.Prove value withDrafting time, expert correction rate, proposal turnaround and win-rate quality.First controlled moveStart with one repeatable service line and a controlled library of approved source material.03Trusted knowledge assistant
Give every team member fast answers grounded in company evidence
How it should workA secure assistant searches policies, methods, previous work and approved guidance, cites its sources and sends low-confidence answers to a subject-matter expert.Prove value withSearch time saved, cited-answer rate, unanswered questions and user confidence.First controlled moveConnect a small, high-value document set first; test accuracy with real questions before wider release.04Client-risk radar
Detect delivery, relationship and commercial warning signs earlier
How it should workAI reviews permitted project signals—milestones, actions, service messages and capacity—to highlight accounts requiring human attention before a missed commitment becomes a complaint.Prove value withEarlier interventions, overdue actions, client escalations and retained revenue.First controlled moveDefine five observable warning signals and test them against completed projects before using live alerts.05Meeting-to-action agent
Turn conversations into owned work without manual follow-up
How it should workWith participant consent, an assistant produces a structured summary, separates decisions from discussion, proposes owners and deadlines, and updates the approved workflow after human confirmation.Prove value withAdministration time, actions captured, overdue actions and correction rate.First controlled movePilot with one internal leadership meeting and prevent automatic external communication.06Opportunity signal engine
Reveal unmet client needs hidden across permitted business data
How it should workAI groups recurring questions, delivery friction and sector themes so account leaders can identify relevant service opportunities—not indiscriminate sales prompts.Prove value withQualified opportunities, relevance rate, conversion quality and client value.First controlled moveAnalyse anonymised themes first and require an account owner to validate every proposed opportunity.