解决流程：将您的数据供应链转变为AI智能.pdf

1、Meet the DataBank teamCharlie BauerStephen BrooksPractice DBusiness Development DInvoices,claims,permits,case files.Information arrives faster than teams can process it,and rekeying or scanning becomes a bottleneck.Backlogs and Manual Processing Slow Critical WorkDocuments live in shared drives,inbo

2、xes,legacy systems,or multiple platforms,making retrieval slow,governance inconsistent,and audit response stressful.Information Is Hard to Control,Find,or DefendWorkflows exist,but teams still rely on email approvals,spreadsheets,and human validation to keep things moving.Processes Depend Too Heavil

3、y on Manual Checks and WorkaroundsWhy We ExistOur role is not to sell a tool or complete a one-off project,its to guide organizations through these breakdowns with durable,operational solutions.Where are you with AI today?A.ProductionB.PilotingC.Not startedD.No ideaAI isnt failingYour data is.The Re

4、alityof enterprise data is unstructured.80%+DataUp to 60%Manual WorkPoor data&fragmentationAutomationWhy do AI solutions fail?Most organizations dont have a strategy problem,they have an execution problem.70%ModernizationIDCstall 85%+of automation and AI projects.GartnerMcKinseyof digital transforma

5、tions fail.of employee time spent is on manual work.McKinseyAI Models dont understand data without heavy preparationThe problemData is fragmented at the point of intakeIt requires too much manual workGovernance&security post a challengeAI Pilots stall before productionIf a manufacturer has an idea f

6、or a new product,whats the first thing they do?The Data Supply ChainWhy do AI,analytics and automation struggle?When data isnt enriched,validated or cleansed,you cant access accurate analytics,trusted automation or scalable AI.The real question isnt“Which AI model are we using?”Its“How strong is our