Management Consulting Delivery

Business Problem

Consulting teams need to coordinate hypotheses, interviews, data requests, analyses, findings, steering committees, recommendations, and benefit tracking across many workstreams. The workflow is structured enough to model but too complex for generic project tools alone.

Four-Step Application

This scenario works best as a four-step, human-in-the-loop application. The required object model already gives this scenario a strong delivery backbone for a four-step operating experience.

• Mission metric focus: higher utilisation, stronger SLA attainment, and better client experience.
• Human + AI pattern: Each step combines structured workflow data with chat assistance, background generation, document understanding, and accessible interaction patterns when they improve the experience.

Step 1. Capture demand and context

• Goal: Make it easy for the user to start the Management Consulting Delivery journey with complete, trusted context.
• Required data: ClientEngagement (consulting engagement), Workstream (workstream record), Hypothesis (working hypothesis), InterviewPlan (planned interview program), and InterviewNote (interview note).
• AI support: Use chat to guide intake, generate clearer prompts, create accessible summaries, and assist with voice or vision-led capture when a form alone is not the best experience. EAI can support structured intake, chat workflows, and document-centred capture today; richer native multimodal capture may still need workflow extensions or connected services.
• Business impact: Improve completion rate, reduce first-touch effort, and raise customer or staff confidence in the UX from the very first interaction.
• EAI delivery: Model the intake as tenant-isolated object types and resources, then use actions, chat workflows, and document indexing or classification to keep the initial record complete and usable.

Step 2. Prepare the decision

• Goal: Turn the captured context into the next best action for Management Consulting Delivery without forcing the human reviewer to assemble the case manually.
• Required data: DataRequest (client data request), DataSet (received data set), AnalysisModel (analysis model), Finding (insight finding), and Recommendation (recommended action).
• AI support: Run background summarisation, extraction, classification, recommendation drafting, and answer generation so a reviewer sees a prepared case instead of raw fragments. EAI delivers the structured records and AI workflow hooks for this today; specialised scoring engines, external rules, or advanced reasoning controls may still need integration work.
• Business impact: Reduce cycle time, improve quality and consistency, and protect the mission-critical metric before the case moves into execution.
• EAI delivery: Link records across the scenario, persist decision state as resources, and use workflow actions plus chat assistance to keep humans in control while AI prepares the work.

Step 3. Execute and collaborate

• Goal: Coordinate the actual work, handoffs, approvals, and user updates needed to deliver the service or outcome.
• Required data: SteerCoMeeting (steering committee meeting), DecisionLog (decision record), Deliverable (client deliverable), RiskIssue (engagement risk), and Dependency (delivery dependency).
• AI support: Draft replies, produce work packets, monitor exceptions in the background, and surface the next action for each operator. EAI can orchestrate tenant-isolated records, actions, chats, and document workflows today; deeper system-to-system automation may require additional connectors or workflow capability.
• Business impact: Increase operator productivity, reduce rework across handoffs, and improve service consistency across the application journey.
• EAI delivery: Use linked object types, actions, resource updates, and workflow-triggered AI assistance so the team can execute in one model instead of splitting work across disconnected tools.

Step 4. Resolve, explain, and improve

• Goal: Close the loop with a clear outcome, an understandable explanation, and feedback that improves the next case.
• Required data: BenefitCase (expected value case), Milestone (delivery milestone), StaffingPlan (staffing plan), Consultant (consultant record), and Proposal (proposal record).
• AI support: Generate outcome summaries, customer-friendly answers, compliance-ready notes, management insights, and accessible follow-up content. EAI can store outcome records and support answer generation today, while richer proactive agents, advanced analytics, or channel-specific accessibility features may need additional product capability.
• Business impact: Increase trust, quality, and measurable business value through higher utilisation, stronger SLA attainment, and better client experience.
• EAI delivery: Keep the full audit trail in structured resources, use AI workflows to explain outcomes, and feed the resulting signals into future product, service, and operational improvement work.

EAI Platform Support By Step

EAI provides the safe service boundary for Management Consulting Delivery through Object Types, tenant-scoped resources, document processing, chat workflows, and CLI verification. For this scenario, the main records are ClientEngagement, Workstream, Hypothesis, InterviewPlan, InterviewNote, and 17 more Object Types.

Process step	What EAI provides	Calling pattern
Step 1. Capture demand and context	Tenant-scoped intake resources for ClientEngagement (consulting engagement), Workstream (workstream record), Hypothesis (working hypothesis), InterviewPlan (planned interview program), and InterviewNote (interview note). Object Type validation, starter forms, optional document intake, and chat-guided capture keep the first record complete.	Define fields in src/eai.config/object-types.ts, run eai types validate and eai types seed, create initial ClientEngagement records with useResources('ClientEngagement') or eai resources create ClientEngagement, and keep browser calls behind /api/eai/....
Step 2. Prepare the decision	Linked resource queries over DataRequest (client data request), DataSet (received data set), AnalysisModel (analysis model), Finding (insight finding), and Recommendation (recommended action). Search, schema checks, document classification or RAG indexing, and chat summaries turn raw context into a prepared decision.	Use useResources('ClientEngagement') list/query/search patterns, verify shape with eai resources schema, use useDocuments().upload/classify/ragIndex, eai docs upload, eai docs classify, and eai docs index where supporting material exists, and send decision-support prompts through useChat(workflowId, 'chat') or eai chat send.
Step 3. Execute and collaborate	Resource updates and actions for SteerCoMeeting (steering committee meeting), DecisionLog (decision record), Deliverable (client deliverable), RiskIssue (engagement risk), and Dependency (delivery dependency). Status changes, assignments, notes, generated work packets, and chat support keep humans in control during execution.	Model actions in the Object Type code, call client.resources.executeAction(type, id, action) or the app hook equivalent, update records through the app service layer, and verify with eai resources get/list/query.
Step 4. Resolve, explain, and improve	Outcome resources for BenefitCase (expected value case), Milestone (delivery milestone), StaffingPlan (staffing plan), Consultant (consultant record), and Proposal (proposal record). Audit-friendly links, indexed final documents, reporting snapshots, and answer generation make the result explainable and reusable.	Persist outcomes as resources, index final material with eai docs index or useDocuments().ragIndex, send explanation prompts with useChat or eai chat stream, and use eai resources aggregate/search for reporting checks.

Prompt, Code, And Service Pattern Mapping

When this scenario is turned into code, eai-gofer should generate Object Type definitions and app calls from the process model instead of inventing direct backend calls.

Use this prompt shape when asking eai-gofer or another coding agent to implement the scenario:

Use the EAI App Template. Model Management Consulting Delivery with Object Types for ClientEngagement, Workstream, Hypothesis, InterviewPlan, InterviewNote. Use useResources for records and actions, useDocuments for uploads/classification/RAG where documents appear, useChat for workflow assistance, and verify with eai types/resources/docs/chat commands. Use eai publicapi only when no named command covers the required platform call.

Scenario artifact	How it maps to EAI service calls
Four-step process	Step 1 becomes resource creation, Step 2 becomes resource query/search plus optional document or chat preparation, Step 3 becomes resource update/action calls, and Step 4 becomes outcome persistence plus explanation/reporting calls.
Object Type definitions	eai types validate, eai types seed, and eai resources schema make the model available and checkable before UI work starts.
Properties and indexes	Fields become useResources payloads, filters, list views, and eai resources create/list/query/search checks. Indexed fields should support lookup and triage, not duplicate canonical records.
Links between Object Types	Relationships become linked-resource UI, timeline context, and audit trails that app code loads through resource queries rather than separate bespoke stores.
Actions and status fields	Workflow buttons and operator transitions call resource action/update helpers, then verify state with eai resources get/list/query.
Document and chat prompts	Prompts should call the platform documents and chat patterns: useDocuments().upload/classify/ragIndex, eai docs upload, eai docs classify, and eai docs index for documents, and useChat, eai chat send, or eai chat stream for conversational assistance.

Required Object Model (22 object types)

This scenario needs more than 20 object types because it spans intake, delivery, exceptions, governance, and reporting.

Engagement and Workstream Setup

• ClientEngagement — consulting engagement
• Workstream — workstream record
• Hypothesis — working hypothesis
• InterviewPlan — planned interview program
• InterviewNote — interview note
• DataRequest — client data request
• DataSet — received data set
• AnalysisModel — analysis model

Insight and Decision Workflow

• Finding — insight finding
• Recommendation — recommended action
• SteerCoMeeting — steering committee meeting
• DecisionLog — decision record
• Deliverable — client deliverable
• RiskIssue — engagement risk
• Dependency — delivery dependency
• ActionTracker — action item

Commercial and Staffing Control

• BenefitCase — expected value case
• Milestone — delivery milestone
• StaffingPlan — staffing plan
• Consultant — consultant record
• Proposal — proposal record
• ChangeRequest — scope change request

Delivery Workflow

1. Authenticate and choose the tenant you want to work in.

   eai login
   eai tenant select

2. Pull environment values, validate the type definitions, and seed the model.

   eai env pull --include-secrets
   eai types validate
   eai types seed

3. Verify that the full model is available for the active tenant before building UI and workflows.

   eai resources schema --format json
   eai verify calls --format json

4. Load pilot data and exercise the operational workflows for the scenario.

AI and Document Opportunities

• Summarise interview notes and client data findings into structured hypotheses and recommendations.
• Classify deliverables and working papers by workstream, milestone, and steering committee relevance.
• Generate action trackers and risk summaries from steering committee decisions and scope changes.

Why This Scenario Is High-Value

Consulting firms scale by making delivery repeatable without making it rigid. This scenario creates a reusable operating model for complex advisory work.