CSP on AINPX Future Vision
From fragmented workspaces to a personalized AI operating environment
A role-agnostic, context-aware platform that anticipates customer needs, coordinates work across roles, and assembles the right experience for every customer moment.
Today
Users navigate fixed, role-based experiences and manually assemble customer context across disconnected tools.
Future state
CSP continuously understands customers, workflows, roles, and priorities, then assembles the right context, actions, and collaboration around the moment.
Outcome
Users move from finding information to making informed decisions and advancing customer outcomes.
Horizon
Research confidence
Section 01
Strategy at a glance
The full argument in one screen: the problem, the opportunity, the anchor, and the foundation.
Strategic problem
ContextCSP has grown feature by feature and role by role.
The accumulated experience places the burden on users to search, interpret, validate, and decide what matters.
Strategic opportunity
DirectionAINPX creates an opportunity to redesign the underlying model rather than add AI to the current interface.
Primary user anchor
ScopeUse the CSM as the initial point of view. The model extends beyond the CSM.
Primary scenario
ScopeUse a customer risk or escalation as the primary demonstration scenario.
Experience foundation
ArchitectureAwareness model
ArchitectureInteraction model
ExperienceHybrid is the dominant future-state pattern.
Delivery horizons
SequencingSection 02
Why CSP must change
Six structural problems that no additional dashboard, tab, or agent can solve.
Fixed experience growth
CSP expands through tabs, scorecards, tables, columns, and persona-specific workspaces as new needs emerge.
Impact
Navigation and cognitive load increase for users, while product and engineering inherit additional surfaces to maintain.
Fragmented context
Customer and operational context is spread across CSP, support systems, project systems, CRM, email, Teams, documents, dashboards, spreadsheets, and individual knowledge.
Impact
Users repeatedly assemble the same information before they can act confidently.
Reactive monitoring
Users must continually check dashboards, account records, support cases, project data, and health signals to determine whether intervention is needed.
Impact
Issues are often discovered after the customer raises them or after the situation has become urgent.
Incomplete handoffs
Context is lost when work moves across CSMs, Support, SAMs, PAs, TSMs, EMs, RMs, Sales, partners, and leadership.
Impact
Users repeat investigation, customers repeat information, and ownership becomes unclear.
Repetitive administrative work
Meeting preparation, follow-up, reporting, status creation, project planning, resource evaluation, and customer communication are repeatedly recreated.
Impact
Administrative work crowds out strategic judgment and relationship-building.
Trust gap
Data may be stale, contradictory, incomplete, or disconnected from its source. AI output may not distinguish fact, inference, synthesis, and user-entered information.
Impact
Users re-verify information and are reluctant to act on recommendations.
Different roles experience different symptoms, but the underlying pattern is the same: too much manual work before confident action.
CSMs
- Manual meeting preparation
- Reactive support visibility
- Repeated account synthesis
- Customer follow-up burden
- Difficulty anticipating customer needs
SAMs and Support
- Fragmented case history
- Unclear ownership
- Internal activity not visible to customers
- Escalation coordination burden
PAs and TSMs
- Technical context separated from adoption and customer goals
- Difficulty connecting configuration, usage, cases, and roadmap implications
EMs
- Project planning outside core systems
- Duplicate internal and customer reporting
- Disconnected forecast, budget, timeline, and resource information
RMs
- Late pipeline visibility
- Incomplete skills and availability information
- Manual partner outreach
- Spreadsheet-based capacity planning
Leadership
- Manual portfolio synthesis
- Inconsistent reporting
- Limited ability to identify emerging risk across accounts
Today’s tool sprawl
Section 03
Vision and North Star
What CSP becomes, what it always knows, and what it should know right now.
Vision statement
CSP on AINPX is a personalized AI operating environment for the people responsible for customer success.
The platform should understand the customer situation, assemble the right context, involve the appropriate roles, and guide work toward an outcome.
Role-agnostic in structure. Role-aware in presentation. Context-aware in behavior. Permission-aware in execution.
Strategic shift
CSP should stop growing as a collection of role-based workspaces, tabs, dashboards, and individual features. It should become a shared intelligence and orchestration layer that dynamically assembles around the customer moment.
Core hypothesis
If CSP becomes AI-native, users will spend less time searching, stitching, validating, and monitoring information, and more time making decisions, coordinating work, and helping customers achieve outcomes.
When CSP continuously interprets customer context and anticipates emerging needs, CSMs can intervene earlier, prepare less, and spend more time guiding customer outcomes rather than reacting to requests.
North Star
A self-assembling, continuously maintained customer-success environment with explainable memory, trusted decision-point interruptions, and coordinated action across roles.
The platform promise
The platform knows the user, the customer, the current moment, what has changed, what matters, who should be involved, and what decision or action needs to happen next.
What the platform should always know
What it should know right now
| Instead of | The platform says |
|---|---|
| Go find the information. | Here is what needs attention. |
| Monitor every dashboard. | You will be interrupted when judgment is needed. |
| Prepare from a blank page. | Review, confirm, and adjust a continuously maintained brief. |
| Move between role workspaces. | Enter one shared environment that adapts to the role and moment. |
| Ask AI where to find something. | AI anticipates what matters and helps move the work forward. |
Section 04
Strategic shifts
Six changes to architectural assumptions. Select a shift for the full from-to detail.
Section 05
The future experience model
Detect, Understand, Coordinate, Act, Learn — the loop every customer moment runs through.
Stage 1
Detect
Identify a meaningful customer or operational signal before the user begins searching.
Examples
Platform
- Monitor signals
- Identify meaningful change
- Rank urgency
- Consider customer context
- Avoid unnecessary interruption
Stage 2
Understand
Assemble the context needed to evaluate the situation.
Context assembled
Platform
- Explain what changed
- Connect related information
- Identify gaps
- Show source and confidence
- Distinguish facts from inferences
Stage 3
Coordinate
Identify who should participate and create a shared path forward.
Platform
- Determine primary owner
- Identify supporting roles
- Recommend next actions
- Create tasks
- Route approvals
- Share relevant context
- Preserve permission boundaries
- Avoid duplicate investigation
Stage 4
Act
Help each role complete the appropriate work.
Platform
- Prepare outputs
- Execute approved workflows
- Update systems
- Draft communication
- Escalate
- Schedule meetings
- Replan
- Assign ownership
- Track completion
Human
Stage 5
Learn
Update the shared customer and operational context based on the outcome.
Platform
- Record decisions
- Capture results
- Update risk
- Update account memory
- Adjust recommendations
- Improve future prioritization
- Preserve an audit trail
Section 06
The critical customer moment
Primary future-state scenario: anticipate and coordinate a customer risk before the customer asks.
Trigger
A critical customer support case has not received a substantive update. The customer has an executive meeting the following day. Recent sentiment is negative, and the case may be raised during the meeting.
Detect
- Support Intelligence identifies stale activity.
- The platform recognizes the upcoming meeting.
- Sentiment and account importance increase the priority.
- The system determines that a human decision is needed.
Understand
- Case priority
- SLA status
- Current assignee
- Last substantive response
- Internal work notes
- Customer-visible activity
- Release and patch information
- Related customer commitments
- Upcoming meeting
- Customer sentiment
- Previous escalation history
- Account-team ownership
Coordinate
- Primary relationship owner: CSM
- Support coordination: SAM
- Technical owner: Support Engineer
- Technical context contributor: PA or TSM
- Customer-facing approver: CSM
- Proposed: internal escalation, ownership confirmation, customer communication, meeting talking points, follow-up monitoring
Act
- SAM receives an escalation task.
- Support receives the complete case context.
- The CSM receives a customer-ready brief.
- Communication capability prepares an external update.
- The CSM reviews and approves the communication.
- The customer receives an update before needing to ask.
Learn
- Case and account context are updated.
- The outcome is attached to the customer history.
- The system records which recommendation was accepted.
- Future risk assessment incorporates the outcome.
- The account team retains a complete decision trail.
Customer outcome
The customer feels that ServiceNow is informed, coordinated, and proactive.
Operational outcome
Roles act from shared context rather than repeating investigation across tools.
Strategic outcome
CSP demonstrates that AI-native architecture can solve a category of customer-service problems rather than add another support dashboard.
Section 07
Role-agnostic operating model
One shared platform. Different responsibilities. Personalized access and dynamically assembled workflows.
Role-agnostic means
- One shared platform
- One connected context model
- Shared capabilities
- Shared orchestration
- Shared interaction patterns
- No requirement for a separate workspace for every role
Role-aware means
- Different default information
- Different recommended actions
- Different decision authority
- Different level of detail
- Different workflow participation
- Different permissions
- Different customer-facing responsibilities
Assembly inputs
Default
Automatically available because it is central to the user's responsibilities.
Contextual
Available when the user becomes involved in a relevant customer, project, case, escalation, or workflow.
Requestable
Available through a user-initiated access request.
Approval required
Requires authorization before data can be viewed or an action can be completed.
Restricted
Unavailable because it falls outside responsibility, policy, privacy, or security boundaries.
The platform should reveal capabilities when they become relevant without exposing every tool, agent, action, or dataset to every user.
CSM lens
SAM lens
PA lens
EM lens
RM lens
Section 08
Intelligence architecture
Seven layers from source systems to measurable outcomes. Select a layer for inputs, outputs, dependencies, and trust requirements.
Section 09
The Three Awareness types
System, workflow, and role awareness together make anticipation possible.
System Awareness
What is happening?
Example
A P1 case has not received a substantive update in five days.
Workflow Awareness
What is affected, and what should happen next?
Example
The case may become an executive escalation because the customer has a meeting tomorrow and sentiment is declining.
Role Awareness
What does this user need to know or do now?
Example
The CSM needs the customer impact and communication plan, while Support needs technical history and the SAM needs escalation status.
Anticipation requires all three forms of awareness. The system must understand the customer state, the workflow implications, and the responsibilities of the person receiving the information.
Section 10
Interaction modes
Hybrid is the primary future-state interaction model because it combines proactive intelligence, structured evidence, workflow controls, and conversation.
Conversational
Best for
Example
“What should I know before tomorrow's meeting?”
Conversation alone can hide structure, state, completeness, and available actions.
UI-driven
Best for
Example
A prioritized portfolio list with support risk, SLA, owner, and available actions.
Static UI can reproduce current navigation and dashboard problems if it is not dynamically assembled.
Hybrid
DominantBest for
Example
The platform assembles a support-risk brief with evidence, roles, actions, and a conversational area for follow-up questions.
Mode selection depends on
Section 11
Agents and orchestration
Capabilities operate behind one experience. Select an agent for signals, outputs, approvals, and visibility.
- Agents may operate behind a unified Otto experience.
- Users should not need to know which agent owns every step.
- Agent identity should be visible when it supports transparency, control, troubleshooting, or trust.
- The platform should orchestrate agents around user goals and customer moments.
- Agents should not compete for attention or create disconnected destinations.
Orchestration in the primary scenario
- 1Customer IntelligenceUnderstands account importance, sentiment, and meeting context.
- 2Support IntelligenceDetects stale case activity and SLA risk.
- 3Risk and OpportunityDetermines likely customer impact.
- 4InterlockIdentifies appropriate internal roles.
- 5CommunicationPrepares the external update.
- 6Reporting and InsightsUpdates the customer and portfolio history.
Section 12
Current tools to future capabilities
Source systems remain. What they are used for changes.
| Tool | Used today for | Future capability | Agents |
|---|---|---|---|
| NowSupport | CasesActivitySLAAssigneeEscalation detailsWork notes | Anticipate and coordinate customer support concerns. | Support Intelligence |
| Surf | SalesOpportunityContractCustomer commitmentsPipeline | Understand upcoming customer and delivery demand. | Customer IntelligenceRisk and Opportunity |
| Orca | ProjectsResourcesDeliveryHoursBudgetAllocation | Coordinate delivery, project health, staffing, and resource risk. | Project DeliveryResource Recommendation |
| Teams and Outlook | Informal decisionsRelationship contextCustomer communicationInternal coordinationSentiment | Preserve account memory and coordinate work. | Meeting PreparationCommunicationInterlock |
| Zoom | MeetingsTranscriptsParticipant context | Maintain meeting history, decisions, commitments, and follow-up. | Meeting Preparation |
| Tableau | ReportingPortfolio viewsProject healthOperational trends | Surface prioritized insights and current status. | Reporting and Insights |
| Excel | ForecastingPlanningResource analysisProject trackingWorkarounds | Provide dynamic planning, scenario modeling, and structured comparison. | Project DeliveryResource Recommendation |
| PowerPoint | Customer statusExecutive reviewQBR and EBR materials | Generate customer-ready narratives and adaptable outputs. | Reporting and InsightsCommunication |
| Product telemetry | UsageAdoptionConfigurationProduct health | Identify risk, opportunity, and next-best action. | Adoption and Value |
Section 13
Time horizons
Now, Next, and North Star are separated so the vision is not read as an immediate roadmap commitment.
Now
NowFoundation
- Improve visibility
- Unify critical information
- Introduce AI-generated summaries
- Reduce tool switching
- Surface source and freshness
- Improve current workflows
- Support existing CSP structures
Example capabilities
Interaction
UI-driven with embedded AI
Why this matters
Useful, achievable, and compatible with current workflows.
Next
NextAssisted workflows
- Connect signals
- Identify risks
- Recommend actions
- Prepare outputs
- Coordinate work across roles
- Route approvals
- Preserve context through handoffs
- Maintain customer memory
Example capabilities
Interaction
Hybrid
Why this matters
Demonstrates measurable operational value and builds trust.
North Star
North StarAssembled experiences
- Dynamically construct the environment around the customer moment
- Use ambient watching
- Anticipate emerging needs
- Assemble relevant roles and capabilities
- Execute approved multi-step workflows
- Maintain explainable memory
- Personalize by role, context, and preference
- Continuously learn from outcomes
Interaction
Adaptive hybrid, conversational, and UI-driven experiences
Why this matters
Shows the complete AI-native vision without presenting it as an immediate implementation commitment.
Section 14
MVP recommendation and validation
Prove the architecture through one high-value moment.
MVP statement
Prove that CSP can tell a CSM what needs attention today, why it matters, where the information came from, who should be involved, and what action should happen next.
Scenario: Stale critical support case before a customer meeting.
Detection
- Identify meaningful stale-case activity
- Connect the case to the customer and upcoming meeting
Context
- Priority
- SLA risk
- Current owner
- Last substantive response
- Customer-visible status
- Customer sentiment
- Meeting context
- Source and freshness
Recommendation
- Explain likely customer impact
- Recommend escalation
- Recommend communication
- Identify roles
Coordination
- Route internal task
- Share context
- Assign ownership
Action
- Prepare customer communication
- Require CSM approval
- Update account context
Trust
- Show sources
- Show confidence
- Distinguish factual and inferred information
- Allow correction
- Record approval
Explicitly out of scope
- Fully autonomous external communication
- Universal coverage of every CSP role
- Complete replacement of source systems
- Unrestricted cross-role access
- Autonomous handling of sensitive escalations
- Complete predictive accuracy
- Final agent naming and visibility model
Validation focus
Desirability
- Does the signal help users intervene earlier?
- Is the recommendation relevant?
- Does the assembled context reduce preparation?
- Does the flow match actual role responsibility?
Trust
- Do users understand why the signal appeared?
- Are sources and freshness sufficient?
- Do users know what is fact versus inference?
- Do users feel in control?
Usability
- Can users understand the priority?
- Can users inspect evidence?
- Can users complete the recommended action?
- Can users distinguish internal and customer-facing content?
Feasibility
- Are required sources available?
- Is the data sufficiently fresh?
- Can ownership be resolved?
- Can actions be executed through connected systems?
- Are permissions enforceable?
Value
- Does the flow reduce manual work?
- Does it shorten time to intervention?
- Does it improve cross-role coordination?
- Does it reduce customer effort?
Section 15
Value and outcomes
What changes for customers, roles, leadership, and the platform itself.
For customers
- ServiceNow appears informed and coordinated
- Customers repeat less information
- Risks are addressed earlier
- Communication becomes more proactive
- Support and success feel continuous
- Customer effort decreases
- Value realization becomes clearer
For CSMs
- Less context gathering
- Faster preparation
- Earlier risk awareness
- Better account prioritization
- Stronger customer conversations
- More time for strategic guidance
- Reduced administrative burden
For specialized roles
- Better-quality handoffs
- More relevant context
- Clearer ownership
- Fewer ad hoc status requests
- Reduced duplicate investigation
- Improved collaboration
For leadership
- More consistent service
- Better portfolio visibility
- Greater capacity
- Earlier risk detection
- More measurable outcomes
- Better alignment across service tiers
For product and engineering
- Fewer bespoke role workspaces
- Reusable architecture
- Shared capability model
- Lower long-term UI maintenance
- Clearer assembly rules
- Scalable agent orchestration
For design and research
- Context models
- Assembly logic
- Interaction rules
- Agent behavior
- Trust patterns
- Role lenses
- Workflow orchestration
- Evidence-backed future states
Section 16
Success measures
Directional measures grouped by category. Status shows how ready each measure is today.
User efficiency
- Time spent gathering customer contextNowBaseline needed
- Time spent preparing for meetingsNowBaseline needed
- Time spent creating status updatesNextProposed
- Systems opened per workflowNowAvailable
- Reduction in duplicate entryNextProposed
- Reduction in manual follow-upNextProposed
Proactive service
- Risks identified before customer escalationNextProposed
- Time between signal detection and human actionNextIn development
- Customer updates sent before customer requestNextProposed
- Preventable escalations avoidedNorth StarProposed
- Proactive versus reactive intervention ratioNorth StarProposed
Workflow coordination
- Time to establish ownershipNextBaseline needed
- Handoff completion rateNextProposed
- Context completeness at handoffNextProposed
- Duplicate investigation rateNextBaseline needed
- Cross-role task completionNorth StarProposed
- Approval-cycle timeNextAvailable
Trust
- Recommendation acceptanceNowIn development
- Recommendation correctionNowIn development
- Source-inspection rateNowProposed
- Confidence understandingNextProposed
- User trust scoreNextBaseline needed
- AI output requiring re-verificationNowProposed
Customer outcomes
- Customer Effort ScoreNextAvailable
- Customer satisfactionNowAvailable
- Support satisfactionNowAvailable
- Adoption improvementNextAvailable
- Time to valueNorth StarBaseline needed
- Risk resolutionNextProposed
- Value realizationNorth StarProposed
- Retention or expansion influenceNorth StarProposed
Platform outcomes
- Role-specific surfaces retired or avoidedNorth StarProposed
- Reuse of shared capabilitiesNextProposed
- Agent orchestration reuseNorth StarProposed
- Reduction in navigation complexityNextBaseline needed
- Workflows supported through shared contextNorth StarProposed
- Assembled versus fixed experiencesNorth StarProposed
Section 17
Experience principles
Ten design rules, each with what good looks like and the anti-pattern it replaces.
Organize around customer moments, not product navigation.
The organizing unit of the experience is a situation, not a screen.
Why: Navigation growth is the root cause of the current complexity.
What good looks like
An escalation assembles case, meeting, sentiment, and owners in one place.
Anti-pattern
Adding an escalation tab to every role workspace.
Anticipate before asking, but interrupt only when action or judgment is needed.
Detection is continuous; interruption is rare and justified.
Why: Alert volume destroys trust faster than missing signals.
What good looks like
One high-confidence interruption before an executive meeting.
Anti-pattern
A daily digest of every changed field.
Assemble context rather than sending users to gather it.
The platform does the stitching work before the user arrives.
Why: Context gathering is the largest reported time cost.
What good looks like
A support-risk brief already contains SLA, owner, and last response.
Anti-pattern
A link list to five source systems.
Recommend action, not only information.
Every insight names a next step and an owner.
Why: Users value action-oriented output over summaries.
What good looks like
“Escalate to SAM and send the customer an update” with both actions available.
Anti-pattern
A summary paragraph with no available action.
Maintain one shared context while adapting presentation by role.
One context model, many lenses.
Why: Duplicated role surfaces are the maintenance problem being solved.
What good looks like
CSM and SAM see the same case through different defaults.
Anti-pattern
A separate SAM workspace with its own data model.
Keep humans accountable for sensitive decisions and customer commitments.
AI prepares; people approve anything the customer will see.
Why: Accountability is a precondition for adoption.
What good looks like
A drafted customer update waits for CSM approval.
Anti-pattern
Autonomous external communication.
Make facts, inferences, sources, freshness, and confidence visible.
Every statement can be inspected back to its origin.
Why: Users re-verify anything they cannot trace.
What good looks like
An inferred risk is labelled and links to the signals behind it.
Anti-pattern
A confident sentence with no source.
Preserve context across roles, systems, and time.
Handoffs carry the full picture, not a link.
Why: Lost context is the main cost of cross-role work.
What good looks like
Support receives the assembled case package with the task.
Anti-pattern
A Teams message saying “please look at this case”.
Use conversation as one interaction mode, not the entire experience.
Conversation supports structured surfaces; it does not replace them.
Why: Blank conversational interfaces were insufficient in research.
What good looks like
A brief with a follow-up conversation area.
Anti-pattern
A chat box as the primary entry point.
Solve reusable categories of problems instead of adding isolated features.
Each solution should generalize to other moments and roles.
Why: Feature-by-feature growth created the current cost curve.
What good looks like
The stale-case pattern reused for stale commitments and stale milestones.
Anti-pattern
A single-purpose stale-case dashboard.
Section 18
Risks and assumptions
What could go wrong, how it is mitigated, and which assumptions still need validation.
The vision becomes another CSM workspace.
Mitigation
Anchor the story with the CSM but demonstrate reusable architecture, shared context, and cross-role participation.
Role-agnostic is interpreted as identical access.
Mitigation
Use role-aware defaults, contextual access, requestable capabilities, approvals, and restrictions.
AI produces too many alerts.
Mitigation
Focus on decision-point interruption, relevance, confidence, and user-controlled thresholds.
Users do not trust the data.
Mitigation
Show source, freshness, confidence, verification status, and correction pathways.
Conversational AI becomes the primary design metaphor.
Mitigation
Use hybrid experiences with structured context, evidence, controls, and actions.
Agents become disconnected destinations.
Mitigation
Orchestrate agents behind customer moments and unified user goals.
Cross-role coordination ignores actual responsibility.
Mitigation
Validate ownership, authority, handoff, and service-tier differences with representative users.
The North Star is interpreted as an immediate roadmap commitment.
Mitigation
Clearly separate Now, Next, and North Star horizons.
Personalization becomes unpredictable.
Mitigation
Explain why content is shown and allow users to inspect, adjust, pin, dismiss, or request capabilities.
The model depends on unavailable or poor-quality data.
Mitigation
Identify source dependencies, quality gaps, confidence thresholds, and fallback states.
| Assumption | Evidence | Confidence | Status | Owner | Validation |
|---|---|---|---|---|---|
| CSMs benefit from proactive customer-risk intelligence. | Support Intelligence research: stale case detection rated highly valuable. | Validated | Supported | Research | Concept test with guided and self-serve CSMsBy Before MVP build |
| Hybrid interaction will be more useful than conversation alone. | CSP AI Workflows research: blank conversational interfaces were insufficient. | Directional evidence | Supported | Design | Comparative prototype studyBy Before MVP build |
| Shared context can support multiple roles without duplicating the interface. | Design hypothesis derived from the role lens model. | Design hypothesis | Open | Product and Engineering | Architecture spike plus cross-role reviewBy Next planning cycle |
| Users will accept dynamic assembly when they understand why information is shown. | Directional evidence on explanation and trust. | Directional evidence | Open | Research | Usability study with explanation variantsBy Before North Star commitment |
| Agents can coordinate across systems while preserving human approval. | Unproven at scale. | Design hypothesis | Open | Engineering | Technical feasibility spikeBy Before Next horizon |
| Current tools can remain systems of record while CSP becomes the operating environment. | Integration dependencies not fully assessed. | Open question | Open | Platform | Source dependency auditBy Before MVP build |
| Customer moments are a more scalable organizing principle than persona workspaces. | Vision strategy position; conflicting internal views exist. | Conflicting evidence | Open | Product | Stakeholder alignment workshopBy This quarter |
| Role and permission models can support contextual access. | Access state model is proposed, not implemented. | Design hypothesis | Open | Platform and Security | Permission model reviewBy Before Next horizon |
Section 19
Strategic decisions to make
Open questions this vision is intended to force, grouped by decision area.
Vision scope
Which customer moment carries the highest strategic value?
ProposedProductShould risk and escalation remain the primary scenario?
ProposedProductHow broadly should the first concept represent non-CSM roles?
OpenDesign
Product direction
Which capabilities are foundational?
OpenProductWhich capabilities are near-term, emerging, or aspirational?
OpenProductWhich existing surfaces should evolve, remain, or eventually retire?
OpenProductHow does the vision align with Project Venus, CRMI, AINPX, and Horizon 2.0?
OpenStrategy
Experience direction
Should users see individual agents or one unified Otto experience?
OpenDesignWhat should be proactively surfaced?
ProposedDesignWhat should remain available on demand?
ProposedDesignWhen should the platform use conversational, hybrid, or UI-driven modes?
AlignedDesignHow much personalization should the MVP include?
OpenProduct
Data and trust
Which sources are authoritative?
OpenPlatformWhat freshness thresholds are acceptable?
OpenPlatformHow should conflicting data be presented?
OpenDesignWhat requires user confirmation?
ProposedDesignHow should inference and prediction be labeled?
ProposedDesign
Role and governance
Which roles require default access?
ProposedProductWhat becomes contextual or requestable?
OpenProductWho approves temporary access?
OpenSecurityWhich actions require explicit human approval?
AlignedProductWho owns the customer-facing communication?
AlignedService
Validation
Which PM, design, engineering, research, and user representatives must review the concept?
OpenProgramWhat evidence is required before the strategy becomes a shared direction?
OpenResearchWhich assumptions must be resolved before prototyping?
OpenResearchWhich assumptions can be tested through the prototype?
ProposedResearch
Section 20
Research traceability
Where the strategy is grounded in evidence, and where it remains a hypothesis.
Future of Service at ServiceNow
Directional evidence- Service is both a strategic risk and differentiating opportunity.
- The future model should focus on relationships, moments, and outcomes.
- AI should enhance people.
- Service should be evaluated end to end.
- Trust and continuity must persist across surfaces.
- The organization needs a bridge from current case-centric service to a future service model.
CSP AI Workflows Research
Validated- Context is fragmented and often inaccurate.
- Users spend significant time gathering information before customer interactions.
- Administrative work prevents proactive customer engagement.
- Users want AI to surface what they need before they ask.
- Users value action-oriented outputs.
- Customer Touchpoint Automation received strong interest.
- Blank conversational interfaces are insufficient.
Support Intelligence Research
Validated- CSMs lack a consolidated view of support cases.
- Guided CSMs experience greater support-monitoring burden.
- Stale case detection is highly valuable.
- Users want priority, owner, last activity, SLA, and case summary.
- Users prefer a prepared snapshot with drill-down and follow-up questions.
- Accuracy is critical for trust.
Expert Services Research
Directional evidence- RMs and EMs work across fragmented tools.
- RMs need early pipeline awareness.
- Skills and availability data are incomplete.
- Partner sourcing is manual.
- EMs use external spreadsheets for hours, budgets, and project plans.
- Internal and customer-facing reporting is duplicated.
- Cross-role context is required for successful delivery.
CSP on AINPX Vision Strategy
Design hypothesis- Use a moment-led approach.
- Anchor with the CSM but reveal cross-role orchestration.
- Use the Three Awareness model.
- Demonstrate one deep scenario.
- Separate Now, Next, and North Star.
- Connect the future concept to reusable architecture.
| Strategy element | Supporting research |
|---|---|
| From feature-by-feature growth to architecture-led intelligence | CSP on AINPX Vision Strategy |
| From navigated views to assembled experiences | CSP on AINPX Vision StrategyCSP AI Workflows Research |
| From reactive dashboards to ambient watching | Support Intelligence Research |
| From role-specific tools to a shared context graph | Expert Services ResearchCSP on AINPX Vision Strategy |
| From tribal knowledge to durable, explainable memory | CSP AI Workflows ResearchSupport Intelligence Research |
| From periodic preparation to a continuously ready state | CSP AI Workflows Research |
| Fixed experience growth | CSP AI Workflows Research |
| Fragmented context | CSP AI Workflows ResearchExpert Services Research |
| Reactive monitoring | Support Intelligence Research |
| Incomplete handoffs | Expert Services Research |
| Repetitive administrative work | CSP AI Workflows ResearchExpert Services Research |
| Trust gap | Support Intelligence Research |
Current state to future state
Compare how the operating model changes across nine dimensions.