J.P. Morgan Payments · GTM Digital Transformation · historical workflow work

GTM Digital Transformation

Client Solutions Dashboard research became the starting point for redesigning a Product-to-market journey that could take 23–34 weeks and crossed fragmented Product information, tools, pricing, approvals, demonstrations, handoffs, and more than a dozen internal functions.

2023–2024 Product-to-market modernization · overlapping enterprise work

90-second case

Problem
Field teams navigated disconnected Product constructs, Sales content, pricing, approval forms, RFP support, demos, SMEs, implementation resources, and governance across a 23–34 week journey.
Mandate
Lead the cross-functional GTM Digital Transformation working team—including UX resources assigned to the initiative—to redesign the field journey and target-state Product-to-market experience.
Decision
Redesign the discovery-to-onboarding system around guided solutioning, shared Product truth, and visible decision state—not a standalone dashboard or another PDF.
What happened
Research planned across roughly 13 RMs and BDs, a cross-functional Sales Journey, a target-state Dashboard, and a more maintainable Sales Playbook established a coherent modernization direction.
Why it matters now
The work established the importance of shared state and observable decisions before introducing intelligent or adaptive operation.

My role

How I contributed.

I led the cross-functional GTM Digital Transformation working team and owned the Client Solutions Dashboard target-state scope, journey, and experience direction. UX resources were embedded in the initiative and operated dotted-line to me for the project; they were not permanent direct reports.

The working team moved from current-state research through guided solutioning, prototype refinement, and architecture implications across UX, GTM, Sales, Product, Technology, and field contributors. The UX researcher retained authorship of the research readout; I do not claim sole authorship of every interview or finding.

I connected that work to the wider Sales Journey and Sales Playbook so the intervention addressed discovery, recommendation, value articulation, pricing, approvals, demos, implementation guidance, onboarding, and maintainable digital delivery—not a standalone interface.

Current reconstruction

The core mechanism.

A present-day visual of the system described in the case.

From Product capability to market learningCustomer and market signals shaped value architecture, positioning, targeting, field conversations, and the next commercialization decision.

Judgment under constraint

Decisions that shaped the work.

Decision

Treat the Dashboard and Sales Playbook as one Product-to-market system—not separate UX and enablement projects.

Why it was hard
Local friction appeared in tools, Product information, demos, pricing, approvals, legal process, stakeholder coordination, and handoffs. A faster local artifact could leave the end-to-end outcome unchanged.
My judgment
Use the journey to expose shared state and decision dependencies, then pair guided discovery and recommendations with maintainable Product, value, pricing, demo, implementation, and training resources.
Consequence
The work produced a coherent target state for discovering, recommending, positioning, and operationalizing Payments solutions across the field journey.

Historical work

The commercial experience was a workflow, not a funnel snapshot.

The mapped Sales Journey spanned 23–34 weeks from pre-proposal work through onboarding and named more than a dozen participating functions and governance groups. Friction appeared in CRM separation, Product constructs, Sell Box access, content discovery, pricing, approvals, RFP support, demos, SMEs, contracts, implementation resources, and forms.

Dashboard research planned interviews with approximately 13 RMs and BDs across multiple Sales and Banking groups. The evidence made clear that field friction was not one interface problem; it was a Product-to-market operating problem.

  1. 01Prospect

    Target · context · initial value hypothesis

  2. 02Discover

    Need · stakeholders · workflow · evidence

  3. 03Solution

    Capability · demonstration · fit · dependencies

  4. 04Commit

    Pitch · pricing · approvals · legal · decision

  5. 05Onboard

    Handoff · implementation context · next owner

Target-state system

The response connected guided discovery to Product truth and downstream operation.

The Dashboard direction used a guided conversation to recommend Products and solution bundles, surface authoritative Product information, generate recommendation reports, and connect to pricing, onboarding, Merchant Self-Service, and additional field resources.

The Sales Playbook complemented that interface with personas, business-value mapping, roadmap and partner context, how-to-sell guidance, pricing paths, demos, implementation resources, training, and case evidence. A maintainable web delivery path was preferred over a long static PDF because it could be updated, measured, and extended.

  1. 01Discover

    Client context · need · personas · business value

  2. 02Recommend

    Product fit · solution bundles · partner path

  3. 03Position

    Value proposition · roadmap · proof · demo

  4. 04Operationalize

    Pricing · approvals · implementation · onboarding

  5. 05Maintain

    Digital delivery · usage signal · Product updates · training

Operating implication

GTM Digital Transformation matters when it makes decisions and dependencies observable.

A workflow can be technically digital and still lose context at every boundary. The useful modernization question was not simply which document or task should move online; it was what state, semantics, owner, decision, and exception needed to remain visible across the lifecycle.

The work made the Product-to-market system and its friction more legible for modernization decisions.

What I would redesign differently with AI now · retrospective

AI should operate on a coherent commercial state—not accelerate fragmented handoffs.

Today I would begin by defining durable commercial objects: customer state, Product truth, needs, commitments, permissions, decisions, owners, exceptions, and realized outcomes. AI could then assist synthesis, retrieval, routing, preparation, and anomaly detection inside visible authority boundaries.

I would not begin with an agent at every stage. The historical case shows why: moving inconsistent context faster can make the overall system harder to understand and govern.