AI Solutions · Enterprise Transformation · Consulting

Jim Broghammer Senior AI Solutions Consultant

I help enterprises operationalize AI from strategy through execution. 15 years of enterprise SaaS experience combined with hands-on AI building: vibe coding, MCP integrations, API architecture, prompt engineering, and change management that gets teams actually using the tools.

Start a Conversation See the 30-60-90 Plan
🏆
162%
Net revenue retention driving AI-powered CS
💼
$3.2M
Enterprise ARR managed with AI-first operations
📈
18
Enterprise accounts running on AI-built systems
🎯
75%
AI-sourced opportunity close rate
The Value

What I bring to
every engagement

The rare combination of enterprise business depth and hands-on AI building that most consultants can't offer.

🤖

Vibe Coding and AI App Building

I build working AI-powered tools, not slide decks about them. Custom dashboards, automated briefing systems, health scoring engines, and internal apps built through conversational coding with Claude, Cursor, and Replit.

· See the CSM Command system I built
🔌

MCP Integrations and API Architecture

Hands-on experience connecting AI systems to enterprise tools through Model Context Protocol servers, REST APIs, and workflow automation. I design the integration layer that makes AI useful inside existing tech stacks.

· CRM, support, billing, and usage data connected through live MCP pipelines
🔍

AI Output QA and Validation

AI generates output. Someone needs to know if it's right. I bring deep domain knowledge in enterprise SaaS to validate, refine, and quality-check AI-generated insights, reports, and recommendations before they reach clients or stakeholders.

· 15 years of enterprise context that machines can't replicate
🔄

Change Management and AI Adoption

The hardest part of AI transformation isn't the technology. It's getting teams to actually use it. I've led adoption programs, built enablement frameworks, and coached teams through the transition from manual to AI-augmented workflows.

· Built peer AI enablement programs at monday.com
🏗️

Enterprise AI Strategy

I help companies move from "we should use AI" to a concrete implementation plan. Audit current workflows, identify high-impact automation targets, design phased rollout roadmaps, and measure ROI at every stage.

· Deployed AI systems across 18 enterprise accounts at scale
🤝

Enterprise Relationship Depth

AI consulting fails without trust. 15 years of Fortune 500 account management at LinkedIn, SeekOut, and monday.com means I know how to navigate C-suite conversations, manage stakeholder alignment, and position AI initiatives for executive buy-in.

· C-suite relationships across Fortune 500 organizations
The Journey

15 years of enterprise depth,
now applied to AI.

The enterprise context that makes my AI consulting effective didn't come from a bootcamp. It came from managing millions in ARR across the companies building the future of work.

monday.com
Senior High Touch Customer Success Manager · Present
Managing 18 enterprise accounts at $3.2M ARR. Built and deployed AI-powered operating systems for account intelligence, led peer AI enablement programs, and achieved 75% close rate on AI-sourced expansion opportunities.
$750K+ expansion revenue generated
SeekOut
Enterprise Customer Success Manager
Full ownership of enterprise accounts in AI-powered recruiting technology. Achieved 162% NRR as the #1 CSM company-wide through deep technical fluency and proactive account strategy.
162% NRR · #1 CSM company-wide
Checkr & Sterling Check
Enterprise Customer Success
Managed complex, compliance-heavy enterprise accounts in background screening, environments where data integrity, regulatory adherence, and stakeholder trust were non-negotiable.
Fortune 500 accounts · Compliance-critical SaaS
AIQ
Customer Success & Account Management
Developed technical account management skills in an AI-adjacent SaaS environment, learning to navigate complex product ecosystems and enterprise buying dynamics.
LinkedIn
Enterprise Sales & Relationship Management · 6.5 Years
6.5 years of enterprise sales and relationship management at world-class scale. Fortune 500 accounts, C-suite navigation, and deep understanding of how large organizations evaluate and adopt technology.
World-class enterprise go-to-market foundation
The Plan

How I deliver
in the first 90 days

Whether it's a consulting engagement or a direct role, this is the phased approach I use to audit, build, and operationalize AI systems that stick.

Phase 1 · Days 1–30

Audit, Map, and Assess

Before building anything, I need to understand your current tech stack, workflows, team capabilities, and where AI can create the most leverage.

Primary focusCurrent state assessment
Key deliverableAI readiness audit + roadmap
Success signalHigh-impact targets identified
01

Tech stack and workflow audit

Map every tool, API, data source, and manual process across the team. Identify integration gaps, data silos, and workflow bottlenecks where AI can create immediate value.

02

AI readiness assessment

Evaluate team AI literacy, existing tool usage, data quality, and organizational appetite for change. Build a realistic maturity model, not aspirational, grounded in where things actually are.

🔑 Critical input for everything that follows
03

Stakeholder interviews and alignment

Meet with leadership, ops, and end users to understand priorities, pain points, and what "success" means to each group. AI projects fail when stakeholders aren't aligned on the outcome.

04

Identify quick wins and high-impact targets

Find 2-3 workflows where AI can deliver measurable improvement within weeks, not months. Early wins build momentum and executive confidence for larger initiatives.

05

Deliver the AI transformation roadmap

Present a phased implementation plan with clear priorities, resource requirements, expected ROI, and risk factors. Concrete enough to start building from immediately.

⚡ Actionable plan, not a strategy deck
Phase 2 · Days 31–60

Build, Integrate, and Test

Move from planning to building. Stand up the first AI systems, connect the integrations, and start getting real output in front of users.

Primary focusImplementation and integration
Key deliverableFirst AI system live in production
Success signalTeam using AI daily
01

Build first AI-powered workflows

Using vibe coding and rapid prototyping, build the initial AI tools identified in the roadmap. Custom dashboards, automated briefing systems, intelligence engines, or whatever creates the most leverage for your team.

02

Connect MCP integrations and APIs

Wire the AI systems into your existing tech stack through MCP servers, REST APIs, and data connectors. CRM, support, billing, usage data, and communication tools all feeding into a unified intelligence layer.

🔌 This is where the system comes alive
03

QA and validation framework

Establish the review process for AI-generated outputs. Set up validation checkpoints, accuracy benchmarks, and feedback loops so the team knows when to trust AI output and when to verify.

04

Begin change management and training

Run hands-on workshops with the team. Not theory sessions, but real work done with AI tools on real tasks. People adopt what they practice, not what they're told about.

💡 Adoption is the make-or-break factor
05

Measure and iterate on early results

Track time saved, output quality, adoption rates, and user feedback on the first deployed systems. Use the data to refine before scaling.

Phase 3 · Days 61–90

Scale, Document, and Transfer

Systems are running. Now scale what works, document everything, and ensure the team can operate and extend the AI infrastructure independently.

Primary focusScale and sustainability
Key deliverableSelf-sustaining AI operations
Success signalTeam running AI workflows independently
01

Scale AI systems across the organization

Expand proven workflows to additional teams, departments, or use cases. What worked for the pilot group gets packaged and deployed more broadly with the same validation framework.

02

Document architecture and runbooks

Every system, integration, prompt template, and workflow gets documented so the team can maintain, troubleshoot, and extend everything without depending on me.

📄 No vendor lock-in, full knowledge transfer
03

Advanced team enablement

Move beyond basic training into advanced use cases: prompt engineering, output validation, building their own AI-assisted workflows. The goal is a team that can extend the system on their own.

🏗️ Building internal AI capability, not dependency
04

ROI review with leadership

Present measurable outcomes: time saved, quality improvements, revenue impact, adoption metrics. Concrete data on what the AI investment produced, not anecdotal wins.

05

Phase 2 roadmap and next horizons

Based on results, present the next set of AI opportunities. What worked, what to expand, where the next wave of automation and intelligence should focus.

📊 Continuous improvement, not a one-time project
How I Work

The operating model
behind the results

How I approach every engagement, whether it's a 90-day contract or a full-time role.

🛠️

Build with AI, Not About AI

  • Vibe coding with Claude, Cursor, and Replit to rapidly prototype working tools
  • AI-generated outputs reviewed and validated against real domain expertise
  • Focus on tools that solve specific problems, not generic AI demos
  • Every build designed for non-technical users to operate independently
🔌

Integration-First Architecture

  • MCP server connections to CRM, support, billing, and communication platforms
  • REST API integrations between AI systems and existing enterprise tools
  • Data migration planning and execution for platform transitions
  • Unified data layers that give AI systems the context they need to be accurate
🔄

Change That Actually Sticks

  • Hands-on training where teams do real work with AI tools, not just watch demos
  • Adoption tracking and friction identification in the first weeks of deployment
  • Champions identified and coached to become internal AI advocates
  • Resistance addressed through results, not mandates
📊

Measurable Outcomes, Always

  • Every initiative tied to a specific business metric before work begins
  • Time-to-value tracked and reported at each phase
  • ROI documentation built continuously, not assembled at the end
  • Clear success criteria agreed with stakeholders upfront
Proof of Work

I don't pitch AI.
I build it.

This is a system I designed and built to manage enterprise accounts at monday.com. It's not a concept deck or a future-state vision. It's running in production across my current book of business, pulling live data through API and MCP integrations, and generating the intelligence I use in every client interaction.

📥

Ingest

Pull CRM data, usage metrics, support tickets, renewal dates, and stakeholder history into a structured intelligence layer

🔍

Analyze

AI identifies health patterns, churn signals, expansion triggers, and engagement gaps across the full book of business

🎯

Strategize

Generates account-specific action plans: pre-call briefs, risk interventions, QBR frameworks, and expansion plays

⚡

Execute

Walk into every conversation prepared with data-backed insights, tailored recommendations, and a clear next-best-action

Production System: CSM Command
CSM Command — Jim Broghammer AI-powered customer success dashboard showing time-to-value tracking, implementation milestones, account health, and renewal intelligence modules
BUILT & DEPLOYED

CSM Command: my strategic intelligence system for tracking account health, risk signals, expansion opportunities, time-to-value, and renewal forecasting.

📋

Pre-Meeting Strategic Briefings

  • Automated account dossier pulled before every call: contract details, recent tickets, usage trends, and stakeholder changes
  • AI-generated talking points aligned to the client's current priorities and pain points
  • Competitive signals and industry context layered in for executive-level conversations
Output

A single-page strategic brief per account that turns 30 minutes of prep into 3 with sharper, more relevant conversations

🚨

Early Warning Risk Detection

  • Multi-signal health scoring: usage decline, support ticket velocity, stakeholder disengagement, NPS shifts
  • Proactive churn risk alerts triggered 90–120 days before renewal, not weeks
  • Root cause hypotheses generated alongside intervention recommendations
Output

A prioritized weekly risk dashboard with specific action items. no account goes dark unnoticed

💰

Expansion Signal Engine

  • Pattern detection across product usage, team growth, feature adoption, and whitespace analysis
  • Expansion opportunity scoring ranked by likelihood, deal size potential, and timing
  • Pre-built positioning narratives that frame upsell as solving the client's stated goals
Output

CS-qualified leads surfaced with context and positioning, ready to hand to sales or run directly

📊

Renewal Intelligence & QBR Automation

  • ROI narratives built from real usage data: specific outcomes tied to their goals
  • QBR decks pre-populated with client-specific metrics, adoption trends, and forward-looking recommendations
  • Renewal forecast enrichment with confidence scoring and influencer mapping
Output

QBRs that land with executives because they're data-driven, relevant, and tied to business outcomes

🚀

Portable, adaptable, ready for your stack.

This system transfers to any enterprise environment. The architecture adapts to your CRM, your data sources, and your team's workflow. I bring the methodology, the build capability, and the enterprise context to make it useful from day one.

Work Samples

Frameworks I've built
and put to work.

These aren't templates I downloaded. They're systems I designed, tested, and deployed across enterprise accounts.

AI Center of Excellence
Model
Selection
Tokens
Optimization
Cost
Governance
Prompt
Engineering
Output
QA Framework
Team
Enablement
Token cost reduction 40-60%
Output accuracy after QA 95%+
Team adoption rate Tracked
Playbook

AI Center of Excellence: Token Efficiency Guide

A complete operational playbook for enterprise AI governance. Covers model selection criteria, token optimization strategies, prompt engineering standards, output QA processes, cost tracking, and team enablement. Designed to help organizations scale AI usage while controlling spend and maintaining quality.

Token Optimization Prompt Engineering Cost Governance Output QA Team Enablement
Change Management Framework
A
Awareness
D
Desire
K
Knowledge
A
Ability
R
Reinforcement
Framework Prosci ADKAR
Applied to AI Transformation
Framework

AI Transformation Change Management

My change management approach built on the Prosci ADKAR model, tailored specifically for enterprise AI adoption. Maps each phase of AI rollout to structured interventions: stakeholder alignment, resistance management, hands-on training, adoption tracking, and reinforcement systems that make new workflows permanent.

Prosci ADKAR AI Adoption Stakeholder Alignment Training Design Adoption Metrics
Verticals

Industries where
I've done the work

Deep familiarity across the verticals that matter most in enterprise B2B SaaS today.

🏥

Health Tech

Compliance-first environments, regulated data, clinical workflows

🔍

Recruiting & HR Tech

ATS, sourcing platforms, background screening infrastructure

⚙️

Work OS & Productivity

Enterprise workflow platforms, API integrations, complex implementations

🤝

Professional Networks

Sales intelligence, talent acquisition, enterprise relationship tools

Let's Work Together

Ready to drive
your next chapter.

15 years of enterprise depth, hands-on AI building skills, and a track record of making technology adoption stick. Available for consulting engagements, contract roles, and direct hire opportunities.

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Jim Broghammer · Senior AI Solutions Consultant · Available for engagements
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