Revenue Operations Analyst (GTM AI Focus)

Overview

You're a Revenue Operations Analyst focused on AI implementation for Aptean's go-to-market teams. Aptean sells vertical-specific ERP software to mid-market and enterprise companies in manufacturing, retail, and healthcare. You'll work with sales leadership, enablement, and IT to evaluate AI tools, build use cases, and figure out what actually drives pipeline and revenue vs. what's just hype.

Role Snapshot

Company Context

Stage: Private, established (3,000+ employees)

Size: 3,058 employees globally

Growth: Mature company actively modernizing GTM with AI investments

Market Position: Established player in vertical ERP space - competing on industry expertise and customization against general ERP providers

What They Sell: Industry-specific ERP platforms (manufacturing, retail, healthcare focus). These are complex, multi-module systems that integrate finance, operations, and supply chain. Sales cycles are long, deals are consultative, and implementations take months.

GTM Reality

Sales Structure: Large, distributed sales organization with separate teams for different verticals and products. You're supporting multiple sales segments (likely SMB, mid-market, and enterprise teams).

Current State: Like most enterprise software companies, Aptean is trying to figure out how AI actually helps their reps be more productive. Expect a mix of pilot programs, vendor evaluations, and "let's see if this actually works" initiatives.

Your Stakeholders:

• Sales Ops team (your core group)
• Sales Enablement (training reps on new tools)
• IT/Systems (integrations and security approvals)
• Sales leadership (want ROI proof before scaling)
• Finance (budget approvals for new tools)

What You'll Actually Do

Time Breakdown

AI Tool Evaluation (25%) | Data Analysis (30%) | Implementation Support (25%) | Meetings/Internal (20%)

Key Activities

• AI Vendor Evaluation: You're researching and testing AI sales tools (conversation intelligence, email assistants, pipeline prediction, etc.). This means sitting through demos, running pilots with 5-10 reps, and trying to figure out if something actually helps or just adds noise. Expect a lot of "this looks cool in the demo but doesn't work with our Salesforce setup."

• Use Case Development: Working with sales managers to identify where AI could actually help. Example: "Our AEs spend 2 hours/day on meeting notes and CRM updates - can we automate that?" or "Our forecast is always off - can AI predict better?" You build the business case, define success metrics, and document the workflow.

• Data Analysis: Pulling reports to measure impact. Did call recording AI actually improve win rates? Are reps using the email tool? What's the before/after on time spent in CRM? You're in Salesforce, Excel, and whatever BI tool they use (probably Tableau or Power BI) building dashboards and running analyses.

• Implementation Support: Once a tool is approved, you help roll it out. This means writing documentation, training sales ops on admin, troubleshooting integration issues, and fielding questions from reps who don't understand how to use it.

• Salesforce Optimization: A lot of AI tools require clean data to work. You'll spend time cleaning up fields, standardizing processes, and building automations so the AI has good inputs.

• Cross-functional Projects: Sitting in meetings with IT on API integrations, with Enablement on training plans, with Finance on ROI reporting. Enterprise software companies have a lot of stakeholders, so expect to present updates, write memos, and justify decisions.

The Honest Reality

What's Hard

• Hype vs. Reality: Most AI sales tools overclaim and underdeliver. You'll spend a lot of time testing things that don't work as advertised, dealing with integration issues, and managing expectations when tools don't live up to the demo.

• Change Management: Sales reps resist new tools, especially if they add steps to their workflow. You'll build something useful and then have to convince people to actually use it. Adoption is always harder than implementation.

• Data Quality Issues: Aptean's CRM data is probably messy (most are). AI tools need clean, consistent data. You'll discover fields that aren't filled out, inconsistent naming conventions, and duplicate records. Fixing this is tedious work.

• Slow Enterprise Pace: Getting anything approved takes weeks. Security reviews, budget approvals, legal negotiations with vendors. A tool you want to test in March might not go live until Q3.

• Proving ROI: Leadership wants to see that AI investments drive revenue. But attribution is hard - did win rates improve because of the AI tool or because you hired better reps? You'll be asked to prove causation with imperfect data.

• Balancing Multiple Priorities: You're supporting multiple sales teams with different needs. Mid-market AEs want prospecting help, enterprise AEs need proposal automation, managers want better forecasting. You can't do everything at once.

What Success Looks Like

• You implement 2-3 AI tools that get adopted by >70% of target users and show measurable time savings or productivity gains
• Your dashboards become the source of truth for "is this AI thing actually working?"
• Sales leadership asks for your recommendation before buying new tools
• Reps stop complaining about manual admin work because you've automated parts of it
• You build a repeatable evaluation framework that Aptean can use for future AI investments

Who You're Working With

Internal Stakeholders:

• Revenue Operations leadership (your direct manager)
• Sales Ops analysts (your peers - likely 2-3 others)
• Sales Enablement team (training and adoption)
• IT/Systems (integrations and security)
• Sales VPs and Directors (need to buy in on changes)
• Finance (budget and ROI reporting)

What They Care About:

• Sales Leadership: Does this help us hit number? What's the ROI?
• IT: Is this secure? Does it integrate with our stack?
• Enablement: Can we train reps on this? Will they use it?
• Finance: What's the cost? Can we measure return?
• Sales Ops: Does this create more work for us to maintain?

Requirements

• 2-4 years in sales operations, revenue operations, or sales analytics (probably not a first ops role)
• Strong Excel/Google Sheets skills - you're building models and analyzing data daily
• Salesforce experience - you need to understand CRM workflows and reporting
• Some exposure to sales tech stack (Gong, Outreach, ZoomInfo, etc.) - understanding what these tools do
• Analytical mindset - comfortable pulling data, finding insights, and presenting recommendations
• Project management skills - you're juggling multiple initiatives with different timelines
• Communication skills - you're translating between technical teams and sales leaders
• Curiosity about AI - you don't need to be a data scientist, but you should be interested in how these tools work and where they fail
• Comfort with ambiguity - this is a new focus area, so processes aren't fully defined yet