Building a Revenue Operating System that Leadership Trusts

Building a Revenue Operating System Leadership Actually Trusts

A conversation with Aidan Nevin.

Executive Summary

Most revenue teams don’t fail because of missing tools. They fail because leadership doesn’t trust the system.

At Fidelity Labs, Aidan Nevin built a revenue operating system from scratch that leadership relies on daily. Not by adding more dashboards, but by designing the system around data integrity, centralized ownership, and repeatability at scale.

Instead of cleaning data downstream, his team controls it at ingestion. Instead of fragmented ownership, they operate with a unified tech stack. Instead of reactive reporting, they built a structured system where every metric is defined, documented, and trusted.

Readers will learn:

  • Start with structure, not tools: CRM is treated as both interface and warehouse, with strict validation at entry
  • Centralize the core, flex the surface: Data models stay consistent while reporting adapts to each business
  • Control data at ingestion: An “air traffic controller” system ensures clean data before it enters CRM
  • Documentation builds trust: Data dictionaries, definitions, and workflows eliminate ambiguity
  • Avoid one-off solutions: Optimize for repeatability across the portfolio, not individual team requests
  • AI as a deflection layer: Enable self-serve insights to reduce RevOps support load
  • RevOps as a strategic partner: Prioritization and discipline elevate the function beyond execution

From Sales to System Builder

Aidan’s path into RevOps didn’t start in operations. It started in sales.

He began in high-pressure sales environments, consistently ranking among top performers before moving into early-stage companies where roles were fluid and systems were non-existent.

That environment forced a shift. From Closing deals to understanding how systems enable deals.

At Fidelity Labs, he was given something most operators never get.
A blank slate! That constraint became the advantage.

“I joined and there was nothing here. No infrastructure, no systems. We were building from zero.” — Aidan Nevin

Designing RevOps Inside an Incubator

Fidelity Labs operates differently from the broader enterprise.

Each venture inside the incubator:

  • Has its own sales, marketing, and product teams
  • Functions like an independent startup
  • Shares a centralized RevOps backbone

At the center of it all sits Aidan’s RevOps team.

They are not tied to one business.
They support all of them.

This creates a unique operating model:

  • Multiple business units
  • One shared RevOps system
  • Constant context switching

The challenge is not just scale. It’s designing a system that works across fundamentally different go-to-market motions.

Traditional vs Incubator RevOps

Traditional Enterprise

Fidelity Labs Model

Siloed ownership

Single ownership of tech stack

Reactive processes

Designed systems

Tool-first

Architecture-first

Data cleanup later

Data integrity at ingestion

Reporting conflicts

Unified definitions

The Core Principle: Centralize What Matters, Flex What Doesn’t

What Gets Centralized

  1. Core revenue architecture
  • Lead to revenue flow
  • Opportunity structures
  • Field-level definitions
  1. Data schema
  • Same field names across all businesses
  • Same definitions and logic
  • No room for interpretation
  1. Enrichment systems
  • Data enriched once and reused
  • Eliminates duplication and cost inefficiency

What Stays Flexible

  1. Business dashboards
  • Tailored to how each team operates
  • Reflects their KPIs and language
  1. Reporting views
  • Custom at the surface
  • Standardized underneath

“Underneath it all has to be the same, but the way it’s presented should reflect the business.”” — Aidan Nevin

CRM as the System’s Backbone

Most teams treat CRM as a system of record.

Aidan treats it as both:

  • A system of interaction
  • A structured data warehouse

The Shift

Typical CRM UsageAidan’s Model
Input layerStructured data system
Flexible entryRestrictive by design
Clean laterClean at entry
Reporting strugglesReporting ready

The Design Principle

Start as restrictive as you possibly think we should get, and then open it up from there.” — Aidan Nevin

Why This Matters

If data is clean at the CRM level:

  • Warehousing becomes simpler
  • Reporting becomes reliable
  • BI becomes trustworthy

If it’s not:

  • Every downstream system compensates
  • Complexity multiplies

The Air Traffic Controller for Data

One of the most practical aspects of Aidan’s system is how it handles incoming data.

Instead of allowing raw data into CRM and fixing it later, they built a system that controls and processes it before it lands.

What Happens Before Data Enters CRM

Every record goes through:

  • Attribution logic
  • Metadata enrichment
  • Scoring models
  • Validation checks

Only after passing these steps does it become part of the system.

Data Trust Is Built Through Documentation, Not Dashboards

Clean data is necessary. It’s not sufficient.
Trust comes from clarity.

What They Built

  • Data dictionaries
  • Field definitions
  • Data maps
  • Validation logic documentation

When stakeholders question a metric, the response is not interpretive. It’s definitive.

“I can tell you how it’s defined, how it’s structured, and where it comes from.” — Aidan Nevin

The Hard Part: Internal Education

RevOps transformations are not just technical. They are organizational.

When Aidan joined, RevOps meant different things to different people across Fidelity.

The first step was alignment.

At Fidelity, that meant:

  • Explaining what RevOps actually does
  • Aligning with legacy enterprise teams
  • Running internal roadshows for months

“The first task I was given was to explain to people what I do.” — Aidan Nevin

AI’s Role: Reducing Dependency on RevOps

The biggest opportunity Aidan sees in AI is not automation for reps.

It’s reducing dependency on RevOps teams.

The Problem Today

  • Constant Slack and Teams messages
  • Reporting requests
  • Basic system questions

The Opportunity

Enable users to:

  • Ask questions directly to the system
  • Access insights without intermediaries

“How can we let users ask the system, not us?” — Aidan Nevin

Building for Repeatability at Scale

The ultimate goal of the system is not just efficiency.

It’s scalability.

Without Structure

Each new business:

  • Starts from scratch
  • Rebuilds systems
  • Repeats mistakes

With Structure

Each new business:

  • Inherits infrastructure
  • Uses proven workflows
  • Launches faster

“The next company doesn’t start at day zero. It starts at day 365.” — Aidan Nevin

The Takeaways

Aidan’s approach reframes what RevOps should be.

Not a support function.
Not a reporting layer.

But a system design discipline.

The Model

  • Clean data at the source
  • Structured systems underneath
  • Consistent definitions across teams
  • Flexible reporting on top

The Result

A system where:

  • Leadership trusts the data
  • Teams align on metrics
  • Decisions move faster

You don’t fix trust at the dashboard level.
You build it at the system level.

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More Resources

How Nektar helps AI Hypergrowth companies move even faster

How Nektar helps AI Hypergrowth companies move even faster

How Nektar Helps AI Hypergrowth Companies Move Even Faster Artificial Intelligence 10 min Fast-moving AI companies are having a moment. Every week a new AI-native startup crosses $100M ARR in what feels like record time. Accel’s 2025 Globalscape report shows a “new breed of AI-native applications” hitting scale much faster than previous generations of SaaS, with some reaching $100M ARR in just a few years. That velocity is backed by unprecedented capital. Prominent AI companies like Cursor, Writer, Groq and Fireworks are raising huge rounds, hiring at triple-digit growth rates, and building products that spread virally from individual builders into the world’s largest enterprises. AI application categories like developer tools, finance, cybersecurity and vertical AI each attracted multiple billions of dollars in 2025 funding alone. Nektar sits right in the middle of this wave. 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The winners look very different from the last SaaS cycle: over 65% of the Accel US & Europe AI 100 are 0–3 years old, and US winners skew especially young at 2.4 years on average. Put simply: AI companies are raising big, hiring fast, and still figuring out their GTM motion on the fly. Bottom-up adoption and insane efficiency AI-native tools are spreading from the bottom up: Developers using AI coding assistants jumped from 36% in 2023 to 90% in 2025 – in just two years. Tools like AI IDEs, agents and copilots are hitting milestones such as “$100M ARR in 8 months” and “10x YoY growth,” according to Accel’s case studies of leading AI-native apps. This isn’t just fast growth – it’s efficient growth. Accel estimates that leading AI applications now generate 3–10x more ARR per employee than prior generations of SaaS companies. But that speed and efficiency create a GTM paradox: You can scale product adoption and revenue incredibly fast. 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But nobody can answer basic questions like: “How many net new stakeholders did this SDR actually bring in?” “Which deals progressed because we pulled in the economic buyer early?” Broken marketing attribution for enterprise deals First-touch and last-touch models collapse when there are 10–20 stakeholders, dozens of events and campaigns, and long sales cycles. “Marketing sourced” covers only a small fraction of reality. No shared view of the customer journey Pre-pipeline engagement, in-pipeline meetings, onboarding, success reviews, expansion conversations – they live in different systems owned by different teams. This is exactly the gap Nektar is built to fill. Nektar as the data backbone for AI GTM At its core, Nektar is a revenue data platform that: Harvests metadata from communication tools (email, calendar, meetings, sequences). Cleans and transforms that data. Writes it into Salesforce against the right opportunities, accounts, contacts and leads. 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Nektar.ai vs People.ai: A Buyer's Guide

Nektar.ai vs People.ai: A Buyer’s Guide

2026 Guide for Enterprise GTM Teams Seeking People.ai Alternatives RevOps 10 min Introduction: Two Different Approaches to the Same Problem Both People.ai and Nektar.ai operate in the revenue data capture category, helping enterprises automatically capture GTM activity and enrich their CRM using AI. However, they solve fundamentally different problems for different buyers. People.ai is an established revenue intelligence platform with strong analytics capabilities, recent recognition as a Visionary in the 2025 Gartner Magic Quadrant for Revenue Action Orchestration, and a mature suite of tools including ClosePlan, account planning, and leadership dashboards. Nektar.ai is an advanced data-first GTM telemetry solution focused on delivering clean, accurate, AI-ready CRM data directly into standard Salesforce objects, designed specifically for enterprises that want to power their existing BI stacks rather than adopt another analytics platform. This guide is intended for GTM leaders, RevOps leaders, Sales Operations teams, and Data teams evaluating both solutions. It draws on direct enterprise evaluation feedback, product analysis, and independent research to help you determine which solution fits your specific needs. Who This Guide Is For This comparison is most relevant if your organization: Already operates a mature BI stack (Databricks, Snowflake, Looker, Tableau) Has dedicated RevOps or SalesOps teams building custom analytics Prioritizes CRM data accuracy over out-of-the-box dashboards Needs granular control over what data syncs to Salesforce Requires specific details around internal and external participation or meeting attendance intelligence (not just invitee data) If your priority is comprehensive analytics UI, pre-built dashboards, and account planning tools, People.ai may be the stronger fit for your organization. But if you are looking at solving the data problem at its core without putting the additional enablement effort on a new training, Nektar is a better bet. This guide focuses on scenarios where data infrastructure is the primary buying criterion. The Core Difference: Analytics-First vs Data-First The fundamental difference between these platforms comes down to philosophy: People.ai is built around the premise that revenue teams need better analytics and insights delivered through their platform. Data capture exists to power their dashboards, scorecards, and AI-driven recommendations. Nektar.ai is built around the premise that enterprises already have analytics tools they trust. What they lack is clean, accurate, complete, unified rep activity data in CRM to feed those tools. Nektar focuses on being the best possible data layer. Neither approach is inherently superior; they serve different organizational needs. The question is which approach matches your GTM infrastructure strategy. 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Accurate attribution matters for: Understanding true time allocation Measuring team effectiveness Forecasting with complete engagement data Pod-based and team selling models People.ai primarily attributes activities to the organizer, which can underreport involvement from SEs, CSMs, and other team members.

activity tracking

How Activity Tracking Can Help You Get Better Visibility Into Deals

How Activity Tracking Can Help You Get Better Visibility Into Deals RevOps 10 min There hasn’t been a time that demanded sustainable revenue growth more than now. Economic headwinds of the last few months have forced businesses to rethink their revenue growth strategies and focus on efficiency. This means getting away with anything that does not make a positive dent in revenue or causes revenue to leak across the sales funnel. But cutting deep costs is not the only way to increase profits. It’s about doubling down on what’s working. And investing time and resources in strategies that help the whole company march towards the same objective – increased revenue.  And there is one sure shot way of achieving this. By knowing exactly what’s happening with your deals. And how can you do that? Activity tracking. Let’s dive deep. What is Activity Tracking? Activity tracking in sales refers to monitoring and recording the various actions and behaviors undertaken by sales professionals as they engage in their sales activities. It involves tracking and measuring the specific activities performed during the sales process, such as the number of calls made, emails sent, meetings scheduled, demos conducted, and deals closed. The purpose of activity tracking in sales is to gain insights into the sales process, assess individual and team performance, and make data-driven decisions to improve sales effectiveness.  What is an Activity Tracking Software? An activity tracking software is designed to monitor and record the various activities performed by sales representatives or teams. These activities typically include interactions with leads and prospects, customer communication, follow-ups, and other sales-related tasks. The primary purpose of activity tracking software is to help sales managers and team leaders assess and improve the productivity and effectiveness of their sales teams. Why Do We Need Activity Tracking? Accurately and comprehensively capturing activity data poses a significant challenge. Despite 67% of businesses utilizing 4 to 10 digital tools, they need to track the activity data generated by these tools completely and precisely. Additionally, 79% of opportunity-related data sales representatives collect never enters the CRM. Moreover, the data recorded in systems like CRM could be more reliable, plagued by issues like outdated, missing, or incomplete entries. This lack of data accuracy is a concern for as many as 70% of revenue leaders, leading to substantial financial losses averaging around $15 million per year for organizations. The presence of accurate and complete activity data in systems like CRM creates misalignment among teams in terms of their technological tools and objectives. When sales teams grapple with questions about updated prospect contact information or the correctness of email IDs in the CRM, their efficiency could improve, positively impacting both businesses and customers. Due to lacking confidence in the data, sales, and marketing teams work in the dark, unable to leverage the full potential of significant investments like CRMs. This situation results in poor returns on investment for such resources. https://www.youtube.com/watch?v=GO6zZpHUoIg&t=1s How Does Poor Activity Data Affect Revenue? Poor data and a lack of activity data in the CRM can harm gaining accurate insights and lead to revenue leakage throughout the customer journey. Here are some key points to consider: 1. Inaccurate or incomplete data When data quality is compromised, it becomes challenging to extract meaningful insights. Only complete or updated information can lead to correct assumptions and flawed decision-making. 2. Missed opportunities Important customer interactions and touchpoints may go undocumented without comprehensive activity data. This lack of visibility can result in missed opportunities to engage prospects, address their needs, and nurture relationships, leading to potential revenue leakage. 3. Ineffective sales strategies The absence of activity data hinders the ability to analyze and optimize sales strategies. Without a clear understanding of which activities drive results, aligning sales efforts with customer preferences and needs becomes difficult, resulting in suboptimal outcomes. 4. Inefficient resource allocation With activity data, it’s easier to assess the productivity and effectiveness of sales teams. This can lead to misallocation of resources, including time, effort, and budget, resulting in revenue leakage and diminished returns on investment. Clean data is essential for Activity Tracking Software as it ensures accurate and error-free information, leading to reliable insights into sales team activities and facilitating better decision-making and performance analysis. With clean data, the software can provide a comprehensive view of sales interactions, prospect engagement, and customer behavior, enabling businesses to identify opportunities, optimize processes, and enhance overall sales efficiency.  Moreover, clean data minimizes the risk of misinterpretation or skewed reporting, fostering greater trust in the software’s output and empowering sales managers and teams to take data-driven actions to achieve their goals. Benefits of Activity Tracking Software Here’s a look at the various advantages of an activity tracking software: 1. Clear visibility into deals Increased visibility into deals serves as a prerequisite for enhancing productivity. When you have comprehensive activity data, you better understand each deal’s status, identify areas that require improvement, and prioritize values that need immediate attention.  Consider the importance of deal reviews in a successful sales process. By utilizing insights derived from unified activity data, deal reviews can evolve from impromptu events to impactful sessions, where sales managers gain clear visibility into the intricacies of every deal.  As activities related to each deal are automatically captured and updated, managers no longer need to remind sales representatives to input data into the CRM constantly. Instead, both reps and managers can access a comprehensive view of contacts and deal specifics within the pipeline, allowing them to focus on urgent matters. 2. Identification of winning rep behaviours Activity data enables you to correlate the productivity of your sales representatives with their performance. For instance, you can obtain crucial insights to answer important questions such as:  Activity data helps map sales reps’ productivity to their performance It provides answers to critical questions such as time allocation, engagement with high-value customers, decision-maker involvement, adherence to best practices, sales target progress, account engagement, and lead follow-up Insights from activity data serve as leading indicators for real-time coaching and decision-making Managers gain visibility into sales reps’

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