Why Ops Skills Are Your AI Superpower

How your ops skills supercharge AI effectiveness - plus a practical roadmap for building AI solutions for your business

Six months ago, I couldn’t write a single line of Python. Today, I’ve built AI tools with Python that generate hundreds of automated reports for over 70% of our sales reps. And I built the first working version of it in 1 month. How? My Marketing Operations background (and AI of course).

If you work in an operations role: Marketing Ops, Sales Ops, Revenue Ops, IT Operations, or even project management with operational responsibilities, you already have the exact skillset needed to build AI solutions that your organization needs.

My last post was fairly technical. I walked through a failed multi-agent experiment and the lessons learned about context dilution in AI workflows. Today, I want to zoom out and share why operations skills are the hidden superpower for AI development success, plus a practical roadmap for turning your expertise into AI solutions.

The Ops Superpower

Operations teams have been building the exact skillset needed for the AI era.

1. We work with data and tools every day. We know where the data lives, what format it’s in, and how to get it where we need it. Need to pull account or contact information from Salesforce? We do it everyday. Need to enrich it with third-party data? We know those integrations.

2. We’re experienced with integrations. We spend our careers connecting systems that don’t talk to each other. We know how to transform data from one format to another, how to handle APIs and API limits, and how to build workflows across multiple platforms. This is exactly what AI requires: pulling data, standardizing it, and feeding it to an LLM in the right format.

3. We live in the world of process, project management, and documentation. We map data flows, we document processes, and we think in systems. When you’re building AI workflows, you’re not just writing prompts, you’re architecting systems. And we’re already trained for this.

4. We know data privacy and compliance. I’ve built entire data privacy processes and workflows for collecting consent, something most Marketing Ops teams are familiar with. Ops teams already have the data governance muscle that is non-negotiable for enterprise-grade AI.

5. We’re the low-code automation experts. This is our superpower. While other teams may be discovering automation, MOPs, in particular, have been living in low-code tools like Zapier, Make, and Workato for years. We’ve been building complex, triggered workflows long before AI made them “agentic.”

AI doesn’t replace this skillset—it supercharges it. This is where operations skills become so valuable. Operations teams can bridge the gap to actual business processes. We’re the ones who know that the lead scoring model needs to account for data decay (that 6-month-old job title is probably outdated), that an email send could fail to deliver if we run into communication limits, or that the routing workflow needs to handle the reality that a certain percentage of our leads have incomplete company data.

This systems-thinking translates directly to building an AI workflow that analyzes a 10-K and automates account research.

From Individual Productivity to Organizational Value

Individual chatbot use delivers productivity gains, but they’re linear: one person gets 25% faster. Operations professionals think in systems, which means we see opportunities for exponential impact. Instead of making one person faster, we can eliminate entire manual processes for whole teams.

That’s how my Analysis Dossier tool got started. Our leadership asked about AI for account research efficiency and my Ops brain immediately started thinking about automation. I knew I could pull 10-K filings, earnings calls, and org charts. I knew how to structure the process. And I knew how to handle the errors (because I’ve debugged enough workflows to know what breaks).

What made it revolutionary was AI. AI was the final piece that let me automate the “analysis” part, not just the “data pulling” part.

In one month, I built a working version that pulled account info, processed 10-Ks with Python (which the LLM coached me through), called APIs for org charts, and generated analysis, all in a secure, compliant Azure environment.

How AI Raises the Bar for What We Can Do

What changed everything for me was realizing that LLMs have broken down the technical barriers that separated Ops from engineering. Anyone willing to learn can access coding, API development, and complex automation logic.

LLMs can coach you through Python, help you build and debug API calls, and walk you through multi-step workflows that would have required developer resources just months ago. And it’s not just about efficiency, it’s about expanding what’s possible for Ops professionals to build independently.

AI accelerates learning across technical domains that used to take years to master. The learning curve that once felt insurmountable feels like more of a gentle slope. (Although to be clear, AI raises the bar, not the ceiling. Years of deep expertise is still invaluable for writing robust, scalable code.)

This shift has been revolutionary for how I approach business challenges. With new capabilities at my fingertips, I genuinely believe I can tackle almost any challenge that comes my way. When you can quickly acquire new technical skills and combine them with the expertise we already have, the possibilities feel limitless. It’s fundamentally changed what I think is achievable.

A Practical Roadmap

If you’re in an Ops role, or just curious and wanting to add value to your business through AI, here’s what I’d recommend:

1. Identify manual workflows that drive value. Start with processes that are currently time-consuming and manual, but deliver real business impact. This builds credibility and proves ROI quickly.

   1. Action: Find a high-pain, low-complexity process. Look for repetitive tasks your colleagues (like Sales) complain about.

   2. Example: Account research, campaign performance analysis, content personalization, or project management processes.
      

2. Strategize on your output and goals. Be specific about what you’re trying to create before you build. This defines your “definition of done” and guides every technical decision thereafter.

   1. Action: Define the final deliverable and the best format for it. Is it a report in a Word doc? A dashboard? A custom-trained chatbot? What sections and information should it contain? Get stakeholder sign-off on the desired end-state and start from there.

   2. Example: A 2-page strategic brief for high-value accounts, a weekly email that analyzes email metrics and suggests improvements, or a tool that researches competitors and delivers regular insights for battlecards.
      

3. Map where your data lives and how to access it. This is where Ops experience shines. Document every data source you need for your output, but don’t try to boil the ocean. Start with the “must-have” data that can give the LLM the context it needs for your goals.

   1. Action: Create a simple flow-chart. Where does the data start (CRM, APIs, financial databases)? How will you extract it? How will you standardize it for the LLM?

   2. Example: I use LLMs to help me write Python scripts to structure my data before the main AI analysis even begins. I also use it to transform markdown text into formats I need, like HTML or even PowerPoints.
      

4. Use low-code tools to piece it together. Use automation platforms like Zapier, Make, n8n to orchestrate the steps. Don’t be scared of code like Python or JavaScript, LLMs can guide you through it.

   1. Action: Build the end-to-end flow: pull data, structure it, send it to an LLM, process the results, generate the output, and deliver it to the user.

   2. Example: A Zapier workflow that triggers when a weekly email report is sent, structures the information, sends it to Azure OpenAI for analysis, and then posts a summary into a Teams channel.
      

5. Consider build vs. buy (but don’t default to buy). A combination of both is often best. It allows you to stay secure and scalable, while giving you the agility to design solutions that actually fit your use case.

   1. Action: Evaluate vendor solutions. If they are too generic, don’t fit your specific needs, or can’t integrate with your data, don’t be afraid to build.

   2. Example: You might “Buy” a core AI platform (like Azure OpenAI) but “Build” the custom workflow that connects it to your specific data and processes.
      

6. Don’t forget enablement and rollout. This is the most critical and often overlooked step. An amazing tool is worthless if no one uses it.

   1. Action: Create a formal adoption plan. Start with a pilot group, gather feedback, and iterate before a full launch. Create learning modules, hold live training sessions, and open a feedback channel.

   2. Example: For the Analysis Dossier, I did a pilot, ran multiple live enablement sessions, and worked with individuals to gather feedback and implement improvements. This is how you build trust, prove value, and gain adoption.
      

7. Design for business impact and not just productivity gains. Efficiency metrics are helpful, but the business cares about revenue. If your AI tool saves someone 2 hours but they don’t use that to drive pipeline, you’re missing the full ROI potential.

   1. Action: Design workflows that channel efficiency gains into high-value activities. Map out what strategic actions could naturally follow your AI output, then build workflows that make those actions easy. This could become the next iteration of your tool.

   2. Example: My Analysis Dossier generated account insights, but people still struggled with what to do next. So I built the “Strategy Brief” to take that intelligence and translate it into actionable sales strategies, personalized messaging frameworks, and specific conversation starters. You could extend this even further by having the output automatically populate fields, trigger targeted sales sequences, or generate customized battle cards. The key is designing each tool to flow into revenue-generating activities rather than just creating more free time that may not be used strategically.

The Bottom Line

If you work in Operations, you have a unique opportunity to drive meaningful AI transformation. You have the right combination of skills: data fluency, integration expertise, process thinking, automation experience, and compliance awareness.

AI is the final layer that supercharges this skillset, unlocking technical capabilities to automate complex workflows, build new tools, and solve problems we couldn’t tackle before.

And you don’t need to work in isolation. Partner with other teams, like IT, to leverage secure, governed platforms like Azure OpenAI, then use your expertise to build the “last-mile” solutions that solve real business problems to create real business value.

So figure out what workflows and processes are manual yet valuable, architect the solution, and start building. The tools are available, the knowledge is accessible, and you already have the skillset. You just need to start building.

I’d love to hear from those who are getting started or who have already built solutions. What workflows are you automating with AI? What challenges are you facing? Let me know your thoughts in the comments!