That’s exactly why, at Inverta, we’ve focused on building an AI-aware culture from the ground up. Our early AI adoption efforts had two key objectives:

1. Engage the entire team in identifying AI opportunities - creating an ongoing pipeline of high-value use cases.
2. Develop AI agents to streamline the client discovery process - one of the most time-intensive and high-impact areas of our business.

Why these two? Because they generate enthusiasm through ownership, and they guarantee every new client has a great first impression.  AI adoption isn’t just about efficiency - it’s about transformation. Transformation requires the whole team, and teams that have a sense of ownership are the most engaged. As for the discovery tools: every new client requires discovery, and it's every new client’s least favorite part of the process.  We prioritized making that progress faster and more insightful. 

Before we dive in, one more note: we’re using the word “agents” loosely throughout this document.  Think of it as a catch all term for custom instructions in various platforms (ChatGPT, Embrace, Gemini) that tell our chat to act in a specific way, look for specific information, and output it in a specific way.  They help us drive consistency across the team.

How We’re Managing AI Workflows with Monday.com

Why "Tribal Knowledge" Was Slowing Us Down

In the early days (you know, a month ago), AI adoption was scattered. Different teams were running different AI experiments, but there was no central process - resulting in:

Duplicated efforts across teams.
Overreliance on individual knowledge (if someone left, so did their AI expertise).
Too much focus on custom instructions, not enough on repeatable process automation.

How We Fixed It: Centralized AI Management

We needed one source of truth - a structured system for managing AI initiatives.

Enter Monday.com.

• AI Request Intake Forms: Every request (custom instructions, new automations, tool evaluations) is categorized, tracked, and prioritized.
• Workflow Optimization: The team can see where each AI project stands - who’s responsible, what stage it’s in, and when it’s ready for deployment.
• AI Dashboard: A single, transparent view of all AI-enabled processes, updated regularly and reinforced via Slack and team meetings.

Real-World Example: One of our quickest wins? AI-powered BDR outreach sequences (see below for details). By integrating client messaging insights with existing content assets, we automated multi-touch sequences - reducing manual effort while improving personalization.

How We’re Identifying Friction Points to Fuel an AI Request Pipeline

AI is at its best when solving real, persistent challenges. But how do you surface those challenges in a structured way without making it feel like more work?

1. Making AI a Part of Everyday Thinking

The first step was a mindset shift: AI isn’t an add-on; it’s a tool for solving daily problems.

• We reinforced this during our consultant and client partner team meetings, actively encouraging people to flag tasks that felt slow, repetitive, or inefficient (or that they just didn’t like doing).  This is key to driving engagement internally - give team members a shared planning experience, and make sure it affects them positively.
• Every month, we rolled up key insights into written guidelines, instructional videos, and AI-generated podcasts (thanks to Google Notebook LM), making it easier for people to learn and contribute at their own pace, using the learning style they liked best.

2. Creating Clear Communication & Feedback Channels

Great ideas don’t always come in a structured format - which is why we needed multiple channels for input.

• Formal Intake: We set up Monday.com forms where team members could submit AI opportunities, ensuring every idea was captured, prioritized, and tracked.  This same board drives a dashboard view that lets every team member see every agent, tool, and process we’ve deployed.  It also links to instructions and a demo video for each one.
• Informal Slack Channel: A dedicated “AI Nerds” Slack space created an open forum for quick discussions - no pressure, no expectations, just a place for candid idea-sharing.  It’s hard to control when inspiration strikes, and LLMs make identifying insights in unstructured conversations (like Slack) easy.

3. Early Wins: Generate Enthusiasm by Making Life Easier

Ideas are great, but execution and solving problems wins the day.  We had to demonstrate that, at minimum, we could enable our staff to get the same quality output, faster (out better output in the same amount of time).  Two opportunities jumped out at us from the list of submissions:

• Reducing manual review of client calls - what used to take hours of transcript analysis now takes minutes with AI-generated insights.  We trained the team on a process for using our RAG system (Embrace) to store our client calls, then equipped them with some prompts and a process for lifting up common themes, opportunities, insight, or points of disagreement across dozens (sometimes hundreds) of hours of calls.
• Best-practice sales outreach at lightning speed - BDR outreach, like marketing communications, need to enable your buyer and provide value.  They need to do that over weeks, and across multiple channels, while referencing relevant content and incorporating the campaign’s messaging framework.  It can be tough for a consultant to juggle, unless you have an agent within Embrace review a client’s messaging framework, available content, and personas, then build out a best-practice cadence that includes stats, insight, and trends.  Now we’re providing clients with the same great outreach but doing it in half the time.

Building buy-in through a sense of ownership and by addressing common pain points early on drove engagement with the initiative, generating 20 AI process requests in the first two months - one request every other working day.

Building AI Agents to Streamline Discovery

Why Discovery Was Our First Focus

An engaged team is great, but an agency lives and dies by how engaged its clients are.  Discovery for us is essential - we need to know what you know, and we need to understand what’s missing.  Discovery for clients is…less fun.  They know what they know, and don’t like to recap problems they were already aware of.  Discovery sets the stage for every client engagement. It’s where we define challenges, align on strategy, and surface high-value opportunities. But here’s the problem:

• Reviewing transcripts for key insights takes hours.
• Extracting relevant data from complex strategic documents is slow and manual.
• Distilling findings into actionable next steps is inconsistent.

AI could change all of that.

What Our AI Discovery Agents Do

We focused on decreasing the time it takes for us to be knowledgeable, and focused on questions on problem areas.

Every engagement starts with a documentation dump, and it’s difficult and time consuming to review every asset looking for answers to specific questions.  We turned this process around by adding those documents to Embrace and building agents that know what our services look like, analyze hundreds of documents, summarize a client’s current state, and highlight gaps.  This brings our team up to speed faster and lets us ask more focused, probing questions of the right people, getting to strategy and solutions faster.

These agents are specifically designed to analyze client discovery materials, surface insights, and generate structured recommendations. Here’s how they help:

Categorization and Summarization: AI scans transcripts and strategic docs, highlighting recurring themes, client pain points, and potential opportunities.
Automated Question Generation: Instead of manually brainstorming, AI suggests targeted discovery questions based on existing documentation, where the client said their problems were, and what we know are recommendations to address those problems.
Role-Based Insights: Agents tailor findings to specific stakeholders (e.g., CMO vs. Demand Gen Lead), making recommendations more relevant.

What Didn’t Work - and How We Fixed It

Early iterations of our Discovery Agents struggled with confusing their knowledge base with the client’s actual data. In some cases, AI would mistakenly state that a client was already using our recommended processes when they weren’t.

The fix? We reworked our custom instructions to remove explicit examples of what “good” looked like.  That’s what consultants and client partners are for.   Instead, the AI was trained to:

• Provide a neutral readout of the client’s current state.
• Surface questions we could ask to identify potential improvements, rather than making assumptions.

This change immediately improved accuracy, ensuring AI remained a tool for discovery - not a source of false conclusions.

Lessons Learned (So Far)

1. AI-Driven Culture Starts with Engagement

When people see AI solving real problems, they engage more. Our structured approach led to a steady increase in idea submissions, proving that process and communication drive adoption.

2. AI Use Cases Must Be Prioritized

Not every AI idea can be implemented at once. One of our biggest challenges?

• Different team members had different pain points and focus areas.
• We had to balance quick wins with long-term, complex projects.

By focusing on fast, tangible wins first, we built trust and engagement - giving us more runway to tackle larger AI initiatives.

3. AI Requires Continuous Learning & Adaptation

AI isn’t "set it and forget it." Prompting techniques, model strategies, and best practices evolve fast. We now have:

• Regular internal training sessions on AI updates.
• A plans to review and update AI processes and training as the models improve.
• Gemini AI integration with Google Drive for quick, AI-driven discovery of internal docs.  Don’t get hung up by not knowing what you know - train your team to turn organizational knowing into individual knowledge.

What’s Next? The Future of AI at Inverta

Our next focus? Staying ahead of AI’s rapid evolution.

• Expanding AI-driven discovery tools - training agents on more specialized marketing use cases.
• Building more autonomous AI systems - moving from LLM chat assistance to automation.
• Refining our approach to synthetic personas - enhancing personalization, targeting, and feedback capabilities.

And most importantly - we’ll keep sharing everything we learn.

Final Thoughts: Your AI Journey Starts Now

1. Start by identifying friction points.
2. Create a structured way to track AI opportunities.
3. Pilot fast, refine often, and make AI a part of your team’s daily workflow.

So, what inefficiencies are slowing your team down? That’s where AI should start.