How to Clone Yourself with AI (in 4 Steps)

From knowledge bases to swarms of AI agents

Dear GTM Strategist!

In my career, I had to learn about some really hard-to-understand technical fields.

Smart grids, traceability with blockchain, dRAGs and RAGs, autonomous driving vehicles, cooling systems for computers, injection molding, pharmaceutical compliance, cloud vs. on-prem system engineering, data interoperability spines, and open information exchange protocols in healthcare, and we just onboarded a new client in the forensic source science space - I think you got the picture.

How did I (Business School Grad, lover of art history and canine sports) manage to grasp the essence of these massively complex fields?

You guessed it, I did whatever it took.

I understood that my ability to do well would be compromised if I “don’t get what the company does”.

I spent hundreds of hours decoding whitepapers, listening to sales calls, attending courses and trainings, conducting lots of research, and, if I was lucky, making friends with a technical person or a product manager who was kind and patient enough to mentor me or at least answer my questions.

Me discovering computer liquid cooling (as you could guess, it was in 2020 😷)

Domain knowledge can be challenging to grasp, especially for individuals working in marketing, sales, and growth who lack technical training or prior exposure to the field. However, it is necessary. Domain knowledge is mission-critical to craft effective messaging, copy, sales, and marketing materials.

In my experience, it takes an average marketer 3-6 months to grasp the field they are working in. That is approximately $15-30K investment for a company by still risking that this person will not stay or do well - that is a gamble.

Worse, I have met marketers and sales operators who were working 3-5 years in companies without understanding what the company does or meeting a single client in person, and that scares the s*** out of me.

Do people simply not bother to learn what they are working on, or are we as an organization extremely ineffective in providing them with accessibility, guidance, and motivation to learn?

And then one day, I realized I had become a leader and domain expert in my field.

Everything changed.

Now I was responsible for “empowering my people” to “get it” - to onboard them, answer questions swiftly and firmly. I had to balance the time and resources spent on “training them” and making sure that the work is done well, while doing 100 other things aside.

And just between you and I -

I often daydream about being able to clone myself.

I am slowly growing tired of repeating myself …

70% of questions are “on repeat”.

We go through the same loops, processes, and I have to do it with love, patience, and without the loss of enthusiasm, because that could seriously impact the motivation of my team.

Sure, I prepared docs - no one reads them 😱

Gen Z is not particularly interested in reading 150 slides and 70+ pages of whitepapers - I don’t mind.

At best, they throw them to ChatGPT and ask for a summary.

Wait - I can do that for them, and while I am at it, let’s make sure that the thing does not hallucinate and it gives them what they want - crisp, hands-on answers at midnight :)

In this newsletter, I’ll invite you to consider 4 forms of organizing your knowledge assets for better knowledge transfer in the company, power the processes with agents and ultimately - bring in the agents to do some of the work instead of you.

We’ll try to clone our domain experts 🙂

Step 1: DIY a simple Knowledge Base

Every org that I’ve ever worked with has terabytes of data - presentations, documents, technical specs, videos … The knowledge is there - often even transcribed. So whenever a client comes and says “we have no idea what to post on LinkedIn” - I am like “send me the transcript of your last call/webinar or just dump me some docs and I’ll send you 10 edited LinkedIn posts in an hour or two.”

The problem is not that “there is no transcript of knowledge” in the organization - the problem is that there is too much of it, that it is not well organized, and that most people have no idea that it exists.

So the first thing you should work on is creating a knowledge base. This is not a “data dump” of all the possibly relevant docs you come across.

Rather, a knowledge base is a centralized repository where an organization’s documents, transcripts, presentations, and other content are stored, structured, and made searchable.

When enhanced with AI tools, the knowledge base transforms from a static archive into a dynamic resource - allowing you to instantly surface relevant information, repurpose assets into new content and formats, and dramatically reduce the time wasted on repetitive knowledge hunting.

One of the first things we do with our clients is create a knowledge base for inbound, so they can start producing content that makes sense in days, not months. This is extremely simple to set up, and you can even do it on the free tier of ChatGPT if you just learn how to utilize Projects effectively.

Another thing you can master quickly is in-depth research. My colleague Kyle Poyar did a wonderful job explaining this on his Substack, so hop over there if you want to research with ChatGPT and Perplexity like a pro.

Also, Kyle and I are doing the 2025 State of GTM Motions survey - please pitch in here if you haven’t yet!

Step 2: Create digital clones of your domain/subject matter experts

We have reached the point of AI-agent development where I can not only “clone myself”, but also other domain matter experts, and serve operators in marketing, product, and support with reliable, swift answers exactly when they need them.

The hidden trap of being the “go-to” person inside a company is that your expertise gets consumed by repetition.

A typical Tuesday looks like this:

• Answering the same questions dozens of times a week

• Being pulled into every sales call for competitive intel

• Rewriting the same explanations in email after email

It feels like supporting the team.

But in reality, it traps some of the most capable people in the company and prevents them from doing strategic, deep work that adds much more value than “answering to the same questions again and again and again”.

In companies with 3,000 or more employees, more than 450,000 hours are wasted each year on repetitive questions.

37% percent of employees spend over two hours a day searching for information that already exists.

That is the equivalent of 216 full-time employees doing nothing but asking and answering repeated questions for an entire year.

That translates into roughly $31.5 million lost annually for a company, assuming a typical U.S. knowledge worker costs around $70 per hour (fully loaded with salary and benefits).

One of the companies solving this problem is Scroll. Cornell University and Nikkei already use it to scale expertise across the company.

The process is straightforward:

1. Create an AI expert – specialization and setup take less than a minute.

2. Curate the knowledge base – upload documents, slides, call notes, and reports. The system learns from existing expertise, not fabricated data.

3. Share with the team – permissions ensure quality control while making knowledge widely accessible.

4. Enable instant answers – colleagues receive expert-level responses with citations, freeing strategic time for higher-value work.

5. Integrate with Slack – answers flow directly into existing workflows, without disruption.

Here is a short demo of how I built my knowledge base, integrated it into Slack, and invited the teams I work with to interact with it. It took me like 5 minutes to build it. Super easy.

Use the code MAJA2025 to get 2 months of premium access for free (Starter plan, $158 value)

Step 3: Tailor the value delivery to the user’s preferences

Greg Isenberg has predicted the end of the user interface as we know it. He explained the main difference between UX and AX (agentic experience):

Source

While predominant UX in AI atm is chat and “copilots”, experts predict that, like agents, future interfaces would become dynamic and tailored to users.

Every time that I say that I am a visual learner, it backfires, and someone will comment that there is no such thing as learning styles. But I know for sure that I learn a lot more from seeing images and watching videos than if I had to read through 80 pages (which is an irony because I am a long-form content writer and I have written a 350-page book).

I am not alone.

91% of employees want personalized training, not the cookie-cutter content they’re getting today.

Companies are losing $13 million for every 1,000 employees because training takes too long and costs too much. Most organizations need six months and a six-figure budget just to turn existing PDFs, SOPs, and slides into “courses”.

You know “courses” such as technical presentation of new products, medical training, compliance training, presentation of new “exciting” regulations on one of key markets by legal, safety at work … Most experts that have to deliver them are not specifically keen on doing that and have little to no training in how to deliver knowledge effectively - spoiler: 6h workshops and 200 slides is not always the answer

I am working with NovaSkill AI - an LG venture that tackles the problem of organizational learning at scale, tailored to user groups.

Here is a demo of how we transformed a clinical poster for a medical device company into an AI-narrated, verifiable training course. Each learner can select how they want to absorb this knowledge—video, podcast, reading the document, etc.—and there is a copilot that acts as their mentor. There is no room for mistakes and hallucinations when it comes to such training; everything has to be bulletproof and verifiable in the knowledge base.

See how we did it:

Now, let’s unlock multiple diverse AI agents to work as a team!

(despite our better judgment 🤠)

Step 4: It is buzzing 🐝- Swarms of agents that work instead of humans (and are supervised by a human)

Care not about the buzzwords; you may be more familiar with the terms “multiagentic workflows” or “teams of agents.”

In essence, we are discussing how you can utilize multiple agents to accomplish tasks autonomously, create new value, and redefine existing workflows.

Let’s be specific: echowin is a platform for building AI voice agents, such as AI receptionists. They have recently empowered Budget Power, an energy broker from Texas, to automate over 90% of their support calls, saving the company 40 hours per week - an equivalent of one full-time employee.

echowin enables a multi-agent workflow aka a swarm of agents. And similarly to other cases, the logic of this build is - knowledge base-agents-human supervision-feedback-loops.

We are looking at the next frontier in AI adoption, where prospects are less impressed with use cases and demand relatable case studies with proven value added (ROI). Our PoCs (proof of concepts) are no longer explorations if we can integrate well in clients’ tech stack, but business cases for them to understand the value they are getting from the product. Just like this ...

You can start experimenting with your no-code AI agents for free:

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As a summary, these are 4 steps for scaling expertise we just uncovered:

What will you build in Q4?

Experiment with some of the tools we mentioned here and share your go-to tools in the comments. Maybe we can co-create a lovely visual together :)

Let’s go to market!

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