How Lana Trains AI Models and Helps Teams Use Them

I come from a background in business intelligence and product analytics, a world of data, processes, and constant pressure to do more with less. Today I work as a large language model trainer, which means I spend a lot of time understanding how AI models are built, how they work, and how to make them genuinely useful in real business contexts.

It all started with one simple question I asked myself: Can I do my job faster and better? Prompt engineering led to automation tools, and over time, that evolved into what I do now, helping non-technical teams adopt AI solutions that actually work.

Working in the tech industry, the question of efficiency was always present. I didn't experience AI as a sudden revelation.

It was a logical next step. The constant pressure of manual work made the move toward AI tools feel inevitable, and once I started experimenting, the experiments compounded into a whole new direction.

I work with clients in consulting, services, agencies, growing sales teams, and marketing teams. They all hit the same wall eventually: the business starts moving fast, and 24 hours in a day is no longer enough.

That is where I step in. For me, the real motivation is working with open-minded people who want real results over the hype, and being there for the moment when AI stops feeling intimidating and starts genuinely changing how they work. Nothing beats that aha moment. It's what keeps me going.

Every day, I get very similar questions: what is an AI agent, is AI useful if you don't have a technical background, and what is the best email outreach tool right now?

The questions vary, but the underlying mindset is usually the same. People treat AI like a magic fix they can plug into a messy process, with raw, unstructured data and no clear strategy, and then wonder why the outputs are bad. That is the most common mistake I see, and it is the first thing I address with any new client.

I flip the usual approach. Instead of arriving with a tool recommendation, I start by mapping what is already happening in the business: what processes are rule-based, repetitive, and time-consuming but do not require constant human creativity. Follow-ups, outreach, data cleanup, lead enrichment, onboarding, recurring reports.

Those are perfect candidates for AI. From there, I feed the AI proper context, clean rules, and clearly defined goals, then set measurable KPIs: higher reply rates, more booked meetings, and hours saved per week. Real outcomes, not vague improvements. The tools only perform when the foundation has been built first.

One area where I see a strong impact is client onboarding. Inception churn, the rate at which clients abandon an onboarding process before seeing value, can reach as high as 48%. That is a number that gets a business owner's attention immediately, because it hits revenue directly and is often invisible until someone measures it.

My approach is to map real-life context first: client preferences, priorities, history. From there, AI can make the onboarding experience feel personalised and responsive without pretending to be human. The practical result is that onboarding timelines that used to take weeks shrink to days.

Professionally, the shift was significant and well-documented in my work. Personally, I frame it differently. I grew up before the internet existed and started my career as it was taking shape, so adapting to major technological shifts is something I have done before.

AI is another iteration of that same process: you learn the tools, figure out what they are good at, accept that they can create chaos as well as clarity, and keep going. It is the same discipline applied to a new context.

Start small. Pick one real, painful problem and solve that. Don't reinvent the wheel from scratch before you understand the parts. Ignore the YouTube gurus and the content designed to generate clicks rather than results.

Focus instead on understanding how AI works for your specific job, business, or situation, because what works for someone else's use case may have nothing to do with yours. The skills that matter most are not technical: they are curiosity, persistence, and a willingness to experiment without the pressure of getting it right the first time. That playful approach is very important.

Many of my clients and peers are solo entrepreneurs or small team founders carrying significant responsibility with limited support structures. The combination of information overload, rapid change, and the pressures of ordinary life: family, finances, time, can make the whole journey feel genuinely overwhelming.

A community of peers who share honest experiences, what worked, what failed, what they are still figuring out, provides something no tool or tutorial can: the feeling that you are not alone in the process. That is what keeps people motivated long enough to actually get good.