When people hear “AI transformation,” they often envision integrating ChatGPT into customer service or automating backend processes. But according to Oleksandr Holovko, Lead Product Designer at Lazarev.Agency, this perspective completely misses the mark.
“AI is a powerful tool, but real transformation happens when you design the right solutions around it to reinvent how your organization works. Think of a hammer. On its own, it’s just a stick with some metal. But in the right hands and context, it becomes essential for building complex structures. The same goes for AI: its true value comes from how well it’s integrated into your processes,” he explains.
Drawing from his experience guiding companies through AI integration, Oleksandr emphasizes that successful AI transformation hinges on design thinking, cross-functional collaboration, and a clear strategy.
A Quick Reality Check on AI in 2025
Across industries, businesses are integrating AI into everything from customer experience to product development and operations. According to Boston Consulting Group’s 2024 survey, companies leading in AI adoption have seen 1.5× higher revenue growth and 1.6× greater shareholder returns than their peers over the past three years.
Leaders like Microsoft, Nvidia, and OpenAI are setting the pace, while fast-moving startups and agencies are carving out new markets entirely. What was once considered experimental is quickly becoming table stakes.
Take VTnews.ai, for example, a project Lazarev.Agency helped bring to life. This AI-powered news aggregator redefined how news is consumed by not only offering a personalized, bias-aware feed, but also using AI as an engine to aggregate and analyze information at scale — far beyond the typical chat-based assistant. In under a year, it reached 240,000 monthly readers.
A few more recent examples of AI transformation from well-known brands:
- Intercom has embedded AI into its core offering by launching Fin, an AI-powered customer support chatbot built on GPT-4. But they didn’t stop at adding a chatbot, they restructured their entire support experience to be AI-first. Fin can handle complex customer queries using knowledge base articles and natural language understanding.
- Moderna uses AI and machine learning across the entire drug development lifecycle from mRNA sequence design to clinical trial optimization.
- Duolingo uses AI for personalized learning, automated feedback, and content generation. The Duolingo Max subscription tier uses GPT-4 to power features like “Explain My Answer” and roleplay conversations.
Disclaimer: Oleksandr notes that despite the popularity of the AI, its real impact on work and employment has been gradual. For example, Duolingo has already stated that AI will not affect their hiring plans, while Klarna’s case raises questions about the effectiveness of AI solutions.
It seems that the concept of being “AI-first” appeals much more to investors and management than to everyday users or employees of these companies — which makes sense, since AI takes a long time to implement, typically relies on large volumes of data, and can make mistakes that a human would never make.
But for those ready to rise to the challenge and do it right, here’s your prep guide.
The 5 Pillars of Preparing for AI Transformation
Pillar 1: Audit Your Current Workflows
Before you explore AI solutions, zoom out and map your existing workflows.
- Where are the bottlenecks?
- Which processes involve repetitive tasks, manual data entry, or complex decision trees?
One useful method is to create a visual process map of each department. Then, assess where AI might improve speed, accuracy, or scalability. Tools like Miro, Lucidchart, or Notion can help you document processes and tag opportunities.
Pillar 2: Align Leadership and Culture
AI can’t thrive in a culture that resists change. Leaders need to create a culture where experimentation, transparency, and psychological safety are the norm.
This starts with internal messaging. To pitch AI transformation to your leadership team effectively, you need to go beyond the hype and tie AI to core business outcomes, risk mitigation, and competitive advantage.
Here’s Oleksandr’s take on how to approach it:
✅ Start with strategic framing
Explain that AI transformation is about rethinking how your company works, not just what tools it uses. Position it as a business strategy, not a tech trend.
✅ Show the business case with data
Use compelling data to make the impact tangible:
- 1.5× higher revenue growth and 1.6× greater shareholder returns for AI leaders vs. peers
- Stitch Fix blends AI with human stylists to scale personalization in retail
- Duolingo Max drives premium revenue through AI-powered features
Focus on KPIs your leadership cares about: revenue, operational efficiency, time-to-market, retention, cost savings.
✅ Highlight the risk of inaction
Frame the opportunity cost:
- AI-native startups are moving faster and leaner
- Customers now expect AI-enhanced experiences
- Investors increasingly prioritize AI-readiness
Ask yourself: “What part of our business could be disrupted by someone with better AI integration?”
✅ Propose a low-risk, high-learning pilot
Instead of asking for a company-wide shift, suggest starting small:
- Choose one product, process, or department
- Define a clear goal (e.g., reduce support cost by 20%, accelerate content production)
- Measure impact and share learnings internally
✅ Focus on people
Emphasize that AI transformation is about culture:
- Upskilling teams
- Encouraging experimentation
- Designing for trust and transparency
- Restructuring workflows
✅ Wrap with a vision statement
- A smarter product experience
- Teams spending less time on repetitive work
- Faster, more informed decision-making
- A competitive edge that compounds
Pillar 3: Upskill and Restructure Your Team
Some jobs will evolve, others may fade, and entirely new ones will emerge. Preparing your team starts with skills mapping: what capabilities do you need to make the most of AI?
Key focus areas include:
- Data literacy and prompt writing: The ability to communicate with AI models effectively and extract value from outputs.
- Cross-functional collaboration: Teams that blend design, engineering, and strategy are better positioned to build meaningful AI-powered experiences.
- Ethical and legal awareness: Understanding bias, consent, data privacy, and explainability is no longer optional.
You’ll also need to restructure teams around AI-enabled workflows. That might mean embedding AI specialists into product squads or pairing designers with data scientists to co-create more dynamic, intelligent interfaces.
The Roles You Need to Transform Your Business
Please note that you don’t need to hire all these roles below at once. In early phases, one person might wear multiple hats or you might work with specialized agencies like Lazarev.Agency to fill critical gaps in design and product strategy while your internal team evolves.
These external teams bring the AI-native design thinking, strategic foresight, and hands-on experience needed to guide transformation without overwhelming your internal resources. While your team ramps up, the right agency can accelerate the process, designing future-ready products, building internal capabilities, and avoiding costly mistakes.
Think of it as parallel transformation: while your team evolves internally, an experienced partner keeps the momentum going.
Pillar 4: Design with AI in Mind
Oleksandr spent over a decade designing digital products for startups, scale-ups, and enterprise teams. Here’s what he learned: if users don’t understand what the AI is doing or why, it doesn’t matter how smart your model is. They’ll abandon it.
So what actually works? Start with transparency.
- Always show when AI is involved. People need to know when a decision or suggestion is coming from a human vs. a machine.
- Explain the “why.” If your AI recommends something, say why. Give users a glimpse into the logic behind the curtain. For example, at Lazarev., we worked on a native integration with the SearchAI search engine, where AI-generated responses were displayed separately from traditional algorithm-based results and website rankings — clearly indicating which answers were powered by AI for a given query.
- Let users fine-tune or override the output.
3 UX Pitfalls to Avoid
❌ Burying AI features in hard-to-find menus → Make smart tools accessible, don’t hide them.
❌ Defaulting to automation where users expect control → Offer manual options, especially for high-stakes decisions.
❌ Giving your AI a personality that doesn’t match your brand → Don’t pretend it’s a human if it’s not. People can tell.
Pillar 5: Start Small
One of the biggest mistakes Oleksandr sees is teams trying to “AI-ify” everything at once. That’s a fast track to chaos. Instead, he suggests starting with one product, one process, or one department (somewhere with a clear problem and measurable impact).
Choose a pilot that lets you test how AI can improve:
- Speed: Are you saving time on repetitive workflows?
- Cost: Can automation reduce manual overhead?
- Accuracy: Is AI improving decision-making?
- User experience: Are people actually enjoying the change?
Here’s what you should track from day one:
- Time saved per workflow or task
- Improvement in decision quality (fewer errors, better outcomes)
- User satisfaction with AI-assisted experiences
Think of AI transformation like product development with a series of experiments.
What Else to Prioritize?
As you integrate AI into your business, there are a few key focus areas that will set you apart and set you up for success:
- Build with context. AI isn’t plug-and-play, it performs best when it’s customized to your workflows, trained on the right data, and integrated into the user journey.
- Prioritize the human experience. The most effective AI tools feel intuitive. Always design with empathy and clarity in mind. For example, if you’re designing a chatbot, empathy might look like this: If a user says, “I’m really frustrated right now,” the bot could respond with, “I’m sorry you’re feeling that way. Let’s get this sorted out together.”
- Make ethics a design principle. Transparency, privacy, and fairness are your opportunities to earn trust. Build systems that explain their decisions, protect user data, and reduce bias from the start.
Tools, Templates, and Team Models
The tools you choose should match your team’s goals, skill sets, and workflows. Here’s a curated list of what we use at Lazarev.Agency and what’s gaining traction across forward-thinking companies:
Notion AI. Perfect for speeding up internal operations: summarizing meeting notes, writing briefs, generating docs, and keeping teams aligned without the manual lift.
Figma + UXPin. Essential for prototyping and testing AI-infused interfaces. Rapid iteration with real-time feedback helps us validate ideas before they hit production.
LangChain / Pinecone. For the builders. These frameworks make it easier to create and scale LLM-powered apps, especially when working with retrieval-augmented generation (RAG) and custom knowledge bases.
Zapier + OpenAI. No-code automation meets AI smarts. We use this combo to create internal tools, automate redundant tasks, and trigger actions from AI prompts.
Whimsical / Miro. Mapping out user flows, AI system architecture, or prompt logic? These are our go-to canvases during early-stage strategy and team syncs.
💡 Pro tip: Don’t let tool fatigue slow you down. Start with one use case, pair it with the right tool, and expand from there. Simplicity wins in the early stages.
One thing we recommend to clients early on: visualize progress. A simple, shared dashboard can track:
- Pilot success metrics (e.g. time saved, engagement uplift)
- Training and upskilling progress
- Workflow automation coverage
- Feedback from users and internal stakeholders
Use this as a single source of truth to keep leadership aligned and momentum strong as you scale AI efforts across teams.
Conclusion: Build AI People Trust with Lazarev.agency
The companies that win with AI won’t be the ones reimagining how their teams work, collaborate, and create value. That shift starts with design thinking.
Design is where human insight meets machine capability. It’s how we turn complex algorithms into experiences people actually trust, use, and enjoy. And in the age of AI, that’s your real differentiator.
Looking for expert guidance? Our AI consulting services at Lazarev.agency help companies lead with design from team readiness assessments to custom workshops and strategy sprints. Let’s build smarter, more human AI together.
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