How should companies tackle AI transformation?

Reviewed by: Lazarev.agency AI Product & Strategy Team

Last updated: December 2025

Expert sources: BCG “AI Adoption in 2024”, Intercom Fin (GPT-4 customer support bot), Moderna AI value chain case, Klarna AI workforce impact, Duolingo AI & jobs statements

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.

AI transformation is the organizational shift toward AI-powered workflows, decision-making, and product experiences. Unlike digital transformation, which moves processes online, AI transformation redesigns how work happens, using intelligence, prediction, and automation at the core.

Let’s dive into the details of AI transformation.

Key takeaways

• AI transformation ≠ “adding a bot” — it’s redesigning how work, decisions, and products function with intelligence at the core.
• Start with a workflow audit + AI maturity assessment so you don’t overpromise with underpowered pilots.
• Align leadership and culture: frame AI as a business strategy and tie it to specific KPIs.
• Upskill and restructure teams around AI-enabled workflows; use external partners to bridge early capability gaps.
• Design with AI in mind: show when AI is active, explain “why”, and keep humans in control where it matters.
• Start small and measure hard: pick one use case, ship a pilot, and track workflow automation coverage, time saved, accuracy lift, AI-specific CSAT, cost-to-serve, model drift, and feature adoption.
• Use a repeatable loop (workflow discovery → opportunity mapping → human-centered redesign → model integration & guardrails → continuous learning) to scale AI without breaking trust.

A quick reality check on AI in 2026

Lazarev.agency’s research shows that searches for “AI transformation” have surged globally since mid-2025 as organizations shift from AI experimentation to full-scale redesign. This trend is driven by GPT-5 adoption, the rise of agentic systems, and growing pressure to modernize workflows before competitors do.

Not only the search proves the tendency. 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.

🔍 Read the full case study here.

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.

Assess your AI readiness

Before moving on, evaluate where your organization stands today. A clear maturity read prevents overestimated ambitions and underpowered pilots.

AI maturity levels

Level 1 — No AI adoption
Manual workflows, siloed data, no automation beyond basic tooling.

Level 2 — Tool-level AI experiments
Teams use isolated AI tools (ChatGPT, Notion AI, Midjourney), but nothing connects to core workflows.

Level 3 — Department-level AI workflows
Certain teams (e.g., support, product, ops) integrate AI into daily processes, but adoption isn’t consistent org-wide.

Level 4 — Product-level AI orchestration
AI powers major parts of the product or service (recommendations, summaries, automations) supported by structured data and governance.

Level 5 — Fully AI-native operating model
AI becomes the default interface layer across decision-making, workflows, and product experiences. Guardrails, governance, and continuous learning loops are in place.

Quick AI workflow audit checklist

Use this simple checklist to identify where AI can move the needle fastest:

• Do we know which workflows contain repetitive manual tasks?
• Where do decisions slow down due to data overload?
• Which processes consume the most time or resources?
• Do teams rely on tribal knowledge instead of structured systems?
• Where do customers experience friction or wait times?
• What tasks produce diminishing returns when handled manually?
• Which workflows could improve with prediction, personalization, or summarization?

Run this assessment once per quarter. It becomes your map for prioritizing AI use cases that deliver real business impact.

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. Position it as a business strategy.

✅ 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.

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🔍 If you want to connect these team changes to a broader organizational shift, explore our guide on why AI change management starts with people and how the right structure, skills, and culture accelerate transformation.

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.

🔍 For a deeper look at how we turn these transparency rules into full AI-native experiences, check out our breakdown on how to build better AI products.

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. AI 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.

🔍 To understand how empathy, clarity, and trust scale across entire AI systems, check out our article on AI product design.

How to measure AI impact

AI transformation only works if you can measure it. Leaders want indicators that show where AI reduces friction, accelerates workflows, and compounds business value over time.

Below are the KPIs we track with clients at Lazarev.agency. These metrics help teams validate early pilots, guide scaling decisions, and maintain accountability as AI becomes part of the operating system.

1. Workflow automation coverage (%)

How much of a workflow is now supported or handled by AI? This shows whether AI is actually replacing manual effort or just sitting unused in the interface.

2. Time reduction per task

Measure how long tasks took before vs. after AI adoption. This is one of the clearest indicators of ROI, especially for repetitive or data-heavy workflows.

3. Accuracy lift

Calculate improvements in precision, error rate, or decision quality. Examples include better classification, fewer manual corrections, or more reliable forecasts.

4. User satisfaction (AI-specific CSAT)

Gauge how people feel about AI-powered experiences:

• Are they using it?
• Do they trust it?
• Do they feel it makes their work easier?

AI features succeed only when humans prefer them over old workflows.

5. Cost-to-serve reduction

Track decreases in operational costs:

• Fewer tickets escalated to humans
• Less manual processing
• Lower time spent per support case
• Reduced content production overhead

Leaders pay attention to this metric, especially at scale.

6. Model performance & drift monitoring

Monitor whether the model is still performing well as data, behavior, or market conditions change. Drift is inevitable, tracking it prevents silent decay of accuracy over time.

7. Adoption & retention of AI features

The critical question: Are people using what you built?

Track:

• Activation rate
• Repeat usage
• Drop-off moments
• Cross-team adoption patterns

High adoption = high trust.
Low adoption = UX, alignment, or customer training issue.

8. Business impact metrics (advanced)

For mature teams, measure:

• Conversion lift (AI-driven recommendations)
• Faster time-to-market (AI-assisted production)
• Increased average order value (AI personalization)
• Improved forecast reliability (agentic workflows)

These are the metrics leadership cares about when scaling AI org-wide.

Why these KPIs matter

Without clear, shared metrics, AI transformation becomes a collection of disconnected experiments. With them, you gain:

• Alignment across teams
• Evidence for scaling decisions
• Early detection of issues or model drift
• Budget justification for future AI investment
• Clarity on what’s actually working

🔍 If you're deciding who can help operationalize these metrics across your organization, our guide to the 20 leading digital transformation consulting firms will point you in the right direction.

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.

The Lazarev.agency AI transformation loop

Before you dive into workflow audits, team structures, or UX patterns, you need a blueprint, a way to understand AI transformation not as a technical project, but as a continuous cycle of discovery, redesign, and optimization.

At Lazarev.agency, we developed the Lazarev.agency AI transformation loop after working with startups and enterprises navigating the shift toward AI-powered products. The model reflects what actually works in the field: a sequence of decisions that turn abstract AI ambitions into practical, trusted, high-usage experiences.

This loop consists of five stages you can apply to any product, department, or organization, no matter your AI maturity. Each stage proved with VTnews.ai case study examples.

🔍 If you want to see how this loop connects to broader, enterprise-level change, explore our guide on how to build a digital transformation strategy with lasting impact.

1. Workflow discovery

Uncover how work really happens today.

Every transformation begins by mapping real workflows — not the official ones written in documentation, but the invisible processes teams rely on day-to-day.

This stage focuses on:

• Identifying friction points
• Exposing repetitive tasks and decision bottlenecks
• Mapping data flows, dependencies, and emotional “pain moments”
• Understanding user behavior beyond surface-level analytics

✅ VTnews.ai example:
Before any model design, we audited how people consume news today — tabs full of articles, biased YouTube recommendations, and endless scrolling with little context. This discovery shaped the decision to create unified “Stories” that merge dozens of articles into a single, easy-to-understand view.

Without dissecting how people consume information, AI becomes ornamental instead of impactful.

2. AI opportunity mapping

Locate where intelligence can meaningfully shift outcomes.

Once workflows are understood, pinpoint where AI makes the biggest difference.
Not every task needs AI. The question is: where does intelligence add leverage?

We look for:

• Decisions slowed by manual analysis
• Content that needs personalization at scale
• Workflows overloaded with data humans can’t parse
• Tasks that compound inefficiency across the organization

✅ VTnews.ai example:
Scanning 130,000+ sources in real time is impossible for humans. AI became a core opportunity not to automate headlines, but to analyze media bias, cluster perspectives, detect underreported events, and summarize cross-ideological narratives.

AI wasn’t added for novelty. It solved something humans physically can’t.

3. Human-centered redesign

Redesign workflows, interfaces, and interactions around people.

This is the heart of Lazarev.agency’s approach: AI fails when teams design for the model instead of the human.

Human-centered AI redesign includes:

• Making invisible machine logic visible and trustworthy
• Replacing confusing automations with guided choice
• Giving users control, not just AI-driven “suggestions”
• Designing for clarity, transparency, and explainability

✅ VTnews.ai example:
AI summaries are powerful but they’re nothing without trust. To ensure transparency, we designed:

• A three-perspective summary (left, center, right)
• A bias scale
• A separate AI-generated summary box
• Clear badges showing what’s AI-driven vs. editorial

This transformed AI from a black box into a guide the user understands and controls.

4. Model integration & guardrails

Integrate the right models and design the guardrails that make them safe.

AI requires intentional integration, alignment with business goals, and UX guardrails that shape its behavior.

We help teams:

• Choose between GPT-5, Claude, open-source LLMs, or hybrid models
• Build retrieval and context systems (RAG)
• Design constraints that prevent AI hallucinations
• Define override mechanisms and fallback states
• Establish auditability, traceability, and consistent tone

✅ VTnews.ai example:
The interactive AI Chat Assistant didn’t simply answer user questions. It:

• Surfaced sources behind each answer
• Used guardrail prompts to remain factual
• Avoided speculation without data grounding
• Aligned responses with the platform’s non-biased mission

Predictive features were also guardrailed: users pick from scenario options instead of the AI inventing outcomes. This ensured safe exploration.

5. Continuous learning & optimization

AI transformation is a living system.

This phase ensures AI evolves with your users, data, and market because models, content, and behavior patterns shift constantly.

This includes:

• Monitoring real-world performance
• Detecting model drift
• Collecting AI-specific UX feedback
• Updating guardrails, prompts, and datasets
• Measuring ROI and redefining success criteria

✅ VTnews.ai example:
The prediction feature is a continuous learning loop:

• Users choose future scenarios
• AI analyzes accuracy as events unfold
• The model improves its narrative forecasting
• The platform adapts based on real-world political and media dynamics

It’s a living ecosystem.

Why this framework works

The Lazarev.agency AI transformation loop gives teams a way to structure AI initiatives so they’re:

• Human-driven, not model-driven
• Measurable, not experimental
• Transparent, not black-box
• Strategic, not opportunistic
• Scalable, not fragile

It turns confusion into clarity and hype into action.

This is the foundation we use for AI-powered systems we design, whether for a news aggregator analyzing 130,000 sources per day or a B2B platform integrating agentic workflows.

Build AI people trust with Lazarev.agency

In 2026, the competitive gap will not be between companies that use AI and those that don’t but between companies that redesign their operating systems around it and those that keep patching legacy workflows.

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.

Contact us and get an advantage today.