The Real AI Transformation Has Nothing to Do with AI

From the desk of Mikko Räty, Head of Product at LeadDesk :

AI is everywhere. It is dominating conversations, roadmaps, and product strategies. At LeadDesk, it is already part of how we work. But along the way, we realized that there is a same quiet trap that many organizations are falling into.

A company often buys the tools, adds “AI” to the roadmap, ships a feature with “AI” branding, and declares the transition done. The technology might have changed, but the team, however, has not.

The truth is that conversations that matter within the product team and customers are almost never about the technology. AI models are impressive and they only keep getting better, so that part more or less takes care of itself.

The hardest part, however, is people. How do they work? How are they organized? What are they asked to do next? A product team built for the world before AI will mostly use AI to do its old job a little faster. But that’s not transformation. That’s just a faster version of the same thing.

Nobody has all the answers here yet. At a recent event LeadDesk attended, one question came up again and again. What skills will people actually need in the future? There were many opinions and no clear answers. The honest take on this is that organizations and teams must explore the possibilities together. Openly and courageously.

What follows is a guide about how to get an entire product organization into the AI conversation, how to break the silos that slows down progress, and how to set product up to win.

Get everyone into the room together quickly

One of the most common failure patterns we noticed is treating AI as a specialist function. A small team owns “the AI product offering,” while everyone else waits to be told how the development happens. As a result, the gap between the two groups widens every week.

The problem to this approach is very simple. AI is not confined to silos and affects every business function. Product and Design need to understand what an AI model can and cannot reliably do. Engineers need to reason about cost, latency, and failure. Sales and customer facing teams knows exactly where customers run into problems daily. When discussions stay siloed within one team, the best ideas never reach the people who have the context to act on them.

So, let’s make sure we keep the conversation open. Product, engineering, design, data, sales, support all in the same room, same demos, same hard questions. The goal is not to turn everyone into an AI expert. Instead, we want to create a common language and platform for everyone to discuss AI and contribute. AI should become something the whole company owns, not just a single specialist function.

AI is a team sport. The moment it becomes one team’s responsibility, the advantage is already gone.

Break the silos that slow you down

Working in silos was manageable when product cycles ran in quarters. In the AI era, the market changes in weeks. Organizations that silo teams simply move too slowly. The old way of working reflects that slower pace. Product writes a spec. Design turns it into an experience. Engineering builds it. Someone measures the results later. That process assumes things stay the same while each team takes its turn.

But with AI capabilities nowadays, things change quickly. A new model can make something easy on Friday that seemed impossible on Monday. A step-by-step process cannot keep up.

The answer is to bring different teams together around a shared goal instead of keeping them in separate silos. Cross-functional teams should exist to explore an idea, build it, test it with customers, and improve it quickly. Give these teams a real problem to solve, access to customers, and the freedom to change direction when something is not working. Organizations that learn fastest will win and silos are one of the biggest barriers to that speed.

The starting point should always have the customer in mind. Cross-functional teams should be organized around solving real customer problems and internal organizational structures should follow that goal. The customer’s business goals, workflows and pain points should become the common objective that aligns Product, Engineering, Sales and Customer Success.

Retrain the team. Not replace it.

Here’s the fear behind many AI conversations: automation means fewer people are needed. However, the opposite is actually true. AI does not remove the need for skilled people. It changes what those people spend their time doing.

The Classic Ikea Example – From the Warehouse to Interior Design

 

When IKEA introduced its AI chatbot, Billie, they did not respond by cutting jobs. Instead, they retrained 8,500 customer service employees. AI took over routine tasks like stock checks and delivery tracking. Retrained employees moved into remote interior design advisor roles, where they could spend more time helping customers with decisions that require judgement, taste, and conversation.

 

The result wasn’t just retained jobs. IKEA also created a new multibillion euro business line.

 

The lesson is simple. AI doesn’t replace people. It takes over repetitive work so people can focus on the work only humans can do. Understanding customers, using judgement, and building experiences that earn trust.

In practice, this means people spend less time on repetitive work and more time on work that matters.

An analyst who used to spend days putting reports together now spends that time finding insights and helping make better decisions. A support specialist who answered the same question hundreds of times now helps to build the AI system that answers those questions automatically and focuses on the conversations where only a person can help.

The work becomes more valuable. That is an opportunity for the team, not a threat. That is why AI should also create optimism about the future. Organizations would benefit from clearly communicating how roles will evolve, what new skills are needed, and what new career opportunities AI will create. When people understand their future, they are far more likely to embrace change.

Build AI products that create business value

An AI feature that nobody pays for is just another cost. Sooner or later, every AI investment needs to generate revenue or save cost.

That is why you should think about monetization from the start, not after the product is built. Focus on the value your AI solution creates for customers, and what they are willing to pay for. Price it for the outcome you deliver, not the fact that it uses AI.

• Tie price to outcomes, not features
  Customers pay for results and value. Conversations handled, faster resolutions, deals won. Anchor pricing to the outcome the AI delivers, not to the fact that AI is involved.

• Make the value measurable
  If a team cannot show the time saved or the revenue generated, it will struggle to defend its price tag. Build measurement into the product so the value is visible to the customer.

• Build pricing that scales
  AI carries real, usage-based costs. Your pricing should account for these costs, or growth will minimize your profit margin.

The pricing for AI products and solutions is constantly evolving across the industry. Instead of trying to define the perfect pricing model internally, organizations should involve customers early to co-create pricing through pilots and continuous feedback. The best commercial models are often built together with customers, not for customers.

Focus on what makes you unique

No organization can build everything in the AI era. AI models, infrastructure, and tools are improving too quickly for any one team to keep up.

The best approach is to focus on what makes your product unique and connect with the rest. For LeadDesk, it is the deep understanding of high-volume sales and customer service for enterprises. The data from hundreds of millions of conversations, and the trust customers place in keeping that data secure and sovereign in Europe.

Everything else can come from the wider ecosystem of AI models, platforms, and business tools. Customers already have the tools they rely on. They expect new products to connect with those tools, not replace them. They don’t want a walled garden. They want the best tools working together.

For product teams, this changes the question. Instead of asking, “What can we build ourselves?”, ask, “What makes us unique, and what can we integrate instead?” That keeps the team focused on where it creates the most value while the ecosystem provides the rest.

Your competitive advantage comes from what only you can do. Not from building every piece of the stack.

For many organizations, that unique advantage is proprietary data. AI models are increasingly becoming commodities, but the data that trains, improves and differentiates those models is not. Organizations should identify what unique data assets they possess and build their long-term AI strategy around them.

The 5-step guide to transforming your product team in the AI era

Nobody has this completely figured out yet. AI is developing too quickly for anyone to have all the answers. That’s why organizations should learn together with their customers and partners instead of waiting for certainty. However, the direction is very clear. Success is not about adopting another tool. It is about changing how your product team operates daily.

Here are five steps to get started:

1. Bring everyone into the conversation
Treat AI as a company-wide capability, not a specialist function. Give every team the shared language and platform to contribute and solve customer problems together.

 

2. Reorganize around outcomes
Break down silos and build agile, cross-functional teams that solve the problem from end-to-end. Consider focusing on niche industries where you can deeply understand customer workflows, regulations and business challenges. AI creates significantly more value when combined with deep industry expertise.

 

3. Retrain toward higher-value work
Use AI to automate routine tasks and help your team focus on the work only people can do. You may even discover or create entirely new areas of your business for growth.

 

4. Build AI products that create business value
From day one, build AI that creates clear value for customers. Price that value, measure it, and make sure your pricing scales with the cost of using AI.

 

5. Focus on what makes you unique
Build what only you can offer and integrate the rest instead of building everything yourself. Your competitive advantage comes from what only you can do. Not from building every piece of the stack.

The companies that win in the AI era are not the ones with the most AI features. They are the ones whose teams learn to work differently. Together, faster, and focused on creating real value for customers. AI technology is accessible to everyone in this era and that is the easy part. The real advantage comes from the transformation, which creates the gap that is hardest for competitors to replicate.

Last but not least, remember that AI is only one part of the equation. The greatest opportunities often come from combining AI with the channels where your customers create the most value, and the unique data generated through those interactions. Organizations that build on these strengths will be best positioned to create lasting competitive advantage.