BEYOND VIRTUAL

Remember when everyone was talking about AI's potential? Well, the talking phase is over. Companies are now quietly forging ahead or falling behind - based on one thing: execution.

In our last issue, we explored how AI is reshaping product development - making innovation faster, smarter, and more cost-effective. We saw BMW using AI to improve precision in car manufacturing, Corbion cutting down development cycles from years to months.

This time, we’re getting practical. We take a look at how companies are using it. What's working, what's not, and how businesses know whether their AI efforts are paying off.

We've reached the point where ideas meet execution, where technology starts to show measurable impact. 

Feature Story

Six Stages of Integrating AI into Your Product Development Process

Here's what most companies get wrong: they think AI integration means buying fancy tools and hoping for magic. It doesn't work that way.

After studying dozens of successful AI implementations, I've noticed a pattern. The winners all follow roughly the same playbook, though none of them would call it that.

Stage 1: Start With the Problem, Not the Tool

First, you need to understand the challenges in your product development cycle and the problems AI can help with. The best companies resist the temptation to chase technology for its own sake. Usually, companies fail to have a clear plan at this stage, and this is a contributing factor to why 95% of GenAI companies fail, according to MIT reports. You should integrate AI to serve your business goals.

Stage 2: Data Readiness, Get Your House in Order

AI runs on data like your car runs on fuel. If your data is scattered across spreadsheets, platforms, and inboxes, AI can’t help you much. This stage is all about collecting, cleaning, and organizing what you already have: customer feedback, product metrics, usage patterns, and sales records. The better your data, the smoother your AI journey.

Stage 3: Build Your AI Brain

You may not need an in-house data science team from day one.

But you do need someone who understands both your business and what AI can do for it - that might be a consultant, a tech partner, or a smart hire.

This is also when you train your existing team: they don’t need to learn to code, but they do need to understand how to work with AI tools and insights.

Also, you run tests on the model you have built and see how well it performs in real-world scenarios. Successful companies view this stage as a feedback loop, not a one-off task.

Stage 4: Model Testing and Validation

This is where the model leaves the lab and enters the real world.

Imagine a prototype getting tested by actual customers or a service workflow being monitored live. AI helps track how it performs, what’s working,  and where adjustments are needed.

It’s one thing for an algorithm to look good on paper; it’s another to see how it behaves in the hands of customers. During this stage, you should gather feedback fast and refine before a full-scale launch.

Stage 5: Roll-Out Stage

Once validated, the model is deployed across the organization. But the job doesn’t end there.

Monitoring is key. AI systems require ongoing oversight to ensure they remain accurate, relevant, and aligned with business objectives. The companies doing this well don’t treat “go live” as the finish line; they treat it as the start of a new cycle of learning.

Stage 6: Continuous Improvement, Keep It Alive

Unlike traditional software that stays frozen in time, AI gets smarter with every interaction. As your product evolves and your customers change, your AI systems need updating, new data, retraining, and occasional fine-tuning.

Visionary Voices

Johnson & Johnson’s AI Wake-Up Call

When Johnson & Johnson first started using AI, they went all in. Every team wanted to try something new, from marketing to research and supply chain. At one point, almost 900 AI experiments were running across the company. It sounded impressive, but there was a problem: most of them weren’t delivering real results.

After a while, J&J noticed a pattern. Only a small fraction of those projects were creating real business value, so they decided to shift their approach. Jim Swanson, Johnson and Johnson’s Chief Information Officer, said that they had to focus on what worked instead of trying everything at once, since only a handful of the projects were yielding results. 

Today, J&J’s use of AI looks very different. They use it to help sales teams work smarter, improve supply chain decisions, and speed up research. But more importantly, they have learned how to make AI useful, not just interesting.

The big lesson here is that you don’t need hundreds of AI projects to make an impact. You just need a few that solve the right problems. Start wide if you must, but be ready to narrow down fast. Measure what’s actually working, let the right people make the calls, and don’t spread your energy too thin.

The Trend

What Successful AI Integration Looks Like

When AI works the way it should, the difference is easy to see - faster launches, fewer mistakes, lower costs, and products that truly connect with customers. Successful companies don’t just use AI as a fancy add-on; they build it into how things get done every day.

Take Unilever, for example. They used to spend months creating product images for different regions-  every label, language, and design variation done manually. Now, with digital twins and AI-powered workflows, they produce those assets twice as fast and at half the cost. In Thailand, one of their brands, TRESemmé, cut content creation costs by 87% and saw people spending three times longer engaging with their ads.

At one of Unilever’s manufacturing sites, AI helps identify production issues before they happen, reducing downtime and improving quality. It also sped up packaging trials for sustainable materials from just a few each year to nearly thirty, saving time, money, and cutting waste in the process.

Siemens, a German tech giant, uses AI to predict when machines might break down. Instead of reacting to problems after they happen, the company now fixes things before they fail. The result? Reduction in maintenance cost by 40%, an increase in staff productivity by 55%, and a huge drop in machine downtime as much as 50%. A huge win in an industry where every minute counts.

Nestlé has also seen what happens when AI is used thoughtfully. The company now uses machine learning to understand local taste preferences and predict which new products will perform well before they even hit the shelves. That insight has led to faster product launches and fewer failed rollouts — saving millions in development costs while keeping customers happy. They’ve also used AI in quality control, catching product inconsistencies early and reducing waste in production lines.

My favorite example is Netflix, a company practically built on AI. Its recommendation system is famous for knowing what viewers want before they do - but what’s less talked about is how those same predictive systems guide their content strategy. They're not guessing what you'll watch next; they're subtly dictating what you’ll watch next, keeping you engaged and your money flowing. That's what happens when you ‘think’ with AI.

Things to Consider

Here are a few things every company should think about before or while rolling out AI in product development:

1. Data Privacy and Security
AI depends on data. If that data isn’t protected, it becomes a risk. Make sure your systems are secure, compliant, and transparent about how data is collected and used.

2. Compatibility
AI tools need to fit your current systems. Outdated software or scattered data can slow everything down. Audit what you have before adding anything new.

3. Skills Gap
AI still needs people who understand it. Many teams lack the right skills to manage, interpret, or adjust AI models. Upskilling can make a big difference.

4. Ethics and Responsibility
AI can amplify bias if left unchecked. Always review how your models make decisions and ensure they align with your company’s values.

5. Measuring ROI
Don’t assume AI success means instant profit. Look for efficiency gains, faster development cycles, or better customer satisfaction as early indicators of ROI.

6. The Future
AI keeps evolving, just like customers' interests.. What works today might not work tomorrow. Keep testing, updating, and learning; it’s the only way to stay ahead.

A Final Note

Here's what separates the companies thriving with AI from those just talking about it: they started. Not with a massive transformation. Not with a million-dollar initiative. They started with one problem and a solution.

The gap between companies using AI and those actually benefiting from it grows wider every day. The technology is the same; the execution is what differs. While your competitors are still debating whether to use AI or even where to apply it, you could be learning how to use it well and gaining all the market advantage.

Pick your pain point. Check your data. Find your guide. Test it. Scale smart. Improve constantly. These aren’t just buzzwords. That’s just what works.

Until next time,

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