Here’s one thing you shouldn’t outsource to an AI model

In a world where success is good and failure creates billion dollar markets at the same time, it is inevitable that businesses are looking. generative AI as a powerful agent. From OpenAI’s ChatGPT’s human-like document creation, to DALL-E’s creative creations when inspired, we’ve seen a future where machines create alongside us – or even lead. Why not expand this into research and development (R&D)? After all, AI can turbocharge ideas, calculate faster than human researchers and can easily find the “next big thing”, right?

Hold it. All of this sounds good in theory, but let’s get real: Betting on gen AI to take over your R&D can lead to serious, possibly even dangerous, problems. Whether you’re a beginner chasing growth or a seasoned player protecting your pages, outsourcing manufacturing operations in your new way is a dangerous game. In the rush to embrace new technologies, there’s a looming risk of losing what creates real innovation — and, at worst, sending your entire business into the death throes of homogenized, unmotivated products.

Let me explain why over-reliance on gen AI in R&D can be an Achilles’ heel.

1. The unknown intelligence of AI: Prediction ≠ thinking

Write the AI It’s basically a supercharged prediction machine. It creates by predicting what words, images, designs or group summaries should be based on history. As fancy and advanced as this may seem, let’s be clear: AI is only as good as its data. It does not really create in the human sense of the word; It doesn’t think deeply, it’s confusing. It’s backward-looking – always relying on what’s already been done.

In R&D, this is a bug, not a feature. In order to be able to make new changes, you need more than just adding extras from history. Great innovations often come from leaps and bounds, and re-imaginings, not from slight variations on an existing theme. Consider how companies like Apple with the iPhone or Tesla in the electric car space didn’t just change existing products – they turned paradigms on their heads.

Gen AI may repeat the design of the next smartphone, but it cannot save us from the smartphone itself. Bold, revolutionary moments around the world — ones that redefine markets, systems, and even industries — come from human imagination, not from algorithmic probabilities. When AI is driving your R&D, you’re able to have a better understanding of existing ideas, rather than the next step—defining the next step.

2. Gen AI is a homogenizing force in nature

One of the biggest risks in letting AI start to control your content thinking is that AI creates content – whether it’s design, solutions or technical changes – in ways that lead to exchanges rather than vice versa. Considering the more educational start-ups, AI-driven R&D will lead to similar products in the market. Yes, different flavors of the same idea, but still the same idea.

Imagine this: Four competitors are using it gen AI systems creating their own user interfaces (UIs). Each system is trained more or less by a lot of information – extracted from the Internet about what consumers like, available designs, best sellers and so on. What do all these AIs do? The difference is the same result.

What you will see develop over time is a confusing mix of ideas and concepts where competing interests begin to look at each other. Of course, the pictures may be a little different, or the appearance of the products will be different at the edges, but the products, identity and uniqueness? Soon, they evaporate.

We’ve already seen the first signs of this in AI-generated art. In platforms like ArtStation, many artists complain about the abundance of AI-generated products that, instead of showing unique human skills, feel like embellishments that have been reworked to rehash familiar cultures, visual cues and styles. These aren’t the new features you want to fuel your R&D engine.

If every company runs gen AI as its innovation strategy, then your company won’t get five or ten disruptive new products every year – it’ll get five or ten great innovations.

3. The magic of human evil: How danger and ambiguity fuel creativity

We’ve all read it in the history books: Penicillin was discovered by accident after Alexander Fleming left cultures of bacteria. The microwave oven was born when engineer Percy Spencer accidentally melted chocolate while standing too close to a radar device. Oh, and a Post-it note? Another interesting accident – a failed attempt to create a super strong adhesive.

In fact, failure and accidental discovery are the essence of R&D. Human researchers, who are particularly interested in the value hidden in failure, can often see the unexpected as an opportunity. Serendipity, intuition, gut feeling – these are as important to successful design as any well-planned map.

But here is the crux of the problem type of AI: It has no concept of ambiguity, let alone the flexibility to define failure as an asset. AI programs learn to prevent errors, optimize accuracy and eliminate data ambiguity. It’s great if you’re improving things or adding factory output, but it’s terrible for good research.

By removing the possibility of ambiguity – defining risk, pushing against errors – AI streamlines possible ways to innovate. People accept complexity and know how to let things rest when the unexpected happens. The AI, meanwhile, will double down on determination, consolidating middle-of-the-road ideas and setting aside anything that seems unusual or untested.

4. AI lacks empathy and vision – two intangibles that make things change

Here’s the thing: Innovation isn’t just about creativity; it is the result of compassion, knowledge, desire, and vision. People create new things because they care, not just about working with logic or fundamental principles, but responding to human needs and emotions. We dream of making things faster, safer, more enjoyable, because at a fundamental level, we understand the human experience.

Think of the creativity of the first iPod or the minimalist look of Google Search. It wasn’t technology that made the game changers successful – it was empathy to understand user frustration with clunky MP3 players or search engines. Write the AI I can’t repeat this. It doesn’t know what it’s like to struggle with a cart app, to be surprised by its design, or to be frustrated by its lack of functionality. When AI “innovates,” it does so without thought. This lack of vision limits his ability to create ideas that resonate with real people. Worse, without empathy, AI can create things that are impressive but lifeless, sterile and changeable – inhuman. In R&D, it is the killer of innovation.

5. Over-reliance on AI destroys human creativity

Here’s a final, worrisome thought for those interested in our AI future. What happens to you let the AI ​​do more? In any field where machines destroy human interaction, creativity declines over time. Just look at the industries where automation was introduced: Employees lose the “why” of things because they don’t flex their problem-solving muscles as often.

In rich R&D environments, this poses a real threat to the people who create new long-term culture. If research teams are limited to overseeing AI-generated work, they may lose the ability to challenge, think critically or go beyond AI’s output. The more you don’t innovate, the less creative you become. By the time you realize you’ve overdone it, it may be too late.

Erosion of human skills is dangerous when markets change dramatically, and no amount of AI can guide you through the fog of uncertainty. Disruptive moments require people to break outside of normal frames – something AI can’t be good at.

The way forward: AI as a supplement, not a replacement

To be clear, I’m not saying that gen AI has no place in R&D – it does. As a collaborative tool, AI can empower researchers and designers to rapidly test hypotheses, iterate through creative ideas, and refine data faster than ever before. When used correctly, it can increase productivity without compromising creativity.

The trick is this: We need to ensure that AI acts as a supplement, not a replacement, for human creativity. Human researchers need to be at the forefront of these new technologies, using AI tools to enrich their efforts – but without losing control of creativity, vision or technical direction.

Gen AI has arrived, but so has the need to continue with the rare, powerful need for human interest and resilience – a quality that cannot be reduced to a machine learning model. Let’s not forget that.

Ashish Pawar is a software engineer.